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DATE DUE I DATE DUE DATE DUE

W! R 1 8260‘

 

 

 

 

 

 

 

 

APR 015-72503

 

 

 

 

 

 

 

 

 

 

EV ,-
WIT}

EVALUATING FACTORS ASSOCIATED
WITH PERCEIVED DOWNTOWN HEALTH
IN SIXTEEN MICHIGAN CITIES

By

Norman Raymond Tyler

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Geography
1 998

Professor Roger Hamlin

ABSTRACT

EVALUATING FACTORS ASSOCIATED
WITH PERCEIVED DOWNTOWN HEALTH
IN SIXTEEN MICHIGAN CITIES

By

N orrnan Raymond Tyler

Local officials and planners in cities across the country have often undertaken
efforts to revitalize their downtowns. A major problem, however, is there has been no
generally accepted method of evaluating such efforts.

This study develops a tool, the Health Perception Index, which is useful in
evaluating efforts by downtown business and community leaders to revitalize their
downtowns, and in determining factors most closely correlated with downtown
health.. The Index is multi-dimensional; it can be utilized both in different
communities and over varying time periods.

In this study, the Health Perception Index was used to evaluate the downtown
health of sixteen study cities in Michigan. The health of the study downtowns were
evaluated both as business environments and as social/cultural centers of their
communities. The evaluation data was based on a survey of downtown business
owners, merchants and local officials. Various factors from the survey were
examined to determine which were perceived as most closely linked to downtown
health.

Primary findings of the study were that “business mix” was the factor most
closely correlated to downtown health and that “browsing shopping” was the business

type most closely correlated with a healthy business mix.

Copyright by
NORMAN RAYMOND TYLER
1998

 

[ungrateful to a north
the detelopment of thy
sened as Chair of m}

huriles inherent in Will.

then the problem size"

0
5 .

daft was gong to the r
Dissertation Committee

intellectual context. 8:

accuracy and internal cw

PCBPCCIIVC. and prmrde

(than and Regional P

in
hintnistrante assistance .
“Wally want to

Rule
theessential ingredient th.

n , ~ .
“We this aeknou led-w

ACKNOWLEDGMENTS

I am grateful to a number of people who have provided invaluable assistance during
the development of this report. I am especially indebted to Roger Hamlin, who
served as Chair of my Dissertation Committee and guided me through the many
hurdles inherent in such a process. He served as my primary advisor in the beginning,
when the problem statement was first being defined, until the end, when the final
draft was going to the printer. I also want to recognize the other members of my
Dissertation Committee. Assefa Mehretu showed me how this work fit into a larger
intellectual context. Bruce Pigozzi reminded me of the importance of academic
accuracy and internal consistency. Warren Rauhe represented an important outside
perspective, and provided encouragement throughout the long process. Within the
Urban and Regional Planning Program office, Dawn Brown provided important

administrative assistance and helped me through the paperwork.

I especially want to recognize the study's 365 survey respondents, for they provided
the essential ingredient that made this research possible. Although they probably will

never see this acknowledgement, I want to recognize their crucial role nonetheless.

Finally, I want to thank my wife, Ilene, for her patience and support. It is difficult
enough being supportive of a spouse as they struggle through one dissertation, but her
tolerance during the difficult period of completing a second one was very much

appreciated.

iv

TABLE OF CONTENTS

LIST OF TABLES .................................................................................................... x
LIST OF FIGURES .................................................................................................. xii
CHAPTER I: INTRODUCTION ............................................................................. 1
Problem Statement ........................................................................................... 2
Scope of Study ................................................................................................. 3
Research Questions .......................................................................................... 3
Study Assumptions .......................................................................................... 4
Organization of the Study ................................................................................ 6
CHAPTER II: LITERATURE REVIEW ................................................................ 7
Theories of Urbanization ...................................................................................... 7
Urban Land Use and Central Place Theory ..................................................... 7
Von Thfinen’s Concentric Rings ................................................................ 8
Description of Central Place Theory .......................................................... 8
Current applications of Central Place Theory ............................................ 9
Human Ecology Approach ......................................................................... 10
Urban Economics and Retail Location Theories .................................................. 10
Gravity Models ................................................................................................ 11
Reilly’s Law of Retail Gravitation ............................................................. 11
Huff‘s Spatial-interaction model ................................................................ 12
Nearest Neighbor Model ............................................................................ 12
Index of Retail Saturation ................................................................................ 13
Location-Allocation Theory ............................................................................. 13
Berry: Structure of urban retail ................................................................. 14
Behavioralist Theories of Downtowns ............................................................. l4
Schwartz: Inorganic and organic views of downtown .............................. 14
Downtown Blight as a Deviant Behavioral Process .................................. 15
Cities as Centers of Growth .................................................................................. 16
The Optimal Size for Urban Places .................................................................. 17
The Preference for Smaller Cities .................................................................... 18
The Need For a New Method of Evaluation ......................................................... 21
The Need for Downtown Models ..................................................................... 30
Traditional Assumptions of CBD Policy ................................................... 30
Alternative Perspectives ............................................................................. 31
The Need for Qualitative Variables ................................................................. 32
Conclusion ....................................................................................................... 37

CHWRIII; M
Selection of St.‘
General Desert;

Albion:
Douagxac: ..
Howe”; ........
lonra: ...........
Latter: ........
hlanrstee:.

a...
cccccc

Alpena: .........
Big Rapzds;
Cadillac: .......
Cold“ ater: .....
Hillsdale:
Ouosso:.........
South Hatens.
turgi :
Demographic (
Respondents
election of Re
Réxponse R
UBC) Bub
Non-reap
”ERIE-Orr

"Self-int:

...........

 

CHAPTER III: METHODOLOGY ......................................................................... 38

Selection of Study Cities ...................................................................................... 38
General Descriptions of the Sixteen Study Cities ................................................. 44
Albion: ............................................................................................................. 4S
Dowagiac: ........................................................................................................ 49
Howell: ............................................................................................................. 50
Ionia: ................................................................................................................ 52
Lapeer: ............................................................................................................. 54
Manistee: .......................................................................................................... 56
Marshall: .......................................................................................................... 59
Tecumseh: ........................................................................................................ 61
Alpena: ............................................................................................................. 62
Big Rapids: ....................................................................................................... 64
Cadillac: ........................................................................................................... 66
Coldwater: ........................................................................................................ 68
Hillsdale: .......................................................................................................... 7O
Owosso: ............................................................................................................ 72
South Haven: .................................................................................................... 74
Sturgis: ............................................................................................................. 75
Demographic Characteristics of the Study Cities ............................................ 76
The Respondents ................................................................................................... 79
Selection of Respondents ................................................................................. 79
Response Rate ............................................................................................ 80
Survey Bias ................................................................................................ 84
Non-respondent bias: ............................................................................. 84
Hawthorne Effect: .................................................................................. 85
“Self-lifting” Bias: ................................................................................. 86
The “Habit” bias .................................................................................... 86
Stopping Rule: ....................................................................................... 86
The Questionnaire ................................................................................................. 87
Developing the Questionnaire .......................................................................... 87
The Initial Development and Testing of the Questionnaire ....................... 87
The Questionnaire Format ......................................................................... 88
Question Sequence ..................................................................................... 90
The First Question ................................................................................. 90
Detailed Evaluation of Downtown Characteristics ................................ 91
Significant Events .................................................................................. 92
Competitor Areas ................................................................................... 94
Evaluation of a downtown’s past ........................................................... 97
Evalution of a downtown’s future ......................................................... 97
Personal Information on the Respondent ............................................... 98
Pretesting the Questionnaire ...................................................................... 98
Administering the Questionnaire ............................................................... 99
Lists of Variables ............................................................................................. 10]
The Use of Archival Data ................................................................................ 102

vi

 

 

 

CHXI’TER IV:
chxazron 0'
Factors M

l. Q's:

unhaiit’n
[5: of a P:
The SCHCh FU-
L'se of Pror
Use of DL’YT‘.
Poptis:
Populat;
Olht’r Demo
Percent I
Househo
Percent 1;
Percent E
Percent o
Medan A
Count} 5;

PM Search {Or P
“Mann 0
Analtsis 0
Retail 5
Billidlné
Employ
ShOppm
Street“
DCang G
Groupmé
haracte
Parking ,t
“1th Re,'

The .COmPCifls

CHAPTER IV: ANALYSIS .................................................................................... 103

Derivation of the Downtown Health Perception Index ........................................ 103
Factors Making Up a "Health Perception Index (HPI)" ................................... 103
1. Question l—Downtown Health Compared .......................................... 105
Use of the Normative Scaling Technique .............................................. 105
2. Historical Look at Downtown Health ................................................... 108
3. Optimism ............................................................................................... 114
4. Evaluation of Individual Downtown Characteristics ............................ 117
Derivation of the Downtown Health Index ...................................................... 122
Use of a Perceptual indicator ........................................................................... 126
The Search For Proxies Using Demographic Data ............................................... 127
Use of Proxies .................................................................................................. 127
Use of Demographic Data ................................................................................ 128
Population Size .......................................................................................... 130
Population Growth ..................................................................................... 131
Other Demographic Factors ............................................................................. 132
Percent in Poverty ...................................................................................... 132
Household Income ..................................................................................... 132
Percent in Labor Force ............................................................................... 133
Percent Black ............................................................................................. 133
Percent of Population 25-44 Years Old and .............................................. 133
Median Age ................................................................................................ 135
County Seat ................................................................................................ 135
The Search for Proxies From Survey Data ........................................................... 135
Downtown Characteristics ............................................................................... 136
Analysis of Individual Characteristics ....................................................... 137
Retail Sales As a Characteristic ............................................................. 137
Building Condition As a Characteristic ................................................. 138
Employment As a Characteristic ........................................................... 139
Shopping As a Characteristic ................................................................. 141
Streetscape As a Characteristic .............................................................. 142
Deriving Groupings of Characteristics ...................................................... 144
Groupings As Shown Through Principal Components Analysis .......... 146
Characteristics Not Included in Clusters ............................................... 149
Parking As a Characteristic ................................................................... 150
Graphic Representation of “Characteristics” Clusters ............................... 150
Comparison of Characteristics for 1986 and 1992 Surveys .................. 152
The Use of Proxy Data ..................................................................................... 153
Proxies For Shopping Group Characteristics ............................................. 153
Retail Sales Data: ................................................................................... 153
Data available on Local Cooperation: ................................................... 155
Business Mix Data: ................................................................................ 156
Employment Data Available: ................................................................ 156
Analysis of Business Mix ..................................................................................... 157
Businesses in the study downtowns ................................................................. 157
Quantifying business mix ........................................................................... 160
Business Mix Analysis ..................................................................................... 161
Analysis by Business Type Groupings ...................................................... 161
Deriving a Business Mix From the Data .................................................... 164
Business Mix and Retail Sales Data .......................................................... 167
Analysis by Respondent Business Category .............................................. 168
Evaluations by Small Retailers: ............................................................. 169
Evaluations by Food and Entertainment Grouping Respondents: ......... 170

vii

 

 

Etaluatz
Emluat:
Emiuatz

Anal} gas 0:
Competition to Dr
Respondent Ex

CHAPTER V: SL'.\
Summary .............
Puncse of Ste.
Rene“ of Rel:
Focus of 53.3)
Deniatron of 1.!
Findings..........
Use of Prom C
Precursor Stud;
Conclusions .........
Significance of
PolIC) Implied:
Future Rexearcl

.nncxorcns
APPENDIX 1: corn
APPENDIX 2; COP}

APPENDIX 3: LIST!
Individual Rev

. From Studi

Cit} and Countt

-CCnSU\
KIlChlgan D
PI SOurce
Dem'cd l'I'Or
Ctsona] 0b:

 

Evaluations by Large Retailers .............................................................. 170

Evaluations by Respondents in Downtown Offices .............................. 171
Evaluations by Other Respondents ........................................................ 171
Analysis of Respondent Evaluations of Business Types ........................... 172
Competition to Downtowns .................................................................................. 176
Respondent Evaluations to Competition .......................................................... 176
CHAPTER V: SUMMARY AND CONCLUSIONS .............................................. 180
Summary ............................................................................................................... 180
Purpose of Study .............................................................................................. 180
Review of Relevant Literature ......................................................................... 180
Focus of Study ................................................................................................. 182
Derivation of the Health Perception Index ...................................................... 183
Findings ............................................................................................................ 184
Use of Proxy Data ............................................................................................ 185
Precursor Study ................................................................................................ 186
Conclusions ........................................................................................................... l 87
Significance of This Study ............................................................................... 187
Policy Implications and Recommendations ..................................................... 188
Future Research ................................................................................................ 190
APPENDICES
APPENDIX 1: COPY OF 1992 SURVEY FORM ................................................... 192
APPENDIX 2: COPY OF 1986 SURVEY FORM ................................................... 199
APPENDIX 3: LISTS OF VARIABLES .................................................................. 207
Individual Respondent Variables ..................................................................... 207
From Study Survey .................................................................................... 207
City and County Variables ............................................................................... 210
US. Census of Population ......................................................................... 210
US. Census of Retail Trade ....................................................................... 212
Michigan Database ..................................................................................... 212
Other sources .............................................................................................. 212
Derived from study survey ......................................................................... 213
Personal observation .................................................................................. 214
APPENDIX 4: MAPS OF PERCEIVED BOUNDATIES OF STUDY
DOWNTOWNS TAKEN FROM 1986 SURVEY ............................................. 226
APPENDIX 5: ITEMS USED BY E.F. THORNDIKE TO DERIVE HIS
“GOODNESS INDEX” ...................................................................................... 226
APPENDIX 6: HISTORY OF DOWNTOWNS ...................................................... 228
The Changing Nature of Retail ............................................................................. 228
Emergence of Shopping Centers and Malls .................................................... 230
History of the Shopping Center ................................................................. 230
Differences between shopping centers/malls and downtowns ................... 233
Advantages of the Shopping Center/Mall: ................................................. 235
Competition from large discount stores, using Wal-Mart as a
representative retailer ................................................................................. 237
Competition from Strip Malls .................................................................... 237
Problems Resulting From Growth of the Retail Industry .......................... 239
The Future of Downtowns .................................................................................... 240
Major Trends Impacting Downtowns ................................................................... 242

viii

 

hlzmtion Pa
Population d
Suburban
Regional.
Dountoun Rex:
Federal Pfogt
Commun:
Commun:
[than De
HISI‘OFIC I
State and Loc
Tax abate:
Tax Increr
Dounrour
Enlcrpngc
Others Piggy,“
Main Strce
The F01;
Elaluatz
PCdCSInan
Transit S1,,

HISIOQ‘ of “'AJ
CBC SlUd\ .
DWntoun Hg;1
Onelalion of E
OTTL’lation of I]
mpacr 0f WAL

e lmpact I
The[

Migration Patterns: Growth and Decline ........................................................ 243

Population dispersion ....................................................................................... 244

Suburbanization ......................................................................................... 244

Regionalization .......................................................................................... 245

Downtown Revitalization Programs ..................................................................... 246

Federal Programs ............................................................................................. 248

Community Conservation Guidance (CCG) Policy ................................... 248

Community Development Block Grants (CDBG) Program ...................... 250

Urban Development Action Grant ............................................................. 250

Historic Tax Credits ................................................................................... 252

State and Local Government Programs ........................................................... 255

Tax abatements .......................................................................................... 255

Tax Increment Financing (TIP ......................................................... 255

Downtown Zoning ..................................................................................... 257

Enterprise Zones ........................................................................................ 259

Others Programs and Strategies ....................................................................... 260

Main Street Program .................................................................................. 260

The Four Point Approach ...................................................................... 261

Evaluation of the Main Street Program ................................................. 263

Pedestrian Malls ......................................................................................... 264

Transit Malls .............................................................................................. 265

APPENDIX 7: ........................................................................................................... 266

The Impact of WAL-MART ................................................................................. 266

History of WAL-MART in Michigan .............................................................. 267

Case Study—Wal-Mart in downtown Carroll, Iowa ................................. 274

Downtown Health As Shown Through Local Economic Indicators ................ 276

Correlation of Economic Indicators to "Health Perception Index (HPI)" ........ 279

Correlation of the Health Perception Index and the economic indicator ......... 280

Impact of WAL-MART stores on study cities and their downtowns .............. 280

The impact of distance ............................................................................... 282

The Impact of All Competitor Discount Department Stores ........................... 285

Summary of The Impact of Wal-Mart and Other Large Discount Stores ........ 290

APPENDIX 8: RECENT PUBLICITY ON THIS STUDY ..................................... 291

BIBLIOGRAPHY ..................................................................................................... 300

INDEX ...................................................................................................................... 314
ix

 

“4.03. 'fi_ _. ._—

 

Table 1
Table 2
Table 3
Table-l
Table 5
Table 6
Table 7

Table 8
Table 9

Table l0
Table 11
Table 12
Table 13

Table 14

Table 15
Table 16

Table 17
Table 13
Table 19
Table 20
Table 2]
Table 22
Table 23
Tlittle 24
Table 25
Table 36
Table 27

Table 38

Table 39
Table 30

Table 1
Table 2
Table 3
Table 4
Table 5
Table 6
Table 7

Table 8
Table 9

Table 10
Table 11
Table 12
Table 13

Table 14

Table 15
Table 16

Table 17
Table 18
Table 19
Table 20
Table 21
Table 22
Table 23
Table 24
Table 25
Table 26
Table 27

Table 28

Table 29
Table 30

LIST OF TABLES

List of Cities Fitting Study Criteria ........................................................ 43
Selected Demographic Characteristics For 16 Study Cities .................. 76
Questionnaire Response Rates ............................................................... 80
Respondents’ Length of Time Listed by City ........................................ 83
Question 1 Rating for Study Cities ...................................................... 108
“Future of Downtown” Question Ratings ............................................ 116
“Future of Downtown” Question:

Optimistic vs. Pessimistic Responses ............................................ 117

Normalized Values for Ratings for 16 Downtown Characteristics ...... 1 18
Mean ratings For All Study Cities

for Characteristics before Normalization ....................................... 120
Normalized Values For Components of Health Perception Index ....... 123
Health Index Values for 1986 and 1992 Surveys ................................ 125
Coefficients: Demographics and Index ................................................ 129
Statistically Significant Correlations of Demographic Data

and the Health Perception Index .................................................... 132

Correlation Coefficients of Age Groups to Respondent
Evaluations of Downtown Health For Other Cities in Michigan... 134

Correlation Coefficients of 16 Downtown Characteristics .................. 136
Sixteen Downtown Characteristics Ranked

By Correlation Coefficients ........................................................... 137
Mean difference in evaluations between 1986 and 1992 surveys ........ 141
Correlation Coefficients of Characteristics Groupings ........................ 146
Values of Four Groupings Using Principal Components Analysis ...... 147
Rotated Factor Matrix for Principal Components Analysis ................. 148
Retail Sales and Retail Sales Per Capita, 1992 .................................... 154
Correlations for Retail Sales Data ........................................................ 155
Correlations of Population and Employment ....................................... 156
Number of Each Business Type in Study Cities, 1992 ........................ 158
Correlations of Business Groupings .................................................... 167
Mean Health Perception Index Ratings By Business Category ........... 168
Mean Evaluations (Normalized) of Characteristics

by Respondent Business Category ................................................. 169
Evaluation of Relative Importance

of Various Types of Businesses ..................................................... 174
Distances to Competition (in miles) ..................................................... 177

Mean Ratings for Strength of Competition By Business Category ..... 178

X

Table 31
Table 32

Table 33
Table 34

Table 35
Table 36

Table 37
Table 38
Table 39

Table 40
Table 41
Table 42
Table 43
Table 4.1

See
Salt

Sale
Rani

C om

C 0th
l(

COTE]
DATA

C orre

Dlxldl
C orre
Gratt
Grax r'.
Cone:

Table 31
Table 32

Table 33
Table 34

Table 35

Table 36

Table 37
Table 38
Table 39

Table 40
Table 41
Table 42
Table 43
Table 44

Selected Changes in Retail Institutional Structure ............................... 235
Sales Change in Wal-Mart Towns vs.

Other Same Size Towns (in percent) .............................................. 272
Sales Tax and Retail Sales for Study Cities, 1987 and 1992 ............... 276
Ranking of Study Cities by Change

in Economic Indicators, 1987—1992 .............................................. 277
Correlation Coefficients for

Population and Change in Sales Tax and Retail Sales ................... 278
Correlation Coefficients of HPI

to Population and Economic Indicators ......................................... 280
Comparison of Wal-Mart vs. non-Wal-Mart Cities .............................. 282
Distance to Wal-Mart ........................................................................... 283
Correlation Coefficients for

Distance to Wal-Mart and Size of Store ......................................... 283
Distance Ranking to Wal-Mart ............................................................. 284
Correlation Coefficients for Distance Ranking .................................... 285
Gravity Model Values .......................................................................... 286
Gravity Model Ratings ......................................................................... 289
Correlation Coefficients for Gravity Model Ratings ........................... 289

xi

 

 

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Egml
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hpn4
fing
Figure 6
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Hamil
Figure 12
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Figure 16
PM I7
figure 18
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figure 22
F1Eur: 33
light: 24
Fangs
ngmzs

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14

Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20

Figure 21

Figure 22
Figure 23
Figure 24
Figure 25
Figure 26

LIST OF FIGURES

Berry’s Elements of the Urban Retail Structure .................................... 14
Locations of 16 Study Cities .................................................................. 39
Respondents’ Length of Time As Residents .......................................... 8]
Respondents’ Length of Time In Business ............................................ 82
First Question ......................................................................................... 91
“Significant Events” Question ............................................................... 91
“Other Cities” Question ......................................................................... 96
“Historical Health of Downtown” Question .......................................... 97
Number of Days For Return Of Questionnaires .................................... 98
Question 1 From Survey ...................................................................... 105
Historical Health Question From Survey ............................................. 109

Dowagiac Historical Health Chart (Not normalized): 1986 Survey 110
Dowagiac Historical Health Chart (Not normalized): Current Study .. 110
Albion Historical Health of Downtown Chart—

1986 and 1992 Surveys Compared ................................................ 11 l
Dowagiac Historical Health of Downtown Chart-

1986 and 1992 Surveys Compared ................................................ 111
Howell Historical Health of Downtown Chart—

1986 and 1992 Surveys Compared ................................................ 1 12
Ionia Historical Health of Downtown Chart—

1986 and 1992 Surveys Compared ................................................ 112
Lapeer Historical Health of Downtown Chart—

1986 and 1992 Surveys Compared ................................................ 1 13
Manistee Historical Health of Downtown Chart—

1986 and 1992 Surveys Compared ................................................ 113
Marshall Historical Health of Downtown Chart-

1986 and 1992 Surveys Compared ................................................ 1 14
Tecumseh Historical Health of Downtown Chart—

1986 and 1992 Surveys Compared ................................................ 114
“Future of Downtown” Question ......................................................... 115
Formula for Deriving Health Perception Index ................................... 122
Rankings of Study Cities According to Health Perception Index ........ 124
Health Index Values for 1986 and 1992 Surveys ................................ 123
Health Perception Index Compared to 1990 City Population .............. 130

xii

%

 

 

 

figure 28
.‘w “
Fig.1?! -9
Figure 30
fipm3l
Fare 32
Fag-are 33
Figure 34
Figure 35
Fzgcre 36
Figure 37
Figure 38
Figure 39

Figure 40

Figure 27

Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39

Figure 40

Health Perception Index Compared to

Population Growth Rate (%), 1980—1990 ...................................... 131
Graph of Correlation of Retail Sales to HPI ........................................ 138
Graph of Correlation of Building Condition to HPI ............................ 139
Graph of Correlation of Employment to HPI ....................................... 140
Correlation Coefficients for Downtown Characteristics ...................... 144
Correlation Coefficients for Downtown Characteristics (Revised) ..... 145
Proximity Matrix of Downtown Characteristics .................................. 151
Survey Question on Importance of Business Types ............................ 173
Survey Question on Competition ........................................................ 176
Locations of Discount Department Stores, 1986 ................................. 268
Locations of Discount Department Stores, 1992 ................................. 269
Locations of Discount Department Stores, 1993 ................................. 269
Changes in Total Retail Sales in Illinois Towns

Before and After Opening of a WaLMart Store ............................. 270
Average New Sales Change After 3 Years

In Wal-Mart Host Town ................................................................. 271

xiii

 

The dounttw
suffering from c
mobile than 11'] 1
highway. render

lotalt) to local n

shopping center c

centers began an

competition that 1

commercial areas a

Giien these ch.
traditional do“ nto

should be [h

eir ecor
TD&_10leSC‘0UTIICTR c

“Capture their relat
husi ~~
messes that don‘

3m? Should don

become

CHAPTER I
INTRODUCTION

The downtowns of smaller cities are under stress. Retail businesses, especially, are
suffering from changing retail patterns and shopping habits. Local residents are more
mobile than in past decades. With more comfortable automobiles and convenient
highways, residents no longer feel a strong physical connection to their downtowns nor a
loyalty to local merchants and their businesses. They feel free to travel to the nearest
shopping center or mall, or to another town or city. In the 19605, suburban shopping
centers began growing into serious competitors for downtown's customers. It is a
competition that largely has been won by the ubiquitous shopping centers, malls, strip
commercial areas and major discount centers.

Given these changes, critical questions arise for those who are interested in preserving
traditional downtowns. How should they be planned to face an uncertain future? What
should be their economic strategy? Should downtown businesses compete directly against
major discounters or chain stores, such as Wal-Mart or Banana Republic, in an attempt to
recapture their retail dominance? Should they evolve into new kinds of retail centers, with
businesses that don’t compete directly with the new chain stores, but are complementary to
them? Should downtowns in smaller cities abandon their traditional retail role entirely and
become service centers, relying increasingly on office and financial functions? Or are
downtowns now obsolete? Should they be allowed to die a natural death, as have other
elements of our 19th and early 20th century cities? The larger question underlying all these

is—What is the role of downtowns in the 1990s and beyond?

Various t};
community lea.
issues. as do“ 1'
socially. Spatt.‘
benefits of spa::
of most urban .
importance in re
communrt} 3cm
Olthe multrpurp

health) commun:

PROBLEM Sr;

Therc is a net

community leader

Ihem to evaluate 1h

Various types of individuals (including downtown business owners and merchants,
community leaders and city officials, and urban planners) need to better understand these
issues, as downtowns still play a central role in our cities—spatially, economically, and
socially. Spatially, they are located at the center of urbanized areas, and thus retain the
benefits of spatial centrality and focus. City centers also still represent the economic core
of most urban areas, even though suburban areas have taken on increased economic
importance in recent decades. Additionally, downtowns remain the traditional center of
community activity in many cities, and still represent the focus for a community. Because
of the multi-purpose role downtowns play, it can be assumed healthy downtowns represent

healthy communities.

PROBLEM STATEMENT

There is a need for a tool to evaluate the general health of downtowns. Business and
community leaders often initiate efforts to revitalize their downtowns, but it is difficult for
them to evaluate the impact of these efforts over time because there is no accepted method
for such evaluation. What is needed is an evaluation tool which incorporates the breadth of
factors affecting general downtown health. Such a tool should look at both economic and
social factors. It should incorporate standard evaluation measures (census data, sales
figures) with input representing the subjective perceptions of the people who live and work
there. Such an evaluative tool should be able to be applied to all downtowns, and be able
to monitor the health over time. Without such a tool, efforts at improving downtown health
are not soundly based, and lessons learned in one city are difficult to apply to others. As
stated by Robertson, "Most studies and policies that relate to the city core address
themselves to the question of viability, health, vitality or some other term conveying the

desired well-being of the downtown. However, few of these efforts have directly

 

confronted {hf 9

boss the) Can he

SCOPE OF ST

This study it
Michigan. The c:
representatisenex
the Upper Peninst

The stud} rec.
these cities. lts pr
these indit'idudie
this stud}. The st

W11. and had an c

RESEARCH Qt”;

This study lool

Till. ' -
‘ m“) fOCLlS can

Studs,

' Can a meanlnzlt

confronted the question of what criteria are to be included in the definition of health and

how they can be measured."1

SCOPE OF STUDY

This study focuses on the downtowns of sixteen small and medium size cities in
Michigan. The cities were purposively selected because of their functional and geographic
representativeness. Not included were cities within larger metropolitan areas and cities in
the Upper Peninsula.

The study records and analyzes the evaluations of business and community leaders in
these cities. Its primary source of information is data collected from an inclusive survey of
these individuals. This survey was conducted during one summer (1992) specifically for
this study. The survey was distributed to 40 individuals in each of the study cities (640

total), and had an overall response rate of 57 percent.

RESEARCH QUESTIONS

This study looks at many factors relating to the health of downtowns. However, its
primary focus can be defined by listing three primary questions asked throughout the

study.

1. Can a meaningful index of downtown health be created?

Such an index is needed to be able to compare a variety of factors in meaningful
ways. Through its uniform application, it could be used to compare "apples
with apples," rather than "apples with oranges."

2. Can such an index be used to compare various cities and define factors most closely
associated with downtown health?

 

' James and Carolyn Robertson. 1978. The Small Towns Book. Garden City, New
York: Anchor Press, Doubleday. 136.

 

For an ir
index ca:
then dov

program

3. Can the inde-
downtouns—
based 011 this

Previous ;
the mix or
to also det

question. a

511m Assn

This studs atte
‘h‘mgmg in the 19
role as mcial and (

dou‘ntouns mel n

T .
0 lllCQrpOrale lhl'x
made.

 

For an index to be useful, it must be shown that it can be put to work. If the
index can delineate the factors most closely associated with downtown health,
then downtown leaders can better understand which types of revitalization
programs on which to focus.

3. Can the index be used to focus on one factor—the mix of business types found in
downtowns—and provide relevant recommendations for downtown revitalization
based on this one factor?

Previous research has indicated a primary determinant of downtown health is
the mix of businesses found in a downtown.2 If this is true, then it is necessary
to also determine what mix is optimal. This research will look carefully at this
question, and suggest whether an optimal business mix can be defined.

STUDY ASSUMPTIONS

This study attempts to provide a fresh perspective on the role of downtowns as they are
changing in the 19903. It also evaluates the success these downtowns have had in their
role as social and cultural centers of their communities. The evaluations of the study of
downtowns forming the basis for this analysis include both economic and social aspects.
To incorporate this fuller perspective throughout the research, three basic assumptions are
made.

AW A meaningful evaluation of a downtown's health can be
derived from perceptions of that downtown's merchants and business owners.

It is a basic assumption of this research that insight on the health of downtowns can
reliably be provided through surveys of downtown merchants and business owners. They
are the individuals who know each downtown best, and have the most at stake in its
success or failure. Such individuals can be considered "customers" who choose to

"purchase" the product offered by cities in their decisions to buy or rent space for their

 

2 Norman R. Tyler. 1987. An Evaluation of the Health of the Downtowns in Eight
Michigan Cities (Dissertation). University of Michigan.

4

 

 

busineSx"

The follow
cit} uith thoxe :
made regarding

downtou n CPI-1““

“h poor health
health.

.AQQIi'fi'fi’i \'

collection a.

Fora comparat:
Sampling and ex alu:
1n the sixteen studs I

. e ,
lll.OTTndllOll SOUTCCS

consistent nner th
sise -

. t ofdata collect.
angina:

businesses. From this perspective, the study is a consumer survey, with merchants and
business owners being the customers and the cities serving as the provider of goods and
services.

The usefulness and reliability of such perceptual input is argued in the next chapter.

Assumption}, Input from downtown merchants and business owners can be
used to determine factors closely correlated with downtown health.

The following study is a comparative study. By making comparisons of data from one
city with those from fifteen other cities, conclusions can be drawn and recommendations
made regarding factors correlated with downtown health. Such comparisons identify
downtown characteristics closely aligned with good health, as well as those closely aligned
with poor health. They also indicate what factors are irrelevant to improve downtown
health.

Assumption}; Comparisons are valid only if a consistent method of data
collection and analysis is used.

For a comparative study of cities to be reliable, data should be collected from a broad
sampling and evaluated in a consistent manner. This study includes surveys administered
in the Sixteen study cities. As opposed to most downtown studies, which rely on existing
information sources for the bulk of their input, this study derives its own data, in a
consistent manner, through use of identical surveys in each city. Without such a consistent
system of data collection and analysis, a study Should be considered a multiple case study
approach.

A significant contribution of this study is the formulation of the Health Perception
Index for downtowns. It establishes a method for comparing downtown health, as
perceived by merchants, business owners and others. The Index was first derived from a
1986 survey of respondents in eight Michigan downtowns, and formed the basis of a
previous dissertation study and report by the author, titled An Evaluation of the Health of

the Downtowns in Eight Michigan Cities (1987). The eight cities in the previous study

 

acre lnClUdCCl tn 1
current sun e). co:
eight new crtiex pr:
The follou in;
validates the concl
Second. it expand
allows for better \1
described in the f
correlated uith pg;
deta:led data on the

business mix is tied

Oncmzmm

The folloutng.

Review. puts this

Specifically in the r

chapter concludes

needs

(1. Chapter 11

Ellie ' .
S '5 Preceded |

were included in the sixteen cities in this study. By including the original cities in the
current survey, comparisons could be made over a longer time period, and the addition of
eight new cities permitted comparisons through a larger cross-section of cities.

The following study does not duplicate the earlier research, but builds on it. First, it
validates the conclusions of the initial Study by showing that the results can be duplicated.
Second, it expands the scope of the original survey from eight cities to sixteen, which
allows for better statistical analysis of the data. Third, and most important, the study as
described in the following pages focuses on the factor previously found to be most
correlated with perceived downtown health—the business mix of downtowns. It includes
detailed data on the businesses found in each of the study cities, and looks in detail at how

business mix is tied with downtown revitalization.

ORGANIZATION OF THE STUDY

The following chapters make up the main body of this report. Chapter II, Literature
Review, puts this study in the context of previous theories of urbanization, and more
specifically in the realm of urban economics and retail location theories and models. The
chapter concludes with the argument that a new model for evaluating downtowns is
needed. Chapter III, Methodology, details the procedures used to conduct this study and
includes an explanation of the method of analysis. A description of each of the 16 study
cities is preceded by the criteria used for their selection. Also included are detailed
descriptions of the survey questionnaire, the survey procedures, and a profile of the survey
respondents. Chapter IV, Analysis, forms the heart of this report. It describes the
derivation of conclusions from factors perceived as being correlated with downtown health.
Chapter V, Summary and Conclusions, briefly restates the important findings and offers
recommendations on how this information can be useful to merchants, business leaders,
city officials, planners and others who are concerned about downtowns and how to
develop relevant strategies for improving their health.

6

 

moan-:5 or [I

This study es:
Downtowns hate tr
the focus of actis in:
area. Many resear.

changed over time it

changes.

This chapter beg
It then reviews impoi
they apply to doun
downtowns in smalle

”man cnl’ironmem l

CHAPTER II
LITERATURE REVIEW

THEORIES OF URBANIZATION

This study evaluates the health of downtowns in a sampling of Michigan cities.
Downtowns have traditionally served as the core of their urban areas, and this function as
the focus of activities has been true both at the scale of smaller cities and large metropolitan
areas. Many researchers and theorists have analyzed the urban system. Their thinking has
changed over time as cities have changed, and a thorough literature review will reflect these
changes.

This chapter begins by looking at the most important classic urban theories and models.
It then reviews important urban economics and retail location theories and models and how
they apply to downtowns. The review then focuses more specifically on the role of
downtowns in smaller cities and current thinking on the future of downtowns in the new

urban environment. It concludes by arguing the need for a new method of evaluation.

URBAN LAND USE AND CENTRAL PLACE THEORY

One of the most distinctive characteristics of downtowns is their centrality. In both
large and small cities, downtowns have been located at urban centers, where they have
traditionally served as a focus for a community's activities, both social and commercial.
Some of the earliest and most basic of geographic theories have dealt with the importance

of this centrality.

 

 

dependent on dtsta:
based on the mark:
From this basis. h
following a concert:
use seas found at it
This simple theory

formed the basis of

 

A classic mode

Chnstaller's Centr'
location of cities.
explained hon the

ve
T33. Accordin g

As early as 1826, von Thiinen postulated that land use around a city is primarily
dependent on distance from the city center.3 His arguments assumed the value of land is
based on the market price of goods, the cost of producing, and the cost of transportation.
From this basis, he showed how land rent curves formed around a central market,
following a concentric land use pattern. According to von Thiinen, the most intensive land
use was found at the market center, with intensity decreasing with increasing distance.
This simple theory, although based upon factors of production rather than consumption,

formed the basis of most of the early analysis of city patterning.

I: . . E E l E] I]

A classic model which looked at the centrality function of cities in more detail was
Christaller’s Central Place Theory.4 He recognized an inherent spatial patterning to the
location of cities. His spatial/economic model, based on rural Germany in the 19305,
explained how the location of cities is determined by their size and functions, and vice
versa. According to Christaller, larger cities containing more functions tend to space
themselves at further distances from other larger cities, while smaller cities tend to be
spaced more closely together. He also postulated a distance threshold based on the range
of services and functions offered in a city. AS Christaller stated, "Cities tend to arrange
themselves as a series of satellites based on market patterns."

Working from Christaller, Ldsch (1954) established a distinctly different hierarchy of
urban spaces, based not so much on spatial patterns as on functional patterns. Whereas

Christaller suggested that larger cities contain all the functions of smaller cities plus

 

3 Johann Heinrich von Thiinen. 1875. Der Isolierte Staat in Beziehung auf
Landwirtschaft und Nationalokonomie (3rd Edition). Berlin: Schumacher-Zanchlin.

4 Walter Christaller. 1933. Central Places in Southern Germany, translated in 1966
by CW. Baskin from the 1933 German version. Englewood Cliffs, N.J.:
Prentice-Hall, Inc.

 

 

 

additional function~
related to their WC
ma) depend on lax-1“
function should be C
t'anous market are.»
point referred to as '.
areas.

Lbsch was not t1”.
refined the basic CK
centralit). and props
are not alua} s lOCdI';

Rushton (1971 l
uhich sent beyond
would he represents
distances for the c

perspective by sax 1'1

 

additional functions, Losch concluded cities can provide sets of functions not directly
related to their size. Spatial and functional hierarchies are not necessarily the same, but
may depend on locational and resource irregularities. Losch assumed the market for each
function should be considered separately, in isolation, allowing for much more overlap in
various market areas. These functional areas would be centered around a common supply
point, referred to as the “metropolis,” forming the center of a hierarchy of functional market
areas.

Lbsch was not the only one to revise Christaller’s basic theory; many others have also
refined the basic concept. Preston (1971) recognized a limitation to the concept of
centrality, and proposed instead the concept of “nodality.” He concluded primary functions
are not always located in the geographic center of an urban area, but rather at nodal centers.

Rushton (1971) postulated larger cities had an inherent advantage over smaller cities
which went beyond size. He concluded there was more satisfaction in larger cities than
would be represented by relative populations because people are willing to travel further
distances for the convenience of higher order goods. Murdie gave an alternative
perspective by saying the demand for goods and services may vary according to population
subgroups, and that populations should be disaggregated to understand demand fully.

C l' . E E l El I]
Central Place Theory is elegant in its simplicity, and has served as the early standard

against which other spatial theories are compared. However, some are concerned it has

inherent weaknesses when applied to the current system of cities. As explained by Berry,

The classical patterns of the central-place hierarchy break down and are replaced
by business patterns characteristically internal to cities. . . . Each such region is
specialized internally instead of being successively subdivided into
progressively smaller regions for the retail distribution of goods and services.
Locational specialization, rather than the repetitive, nested levels of a central-
place hierarchy, appears to be the key to understanding the most modern forms

 

of metro]

maids Oi

 

Central Place

OthT. and their

urbanolog} that t
aproach to under
19:05 Mill the v»
spatial patterns n
organism. Their re
neighborhoods. 5
forces and process:
compete for domin
attributes of the \a
from their early u e
Eeologists, which

applicable.

[REM ECONOMI

The defining ch

0m: lCSS don-I

bl comprehending th:

of metropolitan retail geography. In this, the modern system resembles the
models of Losch perhaps, instead of Christaller.5

W

Central Place Theory looks at the macro-patteming of cities, how they relate to each
other, and their relative hierarchies. However, there is also an important school of
urbanology that theorizes on the internal organization of cities. The Human Ecological
approach to understanding urban patterns had its roots at the University of Chicago in the
19205 with the work of Park, Burgess and McKenzie (1925). They saw the city in its
spatial patterns not pre-defined through geographic models, but as a kind of social
organism. Their research interests were uncovering interactions between a city’s “cells,” or
neighborhoods. Ecologists tried to understand not just the fact of urban geography, but the
forces and processes that controlled and changed it. The city was a place for these forces to
compete for dominance, shaped partly by their physical attributes, but also by the cultural
attributes of the various groups. Most of the behavioral approach to urbanism followed
from their early work. Because this study looks at smaller cities, the work of the Human
Ecologists, which looks at patterns of urban growth in large cities, is not directly

applicable.

URBAN ECONOMICS AND RETAIL LOCATION THEORIES

The defining characteristic of cities is its centralized core. Although this traditional core
has become less dominant in recent decades, it is still important. This is best understood

by comprehending the core’s importance as a retail center. Many theories explain how the

 

5 Brian J.L. Berry. 1963. Commercial Structure and Commercial Blight. Research
Paper No. 85. Chicago: Department of Geography, University of Chicago. p. I64.

10

city‘s core is a P:

gzgnificant.

Gnavm' Mo:

Based on Nev» :-
of urban function~
attraction and its d:
the relative potentia
simplicrt). and has l

Taaffe and Gas
model “as used to C

the} have shou n hot

CClllEl'S.

 

city’s core is a product of its functional nodality. Below are listed some of the most

significant.

GRAVITY MODELS

Based on Newton’s law of gravitation, gravity models are useful in predicting locations
of urban functions. They determine locations based on two factors—the size of an
attraction and its distance from a potential user. Gravity models can also be used to derive
the relative potential for competing downtown or retail areas. The concept is elegant in its
simplicity, and has been used as the basis of a number of urban and retail models.

Taaffe and Gauthier (1973) used a gravity model to study hinterland areas. Their
model was used to delimit the hinterland areas, their systems and hierarchies. From this
they have shown how to establish boundaries of expected areas of influence for competing

centers.

B'll’l [E 'lEV"

Reilly (1931) used a gravity model to develop the Law of Retail Gravitation. This was
used to determine the likely number of trips from generator areas (usually residential
districts) to attractor areas (retail districts). The formula he developed multiplies the
population of a generator by the population of an attractor, divided by the distance between.

Reilly felt his Law of Retail Gravitation should be considered a basic tool of analysis,
since it was based on evidence rather than theory. As he said, “A theory is an attempt to
explain something on the basis of what we think might be true. It is merely an explanation
which exists in someone’s mind and which can be disputed by anyone until such time as its
truth is demonstrated by scientific measurement. A Law, on the other hand, is based on the
measurement of actual conditions which no one can dispute....the law of retail gravitation,

based as it is upon actual measurement of existing conditions, is not a matter of theory or

1]

 

opinion but a la
man} researche

techniques.

 

mm 1063 J n
terms of probabi
continuous and pr
from another. as .\
or 'Huff Model."
Pmdlfling market

fitanges in a markw

 

opinion but a law in the true sense of the word.”6 Reilly’s model has been referred to by
many researchers, and is one of the basic building blocks of retailing spatial analysis

techniques.

H EC 5 . l-' . l l

Huff (1963) refined Reilly’s simple gravity model by modeling consumer preference in
terms of probabilities. He was the first to suggest that trade areas should be seen as
continuous and probabilistic rather than with clearly demarcated boundaries dividing one
from another, as suggested by both Christaller and Reilly. The spatial-interaction model,
or "Huff Model," has been an important model for store choice studies, being used for
predicting market share for new and existing retail centers and to simulate the effect of

changes in a market on store perfromance.

ll 11'” Hill 'll’

Another simple and straightforward model of store choice is the Nearest Neighbor
Model, described by Clark and Rushton (1970) and Ghosh and McLafferty (1987). It
predicts consumers will patronize the nearest store that has a needed good or service. In
other words, customers would minimize distance travelled. This is referred to as the
"Nearest Neighbor," or Proximal Area, method of analysis. This procedure is simplistic
because it assumes all commercial areas are relatively equal in stature (size and
attractiveness) and are differentiated primarily by distance. "Convenience stores,
emergency medical centers, branch banks, automatic teller machines, liquor stores, dry
cleaning services, copy centers, and drug stores are examples of services whose trade areas

can be estimated quite accurately by this procedure."7 The assumption is modified when

 

5 WJ. Reilly. 193]. The Law of Retail Gravitation. New York: Knickerbocker
Press. 32-33.

7 Avijit Ghosh and Sara L. McLafferty. 1987. Location Strategies for Retail and
Service Finns. Lexington, Massachusetts: Lexington Books. 67.

12

considering to o oth
costs. It also recogn
distance hetueen cor
les~ the relatne impa
The Nearest Ne
drawn using simple
drain surrounding ea

1 a
P01) Eons tpotential Ur

trust or RETAIL

Moving be)ond gr
for any given bUsinev
t 1961). The Index con
supply of retail selltn:
attractiveness of an are
saturated commercial e

useful when applied ll

ml‘tronments can be e\ a

 

considering two other factors—varying prices for goods and services, and transportation
costs. It also recognized that the pull of different businesses varies according to the relative
distance between competing establishments—the greater the difference in the distances the
less the relative impact.

The Nearest Neighbor model is generally implemented using Theissen polygons,
drawn using simple geometric procedures. In general, the larger the Theissen polygon
drawn surrounding each retail area the less the competition; with more commercial areas the

polygons (potential trade areas) become smaller.

INDEX OF RETAIL SATURATION

Moving beyond gravity models, a model evaluating the attractiveness of a market area
for any given business type is the Index of Retail Saturation, first presented by Lalonde
(1961). The Index compares the level of retail expenditures in a given area with the level of
supply of retail selling space. The higher the value of the IRS figure the greater the
attractiveness of an area for additional retail. A low retail saturation index represents a
saturated commercial environment for a particular business type. Since the index is only
useful when applied in a comparative manner, the relative attractiveness of various

environments can be evaluated and ranked using the index.

LOCATION-ALLOCATION THEORY

The use of location-allocation models has grown in recent decades largely because of
the increased use of computers for data analysis. Location-allocation theory allows
evaluation of multiple locations based on specified criteria, and selects the one best
location. Because of the widespread use of computers for analysis, this mathematically
rigorous procedure, which allows for more complex analysis, has become the dominant

theory for dealing with determining optimum locations for retail businesses. Examples

13

 

 

 

include the model.

(1984).

 

Berry U963)

tncreasingh frazm
diagram below, rczg:

center. but took on L‘

 

 

 

include the models developed by Achabel, Gorr, and Mahajan ( 1982) and Ghosh and Craig
( 1984).

W

Berry (1963) illustrated that the retail function was no longer centralized, but
increasingly fragmented. Retail functions in contemporary urban areas followed three
distinct patterns: retail nucleations, ribbons, and specialized areas. As shown in Berry’s
diagram below, retail was no longer simply concentrated in a nuclear grouping at the city

center, but took on the forms of strip commercial and specialty commercial areas.

 

 

 

 

 

 

Traditiona| W993“! [“Automobflo reef]
street

I

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Urban arterial .
Neighborhood ribbon “mm" Ml“
Community 1 I
Regional "‘Wfi'gbfnm‘“ Medical om
can
III (N 16’

Figure 4.1 Elements of the urban retail structure: (a) retail nucleation: (b) ribbons;
(c) specialized area.

Figure l - Berry’s Elements of the Urban Retail Structures

BEHAVIORALIST THEORIES OF DOWNTOWNS

W’ ' 'v'w ww

Schwartz (1984) took a behavioralist view of cities, and postulated there are two basic
ways to look at cities and their downtowns. They can be viewed as either "inorganic" or

"organic." An inorganic perspective assumes cities and their cores are simply organized

 

3 Brian J.L. Berry. 1963. Commercial Structure and Commercial Blight. Research
Paper No. 85. Chicago: Department of Geography, University of Chicago.
20 (Table 2).

14

 

 

functions 353:3“
satisf) the need
sen ices and got
their purposc bf
seem the econo
reg. malisi car.
largely unneedet

in contrast.
'beingf Each
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fabric Of the ptd
This PCTSpccm 6
than the sum 0.

“Present the

CU

 

 

functions designed to serve explicit needs. They are machines, basically built to efficiently
satisfy the needs of business, government and the population in general, for providing
services and goods. In this light, downtown buildings remain only as long as they fulfill
their purpose better than those elsewhere. As long as downtowns could most efficiently
serve the economic needs of society they were central to that society. When other areas
(e.g., malls) can fill this need more efficiently, downtowns should be seen as obsolete and
largely unneeded. This "inorganic" view is based primarily on economic considerations.

In contrast, seeing downtowns as organic recognizes that cities have an inherent
"being." Each building in the downtown is not just an economic machine for the
businesses located there, to be discarded when obsolete, but is also part of the overall
fabric of the place and part of its context, contributing to the district's overall character.
This perspective, a core assumption of historic preservationists, sees the whole as more
than the sum of its parts. Together, the buildings and places found in a downtown
represent the culture of a place, and serve as a focus to a community. In this "organic"

view, there are cultural imperatives tied in to understanding downtowns.

v' n h v' e

Similarly, Marcus (1983) looked at blight in downtowns and saw it not in the typical
view of physical decline, but rather in terms of a behavior. He gave a unique perspective to
downtown problems by arguing that “blight” is not caused by a breakdown in the structural
framework (e.g., vacancies, physical deterioration), but it is an outcome of a “deviant
behavioral process.” Thus, downtown blight is a symptom; the cure is not to clean up
blight, but to change the deviant behavior. In his study of British cities, he concluded the
true source of blight was inappropriate functional decision-making (in his study, the leasing
of retail space). His analogy to medical diagnosis techniques concludes that a distinction
should be made between chronic blight and acute blight—not all blight is alike—and

different intervention techniques may be appropriate.

15

 

 

 

crrIES AS CEfl

This stud) we
focus is 5653”“ m
The question of u.
While larger and la
has also been prof"
smaller cities.

The penod of ti.
from rural hinterlan-
others here dCSCl’ll‘K
pushed off their fa:
OPPOHURII) and being

the stories of “the gm
Many have quest}.

lamsts have argued

tun
ent and past ills of

oler , ' "
er} leSth of la

CITIES AS CENTERS OF GROWTH

This study specifically focuses on the status of smaller cities. One reason it takes this
focus is because most research has been oriented toward large cities and metropolitan areas.
The question of urban growth and scale has always been a question for urbanologists.
While larger and larger cities have been the engines of progress, the growth of such cities
has also been problematic. Many writers have looked at the question of larger versus
smaller cities.

The period of the Industrial Revolution established a significant pattern of migration
from rural hinterlands to urban centers. Myrdal (1957), DeBlij and Muller (1994), and
others have described in detail this “push-pull” phenomenon, with rural farmers being
pushed off their farms by labor-saving improved farming technologies and lack of
opportunity and being pulled to cities because of the availability of employment, as well as
the stories of “the good life” to be found there.

Many have questioned whether the trend toward increasing urbanization was healthy.
Marxists have argued this split between city and country life has been the cause of many
current and past ills of both urban and rural society. Marx himself said “...the foundation
of every division of labour that is well developed, and brought about by the exchange of
commodities, is the separation between town and country. It may be said that the whole
economic history of society is summed up in the movement of this antithesis.”9

Engels argued the problems of urbanization could only be addressed by moving the
masses into the rural areas to equalize population density. “The present poisoning of the
air, water and land can be put an end to only by the fusion of town and country; and only
such fusion will change the situation of the masses now languishing in the towns. . . .

Only as uniform a distribution as possible of the population over the whole country, only

 

9 Karl Marx. 1967. Capital Volume III (3 volumes). New York: International
Publishers.

l6

 

an intimate connect:
extension of the meaf
megapitalist mode C
isolation and stupor II
Hoseiitz i; 1954-55 I M
were generattse hecc
because urban entrepr
themselves. spendzn g
Ebenezer Hou ard
resulting from the ind;
book. Garden Cities ”.
behind the urban frtr. g
lnagarden~centered c
Letchtsonh and Welw
Film”) influence in b,

suburbantzation and th...

THE OPTIMAL SIZE:

 

an intimate connection between industrial and agricultural production together with the
extension of the means of communication made necessary thereby—granted the abolition of
the capitalist mode of production—will be able to deliver the rural population from the
isolation and stupor in which it has vegetated almost unchanged for thousands of years.”10
Hoselitz (1954-55) saw the changes in terms of “generative” and “parasitic” cities—cities
were generative because they act as catalysts for economic development, but parasitic
because urban entrepreneurs extract resources from the rural areas, but keep the wealth for
themselves, spending it through conspicuous consumption.

Ebenezer Howard recognized the problems of pollution, congestion, and overcrowding
resulting from the industrial revolution in London, and proposed a new type of city. In his
book, Garden Cities of T o-Morrow (1902), he proposed the construction of satellite cities
beyond the urban fringe, where residential living would be tied harmoniously with industry
in a garden-centered city. His proposal led to the construction of two “garden cities,”
Letchworth and Welwyn. More importantly, he captured the feeling of an age, and was a
primary influence in both England and the United States in establishing the trend toward

suburbanization and the New Towns movement.

THE OPTIMAL SIZE FOR URBAN PLACES

Many urbanologists have been concerned with the dominance of large cities, and have
looked at the issue of an optimal size for cities. Lynch (1987) showed that optimal size
considerations have been part of urban thinking since the time of Plato, who considered a
population of 5,040 citizens as ideal. More recent urban theorists have suggested that, with
modern technology, an optimal size varies from 100,000 to 250,000 population.

Richardson (1977) suggested an optimal size derives from maximization of per capita

 

1° F. Engels. 1970. The Housing Question. Moscow: Progress Publishers (originally
published in 1872-73). 89.

17

 

 

income based on I
as determining or
ma'ginal costs of
where up to a port
cost. but that ese
require dispropom

Is there an opt
(1969) indicated 1
minimized and her
cities hate Optrmd
Optimal size is no
fomard a number
and that it is alvt a}
big. and medium,

concludes by sax it.

half an eqmllann.

income based on production functions. Henderson ( 1974) looked at external diseconomies
as determining optimal city size. Sega] (1977) considered optimal size in terms of the
marginal costs of providing urban services. He compared city size to building a road,
where up to a point increased traffic (population) could be added with minimal additional
cost, but that eventually a threshold of congestion is reached where further increases
require disproportionate expenditures.

Is there an optimal size range for cities, and should it include smaller cities? Malisz
(1969) indicated there may be various threshold populations at which major costs are
minimized and benefits maximized. This would suggest that both smaller cities and larger
cities have optimal characteristics at certain sizes. Clawson and Hall (1973) concluded
optimal size is not important, but instead an optimal rate of change. Sega] (1977) put
forward a number of ideas. First he suggested each city has its own unique optimum size
and that it is always at its optimum. Another is that the very largest cities have grown too
big, and medium-sized “growth nodes” should be encouraged through urban policies. He
concludes by saying there is no such thing as an optimal size for all cities. Any city can

have an equilibrium at a certain size based on factors which vary from city to city.

THE PREFERENCE FOR SMALLER CITIES

The United States is an urban society, and large cities exert a tremendous influence over
our society in general. However, Americans love small towns, and prefer to live there.
Smaller cities represent the best of American values, as represented in the following from
Thorstein Veblen’s The Country Town:

The country town is one of the great American institutions; perhaps the greatest,

in the sense that it has had . . .a greater part than any other in shaping public
sentiment and giving character to American culture.

18

 

liter) w” and 5'“
prefer to live in small
shorted that. gisen lb
suburbs and 20‘? prt’
respondents preferred

The l970s saw f
toss ard urbanization.
population experience
the 1980s and 90s. n1
cinesorrural areas. l
Which represents the
ma llldl have been I
Ll” ”976.) estahl

l\l

dimensions of econt

Every poll and survey taken in recent years has shown that the majority of Americans
prefer to live in smaller cities.11 A 1979 HUD survey of people's attitudes and preferences
showed that, given the choice of "the best place to live," 44% chose small cities, 30% the
suburbs and 20% preferred to live in a larger city.12 Another poll found that 52% of the
respondents preferred to live in small towns vs. 8% for larger cities. 13

The 1970s saw, for the first time, a reversal of the United States’ 200 year long trend
toward urbanization. Between 1970 and 1974, large metropolitan areas over 2 million in
population experienced a net loss in population. Although the trend has not continued into
the 19803 and 905, many feel it was an indication of people’s preference to live in smaller
cities or rural areas. Lessinger has described recent changes through the term "Penturbia,"
which represents the fifth major American migration, turning away from the metropolitan
areas that have been the focus of their lives in this century and to smaller towns and cities.
Liu (1976) established a quality-of-life index for cities of various sizes based on
dimensions of economic, political, environmental, health and educational, and social
relationships, and found that the highest quality-of-life ratings were for smaller cities.

Fuguitt and others (1979) have been looking at the reasons for the significant shift in
migration patterns back to small cities and less densely populated areas. Surprisingly, he
has found that economic incentives are playing a much smaller role than anticipated.

"Among the most important findings echoing throughout the growing literature
concerning the migration turnaround is that economic incentives are playing a

much smaller role than has been the case previously. Individual economic
motivation, the cornerstone of human capital migration theory, has long been a

 

'1 Herrington J. Bryce. 1979. Planning Smaller Cities. Lexington, Massachusetts:
Lexington Books. XV.

‘2 United States Department of Housing and Urban Development. 1979.
Developmental Needs of Small Cities. Washington, DC: US. Government
Printing Office. 2.

13 Risa Palm. 1981. The Geography of American Cities. New York City: Oxford
University Press. 498.

19

 

 

determrnan

influenCc 3?

-0n the 0th
emergC 1”
qualit) Oil
gummdilll

migrants."

Crascroft and
Cities “as. the de~
m the "pull" to
factor is perhaps
with small toss n~

Park 1 I936; i
size includes su
boundaries. and
organization Th
their lack of spr-
in eseryth‘m o

g.

“hole. This '-
llfes‘ble and en

other may:

lOttnd three rec

determinant of migration having considerable empirical support. But its
influence apparently has been reduced in recent years.

"On the other hand, quality of life factors, variously measured, are beginning to
emerge in the migration literature with unprecedented clarity... In fact, the
quality of life factor is the best single structural element for understanding and
summarizing what is presently known about metropolitan to nonmetropolitan
migrants. " ‘4

Craycroft and Fazio (1983) also found that the primary reason people moved to small
cities was the desire to improve their quality of life, rather than economic motivations. It
was the "pull" to rural living rather than the "push" by urban living. The quality of life
factor is perhaps the most important reason for the continuing love affair this country has
with small towns, and is also one of the most elusive.

Park (1936) has suggested people prefer a sense of “neighborhood,” where the smaller
size includes such reassuring features as social homogeneity, street patterns, identity of
boundaries, and common services. Clayton Denman, who has been head of the national
organization The Small Towns Institute, argues that what makes smaller cities desireable is
their lack of specialization. To a larger extent than found elsewhere, everyone is involved
in everything. A small city community can be seen in its entirety and comprehended as a
whole. This "sense of community" is the critical factor in determining a satisfactory
lifestyle and environment.

Other reasons have been suggested for the shift back to small towns. A Canadian study

found three reasons:

1. The suggestion that the benefits from economies of scale and agglomeration
may have been overstated, and such benefits may be captured at a lower level of
urban development than originally assumed.

2. Smaller cities are now providing the infrastructure and organization
necessary to attract both manufacturing and retail relocation.

 

‘4 Glenn Fuguitt, Paul R. V055 and J.C. Doherty. 1979. Growth and Change in Rural
America. Washington, DC: Urban Land Institute.

20

 

3, The net

&Sll’€> {01'
Another reav
transportation": or
computers and c
cities'— cities r
enters and deer
sholesale Chan
limited conseq;
1. that a
people

2. that
culture
3. that
4. the:
Dem:
CUVR

3. The newly realized ability of both companies and individuals to fulfill their
desires for enhanced quality of life.15

Another reason smaller cities take on increased importance is the effect of the
transportation/communications revolution. Referred to as “telematics,”16 this integration of
computers and communications linkages is allowing for the development of “wired
cities”—— cities of relatively low density where workers are connected to larger urban
centers and decision-makers through home computers. This trend is capable of making
wholesale changes in the urban landscape. But Gottman (1977) feels it will have only
limited consequences because it is based on a number of questionable assumptions:

1. that access to the material available through telematics will fully satisfy most
people in their work and leisure;

2. that isolated living with good communications will satisfy people’s social and
cultural needs;

3. that personnel issues can be handled through remote control;

4. that it would not be too costly to provide energy, food, consumer goods,
personal services, amenities, and so on in an extremely low-density
environment. 17

THE NEED FOR A NEW METHOD OF EVALUATION

The above sections have described many perspectives presented in research on urban
areas in general, and downtowns in particular. However, there is a critical component

missing from these discussions, concepts and theories. What is not described is a way to

 

15 D. Todd. "The Small Town Viability Question in a Prarie Context." Environment
and Planning A. 15:903-916.

'5 Paul L. Knox. 1994. Urbanization: An Introduction to Urban Geography.
Englewood Cliffs, New Jersey: Prentice Hall. 131.

17 J. Gottman. 1977. “Megapolis and Antipolis: The Telephone and the Structure of
the City.” in The Social Impact of the Telephone. 1. de Sola Pool (Editor).
Cambridge, Massachusetts: MIT Press.

21

 

cxaluate urban i

exaluatis'e tool.

etaluatiie tool.

cities and their c

"MOM \I
question
well-bet

confront:
health a:
stabtltt}.
ld0“l'1ifi\
these terr
"The Que
d1ff€l€nu
dowan
the Same
judSemer

comparlsc

Good health Is

that

term: r«Ether th

grout-[h is an t

iii If
A Smart m
f

Purely qur

(lus’illl,3[]‘\.e C

58)" One em

evaluate urban health. It is impossible to consider the impact of changes without a reliable
evaluative tool. One problem is that it is unclear what criteria to use to formulate such an
evaluative tool. In other words, what criteria should be used to evaluate the "health" of

cities and their downtowns? As discussed by Robertson (1978):

"Most studies and policies that relate to the city core address themselves to the
question of viability, health, vitality or some other term conveying the desired
well-being of the downtown. However, few of these efforts have directly
confronted the question of what criteria are to be included in the definition of
health and how they can be measured. In other words, although terms such as
stability, viability, and healthiness are used to describe the desired state of
[downtowns], there has been little attempt to provide precise interpretations of
these terms.

"The question remains: Viability compared to what? Viability makes a
difference if one is judging the current state of a downtown against: the same
downtown many years ago; the surrounding... area; other downtowns within
the same region; or downtowns of a similar ilk across the country. Viability
judgements would undoubtedly incorporate aspects of all of these
comparisons..." 1 3

Good health is typically seen in terms of growth. Yet, Schumacher ( 1973) has argued
that growth is an unreliable indicator of health, for growth is inevitably seen in economic

terms, rather that in terms of quality of life.

"A small minority of economists is at present beginning to question how much
further growth will be possible,... but even they cannot get away from the
purely quantitative growth concept. Instead of insisting on the primacy of
qualitative distinctions, they simply substitute non-growth for growth, that is to
say, one emptiness for another." 19

 

‘3 James and Carolyn Robertson. 1978. The Small Towns Book. Garden City, New
York: Anchor Press, Doubleday. 136-137.

19 BE. Schumacher. 1973. Small is Beautiful: Economics As If People Mattered.
New York City: Harper and Row. 46.

22

 

 

Thegm:

and Elf) (Tape,

A SOCt.
facilities:
Of mdju
ll Chang

gotten [x

“hers ( 19.33,-

 

He goes on to say:

"In a sense, everyone believes in growth, and rightly so, because growth is an
essential feature of life. The whole point, however, is to give to the idea of
growth a qualitative determination; for there are many things that ought to be
growing and many things that ought to be diminishing."20

The government developed a means of evaluating health in terms of “social indicators,”

and developed the following definition:

A social indicator may be defined as a statistic of direct normative interest which
facilitates concise, comprehensive and balanced judgments about the conditions
of major aspects of a society. It is in all cases a direct measure of welfare if
it changes in the “right” direction, while other things remain equal, things have
gotten better or people are “better off.”21

Myers (1988) lists four approaches to quality-of-life analysis. They are:
0 the personal well-being approach which measures life-satisfaction of
individuals;
0 the community trends approach which focuses on quality-of-life components
and trends within the community;

. the liveability comparisons approach which focuses on comparing different
urban areas according to a number of objective indicators assumed to reflect
quality of life; and

0 the market/resident approach in which housing price and/or wage differentials
are theorised to compensate for quality-of-life differences between urban
areas.22

In 1939 psychologist E.F. Thorndike presented a method for evaluating the well-being
of cities using what he termed a “Goodness Index.” Using this method he came up with a

number of controversial conclusions. The study was described in two books; in his book,

 

20 Schumacher. 148.
21 United States Department of Health. 1969.

22 D. Myers. “Community-relevant measurement of quality of life.” Urban Affairs
Quarterly. 23: 108-125.

23

 

Your C1431:
has follovs-U‘
cities tsce .r‘
"COOLIDCSS In

'G' and other

"“HA'
"Is it b:
Bork f0
Churches
have a c.
lease lht~

about uh,

'Imrana;

Correlatror

presumpm

£068 again.
Thomdike's Q
or}, “lib lest er dea

' md' ,
0 .

26 mdlkc_ Your
Appendix A f

Your City,23 he collected data on all US. cities with a population of over 30,000, and in
his follow-up book, 144 Smaller Cities, 24 he added data on a significant number of smaller
cities (see Appendix 5). His purpose was to rate each city studied according to a
"Goodness Index" and then see what correlations were found between the Goodness rating

"G" and other factors. As he stated:

"WHAT MAKES A CITY GOOD

"Is it better for a community to have its married women stay in the home or
work for a wage? To have a few churches with large membership or many
churches each with few members? To have chain stores or independents? To
have a city manager or a mayor? To own and manage its lighting plant or to
leave this to private enterprise? These are samples of a multitude of questions
about which there is disagreement.

"Important evidence can be obtained concerning many of them in the shape of
correlations of the fact in question with G. ...on the whole there is a primary
presumption that whatever goes with G is itself good for a city and that what
goes against G is itself bad for a city."25

Thomdike's Goodness rating was based heavily on death-rate data (thus assuming a
city with fewer deaths was a "good" city), but also incorporated education, economics,
"social" items, and "creature comforts" as lesser determinants.26 However, they were all
factors that he had selected intuitively, and he gave little definition to the basis for his
selections. Although correlation analysis is a legitimate research technique, in his case both
the dependent variable (the Goodness Index) and the determinants needed to have a sound
basis or the rest of the research would not be well founded. He himself recognized the

dangers inherent in this form of analysis.

 

23 ER Thorndike. 1940. Your City. New York City: Harcourt Brace and Company.

24 BE Thorndike. 1940. I44 Smaller Cities. New York City: Harcourt Brace and
Company.

25 Thorndike. Your City. 70.
26 See Appendix A for a listing of the twenty-four items Thorndike incorporated.

24

 

 

“Such CH
bod.butb
but be 8‘
arguing fr

relevant 31

Through his 2
factors uere assoc
ratings. For exam
dentists. desrgner~
home on nershtp.
"Negroes" and a h
lying with Slall\l1C\

One of INS conc

'ClllCS becc
the larger or
differences i
IH'eoodnec

"The comm,
Population 13
a SUPCFStitior
having 750C

having “filer
Store'."28

"Such evidence is not conclusive for two reasons: A thing may be intrinsically
bad, but be associated with something good; a thing may be intrinsically good,
but be associated with something bad. Caution is therefore necessary in
arguing from correlations with G, and pains should be taken to inquire into all
relevant affiliations of the fact in question."27

Through his analysis of correlations he developed conclusions that described which
factors were associated with cities with higher ratings for Goodness and which with lower
ratings. For example, factors associated with "Good" cities included a high proportion of
dentists, designers and musicians, as well as a high level of downtown retail sales and
home ownership. Cities with a low Goodness rating had in common a high percentage of
"Negroes" and a high level of church membership. But this approach is tantamount to
lying with statistics, obtaining spurious statistical relationships.

One of his conclusions related directly to the factor of growth discussed above:

"Cities become little or no better by becoming bigger... Within the 144 cities,
the larger ones are neither better nor worse than the smaller. On the whole,

differences in size may perhaps account for a fiftieth of the differences of cities
in 'goodness'.

"The common ambition of citizens to have their city grow bigger in area or
population is misguided. The glorification of size in the case of a city is largely
a superstition,... it probably harks back... to the times when the cities now
having 75,000 or more were contrasted sharply with rural communities by
having water, gas, sewers, lighted streets, and shops other than a 'general
store'."28

Three weaknesses found in this study's approach, however, make many of its resulting
conclusions unreliable. First, Thomdike's intuitive derivation of the Goodness Index made

his Index subject to considerable personal bias. Whether or not he intentionally meant to

 

27 Thorndike. Your City. 70.
28 Thorndike. I44 Smaller Cities. 66.

25

 

bias his data m
Tnorndil;c ”58d
selection 0f dd“
his ossn thm”
stud). but “15 at
Second. Thor
and (ltd not Con‘
sampling of Ol‘m'
inconsistencrft 9
conditions. and 5“
Third. his anal
hon each cit) 's Gs
was improving or E
and effect relations
A contemporar
I985. l989. and 19'
metropolitan areas.
Education, culture.
quantitative. factual
the reSearchers. A2;
those cities. Also, it
how conditions were

What factors were all

CO ,- '
a i c

bias his data (and there is every reason to believe he did not intend to), it appears that
Thorndike used demographic data selectively and "massaged" it for his purposes. The
selection of data was his own choice, and this selection could not help but be swayed by
his own preliminary preconceptions. This is true to some degree in almost any research
study, but was an especially vulnerable weakness of this Study.

Second, Thomdike's analysis was based on secondary data covering factual measures,
and did not consider perceptual input from the residents of the cities studied. Such a
sampling of opinion from residents in the Study cities could have quickly pointed out any
inconsistencies between the theoretical framework of his analysis and and the actual
conditions, and could have served as an important and critical check of his conclusions.

Third, his analysis did not include a historical perspective. The study did not look at
how each city‘s Goodness Index rating was changing overtime, and whether its condition
was improving or getting worse. Such a perspective could have aided in determining cause
and effect relationships.

A contemporary effort at rating cities was developed by Boyer and Savageau (1981,
1985, 1989, and 1993) and published as the Places Rated Almanac. This study ranked 329
metropolitan areas, using as criteria climate, housing, health care, crime, transportation,
education, culture, recreation and economics. The study was based completely on
quantitative, factual data, and made a comparison of cities according to criteria derived by
the researchers. Again, no attempt was made to include perceptual input from residents of
those cities. Also, there was no effort to give a historical perspective to the data, to show
how conditions were changing, whether the condition of a city was improving or not, and
what factors were affecting change. However, “the findings of Boyer and Savageau are

controversial because they give certain metropolitan areas a higher or lower ranking than

26

 

mg as’erage chSO
quantitatne meast

Berger. et at. t
ueighttng sariablt
reflected either th:
procedure."3‘3 In“
and denied the u e
consumers. The ch
housing prices tsaf:
urban size and urb.
desirable amenities
collected from dhd
charaetenstics. Sm.
data which give a b;
areas.

Bumell and Gals!

Berger, and found tha

 

 

the average person would....Such disparities are due to Boyer and Savageau’s use of only
quantitative measures.”29

Berger, et a]. (1987) criticized the approach of Boyer and Savageau and their system of
weighting variables because they “imposed weights in some ad hoc fashion. The weights
reflected either the analysts’ subjective values or the outcome of an atheoretic statistical
procedure.”30 Instead, Berger created a quality-of-life index used to compare urban areas
and derived the weighting of the characteristics from observable behavior of workers and
consumers. The characteristics are weighted based on the assumption that the difference in
housing prices (sales and rents) and wages for otherwise comparable cities (adjusted for
urban size and urban structure) can be explained in terms of amenities available, with
desirable amenities driving up housing costs and driving down wages. The data is
collected from disaggregated data from the US. Census for population and housing
characteristics. Sixteen amenity variables are used, primarily climatic and environmental
data, which give a bias to smaller cities, rather than the amenities offered by larger urban
areas.

Bumell and Galster (1992) compared the methodologies of Boyer and Savageau and
Berger, and found that the inherent biases they incorporated made city size a determining
factor. Boyer and Savageau’s method, using 55 aggregate variables, was shown to favor
larger urban areas, while Berger’s approach, using l6 variables (primarily environmental)
favored smaller urban areas.

Using a market/resident approach, Rosen (1979) found location decisions were best
viewed in terms of three factors—wages, rents and amenities. Blomquist, et.al. (1988)

followed this approach by calculating a dollar value for the bundle of amenities and

 

29 Robert M. Pierce. 1985. “Rating America’s Metropolitan Areas.” American
Demographics. (July):22.

30 Mark C. Berger, Glenn C. Blomquist and Werner Waldner. 1987. “A Revealed-
preference Ranking of Quality of Life for Metropolitan Areas.” Social Science
Quarterly. 68:763 (December).

27

 

tricol'i‘orating [hi
urban counllCS 35‘
In a stud}? b}
ingredient. Their
commonalit) "; 1h;
could be gTOUPCd
lSéO to I960. it de-
l'hrs research it as II
US. in terms of th
shat changes. The
since it alloss ed for a
The study '5 need.
Whfls “as based or

{Oftign‘bom‘ black

C

 

incorporating that value directly into the equation for wages and rents. Rosen ranked 253
urban counties according to these criteria.

In a study by Warner and Fleisch (1977) a historical perspective was an important
ingredient. Their study evaluated urban areas by grouping them according to "clusters of
commonality"; that is, if communities were similar according to selected criteria, then they
could be grouped into pattern types. Using census data for each of its study cities from
1860 to 1960, it described what other cities each were most like at different points in time.
This research was intended to allow historians to describe metropolitan development in the
US. in terms of these groupings, and thus draw distinctions about what factors caused
what changes. The historical perspective of this study is both unusual and significant,
since it allowed for a long-range and thus more accurate perspective.

The study's weaknesses should not be overlooked. It's first weakness is that the entire
analysis was based on only six population characteristics: size of population. the percent of
foreign-born, black, male, and adult, value added in manufacturing per capita, all items
readily available from US. Census data. It is difficult to form general conclusions of any
kind based on this limited set of data. Thus, the results are highly vulnerable, and the
authors admit that "...the addition or subtraction of a single variable will change the
membership in these groups."31

Also, the study was formulated based on a determination of what data were available.
Census data were used because they were readily available. This can be compared to the
proverbial cart leading the horse; that is, the study parameters were based on the availability
of data, a questionable research approach.

Graves (1979) showed amenities to be powerful contributors in the choice of location,
and also showed how the migration patterns of the white population were closely correlated

with climate. Greenwood and Hunt (1989), however, found that jobs and wages are the

 

3‘ Sam Bass Warner, Jr. and Sylvia Fleisch. 1977. Measurements for Social History.
Beverly Hills, California: Sage Publishers, Inc.

28

most imt-‘Ortdnl dc‘:
are less imPOnani'
because the} d‘dn.
and quality-of-life
patterns are mOst re
Ochrmann t l9“
such objective indit
by incorporating a
residents to weight
weighting \sas sinz;
undoubtedly skeuec
climate and crime as
A further attemp
combination softua
itfightings to s'ariot

Variables for 300 men

 

 

 

 

most important determinant of urban migration and growth, that location-specific amenities
are less important in location decisions, and said Greenwood’s results were spurious
because they didn’t include employment information. Porell (1982) found both economic
and quality-of-life factors are important determinants of migration, but that migration
patterns are most responsive to economic factors.

Gehrmann (1978) suggested that progress could be made in the evaluation technique by
such objective indicators weighted by differing groups. Pierce (1985) used this approach
by incorporating a subjective measure to city ratings, asking a sampling of New York
residents to weight the nine categories in terms of importance in selecting a home. The
weighting was simply on a scale of nine to one, based on each categories ranking, and
undoubtedly skewed the results as much as no weighting at all. They ranked economics,
climate and crime as most important and recreation, transportation and the arts as lowest.32

A further attempt to resolve the issue of weighting was made with development of a
combination software/database program which allows users to provide their own
weightings to various factors. Called “Places, U.S.A.,”33 the database includes 70
variables for 300 metrOpolitan areas.

All of these systems are flawed because the measurements and weightings of various
characteristics are selected by the researcher(s), rather than derived from the data itself.
Each brings his or her own perspective on how to measure, and this leads to great diversity
in the results. For instance, Berger’s work found the metropolitan area ranked first for
quality of life was Pueblo, Colorado, while Liu ranked the Pueblo area 42nd and Boyer

and Savageau ranked it 111th.34 Similar discrepancies are found throughout the respective

 

32 Pierce. 23.

33 Diane Crispell. 1987. “Rating With Places, USA.” American Demographics.
(August):58-59.

34 Mark C. Berger, Glenn C. Blomquist and Werner Waldner. “A Revealed-
preference Ranking of Quality of Life for Metropolitan Areas.” Social Science
Quarterly. 769.

29

 

 

1155. In {35" the
analjsis. had it

Significant)"

THE NEED HI).

Perhaps the be
was Kent Robertst'
Problem of Knou l:

the basis for dou ntt‘

Assumption: A it

This approac
downtou n.“
number of re;

° The
° It sc,
‘ It pl;

. “5 Ck

ASSlllnpn‘On: FPO.
e

g. direm l

 

 

lists. In fact, these three listings, which are often used as basic references for quality of life
analysis, had no correlation in their rankings that was shown to be statistically

significant.35

THE NEED FOR DOWNTOWN MODELS

I 1.. l! . [@1221

Perhaps the best overview discussion written on downtown policies and their impact
was Kent Robertson’s 1985 article, "Designing Downtown Redevelopment Policy: The
Problem of Knowledge.”36 In it he outlines the various assumptions that have served as

the basis for downtown revitalization efforts in the past.

Assumption: A healthy CBD is critical to a healthy urban area.

This approach is based on a belief in the statement, “The heart of a city is its

,9

downtown. Vitality of an urban area “trickles down” from this core for a

number of reasons:
' The central business district is still the primary employment center.
0 It serves as the cultural, service, distribution and transportation center.
0 It plays a critical role in city future.

0 Its economy creates a “multiplier effect” on economy.

Assumption: Federal government intervention is necessary for revitalization.

Urban renewal programs between 1949 and 1974 represented an approach with
strong, direct federal involvement. Urban Renewal lost the focus on central
business districts, and focused instead on the clearance of “blighted” areas;
thus, it became primarily a residential removal and redevelopment program.

 

35 Berger. 773.

36 Kent A. Robertson. 1985. "Designing Downtown Redevelopment Policy: The
Problem of Knowledge.” Journal of Architectural and Planning Research. Volume
2 .

3O

 

nit Com
Grant Pm
and ct?“
spec”! 58:

green ‘0 d‘

Assumption: f

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crampICS 0
[he {30ij
projects ha
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shoPP‘"g m
public PTO.”
activity 110*
waterfronl C
districts; 0i“
amenities; ac
of new office

anea from tell

.' lternatis'e Pers‘e

The traditional pct

issue of downtowns

desenbed by Robertsc

Aiswnption: Cent

This altemati‘»
that suburbs r.I
Omierly cent '
corridors," “If.

1‘

 

 
   

The Community Development Block Grant and Urban Development Action
Grant programs attempted to put policy decision-making back into local hands,
and created incentives for private investment tax breaks, land write-offs, and
special services, but little attention either in the private or public sectors was
given to downtown revitalization.

Assumption: Physical improvements are critical to CBD revitalization.

This approach puts reliance on the success of physical improvements. The
examples of physical improvement projects are overwhelming, for this has been
the favored approach to downtown revitalization for most communities. Such
projects have included converting the main streets to pedestrian malls in an
attempt to make the downtown more pedestrian friendly; downtown indoor
shopping malls, intended to compete directly with suburban malls; downtown
public projects, such as convention centers, sports stadiums, which create new
activity nodes; historic districts, used to emphasize a unique ambience;
waterfront development, utilizing an overlooked area in many center city
districts; open space improvements, such as parks and walkways, to provide
amenities; additional parking to improve access convenience; and construction
of new office buildings, in an attempt to shift the functional focus of a central
area from retail to the financial/service sectors.

W
The traditional perspectives described above are not the only perspectives brought to the

issue of downtowns and their revitalization. Other alternative perspectives are also

described by Robertson.

Assumption: Central Business Districts are obsolete.

This alternative perspective recognizes society is now suburban oriented, and
that suburbs now provide all the needs for homes, shopping and jobs. The
formerly central city and downtown functions have migrated to “freeway
corridors,” where it is less risky and easier to develop.

3]

 

Ajjumpiltm.’ F‘

From this p
economic n
should not l
neighborhm‘

The perspectrsev
look at the role of d

for further anal _\ sis.

THE NEED FOR Q

Each stud} desc
according to cntena d
input from residents c
qualitative indicators

being. the understant

 

 

 

Assumption: Focus should be on center city neighborhoods, not downtowns

From this perspective, the city should deal with people’s needs first, not the
economic needs of business. The scale of development to be encouraged

should not be regional, but local, since the core unit of an urban area is the
neighborhood.

The perspectives outlined above by Robertson recognize that there are many ways to
look at the role of downtowns, both in the past and in the future, and lay out a framework

for further analysis.

THE NEED FOR QUALITATIVE VARIABLES

Each study described above was based on quantitative data and compared cities
according to criteria derived by the researchers. No attempt was made to include perceptual
input from residents of those cities. However, there is a need for the development of such
qualitative indicators. As stated in a federal government report, “...the charting of well-
being, the understanding of the relationship between objective changes and subjective
responses, and the development of reliable indicators by which we might reckon advances
in our individual and social well-being are still relatively undeveloped.”37 The current
interest in quality-of-life factors is largely due to two recent societal changes. The first is
the great number of college graduates who have been exposed to humanistic concerns, such
as social and environmental issues as well as concern for personal growth. The second in
the increasing affluence in American society, which gives more confidence that the

essentials are readily accommodated and allows us to look at “higher needs.”38

 

37 “The Quality of American Life in the Eighties.” 1980. US. President’s
Commission for a National Agenda for the Eighties. Report of the Panel On the
Quality of American Life. US. Government Printing Office. 10.

33 “Quality of American Life.” 10.
32

 

Frank Andrew:
question of is but it
for No types of in.

a progra
both perce;
concerns...
assessing p

perceptual d
Terms such as \
beareliance in the
Francis (1973: desc
is. of course. not on
Horn (1993i Ci
quantitative informs:

1“ a genera

me] than of
iOad inhefcn
statistics, am.

AS it is further dc
Quantitative 1
defined than ‘

Frank Andrews (1976) of the University of Michigan has directed research toward the
question of what makes up the concept of "well-being." In his work he stresses the need

for two types of indicators:

"... a program designed to assess well-being would be most useful if it included
both perceptual and non-perceptual social indicators relevant to the same
concerns... In short, we would envisage two parallel series of data: one
assessing perceptions about well-being,... the other providing various non-
perceptual data for the same concems."39

Terms such as well-being, viability or health cannot be measured directly. There must
be a reliance in the research on obtainable data to serve as proxies for these concepts. As
Francis (1973) described the use of proxy data, "A major cause of this reliance on proxies
is, of course, not only conceptual. Some things are just hard to measure.”0

Horn (1993) explains that it is sometimes difficult to separate qualitative from

quantitative information, for there are elements of each in the other.

In a general way the objective-subjective distinction is often more a matter of
form than of substance. All objective-type indicators carry a subjective value
load inherent in the process of the collection, selection and presentation of
statistics, and subjective-type indicators borrow objective modes of grouping,
ranking and partitioning the data.“

As it is further described by Schumacher (1973),

Quantitative differences can be more easily grasped and certainly more easily
defined than qualitative differences; their concreteness is beguiling and gives
them the appearance of scientific precision, even when this precision has been
purchased by the suppression of vital differences of quality.42

 

39 Frank M. Andrews and Stephen B. Withey. 1976. Social Indicators of Well-
Being. New York City: Plenum Press. 340.

4° Walton J. Francis. 1973. “A Report on Measurement and Quality of Life and the
Implications for Government Action on ‘The Limits to Growth.’” Washington,
DC: Department of Health, Education and Welfare.

4] Robert V. Horn. 1993. Statistical Indicators for the Economic and Social Sciences.
New York: Cambridge University Press. 9.

42 ER Schumacher. 1973. Small is Beautiful: Economics As If People Mattered.
New York: Harper and Row. 46.

33

 

chtg concurs

The omlSSlC
[he mOSl im
interactHC-‘i
Yet there is an tr
grossing realization
more material good
neglected~the kind
300_sears ago b) Cir
not being unhapp}
commonly seen as \l
dOWOWnS. Similarl}
lack of down“ ardnes.
A Stanford chcar
Chtiicult because it l’L’il'
- hard to Obtaii
~elaborate mu.

Ta EOOd deal 0

“explained by 3
lfLsti

 

tuted and collecte-

 

 

Francis concurs,

The omission of items which cannot be measured is itself a major bias—often
the most important facts about social conditions are qualitative, derivative or
interactive.43

Yet there is an increasing awareness of the need for such indicators. “There has been a
growing realization of what we give up, individually and collectively, by clamoring for
more material goods, a realization that there are other kinds of goods that have been
neglected—the kind we try to encapsulate in the phrase ‘well-being.”’44 Yet, as observed
300 years ago by Girolomo Cardono, happiness [satisfaction] is perhaps simply the state of
not being unhappy [dissatisfied].45 As used in the health professions, good health is
commonly seen as simply the absence of medical problems. Perhaps the evaluation of
downtowns, similarly, should not be upward on a positive scale. but rather be seen as the
lack of downwardness on a negative scale.

A Stanford Research Center handbook suggests qualitative evaluation and forecasting is

difficult because it relies on:
- hard to obtain data,
- elaborate model building and testing,
- a good deal of consultation with experts in diverse fields.46

As explained by Marans and Rodgers (1975), “Only when subjective indicators...are

instituted and collected over time can we as a society begin to have confidence in the

 

43 Francis. 548.

44 “The Quality of American Life in the Eighties.” 1980. US. President’s
Commission for a National Agenda for the Eighties. Report of the Panel On the
Quality of American Life. US. Government Printing Office. 10.

45 Robert V. Horn. 1993. Statistical Indicators for the Economic and Social Sciences.
New York: Cambridge University Press. 96.

46 Center for Study of Social Policy, Stanford Research Center. 1975. Handbook of
Forecasting Techniques. Menlo Park, California: Department of Commerce
(December). 72.

34

usefulnCSS 0f Ob-
qugt: distinct f ror
{liar either 0”!”
Rather. there 15 ‘
meaning as I! (at:
mammgmd“:
obt.ained.""8 Th
eniironment is d:
Standard against H
Cunerll985isi
nuuaontoeiahni
factors. both social
indicators are based
Used this approach a:
life in British cities.
affected by three fat
status being the most
Perceptual. or soc
“kid‘HWreliablt d
tttdicator is to

trade

mood. Mallhm

 

 

usefulness of objective indicators.”47 They further explain that subjective indicators are
quite distinct from more traditional objective indicators. “The implication, however, is not
that either objective or subjective indicators are better or more useful than the other.
Rather, there is urgent need for both kinds of indicators; each type takes on depth of
meaning as it can be related to the other. By themselves, objective indicators are often
misleading and will remain so until indicators of the human meaning attached to them are
obtained.”8 They explain that how a person evaluates the condition of his or her
environment is dependent on two things: how the environment is perceived and the
standard against which the evaluation is given.

Cutter (1985) suggested a “conceptual model” which used both subjective and objective
indicators to evaluate parameters of quality of life. In this context, objective indicators are
factors, both social and physical, external to the population being studied; subjective
indicators are based on the perceptions of the study population. Rogerson, et.al. (1989)
used this approach and combined subjective and objective indicators to assess the quality of
life in British cities. Mookherjee (1992) concluded that perceptions of well-being are most
affected by three factors—financial status, marital status, and education. with financial
status being the most prominent of the three.49

Perceptual, or social, indicators can be created in many ways, but the question must be
asked, How reliably does such an indicator represent reality? One way to validate such an
indicator is to compare it to other data sources which are more common and more

understood. Malthus first recognized that the social well-being of a society was directly

 

47 Robert W. Marans and Willard Rogers. 1975. "Toward an Understanding of
Community Satisfaction." from Metropolitan America inContemporary Perspective.
Amos H. Hawley and Vincent P. Rock (Editors). New York City: Sage
Publications. 302.

43 Marans and Rogers. 303.

49 Harsha N. Mookherjee. 1992. “Perceptions of Well-being by Metropolitan and
Nonmetropolitan Populations in the United States.” Journal of Social Psychology.
132(August):52 l.

35

related to "5 d
could be den“
ratios. and Chii‘
Israeli to“ ns. h‘
to be used to to 21
Although tht‘
based on pctCCi"
indicator has beef
a \say Similar to .
market. “After tv.
proposing sarious
time. we appear to
measured than \se it
lfa social indic
Ways. It should help
downtowns) has char

0i policies to impro

50. t"
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. ii

cons
equences are

con-
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,\

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. l
ADSOIL 444 F

Sl

~\

related to its demographic condition. Anson (1991) suggested a reliable social indicator
could be derived from demographic data (population age structures, mortality rates, sex
ratios, and children-women ratios) as measures of the quality of life in a society. Using
Israeli towns, he found just two types of readily available demographic data were enough
to be used to to accurately evaluate the impact of policy decisions.

Although the need for reliable social indicators is great, and must by nature be largely
based on perceptual input, the search for an appropriate methodology and a perfect
indicator has been less than satisfactory. Such social indicators have not been developed in
a way similar to how economists have been able to represent the state of the financial
market. “After twenty years of intensive effort, and over a thousand articles and books
proposing various indicators at national and local levels, for comparison over space and
time, we appear to be no nearer to an accepted definition of how quality of life should be
measured than we were when the social indicators ‘movement’ started.”50

If a social indicator is to become useful and relevant, it must serve in a number of
ways. It should help evaluate how the well-being of the thing being studied (in this study,
downtowns) has changed relative to others; it should provide guidance in the formulation
of policies to improve the well-being of the thing being studied; it should measure
something well-defined enough to be unambiguous; it should be readily usable and easily
duplicable. As Anson has suggested, however, such an indicator is conceptualized; “its
consequences are easier to measure than the concept itself, if only because the
consequences are tangible and unambiguous whereas the concept remains obscure and

amorphous.”51

 

50 J. Anson. 1991. “Demographic Indices As Social Indicators.” Environment and
Planning A. 23:434.

5‘ Anson. 444.
36

CONCLUSION

There hate bee:
donntoons. The) t
continued through it
chapter hate taken a
sociological perspeci
means to eialuate L
deieloping such a n
reluctance to rely on
difficulties and preset
0f merchants and bu

Perception Index.

 

 

CONCLUSION

There have been many efforts at describing cities in general, central cities, and their
downtowns. They began with von Thiinen’s concept of central land use patterns and have
continued through to modern, complex computerized models. The models described in this
chapter have taken a variety of perspectives on evaluating cities, including spatial concepts,
sociological perspectives, quality-of-life based studies, and others. But none has presented
a means to evaluate urban health focused on downtowns. As noted, there are difficulties in
developing such a model, most notably the lack of consistent evaluation criteria and a
reluctance to rely on perceptual data. It is the attempt of this study to overcome these
difficulties and present and test a model for the evaluation of downtowns based on a survey
of merchants and business owners, which will be used to derive a Downtown Health

Perception Index.

37

This study U‘
ofa suryey‘. ”7?“
suney collects in
and other leaders .
sixteen study citie
cities. The SUFVC‘j
following sections t

Analysis of the i
analysis. Correlatii
statistically signifies
measure the strengtf
“Pit causation. and

other. Cause-effect r:

and controlling factor

SELECnox or 311‘

This is a study of c

lit
tired to larger cities.

 

Stud)" includes a p

CHAPTER III

METHODOLOGY

This study uses survey research as its primary methodological approach. Through use
of a survey, input has been collected which is not available from any other source. The
survey collects information on the perceptions of downtown business owners, merchants
and other leaders on the condition of their downtown. By administering the survey in the
sixteen study cities in a uniform format, the responses are able to be compared between
cities. The survey, its development and its administration, is defined in detail in the
following sections of this chapter.

Analysis of the data accumulated from the survey is done primarily through correlation
analysis. Correlation analysis tests whether two factors are associated in a way that is
statistically significant. It can indicate whether factors are statistically related, but can also
measure the strength of that relationship. However, a relationship (correlation) does not
imply causation, and correlation analysis will not indicate which factor is dependent on the
other. Cause-effect relationships can be determined with confidence only by incorporating

and controlling factors prior to the survey.

SELECTION OF STUDY CITIES

This is a study of cities, a) between 5,000 and 20,000 population, b) not economically

linked to larger cities, and c) located in outstate areas of the lower peninsula of Michigan.

The study includes a purposive selection of sixteen cities, shown on the map below.

38

 

Manistee ’Cadillac

'Big Rapids
'Ionia
South Haven
'Marshall
’Albion .
'Dowagiac ‘Coldwater Tecumse
03“".ng 'Hillsdale f

Figure 2 - Locations of 16 Study Cities

The study cities were selected to represent a variety of characteristics, including city
size, market autonomy, and geographic location. Since the specific focus of this study was
on smaller cities, population size was a primary characteristic.

Christaller’s Central Place Theory recognized various thresholds of population size as
determining a town’s ability to serve as a central focus of functions for a larger hinterland
community. He suggested a city’s size relates directly to its ability to successfully service a
resident population.

As discussed in the Literature Review chapter, a series of urban theorists have looked at
the question of optimum size for cities. Malisz (1969) indicated there may be various
optimum threshold populations. Segal (1977) suggested each city has its own unique

optimum size and that it is always at its optimum. However, Americans have always

39

shown a Pref
from “man re
of smaller C“:
“Small C 1‘
Housing and
define 5’1““
most Out-stat:
stud) includt’c
cannot SUPP”
described b)
population. T
cities included
Study Citie
recognized in t
become closely
distinct entities
n0t included. sir
and would not i
Cities like Birrr,
the defined popu
The third crit

Michigan so coll:

v
“Woman:

N

- . Borch
U .
53 Pper Mid

156d on

shown a preference for smaller cities. And yet smaller cities have received little attention
from urban researchers. That is a primary reason this study has focused on the downtowns
of smaller cities.

“Small cities” have been defined using various population ranges. The Department of
Housing and Urban Development has used the population range of 2,500 to 50,000 to
define small cities. The range from 20,000 to 50,000 is questionable, however, for in
most out-state areas a city of this size would generally be considered medium size. This
study included an upper population limit of 20,000. Conversely, cities which are too small
cannot support a downtown commercial district with a balanced mix of business types, as
described by Borchert's hierarchy.52 Such is the case with cities of 2,500 to 5,000
population. This study sets as a lower limit a population of 5,000. The final list of study
cities included p0pulations from 5,563 (South Haven) to 16,322 (Owosso).53

Study cities were also selected based on their relative economic autonomy. As
recognized in the work of Christaller and Lbsch, cities in close proximity to larger cities
become closely tied with the market areas of those cities, and are not able to be studied as
distinct entities. Therefore, Michigan cities in close proximity to larger urban areas were
not included, since the smaller cities would not be sufficiently distinct from the larger cities,
and would not allow meaningful analysis of the characteristics of the smaller city itself.
Cities like Birmingham, East Grand Rapids and Muskegon Heights, while falling within
the defined population range, were therefore excluded from the list of considered cities.

The third criteria used for selection was geographic location. The study was limited to
Michigan so collected information had common state-wide data. This would focus research

variables on cities, not on states, and would eliminate the need to stratify variables by state.

 

52 LR. Borchert and RB. Adams. 1963. Trade Centers and Tributary Areas of the
Upper Midwest. Minneapolis, Minnesota: University of Minnesota.

53 Based on 1990 US. Census Bureau statistics.

40

 

 

within L

[hC 10“ cf I16
cities vsere S
TecumSCh' }
the center an
ssh-it‘ll were t
which had ‘6
collar cit) “ t'
The 16 >
response to C
important as s
i'iessed as a u
of Fuguitt“ a
courthouse brt
contributed to a
Three cities
tSouth Has en 2
included to stuc

locations. A5 r

 

Within the lower peninsula, cities were selected for their geographic diversity. Since
the lower tier of counties was the area of the state to be first settled, representative study
cities were selected from the eastern, central and western sections of this area, including
Tecumseh, Hillsdale, Coldwater, Sturgis, and Dowagiac. Other cities were selected from
the center and northern areas of the lower peninsula. In one case, two cities were selected
which were in close proximity and located in the same county (Albion and Marshall), but
which had very different images, one a blue-collar, working-class city, the other a white-
collar city with a strong sense of local history.

The 16 study cities included seven which are county seats. Preston's work, in
response to Central Place Theory, recognized the importance of “nodality” as a factor as
important as size and location in establishing a city’s importance. County seat status was
viewed as a way to see if such status impacted on perceived downtown health. The work
of Fuguitt54 and others suggested such status played an important role, since the county
courthouse brought residents into the city for purposes other than shopping, and also
contributed to a proliferation of offices related to legal work.

Three cities located along the Great Lakes shore were included, two on the west side
(South Haven and Manistee) and one on the eastern shore (Alpena). These cities were
included to study the impact of tourism, a specialization directly related to their distinct
locations. As recognized by Berry and Pan”, locational specialization may be more
important than either size (Christaller) or functional hierarchy (Losch) in determining
relative success.

Thirty—five cities satisfied the populations size criteria. From the total list of 35 cities,

the final sixteen were selected as representative of the above stated criteria. Although the

 

54 Glenn V. Fuguitt. 1965: “County Seat Status as a Factor in Small Town Growth
and Decline.” Social Forces. 44:245-251.

55 Brian J .L. Berry and John B. Part. 1988. Market Centers and Retail Location:
Theory and Applications. Englewood Cliffs, New Jersey: Prentice-Hall, Inc.

41

 

for o:'r
UPPCtl
Tilt

Shams n

larger sample of 35 cities was used for some overall comparisons, the purposive sample of
the final 16 cities (which includes the eight cities used in the original 1986 study) was
selected as a cross-section large enough for meaningful statistical analysis. Conclusions of
the study are not valid beyond the study boundaries, and interpretation of the conclusions
for other parameters, such as larger cities, cities within metropolitan areas, cities in the
upper peninsula or in other regions, should be made with caution.

The list of 35 cities is shown below, listed from largest to smallest, with the study cities

shown in boldface.

42

Table l - List of Cities Fitting Study Criteria
mutton“

\OOONIOsUI¢.wN.—.

10

 

56 Based on 1990 US. Census Bureau statistics.

£211!
Ypsilanti

Mt. Pleasant
Monroe
Adrian
Owosso
Benton Harbor
Big Rapids
Niles

Grand Haven
Alpena
Sturgis
Cadillac
Albion
Coldwater
St. Joseph
Alma
Ludington
Fenton
Howell
H'illsdale
Greenville
Charlotte
Lapeer
Tecumseh
Three Rivers
St. Johns
Marshall
Manistee
Hastings
Dowagiac
Petosky
Ionia
South Haven
Cheboygan
Allegan

43

24,846
23,285
22,902
22,097
16,322
12,818
12,603
12,458
11,951
11,354
10,130
10,104
10,066
9,607
9,214
9,034
8,507
8,444
8,184
8,170
8,101
8,083
7,759
7,462
7,413
7,284
6,891
6,734
6,549
6,409
6,056
5,935
5,563
4,999
4,547

GEDCEFt

that is.
Should
mathmt

Of The C

GENERAL DESCRIPTIONS OF THE SIXTEEN STUDY CITIES57

This study compares aspects of the sixteen study cities using a survey format.
However, each city has unique qualities which may not be apparent from objective data or
tabulated survey results, but which are important to a full understanding of them. As
Schwartz explained, although cities can be seen in terms of “inorganic” characteristics—
that is, in terms of simply organized functions designed to serve explicit needs—cities
should also be seen from an “organic” viewpoint. Downtowns are not just economic
machines for the businesses located there, to be discarded when obsolete, but are also part
of the overall fabric and context of a communtiy, contributing to it's overall character.
Craycroft and Fazio reinforced this notion when they found the primary reason people
moved to small cities was not economic motivations, but the desire to improve their quality
of life.

The following section presents both perspectives, and includes important demographic
information as well as a more general description of each city based on site visits.
conversations with residents and officials, and published materials. Most of the
information was collected in 1992. Many of the comments are impressionistic, based on
conversations with downtown merchants and city officials. Additional demographic data
on each city is presented in the Analysis chapter, which looks at the effects of demographic
characteristics on downtown health.

First, described alphabetically, are the eight cities included in the 1986 survey. They

are followed with descriptions of the eight cities added for the 1992 survey.

 

57 All demographic data is based on the 1990 US. Census unless otherwise noted.
44

Albion's '
1960 to 10.0
i055 in the ‘11
economy \,
mm”): the
during W0r
on a do\an
dtibanded (
the dost Hto'

Albion
in the 303‘
m of Cult
which “as
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mpuiaIIOn

ALBION:

“Albion

Albion's population has been decreasing in recent decades; it dropped from 12,749 in
1960 to 10,066 in 1990. This decline was due primarily to a loss of industrial, blueocollar
jobs in the immediate area. Throughout this century Albion has had an industrially-based
economy, with economic peaks and valleys. Although prosperous in the early part of the
century, the city was severely hit by the depression of the 1930s. Local industry was busy
during World War II, and the area became prosperous again in the 505. Albion has been
on a downhill slide since the 703, when a number of important local industries either
disbanded or relocated their plants. The community was devastated by these losses, and
the downtown retail core lost much of its customer base.

Albion has been racially cosmopolitan since the 19008. Blacks migrated north for jobs
in the 305, Hispanics in the 40s and southern whites during the Second World War. This
mix of cultures, coupled with the up and down local economy, gave the city a reputation
which was described in a 1953 newspaper article as "A little city with big city problems."

The community is made up of three primary population sectors: 1) a blue-collar
population which is young, ethnic, and has a high rate of unemployment and public
assistance, 2) a college population connected with Albion College, located in the city on
the east side of the Kalamazoo River (the college population has been shown to have little

contact with the rest of the community, however, and has had little impact on the

45

downtown), and 3) farming families living in the agricultural areas surrounding the city.
(Albion served as a primary retail center for these families in earlier times, but has lost
much of this customer base in recent years.)

In 1984, the Michigan Main Street Program selected Albion as a case-study for its
comprehensive downtown evaluation survey. A year later the downtown community
formed Downtown Albion Inc., an umbrella organization with a full-time director. It began
a successful effort to revitalize the downtown. Although Downtown Albion Inc. generated
$1.7 million, it began to falter in a few years. The city responded by creating a Downtown
Development Authority in 1988 and a Tax Increment Financing Act district in the same
year. By 1992, the organization had been scaled back, and downtown merchants no longer
had any significant coordinated efforts at downtown improvements. By 1993, the
Downtown Development Authority had lost its tax increment financing funding due to
school tax reforms, and now operates without a budget. As an example of the foiled
efforts, in 1986 the city supported a new business, Brian’s Department Store, in an attempt
to bring a larger retail base to the downtown. By 1992, Brian’s was gone and replaced by
a Goodwill second-hand store.

In 1992, the Michigan Department of Transportation made public plans to repave the
main downtown thoroughfare, Superior Street. Merchants were generally upset about the
potential loss of business from having Superior Street closed to traffic for seven to eight
months. This was a very controversial project, with many merchants in favor of putting
smooth asphalt over the existing brick paving, but local historians wanting to replace it with
new brick paving to match the old. Ultimately, the DDA contributed $1 12,000 for the
additional costs to have new brick pavers installed.

Albion’s retail stores have no nearby competition. The Battle Creek Mall, 25 miles
away, is seen as their biggest competitor. A new Wal-Mart recently opened in Jonesville,
27 miles distant. A local strip commercial district on the edge of town is anchored by an

aging KMart. Even with this minimal competition, it is apparent Albion’s downtown holds

46

little 0f 11
and has i
frequent:

Tth

I)» TJI'U

If?

BIC"

l2.”

lo.

re,

little of its local customer base. Albion College is located a few blocks from the downtown
and has no shopping or recreation on campus, yet merchants indicated virtually no students
frequented the downtown.

The current situation in Albion’s downtown was summarized in a description from a

paper by Heather Richards.

Am

mm: Downtown Albion possesses an architectural heritage that sets it
apart from most small towns in southern Michigan. Downtown Albion is
composed of a core of continuous and cohesive commercial buildings dating
from the late nineteenth and early twentieth century. Although some have
suffered unsympathetic remodeling efforts, the historic fabric of downtown
Albion’s buildings are intact, complementing and reinforcing each other through
their similarity in scale, size and style. In addition, Albion’s Main Street
(Superior Street/ M-99) is one of three remaining examples of a brick-paved
street in Michigan, and was recently resurfaced with clay bricks. This historic
architectural tradition ultimately defines downtown Albion’s identity and sense
of place.

Location: Not only does downtown Albion front the Kalamazoo River,
including two riverfront parks. but the downtown is located right off I—94.

Barking: Downtown Albion has plenty of both on-street and off-street, free
public parking.

AIIZIQILCQILCKQ: Approximately 1,600 students attend Albion College, located
just four blocks from downtown.

Emblems
W3 In the late 1970s and early 19805,

most of the industry in Albion either consolidated or folded, resulting in a net
loss of 922 industrial jobs.

W: Although there are no malls or substantial shopping centers
located within the city limits, recent studies have shown that most
residents/customers shop in Jackson or Battle Creek, both over fifteen miles

47

away. Retail selection in downtown Albion is limited, and not targeted for the
Albion market, which is multicultural, blue-collar and academic.

WM: Although downtown Albion is clearly suffering
commercially, most merchants have not demonstrated the initiative to change

their situation, nor do they exhibit much allegiance amongst themselves. Very
few merchants cater to the students at Albion College, store hours are
uncoordinated, and support of revitalization efforts in the past and/or present are
either non-existent or inactive.

WM: Downtown vacancy rates are high (currently 17%), and with
high vacancy rates building improvements are minimal. High vacancy rates
also lend themselves to a “lack of critical mass”—merchants, current or
potential, suffer when surrounded by vacant storefronts.

W: Although Albion is located right off I—94, there is little or no
signage on the highway, nor on M—99 leading towards downtown Albion.

Wagon: Although the City and the Chamber of Commerce
support efforts in revitalizing downtown Albion, there seems to be no common
thread connecting the downtown merchants, the City and the Chamber.
Business recruitment activities are minimal, with little or no incentives and no
business recruitment packets that address the downtown commercial district.
There is minimal involvement in downtown retail awareness workshops and
seminars. There is no agency that acts as a bridge and a monitor between these
three groups. The Downtown Development Authority seems to exist simply as
an image of its past self.58

 

58 From a paper by Heather Richards for the course, Community Development and

Downtown Revitalization, at Eastern Michigan University. October 1994.
48

DOWAGIAC:

i

f 'Dowagiac

Located in southwestern Michigan, Dowagiac has almost been forgotten by outsiders.
Located on the historically important Michigan Central railroad line running from Detroit to
Chicago, it had previously been along the path of progress. The downtown’s “front” is
still formed by the railroad tracks, with important industries located just across the tracks.
With no major highway links, Dowagiac lost much of its status with the decreasing
importance of the railroad and increased dependence of highway transportation. As with
Albion, local industries brought prosperity to the community during and after World War
II, but their post-war attrition brought a general decline to the local economy.

Dowagiac’s demographics represent a poorer community. Of the sixteen study cities,
Dowagiac's residents have the lowest median value for housing ($31,400) and lowest
percentage of high school and college graduates (62.5% and 7.7%).

The downtown had been in a slow decline through the 1980s. An older Woolworth’s
store remained open and, as one merchant explained, brought in more customers than all
the other downtown businesses combined. Few sustained efforts had been made to
improve the downtown until a Downtown Development Authority was established in the
mid-19803. In its first year the DDA had a substantial improvements budget. By 1992, it
had funded and completed construction of a new downtown Streetscape on Front Street, the

primary retail street. The Streetscape was designed to complement the historic character of

49

the downtou n.
merchants. Th:
outdoor amphit':
areas. These i
positive attitude

economic vigor.

Hoot-1L;

 

the downtown, and seemed to generate a new sense of pride and optimism among local
merchants. This led to other plans for improvements in the downtown area, including an
outdoor amphitheater, a new farmers market pavilion, and reconstruction of the parking
areas. These improvements brough substantial change to the downtown, creating a
positive attitude among both merchants and city officials, and seems to have led to renewed

economic vigor.

HOWELL:

Howell.

Howell is a community with abundant resources. It is strategically located between the
major centers of Detroit, Ann Arbor, Flint and Lansing, and is located along I-96, an
important connector between the east and west side of the state.

Howell's downtown community is taking advantage of the new consumer market
building up around it. It has an affluent renter population; of the sixteen study cities, it has
the highest percentage of multi-unit dwellings, the most new residences (built between
1970 and 1980), the most new householders, the highest rental rates, and the highest
median income of the sixteen study cities.

The city has given considerable attention to its downtown in recent years. Four Master
Plans, a Streetscape Plan, a Historic Buildings Survey, a Main Street Center Resource
Team Report, a Urban Development Action Team (UDAT) study sponsored by the

American Institute of Architects, various university studies, a Downtown Development

50

Authori
ideas or

Hov

rei'itali;
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to the C

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Authority, three planning consultants, and a downtown consultant—all have contributed
ideas on how to revitalize downtown Howell.

Howell was a Main Street Program59 community in 1986 and had a Main Street Project
manager serving as a downtown advocate. However, three individuals held the project
manager position in three years, and there was little followthrough in planning. The
Program was criticized for having an historic preservation focus rather than a downtown
revitalization focus. Its biggest accomplishment was to place the downtown on the
National Register of Historic Places in 1987. By 1992, control of the Program was given
to the City Manager, effectively killng it as a separate revitalization effort.

In 1991 the city established 3 Downtown Development Authority (DDA). The
Authority appointed Carlisle Associates as long-term planning consultants. A ten-year plan
for downtown improvements included funding for streetscape improvements, new trees,
lighting and a two-story parking deck. The first project was to be streetscape
improvements, including lighting and signage, but high costs put much of it on hold.

Other changes are affecting the downtown. The City Hall and the Police Department
will have moved out of their current locations in the central downtown to a new location
outside the downtown, on Grand River Boulevard. Wal-Mart recently opened a nearby
store, but most merchants indicated they have felt little effect from it, since the downtown
is increasingly becoming a specialty shopping district. Few of Howell’s merchants see

large discounters as competitors, but as contributors to the local retail environment.

 

59 The Main Street Program is a demonstration project for downtown revitalization
efforts sponsored by the National Trust for Historic Preservation. It was most
active in the 19803.

51

 

 

10m:

Ionia is one
of its local hart
known "Free FL
lonia also has lt'
employment stal

For most of
willed business

broadened cons

downtown imprt

A 1978 plan

dOtt‘titown rei it

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IONIA:

S 'Ionia

Q

Ionia is one of the oldest communities in central Michigan. The city has a strong sense
of its local history, from which it has drawn much of its community identity. It has a well-
known "Free Fair” during the summer, which for many decades has drawn large crowds.
Ionia also has long had a state prison located in the city, and the resulting jobs have given
employment stability during otherwise rough periods.

For most of its history, the downtown was owned and controlled by a few strong-
willed businessmen. Recently, however, the ownership of downtown businesses has
broadened considerably. This has increased the level of commitment and activity in
downtown improvement efforts.

A 1978 planning proposal proposed using historic preservation as the primary focus of
downtown revitalization efforts. Its objective was “to develop pride in the past, image and
resources of Ionia.”6O As a result, a series of successful efforts at streetscape and building
facade improvements were initiated.

Between 1979 and 1982, the City had accomplished a number of downtown projects,
including landscaped parking, repaving of streets, new street lights, free parking, and
regular street cleaning. A sign ordinance was passed to standardize signage. A revolving

loan fund permitted downtown businesses to borrow from local banks at 2 percent interest,

 

60 Ionia Revitalization Plan. 1978. City of Ionia, Michigan.

52

and ma;
busines;
resxstan.

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mercha:
to fill c

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and many small businesses benefited from this program. Attempts to establish common
business hours for downtown businesses was not successful, however, due to merchant
resistance.

After languishing for many years, Ionia’s Downtown Development Authority was
reinvigorated in 1989, due largely to the encouragement of a new city manager. It has
become a prominent city agency with a relatively large staff and a Main Street Program.
The DDA staff works primarily on business recruitment, and has used consumer surveys
from 1987 and 1991 and a 1992 business survey to focus on three business types that are
most needed—a variety store, another men’s clothing store, and a chain motel. One young
merchant was encouraged by the DDA to expand his existing business into men’s clothing
to fill one of these needs. As another merchant said, vacancies should not be seen as
problems, but as opportunities for young entrepreneurs.

The DDA has a retailers’ group, which is the marketing arm of its activities. The local
Chamber of Commerce also sponsors a separate merchants organization, the Progressive
Merchants Group.

Ionia’s merchant groups have used a number of strategies for improving the
downtown’s attraction as a commercial area. One strategy is to have the downtown appeal
to younger customers, and let Wal-Mart and KMart cater to older residents. Another idea is
to cover the downtown sidewalks with an overall canopy, but this has not received
widespread support. Common store hours have also been discussed, but this idea has not
yet received sufficient support to be implemented.

Quality of life issues have also been important objectives. A new organization,
Progress 90, was created in the mid 19805 to broaden the scope of revitalization efforts,
and has assisted in creating and renovating recreational areas in Ionia. This included the
renovation of the historic Ionia Theater, a downtown Art Deco style theater that had been
underutilized. The city purchased the structure, renovated it, and operates it as a focus for

evening activity in the downtown.

53

lonia's dou

owner said. "At

Ltpeerhasl
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Detroit. and int;
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61 _
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Ionia’s downtown business owners seem willing to discuss many ideas and, as one

owner said, “At least downtown Ionia is trying things.”

LAPEER:

S Lapeer'

Lug

Lapeer has historically served as a hub of the agricultural community in the eastern part
of the state. Its population has remained steady throughout much of its history. In recent
years, however, the area has achieved closer ties with the industrial areas of Flint and
Detroit, and interstate highways have reduced the driving time to those areas. The number
of county residents employed in manufacturing increased almost 700% from 1950 to
1980.61 In the last two decades, the local population has grown substantially, both within
the city and in surrounding Lapeer County. The Michigan Department of Commerce
recognized the Lapeer area as the second-fastest growing area in Southeastern Michigan.

Lapeer's downtown is located off the city's primary through roads. As a result, the
downtown has experienced great competition from newer retail stores located in shopping
centers along those through roads. To better compete, a downtown renewal plan was
adopted in the early 1970s. The most significant change resulting from the plan was the
implementation of a one-way street system around the downtown perimeter. Large parking

areas surrounded the downtown core, and effectively separated downtown businesses from

 

61 Michigan Statistical Abstract.

54

 

 

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surrounding residential neighborhoods. This caused a major disruption in the fabric of the
downtown, and has remained a source of controversy ever since.

The Lapeer Downtown Development Authority was established in 1982, and has
included as projects the development of downtown parking areas, a downtown
beautification program, and a downtown office space survey. The DDA sponsors a
Farmers’ Market, which has been successful in attracting shoppers to the downtown area
twice a week. The DDA also funded the position of Business Recruitment Coordinator,
whose role is to help interested entrepreneurs find the type and size of space they need in
the downtown.

A facade low-interest (4%) loan program was initiated in 1985, cooperatively funded
by the City and three local banks. Fifteen businesses have thus far benefited from the
program.

In the 1992 survey, a number of problems were described by local merchants. The
Primary concern on the minds of many merchants was the construction of new Meijers and
Wal-Mart stores at the fringe of the city. Many merchants described the problems that
would result, and the unfairness of this new competition. The Chamber of Commerce had
sponsored a program on how merchants could prepare for the arrival of Wal-Mart, based
on lessons learned in other communities. Some merchants felt the new superstores would
be a direct threat to their future business; others felt their stores had a market niche which
would not be negatively impacted.

Another concern of merchants was frustration with annual Gus Macker tournaments.
These annual three-on-three basketball tournaments take place on temporary courts located
throughout the downtown, and although the tournament attracts huge crowds, it also
disrupts downtown businesses for an entire weekend. Complaints were also expressed
about the city’s refusal to remove parking meters from the downtown, even though

merchants indicated they were willing to compensate the city for lost revenues.

55

 

 

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for the

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Alternatively, the city has suggested that merchants adopt a policy of paying parking tickets

for their customers.

MANISTEE:

'Manistee

In the late 19th century, Manistee was an important and prosperous center of
Michigan's lumbering industry. Since that time both its population and prosperity has been
declining at a steady rate. Its current population is half of what it was at the turn of the
century. Its population is elderly, with the average age of residents older than any of the
other study cities. Manistee's residents have the highest median age, the highest proportion
of residents over 65 years of age, and the highest percentage receiving Social Security.

For most of this century Manistee's residents have been living with the ghost of what
had been. As a result, the city has a downtown didn’t change appreciably from the turn of
the century until recently. Some residents have seen this as an asset, since the preservation
of the downtown's historic character could be used to encourage tourism. Toward this
end, the central business district is now listed on the National Register of Historic Places.

An interest in downtown revitalization was first evident with Project Facelift, a clean-
up, fix-up program begun in 1970 by the local Jaycees. A Uniqueness Committee was
established to aid both business and home owners in their efforts to restore and renovate

their buildings.

56

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A number of proposals for downtown revitalization have been presented since then—
for downtown restoration, facade improvements, riverfront development, etc. A “Victorian
Village” theme was established for downtown facades as part of a redevelopment effort
sponsored jointly by the City, the Chamber of Commerce and the Downtown Development
Authority. The DDA also set up a low interest loan program for downtown store owners.
Four local banks provided the funds for the program, and property owners in the historic
district can apply for loans of $2,500 to $15,000 for storefront improvements. The loans
offer low interest rates, low monthly payments, no processing fees, and up to a 5 percent
rebate from the DDA.

Many merchants feel the most significant project has been the recently completed
RiverWalk, a beautifully landscaped park and boardwalk system located behind the
commercial buildings on River Street. However, this costly project has not yet served as a
spur to downtown revitalization. Even in the middle of the summer the new boat slips,
restaurants, and outdoor seating areas are largely empty. Some explained the reason as the
downturn in popularity for Lake Michigan charter fishing, one on Manistee’s primary
draws. Others felt it was the lack of a coordinated promotions program that has led to the
inactivity.

Two larger stores continue to anchor the downtown, Milliken’s and Penney’s.
However, the lease for the Penney’s building was to be up in 1994, and it is inevitable that
the store will close at that time, since Penney’s feels this store competes with its larger,
newer Traverse City store. Indeed, it was evident from the project survey that residents of
Manistee travel regularly to Traverse City (62 miles away) and Grand Rapids (1 19 miles
distant) for shopping.

The lack of local support for downtown revitalization has been explained as the result
of local demographics—Le, a tight-fisted and “stubbom” Polish population, old-money
families that prefer to hide their wealth, and an elderly population that largely leaves in the

winter for warmer areas. One respondent compared Manistee with nearby Ludington.

57

Ludington
Manistee is

Maniste
in the last I:
reason that
unions.

A 1993
economic re
“ere:

Ex;
bUsli

profi

Bllxl

62 ,,
Dov
Mant
1993

Ludington is younger, progressive, and a city with a commitment to good schools;
Manistee is older, very conservative, where the primary concern is in not raising taxes.

Manistee’s other major problem is unemployment. Many businesses have shut down
in the last few years, and efforts to recruit new ones have been largely unsuccessful. One
reason that has been given is the difficulty that management has had with local labor
unions.

A 1993 study done by HyattPalma consultants, a firm specializing in downtown
economic revitalization, presented four overall strategies for Manistee’s downtown. They

were:

WWW: Further enhance, upgrade and update the

businesses and merchandise of downtown so existing businesses become more
profitable and better serve the marketplace.

Wu: Fill existing vacant building space and broaden the
mix of goods and services offered within the downtown by filling in the gaps

which currently exist.

WW3 Improve the image of downtown and

increase relevant communications with downtown’s current and potential
customers.

Wm: Assure quality real estate development projects and
adaptive use projects within downtown.62

 

62 “Downtown Manistee Economic Enhancement Strategies” Report. Prepared for
Manistee, Michigan Downtown Development Authority by HyettPalma. (January
1993).

58

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MARSHALL:

“Marshall

Marshall is one of the most stable communities in the state. Even its early citizens
recognized the city's potential importance, and were nearly successful in having the city
designated as the state capitol in 1847, losing by one vote. The city also has lost out on
other opportunities for significant growth. In the 18705 the Michigan Central Railroad
moved its primary train yards from Marshall to Jackson, where they became a dominant
local industry. During the same period, Marshall had become a center for “medical
opportunists,” many who became wealthy selling patent medicines. However, this
industry quickly died with the Pure Food and Drug Act. As a result, Marshall has
remained, since the 19th century, a small but prominent Michigan city.

Marshall's residential areas include many large, historic structures. Their continued
maintenance can be credited to the efforts of Harold Brooks, the city’s mayor from 1925 to
1931. He encouraged historic preservation efforts, and purchased a number of primary
buildings to insure the integrity of Marshall’s history, including its famous “Honolulu
House.” The standards he set seventy years ago are the standards by which current
preservation efforts in the city are judged.

In 1973 the Marshall Historical Society commissioned as consultants the firms of
Richard Frank and Johnson, Johnson and Roy. Their efforts resulted in an important

study, Marshall: A Plan F or Preservation, which is used nationally as an example of good

59

presen'a
district.
largest
tour. uh
The l
roman
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preservation planning. One proposal was to establish the downtown as a registered historic
district, but this was not achieved until 1991, when it was also honored as the nation’s
largest “Small Urban” Historic Landmark District. Each fall the city hosts a historic homes
tour, which draws many thousands of visitors.

The downtown has taken advantage of the city’s historic character, and has become a
primary tourist destination in central Michigan. Many of its businesses now cater to this
customer base. A high proportion of shops selling gifts and antiques are included among
the traditional downtown businesses. This has led to a successful business mix, one that
keeps its own local customers while also serving as a regional magnet for tourists from as
far away as Detroit and Chicago. Promotion efforts keep the tourist business healthy, and
include large billboards along the interstate highways, tens of thousands of flyers sent each
year, and good cooperation from many of the merchants.

Even when recruiting new industry, preservation as a quality-of-life amenity is used as
a primary selling point. The local Chamber of Commerce takes an active role in Marshall’s
downtown, long serving as its primary promoter. A recent planning activity has been the
Marshall Forward program. This strategic planning effort had community task forces
dealing with housing, government, business/personal services, education, manufacturing,

tourism and recreation, and retail issues.

TECH

Tecum
1934. Whey
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TECUMSEH:

Tecumseh.

Tecumseh, located in southeastern Michigan, was a sleepy agricultural community until
1934, when a refrigeration company located its plant there. Since then Tecumseh Products
has provided the stability of almost full employment to the city's residents. As a result,
Tecumseh has one of the lowest unemployment rates in any of the study cities (6.6%),the
lowest percentage of its population below the poverty level (5.8%), the highest household
income rate ($33,545). Tecumseh since then has broadened its industrial base, and
residents have an attitude of confidence about the city’s future.

This attitude is also evident among downtown merchants, who recently began a
successful, self-initiated effort to draw more tourists into the downtown shops. With a
strong promotional program, they are attempting to put the city “on the map” as a historic
small town, and have initiated projects and activities to encourage this. One project
includes an annual tour of historic homes similar to the larger homes tour in Marshall.
Several tourist oriented downtown businesses, including gift shops, antiques stores, and a
chocolate store have been successful with period furnishings.

Tecumseh established a Downtown Development Authority in 1982, with a TIF district
established in 1984. Most of the funds generated by the TIF were spent on parking
improvements. A recent streetscape improvements project has had mixed reviews by local

merchants, some who feel its $920,000 cost was a poor use of the money. Plans are being

61

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developed for more improvements throughout the downtown, especially along a primary

cross street.

ALPENA:

Alpena

LLB

Historically, the Alpena area economy was built on two large industries. Its early
period was based on the lumbering industry, and later became an important center of the
cement industry. It is the largest city in the northeastern section of the Lower Peninsula,
and is unique largely because of its remoteness from other large urban areas. In spite of the
distances, many residents feel their closest regional shopping area is in Saginaw, 140 miles
distant.

The city is also located on the opposite side of the state from most of the booming
northern Michigan tourist areas. Alpena hasn’t yet captured a large tourism industry,
although it is making serious efforts in this direction. The city recently received a
Community Development Block Grant to develop a project to connect the downtown with
its nearby harbor. The improvements are meant to encourage sailors and boaters,
especially people on charter boats, to walk into downtown and patronize its businesses. A
new marina and boat launch facility was recently completed at a former industrial
waterfront area, and it also will encourage the city to capitalize on the waterfront attraction.
A small shopping center on the fringe of the downtown, called Harborside Mall, is located

between the marina and the downtown. Some in the downtown feel the mall has hurt

62

do“ mi
area} f
Iv

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Harbor

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downtown businesses because of the competition; others feel it has helped draw boaters
away from the marina and to the downtown core one block beyond.

Alpena’s entire downtown area encompasses three districts—the main downtown,
Harborside Mall, and Old Town. Old Town, located across the downtown bridge, is a
two-block historic area containing antique and specialty shops.

Alpena established a Tax Increment Financing Act district for its downtown in 1982.
Before the TIFA district, downtown property values had been declining; now they are
beginning to rise again. The district is administered by 3 Downtown Development
Authority, which includes a Design Committee to review projects for design and historic
compatibility.

The city and its merchants are promoting its image as a center of antiques stores and by
encouraging festivals. As explained in an editorial in the local paper, “Coupled with the
restoration going on in Old Town, and with the proper plan laid out for waterfront linkage,
antique stores could provide the perfect atmosphere in the area. The rich Victorian heritage
of the city only adds to the appeal of the idea,...”‘53 Yet, sometimes such liveliness has
been discouraged by city ordinances. A 1986 ordinance, for example, does not allow
sidewalk vendors, which has discouraged one merchant from using an old popcorn wagon.
Because of complaints, this ordinance is being rewritten.

Downtown merchants have a volunteer merchants association, primarily dealing with
promotions. The merchants association recently attempted establishing common store
hours, but only six or seven businesses cooperated. The effort was abandoned after about
a year. A merchant initiated program, “Target 2000,” is a current effort attempting to

recruit new businesses.

 

63 Bill Speer. 1992. “Back to the future in downtown Alpena.” Alpena News
(January 11):10-A.

63

 

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BIG RAPIDS:

S 'Big Rap'

9

Big Rapids was established on the Muskegon River, which generated the power for the
city’s early lumbering industry. The river is still a very prominent part of the downtown
and is now the location of a scenic downtown park. A hydro-electric dam built on the river
later provided power for the manufacture of furniture and wood products. A number of
extractive industries are located in the Big Rapids area, including oil, natural gas, sand and
gravel. More recently, fishing has become prominent in the local area as a tourism
industry.

Big Rapids is the county seat for Mecosta County. It is also the home of Ferris State
University, and many of its students live in and near the downtown area. Their presence
has been seen as a benefit to some downtown businesses which cater to their buying
habits, but as a burden by other businesses who see students as discouraging older, more
traditional customers. Students have also been seen as a problem because of their increased
nighttime activity and partying and the increased number of break-ins in the downtown
attributed to them.

A newer commercial district has been developing at the southern end of the city,
generally located between the Ferris State campus and the US. 13] highway intersection.
It includes a number of new plazas, including separate plazas for Ames, KMart, and Wal-

Mart. This district attracts many regional customers, but also draws off customers from the

 

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downtown area. The township and city governments had an intense battle when the city
refused to connect the new developments to the city sewer system, but the city was
ultimately forced to make the connections by the courts.

In 1987, downtown business owners and merchants responded to the need for
downtown revitalization by creating a new Tax Increment Financing district and Downtown
Development Authority. A Giant Store, which was being built adjacent to the downtown,
was included in the DDA downtown boundaries, and its completion generated considerable
new funds for the TIF. In 1989, the DDA undertook a major improvement project,
including the construction of new sidewalks, planters, banners and trees in the downtown.
However, much of the construction was poorly done, and costly rebuilding is now
necessary and is causing considerable discord in the downtown community. One
businessman blamed the problem on a DDA Board dominated by professional people rather
than retailers.

The overall impression one is left with in Big Rapids is a downtown community with
no strong leadership. There were numerous concerns described in the survey of
merchants— problems with recent city managers; the Downtown Development Authority
and Downtown Businessmen’s Association operate independently of one another; retailers
are not adequately represented on the DDA Board, which has no full-time administrator;
there is the fear of the competition from the newer regional discount stores: there is
continuing bickering with the Ferris State administration, which has had its own internal
problems; and revitalization programs have generally not received the full support of the
downtown community.

In 1992, a survey was completed for the Downtown Development Authority by
students in the Ferris Business Development Center. The survey generally asked for
opinions on the downtown. Although answers were wide-ranging, one respondent’s

answers on “Public perception” was especially revealing—under “Strengths” was listed

65

 

“Small tov

“Weakness:

CADILL

Cadillac
not located .
[0 “‘0 recrt
Cadillac‘s l(
of Cadillac‘
meals 0r p”
":31de cusi

Cadillac
Downtown
ngup). an d

districr COnn.

 

“Small town feeling, neighborhood caring, tradition, atmosphere,” while under

“Weaknesses” was listed “Public perception that downtown is ‘doomed.”’

CADILLAC:

'Cadill

)

Z__LL

Cadillac benefits from the tourism industry in northwestern Michigan, even though it is
not located on the Lake Michigan shore. This is partially due to the city’s close proximity
to two recreational lakes, Lake Cadillac and Lake Mitchell. But it is primarily due to
Cadillac’s location as a crossroads of highways to the Traverse City area. As a result, a lot
of Cadillac’s traffic is on its way through town, and sees Cadillac as a stopping point for
meals or provisions. However, Cadillac is a large enough city to also have a significant
resident customer base year-round.

Cadillac has three organizations concerned directly or indirectly with the downtown—a
Downtown Development Authority, the Downtown Cadillac Association (a merchants
group), and the local Chamber of Commerce. The DDA has no tax increment financing
district connected with it, so its financial operations are administered directly by the city. It
was explained that city officials have generally been opposed to a TIF district because it
would take funds away from other city budgets and give them directly to the downtown.

Most of the city’s downtown improvement programs have been oriented to physical
improvements, and have included street repaving, removal of overhead utility lines,

replacing old sidewalks with brick pavers, replacement of street lighting with a more ornate

66

| /

style. and
Lake Cadi
meters “6:
of their ini
for five 3
constructio
revenues ir
Other
businessm
res'italizatit
dOWmCNn |
officials prc
lust nor
diSCOUnI ch.
Center Plaza
Man pram
mailers, an
“Counts of1
at that time.
which had fl
Purchaged 1h
some Small rt
done Quite w
The trad;

l
community.

 

Confirm h-
‘ C

style, and improvements to walkways, docks, and lighting along the downtown shore of
Lake Cadillac. As part of a recently completed streetscape improvement program, parking
meters were removed from the streets and a free parking policy was instituted. As a result
of their initiating the idea, merchants complained that they have had to reimburse the city
for five years for lost parking revenues. The money was intended to go toward
construction of additional parking, but some merchants indicated the city put it in its general
revenues instead.

Other concerns with city government were also expressed. One prominent
businessman was disappointed the city has given no incentives for downtown
revitalization. Although he has spent hundreds of thousands of dollars to fix up his
downtown building, the only reaction from the city has been to raise his taxes, even though
officials promised not to do this.

Just north of the downtown a strip commercial area has developed. Many of the large
discount chains are represented, including an Ames Plaza, KMart Plaza, A Giant Family
center plaza, and representatives of all the major fast food chains. However, when Wal-
Mart Plaza opened in 1990 it was considered a death knell for traditional downtown
retailers, and many of them closed. According to one bookkeeper who handles the
accounts of some downtown businesses, not one downtown business was making a profit
at that time. Especially harmful to the downtown was the closing of Green’s Variety Store,
which had formerly brought a lot of foot traffic into the downtown. The city has since
purchased the building, intending to convert it for lease to smaller shops. By comparison,
some small retailers moved from the downtown to new locations at the strip malls and have
done quite well.

The traditional customer base for Cadillac’s downtown retail had long been the farming
community. However, rural families tend to prefer the discount prices of larger chain
stores and show less loyalty to traditional downtown stores. Wealthier residents, in

constrast, have found it convenient to go to Traverse City to shop because of its larger

67

selection
downtou
specialty
better dos
the traditzé
Cadfll
structure.
count} re;
less expen

have a sir

COLD“

\

 

 

selection and more specialized retailers. This leaves a reduced market niche for the
downtown. In response to this problem, some merchants have suggested encouraging
specialty shopping for tourists. This approach was supported through the survey, since
better downtown health was seen among merchants with specialty businesses, rather than
the traditional business retailers.

Cadillac is considering building a new court house to replace its outgrown Victorian era
structure. City officials would like to build the new structure at a downtown site, but
county representatives prefer a site outside of the city which would allow them to build a
less expensive prefabricated building. This is a major issue for the community, and will

have a significant impact on the downtown either way.

COLDWATER:

'Coldwat

Coldwater is located along the route of the old Chicago Road (US. 12) going from
Detroit to Chicago. The highway serves as the main street of the city’s downtown. More
recently, I-69 was constructed as a north-south interstate highway passing just east of the
downtown. The interchange of the two highways has created a new commercial area,
putting pressure on the traditional downtown to retain its businesses.

An early revitalization study for Coldwater was completed by planning consultants
Vilican-Leman and Associates in 1967. The focus was on “urban renewal,” resulting in

some of the downtown’s historic buildings being either defaced or demolished in an

68

' Eng-fir

attempt to t
which facili
city's zonir
downtown t
Colds at
these came
8350.000 in
program. cc
impon'emer
and asphalt l
Another
0f the dou n
company to
facility, The
Conducting a
lOItgqem1 g0
Coldu-mf

regular Perle

me" the CXI:

 

attempt to modernize. In 1979, a Commercial Redevelopment District was established
which facilitated a 50 percent tax break for new construction and/or improvements. The
city’s zoning ordinance was also modified to encourage upper floor residential use in
downtown buildings, but to this time only one loft has been converted.

Coldwater established a TIF district in 1982, followed a year later by a DDA. From
these came two significant programs—a facade loan program, which has generated over
$350,000 in improvements and $50,000 in DDA rebates; and a streetscape improvement
program, costing $660,000 and completed in 1990. A more recent project brought
imporvements to downtown parking areas, with $500,000 spent on landscaping, signage,
and asphalt paving.

Another DDA project was the renovation of Roosevelt High School, two blocks south
of the downtown. A long term lease for the renovated space was given to an appliance
company to create local jobs, and the gymnasium may become a new community center
facility. The DDA was also working closely with the Downtown Business Association in
conducting a “Visioning” program, a process for gaining public input toward establishing
long-term goals. The city has also participated in the National Main Street Program.

Coldwater’s downtown district includes the historic Tibbitt’s Opera House, which has
regular performances and draws 30,000 people a year. Although considered a downtown

asset, the extent of its impact has not yet been studied.

69

HILLS D.-‘

Hillsdale
in loness ille
lhllsdale “or
result has h
courthouSe a
dOWmOWn.

Hillsdak
COeducan‘ona
Students do r
negligible rm
small college
cheptI‘On).

Hillsdale-

 

desirable TEta
‘ I
Ialll’Oad [rack

regard [0 d0“ I

 

HILLSDALE:

'Hill ale

Hillsdale is the county seat for Hillsdale County. Originally the county seat was to be
in Jonesville, a few miles to the north and along the Chicago Road (now US. 12).
Hillsdale won out because of its greater size and central location within the county, and as a
result has historically benefited from this designation. The city’s historic county
courthouse and surrounding square form an important and dominant focus in the
downtown.

Hillsdale is also the home of Hillsdale College, founded in 1844 as an independent,
coeducational residential college. Although the college now has about 1,000 students,
students do not frequent downtown businesses, and the student population has had a
negligible impact on downtown retail. This is not unusual, and has been found in other
small college towns as well (e.g., Adrian and Albion, with Big Rapids being a significant
exception).

Hillsdale’s downtown tends to be perceived by its business owners and merchants as
having two districts. The upper area around Courthouse Square is seen as the more
desirable retail area, and includes more established businesses. The lower area near the
railroad tracks includes merchants who feel they have been neglected by the city with

regard to downtown improvements.

70

 

A stri]
Joness ilie
There u a
a new Wa

Hillsd.‘
angled cro
confusion .

A serie
partnershi;
Montgome:
ClOlVCd lnlt
efforts~Prt

and establisl

 

A strip commercial area has been established along the highway between Hillsdale and
Jonesville, and includes Ames, Kroger and Perry Plaza and various fast food restaurants.
There was considerable concern among downtown merchants in 1992 over the opening of
a new Wal-Mart in a few months and the impact this would have on downtown businesses.

Hillsdale’s downtown streets form an unusual traffic pattem.. What formerly had been
angled crossing streets are now separated by a confusing median strip, causing continual
confusion in downtown traffic.

A series of downtown revitalization efforts began in 1981, initiated by a local investor
partnership, Hillsdale Development, Ltd. They collectively purchased the unused
Montgomery Ward building and converted it into small retail leased spaces. This group
evolved into a larger, more inclusive organization responsible for downtown revitalization
efforts—Project Hillsdale Committee. The new organization established a TIFA district
and established specific goals, including:

WW5: Low-level period lighting fixtures should replace tall
sodium vapor lighting in the downtown area.

Wes: All aerial utility lines should be placed underground.

Barking: Abundant, attractive and convenient parking should be available to
shopper and merchants.

WW: Create rear entrance store accessibility that is more
attractive and safer for customer access.

Mammal/misting: The Ward’s Building can be subdivided

into retail space and could be used to attract businesses downtown.

WWII—$.53 Install park benches and trash containers
(period design), as well as planters and shrubbery.

Sidewalks: New sidewalks are to be added to the downtown and old sidewalks
are to be replaced.

Won: Business owners should be encouraged to renovate
storefronts and buildings in a manner consistent with the historic character
of the district.

71

I.—

been

fundt

no“ i
DDA.
merch;
the loc

downto

OWC

 

W: Main and enhance the quality of Hillsdale’s Victorian
architecture.

W: Improve the flow of traffic around the area of the Hillsdale City
Hall.

W: Design a logo using a central theme that ties together the
aspects of the community.64

Project Hillsdale is still being implemented, but many of the projects listed now have
been completed. Hillsdale Development Limited and the city have been instrumental in
funding a number of downtown improvement projects, including new parking areas. Plans
now include the restoration of the old railroad station and depot area. The city has no
DDA, and much of this effort was being coordinated by one prominent downtown
merchant. Virtually the only general organization for merchant involvement in Hillsdale is
the local Chamber of Commerce, putting the Chamber in a more primary focus for the

downtown than in other cities.

OWOSSO:

'Owosso

Z__s,5

Owosso is one of the largest of the study cities and has a large enough market base to
support a larger downtown. It is located midway between Lansing and Flint, and is

gradually becoming a bedroom community to those cities.

 

64 Project Hillsdale: Proposed Development Plan for the Community of Hillsdale.
(November 1985).

72

.‘vleijer's

 

the city. 11
downtown. t
damwfle
revitalizatit
Departmer:
prominent .
constructed
encouragin‘
enters thropl
The Cit)“ a}:
me main d.

0“ 08st:
rather that.
ETOWth. the
d0Wn~312e
el’fintugm),

DOWm,
Author“). ;
local ch,”
has been q l
installalloyl
dOWmOWF l
economic

uHOfne Ti

 

Show‘ a) ll; I

0n the Sm!”

Meijer’s, Wal-Mart and Fisher Big Wheel stores recently have located on the fringe of
the city. However, in 1984 J.C. Penney’s was attracted to build a new store within the
downtown, encouraged by an Urban Development Action Grant and the city’s construction
of a new, landscaped municipal parking lot. This store has contributed significantly to
revitalization efforts in the downtown, and helped offset the loss of Weichmann’s
Department Store, a major downtown retailer which closed recently and left vacant a
prominent comer in the downtown. More recently, a unique pedestrian walkway was
constructed from a parking area directly into the downtown’s primary intersection,
encouraging the use by pedestrians of sidewalks instead of rear entrances. The walkway
enters through remaining facade of an older building which was otherwise demolished.
The city also has a West Side commercial district, which is separated by a few blocks from
the main downtown.

Owosso is not the county seat, and historically has relied on its businesses for growth
rather than government services. Because the city is beyond its period of residential
growth, the downtown district is oversized, and there have been attempts to effectively
down-size the downtown, giving it more density, making it more pedestrian-friendly, and
eventually increasing property values.

Downtown activities in Owosso are coordinated by the Downtown Development
Authority and the Owosso Merchants Association, with considerable involvement by the
local Chamber of Commerce. Unlike many smaller cities, Owosso has a city planner, who
has been quite active in stimulating revitalization activities. Recent activities include the
installation of new lampposts and park benches and the use of police foot patrols in the
downtown. The DDA has provided $75,000 in business loans and $300,000 in major
economic loans. The Chamber of Commerce sponsors promotional programs, such as
“Home Town Holidays,” 3 summer Curwood Festival along the river, and an annual car
show, although some downtown merchants indicated the Chamber puts too much emphasis

on the strip commercial areas and not enough on the downtown itself.

73

SOUTH HAVEN:

South Haven

South Haven is one of the two study cities located on the shores of Lake Michigan.
Because of its location, tourism is an integral part of its downtown, and the balance
between providing for local residents and seasonal tourists forms its retail equation.

Although the city has always had a relatively small population, it has long served as the
urban center of Van Buren Township and nearby communities. The population represents
the diversity of the area, with a relatively large African-American community (15%). It
also has a high percentage of elderly residents. These groups form a counterpoint to the
younger, up-scale customers attracted through tourism, boating and fishing.

There has been a significant shift in the business mix toward both lunch and dinner
restaurants, undoubtedly to draw on the tourism potential. A recently improved riverfront
area, with a park and 44 short-term slips, has made a more pleasing link from the harbor
and beach areas to the downtown itself. On the northern edge of the downtown is
“Waterfront Village,” a block of seasonal businesses that open only in the summer. A new
year-round hotel with “mini-conference” facilities is part of this district and may attract
other investment.

In 1989, Wal-Mart built a store just outside the city. Some in the downtown feel the
competition from Wal-Mart almost killed the downtown, while others feel its presence has
contributed to the overall retail environment, since the downtown had a poor retail mix

prior to this and Wal-Mart filled an important void.

74

There r.
years. At c
but it did it
worked w:
banks spot.
among d0\k
Its primar}
it has not 2*

m'italizatt

STI‘RC

 

genflaliv

establishec

There have been a number of downtown revitalization efforts in South Haven in recent
years. At one time South Haven had been designated a Main Street Program community,
but it did not develop this program to any great degree. Cranbrook Academy in Detroit
worked with the city on a Community Design Program, but with little success. Local
banks sponsored a facade improvement program, but there was little interest in the program
among downtown property owners. The city has a DDA, but it is currently non-functional.
Its primary accomplishment was a streetscape beautification project in the early 19805. and
it has not had a significant budget to work with since then. The city hopes to encourage

revitalization, and is now in the process of preparing a new master plan.

STURGIS:

'uri

With the development of a strip commercial area located a mile from its downtown,
Sturgis has become a regional shopping destination. The new commercial district includes
a Wal-Mart, a KMart, other commercial plazas, and most of the major fast food chains.
The impact of the strip on the downtown is mixed, for it has drawn many businesses away
from the downtown, but it also draws many commuting customers from outside areas into
the Sturgis area.

The downtown district has not organized a significant response to this competition, and
generally is suffering as a result. Although a Downtown Development Authority was

established in 1980, it has been largely a volunteer effort and did not have its first Director

75

until 19
by GM
currentf

improse

DEs

The
16 stud}

 

until 1990. The Downtown Sturgis Association commissioned a study of the downtown
by Grove Associates in the early 1980s, but it did not have significant impact. There is
currently a Tax Increment Financing district, with a 20% rebate program for facade

improvements.

DEMOGRAPHIC CHARACTERISTICS OF THE STUDY CITIES

The following chart lists relevant data from the 1990 US. Census of Population for the
16 study cities. The data includes:

0 1990 city population

- Median age of city population

0 Percent of population with a college degree

0 Percent of working population unemployed in 1990

. Percent of population that is African-American

0 Mean household income

0 Percent of population with household income below poverty level
0 Median cost of housing in city

Table 2 - Selected Demographic Characteristics For 16 Study Cities

 

I

fl990 Census Statistics
iPopuI. EMedAge %CoIIGrad %Unem2 %Blacld HHIncome%Povert1Mestg

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

— Albion; 1.0 066; -,_.--.26.- .5. _ -16- 8% 12 4%: 30. 8% :W W . W820, 078 ... 25 4% $31, 700
Dowagiac 64091317: 77% 14 1%W15 5% $20628 ...... 155% 831400
Howell 8.1841312 16 0% 6. 8% 0.1% 831 67419 2% ...§,7§..40.0
Iowa 5 935 .30 8 1.3 2% 9 7% . ..-0 9%...- $23 6141.. . 16 3% 34.1. 200‘
Lapeer 7, 7591 30. W2W 9. 7%W 10 7% .-... 4. 7%: 822. 833 W20. 1% 851 400'

:. Manistee 6734‘ 36. 5.- 124% 17. 3% 0.5%?“ $18010 21 ."6%’W’W'f‘ $34900;
Marshall .-..6 891; 34 9 22 9% 6 196E ....._.-l 4% . .5230. 000 . 8 5% 553 900'
Tecumseh ' 7 462133 6! 17.3% 6. 6% 0 1%; W833, 545 5 8% $62. 900

; Alpena 11, 354 36.0. 13.1% 9. 3% 0 2%?” “”820, 472 "“1‘3. 5% W837, 600.
5......4318349195 12.603 _. -21 3 .31 0% .-.-..1.1. 4%: ..--....5 0% . $14. 9901 44 4% .551 800
Cadillac 10,104 32 8: 1‘5. 5% _W12 1%; W‘WWW‘0 2%1 821,170 15. 5% W837 000';
Coldwater 9 607 32.8 13.5% 9. 5% 0 3% 822, 562 .... 17 5%W83W5W,9W90:
-- 11111568116 8, 170----.- .295-.. 19 2% ,_- 8 9%; _ 0 6% 821, 688 16. 3% s39, 000;
Owosso 16322’ 31.7. 11.3% 12.2% 0.1%; ._- 823220 .. 157% $38000
"'iSouthHaven 5, 563 35. 3’ 21.7% 9. 2% 15.1%‘1‘ $25 967 16 9% .. 850.109..
Sturgis 10113.0 ............. 32 37. - _. 123%} , . 5 3%1---0-8%3 ,,,$23...642 ., - .11 9% 5.5.6 3001

 

 

 

 

76

SIU

Sta!

 

The census statistics include some interesting comparisons which give a better

perspective to the descriptions of the cities from the previous section.

W: Populations of the study cities vary from 5,563 to 16,322. Although
this is a considerable range, with the largest city almost three times the population
of the smallest, this population range represents cities with similar functional
characteristics, and would all fit within the same functional category in the Borchert

Hierarchy of Trade Centers‘55.

WM: Big Rapids has the lowest median age, primarily because of the large
student population from Ferris State University included in the city population
statistics. Fully 59% of the population of Big Rapids is between 18 and 24 years of
age, much higher than the other college towns of Albion (23%), which has Albion
College, and Hillsdale ( 19%), with Hillsdale College. All three are well above the
average for the other 13 cities (10%). The oldest populations are found in Manistee
and Alpena, with 21% and 19% of their respective populations over 65 years of

age, compared with 15% for the entire group of cities.

 

‘55 The Borchert Hierarchy of Trade Centers establishes that cities of any size engage
in all the economic and trade activities of lesser sized cities, and supports the
general notion of a functional hierarchy. The hierarchy proceeds upward from
hamlet to village, town, city (county seat), regional capital, and national
metropolis. The goods traded in cities at different levels in the hierarchy is shown
in the chart below.

Metropolis

Primary Wholesale-Retail Center
Secondary Wholesale Center
Complete Shopping Center
Partial Shopping Center

Full Convenience Center

Minimum Convenience Center
Hamlet

77

at”:

 

W: Big Rapids has the greatest percentage of college graduates

within its population (31%). This and the other cities with college populations

throw askew any general significance to be drawn from this statistic.

unomoloymgm: There is a large variance in the rate of unemployment among the
various communities. Although residents in Albion indicated there has been a big
problem with the loss of jobs in their community, other study cities have
unemployment problems as bad or worse. Dowagiac had the highest
unemployment rate in 1990 (17%), while a number of other cities had rates above

10 percent.

Wagons: Albion, Dowagiac and South Haven are the

three study cities with substantial African-American populations. The other cities
had African-American population percentages well under the national and state

averages.

Hogsoholognoomos: Based on household incomes, Tecumseh, Howell and
Marshall are the study cities with the highest household incomes (over $30,000).
The lowest incomes are found in Big Rapids (because of the large student
population) and Manistee (because of its large elderly population). The average

household income for all the study cities was $23,381.

WI The city with the highest percentage of the population with incomes
below the poverty level was Big Rapids, again because of the large number of
students. Other communities with high rates included Albion, Manistee and
Lapeer. The lowest rates were in Tecumseh, Marshall and Howell, which also had

the highest household incomes.

78

Wm: Housing costs were found to be highest in Howell, which
is currently in an area of great residential growth, located as it is along a major
expressway with easy commutes to Lansing, Detroit and Ann Arbor. Housing
costs are also higher in Tecumseh, with its relatively affluent population. The

lowest housing costs are found in Dowagiac and Albion.

THE RESPONDENTS

SELECTION OF RESPONDENTS

This study evaluates the health of downtowns through the perceptual evaluations of
downtown merchants, business owners, and local officials. They know the downtown
well, and are most impacted by changes. An inclusive survey of these groups was the
method used to gain input. Data was collected in two ways—first, through use of self-
administered questionnaires and, second, through personal interviews. The same topics
were covered using both techniques; sometimes both techniques were used with a single
respondent. The self-administered questionnaire was the primary technique used, and data
collected through its use form the bulk of the information gathered.

Questionnnaires were distributed to downtown businesses within the following

parameters:

1 . Forty questionnaires were distributed in each of the downtowns;
2. Distribution was limited to ground-floor businesses and enterprises;

3. For smaller cities, 40 questionnaires covered virtually all of the downtown
businesses; for larger cities, first priority for distribution was given to
businesses in the heart of the downtown (defined below), with a smaller
proportion given to representative fringe businesses;

4. Stamped, addressed return envelopes were included with each
questionnaire.

79

 

 

 

Of 1
fora re

TCSIX‘R'N

Reco
categon‘e
than fl \‘e 1
first "ripe

made Onlx

alld CDCOU

 

W

Of the 640 questionnnaires distributed, responses were received from 365 individuals,
for a response rate of 57%, which was considered adequate for statistical analysis. The

response rate for each study city is listed below:

Table 3 - Questionnaire Response Rates

City Ramses City W
Albion 48% Alpena 48%
Dowagiac 47% Big Rapids 65%
Howell 55% Cadillac 68%
Ionia 55% Coldwater 58%
Lapeer 65% Hillsdale 58%
Manistee 53% Owosso 60%
Marshall 60% South Haven 65%
Tecumseh 53% Sturgis 60%

Records were kept of the type of contact made, and contacts were coded into three
categories. If the questionnaire and its topics were discussed with a respondent for more
than five minutes, the contact was considered a personal interview; this was considered the
first "type of contact" category. A second category was designated when the researcher
made only a brief personal contact with the respondent to explain the purpose of the survey
and encourage him or her to complete the questionnaire, but did not discuss the survey
topics for any period of time. A third type of contact was designated when respondents
were not personally contacted; typically they were unavailable and the questionnaire was
left with an employee or colleague.

Analysis was made of these types of contact to the survey responses. Personal contact
apparently did not bias respondents, for there was no discemable difference in responses
received to questions based on the type of contact made.

Personal data on respondents was also collected from the questionnaires. This data

indicated respondents knew their communities well, for the mean length of residency was

80

 

ll!

32m.

tea—.37.;— .2.

.5.

I

I

.=Z

 

26 years, and respondents had been involved in the downtown as merchants or in other
capacities for an average of 12 years. Clearly the respondents as a whole represented a

group that could speak about their respective downtowns based on long experience.

40..
35-
3o-
25-

No. of Responses

    

 

0-5

$

21.25, g. 1

11-15

“3

M M

16-20
26—30
1-

41-45
51-55
61-65
71-75
81-85

76—80

66-70

2

No. of Years Resident

55

Figure 3 - Respondents' Length of Time As Residents

81

 

The f
316 fight

 

“100
0
m
E
8.
U) 80
U
M
"5
60
5
Z

40

    

20

 

3 9 "—.‘ 3 3. 5i 3'3 3 5r“ 3
s : é a :5. .7. 9, 3 $

No. of Years in Business
Figure 4 - Respondents’ Length of Time In Business

The following chart lists the mean for these factors for each study city, showing both

the eight study cities from the 1986 survey and all 16 cities from the 1992 survey.

82

Table 4 - Respondents’ Length of Time Listed by City

1286311132! 1222M

Albion 38 l 2 43 21
Dowagiac 34 14 25 8
Howell 34 l 8 23 1 2
Ionia 3 l 1 3 37 17
Lapeer 22 14 2 l 1 l
Manistee 25 l 1 27 11
Marshall 34 1 7 2 l l 1
Tecumseh 29 l 3 32 l 2
Alpena 26 l 1
Big Rapids 21 l 1
Cadillac 23 l 1
Coldwater 24 9
Hillsdale 30 l 1
Owosso 23 l 2
South Haven 23 10
Sturgis 25 10

It is clear from these figures that in some cities there has been more transition in both
the length of residency and downtown businesses in recent years. The original eight study
cities allow this information to be reviewed in a time-series format. In Dowagiac the 1986
survey respondents had lived in Dowagiac an average of 34 years and been in business for
an average of 14; by 1992 the averages had dropped to 25 years and 8 years respectively.
Similar drops were found in Howell and Marshall. In other cities the opposite was found.
Albion’s respondents went from an average of 38 years of residency to 43 years, and from
12 years in business to 21 years, indicating there has been little or no turnover in the six
years between studies. Although some of this may be due to the selection of individual

respondents, the survey was representative enough to indicate this had been a general trend

83

in the cit

cities. sin

um:

It is in
understooc
rcspondenl
significant.
Similarities :

The bias

«lbn-resp

One type
individualS w
indiViduals u
genera”). hart
Collected data,

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67 ,

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in the cities mentioned.66 Such time-series information is not available on the remaining

cities, since they were not part of the 1986 survey.

Was

It is important for any researcher to recognize biases in survey responses. If they are
understood as fully and accurately as possible, such biases can be minimized. Potential
respondent biases were looked at in the 1986 survey, and it was found they were not
significant. Therefore, analysis was not deemed necessary for the 1992 survey because of
similarities in the questionnaire format.

The biases checked for previously were the following:

Non-respondent bias:

One type of bias often found in this type of survey is based on the assumption that
individuals who have responded in a survey tend to represent different attitudes than those
individuals who haven't responded. Other studies have found that non-respondents
generally have a more negative outlook, which may not be otherwise represented in the
collected data. In the 1986 survey,67 which was a precursor to this study, such a bias was

checked for in the following ways:

1. Did respondents who quickly submitted their questionnaires tend to give
different responses from those who procrastinated in returning it? The
assumption made here is that procrastinators tend to be more like non-
respondents, and might to some degree represent the attitudes of non-
respondents. However, analysis showed there was no significant difference in
the responses based on the time taken to return the questionnaire.

2. In some instances respondents who would not otherwise have completed a
questionnaire were encouraged to give responses through the personal interview

\

66 A statistical analysis of this information is included in the Analysis chapter.

67 Norman R. Tyler. 1987. An Evaluation of the Health of the Downtowns in Eight
Michigan Cities (Dissertation). Ann Arbor, Michigan: University of Michigan.

84

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format. If it is assumed that this group would otherwise have been non-
respondents, then they could be used to represent non-respondent attitudes.
Once again, it was found there was no significant difference in responses based
on the type of contact made (personal interview vs. other), so there was no
evidence of a bias.

Based on these two checks, the conclusion was made that there would be no difference

in the responses given by non-respondents, and thus this bias did not need to be

accommodated in the data analysis.

Hawthorne Efi'ect:

Another type of bias commonly found in survey research is the Hawthorne Effect,
which says that respondents tend to respond differently simply because they have been
selected for a survey. Because of the special recognition which has been given them, it is
sometimes found the respondents tend to answer in the way which will most please the

researcher.

This was not likely to be a factor in this study for two reasons.

1. This survey was associated with a major university, and the name of the
university was prominently displayed on the front cover of the questionnaire.
Universities are generally seen as relatively neutral in their perspectives on
issues such as those covered in this study, so respondents felt no pressure to
either agree or disagree with the sponsor institution's viewpoint.

2. As described above, there was virtually no difference in responses based on
the type of contact made by the researcher. If respondents who had been
personally interviewed had responded differently, as a group, from those with
whom no personal contact had been made, then it could be assumed
respondents had been affected by the researcher contact. Such was not the
case.

85

C
respo
accor
respor
more

questir

771g

“Self-lifting " Bias:

Closely associated with the Hawthorne Effect, the “Self-lifting” bias recognizes
respondents want to make themselves appear in a positive light, and will respond
accordingly. This bias was minimized by positioning personal questions about the
respondents at the end of the questionnaire, where they would tend not to affect the other,
more substantive responses. This effect was also minimized by using anonymous

questionnaires, where a respondent’s ego was not tied in with the responses.

The “Habit” bias

If given a series of similar questions, respondents will fall into a habit of answering
them similarly without considering each on its merit. This bias was minimized by changing
the format of questions throughout the questionnaire. The format ranged from simple
“check the box” questions to one-word responses to open—ended responses to completing
information on simple graphs and maps. Through these variations each question was given

its own personality, avoiding the “habit” response.

Stopping Rule:

Another bias which is sometimes found in survey research is based on the fact that the
survey procedure is terminated when the researcher has obtained the desired results, and
the amount of data collected is determined by results. This was avoided by establishing
survey parameters beforehand. This study established two parameters prior to the
collection of Survey forms:

1. 16 cities would be surveyed in depth;

2. A minimum of 20 responses would be obtained in each city.

86

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The
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THE QUESTIONNAIRE

DEVELOPING THE QUESTIONNAIRE

IllulEVl lI' [IQ . .
The questionnaire was designed to survey respondents about their attitudes and
opinions on their downtown. Since it was meant to record perceptions of the sampled
group, the questionnaire needed to present the types of information desired, yet be open
enough to allow respondents to explain their answers in more detail if they so desired.

The 1992 survey built on the original 1986 survey, and included some follow-up
questioning.68 The 1986 questionnaire (see Appendix 2) was revised and field-tested
sixteen times before reaching its final form. Revisions had been made for the following
reasons:

1. Respondents were observed during pre-testing while completing the form

and then interviewed afterwards. Questions that were confusing, repetitious
or unnecessarily difficult to answer were withdrawn or modified.

2. Data from the pretest surveying were analyzed. If data were found to be
confusing or incomplete, the response categories were modified.

3. The location and grouping of question topic areas were varied to see if the
flow from one topic to the next became easier for the respondent for follow.

The 1992 questionnaire (see Appendix 1) was modified to more directly address the
specific topics being emphasized in this round of surveying. The modifications included:

1. A question was added which asked respondents to indicate how much they
felt certain types of businesses contributed to downtown health. This

 

63 Tyler, Norman R. 1987. An Evaluation of the Health of the Downtowns in Eight
Michigan Cities (Dissertation). University of Michigan.

87

attempted to get perceptual input on the importance of business mix as a
factor in downtown health.

2. Questions relating to the respondent’s building were eliminated, since this
information had had little value in the original analysis.

3. A question asking for a relatively detailed evaluation of the history of the
downtown in terms of various characteristics had received a relatively low
rate of response in the original survey and was eliminated.

4. The original questionnaire had included a street map of respondents’ cities;
they were asked to draw a line around what they considered to be the
boundaries of the downtown area. This question had yielded interesting
information, but was considered too complex to administer and not directly
related to the primary questions being pursued, and was eliminated.

Il Cl . . E t

The questionnaire was designed using the "Total Design Method" format described in
the book, Mail and Telephone Surveys: The Total Design Method by Don Dillman.69 The
Total Design Method (TDM) emphasizes the use of a small booklet format made from
standard size sheets of paper folded in the middle and stapled. According to this format, a
full questionnaire should have no more than 12 pages (i.e., three sheets of paper). The
front and back pages are used as "covers" and have no questions. The TDM also stresses a
graphic layout which emphasizes the framework of the questions.

For this questionnaire, the front cover used as its graphic an outline of the lower
peninsula of State of Michigan. This graphic was used because it was immediately
recognizable by virtually all respondents, because it emphasized the fact that this was a
state-wide study, and because it indirectly gave credibility to the questionnaire due to the
associative connection with the state. The name of the study city for each city was printed

on the state map. This made it immediately obvious to a respondent that their city was

 

‘59 Don A. Dillman. 1978. Mail and Telephone Surveys: The Total Design Method.
New York City: John Wiley and Sons.

88

 

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and set
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receiving individual attention. This fact was commented on by a number of respondents,
and seemed to be important in getting a good response rate.

The front cover also included the name of the institutions associated with the study, the
Michigan State University Urban Planning Program and the Eastern Michigan University
Urban and Regional Planning Program. This clarified immediately that the questionnaire
was not a commercial solicitation for a product or service and made respondents
comfortable with the fact that the information collected would be handled in an appropriate
manner.

The first page inside the questionnaire was an introductory letter which told potential
respondents about the survey and its use. The letter stressed the importance of their
participation, and stated that their identity and their individual responses would be kept
confidential. The letter was then signed by myself individually for each questionnaire
using a contrasting color ink to emphasize the individuality.

The back "cover" was also separate from the questions portion of the questionnaire. It
asked respondents for information if they were interested in a follow-up to the survey.
First of all, it included a "rewar " to respondents who completed the questionnaire; by
checking a box, a respondent was told s/he would receive a "Summary Report" which
presented, in brief, the findings of the study. This box was pointed out to most
respondents when personal contact was made, and many respondents asked for this
information. Having the "Summary Report" box also made it necessary for a respondent to
fill in his or her name and address. Although the survey was confidential, and respondents
were promised anonymity (and indeed they will remain anonymous to anyone outside this
study), this encouraged most to include their names, which allowed both for additional data

on respondents, and also formed the basis for a future mailing list.

89

 

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Questinnjequenss

The questionnaire grouped questions into topic areas. Respondents were first asked to
evaluate the existing condition of their downtown, both in a general sense and in a detailed
way. They were also asked to look at their downtown historically—how it’s health had
changed over time, what had changed it, and how they felt about its future. Finally, they

were asked to evaluate other downtowns with which they were familiar on a comparative

basis.70

The First Question

One of the most critical questions on any questionnaire is the first one. It is the
question that determines whether or not a person will complete the questionnaire.
Therefore this question must be intriguing, yet easy to answer. It should draw readers into
the survey topic and make them feel like they will have something to contribute. It should
not make the readers feel they don't have enough knowledge to continue, yet it should not
be overly simplistic and make them feel their answers are inconsequential.

Although other questions were also tried, the question that best filled the needs of the
first question—for brevity, clarity, relevance to topic and perceived usefulness—was the

following:

 

70 A copy of the full questionnaire is shown in the Appendix.
90

 

 

Thi:
the res;
might dt
“‘85 eas;
facts A:
sewed an

important

 

How would you compare the overall health of your downtown with those of
other cities similar in size?

(Note: A number of questions ask about the overall “health" of downtown. Its health
can include economic, social and physical characteristics.

How do you feel it compares — is it

[ ] Much more healthy

[ ] Somewhat more healthy

[ ] About average for this size town
[ ] Somewhat less healthy

[ ] Much less healthy

 

 

 

Figure 5 - First Question

This question was directed specifically at the topic of downtown health. It also asked
the respondent to give their initial opinion on the health of their downtown before they
might develop a bias based on the structure and sequence of other questions. The question
was easy to respond to and asked for an opinion without the need for knowing certain
facts. As a result, this question received a 96% response rate among the respondents and
served as a very satisfactory first question, both for encouraging responses and as an

important piece of data for analysis.

Detailed Evaluation of Downtown Characteristics

Question 1 was followed with a question that asked respondents to consider various
characteristics of their downtown. It listed 16 individual characteristics of downtowns and
asked them to evaluate their own downtown in terms of each of these characteristics. They
were to indicate by checking a box whether their evaluation for each was “Very good,”
“Good,” “Fair” or “Poor.” The list of characteristics was developed from a separate survey
conducted in 1985 which was intended to develop such a list. The characteristics included

the following:

91

1. General condition of buildings downtown
2. Streetscape—sidewalks, benches, trees, etc.
3. Mix of stores and businesses
4. Parking in downtown
5. Your impression of overall retail sales downtown
6. No. of shoppers for “browsing” shopping
7. No. of shoppers for “quick-stop” shopping
8. Merchants association
9. Cooperation of banks

10. Cooperation of city government

1 1. Local political situation

12. Downtown as a community cultural center

13. No. of tourists coming to downtown

14. Employment in the local area

15. Historic character of downtown

16. Safety and security downtown

Significant Events

The first two questions had asked respondents only to check boxes. The third asked
them to list, in their own words, two events that had been significant in changing the health
of their downtown, either for better or for worse. This question provided useful

information, but also encouraged respondents to develop their thoughts on why the

92

 

dox

que

downtown was the way it was, and gave a historical perspective to their responses. The

question was stated as follows:

Significant events...

Now, think back...

What things have happened that have significantly afl'ected
the health of your downtown? Did they make the health of
the downtown (zone; or m?

Indicate below what you consider to be these significant
events.

 

(Significant Event #1... W

 

 

Year (approx.)

Did it make the health of the downtown?...
D Better

\ D Worse

 

 

 

4k

ngificant Event #2...

 

 

Year (approx.)
Did it make the health of the downtown?...
0 Better

L D Worse J

Comments?

 

 

Figure 6 - “Significant Events” Question

93

 

 

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Competitor Areas

Each of the study city downtowns has competition from other commercial districts.
The intensity of this competition can be an important determinant of downtown health.

There are numerous models that look at retail competition, two of which are described
in the Literature Review chapter. The Nearest Neighbor Model of Clark and Rushton
suggests that customers will patronize the nearest store that has a needed good or service.
This suggests that downtowns will effectively service their market area for all the goods
and services they provide if they represent the area in closest proximity. This model was
tested by including a question on the survey dealing with competitor areas.

Berry’s Elements of Urban Retail Structure model illustrated that the retail function was
no longer centralized, but increasingly fragmented. This fragmentation generally follows
three distinct patterns: retail nucreations (e.g., downtowns or malls); ribbons, (e.g., strip
commercial areas); and specialized areas.

The study looked at this question by asking respondents to consider areas which were
competitors to their downtown. Two questions asked about competitor areas in a
comparative way. The first asked respondents to list what area they considered to be their
downtown’s greatest competitor. It specifically did not ask for the name of a city, for a
competitor to a downtown may take many forms; it may be a mall just outside the city or a
regional mall more than one hundred miles distant. Next, respondents were asked to
evaluate the strength of competition—”Very strong,” “Quite strong,” “Strong” or
“Moderate”—and describe in their own words how it was competitive. This question was
included because it allowed for a determination of whether competition had had a
significant impact on downtown health. If it did, it would be necessary to know whether it
was a positive or negative impact, and what factors were most important for that
competition (e.g., distance, size of competitor area, type of competition).

The question, as illustrated below, showed 35 different cities on a map of Michigan.

All the cities were located in the Lower Peninsula, and they were similar in size and

94

 

chm?
ones
whicl
ED“?

omer:

characteristics to the 16 study cities.71 Based simply on their own impressions of these
cities, respondents were asked to indicate which they felt bad healthy downtowns and
which had downtowns with poor health. This procedure gave an evaluation of the larger
grouping, but also allowed the sixteen study cities to be rated by respondents in each of the

other fifteen cities. The question was stated in the following way:

 

71 See page 38 for a description of how the 16 study cities were selected from the
larger group.

95

Other Cities . . .

Shown on the map below are Michigan cities, many about the same
size as yours. Of those with which you are familiar, draw a circle
around those you feel has a particularly “healthy” downtown.

(Circle any number)

Next, draw an “X” through any which you feel has a downtown
which is particularly unhealthy. (Again. “X" any number)

 

   
 
 

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Figure 7 - “Other Cities” Question

96

 

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Evaluation of a downtown ’3 past

Respondents were also asked to evaluate how the overall health of their downtown had
changed over time. A grid was included in the questionnaire with decades indicated back to
1900. They were asked to give their evaluation of downtown health as far back as they felt
they were able, and to indicate by simply drawing a line on a graph (shown below) what

the health was at any particular time.

How downtown health has
changed over time

How has the health of your downtown changed over time?
Its “health ” can be seen in terms of economic, social and
physical characteristics.

0n the graph below, simply draw a continuous line which in
W shows how its health has changed over time.

(Go as far back in time as you can)

1900 1920 1940 1960 70 80 90 92

 

Very good

 

 

Good

 

 

Fair

 

 

 

 

 

 

 

 

 

Poor

 

Figure 8 - “Historical Health of Downtown” Question

Evolution of a downtown ’3 future

As a question which looked toward the future, respondents were asked to give their
impressions of whether they were optimistic or pessimistic about the future of their

downtown—did it look good or poor? They were then asked “Why do you feel this way?”

97

 

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which offered them an opportunity to respond about anything they felt was important to

their evaluation.

By asking respondents to evaluate their downtown in this series of questions, presented
in a variety of formats, respondents were encouraged to dwell on the topic of downtown
health. Such repetition would lead, it was assumed, to more thoughtful responses on this

critical question topic.

Personal Information on the Respondent

Finally, the questionnaire included a series of questions requesting information about
the respondent. It asked for information on length of residency, length of time in business

in the downtown, organizational affiliations, and involvement in revitalization efforts.

13,111..

Tecumseh was used as the pretest community in the 1986 survey. During the
pretesting period (the summer of 1986), five revised questionnaires were developed.
Throughout this period the questionnaire was simplified, both by eliminating irrelevant
questions and by making the remaining questions more "respondent friendly." Because of
this pretesting, there was little need for further pretesting for the 1992 survey. Although
Tecumseh was used for the pretest, the city has remained as one of the study cities, and
was one of the 16 cities in which a full survey was administered in 1992.

Another initial pretest was an informal survey of design professionals used to determine
the list of Downtown Characteristics used in the final survey. The Survey of
Professionals'72 included a wide variety of separate items which could potentially be used

as indicators of downtown health. The list was drawn from interviews, from personal

 

72 The Survey of Professionals is more fully described in the author’s 1987
dissertation, An Evaluation of the Health of the Downtowns in Eight Michigan
Cities. A copy of the Survey is shown in its Appendix.

98

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observations, and more heavily from a review of literature. Although the Survey of
Professionals did not have a large enough sample base to be considered scientific, it did

contribute to the selection of the 16 Downtown Characteristics used in the final survey.

5 l . . . l D i .
Every effort was made to administer the survey questionnaire in a consistent and
impartial way. Questionnaires were personally delivered to potential respondents (as
described in the section, Selection of Respondents) at their places of business. Each
included an addressed, stamped return envelope. All the questionnaires were delivered by
the same person; this allowed for consistency in the introductory remarks, which typically
went as follows:
"Hello. I'm Norm Tyler from Michigan State University. Through the
school’s Urban Planning Program, I am conducting a study asking people like
yourself to give their opinion on how well your downtown is doing. We would
like to know how you feel about your downtown, what efforts have been good
for your downtown and what things have hurt it. From these responses,

recommendations will be developed on what revitalization efforts have been
successful for cities like yours.

(Pause for response...)

"We have included relevant questions in this survey form. (Hand them the
questionnaire). I would appreciate it very much if you would complete it and
return it to me at the address given. An envelope with a stamp is included.

"Note on the back page that a Summary Report, which will describe the study
findings, will be sent to anyone who checks the box."

(Encourage further discussion if possible)

Questionnaires were returned by mail, which took anywhere from 2 to 71 days to

receive. The average length of time was 10 days. Since questionnaires could be returned

99

 

3110

que

anonymously, there was no follow-up mailing to encourage non-respondents to return their
questionnaires.
The Chart below illustrates the time taken for all the questionnaires to be returned:

No.of No.of
Days W (Each X=2 responses)

0-5 52 XXXXXXXXXXXXXXXXXXXXXXXXXX

5-10 59 XXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
10—15 32 XXXXXXXXXXXXXXXX

15-20 14 XXXXXXX

20-25 7 XXXX
25-30 8 XXXX
30-35 3 XX
35-40 1 X
40-45 2 X
45-50 1 X
50—55 0

55-60 0

60-65 0

65-70 1 X
70-75 1 X

Figure 9 - Number of Days For Return Of Questionnaires

The great majority of responses were received within 3 to 4 weeks after initial contact
was made. Analysis of responses showed no significant bias was found based on
response time. Since questionnaires were distributed from June through November, the
data also were checked for seasonal bias; because retail trade is seasonal, one might expect
that merchants who were given the questionnaire in a slow period would respond
differently from those who were given it during a busy time of year. The data showed no

such bias; the evaluations were similar for all periods of the survey.

100

 

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LISTS OF VARIABLES

The questionnaires were tabulated by converting all responses to a computer database
format. This allowed for statistical comparisons. There were two "units of analysis"—that
is, the primary categories used to subdivide the data.

The first unit of analysis was a single respondent. Data could be stratified by many
factors associated with individual respondents. Factors included such personal information
as whether or not they were a downtown merchant, how long they had lived in the
community, and what organizational affiliations they had. The individual factors could also
be used for comparing responses from one question topic to another. For instance, did
respondents who knew a great deal about the history of their downtown tend to rate the
downtown as being in better condition than the general sample, or in worse condition, or
did this not make a difference in their evaluations? Or, if respondents indicated that
competition from other retail areas was very strong, did they tend to rate retail sales in their
downtown lower than other respondents? Or, did respondents who rated their downtown
health as very good also tend to rate other downtowns better than normal, below normal, or
was there no correlation? These were questions which could be looked at on a respondent-
by-respondent basis.

The second unit of analysis was comparison between individual cities and their
downtowns. Instead of a dataset with 365 cases, one representing each respondent, this
dataset had only sixteen cases, one for each of the study cities. Through this analysis, one
was able to look at questions such as—Were certain types of businesses found more often
in a healthy downtown? Were other business types found in greater number in less healthy
downtowns? If the population of a community had grown rapidly, did this correlate with
improving downtown health?

The two datasets were entered onto Microsoft Excel software for recording and for

statistical analysis. A list of variables for each dataset is included in Appendix 3. In

101

h}

for 1

Info.

do

addition to coding and entering all information onto a database, information from open-
ended questions, from interviews, and from personal observations were kept as notes and
referred to throughout the analysis to enrich the analytical perspective. Relying only on
quantitative data did not fully represent the information that had been collected, and this

source of more subjective data was included.

THE USE OF ARCHIVAL DATA

Historical data on the study cities was collected from other resources and was available
for further analysis. Local and state archival data were integrated into each city's dataset.

Information was collected from the following sources:

Mm: used for population figures for all cities and counties; also
included is information on demographics, housing, income, etc.

MiohiganMangal: (printed every 2 years) historical information on municipal
status, annexations, voting results, newspaper subscribership, banks and
media.

W: used chiefly for its comprehensive listings of all businesses
in all Michigan cities (published from about 1870 to 1930).

Was: used to determine when transportation

connections were made to cities, including highways, rail lines, airlines and
ferry routes; also used for establishing distances between cities.

WW: detailed insurance maps showing individual

structures in each downtown (printed irregularly from 1870 to about 1930).

Wagons: lists financial status, including debts, of all

municipalities (published from 1910 to present).

Looalfiommgnjtyfitohjxes: usually found in local libraries, these resources

included old photographs, city directories, newspaper clippings and other
downtown records.

102

CHAPTER IV
ANALYSIS

DERIVATION OF THE DOWNTOWN HEALTH PERCEPTION INDEX

FACTORS MAKING UP A "HEALTH PERCEPTION INDEX (HPI)"

This study uses perceptual input to find factors most closely allied with downtown
health. Downtown "health" is the dependent variable; in other words, other factors are
judged by how they affect the "health" factor of a particular downtown. Because of the
importance of this variable, it is critical to define what is meant by "health."

In this study health is defined in a comprehensive sense, including the condition of
downtowns in physical, economic and societal terms. Downtown health may be seen in
terms of physical factors, such as streetscape improvements, restored facades, one—way
streets, and improved parking. It may also include economic factors, such as growth in
retail sales, public expenditures in the downtown, or the level of employment in the overall
community. Societal factors can be more wide-ranging, and may include the ability of a
downtown to serve as a focus of the community, the activities of organizations connected
with the downtown (e.g, merchants association, chamber of commerce), or even issues of
changing lifestyles and shopping patterns. Through use of the survey questionnnaire,
respondents were asked their opinions about a variety of factors relating to their
downtown.

A system for comparing these responses in a consistent way was established through
the development of a downtown Health Perception Index (HPI). The usefulness of such
an index has been recognized by many researchers, including E.F. Thorndike and his

103

“Goodness Index” for cities and Boyer and Savageau’s more contemporary “Places Rated
Index” described in the Literature Review. However, these indexes, and most indexes of
urban health, have been formulated from available datasets. They are subject to the data
available, and must rely on proxy information for the derivation of conclusions.

This study takes a different approach with its Health Perception Index. It derives the
index directly from raw data collected specifically for this purpose through the study
survey. It creates its index not from secondary sources, but from the primary source on
which the study is based, the input of merchants, business owners, and local officials.

The Health Perception Index was derived by combining responses from a number of
questions from the survey and blending them into a single value. The four survey
questions used to derive the Health Perception Index were:

1. Mun—Respondents were simply asked to evaluate the
health of their downtown compared to other downtowns of similar size.

2. W—They were asked to indicate, to the

best of their knowledge, how the overall health of their downtown had
changed over the decades.

3. WWW—They were asked to indicate how they felt

about the future health of their downtown—were they optimistic that its
condition would improve or were they pessimistic?

4. WWW—They were asked to evaluate their downtown

according to a list of specific characteristics, including such things as retail
sales, parking convenience, the cooperation of the city, etc.

Each of these four questions which make up the HPI will be discussed in turn and
compared to the results from a similar questionnaire administered as part of the precursor

1986 study.73

 

73 Norman R. Tyler. 1987. An Evaluation of the Health of the Downtowns in Eight
Michigan Cities (Dissertation). Ann Arbor, Michigan: University of Michigan .

104

Wand

Stated as an initial assumption of this study, "A meaningful evaluation of a
downtown's health can be derived from perceptions of that downtown's merchants and
business owners." The first survey question was based directly on this assumption, and
asked respondents to evaluate the overall health of their downtown compared with other

cities of similar size. Question 1 was presented in the questionnaire as shown below:

 

How would you compare the overall health of your downtown with those of
other cities similar in size?

(Note: A number of questions ask about the overall “health " of downtown. Its health
can include economic, social and physical characteristics.

How do you feel it compares — is it

[ ] Much more healthy

[ ] Somewhat more healthy

[ ] About average for this size town
[ ] Somewhat less healthy

[ ] Much less healthy

 

 

 

Figure 10 - Question 1 From Survey

This question was placed first on the questionnaire because it was intended to elicit a
general initial impression from respondents, before they knew about the line of questioning
which followed. This placement minimized bias for this critical question. It also
encouraged respondents to begin with a relatively simple question, helping to overcome

their initial resistance to filling out a questionnaire.

Use of the Normative Scaling Technique

The responses to Question 1 for each study city were converted to a 0—100 scale, with
100 representing the most positive response possible and 0 representing the most negative.

In other words, if all respondents in a particular downtown had indicated their downtown

105

was “Much more healthy,” the overall rating for that downtown for Question 1 would have
been 100; if all had indicated their downtown was “About average...” the score would have
been 50.

A similar 0—100 scale was applied to virtually all of the questions used in the
questionnaire. This conversion was necessary to make it possible to compare responses
from many questions and question formats.

Using a 0—100 scale, one might initially assume the mean for each question, taken over
a large enough sample, would be close to a value of 50 (representing for Question 1 the
box labeled "About average"). Survey researchers expect a bias in questions of this type.
and have found that respondents typically rate their own city a little better than average.
Thus, the mean rating is initially valued higher than 50. As an example, the mean response
for this question for all the respondents was 54.5, significantly above the anticipated mean
value of 50.

To adjust for this bias, a “normative scaling” technique was utilized and applied
throughout. Normative scaling is a technique developed by the author to accommodate
such a bias. Through a reiterative technique of adjustment, the initial values were “nudged”
upward or downward sufficiently to derive a new mean with a “normalized” value of 50.

Using the example from Question 1, the values based on the responses were
recalculated so the average for all respondents became 50, rather than 54.5. In other
words, all the initial values were shifted proportionally downward. Using this procedure
the values in the mid-range are shifted more, while values at the higher and lower ends are
shifted less (e.g., a value of 54.5 is shifted downward 4.5, a value of 27 would be shifted
downward 2.2 and, of course, a value of 0 would not be shifted at all).

The formulas used for normalizing the ratings for the sixteen study cities are:

A. If the initial composite mean for all cities is above 50 and the initial mean for
an individual city is above the initial composite mean:

106

Normalized value for a city = initial value — (W x (comp. mean — 50) )
100 - comp. mean

B. If the initial composite mean for all cities is above 50 and the initial mean for
an individual city is below the initial composite mean:

Normalized value for a city = initial value - (MW 1: (comp. mean - 50))
comp. mean

C. If the initial composite mean for all cities is below 50 and the initial mean for
an individual city is above the initial composite mean:

Normalized value for a city = initial value + (W x (50 — comp. mean) )
100 — comp. mean

D. If the initial composite mean for all cities is below 50 and the initial mean for
an individual city is below the initial composite mean:

Normalized value for a city = initial value + (Ill-EM x (50 — comp. mean ) )
comp. mean

The values for Question 1 for each study city were normalized in this way so the mean
composite value for all the study cities would be 50.74 Such normalization accomplishes
two things—first, it eliminates the bias inherent in the questioning and answering as
described above and, second, it allows data collected through a variety of techniques to be
put into a common format for comparisons.

The following chart lists the 16 study cities75 and indicates the mean values for
Question 1 for each city both before and after normalization. (From this point on, only

normalized values will be used unless otherwise noted.)

 

74 Using the reiterative process, values for normalization were assumed acceptable if
within a range from 49.0 to 51.0.

75 Cities are listed alphabetically, with the eight cities from the original study grouped
first, and the cities added for this study grouped after.

107

Table 5 - Question 1 Rating for Study Cities
Non-normalized Normalized

£21m X6111: leflg
Albion 13.9 12.8
Dowagiac 67.9 64.2
Howell 59.5 54.9
Ionia 56.0 50.9
Lapeer 60.2 55.7
Manistee 46.3 41 .9
Marshall 79.2 76.8
Tecumseh 73.8 70.8
Alpena 45.8 41.5
Big Rapids 40.0 36.2
Cadillac 51.9 46.9
Coldwater 53.6 48.5
Hillsdale 54.8 49.6
Owosso 50.0 45.3
South Haven 50.0 45 .3
Sturgis 65.2 61.3
ALL 54.5 50.176
w w 1

As explained at the beginning of this section, the Health Perception Index (HPI) was
derived from the responses to four survey questions, including Question 1 described
above. The second of the questions related to perceptions on how the health of a
respondent’s downtown had changed over time, thus giving a historical perspective to the
evaluation. This historic question served not only as a cross-check on how a respondent
felt about his/her downtown's current status, but gave a dynamic aspect to how they
thought it was changing, and whether they felt their downtown was getting better or worse

over time. The question was stated as follows:

 

76 The figure is not exactly 50 because the figures shown under "ALL" are not the
means of the city ratings shown because some cities had more respondents than
others, and this adjusted the total mean slightly.

108

How downtown health has
changed over time...

How has the health of your downtown changed over
time? It's “health" can be seen in terms of economic,
social and physical characteristics.

0n the graph below, simply draw a continuous line
which, inynuuznininn, shows how its health has
changed over time.

(Go as far back in time as you can)

1900 1920 1940 1960 70 80 9092
Very good

 

 

 

Good

 

 

Fair

 

 

 

 

 

 

 

 

 

Poor

 

Below is an example...

 

 

 

 

 

Figure 11 - Historical Health Question From Survey

109

An example illustrates the significance of this question. Shown below are composite
graphs for Dowagiac for both the 1992 survey and a similar survey conducted in 1986.

Each line shown represents the answer from one respondent.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1900 1920 194_o__ 1960 mo 1930 as
' . Very Good

Good

Fair

l _ Poor

Figure 12 - Dowagiac Historical Health Chart (Not normalized)

1986 Survey
1900 1920 1940 1960 1970 1980 1990 92

 

—\ l . zl/ “”6006

. / a...

\ I .
\ J/ll Fair

\
\

Figure 13 - Dowagiac Historical Health Chart (Not normalized)
Current Study

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Poor

 

 

The first chart clearly illustrates a poor opinion of Dowagiac's downtown health in
1986, showing a generally downward trend with little or no indication of optimism. The
1992 chart exhibits a significant and consistent upshift in the attitudes of respondents.

Virtually every line ends on an upswing. Clearly something is happening in Dowagiac that

110

has significantly changed the opinion of merchants and business owners regarding their
downtown's health.

These composite historic graphs were then normalized for all sixteen cities. This
process of normalization used as its “base line” the average for all sixteen cities for each
decade (with a normalized value of 50 at the midpoint). This allowed the information for
each city to be seen clearly, since external factors, such as the national economy, were
essentially extracted from the historic graphs.

1940 1960 1970 1980 86 1990 92

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Solid line = 1992 survey
Dotted line = 1986 survey

Figure 14 - Albion Historical Health of Downtown—
Normalized Values compared, 1986 and 1992 Surveys

1940 1960 1970 1980 86 1990 92

 

Solid line = 1992 survey
Dotted line = 1986 survey

Figure 15 - Dowagiac Historical Health of Downtown—
Norrnalized Values compared, 1986 and 1992 Surveys

11]

1940 1960 1970 1980 86 1990 92

 

Solid line = 1992 survey
Dotted line = 1986 survey

Figure 16 - Howell Historical Health of Downtown—
Normalized Values compared, 1986 and 1992 Surveys

1940 1960 1970 1980 86 1990 92

 

Solid line = 1992 survey
Dotted line = 1986 survey

Figure 17 - Ionia Historical Health of Downtown—
Normalized Values compared, 1986 and 1992 Surveys

112

1940 1960 1970 1980 86 1990 92

 

Solid line = 1992 survey
Dotted line = 1986 survey

Figure 18 - Lapeer Historical Health of Downtown—
Normalized Values compared, 1986 and 1992 Surveys

1940 1960 1970 1980 86 1990 92

 

Solid line = 1992 survey
Dotted line = 1986 survey

Figure 19 - Manistee Historical Health of Downtown—
Norrnalized Values compared, 1986 and 1992 Surveys

113

1940 1960 1970 1980 86 1990 92

 

Solid line = 1992 survey
Dotted line = 1986 survey

Figure 20 - Marshall Historical Health of Downtown—
Norrnalized Values compared, 1986 and 1992 Surveys

1940 1960 1970 1980 86 1990 92

 

Solid line = 1992 survey
Dotted line = 1986 survey

Figure 21 - Tecumseh Historical Health of Downtown—
Norrnalized Values compared, 1986 and 1992 Surveys

As is shown, the evaluations of historic health appear to remain reasonably consistent
from the 1986 to the 1992 survey, suggesting that the responses are reliable as indicators of

perceived historic health of the downtowns. The historic health charts for the other study

cities are not included, since they cannot be compared to an earlier time period.

i u wn wn

The third question used to derive the Health Perception Index related to the attitudes of
respondents about the future of their downtown. The questionnaire asked whether they

generally saw the future of their downtown with optimism or pessimism. This provided

114

another insight on the time element by asking how respondents saw the present downtown

condition projected into the future. The question was phrased in the following way:

Now we would like your opinion on your
downtown ’s future.

 

What is your opinion of the future of
your downtown?

Does it look good or poor?
[ 1 Good
[ ] Poor

Why do you feel this way?

 

 

 

Figure 22 - “Future of Downtown” Question

This question was more difficult to convert to the 0—100 scale used for the previous
questions, since there was only a “Good” or “Poor” response. A value was established
simply based on the percent of “Good” responses to the total number of responses. In
other words, if 15 respondents checked “Good” and 10 checked “Poor,” the rating for that
study city was “60” (15/25 x 100). This allowed the question to be blended with the 0—100
scale used for responses to the other questions making up the Health Perception Index.

The following mean ratings were given by the respondents from each city:

115

Table 6 - “Future of Downtown” Question Ratings

Ratinglionnalizssl mam

Albion 10.5 8.4 2 l7
Dowagiac 93.8 92.0 15 l
Howell 68.2 59.2 15 7
Ionia 65.0 55.2 13 7
Lapeer 44.0 35.3 1 1 l4
Manistee 63.2 52.8 12
Marshall 86.4 82.5 19 3
Tecumseh 84.2 79.8 16 3
Alpena 38.5 30.8 5 8
Big Rapids 20.0 16.0 5 20
Cadillac 75.0 68.0 18 6
Coldwater 40.9 32.8 9 1 3
Hillsdale 33.3 26.7 7 l4
Owosso 60.9 49.9 14 9
South Haven 60.9 49.9 14 9
Sturgis 75.0 68.0 18 6

Two things should be noted from the review of these numbers. First, the values are
more pronounced than in the other question forms. For example, Albion’s rating for
Question 1 was a low 12.8, but for this question was an even lower 8.4, indicating great
pessimism about the future, while Dowagiac’s 64.2 for Question 1 is exceeded here by a
very high 92.0, indicating great optimism.

Second, eight of the cities listed above were included in the 1986 survey. The same
question was asked, and for some the values have changed considerably in six years. For
instance, in Albion the previous study had 5 respondents optimistic and 4 pessimistic,
while this survey showed only 2 optimistic and 17 pessimistic. On the other hand,
Dowagiac in the previous study had 5 optimistic and 7 pessimistic, while this survey
showed 15 optimistic and only 1 pessimistic. As a result, this question was shown to be

the most dramatic indicator of a shift in attitude, shown by comparing responses for the

116

original eight cities for both the 1986 study and the current study, as shown in the chart

below:
Table 7 - “Future of Downtown” Question:
Optimistic vs. Pessimistic Responses
1986 Survey Current Study
Mimi! £12m £91m am

Albion 5 4 2 l7
Dowagiac 5 7 15 1
Howell 10 2 15 7
Ionia 14 l 13 7
Lapeer 1 1 6 1 1 14
Manistee 5 7 12 7

Marshall 14 4 19
Tecumseh 7 l 16 3

4 v ' 'v' w

As the fourth and final question making up the Health Perception Index, respondents
were asked to evaluate a list of 16 characteristics generally associated with downtown
health, including retail sales, condition of buildings, level of cooperation from the city, and
so on, as shown below. This question was included because the evaluation of such
individual characteristics gave more detail on how respondents rated their downtown’s
health, and revealed important information about the factors that have contributed most to a
downtown's current condition. The 16 characteristics included were:

General condition of buildings downtown
Streetscape—sidewalks, benches, trees, etc.
Mix of stores and businesses

Parking in downtown

Your impression of overall retail sales downtown
No. of shoppers for “browsing” shopping

No. of shoppers for “quick-stop” shopping
Merchants association

Cooperation of banks

Cooperation of city government

OONQO‘M‘hWNu—t

H

117

11. Local political situation

12. Downtown as a community cultural center
13. No. of tourists coming to downtown

14. Employment in the local area

15. Historic character of downtown

16. Safety and security downtown

The respondents were asked to indicate whether they evaluated each characteristic as
“Very good,” “Good,” “Fair,” or “Poor.” As previously described, the values for the
ratings were normalized for each study city. The normalized values were as follows:

Table 8 - Normalized Values for Ratings
for 16 Downtown Characteristics

Bldgfi Street BMix Entkfi Sales Ems Quid: Memh

Albion 20.1 26.0 9.6 36.8 7.9 5.1 35.6 18.0
Dowagiac 60.4 95.7 45.5 49.0 55.8 57.5 50.4 63.5
Howell 55.9 40.2 56.6 22.7 54.0 60.4 57.4 59.5
Ionia 49.8 79.1 34.7 55.0 47.1 45.7 42.8 36.7
Lapeer 59.5 41.8 62.3 50.0 61.5 55.0 57.9 61.1
Manistee 55.8 23.1 51.8 39.7 33.0 36.5 45.1 58.8
Marshall 64.8 46.0 57.0 59.7 61.0 64.7 42.9 55.0
Tecumseh 69.8 89.5 68.1 79.4 69.9 63.2 59.1 57.6
Alpena 45.8 36.0 55.6 35.1 49.6 55.4 38.0 63.0
Big Rapids 33.5 42.3 47.3 30.8 36.2 38.7 29.5 47.9
Cadillac 37.7 59.0 62.3 61.5 57.5 59.8 56.9 49.4
Coldwater 55.9 83.4 45.5 81.8 49.3 37.1 53.8 56.3
Hillsdale 54.1 19.9 38.7 59.4 52.7 55.8 57.4 48.6
Owosso 54.1 58.6 56.3 59.4 49.2 50.0 61.5 54.2
South Haven 38.7 36.0 43.8 28.2 53.7 63.6 48.6 29.6
Sturgis 50.7 37.9 56.0 63.9 53.8 40.0 57.7 35.4

118

Banks

Albion 29.2
Dowagiac 57.6
Howell 54.5
Ionia 46.0
Lapeer 64.8
Manistee 28.4
Marshall 60.0
Tecumseh 65.7
Alpena 49.5
Big Rapids 50.8
Cadillac 48.9

Coldwater 49. 1
Hillsdale 43.3

Owosso 53.0
South Haven 42.6
Sturgis 58.3

Table 8 (cont’d).

25.6
56.7
50.5
57.3
60.6
46.4
61.0
61.9
42.5
37.6
48.2
45.9
36.4
62.4
40.3
58.3

11.7
63.2
56.0
57.3
60.3
48.3
62.9
44.3
32.3
40.6
55.4
53.1
44.3
59.8
42.3
61.0

8.9
58.3
56.3
34.7
58.7
57.5
59.5
56.6
54.3
33.7
47.5
61.5
35.7
62.2
47.1
55.3

8.1
63.9
53.5
33.0
41.4
55.5
80.5
65.2
61.6
27.7
67.4
43.7
40.2
26.8
80.9
40.1

Jobs
11.6
57.3
62.8
61.0
54.8
28.7
69.4
67.1
46.2
50.5
64.2
24.9
42.4
50.1
35.9
68.4

Histiafsnt Mean

29.9
60.3
69.3
67.9
57.5
70.8
87.2
76.6
29.9
32.0
30.3
49.8
46.7
40.3
34.0
28.4

29.2
40.7
57.7
50.8
53.7
60.9
68.1
68.7
59.7
36.0
55.9
47.8
64.3
50.0
33.7
35.6

19.6
58.5
54.2
49.9
56.3
46.3
62.5
66.4
47.2
38.4
53.9
52.4
46.2
53.0
43. 7
50.0

This evaluation by the respondents in each city of their downtown’s characteristics

reveals some clear and interesting information. For example, it is obvious that the

respondents in Albion feel their downtown is doing very poorly. The mean rating of 19.6

for Albion for all characteristics was much lower than the mean rating for any of the other

study cities. In addition, each of the downtown characteristics for Albion was rated well

under 50, the normalized mean, indicating the low evaluations by respondents were

consistent for all aspects of their downtown. Indeed, the low rating for Albion must be

considered as quite exceptional, and could give a more general perspective on what causes

poor downtown health. The mean ratings for the other 15 cities were more closely

grouped, but still ranged from 38.4 (Big Rapids) to 66.4 (Tecumseh), a range wide enough

to indicate a significant difference in the general evaluation of downtown characteristics.

119

However, the real value of these figures is in the analysis of the ratings for the
individual characteristics before normalization. The values shown in the chart below
indicate that some characteristics were generally rated higher by all respondents.77

Table 9 - Mean ratings For All Study Cities
for Characteristics before Normalization

Bliss Sm Mitt Earls: Sales Ems Quick March
Mean rating 59.1 73.9 38.2 50.8 37.0 36.8 38.3 40.6
Standard Dev. 12.8 16.7 11.8 16.9 11.8 12.5 8.5 11.6

Mean rating 57.3 47.8 40.8 32.0 44.8 33.9 67.8 67.0
Standard Dev. 10.6 10.6 11.9 10.4 19.4 14.1 16.3 11.9

The highest ratings were given to “Streetscape,” with a mean rating of 73.9 before
normalization. This characteristic also had the greatest standard deviation, representing a
significant difference of opinion from one city to another. This deviation was found largely
to be based on whether streetscape improvements had recently been completed. Five cities
(Dowagiac, Tecumseh, Coldwater, Cadillac, and Ionia) with very high ratings for
Streetscape (all with ratings in the 905 before normalization) were also cities which had
recently completed downtown streetscape improvements.

Similarly, a number of characteristics receiving the lowest mean ratings (before
normalization) related to more functional aspects of the downtown, including shopping
(Business Mix, Browsing Shopping, Quick-stop Shopping), image (Downtown as a

Cultural Center), and economics (Jobs). If the characteristics are divided into two general

 

77 See Appendix for chart of ratings for characteristics for all cities.

120

categories, physical and functional,78 the average rating for all cities for these two

groupings can be compared, as shown below.

E'll ..

Mix of stores and businesses

Your impression of overall retail sales downtown
No. of shoppers for “browsing” shopping
No. of shoppers for “quick-stop” shopping
Merchants association

Cooperation of banks

Cooperation of city government

Local political situation

Downtown as a community cultural center
No. of tourists coming to downtown
Employment in the local area

Safety and security downtown

Mean value: 42.9

131']! ..

General condition of buildings downtown
Streetscape—sidewalks, benches, trees, etc.
Parking in downtown

Historic character of downtown

Mean value: 62.9

 

78 The author’s 1987 Study, An Evaluation of the Health of the Downtowns in Eight
Michigan Cities, derived these two general categories through correlation analysis
of the characteristics.

121

These groupings indicate that physical characteristics of the downtown are generally
rated higher than functional characteristics. This is likely because physical characteristics
are more apparent and easier to identify, and thus easier to evaluate, while functional
characteristics are “fuzzier” to most respondents, and their significance tended to be

downgraded as a result.

DERIVATION OF THE DOWNTOWN HEALTH INDEX

Described above were the four questions used to derive the Health Perception Index
(HPI). To repeat, the four questions were (A) Compare your city to other cities similar in
size, (B) Evaluate the health of your downtown historically, (C) Indicate whether you are
optimistic or pessimistic about the future of your downtown?, and (D) Evaluate 16
individual downtown characteristics. The normalized values for these four questions were
merged to derive the final downtown Health Perception Index and calculated according to

the following formula:

HPI = mean (2A, B. C, 2D)

A = normalized value of comparison of downtown to other downtowns,
weighted double

B = normalized value of average rating for 1990 and 1992 on historic health
graph
C = normalized value for optimistic/pessimistic response

D = normalized average value of all 16 downtown characteristics,
weighted double

Figure 23 - Formula for Deriving Health Perception Index
As noted, two questions were given double weighting for calculating the Index. The
comparison question (Question #1) was weighted double because of its directness and its

primacy in the questionnaire. The question was considered direct because it asked

respondents to simply rate the health of their downtown. It had primacy because it was the

122

first question on the questionnaire, and was therefore least likely to include normal survey
bias resulting from question wording or sequence. The other question weighted double
asked respondents to evaluate individual characteristics of their downtown. These
responses were weighted double because of the question’s inclusiveness, covering 16
different aspects of downtown health within one question. This weighting was determined
prior to analysis.79

The chart below shows weighting had only minor significance on the value of the
Health Perception Index, and caused only minor shifts in the rankings of the cities. The
unweighted values for each of these four factors and the unweighted and weighted Index

values for each city are as follows:

Table 10 - Normalized Values For Components of Health Perception Index

Unweighted Weighted
HE!

A B C D 11121
Albion 12.8 22.1 8.4 - 19.6 15.7 15.9
Dowagiac 64.2 58.9 92.0 58.5 68.4 66.0
Howell 54.9 61.4 59.2 54.2 57.4 56.5
Ionia 50.9 42.1 55.2 49.9 49.5 49.8
Lapeer 55.7 57.5 35.3 56.3 51.2 52.8
Manistee 41.9 43.5 52.8 46.3 46.1 45.4
Marshall 76.8 62.6 82.5 62.5 71 . 1 70.6
Tecumseh 70.8 70.5 79.8 66.4 71 .9 70.8
Alpena 41.5 42.9 30.8 47.2 40.6 41.8
Big Rapids 36.2 40.3 16.0 38.4 32.7 34.3
Cadillac 46.9 55.5 68.0 53.9 56.1 54.2
Coldwater 48.5 44.5 32.8 52.4 44.6 46.5
Hillsdale 49.6 46.1 26.7 46.2 42.2 44. I
Owosso 45.3 48.2 49.9 53.0 49.1 49.]
South Haven 45.3 52.0 49.9 43.7 47.7 46.6
Sturgis 61.3 50.1 68.0 50.0 57.4 56.8

 

79 In the 1986 Survey of Michigan Downtowns, the precursor to the current survey,
there was an attempt to have respondents themselves weight the sixteen downtown

characteristics as to “how important an indicator of health you feel it would be.”79
This questioning confused respondents, however, who were sometimes unclear
whether they were rating the importance of the characteristic or rating the
characteristic itself in terms of their downtown. For the sake of clarity the final
form of the 1986 questionnaire did not include questions asking responding to
weight their responses, and they were also not included in the 1992 survey
questionnaire.

123

100.0
90.0
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0

0.0

       

Tecumseh
Marshall
Dowagiac
Sturgis
Howell
Cadillac
Lapeer
Ionia
Owosso
South Haven
Coldwater
Manistee
Hillsdale
Alpena
Big Rapids
Albion

Figure 24 - Rankings of Study Cities
According to Health Perception Index
The above chart graphically indicates the significant differences in perceived health of
the various downtowns. Albion’s Health Index rating is 15.9 (on a scale of 0 to 100),
indicating a downtown that is almost universally perceived as poor. Marshall and
Tecumseh have the highest ratings, with almost identical Index values of 70.6 and 70.8.
These should be considered as high positive ratings, especially since factors such as
external conditions, and the normal positive bias found in surveys, have been extracted
through the process of normalizing the values. Not surprisingly, a large number of cities
(10) have Index ratings generally in the 45 to 55 range, an indication of normal evaluations
of the conditions in those cities.
This newly derived Health Perception Index (HPI) also can be compared to the Health
Index derived for the eight cities included in the 1986 survey. The values for both are

shown in the chart below.

124

Table 11 - Health Index Values for 1986 and 1992 Surveys

1986 1992

Index hides

Albion 34 1 5.9
Dowagiac 37 66.0
Howell 48 56.5
Ionia 54 49.8
Lapeer 5 1 52.8
Manistee 43 45.4
Marshall 59 70.6
Tecumseh 64 70.8

These comparisons indicate significant shifts in some of the original study cities in the
six years from 1986 to 1992. For example, Albion’s low rating of 34 in 1986 has dropped
precipitously since then, and is now much lower. In contrast, Dowagiac changed from an
Index rating well below the mean (37) to one well above (66.0). Other cities do not show
changes as dramatic, but both Marshall and Tecumseh, which had relatively high ratings in
1986, have improved their ratings in recent years, indicating even better downtown health.

Some initial conclusions can be drawn simply on the basis of these comparisons. For
Albion, it is apparent that whatever was causing poor downtown health in the 1980s has
only become worse in the 19905. However, in Dowagiac some significant change(s) has
happened in six years that has not only improved the perception of downtown health, but
also encouraged merchants and owners to be optimistic that their downtown will be even
healthier in future years. Finally, in Tecumseh and Marshall successful downtown
programs and activities have continued to be improved over time. Three different lessons
can be learned from these examples by looking in more detail at some of the factors

impacting downtown health.

125

USE OF A PERCEPTUAL INDICATOR

The downtown Health Perception Index (HPI) derived for this study is an indicator
based on the perceptions of survey respondents. As such, it is subject to the shortcomings
of perceptual input, and should be considered a social indicator more than an economic
indicator. Although the HPI is used for statistical analysis, it is derived from data based on
the perceptions of respondents, and can have as much to do with their attitudes as with
facts. Cazes (1972) has called these types of indicators “trans-economic.”80

The Health Perception Index was developed for this study because no other satisfactory
analytical tool was found for evaluating downtown health. As discussed earlier, Andrews
recognized the need for assessing well-being with both perceptual and non-perceptual
indicators. However, as Francis (1973) said in describing the use of such indicators,
“Because...an indicator in inherently arbitrary, it presents a special political danger. We
may delude ourselves into believing that the index really measures what it purports to
measure.”81 Horn recognized the difficulty with both types of indicators, when he stated,
“All objective-type indicators carry a subjective value load inherent in the process of the
collection, selection and presentation of statistics, and subjective-type indicators borrow
objective modes of grouping, ranking and partitioning the data.”82 Indeed, the Health
Perception Index does not measure downtown health. But it does allow comparisons to be
made between the relative states of the study downtowns, and through these comparisons

allows for analysis. Thus, a Health Index rating of 75, for example, does not establish a

 

30 From A. Shonfield and S. Shaw (Editors). 1972. Social Indicators and Social
Policy. London: Heinemann.

31 Walton J. Francis. 1973. “A Report on Measurement and Quality of Life and the
Implications for Government Action on ‘The Limits to Growth.”’ Washington,
DC: Department of Health, Education and Welfare. 552.

82 Robert V. Horn. 1993. Statistical Indicators for the Economic and Social Sciences.
New York: Cambridge University Press. 9.

126

firm quantitative value on the health of a given downtown, but it does indicate that the
perception of general health was considerably higher than a city which had a Health Index
rating of 50.

THE SEARCH FOR PROXIES USING DEMOGRAPHIC DATA

USE OF PROXIES

This study’s Health Perception Index can be derived only through use of an intensive
survey, making it impractical to rely on as an everyday evaluative tool. Cities would
benefit from more easily obtainable evaluation techniques. If other more readily available
data was found to correlate with the Health Perception Index, that data could serve as a
useful proxy for the Index. Such use of proxy data would allow every downtown to be
compared and evaluated more straightforwardly as to the relative success of its
revitalization strategies.

As presented in an earlier chapter of this report, Andrews (1976) recognized the need

for both perceptual and non-perceptual indicators.

"... a program designed to assess well-being would be most useful if it included
both perceptual and non-perceptual social indicators relevant to the same
concerns... In short, we would envisage two parallel series of data: one
assessing perceptions about well-being... the other providing various non-
perceptual data for the same concerns."83

Anson (1991) suggested social indicators derived from demographic data could be

reliable measures of the quality of life in a society. Demographic indices could provide a

 

33 Frank M. Andrews and Stephen B. Withey. 1976. Social Indicators of Well-
Being. New York City: Plenum Press. 340.

127

satisfactory solution to the problems inherent in relying on an intensive survey of attitudes.
“Their cost of collection is small, because the required data will be collected by the state in
any case; their estimation errors are the smallest that can be obtained, as they are based on
total populations and not on samples; they are robust in the face of interventions which
may seek to improve the indicator without seriously affecting the underlying social reality;
and they are backed by a sound empirical and theoretical knowledge base, which enables us

to specify what it is that these indices mean and what their limitations are.”84

USE OF DEMOGRAPHIC DATA

The search for proxies for the Health Perception Index began by looking at
demographic information. This included analysis of readily available data from the US.
Census and state statistical sources. The methodology used was correlation analysis,
which would indicate the strength of correlations85 between perceived downtown health
and other data.

Correlation analysis was first used to compare the HPI with data from the US. Census
for 1990 for the 16 study cities. The minimum acceptable correlation coefficient was
0.50,86 using a confidence level of 95% with the sample of 16 cities. Each factor taken
from the 1990 census is shown below, including the coefficient of its correlation with the

Health Perception Index.

 

84 J. Anson. 1991. “Demographic Indices As Social Indicators.” Environment and
Planning A. 23:433.

35 Correlation does not imply causation. A high correlation between the Health
Perception Index and streetscape improvements would not imply that streetscape
improvements lead to improved downtown health. It may mean good downtown
health brings in funds to allow streetscape improvements. In other words,
streetscape improvements may lead to good health, or good health may lead to
streetscape improvements. Cause-effect relationships can be determined with
confidence only by incorporating and controlling these factors prior to the survey.
What can be concluded through correlation analysis with some degree of
confidence is that one is somehow related to the other.

36 A 99% confidence level would need a coefficient of at least 0.55.

128

Table 12 - Coefficients: Demographics and Index

Demographic Correlation
Editor W
Population size -0.36
Population size (county) 0.08
Population grth 0.31
Population growth (county) 0.16
County seat status 0.58
(non-seat is better)
Median age 0.50
(older is better)
Percent pop. 65+ yrs. old 0.22
Percent pop. 18-24 yrs. old -0.50
(negative impact)
Percent pop. 25—44 yrs. old 0.59
Percent pop. 45-64 yrs. old 0.34
P0pulation density 0.39
Median income 0.40
Upper quartile income 0.41
Percent black -0.51
Percent black (county) -0.05
Percent urban (county) -0.18
Percent college graduates -0.20
Percent unemployed -0.39
Number experienced unempl. -0.04
Percent in labor force 0.60
Percent in labor force (county) 0.38
Percent executive work force -0. 13
Household income 0.65
Household income (county) 0.32
Percent in poverty -0.68
Median price of housing 0.43
Per Capita income (county) 0.30
Increase in no. of retail stores -0.20
Retail sales -0. 10
Retail sales (county) -0.03

(Notes: An “ "‘ " represents a statistically significant correlation.
All population statistics refer to city populations unless otherwise noted)

129

This list reveals interesting correlations. Some of the more revealing of them will now

be looked at individually.

E l . S'
The sixteen study cities varied in pOpulation from 5,563 to 16,322. The 1990
populations of the cities are shown below, graphed with the Health Perception Index.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

80.0 ,. 18.000
5 70-0 - ' 16.000
‘ —

:1 14.ooo°
— 60.0 .. . a
.5 so..- _ /\ 12,000:
‘5- I I I 10,000 ‘3
3 40.0 -~ " — :3.
3 m I . I _ 8.000 3
°' 30-0-~ "mm. . ' 6000 2.
.5 I t O
. fl-
; 20.01 4.0“)
10.0 -- 2,000
0.0 i : : ' t i ' : : . ' ' ' t . 0
.c - 59. = “3 O r: s- o “I a
gig-53033 sagaéggfig
5 3’ .. o .g '- 3 3 E _ 2 2-
8 2 g m :1: U o E :5 z E
t— 3 0 iii
CI)

(Note: Bars are HPI, line is Population)

Figure 26 - Health Perception Index compared to 1990 City Population

According to Reilly’s Law of Retail Gravitation,87 population size can be used to
determine the size of a retail market area, and thus its potential customer base. According
to Holden and Deller (1994),88 population size can also serve as an adequate proxy for
overall retail sales, the number of retail stores, and the volume and variety of goods

available. However, there is not necessarily a correlation between population size and the

 

37 See Chapter H

83 John P. Holden and Steven C. Deller. 1994. “Measuring Community Retail
Markets: An Analysis of Reilly’s Law.” Small Town (September—October):
1 6—2 1 .

130

health of the retail environment. The above analysis shows no significant correlation
between population size and the Health Perception Index. Downtowns with larger potential

retail market areas are not necessarily more healthy than those with smaller market areas.

Populationfimth

One of the most commonly used demographic statistics relating to cities is population
growth. Growth in population was considered by comparing the percentage growth rate
for each city from 1980 to 1990 with the Health Perception Index. As shown graphically
in the following chart, and shown statistically through a correlation coefficient of 0.0739,

there is no correlation between these factors.

 

 

 

 

 

 

 

 

 

 

 

80.0 .. -~ 30.0
5 70.0 " .. 25_o
'5 a A
E 60.0 I! . "' 20.0 g E
a _ 1.. 0
.g 500 «l- 4 15.0 (D E.
a. — I -- 10.0 v
§ 40.0 -- . 5 0 3 e
0 I- . "' at
a. ' -

30.0 -- . I " e
'5 . . I‘ - I -- 0.0 a:
75 20.0 -- . 50 6°. .-
s - - ‘ ' °

10.0 'I ' - P -10.0

I
0.0 1 i i i i 1' + 1 i 4 i i i ‘T 4 -15.0

 

 

 

 

 

 

 

 

 

 

 

 

Sturgis
Howell
Cadillac
Lapeer
Ionia
Owosso

Hillsdale
Alpena
BigRapids
Albion

Tecumseh
Marshall
Dowagiac
SouthHaven
Coldwater
Manistee

(Note: Bars are HPI, line is Population Change)

Figure 27 - Health Perception Index Compared to
Population Growth Rate (%), 1980-90

 

89 With a sample of sixteen cities, a correlation coefficient must be a minimum of
0.50 (or -0.50) to be considered statistically significant with a confidence level of
95 percent.

131

OTHER DEMOGRAPHIC FACTORS

From the list of factors taken from the 1990 census, the factors that are statistically
correlated with perceived downtown health are listed are grouped by similarity.

Table 13 - Statistically Significant Correlations of Demographic Data
and the Health Perception Index

* Percent in poverty —0.68
* Household income 0.65
* Percent in labor force 0.60
* Percent black —0.51

* Percent pop. 25-44 yrs. old 0.59
* Percent pop. 18-24 yrs. old —0.50

* Median age 0.50
* County seat status —0.58
mm

The demographic factor with the highest correlation is percent of population in poverty.
It is clearly shown that the higher the level of poverty, the poorer the perception of

downtown health.

Householdlncome

As will be shown elsewhere, cities with higher average household incomes tend to rate
their downtown health more positively. This is obviously the reverse of the Poverty factor
just described. The relative evaluations may be due to the fact that there is a direct link
between income and downtown health, and wealthier families tend to spend more on goods
and services. The correlations may also be because relative wealth creates a more positive
community image overall, and respondents see downtowns as benefiting from that positive

image.

132

W
The positive correlation of the percent of people working and perceived health is in line

with the preceding categories, since employment relates directly with household income

and negatively correlates with poverty.

W391;

There is a direct link between the percentage of black population and poverty, and the

negative correlation to downtown health relates to this linkage.

The first four categories—poverty, household income, jobs, and percent black
population, are all linked to each other, and could be grouped into a Principle Component

category termed “Socio—economic characteristics.”

11 i -44
W10

Other statistically significant correlations indicated the age of residents may be
associated with downtown health. The analysis indicated a city with a higher percentage of
its population between the ages of 25 and 44 years old tended to have better downtown
health (with a correlation coefficient of 0.59), while a higher percentage within the 18 to 24
age range was negatively correlated to downtown health (with a coefficient of -O.50). An
initial conclusion could be the 18 to 24 age range represents a population that is relatively
mobile and transient and shows little attachment to local businesses. By comparison, the
25 to 44 age range represents a population which is more family oriented, more stable, and
in their prime purchasing years. This indicates downtowns could benefit from the buying
habits of the 25 to 44 year old age group, and that adults with families (the representative

lifestyle from this age range) contribute to downtown health and should be encouraged by

133

downtown business owners and merchants. Interestingly, older residents (45 years and
older) were not found to be tied statistically with the HPI.

The correlation of downtown health to age of population is strong enough to be worthy
of further exploration. This was done by including a larger sampling of Michigan cities,
based on one of the questions from the survey questionnaire. The question asked
respondents to give their impressions of other cities of similar size across the state,
indicating which they thought had healthy downtowns and those with downtowns in poor
health.90 From the initial list of 35 cities included in this question, seven were eliminated
before analysis because they had local colleges, which resulted in skewed population
characteristics. Of the 28 cities looked at, including the study cities, the following
correlations were derived.

Table 14 - Correlation Coefficients of Age Groups to Respondent Evaluations
of Downtown Health For Other Cities in Michigan

Agefitoun Commit.

18—24 -0.38
25-44 0.46
45—64 0.44

65+ 0.48

With the sample of 28 cities, a minimum correlation coefficient of 0.38 is considered
statistically significant. The above chart shows that all the age groups listed are statistically
correlated to the evaluations of downtown health. The 18-24 year old age group is
negatively correlated, while the others are all positively correlated. This substantiates the
conclusion previously presented on the contribution of certain age groups to downtown

health.

 

90 See page (22) for a full description of this question.

134

mm
This category is in line with the age category just described. and shows that study cities

with younger median ages tend to have lower evaluations for downtown health.

W

Population size (or relative population size) is one way to consider the dominance of a
city. Another way is to look at functional dominance. A historically significant function is
that of county seat. County seat status brings with it many functions (e.g., law offices,
clerical and support services, county residents having business with county offices) which
contribute to the functional vitality of a city.

Of the 16 study cities, 10 are county seats. Analysis indicated that cities that were not
county seats had healthier downtowns than cities that were county seats. This is counter to
other studies which have shown that county seat status contributes to downtown vitality
(Fuguitt 1965), and should be assumed to be a spurious statistical correlation possibly

resulting from measurement problems not directly tied with this study.

THE SEARCH FOR PROXIES FROM SURVEY DATA

As described in previous sections, the Health Perception Index (HPI) not only
establishes a yardstick for evaluating downtown health, but also allows analysis of what
factors have contributed to a downtown’s good or poor health. But what factors are most
closely correlated with good or poor health? This question can be looked at by comparing
respondents’ evaluation of downtown health with their evaluation of other factors on which
they were surveyed. If a significant and consistent statistical correlation is able to be
shown between any of the factors and the evaluations of downtown health, it can be

assumed there is a significant relationship between the two. The following section looks at

135

a list characteristics of the downtowns, as well as historical factors, business mix, external
competition, the general attitude of merchants, and even personal characteristics of the
respondents. Through correlation analysis possible relationships to downtown health are

explored.

DOWNTOWN CHARACTERISTICS

The survey questionnaire listed sixteen downtown characteristics, and respondents
were asked to evaluate each characteristic for their downtown.91 This question revealed
important detail on what respondents consider to be the primary reasons for improving or
lessening downtown health.

The initial analysis of this question looked at which of the 16 characteristics were
statistically correlated with the Health Perception Index. The correlation coefficients are

listed below (Note: Boldface indicates a statistically significant correlation):

Table 15 - Correlation Coefficients of 16 Downtown Characteristics

1. General condition of buildings downtown 0.84
2. Streetscape—sidewalks, benches, trees, etc. 0.5 3
3. Mix of stores and businesses 0.7 3
4. Parking in downtown 0.44
5. Your impression of overall retail sales downtown 0. 88
6. No. of shoppers for “browsing” shopping 0.78
7. No. of shoppers for “quick-stop” shopping 0.54
8. Merchants association 0. 5 6
9. Cooperation of banks 0.75
10. Cooperation of city government 0. 84
1 1. Local political situation 0. 78
12. Downtown as a community cultural center 0. 75
13. No. of tourists coming to downtown 0 . 68
14. Employment in the local area 0. 79
15. Historic character of downtown 0 . 63
16. Safety and security downtown 0. 5 3

 

9‘ This question was more fully described in Chapter III on development of the
questionnaire.

136

As is shown, every characteristic is significantly correlated with downtown health,
except for one—parking. The characteristics, when relisted according to their ranking,
show the highest correlations first.

Table 16 - Sixteen Downtown Characteristics Ranked By Correlation Coefficients

5. Your impression of overall retail sales downtown
1. General condition of buildings downtown
10. Cooperation of city government
14. Employment in the local area
6. No. of shoppers for “browsing” shopping
1 1. Local political situation
9. Cooperation of banks
12. Downtown as a community cultural center
3. Mix of stores and businesses
13. No. of tourists coming to downtown
15. Historic character of downtown
8. Merchants association
7. No. of shoppers for “quick-stop” shopping
2. Streetscape—sidewalks, benches, trees, etc.
16. Safety and security downtown
4. Parking in downtown

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Retail Sales As a Characteristic

“Retail Sales” is most closely correlated with the Health Perception Index (0.88) as is

illustrated by the graph below.

137

100.0 Ii-
90.0 It-
80.0 II
70.0 q» I I
60.0 T
HPI 50.0 -I II
40.0 -~ '
30.0 v-
20.0 --

10.0 «-

 

O O l l l J i l l l l J
- I I I I I I I I I I

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

Evaluation of Retail Sales

Figure 28 - Graph of Correlation of Retail Sales to HPI

Building Condition As a Characteristic

A high correlation is also shown between “Condition of downtown buildings” and
downtown health. It may relate to the general maintenance of downtown buildings, the
amount of renovation or modernization, the number of vacancies, or the general historic
character of the downtown. This characteristic has a much higher correlation coefficient

than in the 1986 survey.92

 

92 Norman R. Tyler. 1987. An Evaluation of the Health of the Downtowns in Eight
Michigan Cities (Dissertation). Ann Arbor, Michigan: University of Michigan.
1 18.

138

100.0 ..
90.0 n
80.0 --
70.0 -- I I
60.0 --
HPI 50.0 -- n n
40.0 -- '
30.0 -r
20.0 ..

10.0 ..

 

 

0.0 ::::::::::
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

Evaluation of the Condition of Buildings

Figure 29 - Graph of Correlation of Building Condition to HPI

Employment As a Characteristic

“Employment” was also shown to be closely associated with downtown health. This

was shown through three different types of analysis.

1 . The evaluation of survey respondents showed the characteristic “Employment in
the local area” closely correlated with the Health Perception Index (with a
correlation coefficient of 0.79).

139

100.0 1-
90.0 ...
80.0 --
70.0 41- ..
60.0 --

HPI 50.0 -L . I
40.0 -- '
30.0 --
20.0 --

10.0 «-

 

O 0 I I l 1 I l l l l I
- I I I I I I I I I I

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0

 

Evaluation of Employment in the Local Area

Figure 30 - Graph of Correlation of Employment to HPI

2. Labor force statistics (percent of population in the labor force) from the 1990
census were also found to be correlated with the Health Perception Index (with
a coeffcient of 0.60).

3. To test for consistency, respondents were identified who had also participated
in the 1986 survey. A total of 39 matching respondents were identified
(although there were likely others not identified). Comparisons were made on
how their responses had changed, or not changed, over the six years. The
mean differences in their answers over that six year time period are listed
below. As shown, the characteristic they evaluated with greatest consistency
over that time period was “Employment in the local area” ( 12.2), giving
additional confidence to the evaluation of this characteristic.

140

Table 17 - Mean difference in evaluations between 1986 and 1992 surveys

 

 

 

 

E] . . I: v' .
1. General condition of buildings downtown 19.2
2. Streetscape—sidewalks, benches, trees, etc. 21.5
3. Mix of stores and businesses 23.1
4. Parking in downtown 19.2
5. Your impression of overall retail sales downtown NA.
6. No. of shoppers for “browsing” shopping 17.3
7. No. of shoppers for “quick-stop” shopping 16.0
8. Merchants association 20.8
9. Cooperation of banks 13.1
10. Cooperation of city government 26.3
1 1. Local political situation 15.1
12. Downtown as a community cultural center 27.2
13. No. of tourists coming to downtown 13.1
14. Employment in the local area 1 2 .2
15. Historic character of downtown 20.2
16. Safety and security downtown 22.4
Shopping As a Characteristic

Two types of shopping were included as characteristics—”Browsing shopping” and
“Quick-stop shopping.” Of the two, the respondents from downtowns that felt they had
good browsing shopping stores tended to rate the health of their downtowns better
(coeff.=0.78), while those with higher evaluations of quick in—and-out stores had a much

lower correlation with overall health (0.54).

141

S treetscape As a Characteristic

The characteristic “Streetscape” was shown as weakly correlated to the HPI.
(coefficient of 0.56). However, the respondents evaluations varied widely from city to
city, indicating a characteristic that is subject to considerable misperceptions regarding its
importance to downtown health.

In the city of Dowagiac there was a +50.0 change in the rating for “Streetscape,” much
greater than typical. Responses were reviewed for the respondents who had participated in
both the 1986 and 1992 studies; it was evident that for each respondent the change had
been positive. Each of the five respondents who completed both surveys indicated there
had been dramatic improvement in their downtown’s “Streetscape.” Chapter 111 included a
background discussion of Dowagiac which described the streetscape improvements that
were just being completed as the 1992 survey was being taken. It is evident that the
respondents in Dowagiac considered this change in a very positive way, especially when
compared to other study cities where streetscape improvement programs had sometimes
been subject to criticism, controversy and disappointment. An analysis of the full surveys
support this positive shift in Dowagiac. The rating for “Streetscape” in 1986 was a very
low 19, but in 1992 it was an incredible 96. Coupled with this, in Dowagiac the rating for
“Condition of buildings” rose from 22 to 60.

Other cities provided less clear information. Tecumseh had also recently completed
streetscape improvements, and the mean rating. given by repeating respondents had
improved from 79 in 1986 to 90 in 1992. Manistee, however, which had recently
constructed an extensive and expensive Riverwalk pedestrian walkway in their downtown,

had a rating which dropped from 41 to 23 during the same time spam” This should not be

 

93 Changes in the normalized values for “Streetscape” for each of the eight original
study cities included in both the 1986 and 1992 surveys are as follows:

142

considered as big a decline as shown, however, for the tremendously high rise in the
values for Dowagiac caused the normalization of numbers to be significantly skewed, and
if non-normalized numbers are used, the decline in Manistee is only from 51 to 48. The
fact that there is a decline at all is nevertheless significant, considering the large amount of
money spent on the Riverwalk project.

Looked at in its entirety, the data indicates the characteristic “Streetscape” was subject
to the largest swings in evaluation values.94 From this analysis an important conclusion
can be reached: If streetscape work had recently been completed, it was typically seen in a

very positive light, but if such improvements were completed years before, or had been

 

Normalized values Before Normalization

1.28.6 1222 128.6 1.222
Albion 43 26 53 48
Dowagiac 19 96 24 98
Howell 41 40 51 70
Ionia 83 79 87 91
Lapeer 58 42 68 72
Manistee 41 23 51 48
Marshall 79 46 84 75
Tecumseh 7 1 90 78 95

94 The standard deviations from the normalized values for the 16 downtown
characteristics are as follows:

1. General condition of buildings downtown 12.3
2. Streetscape—sidewalks, benches, trees, etc. 23.4
3. Mix of stores and businesses 13.5
4. Parking in downtown 16.9
5. Your impression of overall retail sales downtown 13.8
6. No. of shoppers for “browsing” shopping 14.8
7. No. of shoppers for “quick-stop” shopping 9.4
8. Merchants association 12.8
9. Cooperation of banks 10.4
10. Cooperation of city government 10.6
11. Local political situation 13.2
12. Downtown as a community cultural center 13.9
13. No. of tourists coming to downtown 19.6
14. Employment in the local area 16.6
15. Historic character of downtown 18.9
16. Safety and security downtown 12.2

143

controversial in some way, this characteristic did not show the same kind of positive
response. In other words, streetscape improvements can have dramatic short-term impact,

but not necessarily significant long-term impact.

12 'v' E . E :1 . .
Every characteristic listed also can be checked against every other characteristic for

correlation, since each was rated by virtually all respondents. By doing so each pairing of

characteristics has a correlation coefficient, as shown in the chart below:

WSWBMXPdthdSBMQflWIEISCIdeiLQILTquBIh Sty

Building Cond. --

Streetscape .33 --

Busi. Mix .62 .41 ~-

Parking .48 .46 .32 --

Ret. Sales .74.53.82 .47 --

Browsing Sh. .58 36.68 .08.86 --

Quick-stop Sh. .54 .20.56.51.59 .39 ~-

Merch.Assoc. .71 31.63 .12.51 .46 .26 --

CoopofBanks .58.64.71 38.82.61 .45 .47 ~-

CoopofCity .78.58.69 .47.73.50.54 .46.73 --
LocalPolitics .70.50.53 .35.61 .49.51 .44.62.83 --
CulturalCenter .80 .40.70 36.61 .48.53.74.55.73.70 --

Tourism .40 .25.51 02.63.82 .09 .35 .29 .28 .30 .44 --

LocalJobs .50 .47.66 29.73.61 .35 .23.73.72.64 .33 .41 --
Historical .76 .21 .25 .25 .43 .38 .21 .50 .23 .53 .46 .45 .28 .28 --
Safety .72 .l4.60 37.57.54 38.69 .30 .46 .36.51 .36 .37.64 ~-

Figure 31 - Correlation coefficients for Downtown Characteristics
(Statistically significant @ 95% confidence level shown in bold-face)

The chart becomes more descriptive if the correlations are grouped according to their
relative strength. By adjusting the order of the characteristics and removing values not

statistically significant (less than 0.50), the following chart was developed:

144

Ret BMix Brow Jobs Bank City Polit Bldg Cult Hist Merch Safe Tour Street Quick Park

 

 

 

 

 

 

 

 

 

Ret. sales -- -

Busi.mix C82 -- 1 $93;

Brows. shop .86 .68 -- 0°9

Local jobs .73 .66 .61 -- Q‘s?

Coop. banks .82 .71 .61 W3 -- (Po

Coop. city 073 .69 .59 .72 .73 -- ‘94)

Local politics .61 .53 G64 .62 .83 -- ”‘5'

Bldg. cond. .74 .62 .58 .50 .58 .78 C70 -- “9°

Cultural ctr. .61 .70 .55 .73 .70 .80 --

Historic char. .53 .76 -

Merch. assn. .51 .63 .71 .74 .50 --

Safety .57 .60 .54 L .72 .SIJ .64 .69 --

Tourism .63 .5 1 .82 --
Streetscape .53 .64 .58 .50 --
Quick-stop .59 .56 .54 .51 .54 .53 ~-
Parking .51 --

Figure 32 - Correlation coefficients for Downtown Characteristics (Revised)
(Higher correlations shown in bold face and also larger type face)

By recombining the sequence of characteristics in such a manner, clusters of significant
correlations are able to be shown. This clustering can be more revealing than the individual
characteristics themselves.

At the top of the chart is a cluster strongly associated with general retail functions; it
includes “Retail Sales,” “Business Mix,” and “Browsing Shopping.” As was shown in
Figure IV-34, each of these was also strongly correlated with the HPI—Sales (.88),
Business Mix (.73), Browsing Shopping (.78)—all well above the minimal .50 level. For
purpose of analysis this grouping will be termed as the “Retail Group.”

Another cluster of related characteristics is seen further down the chart, and includes
“Cooperation of Banks” (HPI of .75), “Cooperation of City” (.84), and “Local Politics”
(.78), which will be referred to as the “Local Cooperation Group.” Interestingly, the

characteristic “Merchants Association” was not aligned with this group at all, indicating

145

merchants see their own organization as separate and apart from the more public
organizational structure.

A final significant grouping deals with physical aspects of the downtown, and includes
“Building Condition” (.84), “Downtown as a Cultural Center” (.75), and Historic
Character” (.63). These will be referred to as the “Architectural Group.”

A single characteristic, not easily grouped with any of the above described groups, but
statistically significant nonetheless, was “Local Employment.” This will be referred to
individually and be considered a cluster of one.

For each of the groupings described above, the combined correlation coefficients were

computed as shown below.

Table 18 - Correlation Coefficients of Characteristics Groupings

Comm
Retail Group 0.86
Local Cooperation Group 0.86
Architectural Group 0.79
Local Employment 0.80

Each of these groupings has a high coefficient of correlation, and should be considered

closely associated with downtown health.

Groupings As Shown Through Principal Components Analysis

Principal Components Analysis, a form of factor analysis, was used as another method
of deriving groupings of Downtown Characteristics which had commonalities. The
analysis was done with SPSS software, and was based on the data from individual

respondents to the 16 Characteristics. Principal Components Analysis revealed four

146

groupings, with the strength of each grouping based on its Eigenvalue.95 Only

Eigenvalues of more than 1.0 were included.

Table 19 - Values of Four Groupings Using Principal Components Analysis

Grouping Percent of Cumulative
A 5.15 32.2 32.2
B 1.31 8.2 40.4
C 1.22 7.6 48.0
D 1.09 6.8 54.8

Variable A is shown to have the strongest grouping, and accounts for 32.2 percent of
the commonalities of all the 16 characteristics. Variables B, C, and D are also significant as
groupings, but much less so. The Characteristics which make up each of the Variables

shown above can be determined from a salient factor loading to the Rotated Factor Matrix,

as shown below.

 

95 The Principal Components Analysis was based on “Root One” Extraction and
Varimax Rotation.

147

Table 20 - Rotated Factor Matrix for Principal Components Analysis
(Principal Components Shown in Boldface)

Grouping A Group B Group C Group D
Cooperation Retail Architectural Streetscape

Building Condition .083 . 186 .5 6 2 .430
Streetscape .192 .095 .038 .67 6
Business Mix .078 .5 3 3 .146 .347
Parking .077 .061 .180 .67 6
Retail Sales .260 .6 64 .172 .316
Browsing Shopping .122 .815 .153 .085
Quick-stop Shopping .331 .350 .050 .172
Merchants Association .439 .259 .383 .101
Cooperation of Banks .6 1 5 .305 .006 .260
Cooperation of City .838 .088 .130 .153
Local Politics .8 3 7 .034 .178 .135
Cultural Center .5 l 5 .340 .260 -.186
Tourism .145 .756 .120 -.114
Local Jobs .375 .338 .040 .342
Historic Character .201 . 151 .8 0 6 .031
Safety and Security .089 .096 .7 1 4 .107

148

Characteristics with a Factor Matrix value greater than 0.500 were considered to have
significant degrees of commonality, and were included as parts of Factors A, B, C, and D.
These values are shown in boldface type. Grouping A, which includes the following
characteristics—Cooperation of City, Local Politics, Cooperation of Banks, Cultural
Center—strongly resembles the Local Cooperation Group previously described, and
reinforces the significance of these Characteristics as a grouping. Similarly, Grouping B,
which includes Browsing Shopping, Retail Sales, and Business Mix, matches the Retail
Group previously described, but also includes Tourism. Grouping C is similar to the
Architectural Group—both include the characteristics Building Condition and Historic
Character; only Cultural Center has been switched from this group to Grouping A. A final
grouping, Grouping D, which includes the characteristics Streetscape and Parking, has
been previously shown not to be an especially strong grouping, either to other
characteristics or to the Health Index, and will be ignored.

This Principal Components Analysis gives strong statistical reinforcement to the more
visual matrix analysis, and validates the Retail Group, Local Cooperation Group, and

Architectural Group correlations.

Characteristics Not Included in Clusters

Other characteristics were found not to be closely associated with clusters with which
they would seem to have much in common. “Streetscape Improvements” was not
correlated with any of the characteristics from the Architectural Group, and so was not
included. This would support the earlier conclusion that streetscape improvement
programs tend to have dramatic impact on downtowns when first completed, but tend not
to have long-term impact, and are largely disassociated from other factors contributing to
downtown health.

Similarly, “Quick-stop Shopping” was not strongly associated with the Retail Group.

This can be explained by the fact that quick—stop shopping is in-and-out shopping, and

149

quick-stop establishments do not contribute customers to the downtown retail area in

general. This was also clearly shown and described in the 1986 survey, which concluded:
Many cities have modified their downtowns to accommodate shoppers who
want to stop quickly at one destination. The prototype is the strip commercial
shopping center. In many cases this has worked to the overall detriment of the

downtown, for shoppers are not encouraged to pass by other storefronts and
possibly make other stops.96

Another interesting correlation of two downtown characteristics was “Browsing
shopping” with “Tourism,” with a correlation coefficient of 0.83, but this was seen as a
unique relationship not closely tied with others. Other characteristics had less prominent

correlations, and will not be considered in detail.

Parking As a Characteristic

“Parking” had no significant correlation with any other downtown characteristic, and
also had no significant correlation with the Health Perception Index (coeff.=0.44). The
1986 survey also found that “Parking” had an insignificant correlation with downtown
health.97 This is important to recognize, because many cities have allocated a large portion
of their downtown improvement budget to more or improved parking, when there is no
evidence that the amount or quality of parking contributes to downtown health in any
significant way. Of the 16 defined characteristics, only parking had no association with

either the HPI or to other characteristics which contribute to downtown health.

99

tei' s

66

Of the sixteen downtown characteristics evaluated in the survey, some have strong

correlations with each other, some weak correlations, and some have no apparent

 

96 Norman R. Tyler. 1987. An Evaluation of the Health of the Downtowns in Eight
Michigan Cities (Dissertation). Ann Arbor, Michigan: University of Michigan.
122.

97 Tyler. 1 19.
150

correlations whatsoever. These relationships are shown graphically in the illustration

below which symbolically illustrates the magnitude of relationships.

I Browsing
. Shopping )
I -.. ‘

Retail
Sales

 

   
       

  
 
 
 
   

Safety and
Security

    
 

anks Local

 

. Politics
Streetscape uick-stop
S opping
Parking

 

 

 

Figure 33 - Proximity Matrix of Downtown Characteristics

The illustration clearly shows that some characteristics are more central in their
importance than others. As has been described above, “Retail sales” is the most prominent
characteristic in describing merchants' perceptions of the health of a downtown, for it not
only has the closest association with downtown health, but also with many other
downtown characteristics. “Retail sales” is closely tied with “Business mix,” inferring it is

highly dependent on an appropriate mix of business. It is also clustered with “Browsing

151

shopping,” representing a certain type of retail business, but is not at all closely aligned
with “Quick-stop shopping.” Quick-stop shopping businesses are not closely aligned with
any other particular downtown characteristics, indicating respondents' perceptions that the
presence of such businesses is not directly tied with downtown health and revitalization.
Other key factors are shown to be the “Cooperation of the city” and “Cooperation of
local banks.” The “Local employment” rate is illustrated as also a key factor for downtown

health, but it largely unassociated with other characteristics.

Comparison of Characteristics for I 986 and 1992 Surveys

The 1986 study included a survey of eight cities. The 1992 survey included sixteen
cities, and should be considered more definitive in its results. However, there was
considerable consistency in the analysis of downtown characteristics for the two studies.
Therefore, it is appropriate to review both the areas of similarities and differences of the
conclusions of the two studies.

As with the 1992 survey, the 1986 survey found shopping related characteristics to be
most associated with perceptions of downtown health. The primary characteristics were
“Business mix” (#1 in the 1986 survey) and “Browsing shopping” (#2). Also, “Retail
sales” had the highest coefficient of correlation for individual respondents in the 1986
survey.98 These three key characteristics were clustered into a Shopping Group (as they
were with this survey); this cluster had the highest correlation with downtown health. The
perceived importance of “Quick—stop Shopping” did not have a high correlation in either
survey.

Another cluster identified in the 1986 survey was termed the “Architectural Group,”
which is similar to the “Architectural Group” defined above. The 1986 cluster included

“Building condition” and “Historic character,” but also included “Landmarks” (similar to

 

93 However, Retail sales had a relatively low coefficient when analyzed by city.

152

Cultural center in the current survey) and “Streetscape.” The 1992 survey found them
more closely aligned with the Health Perception Index than in 1986.

The “Local Cooperation Group” cluster was not identified in the 1986 survey. The
correlation of its characteristics to the 1986 Health Index was only moderately strong for
“Cooperation of city government” and not at all strong for “Local politics” or “Cooperation
of banks.” The strength of this cluster has only been identified with the larger survey done
in 1992.

“Local employment” has been shown to be an important characteristic with a high
correlation to the HPI in both the 1986 and 1992 surveys. In 1986 it was ranked 3rd in
importance and in 1992 it was ranked 4th. Although not closely correlated with any other
downtown characteristic, it is loosely correlated with a broad number of them.

This analysis of both surveys largely confirms the results of the original 1986 study,
giving additional strong support to the primacy of “Shopping” as the primary determinant

of downtown health and clarifying the role of other important characteristics.

THE USE OF PROXY DATA FOR DOWNTOWN CHARACTERISTICS

This section compares survey data with other available data sources. Each is discussed

in turn, beginning with the Shopping Group.

E'ESI'EZIr"s

Retail Sales Data:

The Shopping Group cluster includes three characteristics—”Retail sales,” “Browsing
shopping,” and “Business mix.” Alternate sources of data are available for each of the

three characteristics, although not as readily as one would hope.

153

Retail sales figures are available on a county basis,99 a regional basis,100 or by larger
metro area.101 Retail sales figures were given in the Michigan Statistical Abstract for
1987, but this was last published in 1987 and there are no plans to continue. Figures for
retail sales are also available through the federal government’s Census of Retail Trade,
which includes figures for each of the study cities. The most recent Census, for 1992, was
released in 1995.

From the 1992 Census total sales figures were converted to a per capita figure for each

city. They are as follows:

Table 21 - Retail Sales and Retail Sales Per Capita, 1992102

Albion $74,600,000 $7,560
Dowagiac $93,800,000 $14,589
Howell $161,100,000 $19,120
Ionia $102,500,000 $17,262
Lapeer $255,300,000 $31,631
Manistee $93,100,000 $14,176
Marshall $97,900,000 $14,336
Tecumseh $68,800,000 $9,185
Alpena $172,800,000 $15,453
Big Rapids $164,100,000 $13,394
Cadillac $175,800,000 $17,432
Coldwater $163,700,000 $16,988
Hillsdale $1 17,300,000 $14,103
Owosso $239,400,000 $14,691
South Haven Not available N/A
Sturgis $149,500,000 $14,568

 

99 Sales Tax data.

100 Available through the Federal Reserve Bank.

101 Available through the National Retail Federation in Washington, DC.

'02 Population figures for 1992 were projected from 1990 census figures based on
historic trends.

154

For US. Census of Retail Trade retail sales figures are to be used as a proxy for the
Health Perception Index, such figures should be correlated. As was shown previously,
there was a strong correlation between the evaluation of Retail Sales and the downtown
HPI, with a correlation coefficient of 0.88. However, there was no meaningful correlation
of the HPI to actual retail sales per capita, with a coefficient of only 0.12, much below the
0.50 figure needed to have even a minimal statistical correlation. Therefore, it is
impossible to conclude that the perceptions of respondents correlates with actual retail sales
figures.

A cross-check of retail sales figures to “Retail sales” survey evaluation showed a
coefficient of 0.35, once again indicating no statistical correlation. The results are
illustrated in the chart below:

Table 22 - Correlations for Retail Sales Data

Retail Sales
H121 Rstailfialss EQLCamta
Retail Sales —0.06
Retail Sales per cap. 0.20 0.71
Retail Sales, Survey Eval. 0.88 0.16 0.35

The lack of correlation between actual retail sales figures and the Retail Sales evaluation
based on the survey can be interpreted in one of two ways. The first is that these results
show respondents' perceptions do not relate to reality, and are largely psychological
responses based on their own misguided view of the situation. The other interpretation is
that downtown health cannot be determined simply by looking at hard data, for downtown

health should be evaluated relative to expectations.

Data available on local Cooperation:

The “Local Cooperation Group” consists of three Characteristics from the survey—

Cooperation of City Government, Local Politics, and Cooperation of Banks. There is no

155

known data that could represent these characteristics, so the perceptions of the respondents

must be used to represent this factor.

Business Mix Data:

Data on “Business Mix” is most easily and readily available through a simple count of
businesses within downtowns. As part of this study, the numbers and types of businesses
in each study city were tabulated by on—site counting. Because this is a primary area of
analysis for this study, it is dealt with in a separate section (See the following section,

“Analysis of Business Mix”).

Employment Data Available:

Previous analysis has shown that the demographic characteristic, “Percent of the local
population in the labor force,” is closely correlated with the Health Perception Index.
However, it was shown not to be correlated with the evaluation given by respondents for

the characteristic “Local employment.” The coefficients are as shown below.

Table 23 - Correlations of Population and Employment

% Pop. in
11121 Lemaitre

% Pop. in Labor Force 0.60
Local Employ., Survey Eval. 0.79 0.41

This indicates that Labor Force data available from the census can indicate a “tendency”
toward evaluating downtown health, but cannot be considered a reliable indicator of

downtown health.

156

ANALYSIS OF BUSINESS MIX

Christaller, in his Central Place Theory, postulated that a city’s success was largely
determined by its population size and relative location. August Lbsch, a contemporary of
Christaller, took a different perspective. He suggested a city’s prominence and success
was related more to the varying functions that city was able to provide. Lbsch concluded
cities can provide sets of functions not directly related to their size. This supports the
conclusion that the mix of businesses is an important factor in determining downtown
health.

As was shown previously, “Business mix” is a characteristic closely aligned with the

Health Perception Index. This section analyzes this characteristic in greater depth.

BUSINESSES IN THE STUDY DOWNTOWNS

The numbers and types of businesses in each downtown were recorded through on-site
observation. The business types were listed as they were found, with no attempt to
combine them into generalized categories. This type of listing was more useful initially
than trying to rely on established categorizing methods, such as the "Standardized
Industrial Classification" Code used by the federal government, which is too inclusive in its
categorizations.

The numbers of businesses (ground floor only) in each downtown were as follows:

157

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fl

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IAN—I

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DOD)
MON

#A-h-b-h
Ul-hbJN—I

OCMQQUIhWNI—I

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COOOQO‘MA

Table 24 - Number of Each Business Type in Study Cities, 1992

Businesslvpe
Appliances
Auto related
Bakery/Ice cream shop
Banks
Beauty/Barber shop
Bike shop
Books/Newspapers
Bridal shop/Tuxedos
Cameras/Film
Carpeting/Floor covering

. Cleaners/Laundry
. Clothing

. Collectibles/Cards
. Dance studios

. Department stores
. Dinner restaurant
. Drug store

. Electronics/TV

. Florists

. Fraternal organizations
. Furnishings

. Furniture

. Gifts/Antiques

. Groceries

. Hardware

Health & fitness centers

. Hobbies/Crafts

. Hotels

. Insurance/Real estate
. Jewelers

. Lumber

. Lunch restaurant

. Medical equipment
. Music stores

Office supplies

. Paints and decorating

. Photographers/Frames
. Plumbing/Elect./Heating
. Printing/Publishing

. Professional offices

. Public agency offices

. Second-hand stores

. Sewing/Fabrics

. Shoes

. Shoe repair

o—ooaooooo-——oo4>oo¢-oo-—o—Nooo—-~oo~4>~ooo~os>t>rou~

158

ooooc;--———mom—Nwooo—oo—~o~oo~oo~o~o~o~oo¢~Nul—

-—~ooafi3ww-—oom——w—oom—mwo—oo~——--—-—rooooo-—~4>Amo~ro

ON—I—I-fimfl—‘N—‘O-‘N—‘OWWONON—I

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9

~—~—omoowlooo—oowouulo—--—-o\rNONN~——oo—ooo———Nocx~wtsoIa!

Table 24 (Cont’d).

46. Sporting goods 0 0 0 2 1 2 1 0
47. Tavem/Bar 2 2 1 3 2 2 3 1
48. Theaters 0 0 2 1 1 1 1 0
49. Travel agencies 0 l 1 0 1 2 1 1
50. Used goods 4 0 0 0 1 1 2 0
51. Vacant storefronts 9 8 4 8 6 9 6 2
52. Variety stores 0 l 1 1 1 2 1 2
53. Video rentals/Arcades 1 2 0 2 0 2 1 1
Total No.01“ Busi. 94 77 89 103 101 101 124 86
1. Appliances

2. Auto related

3. Bakery/ Ice cream shop

4. Banks

5. Beauty/Barber shop

6. Bike shop

7. Books/Newspapers

8. Bridal shop/Tuxedos

9. Cameras/Film

10. Carpeting/Floor covering
11. Cleaners/Laundry

12. Clothing

13. Collectibles/Cards
14. Dance studios

15. Department stores
16. Dinner restaurant

17. Drug store

18. Electronics/TV

19. Florists

20. Fraternal organizations
21. Furnishings

22. Furniture

23. Gifts/Antiques

24. Groceries

25. Hardware

26. Health & fitness centers
27. Hobbies/Crafts

28. Hotels

29. Insurance/Real estate
30. Jewelers

31. Lumber

32. Lunch restaurant

33. Medical equipment
34. Music stores

35. Office supplies

wwwwoww—~uow§-o~o———~w\lwoow-——oAON-—
mNONONNo—o—wmo=ON—NN~o—-Ao—OON—mw—w—
omomomo‘ocboowq—oo—-~oooo-oo~w~o~+~oww—
Isa—Oh—NAo———ow—o——owooo—m——o—v—ooxNo—o
w—Oxoomu~—o—NMmO~N---MO~ONwOmwow—I
N—~—MONGO—‘ONWG-‘O—‘w—WN—~NM-Oo—MOQWN-ho
H—oosoroullooo—Mfg—~o—omoo—ooom—ooooxlw—N-—
Gnome-—AO~OOOA-ON~NC~ONUiO~o—oos-wo—u—

159

Table 24 (Cont’d).

36. Paints and decorating 0 1 1 1 2 2 2 1
37. Photographers/Frames 0 l 1 0 2 3 2 0
38. Plumbing/ElectJHeating 0 0 0 2 0 3 1 0
39. Printing/Publishing 0 2 2 0 4 3 2 1
40. Professional offices 14 11 11 11 15 12 9 10
41. Public agency offices 4 4 l 7 9 9 2 2
42. Second-hand stores 0 2 3 2 0 0 3 0
43. Sewing/Fabrics 1 l l 2 2 2 0 0
44. Shoes 3 1 3 3 2 2 0 1
45. Shoe repair 1 1 0 0 2 1 0 0
46. Sporting goods 3 1 3 0 1 1 0 2
47. Tavem/Bar 4 l 2 3 0 3 l 1
48. Theaters 3 1 1 l 1 2 l l
49. Travel agencies 0 2 2 2 1 1 2 2
50. Used goods 0 2 3 2 0 0 3 0
51. Vacant storefronts 12 7 7 9 l 9 8 11
52. Variety stores 2 1 0 1 1 l 1 0
53. Video rental/Arcades 0 1 0 1 l 0 1 0
Total No.of Busi. 122 87 95 88 109 131 109 70

an i in u i i

As part of the survey, the total number of each type of ground-level business was
recorded. Each downtown was divided into two sub-areas, the central downtown core and
the surrounding downtown commercial area. The central downtown core was defined as
that area where virtually every structure on a block was commercial; it was not found to be
difficult to determine the boundaries of these areas. The surrounding downtown
commercial area was not as clear-cut with its boundaries, but was still generally not
difficult to determine. The rule of thumb used was that if a majority, but not all, of the
ground floor space in a block was commercial, it was part of this sub-area.

This pattern of establishing the functional boundaries of a downtown was more fully

explored in the 1986 study.103 Analysis of these maps illustrated that the simpler

 

‘03 Norman R. Tyler. 1987. An Evaluation of the Health of the Downtowns in Eight
Michigan Cities (Dissertation). Ann Arbor, Michigan: University of Michigan.
pp. 33-42. In this study respondents were asked to draw boundaries of what they

160

procedure described in the previous paragraph effectively represented the same downtown

areas derived from these image maps.

BUSINESS MIX ANALYSIS

Analysis from the previous chapter has shown that “Business mix” is closely correlated
with perceived downtown health. Based on that finding, a study of the business mix in the
sixteen study downtowns should give clues about what a “healthy” mix includes. For
instance, if more of a certain type of retail store is generally found in the healthier
downtowns, then it could be concluded that that type of store contributes to downtown
health.

Some downtowns may have more of a certain type of business simply because the
cities are larger and they have more businesses in general. To get a more accurate picture,
it was important not only to look at the number of businesses, but also the percentages for
each business type. For these reasons both total number and percentage will be used
throughout this analysis.

The 1986 study indicated that certain business type groupings were closely associated
with merchants' perceptions of downtown health, while others had no significant

correlation. This survey was to verify what had been only preliminary conclusions.

1 l . l E . I if .

The number of business types shown on the above list is large because there was no
initial attempt to group business types together. The list was formulated simply by
recording the number of businesses in the downtowns at the time of the survey.

Groupings can better explain the categories of businesses that contribute to downtown

 

perceived to be the edges of the downtown district. (Composite maps developed
from this question are shown in Appendix 4)

161

health, however. For this reason, a number of groupings were formed, based on generally
accepted criteria for downtown planning. These groupings, and their components, are
listed below with their coefficients of correlation (as a group) to the Health Perception

Index shown.

Clothing

Department stores

Furnishings

Gifts/Antiques

Hardware

Jewelers

Music stores

Office supplies

Sewing/Fabrics

Shoes

Sporting goods

Variety stores
Coefficient by number of businesses: -0.01
Coefficient by percent of businesses: 0.07

As can be seen, the grouping of retail businesses most typically associated with the
traditional downtown as the center of local shopping shows no correlation to the HPI.104
Based on this analysis, there is no evidence the traditional downtown business mix is any
longer associated with perceived downtown health.

1 c ' es

Bridal shop/Tuxedos

Clothing

Jewelers

Shoes
Coefficient by number of businesses: 0.14
Coefficient by percent of businesses: 0.25

During interviews in the downtowns many comments were heard about the need for

downtowns to keep clothing and accessory stores, since these are the primary business

types that have been drawn away by new shopping centers. However, correlation analysis

 

‘04 With 16 cities the minimum correlation coefficient which is statistically significant
at a 95% confidence level is 0.50.

162

showed there is no significant tie between downtowns that have retained a higher

percentage of these types of retail and the perceived health of their downtowns.

Qtflm

Banks

Insurance and Real estate

Public agency offices

Professional offices

Travel agencies
Coefficient by number of businesses: 0.17
Coefficient by percent of businesses: 0.28

If downtowns no longer fulfill their traditional retail functions, can they have a better
future through encouraging office use downtown? The analysis showed no significant
correlation between downtowns with more offices and perceived downtown health.
However, a coefficient of 0.28 shows a slight tendency toward this type of business as
having a positive influence.

lounsmil
(shown to be strongly correlated with each other)

Dinner restaurant

Gifts and antiques

Hotels
Coefficient by number of businesses: 0.18
Coefficient by percent of businesses: 0.26

Many downtowns in the study are encouraging tourism as a way to improve the health

of their downtowns. Analysis of the actual business mixes showed a minor tendency

toward this improving downtown health, but no statistically significant correlation.

Clothing

Florists

Home furnishings

Gifts and antiques

Jewelers

Shoes
Coefficient by number of businesses: 0.17
Coefficient by percent of businesses: 0.42

Based on a correlation coefficient of 0.42, the business mix grouping having the

strongest tendency toward association with perceptions of downtown health is “Browsing

163

shopping.”105 This supports the results of analysis of downtown characteristics from the
previous chapter, which showed that respondents associated “Browsing shopping” very
closely with downtown health. The 1986 study also supported this conclusion, and
showed a coefficient of 0.81 between the “Browsing shopping” business mix and the
study’s Health Index. “Browsing shopping” was the only business mix from this study
that was shown to be statistically correlated.106 Since all three analyses reinforce each
other with regard to this business mix, the survey clearly indicates that Browsing shopping

is important to downtown health.

'v' ines 'x

Another avenue of analysis was pursued by using a “cart before the horse” technique.
Rather than group business types prior to analysis, analysis was used to derive potential
business type groupings.

A matrix was created which derived correlations for all 53 individual business types
against one another. This large master matrix was carefully reviewed to look for business
types that had either high positive or negative correlations with each other. Largely an
intuitive procedure to begin with, two lists of business types were derived—those
positively correlated to the Health Perception Index (HPI) and those negatively correlated.

Two distinct lists resulted, as shown below:

 

‘05 Although a correlation coefficient of 0.50 is the minimum to show statistical
significance, a coefficient of 0.42 is close enough to indicate a “tendency.” The
minimal number of cases (16 cities) makes a higher coefficient unlikely.

106 Tyler. 123.
164

Business types positively correlated with the HPI:

Bakery/ Ice Cream shops
Cameras/ Film stores
Electronics/ TV

Florists

Gifts/ Antiques

Travel Agents
Variety Stores

Coefficient for grouping by number of businesses: 0.51
Coefficient for grouping by percent of businesses: 0.62

Business types negatively correlated with the HPI:

Appliances
Furniture
Groceries
Hotels

Department stores
Paint/ Decorating

Used goods

Vacant storefronts

The correlation coefficients show each of these groupings has a statistically significant
correlation, whether looked at by number of businesses or percent of businesses. With
these groupings defined, the next step was to look for commonalities for the business types
in each list. By reviewing various combinations of these business types, smaller groupings

which have some commonalities were discovered. These lists, as refined, are shown

below.

Coefficient for grouping by number of businesses: -0.72
Coefficient for grouping by percent of businesses: —0.66

Refined list of correlated business types positively associated with the HPI:

Bakery/ Ice Cream shops
Electronics] TV

Florists

Gifts/ Antiques

Travel Agents
Variety Stores

Coefficient by number of businesses (central areas): 0.61
Coefficient by percent of businesses (central areas): 0.72

165

Refined list of correlated business types negatively associated with the HPI:

Appliances

Department stores

Furniture

Groceries

Used goods
Coefficient by number of businesses: -0.79
Coefficient by percent of businesses: —0.80

As can be seen, these smaller groupings have higher coefficients, and represent the
business mix groupings with both the closest positive and negative correlations with the
Health Perception Index. The first group, consisting of business types with positive
correlations, are primarily small businesses found in small retail stores. They represent the
type of shopping that encourages interaction and specialty purchases. This group will be
referred to as the “Specialty” business mix.

As described earlier in this section, when the actual businesses in each downtown were
counted, note was made of their locations—whether they were in the central area of the
downtown or on the downtown fringe. As indicated above, the correlations for the
positively correlated businesses were highest when the businesses located in the central
area of the downtown only were considered (although there also were high correlations if
the number included both the central and fringe areas). This indicates that these specialty
shop businesses are most beneficial to downtown health if they are located centrally, and
less beneficial when located at the fringe.

The grouping of negatively correlated businesses included larger stores, and will be
referred to as the “Large store” mix. The grouping includes business types that are not
subject to impulse buying or browsing. Rather, they are stores which tend to attract
customers for larger purchase items, where a central downtown location is less critical to

success. It should not be concluded that these types of businesses decrease the health of a

downtown. Rather, it represents the fact that these types of businesses, because of their

166

appeal to customers making larger purchases where location is not as critical, can hold a

viable customer base even in downtowns that were evaluated as having poorer health.

E. 11' 18 .151 1;

Correlations compared the “Specialty” and “Large Store” business mix groupings
against three other relevant factors. Correlations were derived for the following items:

. “Specialty” business mix

. “Large store” business mix

Health Perception Index (HPI) values

0 US. Census of retail sales figures

Evaluation of “Retail Sales” characteristic on 1992 survey

The table below shows these correlation coefficients (using percent of downtown

businesses):

Table 25 - Correlations of Business Groupings
(min. coeff. for statistical significance: 0.50)

H21 51m. Largest. Saleem.
Specialty 0.72
Large stores —0.80 -0.36
Census ret. sales figures 0.12 —0.04 -0.28
Survey eval. of sales 0.88 0.53 -0.82 0.35

This analysis shows high correlations of these business mix groupings with the
survey’s evaluation of retail sales (0.53 and —0.82), but low correlations against actual
sales figures (—0.04 and -0.28). There is also a high correlation of these groupings with
the Health Perception Index (0.72 and —0.80). This supports the conclusion that perceived
downtown health is closely tied with business mix, but not closely correlated with actual

retail sales figures.

167

l l . l B 1 E . C

During the survey, each respondent was identified as associated (typically as owner or
manager) with their business under one of five categories—Small retail, Large retail,
Office, Food/Entertainment, Other. Based on these categories, the HPI of individuals in
each category was calculated to see if the responses were significantly different between
respondents associated with “Small retail” versus “Large retail.” The responses were as

follows:

Table 26 - Mean Health Perception Index Ratings By Business Category

No. of
H121 respondents
Small retail 50.1 175
Large retail 50.5 76
Office 55.6 53
Food/ Entertainment 43.8 17
Other 58.4 40

The HPI values for respondents associated with “Small retail” and “Large retail” were
similar, indicating no significant bias in their perceptions of downtown health based on the
size of their establishment. Therefore, the significant difference between the two in the
business mix analysis described above cannot be attributed to respondent bias.

However, bias could be attributed to other categories, for respondents associated with
downtown “Offices” tended, as a group, to rate downtown health higher than average
(55.6), while those associated with “Food! Entertainment” businesses tended to rate it
significantly lower (43.8).

The greatest bias, however, was a positive bias in the “Other” category, a group that
included a high proportion of local officials. This group had been shown to be positively

biased in the 1986 survey as well.

168

The evaluations (normalized) by the five business categories for the sixteen downtown

characteristics reveal some interesting information, and possibly reveal other inherent

biases among respondent groups.

Table 27 - Mean Evaluations (Normalized) of Characteristics

by Respondent Business Category

Wagon 131ng
Small retail 51.4
Large retail 52.1

Office 56.3

Food/ Entertain. 42.7
Other 50.1

Banks

Small retail 47.4
Large retail 55.0
Office 56.9

Food/ Entertain. 42.6
Other 51.6

49.8
54.2
55.0
46.6
51.0

City
40.5
51.2
56.8
44.2
57.9

Evaluations by Small Retailers:

52.2
48.6
52.3
39.3
52.7

Polit.
43.2
48.5
51.6
51.2
53.7

Street B.Mix Parkg

46.5
55.4
61.3
33.1
53.9

Cult.
46.3
43.5
52.5
47.3
53.9

Sales Brws Quick Merch

50.4
49.1
50.5
39.7
55.6

Tour.

52.2
48.4
45.9
44.6
56.5

52.3
51.8
46.2
38.8
54.2

Jobs
41.9
48.1
54.9
40.8
55.9

50.4
56.2
47.1
41.9
50.0

46.2
51.5
50.2
51.5
49.3

Hist. Safety Mean

48.0
47.8
53.8
48.5
58.6

48.5
48.4
56.6
48.0
53.9

47.9
50.6
53.0
43.8
53.7

The chart shows small retailers gave significantly lower evaluations to the two

characteristics relating to local govemment—"Cooperation of city government” (40.5) and

“Local political situation” (43.2).107 This indicates small retailers do not positively

evaluate city officials, and the programs they coordinate, and are unsure whether they are

helpful to them.

These two characteristics—”Cooperation of city government” and “Local politics”—

had a high degree of correlation (coeff.=0.83) across all the study cities, indicating that

cities that rated one these two characteristics high or low generally rated the other

 

‘07 The complete list of values is shown in Table 8 on page 1 18.

169

respectively high or low as well. Especially low ratings for these two characteristics were
found in Albion (normalized ratings of 25.6 and 11.7 respectively), Hillsdale (36.4 and
44.3), and South Haven (40.3 and 42.3), all cities which had little involvement of the city

in downtown efforts. The highest ratings were found in cities that had more involvement.

Evaluations by Food and Entertainment Grouping Respondents:

The business category in which respondents gave the lowest overall ratings was the
Food and Entertainment category. Respondents from this category gave especially low
ratings to “Business mix” (39.3), “Parking” (33.1), “Retail sales” (39.7), and “Browsing
shopping” (38.8). They may have been especially critical of parking because convenient
parking is critical to food and entertainment businesses, and such businesses can be
competing for parking spaces with many other businesses. The other three characteristics
to which they gave low ratings—”Business mix,” “Retail sales” and “Browsing
shopping”-—had been shown in earlier analysis to be the most closely tied to the Health
Perception Index, indicating that food and entertainment businesses are hit hardest located

in a downtown with poor health.

Evaluations by Large Retailers

The respondents from the “Large Retail” category tended to rate the sixteen downtown
characteristics near the mean values. Large businesses tend not to be as impacted by what
happens in a downtown as small retailers or owners or managers of restaurants and
entertainment businesses. Because of their larger size, the businesses draw customers
from a wider area, and are not as dependent on a healthy downtown for a healthy business.
As one owner of a downtown appliance business explained, “My business could be
downtown, or out in the new shopping center, or even out in a separate location, and it
wouldn’t matter much. Customers who are buying appliances will go to where the store

is.” They rated the characteristic “Parking” highest (55.4) and “Downtown as a

170

Community Cultural Center” lowest (43.5), indicating their practical orientation to a

downtown location.

Evaluations by Respondents in Downtown Ofi‘ices

Respondents from the “Office” category—Le, people who had private or public offices
in ground-floor downtown space—tended to give higher than average evaluations for most
of the sixteen downtown characteristics. Instead of the low ratings given to “Parking” by
both small businesses and food and entertainment businesses, respondents in offices gave
their highest rating to “Parking.” For them, a downtown location seemed to be a good one,
possibly because downtown provided all the other stores and services useful to office
workers. They also gave higher ratings to “General condition of buildings downtown,”
“Historic character,” and “Streetscape,” as well as “Safety and security,” indicating they
liked the ambience found in the downtown. They also rated “Banks” and “City
government” high, characteristics that relate to convenient services. Coupled with earlier
analysis, these ratings would indicate that offices may not contribute significantly to

downtown health, but they draw from it and thrive in a healthy downtown location.

Evaluations by Other Respondents

The last of the five business categories is “Other,” primarily made up of public agency
officials and community leaders. The respondents in this category gave the highest ratings
overall for their downtowns, indicating a feeling of positiveness and optimism about
downtown health. Indeed, in many instances their professional reputations may be
dependent upon having a healthy community, and the higher ratings may be a natural bias
among this group. The tendency for high evaluations of downtown health among local
officials was first recognized in the 1986 survey, and has been reinforced with these
results. Among these respondents the characteristics with the highest evaluations were

“Historic character” and “Cooperation of city government,” two areas where they probably

171

felt they had some control. However, their evaluations for virtually all the characteristics
was above the mean, with the lowest reserved for “Merchants associations.” Likewise,
small retailers had given “Cooperation of city government” its lowest rating, indicating a
longstanding discord between city officials and small retailers that has been found
throughout this study. As one merchant said about a major repaving project for his
downtown’s main street, which would mean closing the street for a matter of months, “Not
once has a city official come in to discuss this project with me and see if there was a way to
minimize the terrible inconvenience to my customers during this period of construction.”
And as was explained by a city official in that same downtown, “I am really surprised that
some of the largest storeowners have never bothered to come to one of the meetings at City

Hall where we presented the project and asked for comments.”

The survey questionnaire included a question which asked respondents to indicate how
much various types of businesses contributed to downtown health. The question was
placed toward the end of the questionnaire so it would bias respondents when answering
other questions relating to the downtown health. The question was stated in the following

manner:

172

 

 

Types of businesses...

Most people would agree that some types of
businesses contribute more to downtown health
than other types.

From the list below, indicate your feelings about
how important each type of business is to overall
downtown health.

Very Somewhat Not Hurts
important important important downtown

"Browsing" retail stores D D D D

(e.g., gifts, clothing, jewelry, furnishings)

"Quick-SIOp" retail stores D
(e.g., drug store, gas and snacks, fast food)

Large retail stores 0 D
(e.g., department stores, hardware, variety, appliances)

Offices 0 D

(e.g., banks, professional, real esratc, insurance, travel)

13

Public buildings and offices
(e.g., city hall, courthouse. post office)

Evening activities
(e.g., theater, dinner restaurant, bar)

Tourism businesses
(e.g., specialty shOps, photo srore, galleries)

1:10
1:1 1: 1:1 1:3

"Handyman" businesses 0
(e.g., hardware, building supplies, auto-related)

D D D D D D D D
D D D D D D D D

"Small entrepreneur" businesses 0 D
(e.g., beauty shop, health and fitness, crafts and hobbies)

 

Figure 34 - Survey Question on Importance of Business Types

173

 

Responses to this question were generally in the “Very important” to “Somewhat
important” range. Of the average of 350 responses to this question, 300 were in this upper
range, and only 50 below.108 Converted to a O—lOO scale, but not normalized, the

responses in each category had the following means (listed high to low):

Table 28 - Evaluation of Relative Importance of Various Types of Businesses

“Browsing” retail stores 93.2

Large retail stores 84.4

Tourism businesses 82.5

Evening activities 80.]

“Small entrepreneur” businesses 77.4
“Quick-stop” retail stores 70.2

Public buildings and offices 69.5
“Handyman” businesses 66.8

Offices 66.4

These evaluations closely match evidence derived from other areas of analysis of this
study. Browsing retail businesses were seen as being most closely linked to downtown
health. This supports two earlier findings from this study. First, browsing businesses
were found most closely correlated to the Health Perception Index. Second, it was shown
by counting businesses in each of the downtowns that the cities with healthier downtowns
also had a higher proportion of browsing shopping businesses. These three pieces of
evidence clearly indicate browsing shopping is perceived to be the business type most

closely aligned with downtown health.

 

‘03 Total tabulations were as follows:
Browsing Quick Large Office Public Evening Tourism Handyman Entrepr.

Very import. 282 114 215 92 I20 177 2091 92 156

Somewhat imp. 68 167 106 180 I48 I38 115 I72 154
Not import. 2 6O 16 59 75 35 29 78 39
Hurts 0 7 8 l8 7 0 3 7 2

174

Tourism businesses also were evaluated as important to downtown health, receiving an
average rating of 82.5. Tourism businesses overlap closely with browsing businesses, in
that they tend to be small, specialty stores.

From the above chart, large retail stores were shown to be more problematic, for
although respondents felt they were important to downtown health, other analysis has
shown there is no significant correlation between the number of large businesses and the
Health Perception Index. This reinforces a perception of this researcher while visiting the
downtowns. Although comments were commonly heard about the terrible impact of the
loss of a major store—a long-established department store that had recently closed, for
example—it was not clear that downtowns without such stores were suffering noticeably
more than other downtowns. The recent loss of such a store may be perceived as having a
very negative impact, but downtowns which have never had large stores as part of their
downtown business mix, or downtowns which lost such stores many years ago, don’t
appear to have suffered.

Of the business types listed in this question, the lowest importance was assigned to
"Offices." Many small retailers, a group making up the largest proportion of survey
respondents, see offices located in downtown storefronts as limiting the opportunities for
new retail stores to open, for they “steal” the prime street locations. The number of
respondents who said offices actually “hurt downtown” (18) was greater than for any other
business type. However, as described previously, other evidence from the survey
indicated that offices did not hurt downtown, but also did not significantly contribute to the
health of downtowns.

No correlation was found between the respondents' evaluations of "business types"
and the types of businesses with which respondents were associated. In other words,
small retailers were not shown to have a bias toward the importance of small businesses,
nor did respondents in offices have a bias toward evaluating offices as important to

downtown health.

175

COMPETITION TO DOWNTOWNS

RESPONDENT EVALUATIONS TO COMPETITION

The survey questionnaire gave respondents an opportunity to indicate what competition
their downtown had. The question is shown below; they were asked to complete this

questions for up to two competitors. A space for other comments was also included.

 

My downtown’s greatest competitor is

Name of place

 

How distant (miles)?

 

It’s competition is (circle one)

Very strong Quite strong Strong Moderate

How is it competitive?

 

 

 

 

Figure 35 - Survey Question on Competition

Since no scale of competitor was suggested in the question, competitors described by
respondents varied from small stores down the block to larger metropolitan areas up to 150
miles distant. Of the 559 responses, the distances given to downtown competitors had a
mean distance of 18.3 miles and a median distance of 10 miles (Note: the mode value was 2

miles). The descriptive statistics (in miles) for each city are as follows:

176

Table 29 - Distances to Competition (in miles)

Albion 1 6.0 20 30 Alpena 32.5 3 150
Dowagiac 22.3 25 45 Big Rapids 12.6 2 55
Howell 10.8 7 35 Cadillac 25.1 4 100
Ionia 1 3 .7 3 40 Coldwater 14.6 3 45
Lapeer 1 3.3 3 45 Hillsdale l9. 1 20 60
Manistee 45. 1 50 149 Owosso 10.8 3 60
Marshall 1 1.7 12 45 South Haven 16.4 15 45
Tecumseh 1 3 .0 10 45 Sturgis 20.5 2 60

As shown, the distances from which respondents felt there was moderate or strong
competition varied greatly. In Manistee the median value for distance to competition was
50 miles, twice as great as any other city. Some respondents in Manistee listed Grand
Rapids as a competitor, even though it is 119 miles away. Alpena’s respondents identified
competition on the city fringe, as indicated by a median distance of 3 miles, but also saw
competition at some distance, with a mean distance value of 32.5 miles, and a maximum
distance of 150 miles (Saginaw). In cities such as Ionia, Lapeer, Big Rapids, Cadillac,
Coldwater, Owosso and Sturgis, with a median distance of 2 to 4 miles, a majority of
respondents saw the competition as a shopping center just outside of the city.

These figures indicate that competition for a downtown can be felt at both close
proximity and at great distances. By comparing these distances against the strength of
competition, the impact of competition could be derived through use of a gravity model,
which assumes there is an inverse relationship between distance and the pull of a body on
another (in this case, the pull of a competitor shopping area on a study downtown).
According to the Reilly gravity model described in the Literature Review chapter, if
competition is at a greater distance, the pull away from the study downtown should be

proportionally less.

177

Correlation analysis was used to test strength of competition (normalized values derived
from the survey) to distance (from downtowns to competitors). An overall correlation
coefficient of —0.01 was derived, which indicates no “correlation at all, not even a
statistically insignificant tendency. Based on the survey responses, therefore, distance was
not seen as a factor in determining the strength of competition. Cities such as Manistee and
Alpena perceive competition from Grand Rapids, Traverse City and Saginaw in the same
way other cities sense competition from only a few miles away. The question that is
unanswered is whether this is just an inaccurate perception, or whether our mobile society
has reduced such distances to virtual meaninglessness.

Further analysis looked at what types of respondents, with what types of businesses,

perceived the greatest level of competition. The following chart indicates their responses.

Table 30 - Mean Ratings for Strength of Competition By Business Category

Strength of Distance No. of
Competition Lmilesl team—same
Small retail 72.2 109 17.3 350
Large retail 74.4 18.0 152
Office 75.9 21.5 106
Food] Entertainment 80.0 1 1.6 34
Other 69.0 21.8 80

The greatest competition is felt by respondents from the Food/ Entertainment category,
a type of business that is very competitive. It is not surprising that the least sense of
competition is seen in the Other category, which is made up primarily of city officials and

downtown leaders. The Small retailers are the next lowest rating, even though it could be

 

109 To convert to a 0—100 scale, the following values were established,

Very strong competition 100
Quite strong 75

Strong 50

Moderate 25

178

assumed that small businesses would be subject to a lot of competition—more than large
retail businesses which can draw from a larger area.

Competition is perceived as closest (in miles) by Food/ Entertainment business
respondents as well, again because it is the most competitive of the categories. Large and
Small retail businesses have a moderate view of distance to competition, and respondents in

the Office and Other categories perceived the least pressure from competition nearby.

179

CHAPTER V
SUMMARY AND CONCLUSIONS

SUMMARY

PURPOSE OF STUDY

There have been many efforts by cities across the country to revitalize their
downtowns. Some have been more successful than others. A major problem, however,
has been that there is no generally accepted method of evaluating such efforts. For
revitalization efforts to be meaningful, downtown leaders need a method for evaluating
whether a program or activity has been helpful in improving downtown health. Often,
programs appearing successful initially later are recognized as ineffective over the long run.

Development of an evaluative tool is an important need for city planners, local officials,
downtown business owners, and others concerned about the effectiveness of downtown
revitalization efforts. The Health Perception Index (HPI), as developed in Tyler's 1986
study and described and utilized throughout this study, provides a base Index evaluation
against which other factors may be compared. It can be used as a yardstick for evaluating
revitalization efforts. It is multi-dimensional, and can be utilized both in different

communities and at varying time periods.

REVIEW OF RELEVANT LITERATURE

Planning and geography literature includes many models developed for evaluating

urban health. Some, such as Christaller’s classic Central Place Theory, use spatial analysis

180

to derive the potential health of a city based on its location relative to other cities. Others,
including those of Losch and Berry, determine urban health in terms of functional rather
than spatial hierarchies.

Economic models evaluate urban health based on commercial retail patterns. Reilly’s
Law of Retail Gravitation established potential commercial vitality based on a gravity
model. Others have derived economic health based on patterns of consumer preference;
these include Clark and Rushton’s Nearest Neighbor Model and Lalonde’s Index of Retail
Saturation.

Relatively few urban researchers have attempted to incorporate social or community
factors in urban models, for evaluations are difficult to quantify. Schwartz recognized that
downtowns could be seen as “inorganic” bodies that are machines for providing goods and
services, but suggested that they should also be seen from an “organic” perspective as
places that represent the culture of a place, and serve as a focus of community life—in other
words, they can be more than the sum of their parts. Such factors are difficult to quantify,
however. Thomdike’s early attempt at developing a “Goodness Index” had many
methodological weaknesses in its derivation, and relied too heavily on readily available
datasets.

Multi—dimensional indexes of urban health have proliferated in recent years. Perhaps
the best known is the Places Rated Almanac, first developed by Boyer and Savageau
(1981, 1985, 1989, 1993). It has been criticized by Berger (1987) and others for being too
“ad hoc” in its selection of criteria, and most such ratings systems have been exposed as
having significant biases of one form or another.

Many studies, including polls completed by the US. Department of Housing and
Urban Development (1979), have indicated the role of downtowns in community life is
best seen in smaller cities. Craycroft and Fazio (1983) recognized a stronger link to quality
of life factors in smaller cities. Robertson (1978) recognized generally accepted criteria had

yet to be developed for defining and measuring downtown vitality and health. According

181

to Schumacher (1973), the normally accepted criteria of economic growth is no longer
acceptable, for growth must include a qualitative dimension.

Andrews (1976) recognized the need to include qualitative indicators which rely on
perceptual input. As Francis (1973) recognized, “The omission of items which cannot be
measured is itself a major bias—often the most important facts about social considerations
are qualitative, derivative or interactive.”1 10 Marans and Rogers ( 1975) concurred, saying
that only through the use of subjective indicators can we begin to have confidence in the

usefulness of objective indicators.

FOCUS OF STUDY

This study brings its own perspective in looking at the role of downtowns in the 1990s
and beyond. It has focused on the downtowns of a group of representative smaller cities in
Michigan. The cities were selected based on three criteria—they are 1) between 5,000 and
20,000 population, 2) not economically linked to larger cities, and 3) located in the outstate
areas of the lower peninsula of Michigan. Eight of the cities were included in a precursor
study completed in 1987; eight additional cities were purposively selected to give a
representative selection based on city size, market autonomy and geographic location.

The study investigated attempts made by business and community leaders to revitalize
their downtowns and evaluated the relative success of these efforts. In addition to
evaluating the business environments of these downtowns, their role as social and cultural
centers of their communities was also examined.

The study evaluated the health of the downtowns in sixteen cities in Michigan through a

survey questionnaire given to business owners, merchants and local officials. The survey

 

”0 Walton J. Francis. 1973. “A Report on Measurement and Quality of Life and the
Implications for Government Action of ‘The Limits to Growth.’” Washington,
DC: Department of Health, Education and Welfare. 548.

182

was both inclusive and representative, and included at least 20 respondents in each of the
study cities (365 total), with an overall response rate of 57 percent.
The study used as a starting point three basic assumptions:

AM A meaningful evaluation of a downtown's health can be
derived from perceptions of that downtown's merchants and business owners.

Assumption]; Input from downtown merchants and business owners can be
used to determine factors closely correlated with downtown health.

W3; Comparisons are valid only if a consistent method of data
collection and analysis is used.

DERIVATION OF THE HEALTH PERCEPTION INDEX

This study systematically compared and evaluated revitalization efforts by sixteen study
cities and developed a common yardstick—the Health Perception Index—for making such
comparisons. The Index was derived from four questions included in the survey—1)
compare the health of your downtown with other downtowns of similar size; 2) indicate
how the health of your downtown has changed over time; 3) indicate whether you are
optimistic or pessimistic about the future health of your downtown; 4) evaluate your
downtown in terms of sixteen characteristics.

Through correlation analysis, various factors from the survey were examined to
determine which were perceived as most closely linked to downtown health. The factors

looked at included:

- sixteen downtown characteristics (e.g., parking, mix of businesses,
cooperation of city government, etc.);

- significant events that had impacted downtown health;
0 the level of retail competition from other areas; and

0 how downtown health had changed over time.

183

Although the Health Perception Index lacks precision in scientific terms, since it relies
on perceptual input for its derivation, it is important because it is one of few tools yet
developed for evaluating urban health that is not based solely on data from sources outside
the community (U .S. Census, state sales tax records, etc.) As a result, it can incorporate a
broader range of factors and perspectives in its derivation than information drawn from
statistical charts.

The use of the Index could be broadened to other situations. The four questions used
to derive the Index could be included in a survey of any city. The HPI could then be used
to evaluate the perceived impact of revitalization efforts. The Index could also be used for
l) evaluating the importance of various factors impacting downtowns, 2) comparing a
downtown to other downtowns, and 3) observing changes in perceived downtown health

over various time periods.

FINDINGS

A primary finding of this study was that, of sixteen downtown characteristics
considered, “Business mix” was the characteristic that most closely correlated to the Health
Perception Index (HPI). In other words, the factor having the closest association with
perceived downtown health was shown to be the types of businesses located there.

A good business mix includes various types of businesses. The business type having
the strongest correlation with the Health Perception Index was “Browsing shopping.”l 1'
Other characteristics closely correlated to the HPI included “Retail sales” and “Cooperation

of city government.” Most of the other sixteen downtown characteristics had varying

 

1“ This supports the conclusion of a precursor 1986 study, which found a strong
association between “Browsing shopping” and the study’s Health Index.
Norman R. Tyler. 1987. An Evaluation of the Health of the Downtowns in Eight
Michigan Cities (Dissertation). Ann Arbor, Michigan: University of Michigan.
125.

184

degrees of correlation with the HPI. Three factors did not correlate—“Streetscape
improvements,” “Parking," and “Quick-stop shopping.”

One of the most descriptive pieces of information to come out of this study was the
Correlation Proximity Chart of Downtown Characteristics (Figure IV-46). It illustrates
clearly and simply the downtown characteristics that are central to perceived downtown
health, as well as their relationships to each other.

The business types found in each study city were grouped into five categories—Small
retail, Large retail, Office, Food/Entertainment, and Other (primarily public uses). The
business category with the strongest correlation to the HPI was “Small retail.” Such
businesses represent the type of shopping that encourages interaction and specialty
purchases. The business category least correlated to the HPI was “Large stores,” a
category representing stores which attract customers for larger purchase items, where a

central downtown location is less critical to success.

USE OF PROXY DATA

The study looked for proxy data which could reliably represent the Health Perception
Index (HPI). Commonly used statistics, including retail sales figures from the US.
Census of Retail Trade and sales tax figures from Michigan, were compared against the
Index. Although a high correlation had been found between the HPI and the evaluation of
downtown “Retail sales” by survey respondents, there was no statistical correlation found
between the HPI and actual retail sales figures. This indicates government retail sales
figures cannot be used interchangeably with evaluations of downtown health given by
merchants, business owners and local officials.

Demographic statistics were also compared to the HPI. The HPI was found not
statistically correlated with population growth; evaluations of good downtown health could

be found in cities with no population growth as readily as in cities with growing

185

populations. However, some demographic data had significant correlations with the HPI.

These included household income and population age categories.

PRECURSOR STUDY

This study is a follow-up to a similar survey and study completed in 1987.112 A
significant aspect of this study is that its findings support and validate the findings of the
earlier study. Comparing results from this study to the earlier study allowed comparisons
in survey responses to be made over time (between 1986 and 1992). In some cities, most
notably Dowagiac, attitudes about the downtown had shifted considerably in six years,
largely because of downtown revitalization efforts undertaken during the period between
the two surveys. Focused analysis was given to the short-term impact and evaluation of

these changes.

 

 

“2 Tyler. 1987.
186

CONCLUSIONS

SIGNIFICANCE OF THIS STUDY

This study presents a new approach and a new methodology particularly suited to
analyzing and evaluating downtowns. A downtown Health Perception Index is described.
Its derivation and use comes out of the study itself, and is not closely tied with well-
established urban models. The study’s approach doesn’t build on previous models as
much as suggest a new approach to urban modeling.

A significant aspect of this study was the development of a method for comparing
objective and subjective input. Comparisons of hard data from external sources with soft
data generated by the study allowed for checks on the usefulness and relevance of each.
The study’s soft data came from perceptual input from the study’s respondents. It was
used to test whether hard data could be used as proxies for the survey data. Because the
correlations between the study’s survey data and other hard data was weak, the need to use
both types of input for urban modeling was apparent. As a model applied to the
problematic area of downtown revitalization, the Health Perception Index can be used to
bridge the gap between hard and perceptual data.

This study also tested a spatial approach to evaluating downtown health, focusing on
distance as a factor in its examination of competitors. It challenged classic spatial models
by finding that distance to competitor areas had little correlation to perceived downtown
health. The evaluation of downtown health had little or no association with whether its
primary competitors were close or distant, or whether the relative strength of the
competition was strong or weak. This finding suggests downtown health is not unduly
affected by external forces, but is most dependent on factors internal to the downtown.

Downtown health was shown to be more a product of internal factors, such as business

187

mix, the cooperation of city government, local politics and the condition of buildings, than
it was factors external to the downtown and its city.1 13 As a result, downtown merchants
and business leaders should not be overly concerned with competitor districts, whether
they are other cities or nearby shopping centers and malls. Such shopping areas are a fact
of life in American culture today, but the study clearly indicated downtowns can hold their
own, even with significant external competition, if they offer appropriate goods and
services.

Finally, this study was significant because it analyzed in depth the effectiveness of
revitalization efforts in smaller cities, an area of research largely ignored by urbanologists.
The study’s findings can be generalized to include most smaller cities (5,000—20,000
population). However, its relevance to larger populations is untested, and care should be
taken in interpreting these findings to larger cities, where the dynamics of the city center
can be quite different. For example, in larger cities the optimum business mix may vary
substantially from that found for the study cities. Also, factors such as parking may have a

much more important role in determining downtown vitality than was found in this study.

POLICY IMPLICATIONS AND RECOMMENDATIONS

Several clear policy implications come out of this study. First, revitalization efforts
should include design strategies which develop downtowns as pleasant places to spend
time. Downtowns should be seen as more than quick-stop convenience shopping areas, a
direction which many have headed in recent years. Merchants and city officials should see

downtowns as also providing an important focus for community life. The interchanges

 

1 13 A gravity model was also used for an ancillary study on the impact of Wal-Marts
of the sixteen study cities. As described in Appendix 7, this analysis showed no
direct correlation between the size and distance of Wal-Mart stores and the HPI.
This was also true when all discount department stores (not just Wal-Marts) were
grouped.

188

from chance encounters on sidewalks or in stores and restaurants encourage a type of
vitality which builds long-term downtown health.

The study has also shown the image a downtown presents to the local market could be
more attractive if it focused on encouraging certain business types to be located there. As
described previously, the business type shown as most closely correlated with perceived
downtown health was browsing shopping retail stores. Incentives should be given to
attract business types shown as critical to downtown health. To encourage the appropriate
business mix, downtown leaders should not rely on the chance attraction of businesses, but
should proactively solicit businesses seen as most conducive to good downtown health.
The example of Ionia, where the Downtown Development Authority was willing to lease a
vacant storefront at one dollar a year if a young entrepreneur agreed to establish a men’s
clothing store, is an example of this proactive approach.

In contrast, a business type not correlated to the Health Perception Index was “Quick-
stop shopping.” The reasons for this are not immediately obvious. In order to allow
customers to quickly enter and exit their downtown stores, many merchants have reoriented
their entrances to the rear of their buildings for quick access from a parking lot. They see
this convenience as a way to compete with the “Stop 'n Go” type of businesses found
along commercial strips. However, downtowns should not be compared with commercial
strips, for their role is quite different. The general health of a downtown can suffer when
customers are not encouraged to use downtown sidewalks. Using front entrances of
businesses should be encouraged, for it contributes to shoppers perceiving the downtown
as a cohesive district, rather than as a series of individual businesses unrelated to each
other.

Two other factors were shown not correlated with the Health Perception Index—

’9

“Parking” and “Streetscape improvements. Improved parking has been the focus for
many downtown revitalization programs. In an effort to compete with shopping centers

and malls, parking has been seen by many downtown merchants as the key to attracting

189

customers. In many instances, such a focus has diverted money and attention from more
productive activities, and although improved downtown parking should not be seen as a
bad thing, it was shown to not be the key that unlocks the door to downtown health.
Similarly, streetscape improvements, such as sidewalk pavers, trees and benches, and
new lighting, have commonly been viewed as a panacea for poor downtown health. The
study indicates that such improvements play only a minor role. Physical improvements
should be seen as a product of improved downtown health, rather than a cause of it. As is

suggested by the old cliche, “Beauty is only skin deep.”

FUTURE RESEARCH

This study is based on recommendations from a precursor study completed in 1987.
The earlier study suggested a number of areas for future research. These were described in

its Conclusions section, and are quoted below:

First, the number of cities surveyed during the study (eight) was the minimum
number feasible to begin stratification of some of the data by city. A much
fuller and more reliable perspective could be gained by increasing the number of
cities surveyed to twice that number. In addition to improving the database
significantly, such an expansion would also allow for statistical testing to see if
the data were generalizable to a yet larger sample. The data at this time can only
be seen as representing the respondents (primarily downtown merchants and
owners) in the eight study cities, and should not be generalized beyond that
scope. What is necessary is to determine whether a sample from eight cities, or
even sixteen cities, can be generalizable to all small city downtowns.

Second, the results from the eight study cities should be seen as only an initial
survey of the respondents, who would be given a follow-up survey after results
of this survey become available. The respondents would be presented with the
results of this initial survey, and asked to respond to the findings (a "Delphi"
survey technique). This would allow the respondents to indicate whether they
generally agreed with what was found, or if they markedly disagreed to clarify
the areas of disagreement.

190

Also, follow-up surveys could be conducted at certain time intervals (e.g.,
every 5 years), to allow for an expanded database of information on each
downtown which could be analyzed in a time series format. Even in the year
since the questionnaires were received and tabulated to the time of the final draft
of this paper the situation in a number of the study downtowns has changed
significantly. Analysis would further define how certain factors had impacted
on the perceived health of downtowns over time, and how respondent attitudes
were reflected in those changed circumstances.

Perhaps the most important area of future research would be toward refining the
Health Index as a tool for evaluating downtown health. In its initial form, as
presented in this paper, the Index is a reasonable mechanism .for representing
perceptual data in a systematic, comparative format. However, as the scope of
the evaluative technique is expanded, the Index can be further refined, and
eventually include other parameters which may be better indicators of health.
Also, more of a focus could be given to the number and types of businesses
which make up an optimum business mix. Although this may be found to vary
from city to city, the information would be beneficial to business and
community leaders when deciding future goals for their downtown. Further
research could also give more study to the types of revitalization programs that
have had the most impact - what characteristics have the successful programs
had in common.1 '4

Three of the four recommendations were accommodated in this later study—the number
of study cities was expanded from eight to sixteen; a follow-up survey was conducted six
years after the initial survey (1986 and 1992); and the Health Index was refined and
validated as a tool for evaluating the perception of downtown health. The one
recommendation not followed was use of at “Delphi” technique survey to see if respondents
agreed with the study’s conclusions. However, this was tested to some degree by

identifying individuals who had been respondents in both the 1986 and 1992 surveys and

 

“4 Norman R. Tyler. 1987. An Evaluation of the Health of the Downtowns in Eight
Michigan Cities (Dissertation). Ann Arbor, Michigan: University of Michigan.
205-6. -

191

analyzing their responses as a group. It was found there was no significant differences

between this group and other respondents.

There are many opportunities for further study on the topic of downtown revitalization
in smaller cities. To date, there has been little research concerning the evaluation of
downtown health based on perceptual input. Using a systematic, comparative format, such
input should be expanded, developed, and eventually accepted as a reliable indicator. The
Health Perception Index is intended as an initial step in that direction.

Future research should also consider surveying other respondent groups. This study
focused on downtown merchants and business owners, and also included local officials. It
would be important to compare these findings against a similar survey administered to a
random sampling of either downtown shoppers or local residents. This study’s
respondents were oriented primarily to the downtown environment. Shoppers and local
residents may consider the downtown district as only one option among their choices of
districts to frequent for goods and services, and as a result could provide an more impartial
evaluation of the downtown.

Further research could also look more closely at demographic characteristics that
correlate most closely with the Health Perception Index. Special attention could be paid to
the relationship to population age categories and household income, since these were
shown as strongly correlated.

Finally, historians might be interested in looking more closely at a surprising finding of
this study—that the HPI has a negative correlation with county seat status. It has been
found in previous studies by other researchers that cities which are county seats benefited
significantly from increased activities associated with this function, including law offices,
restaurants, etc. This may no longer be as important a factor as it once was, and may have
only a negligible impact on downtown health as the functions of the downtown have

become reoriented in recent decades.

192

APPENDICES

APPENDIX 1

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APPENDIX 3

LISTS OF VARIABLES

APPENDIX 3: LISTS OF VARIABLES

INDIVIDUAL RESPONDENT VARIABLES

W31

wflewewwrppwsewewwrpp

29.
30.
31.
32.

“$99.59!”?

(All data from 1992 survey unless otherwise indicated)

Name of City

City identifier number

Date survey distributed

Date survey returned

Response time (no. of days)

Respondents’ business name

Respondent identifier number

Indication of having met with respondent

Indication of whether had discussion with respondent

Respondent’s business type

Business type category (Large retail, small retail, office, food/enter., other)

Business type category identifier

Evaluation of Question I of survey

Evaluation of Characteristics: General condition of buildings downtown
Streetscape—sidewalks, benches, trees, etc.
Mix of stores and businesses
Parking in downtown
Impression of overall retail sales downtown
No. of shoppers for “browsing” shopping
No. of sheppers for “quick-stop” shopping
Merchants association
Cooperation of banks
Cooperation of city government
Local political situation
Downtown as a community cultural center
No. of tourists coming to downtown
Employment in the local area
Historic character of downtown
Safety and security downtown

Type of Significant Event having impact on downtown

Year of Significant Event

Indication of whether Event had positive or negative impact on downtown

207

33.
34.
35.
36.
37.
38.
39.
40.
4].
42.
43.

45.
46.
47.
48.
49.
SO.
5 l.
52.
53.
54.
55.
56.
57.
58.
59.
60.
61.
62.
63.

65.
66.
67.
68.
69.
70.
7 l.
72.
73.
74.
75.

208

Type of Significant Event #2 having impact on downtown

Year of Significant Event #2

Indication of whether Event #2 had positive or negative impact on downtown

Downtown’s greatest competitor area

Miles to downtown’s greatest competitor area
Strength of competition from greatest competitor area
Type of competition

Another competitor area

Miles to downtown’s other competitor area

Strength of competition from other competitor area
Type of competition from other competitor area

Evaluation of downtown health of other Michigan cities:

Cheboygan
Petosky
Alpena
Manistee
Cadillac
Ludington
Big Rapids
Mt. Pleasant
Alma
Greenville
Ionia

Grand Haven
Hastings
Allegan
South Haven
St. Joseph
Benton Harbor
Dowagiac
Niles

Three Rivers
Sturgis
Coldwater
Marshall
Albion
Hillsdale
Adrian
Tecumseh
Monroe
Ypsilanti
Howell
Charlotte

St. Johns

76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
10].
102.
103.
104.
105.
106.
107.
108.
109.
l 10.
1 1 l.
112.
l 13.

209

Owosso
Fenton
Lapeer
Evaluation of downtown’s historic health: 1900
1920
1940
1960
1970
1980
1990
1992
Evaluation of business type importance: “Browsing” retail stores
“Quick-stop” retail stores
Large retail stores
Offices
Public buildings and offices
Evening activities
Tourism businesses
“Handyman” businesses
“Small entrepreneur” businesses
Indication of optimism or pessimism toward downtown’s future
Types of reasons for optimism or pessimism
Indication whether resident of community
No. of years as resident
Indication whether respondent feels they know the downtown area well
Indication whether own or manage downtown business
No. of years owned or managed
Indication whether a member of a downtown organization(s)
Type of organization
Indication whether respondent has been active in revitalization efforts
Indication whether a public official
Other information on respondent
Indication whether respondent wants copy of summary report on study
Health Index components: Survey Question 1 Index
1990-92 historic evaluation Index
Optimism/pessimism Index
Downtown Characteristics Index
Health Perception Index

210

CITY AND COUNTY VARIABLES

W

1000.
1002.
1003.
1004.
1005.
1006.
1007.
1008.
1009.
1010.
1011.
1012.
1013.
1014.
1015.
1016.
1017.
1018.
1019.
1020.
1021.
1022.
1023.
1024.
1025.
1026.
1027.
1028.
1029.
1030.
1031.
1032.
1033.
1034.
1035.
1036.

(All data from 1990 census unless otherwise indicated)

Identifier number

County seat status

City population, 1990

County population, 1990

City population, 1980

County population, 1980

City population, 1970

County population, 1970

City population growth rate, 1980-1990

County population growth rate, 1980-1990

City population growth rate, 1970-1980

County population growth rate, 1970-1980

Percent of city population/ county population, 1990
Percent growth of city population] county population, 1980-1990
Percent growth of city population/ county population, 1970-1980
Percent of city population under 18 years old
Percent of county population under 18 years old
Percent of city population 18-24 years old

Percent of county population 18-24 years old
Percent of city population under 24 years old
Percent of city population 2544 years old

Percent of county population 25-44 years old
Percent of city population 45-64 years old

Percent of county population 45-64 years old
Percent of city population over 65 years old
Percent of county population over 65 years old
Percent of city population over 45 years old
Median age of city population

Median age of county population

Area of city in square miles

Area of county in square miles

Population of city per square mile (Population density)
Population of county per square mile

Percent of county land area urbanized

Number of households in city

Number of two-parent households in city

1037.
1038.
1039.
1040.
1041.
1042.
1043.
1044.
1045.
1046.
1047.
1048.
1049.
1050.
1051.
1052.
1053.
1054.
1055.
1056.
1057.
1058.
1059.
1060.
1061.
1062.
1063.
1064.
1065.
1066.
1067.
1068.
1069.
1070.
1071.
1072.
1073.
1074.
1075.
1076.
1077.
1078.
1079.

211

Number of female-headed households in city
Number of households with occupant living alone
Number of households with occupant living alone over 65 years old
Number of houses in city with value less than $50,000
Number of houses in city with value $50,000-99,999
Number of houses in city with value $100,000-149,999
Number of houses in city with value $150,000-l99,999
Number of houses in city with value $200,000—299,999
Number of houses in city with value greater than $300,000
Lower quartile value of houses in city
Median value of houses in city
Upper quartile value of houses in city
Number of black households in city
Percent of black households in city
Percent of black households in county
Household income of black families in city
Household income of black families in county
Household income of white families in city
Household income of white families in county
Percent urban population in county
Percent city population high school graduates
Percent county population high school graduates
Percent city population college graduates
Percent county population college graduates
Percent city population unemployed
Percent county population unemployed
Percent city population in labor force
Percent county population in labor force
Travel time in minutes, home to work, city residents
Travel time in minutes, home to work, county residents
County population: Number of workers in executive jobs
Number of workers in professional jobs
Number of workers in technical jobs
Total number employed
Percent of executive employed to total
Co. Pop.: Last occupation of experienced unemployed: Executive
Professional
Technkal
Service
City, mean household income
County, mean household income
City, percent of families with household income below poverty
County, percent of families with household income below poverty

212

1080. Median rent for house in county
1081. Median housing cost in city, 1990
1082. Median housing cost in county, 1990
1083. Median housing cost in city, 1980
1084. Median housing cost in county, 1980

MW
1 101. Number of retail establishments in city, 1992
1102. Number of retail establishments in county, 1992
1 103. Number of retail establishments in city, 1987
1104. Number of retail establishments in county, 1987
1105. Number of retail establishments in city, 1982
1 106. Number of retail establishments in city, 1977
1 107. Number of retail establishments in county, 1977
1108. Retail sales in city, 1992
1109. Retail sales in county, 1992
1110. Retail sales in city, 1987
1 11 1. Retail sales in county, 1987
1 112. Retail sales in city, 1982
1113. Retail sales in city, 1977
11 14. Retail sales in city, 1972
11 15. Retail sales in city, 1963
1116. Retail sales in city, 1958
1 1 17. Retail sales per capita in city, 1992
1 1 18. Retail sales per capita in city, 1987
1 119. Change in retail sales, 1987-1992
1 120. Percent change in retail sales, 1987-1992

W114

1201. Retail sales tax, 1986

1202. Retail sales tax, 1987

1203. Retail sales tax, 1992

1204. Retail sales tax, 1993

1205. Retail sales tax, 1987 (adjusted)

1206. Retail sales tax, 1992 (adjusted)

1207. Change in retail sales tax, 1987-1992 (adjusted)

1208. Percent change in retail sales tax, 1987-1992 (adjusted)

“4 Collected by the Michigan State University Institute for Public Policy and Social
Research.

213

cheuszurces
1301. Size (sq. ft.) of closest Wal-Mart store
1302. Distance (miles) to closest Wal-Man store
1303. Size (sq. ft.) of Meijer’s store in community (if any)
1304. Size (sq. ft.) of KMart store in community (if any)
1305. Ave. size (sq. ft.) of other discount department stores in community (if any)
1306. No. of other discount department stores in community (if any)
1307. Total sq. ft. of all discount department stores in community
1308. Distance ranking (according to Stone) of Wal-Mart stores
1309. Distance ranking, revised (according to Stone) of Wal-Mart stores
1310. Gravity model rating (distanceZ)
1311. Gravity model rating (distance)

D 've rv

(Data taken from individual surveys also interpreted at city level)

1401. City identifier number
1402. Business type category (Large retail, small retail, office, food/enter., other)
1403. Evaluation of Question 1 of survey
1404. Normalized value of Question 1 of survey
1405. Evaluation of Characteristics: General condition of buildings downtown
1406. Streetscape—sidewalks, benches, trees, etc.
1407. Mix of stores and businesses
1408. Parking in downtown
1409. Impression of overall retail sales downtown
1410. No. of shoppers for “browsing” shopping
1411. No. of shoppers for “quick-stop” shopping
1412. Merchants association
1413. Cooperation of banks
1414. Cooperation of city government
1415. Local political situation
1416. Downtown as a community cultural center
1417. No. of tourists coming to downtown
1418. Employment in the local area
1419. Historic character of downtown
1420. Safety and security downtown
1421. Normalized value of Char.: General condition of buildings downtown
1422. Streetscape—sidewalks, benches, trees, etc.
1423. Mix of stores and businesses
1424. Parking in downtown
1425. Impression of overall retail sales downtown
1426. No. of shoppers for “browsing” shopping

1427.
1428.
1429.
1430.
1431.
1432.
1433.
1434.
1435.
1436.
1437.
1438.
1439.
1440.
1441.
1442.
1443.
1444.
1445.
1446.
1447.
1448.
1449.
1450.
1451.
1452.
1453.
1454.
1455.
1456.
1457.
1458.
1459.
1460.
1461.
1462.
1463.
1464.
1465.
1466.
1467.
1468.
1469.

214

No. of shoppers for “quick-stop” shopping
Merchants association
Cooperation of banks
Cooperation of city government
Local political situation
Downtown as a community cultural center
No. of tourists coming to downtown
Employment in the local area
Historic character of downtown
Safety and security downtown

Downtown’s greatest competitor area

Miles to downtown’s greatest competitor area

Strength of competition from greatest competitor area

Another competitor area

Miles to downtown’s other competitor area

Strength of competition from other competitor area

Evaluation of downtown’s historic health: 1900

1920

1940

1960

1970

1980

1990

1992

1900

1920

1940

1960

1970

1980

1990

1992

Indication of optimism or pessimism toward downtown’s future

Normalized value of Optimism or pessimism toward downtown’s future

Indication whether resident of community

No. of years as resident

Indication whether respondent feels they know the downtown area well

Indication whether own or manage downtown business

No. of years owned or managed

Indication whether a member of a downtown organization(s)

Health Index components: Survey Question 1 Index

1990-92 historic evaluation Index
Optimism/pessimism Index

Normalized value of historic health:

215

1470. Downtown Characteristics Index

1471. Health Perception Index

1472. Normalized value of: Health Perception Index
BersenaLtherxatiQn

1501. No. of businesses in central downtown: Appliances

1502. Auto related

1503. Bakeries/ ice cream

1504. Banks

1505. Beauty shops/ barber shops

1506. Bike shops

1507. Book stores/ news stores

1508. Bidal/ tuxedo shops

1509. Camera! film stores

1510. Carpeting/ flooring

151 1. Cleaning/ laundry

1512. Clothing

1513. Collectibles/ cards

1514. Dance studios

15 15. Department Stores

1516. Dinner restaurants

1517. Drug stores

1518. Electronics/ TV stores

1519. Flower shOps

1520. Fraternal organizations

1521. Home furnishings

1522. Furniture stores

1523. Gifts/ antiques

1524. Grocery stores

1525. Hardware stores

1526. Health and fitness centers

1527. Hobbies and Crafts

1528. Hotels

1529. Insurance and rela estate offices

1 530. Jewelers

1531. Lumber suppliers

1532. Lunch restaurants

1533. Medical equipment

1534. Music stores

1535. Office supply stores

1536. Paint and decorating stores

1537. Photographers/ frame shops

1538.
1539.
1540.
1541.
1542.
1543.
1544.
1545.
1546.
1547.
1548.
1549.
1550.
1551.
1552.
1553.
1554.
1555.
1556.
1557.
1558.
1559.
1560.
1561.
1562.
1563.
1564.
1565.
1566.
1567.
1568.
1569.
1570.
1571.
1572.
1573.
1574.
1575.
1576.
1577.
1578.
1579.
1580.

216

Plumbing/ electrical] heating shops
Printers and publishers
Professional offices
Public offices

Sewing/ fabric shops
Shoe stores

Shoe repair shops
Sporting goods
Tavern] bar

Theaters

Travel agents

Used goods

Vacant storefronts
Video rentals/ arcades

Total no. of businesses in the central downtown

No. in downtown, not in central area:

Appliances

Auto related

Bakeries/ ice cream
Banks

Beauty shops/ barber shops
Bike shops

Book stores/ news stores
Bidal/ tuxedo shops
Camera/ film stores
Carpeting/ flooring
Cleaning/ laundry
Clothing

Collectibles/ cards

Dance studios
Department Stores
Dinner restaurants

Drug stores

Electronics/ TV stores
Flower shops

Fraternal organizations
Home furnishings
Furniture stores

Gifts/ antiques

Grocery stores
Hardware stores

Health and fitness centers
Hobbies and Crafts
Hotels

1581.
1582.
1583.
1584.
1585.
1586.
1587.
1588.
1589.
1590.
1591.
1592.
1593.
1594.
1595.
1596.
1597.
1598.
1599.
1600.
1601.
1602.
1603.

217

Insurance and rela estate offices
Jewelers

Lumber suppliers

Lunch restaurants

Medical equipment

Music stores

Office supply stores

Paint and decorating stores
Photographers/ frame shops
Plumbing/ electrical/ heating shops
Printers and publishers
Professional offices

Public offices

Sewing] fabric shops

Shoe stores

Shoe repair shops

Sporting goods

Tavern/ bar

Theaters

Travel agents

Used goods

Vacant storefronts

Video rentals/ arcades

1604. Total no. of businesses downtown, not in the central area

(Note: These counts also available for l 986 survey)

APPENDIX 4

MAPS OF PERCEIVED BOUNDARIES OF STUDY DOWNTOWNS
TAKEN FROM 1986 SURVEY

218

Albion:
Most respondents in Albion saw the downtown as the businesses located along Superior
Street, from the Post Office on the north to the Library on the south. A few respondents

included Clinton Street to the west also, but most did not. Surprisingly, no one defined the
northern boundary as the river, but some used the railroad tracks as the northern edge.

In spite of many natural and building landmarks which could disrupt the image, the
combined map showed fairly clear and consistent boundaries.

.lu'-\
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l
l
l
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i
l

‘ . n non-er dt - Supel'IOI' St."

 

Dowagiac:

The downtown has a very strong edge at the railroad tracks, which has industry on the
opposite side. However, the other boundaries wandered off, especially south on Front
Street. In many instances, respondents included residential areas within their downtown
boundaries.

The city is now defining the boundaries for 3 Downtown Development Authority. The new
DDA boundaries cross the tracks and include much of the industrial area, chiefly as a way
of increasing the tax base for the Authority.

2
v!
.
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E
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e
E
5
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220
Howell:

Howell's downtown has a lot of physical integrity. as is illustrated by the strong definition
given to its boundaries. The core of the downtown is located along Grand River
Boulevard. with its strongest focus the County Courthouse square. A few respondents
included the Library at the west end of the city, most did not. The only other discrepancy
shown was whether the downtown extended on the north to the railroad tracks.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

 

 

 

 

 

 

 

 

 

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Ionia:

Ionia's downtown is along Main Street. It is enclosed on the north (left) by the base of a
hill beginning at Washington Street. On the south its edge is the railroad yard. At the east

(top) edge, respondents were unsure whether to include the many city and county buildings
within the boundaries of downtown. , -

 

' 1

Buildings
, r

u ‘ l . Railroad

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1’31

Lapeer:

Lapeer has very strong boundary definition, probably because an Urban Renewal program
which established a one-way street pattern around the downtown and created a strong edge
defined by the new parking areas. However, a few respondents commented on the need
for the downtown to expand by being able to cross these open areas which separate the
downtown from the surrounding residential areas.

Similar to Ionia, the only discrepancy in boundary definition was whether the city and
county buildings on the west (bottom) should be inelu_ti_ed;_ the respondents were split in

their opinions on this.

 

 

 

 

 

 

 

 

111 _~.L_.l13 _‘-,‘t

Manistee:

    

\'1\,

The respondents in Manistee showed the least agreement on downtown boundaries of any
of the study cities. Most agreed that the north (left) boundary was along the Manistee
River, but some large businesses are located on the opposite side, and some respondents
wanted to include them also. The other boundaries were drawn in many different
locations. There was little agreement as to whether the area to the south (above) River
Street, which is mixed residential and commercial, should be considered as part of

  
  

downtown.
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turnaround to Exchange Street. The definition given the eastern (bottom) boundary was
somewhat surprising, since businesses go only as far as Madison Street, two blocks before
Exchange St. ' " ‘ ’ “'r -- um.- : .1

St. ..:..... "

‘.
l
u

Tecumseh:

Tecumseh was represented by its respondents as a small downtown physically. The
biggest discrepancy in Tecumseh's boundary image was whether the downtown was
limited only to the buildings facing Chicago Boulevard, the city's main street, or whether it
extended to include scattered businesses on adjacent blocks. The respondents were split on
this. Few respondents went cast an extra block, to Maumee Street, in order to include the
City Hall/ Police Station and the Post Office; they were not considered part of the
downtown by most.

" City. Hall
Chlmo B'Vd. ‘ L CNICAGO

Post Office

 

APPENDIX 5

ITEMS USED BY E.F. THORNDIKE
TO DERIVE HIS "GOODNESS INDEX"

APPENDIX 5:

ITEMS USED BY E.F. THORNDIKE
TO DERIVE HIS “GOODNESS INDEX”

Wealth

Infant death-rate (reversed, so that the fewer the deaths the higher the score)
General death-rate (reversed, so that the fewer the deaths the higher the score)
Death-rate from typhoid (reversed)

Death-rate from puerperal diseases (reversed)

Death-rate from appendicitis (reversed)

It m at'

Per capita public expenditures for teachers' salaries

Per capita public expenditures for textbooks and supplies
Percentage of persons sixteen and seventeen attending schools
Average salary of high school teachers

Average salary of elementary school teachers

c
Rarity of poverty as indicated by the infrequency of homes rented for under $15 per

month and homes owned valued at under $1500
Average wage of workers in factories
Frequency of home ownership (number of homes owned per thousand population)

CteatutLCQmflons

Per capita domestic installation of electricity
Per capita domestic installations of gas

Per capita number of telephones

Per capita number of radios

Per capita number of automobiles

226

227

chetltstus

Literacy (number of illiterates aged 10 or over, divided by the population aged 10 or
over, the percentage being reversed so that the fewer the illiterates the higher the
score)

Circulation of the Literary Digest per 1000 population
Death-rate from syphilis (reversed)

Death-rate from homicide (reversed)

Death-rate from automobile accidents (reversed)1 ‘5

The basic conclusions based on Thomdike's research were:

"So far as concerns fundamental causes, then, the golden rules for a small city, as for a
large, are: to (1) make itself attractive to good people, (2) have them raise more children
the better they are, and (3) give them opportunities to improve themselves physically,

intellectually and morally and to earn larger incomes.” '6

 

“5 E.F. Thorndike. 1940. 144 Smaller Cities. New York City: Harcourt, Brace and
Company. pp. 28-29.

“6 Thorndike. p. 78.

APPENDIX 6

HISTORY OF DOWNTOWNS

APPENDIX 6: HISTORY OF DOWNTOWNS

This study has dealt with the situation in downtowns as they currently exist. However,
there have been forces of change which have been impacting downtowns for many
decades. The following section describes the history of downtowns generally, and looks at

the many factors which have determined their evolution for more than a century.

THE CHANGING NATURE OF RETAIL

The nature of retail business is that there is always change. Each decade brings large-
scale and sometimes surprising shifts on both the supply and demand side. Retail
businesses have been affected by a succession of innovations dating from the mid-18005:
“packaging in containers of fixed sizes and weights (18405); standardized methods of
sorting, grading, weighing and inspecting (early 18505); fixed prices (18605); standardized
clothing sizes (early 18805); periodic presentations via catalog (18805); the buffet-style
restaurant (1885) and the cafeteria serving line (1895); fully automated vending machines
(1897); standardization through franchising (1911); drive-through auto service stations
(1913); self-service store layout (1916);...packaging that ‘sold itself’ (late 19205); ‘fair
trade’ enforced price uniformity (1931); and wide selection of competing brands displayed
on open shelves (1934).”117

As described by Hutter (1987), the downtown department store was the first true
modern commercial phenomenon. From the 18805 to the 19205 it provided something

new—a convenient one-stop, one-price shopping establishment. Trachtenberg (1982)

 

“7 The Future of U.S. Retailing. p. 54-55.
228

229

points out that “in department stores, buyers of goods learned new roles for themselves,
apprehended themselves as ‘consumers,’ something different from mere users of
goods.”1 13

Part of the success of the large department store was the technology developing during
the Industrial Revolution, including such new inventions as the elevator, improvements in
gas lighting, and pneumatic tube systems. But there was also a social element to the
phenomenon, for shopping in these “grand emporiums” represented a new ethic of
consumerism that was being glorified during this period and represented the success of the
American economic system. They also were important in teaching women to be proper

users of goods.

As much as the school, and much like the factory, the department store served
its customers as an educational institution. Proferring infinite charm at cheap
prices, it sold along with its goods a lesson in modern living. The departments
taught the social location of goods; trousers as “men’s clothing,” silks as
“women’s wear,” reclining chairs as “parlor furniture.”...department stores
taught families what they needed, taught symbolic as well as practical functions
of things. The lessons inhered in the design of things themselves, their
packaging and advertisement, their place and manner of presentation, the entire
gestalt assuming a continuous act of learning and using, along with buying.‘ '9

The department store also provided post-Victorian women a safe place to go in the
industrial city. Many provided more than basic shopping facilities, including ladies’
lunchrooms, sitting rooms, and even “silence rooms” for a peaceful respite. Detroit’s
Hudson’s store had a room where mothers could nurse their babies and change their

diapers. Ultimately this emphasis led to the “feminization of spending” (Duncan 1965).

 

“3 Alan Trachtenberg 1982. The Incorporation of America. New York: Hill and
Wang. p. 130.

1 19 Tractenberg. p. 132.

230
Technology also had much to do with the new form of convenience shopping, for the
automobile no longer made centrality necessary to retail success, and shopping followed

the development of residential areas of the urban fringe.

EMERGENCE OF SHOPPING CENTERS AND MALLS

In the last sixty years there has been a gradual, but inexorable, shift of retail from the
city center to new suburban centers. At first retail followed new residential development,
but then residential followed retail as the convenience of new commercial centers became
better established.

This shift has been a relatively tranquil process, and has been widely accepted. Little
friction to it has been created either through public policy or private competition. Indeed, it
has generally been the strategy of formerly downtown businesses to join the surge to the
suburbs rather than fight it. Thus, the functions that remain in the downtowns were either

too weak or too established in a special niche to move.

wamnbeshmmgcgm

The first automobile-oriented shopping center is generally agreed to be Country Club
Plaza, just outside Kansas City, Missouri. Jesse C. Nichols, a real estate developer, was
developing much of the area southwest of Kansas City for upper income housing, but
recognized in the 19205 the increasing importance of the automobile to the new suburban
lifestyle. Country Club Plaza was begun in 1922 specifically as an area for shopping by
car. It was deliberately not located near a trolley line, and had 46 percent of its space taken
up with streets and parking, including one of the first parking garages built anywhere. The
Plaza was located at a nexus of roads which Nichols had built over the years for his
housing developments. Nichols believed,

Wide streets, squares, and plazas are needed in these days of parking....Main
traffic ways should have great width, but byways should be side enough only to

231

give capacity to go from one traffic way to another....The shops are built around
a square of plaza ...and the main streets in commercial areas are 100 to 200 feet
in width.120

It is clear that Nichols integrated the automobile into the design of this shopping plaza,
and began an inevitable trend that has impacted cities all over the world ever since. By
including 250 shops, some branches of downtown stores, he also established the suburban
5h0pping center as a direct competitor to the traditional downtown as the center of retail.

One of the first and largest supermarkets established was the Crystal Market in San
Francisco. Built on a former circus grounds, the store building was 68,000 square feet,
with parking for 4,350 cars. “By the mid-19305 the Crystal Palace set a sales record of
twenty-five tons of sugar in an hour, five freightcar-loads of eggs in a month, and an
average of nearly a ton of apples per day for an entire year. Seizing the potential of one-
stop shopping, the Crystal Palace quickly expanded to offer liquor, tobacco, and jewelry as
well as drugstores, barber, and beauty parlors, and a dry cleaner—like the hyperrnarket of
today—and sowed the seeds of its own destruction. While consumers could find almost
anything they wanted,...they could not get past the logjam at the checkout counter to buy
it.”121

"Highland Park in Dallas, developed ...in 1931, synthesized new retailing ideas. Many
consider this project to be the first planned shopping center... Highland Park occupied a
single site not bisected by public streets, its stores were built with a unified image and
managed under the control of a single owner, and the amount of onsite parking was
determined by demand. It was so well conceived that even today it commands rents of

$45 per square foot...

 

'20 James E. Vance, Jr. 1990. The Continuing City. Baltimore: The Johns Hopkins
University Press. p. 491, from a speech given by J .C. Nichols to his company
associates in 1934.

12' The Future of U.S. Retailing. p. 55.

232

"Business and financial innovations may have been more important than design
innovations. The River Oaks Center, developed in 1937 in Houston,... inspired a new way
of thinking about "the shopping center" and contributed operational practices such as the
percentage lease and the merchants' association. Other significant operational innovations
surfaced in later years: careful attention to tenant mix, the professionalization of
management, joint promotions and participative advertising, common area maintenance,
the standardization of leases, the switch to net leases, and the inclusion of escalation clauses
in leases.

"The model for the regional mall—Northgate Center in Seattle— was developed... in
1950. Northgate pioneered the idea of incorporating a full-line department store as an
anchor, and featured a central pedestrian mall and service delivery via an underground
tunnel. Fueled by the inception of the interstate freeway system and regional expressways,
the development of regional malls took off.

"The market then began to refine the shopping center concept. In 1956, the first
enclosed mall—Southdale—was developed, and from then on, the growth in shopping
centers has been dramatic. Extra-large super regional centers have come into existence...
Mixed-use centers, like Houston's Galleria, also represent an important refinement, as do
multilevel malls, like Water Tower Place in Chicago, made possible by technological
innovations in elevators and escalators." ‘22

The evolution of the shopping center has led to its largest example in Edmonton Mall
in Alberta, Canada. It has 5.2 million square feet, and contains “over eight hundred stores
including eleven major department stores, nineteen movie theatres, 1 10 eating
establishments, five amusement areas.”123 It also represents the notion of convenience

shopping taken to its furthestmost expression, and represents how far retail businesses

 

122 A. Alexander Bul and Nicholas Ordway. 1987. "Shopping Center Innovations:
The Past 50 Years." Urban Land (June 1987). pp. 22-23.

123 The Future of U.S. Retailing. p. 57.

233

have gone from the small independent retailer located in a traditional downtown. Yet it
also represents that retailing has taken two paths, one toward the convenience of the
regional megamall, with all services collected in one common location, and the other
toward the diversity of independent retailers filling a niche in the local market. This niche

market is what most downtowns are left with, and with which retailers can capitalize.

tw ' w w

Why are some retail environments currently more successful than others? The
traditional wisdom of real estate experts has been that the three most important factors are

’9

“location, location, location. This was supported in a survey of customer preference
patterns by Hudson (??), which showed preferences were most strongly based on both
location and price. However, a 1990 survey124 of more than 250,000 shoppers revealed

that the primary reasons consumers chose their favorite stores were as follows:

Reason 25mm;
Price 21 .6
Selection 1 8. 1
Quality 16.8
Location 14.7
Service 10.4

The study shows the most important factor to retail success is not location, but price.
Downtowns have traditionally offered central location and service, but as a result are losing
to the discount chains that offer low price and large selection. The discount store has
become America’s favorite place to shop, although some studies show “...it is losing its
competitive edge to ‘category killers’ because discount stores no longer represent off-price,

but represent the price.”125

 

124 The Future of U.S. Retailing. p. 52.
125 Future of U.S. Retailing. p. 53.

234
Even this advantage is being lost as all major retailers are getting products from the
same suppliers at similar quantities and prices, and even factory outlets and category killers
have similar prices. This leaves the small independent retailer in an almost untenable
position. As one appliance retailer in a Michigan downtown explained, “I can’t buy
wholesale as cheaply as the new Wal-Mart is selling off their shelves. It would be
advantageous for me to buy my small appliances from that store rather than from my
distributor, but I refuse to do this on principle.”26
Outlet centers are another concept that are anathema to independent downtown retailers,
for the principle of outlet centers is that manufacturers want to retain more control over
their product distribution. Although prices at outlet centers are typically not lower than at
other discount retailers, the outlet allows manufacturers to both control their markets and
trade more directly on their brand names. “Factory outlets are one modest step toward the
ultimate solution to the problem of maintaining control from the factory to the customer—
strategic organizational alliances.”‘27
The new “power centers,” which feature “category killers,” are the latest in a long line
of competitors to the downtown. The new commercial types may be doing more damage
to each other than to “mom-and-pop independents, however. “What power centers affect
most is the small- to mid-size strip center. They’re taking both customers and tenants.”128
Independent retailers are less affecrted. “Small retail is in the bag on a mom-and-pop
basis. The conglomeration theory says the power center is where everyone wants to be,

but it really doesn’t make sense for the mom-and-pops to be there.”129

 

‘26 Interview for this project survey. July 1992.

127 The Future of U.S. Retailing. p. 54.

128 Quote from David Andrews, Vice President of Commercial Marketing, The
Mitchell Co., Mobile, Alabama, in “Power centers flex their muscles.”Chain Store
Age Executive. February 1989. p. 3A.

129 Quote from Ed Attebury, Vice-President of Maroon Brokerage, Sacramento, in
“Power centers flex their muscles.”Chain Store Age Executive. February 1989. p.
3A.

235
The retail industry has undergone significant changes throughout its lifetime of almost
two hundred years. The following chart, taken from the text, Retailing, shows the gradual

shift from general stores to more and larger specialized stores.

Table 31
Selected Changes in Retail Institutional Structure”1
Period of Inception to Stage of
I . . 11 E E 111 . I 2 11:] E 'v E.
General store 1800-40 100 Declining/obsolete A local institution
Single-line store 1820-40 100 Mature Hickory Farms
Department Store 1860-1940 80 Mature Marshall Field’s
Variety Store 1870 1930 50 Declining/obsolete Morgan-Lindsay
Mail-order house 1915- 1950 50 Mature Spiegel
Corporate chain 1920-30 50 Mature Sears
Discount store 1955-75 20 Mature KMart
Conventional supermarket 1935-65 35 Mature/declining Winn-Dixie
Shopping center 1950-65 40 Mature Paramus
Cooperative 1930-50 40 Mature Ace Hardware
Gasoline station 1930-50 45 Mature Texaco
Convenience store 1965-75 20 Mature 7-Eleven
Fast-food outlet 1960-75 15 Late growth McDonald’s
Home-improvement center 1965-80 15 Late growth Lowes
Superspecialist 1975-85 10 Growth Sock Shop
Warehouse retailing 1970-80 10 Maturity Levitz
Computer store 1980-85 5 Maturity Computerland
Electronics superstore 1980— ? Growth Circuit City Stores
Off-price retailer 1980— ? Growth Burlington Coat Factory
Warehouse club 1985 ? Growth Sam’s Wholesale Club
Electronic shopping 1990 ? Growth Home Shopping Network
Adi/antagmtthefihapninafiemetmal:

There are a number of advantages inherent in the shopping center/mall type of retailing.
Some of these could also be used to advantage by downtown merchants.
Management. Downtowns are essentially managed by individual business owners

who make decisions on management based primarily on their individual concerns. They

 

13‘ J. Barry Mason, Marris L. Mayer and Hazel F. Ezell. 1994. Retailing (5th
Edition). Burr Ridge, Illinois: Richard D. Irwin, Inc. 93.

236
see other, similar businesses in the same vicinity chiefly as competitors, and there is little
or no cooperation one with another regarding common concerns such as hours,
promotions, parking or general planning.

In contrast, businesses located in shopping centers/malls are subject to very tight
management control, and many policy decisions are not left up to the discretion of the
individual storeowners or managers, but are determined by the overall management. The
center management establishes common hours, promotions and many other aspects of
operations to which the individual businesses must comply.

This distinctly different approach to overall management issues is probably the most
significant difference between downtowns and shopping centers/malls.

- accessibility

adequate entrances and exits
good service facilities

- plentiful parking

° modern and well-planned facilities
unified arch., planned space, allows for expansion

- coordinated merchandising and promotion

There are many advantages inherent in managing a controlled retail environment like a
mall which are not available for independent retailers in a downtown. One example is the
Homart Daily Comparative Sales Report,131 compiled daily for retailers in Homart
managed malls. The reports are based on sales figures collected each morning from mall
retailers, compiled and distributed by three o’clock in the afternoon of the same day. The
confidential computerized figures allow retailers to compare their daily performance with

other retailers in the same category and with the center itself. As one retailer says, “It’s like

 

‘31 Designed by W. Joe Larson, Senior VP—Asset Management for Homart
Development Company, Chicago.

237

a daily thermometer reading.”‘32 The report compares figures with the previous year,
along with weather conditions and special events occurring at that time, and also show
daily, monthly and yearly growth and decline. The Report serves two primary purposes—
it gives merchandisers immediate information on their comparative status and it also
creates a daily performance incentive. Also, the reports are hand-delivered by management

staff and keeps them in daily personal contact with the store owners.

Huron 0111."! or or‘ .I‘ «ti-.1 -.. -. (0‘11-..-

Large discount stores have made important inroads into the market areas of small and
medium sized cities. Initially located in larger urban areas, these all-inclusive stores have
found relatively easy market opportunities in smaller urban areas. The discount chain that
has taken most advantage of this more rural market is Wal-Mart, whose founder, Sam
Walton, specifically targeted small cities for his store locations. Wal-Mart used a “down-
home” retailing approach, appealing ot lower middle-class households (40 percent of its
customers have household incomes of $20,000 or lessl33) and was so successful it
became the nation’s leading retailer in just a few years. The advantages of the small town
locations were numerous—there was virtually no competition for selection or price among
small local retailers, municipalities had few regulations over land development (lot sizes
range from five to 25 acres), and were generally very receptive to the increased tax base
created by a large retailer, and access from surrounding communities was relatively
convenient via the interstate highway system which now criss-crossed most of the

countryside.

 

132 Teresa Andreoli. 1991. “Homart: Daily Comparative Reports Help Mall Retailers
Keep Pace.” Stores (September 1991 ). p. 96.

133 Gruidl, John and Steven Kline. 1992. "What Happens When a Large Discount
Store Comes to Town?" Small Town (March-April 1992). pp. 20-23.

238

2 "E 5.!”

Further competition to traditional downtowns has come from strip malls, which service
the convenience shopper by providing basic retail services in an auto-oriented environment.
Although not as complete as the mall or discount center, or as friendly as a downtown
merchant, the strip mall has remained successful because it provides a basic retailing
need—convenient in and out. As described by one shopper, “It’s right off the highway, so
I can get in and out fast. It’s not as nice as the Brookwood Mall, but that really doesn’t
matter. They have what I want here.”134 As families have had more demand put on their
time, strip malls provide the quick in and out shopping most desired. “Big malls have
become overwhelming to people,” says retail consultant Wendy Liebman, who has studied
women’s shoppping habits in the Midwest. “We’ve found that people still want selection,
but for women with children, especially, wandering around a huge mall is hard work.
Smaller centers are easier to manage.”135

Time is also an important factor to shoppers, now more than ever before. In two-
eamer families studies have shown that time is the most important factor in shopping, and
many feel that shopping is a stressful chore chore that interferes with leisure time and
quality time to be spent with children. Therefore, the opportunities for businesses to
develop time—saving services are great. As one study of recent shopping habits concluded,
“Marketers should determine how much their stores are inconveniencing their customers,
reviewing all customer systems and selling contact points. By changing procedures or
retraining personnel, they can eliminate ‘time bandits’ and offer their customers time-

saving alternatives.”136

 

‘34 Chip Walker. 1991. "Strip Malls: Plain But Powerful." American Demographics
(October 1991). p.48.

135 Walker. p. 50.

136 Eugene H. Fram and Joel Axelrod. 1990. "The Distressed Shopper." American
Demographics (October 1990). p. 45.

239

Situated at strategic intersections along transportation lines, strip malls are successful
because of location. They have become ubiquitous, and as a whole accounted for 87
percent of all shopping malls and 51 percent of total shopping center retail sales. Almost
2,000 of these small convenience centers opened in 1990 alone.137 As a result, they will

continue to be major competitors to downtown retailers.

Pro 1 ' w

Because of the growth of the retail industry through the 19805, there are many
problems and areas of concern that have accompanied this growth. As a result, retailing in
general has significant challenges facing it during this decade. As described by Mason,

Mayer and Ezell (1994), they include:

W: The growth in retail sales has proceeded faster than the
growth in either population or consumer spending, resulting in more retail
square footage generally than can be used for the population.

Samengss: Retailers have made a habit of copying retail leaders in terms of
merchandise, layout, marketing and service. This limits the distinctiveness
which in the past has differentiated one retailer from another.

W: Current growth in the retail industry is primarily a
result of promoting price over all other aspects. This has led to the need for
quantity sales to support minimal profit margins, and has cut out many
traditional retailers who offered primarily quality and service.

W: As a result of excessive price cutting, service has
received little attention, and there has been a signficant reduction in customer

loyalty to a store or retailer.

W: Easy money availability in the 19805 led to many

highly leveraged investments. As the 805 boom softened into the more fiscally
conservative 905, many of the highly leveraged companies and investors are
unable to develop new capital for further growth.

 

‘37 Fram. p. 49.

240

AW: The 905 have led to a period where there is a much larger gap
between the large, high-performing retailers with sound management practices
and the rest of the retail environment. This trend discourages start-up
businesses and new entrepreneurs, and leads to an industry controlled by a few
big corporations.”8

These factors have led to the situation where many retail businesses are no longer
viable, especially small independents and start-up businesses—the type that make up the
core of the downtown retail environment. In 1989 David Glass, the Chief Executive
Officer of Wal-Mart, predicted that more than 50 percent of all retailers would not be in
business by the year 2000.139 Clearly downtowns would be hit very hard by these

changes if strategies were not implemented for adapting to the new situation.

THE FUTURE OF DOWNTOWNS

The downtown has traditionally been seen both as the economic center of a city as well
as it’s center of urban life. It formed the focus of community activities, and was the
highest location in the urban hierarchy. The advantages of spatial and functional
agglomeration were such that functions benefited significantly from being at this common
location. However, as Harris and Ullman have shown with their Multi-nuclei Theory (see
Chapter ??), the contemporary city is no longer completely focused on the center city, or
downtown, but has developed many nodes of activity, some directly competing with
downtowns in terms of significance.

The question then becomes—What is the role of downtowns (or Central Business
Districts) in the future? Lord and Guy (1991) have described the historical evolution of

CBDs in their three stage model: CBD dominance (18505-19505); CBD deline (19505-

 

133 J. Barry Mason, Marris L. Mayer, and Hazel F. Ezell. 1994. Retailing (5th
Edition). Burr Ridge, Illinois: Richard D. Irwin, Inc. p. 607.

139 Bill Saporito. 1991. “15 Wal-Mart Unstoppable?” Fortune (May 6, 1991). p. 50.

241

19705); and CBD replacement (19705 to the present). Friedrichs, Goodman et a1. (1987)
present three possible alternatives:

1. Overall growth: The CBD will improve its position in the metropolitan area,
perhaps even expanding its functions.

2. Overall decline: The CBD will become a center like other larger subcenters.

3. Specialization: The CBD will become more specialized.140

Where should growth in our cities be encouraged—in the center city or on the urban
fringe? In the past decades we as a society have oriented our attention to the suburbs and
away from our traditional urban core. It has now reached a point where many of our older
downtowns have become largely forgotten as an activity destination point. In a 1984
editorial, Russell Baker expressed a common viewpoint with respect to the continuing
deterioration of many of our downtowns.

Main Street is dead. Dead as the Bijou Theater with double-feature
programs that changed three times a week. Dead as the dry-goods store that
used to sit at the intersection of Washington Avenue. Dead as the trolley car
that used to clang down the middle from the First National Bank all the way out

to the Bosky Dale Amusement Park. Dead as Sinclair Lewis. Dead, dead,
dead.

I must have been aware of its death for years, but I had never acknowledged
it, had never said right our loud: “Main Street is dead. It died years ago. Main
Street has been dead for years, and it's never coming back." '4'

Berry (1963) defined four types of commercial blight found in retail areas. “Economic
blight” is the loss in market demand brought on by changes in competition of other socio-

economic factors of the population which changes market demand. “Physical blight” is

 

‘40 Jurgen Friedrichs, Allen C. Goodman, et a1. 1987. The Changing Downtown: A
Comparative Study of Baltimore and Hamburg. New York: Walter de Gruyter.

14‘ Russell Baker. 1984. “There’s no Main Street in America anymore, only shopping
centers,” The New York Times (16 September 1984).

242
simply the aging and lack of maintenance of commercial structures. “Functional blight”
results from changing technology, with older businesses not accommodating themselves to
changing markets. It may also be a result of a lack of shopper convenience or inadequate
parking. “Frictional blight” is the impact of external factors of businesses, such as
incompatible adjacent land uses or environmental or social deterioration in the surrounding
area.

The CBD does have some inherent assets which will allow it to keep an important role.
One of the most obvious and most important is its centrality. Most of the the city’s
infrastructure, transportation lines and development are located in the center city. It is also
still the historic center, the tourist center, and cultural center in most cities.

One of the problems with downtown analysis is defining the boundaries of the study
area. A boundary definition is often arbitrary, based on census or administrative
boundaries of convenience rather than functionally defined boundaries. Murphy and Vance
(1954) established boundaries by looking at the ratio of building floor area for downtown
(CBD) uses to total building floor area for all uses. The U.S. Census of Retail Trade,
published every five years, had published CBD boundaries for all cities using census tract
boundaries, but discontinued the practice beginning in the 1987 Census. Tyler (1987) used
a survey of downtown merchants to establish the perceived boundaries of downtown
districts in eight smaller cities in Michigan, and found that cities with well-perceived
boundaries also had a more positive evaluation of their downtown’s overall health.

The great majority of studies looking at Central Business Districts and downtowns
have focused on larger cities. Very few attempts at a systematic analysis have focused on
smaller cities of less than 25,000 population. But downtowns in smaller cities can serve a
more critical function than in larger metropolitan areas, where specialized centers provide a

full range of services within the urban sphere.

243
MAJOR TRENDS IMPACTING DOWNTOWNS

Many changes have historically had an impact on downtowns, but four major trends
which have been defined which have directly impacted downtowns, all long-term and
wide-ranging. They are—population dispersion; the emergence of shopping centers and
malls; the lack of innovation in downtown commercial districts; and the increasing

difficulty of access in older downtowns. Each will be discussed in turn.

MIGRATION PATTERNS: GROWTH AND DECLINE

Population growth or decline can be influenced by factors external to a city. These can
impact on demographic characteristics in a variety of ways. Palm (1981) defined three
primary influences on migration pattems—demographic, environmental and the impact of
federal spending. '42 Demographic causes include impact of the “baby boom” generation
on the population; environmental may include the movement of elderly to warmer
climates in the south and west; federal spending may impact through the closing of a
military base or the funding of a large research project.

Most of these influences on migration are regional or national in scope, and would
generally impact all the study cities similarly. None of the study cities had a significant
migration pattern that was unique to its own circumstance, and this factor could thus be
ignored for purposes of a comparative analysis.

What gives some towns and cities more viability than others? In early patterns of
settlement often new towns would be located very close to one another. In this uniform
terrain, some would grow and prosper and others would die. Although there were many
reasons for this, one of the most important factors was the designation of a town as a

county seat. This gave a reason for early settlers to visit one center over another, since the

 

'42 Risa Palm. 1981. The Geography of American Cities. New York City: Oxford
University Press. pp. 170-172.

244
town which was also a county seat offered functions not offered by adjacent centers. The
early histories of states are full of examples of one properous city withering and a smaller
one prospering because of its fortune, typically through political suasion, at being selected

as county seat.

POPULATION DISPERSION

Two national population trends that have been happening for a number of decades have
had profound impact on center cities and downtowns. Neither has been caused particularly
by changes in the center cities, but center cities and downtowns have felt the effect of them

in many ways.

5 l l . .

The movement of the population to the urban fringe has been the result of “push-pull”
factors which have both made center cities less desireable and suburban areas more
enticing. The Industrial Revolution brought with it increased concentration of industry and
business at the city core. Whether very large cities or much smaller cities, the resulting
congestion and pollution made the urban core less desireable for residential use, and
pushed first the affluent, and later the middle class, to distance themselves from the city
center.

At the same time, transportation system improvements had a significant impact, for the
history of urban land use has been closely tied to the history of transportation technology.
As each new transportation improvement was ushered in, the city adjusted to it in its land
use patterns. The growth of American cities can be subdivided into four eras, each based
on new transportation systems. The early colonial city was circular in form, with its limits
defined by the distance a pedestrian could walk (Era I). Era II was when city growth
followed along the rails of the trolleys. Suburban development was seen, but only in close

proximity to the tracks, since that was the only practical way of getting to the suburbs

245

before the turn of the century (Bra 1]). The automobile allowed for access anywhere on the
city fringe, since city streets could be built and were built in a regular pattern to fill in the
gaps created by the trolleys (Era HI). Finally, the era of the interstate highway opened up
new centers farther out from the center (Era IV). With the highways it was easier to live
further out and still have convenient access to the city center, and also the highway
interchanges tended to define new centers of commercial development.

The pattern of suburbanization is now the dominant pattern of urbanization in most
American cities. Most traditional downtowns have suffered from these changes, and have
been coping for decades with the changes required to make them once again viable, even if

in a new form.

5.1..

The other significant population trend has been regionalization—that is, the population
migration from cities in the north and east to new and growing urban areas in the south and
west. The “push-pull” phenomena can also be used to explain this trend. People were
pushed from older northeastern cities because of their deteriorating infrastructure, industrial
facilities and housing stock. Businesses were also increasingly put off by burdensome
regulations and the high cost of labor resulting from unionization, and were looking for
areas with less government regulation, as well as less unioin dominance. At the same time,
the south and west was appealing because of its lower land prices, warmer climate, cheaper
energy costs, and general newness in many growing urban areas. The areas also had less
burdensome regulations, and attracted private investment as well as federal investment.
The Democratically controlled Southern states became ripe fOr the picking for Republicans,
as both Nixon and Reagan saw many of the traditional southern Democratic voters
switching to the conservative approach of the Republican party. To encourage this switch
many govemment-funded projects were given to southern and western regions. Cities that

had traditionally been agricultural now obtained large industries.

246
DOWNTOWN REVITALIZATION PROGRAMS

It could be concluded that downtowns truly have become obsolete. They have outlived
their usefulness, and should be allowed to die a natural death. Yet there are numerous
reasons why downtowns should not be left to die, but instead should be revitalized to once

again take a prominent role in the community.

1. W: Downtowns have existing streets, existing sewer
and water lines and other utilities, and a central location. It is very wasteful

for American cities to discard this built-up infrastructure and pay again for
new intrastructure at the city’s edge which largely duplicates what already
exists. From both an economic and environmental standpoint, it is a poor
decision for our society not to recycle downtowns rather than “throw them

away.”

2. mm: Downtowns have traditionally provided a focus for
local communities, giving a sense of identity to its residents. By going
downtown, one could associate a place with the concept of community, and
this did much to create a common sense of purpose. Without this focus of
local culture, residents do not feel they belong to a community, and it
becomes difficult to get local support for projects and activities. As our
society becomes more mobile, the need for a place with which to identify
becomes increasing important. With the loss of downtown comes the loss
of the community’s center, and as Gertrude Stein once said, “There is no
there there.”

3. Greater diversity: Downtowns have greater functional diversity than many
of the newer centers being built on the city fringe. Downtowns still often
serve as a center for retail stores, financial institutions, public agencies and
local government offices, local public transportation, historic areas, and
cultural and educational institutions. This diversity comes in many mixes,
but the diversity gives to downtowns a long-term strength. By contrast,
many newer developments are unifunctional, devoted only to specialty
retailing, quick-stop shopping, or single-size residential developments. This
makes these areas much more vulnerable to changing times, and indeed
might make them obsolete long before downtowns. It can already be seen
that many suburabn developments from the 19505 and 60’s are already

247

dated, and have been abandoned, to be replaced by more recently built
fringe developments. Through it all, the diversity of functions found in
downtowns may be what will allow them to persevere and eventually
restore their place of primacy within the local community.

4. W: Statistically, downtowns are still the greatest

employment sectors within cities, with many people coming to the
downtown district on a daily basis for their jobs. This provides the potential
for a regular and continuing user base for functions located in downtowns.

5. W: Downtowns continue to be the center of distribution of
goods and services. Although for many decades there has been a pattern of
dispersion of businesses locating in other sections of cities, downtowns still
retain an inordinate share of these functions, and this gives potential for
many other things to happen around these centers of distribution.

6. Wm: Since the 19705 there has been a

definable trend of people moving back to central cities. This population
tends to be educated and professional, without children, but can form the
base of a new downtown residential community.

7. W: A number of programs have been instituted which
have been directly beneficial to downtown revitalization efforts. They
include the Main Street Program of the National Trust for Historic
Preservation, the Downtown Development Authority program and Tax
Increment Financing Act, and various local programs established by city
governments or merchants’ associations. These have given a particular
focus to the needs of downtowns, and have successfully addressed the
unique needs of downtowns in their program orientations.

There have been numerous programs that have attempted to deal with the problems of
downtown. Most of these have been indirect attempts at revitalization, but they have come
from all levels of government. A review of some of the primary programs illustrates the

many approaches, and also suggests ways that have been relatively successful.

248

FEDERAL PROGRAMS

; . E . 5.! ICCEIEI'

One program which could have had great impact on protecting the retail business
environment of center cities was the Community Conservation Guidance (CCG) Policy,
initiated by President Carter in 1979. Originally called the "Regional Shopping Center
Policy," it was intended to determine the impact new suburban shopping centers had on
businesses in Central Business Districts. If the impacts on businesses in existing
downtowns were determined to be negative, the federal government could withhold
financial support for infrastructure improvements associated with new developments.

The CCG Act recognized that federal participation in development projects had in many
ways led to the demise of central cities. The program was intended to ensure that future
expenditures would not have the effect of eroding existing downtown commercial areas
further. The policy could be invoked whenever a proposal for a new development included
the expenditure of federal monies or other federal action, such as the construction of federal
highway extensions or tumoffs or the extension of water or sewer lines. It required the
preparation of a community impact analysis, which had to be completed within 45 days.
The request for such a review needed to come as a formal written request from a
community's chief elected official (e.g., the mayor). The written request had to include the
following: (1) a statement indicating why local officials were concerned with the federal
action; (2) evidence of support from local elected bodies such as city councils; (3) an
indication of the link between the federal action and the development of the shopping
center; (4) the local official's perception of the effect of the shopping center on an existing
retailing district; (5) an indication of action which had been taken by local officials to

strengthen existing commercial areas; and (6) an indication that local officials had sought to

249

discuss or negotiate the concerns with the applicant (shopping center developer) for the
federal action in question."143

Negative impacts to be considered in the impact analysis were 1055 of jobs, reduction in
the tax base, loss of employment for minorities, increase in population decentralization and
its impact on energy consumption, and the effect on costs of retail goods in the future. If
such negative impacts could be shown, the federal government, under this policy, could not
stop a project, but could discourage its construction by withholding financial support.

The CCG program was strongly opposed by the National Retail Merchants'
Association (NRMA), who argued for a "pro-downtown," rather than an "anti-suburban,"
policy, and by the International Council of Shopping Centers, who similarly argued for
more downtown incentives rather than suburban disincentives. This was not a surprising
stance, for as suggested by one reviewer,

"Although not totally unconstrained, shopping centre development in the USA
has been little influenced by public policy. As long as taxes were paid,

shopping centres were operated with little attention given to any broader public
social and economic costs incurred from their operation."144

The CCG program lasted for one-and—a-half years, but was terminated by Ronald
Reagan soon after being sworn in as President. During that short time span twenty-four
impact studies were requested and completed. The general outcome was that they
represented jurisdictional disputes between cities and their surrounding municipal
governments having more to do with tax revenues, and only indirectly with supporting
existing retail businesses. In addition, the Department of Housing and Urban
Development (HUD), which administered the program, found it had virtually no power to

require other federal agencies to withhold the expenditure of agency funds.

 

'43 Dawson, John A. and J. Dennis Lord. 1985. Shopping Centre Development:
Policies and Prospects. New York: Nichols Publishing Company. p. 11.

'44 Dawson. p. 9.

250

WWW

Enacted in 1974, the Community Development Block Grant program consolidated
seven separate grant programs of the Department of Housing and Urban Development
(HUD) into one grant program. Primarily for larger cities and populous counties, HUD
also provided discretionary grants to smaller communities, which were later given to states
for distribution.

As proposed by President Nixon, the primary thrust of the CDBG program was to
give greater decision-making authority to local officials through revenue sharing, allowing
them more flexibility in the use of the funds. The program was oriented to low- and
moderate-income neighborhoods, with its goal to reduce or eliminate slums and blight. As
the program evolved, the federal role was minimized, and local governments made most of
the decisions on how CDBG money was used. The most frequent use of funds during this
period was for land acquisition and infrastructure development, with the goal being long-
range economic development. In other words, CDBG funds were channeled in two
distinct directions—one of providing physical improvements, the other of promoting

economic development.

W

The Urban Development Action Grant program was introduced in 1977. The
program’s intent was to use public monies to encourage and leverage investment from the
private sector. Unlike the CDBG program, UDAG funds were allocated directly toward
economic development. According to a study done by the Department of Housing and
Urban Development in 1982 hundreds of projects were initiated with UDAG funds

through “pump-priming” grants and loans, a majority of which would not have happened

251

without the leveraging of funds provided through such support. On average, about six
dollars of private investment resulted from every dollar of public money spent.‘“5

About a fifth of these projects dealt directly with retail revitalization, targeted primarily
at severely depressed cities. Central city retail redevelopment fit well with UDAG program
goals, since such businesses provided a significant number of low-income jobs and
minority employment, and because it focused on the redevelopment of "distressed" areas,
as defined by HUD guidelines. As described by one writer, “In the 19705 and 19805,
urban planning and redevelopment reached their heyday in an atmosphere of free-flowing
federal funds... As far as downtown retail development was concerned, the linchpin was
the mighty UDAG.”146

Although there was a program requirement that at least 25% of the UDAG funds go to
smaller communities, there were insufficient numbers of proposals from cities of this size,
so gradually the program became more oriented to larger projects in bigger cities. The
types of project benefiting most often from UDAG funds were new center-city, multi-use
centers. Also important were projects converting historic properties into shopping centers
and multi-use centers. However, the success in obtaining funding for these larger-scale
urban projects may have been counter-productive in the long run, for the higher financing
made it necessary to bring in chain stores to satisfy pre-lease requirements for the centers,
pricing out local retail and converting what had been diverse retail environments into retail
areas not too different from competitor malls.

The Urban Development Action Grant program has been largely abandoned through
de-funding. However, the general opinion is that UDAG was a relatively successful
program for encouraging private sector investment in central cities and served as a

development catalyst in many areas.

 

‘45 Dawson, p. 14.

'46 Alan L. Gilman. 1990. “Downtowns, Post-UDAG.” Chain Store Age Executive
(June 1990). p. 83.

252
H. .I C l.

The 1978147 Tax Act recognized the growing interest in preserving older, historic
buildings in downtowns, and established an incentive program for rehabilitation. The
Rehabilitation Investment Tax Credits program allowed developers a 10% tax credit for the
costs of rehabilitating a historic structure. The 10% credit was a considerable incentive, for
unlike a tax deduction, which is a reduction from gross income claimed on the tax form, a
"tax credit" is subtracted directly from the amount of tax owed, and represented a much
higher savings.

The new Rehab Investment Tax Credit program became an immediate success. A
1979 study showed that $1.3 million in tax credits had generated $27 million in
rehabilitation work. Between 1976 and 1986 nearly 17,000 projects, valued at $11 billion,
took advantage of the program.”8 The focus of urban projects had shifted dramatically
from demolition to rehabilitation. One prominent preservation consultant, speaking at a
preservation forum, concluded, “The tax credits have been enormously successful in cities
and towns around the country in encouraging the preservation of historic buildings?”9
Congressman Richard Gephart (D-Mo.), speaking at the 1994 National Trust’s conference,
referred to the tax credits as “the most important feature for urban redevelopment and
urban renewal” in the 19805.150 Others concurred; “...all kinds of things have been tried to
stop the deterioration of downtowns. The first program that ever really worked was the

investment tax credit.”151

 

147 Revenue Act of 1978 (PL. 95-600).

143 Robert E. Stipe and Antoinette J. Lee. 1987. The American Mosiac: Preserving a
Nation ’5 Heritage. Washington, DC: The Preservation Press. p. 24.

149 William MacRostie, comments for a symposium sponsored by Progressive
Architecture and the AIA Foundation, “What Price Success,” Progressive
Architecture. August 1985. p. 107.

150 Kim Keister and Arnold Berke 1994. “If Not Now, When?” Historic
Preservation News. December 1993/January 1994). p. 22.

15‘ Nellie Longsworth, speaking at the same symposium, p. 110.

253

Because of its success and increasing public support, the federal government expanded
the RITC program in 1981 as part of the Economic Recovery Tax Act (ERTA).152 Instead
of a straight 10% tax credit for Certified Historic Structures, the new Act increased the tax
credit to 25% for historically “significant” structures, truly a substantial return on
investment, and also allowed the tax credit to be used for "contributing" buildings in
"Certified Historic Districts.” It also added two new categories, allowing a 20% credit for
any income-producing building over 40 years old and a 15% credit for any over 30 years
old. The new Act led to the creation of many new historic districts, for through this device
many structures which would not be recognized as historically significant on their own
could qualify for the 25% credit as "contributing" structures.

The tax credit program led to the saving and rehabilitation of many, many historic
buildings. Its purpose was not to restore “significant” older buildings as museum pieces,
but to return them to use to meet current housing, retail, industrial and commercial needs.
Because of the financial opportunities, even developers with no previous interest in historic
preservation wanted to become involved.

For the years 1982 through 1985 the tax credit program alone stimulated the
investment of an estimated $9 billion dollars in over 11,000 structures. An article in
Historic Preservation magazine that included a national survey of developers showed that
36% would have not done rehab work to historic properties without the tax credit program,
that 39% would have done substantially less, and that only 24% would have done as much
as they did with or without the program. Certainly the tax credits provided the engine to
make the historic rehab program run.

The program also made some strange bedfellows. Where old-line preservationists had

for many years opposed most of the proposals presented by developers, now

 

‘52 Economic Recovery Tax Act of 1981 (PL. 97-34).

254

preservationists and developers both supported the programs, the former because it saved
historic buildings and the latter largely because it was profitable.

By 1983 there had been 3,600 applications for tax incentive projects. In 1984 the
Government Accounting Office looked into the impact the program was having on the
federal treasury, and became alarmed at the program's success. They calculated that the
taxes lost through these credits had increased from $2.5 million annually in 1978 to $210
million in 1984, and an increase was projected to $700 million annually by 1988. In a time
of fiscal cutbacks, this kind of largesse could not pass unnoticed.

The GAO also discovered some serious abuses in the program. Some 17% of those
owners claiming tax credits did not qualify, and their buildings had not been approved for
such status. Also, if a building were sold within five years of its rehabilitation, the owner
forfeited his or her tax credit on a prorated basis, and was liable for a "recapture" tax. The
study found that fully 40% of those owners who sold within five years had not paid this
recapture tax. Finally, a serious abuse was found in the use of easements.”3 For
properties where an easement had been donated, and the owner was able to take a tax
deduction based on its value, it was found that the average easement had been overvalued
by more than 200%.

The federal government decided to clamp down, and the tax credit program became in
serious jeopardy of termination. It was saved only because of a strong lobbying effort by
groups impressed by the incredible success of the program in retaining and refurbishing
older buildings and historic districts in cities throughout the country. The 1986 changes in
the tax law trimmed down the program, but also removed some of the abuses and
inequities.

The 1986 Tax Act limited credits to individuals who were actively involved in the

property; passive investors would no longer be able to piggyback onto the investment

 

153 See the discussion “Easements” discussed earlier in this chapter.

255
activity of others only to take advantage of tax credits. Also, tax credits were scaled back to
20% for Certified Historic Structures or contributing buildings in Certified Historic
Districts, and to 10% for any other structure built before 1936. These new values, although
lower, were an improvement in some ways. With the previous values of 25% for a CH8
and 20% for a 40+ year old building not certified as a historic structure, many owners and
developers had preferred working on the latter type of property, for the rehabilitation
standards were much less strict, and rehab work didn’t require a delay while state
certification was obtained. As a result, more secondary buildings were rehabbed than
significant historic structures. This distorted the program's goals. The situation was
largely rectified with the new 20%/10% values, which gives primary encouragement to the

most significant structures.

STATE AND LOCAL GOVERNMENT PROGRAMS

T ae

Although studies have shown that tax abatements can be ineffective when used
indiscriminately by local governments, they can be effective when tied in with well-defined
deve10pment objectives. In downtowns certain types of businesses might create a positive
business mix complementarity and should be especially encouraged. These businesses

could be encouraged by being eligible for a tax abatement program.

I I E. . CIIE I l
w w ev o ' ' 5
Tax Increment Financing (TIP) is a popular tool used by local governments to finance

local economic development. Under the TIF Act, tax revenues generated in a defined

district which exceed a base amount can be used exclusively within that district. As

256
assessed values rise, these incremental funds can be used to complete projects which
enhance a downtown area and attract more business and more tax revenues.

The structure of Tax Increment Financing has been explained in the folllowing way:

Tax increment financing (TIF) allows cities to create special districts and to
make public improvements within those districts that will generate private-
sector development. During the development period, the tax base is frozen at
the predevelopment level. Property taxes continue to be paid, but taxes derived
from increases in assessed values (the tax increment) resulting from new
development either go into a special fund created to retire bonds issued to
originate the development, or leverage future growth in the district.

A tax increment financing district is created by the adoption of a plan for
redevelopment and a TIF plan. The assessed value of the property within the
district is then determined according to the last tax roll and represents the
"original" (pre-redevelopment) assessed value of the tax increment district.
Each of the taxing jurisdictions (municipality, county, school district, and
special taxing jurisdiction) continues to receive its share of the taxes collected on
the assessed valuation that represents the original assessed value, just as though
the district had never been created and there had been no change in the assessed
valuation of the area. When the original assessed value of the district has been
certified, the municipality may begin the redevelopment and the tax increment

financing process.

The tax freeze lasts for a defined period of time, as set forth in the
redevelopment plan. At the end of that period, taxing jurisdictions finally enjoy
the benefit of increased property values.

A community using TIF financing does not lose the tax revenues that were
being collected before the development program. In fact, it is possible for
taxing jurisdictions to increase their millage rates during the increment period,

thus increasing gross tax revenues.154
TIF districts have been used extensively in communities across the state in conjunction

with Downtown Development Authorities (DDAs). The state's primary purpose in

 

154 Nancy L. Minter. 1991. "Tax Increment Financing." Urban Land (May 1991).
p. 38.

257

enabling DDAs to be established by local governments was to "halt property value
deterioration and increase property tax valuation where possible in the business district of
the city " as well as "eliminate the cause of that deterioration and promote economic
growth." Although the Downtown Development Authorities Act (PA. 197 of 1975) was
intended primarily for traditional downtown areas, any type of municipal government,
including counties, cities, villages and townships, has been able to establish one DDA
district.

Establishing a DDA requires substantial commitment by a municipality's governing
authority. A separate Authority board must be established, and technical skills must be
available to implement the tax increment financing plans and/or the issuance of revenue
bonds to raise funds, which sometimes becomes substantial over the years. However, the
advantages of such an entity are also substantial. A DDA allows for the focusing of
economic development activities on a defined area.

Downtown public improvements can be financed in two ways in a TIF district—
improvements can be paid for directly from funds generated from the increase in the tax
base, or tax increment bonds may be issued by the community based on projected
increases in the future in the tax base. The funds can be used for a wide variety of
improvements, as defined by the local TIF agency as part of its charter. A DDA may, by
law, own and manage property, and can thus be directly involved with the economic
renewal of certain parcels. It can also encourage a more direct and much stronger
partnership between the public and private sectors in economic development activities.

Because of a pattern of general misdirection of the DDA concept by many

municipalities, the state is no longer approving new DDA districts.

258
Wain:

Can older downtowns also be revitalized using conventional land use regulations and
zoning? Various cities have established specific overlay zoning to encourage certain kinds
of growth in downtown areas.

In Washington, DC. a SHOP zone was established in an eighteen block area of the
downtown that had been losing its retail businesses. The overlay zone established a goal
that 20 percent of the total floor area in this downtown district must be retail and service.
This was roughly four times the amount typically found in a new downtown building at
that time, and meant that for taller buildings not only the ground floor but some second
floors must be given to retail use. The SHOP ordinance also called for continuous retail
use at the ground floor level. To encourage pedestrian shopping and displays at the street,
it regulated entrances and display windows and restricted interior atriums, which were
common in newer buildings. Some service uses, such as banks and travel agencies, which
did not encourage pedestrian shopping, were limited to 20 percent of floor areas. In
contrast, department stores counted triple for satisfying the square footage requirement and
legitimate theatre counted double. Also, minority or displaced businesses counted at 1-1/2
times the square footage.

Developers heavily criticized the SHOP zoning for forcing them to take on lower-
quallity (non-chain) tenants. They felt they were being forced to fulfill public policy, and
felt this financial burden as unfair in a free market system. The ordinance was challenged
in court, with developers claiming it was a “taking” of their property, but the courts held up
the validity of the zoning as a legitimate use of public regulation for a community purpose.

Other examples give similar emphasis to trying to restore more traditional uses in their
downtowns. Cincinnati established an ordinance similar to the example from Washington,
DC, but established that 60 percent of the ground floor frontage must be retail uses, also
excluding banks, travel agents and airline ticket sales offices. Orlando has an ordinace that

says such requirements apply not only to new buildings, but whenever there is a change of

259
occupancy in esisting buildings as well. Bellevue, Washington has a zoning code
provision which encourages neighborhood businesses for in-town residents. To counter
the preponderance of up-scale, “boutique” retail attractive to tourists and upper-income
white-collar workers, San Francisco established a provision that a certain percentage of
downtown businesses be targeted specifically for less affluent downtown workers.
Some planners feel an innovative approach to zoning may be sufficient to encourage

renewal activities. As described in an editorial from Small Town magazine:

"This country desperately needs an enlightened public policy concerning

downtowns and their relationship to overall land use. Zoning must reflect the

downtown's role as the community social center. Therefore, zoning codes need

to begin the process of pulling multiple uses back downtown. For example, the

code should say that government must stay downtown and that shipping must

stay downtown. Also, downtown is where the movie theater and other

entertainment businesses must locate. It is also the place for offices and for

service businesses. Zoning should exclude these uses from other areas and it

should do away with such designations as commercial highway strip, planned
shopping center, etc.

"In our rush to modernize, we've forgotten the fundamental truth that people need a
central meeting place. The old downtown served that function once. Nothing has ever
replaced that crucial foundation for community in those places that destroyed their
downtowns. The most important agenda item for all concerned citizens and public officials
is not the sewer or water system, or the roads or the police. It is how to forge and maintain

a special sense of community. The solution starts downtown."155

Enterpriseches
The concept of “Enterprise Zones” was first put forward in Britain. It was developed

as a way for depressed neighborhoods to bring about jobs, capital investment and

 

155 "The World From Main Street." Small Town (July-August 1991). p. 3.

260
community renewal. The tools used for this revitalization were primarily tax reductions
given for investment and a loosening of government regulations. The idea was to define
specific areas eligible for these benefits to encourage private investment and the relocation
of firms to these areas.

While President, Ronald Reagan encouraged the idea of enterprise zones as a way for
government to encourage private investment. The Kemp-Garcia Bill on 1981 (HR. 3824)
supported this concept, and was different from other economic development legislation in
that it allowed to geographic differentiation, with only certain zones defined as distressed
being eligible. If the zone had high unemployment and poverty and the local government
agreed to a reduction in real property taxes of at least 20 percent, the Secretary of
Commerce would declare the area an Enterprise Jobs Zone for a period of at least 10 years.

The enterprise zone concept has been brought back under President Clinton, and is
known as the Empowerment Zone and Enterprise Community (EZ/EC) program.
Communities that are designated gain a number of benefits, including eligibility for tax-
exempt bonds, EZ/EC block grants, special consideration for other federal funding, and an
easing of federal regulations.

Although not used specifically for downtowns, enterprise zones and EZ/EC zones
have improved inner city neighborhoods and have had a positive impact on downtowns
indirectly. The enterprise zone concept also set the stage for other designated zone
programs which directly impacted downtowns, including the Downtown Development

Authority program described next.

OTHERS PROGRAMS AND STRATEGIES

Main Street Program

In 1980 the National Trust for Historic Preservation established the Main Street

Program. The purpose of the program was to show that the rehabilitation of older

261
commercial buildings could be an important part of a downtown revitalization effort. The
original concept for the Main Street Program was based on three pilot projects—in
Galesburg, Illinois, where involvement began in 1977; Hot Springs, South Dakota; and

Madison, Indiana.

The Four Point Approach

Encouraged by the results of the pilot projects the Main Street Program established an

"Approach" to downtown revitalization based on four key ingredients:

1. 93311123115211: Perhaps the most difficult aspect of any revitalization effort is to
create the organizational framework that brings together various interest groups and
individuals. Each group comes to the table with its own agenda and sphere of interest.
The merchants association may be interested in the promotion of retail sales, the
Chamber of Commerce in job creation, and city government in providing municipal
services. Without coordination, these efforts may not be supportive of each other, and
in some cases may be at odds. The Main Street Program’s project manager usually
attempts to bring these groups together and reorganize them under an umbrella

organization that deals directly and exclusively with the concerns of downtown.

2. Emotion: In many communities, the downtown has been largely overlooked by
its citizens, who have shifted their consumer shopping patterns. To counter this, the
Main Street Program has shown that downtowns need to compete by promoting
themselves, and presenting an attractive new image. Promotions are considered critical
to attracting people downtown. By first targeting groups that the downtown should try
to attract (e.g., families with children, young professionals, or tourists), the creation of
sales and special events establishes downtown as a place of activity, where there is

always something new and interesting happening.

262
3. Design: Although physical improvements are not enough by themselves to
revitalize an area, such activities, especially storefront rehabilitation and "streetscape"
improvements, can provide visual proof that there is something happening in a
downtown. Thus, the design aspect of the Main Street Program is important because it
provides evidence of revitalization activity, as well as creating a more desirable environ-

ment.

4. WW: Providing financial support for a revitalization program

is the last critical component in the Main Street Program’s four-point approach. This
effort attempts to find financial resources for revitalization work. A typical strategy
may enlist local banks to provide funding for a revolving loan program for
rehabilitation work. In the past, downtowns have largely been ignored by local lending
institutions, who saw little business potential there. By convincing banks to give their
support jointly, none feels greatly exposed to risk. The Main Street Program has
demonstrated that property values can be substantially increased with a coordinated

revitalization program.

Through the National Main Street Center, the National Trust provided and paid the
salaries of project managers in many communities. These Main Street managers were
professionals with some background in downtown revitalization work. Typically, the
manager's initial involvement was for three years, with the Trust paying a project
manager's full salary the first year, two-thirds of it the second year, and one-third the third
year. Cities gradually picked up support for the manager and the program, and by the third
year the program was intended to be self-supportive, at which point staffing was paid

through local program funds.

263

Evaluation of the Main Street Program

The Main Street Program was very successful in its efforts, and was shown to be one
of the best approaches yet developed for revitalizing aging downtowns. Hundreds of
communities entered the Main Street Program, and many others adopted some of its
strategies, even without receiving full support from the National Main Street Center.

With its years of experience to draw on, the National Main Street Center established
some guidelines helpful in understanding the nature of downtown rehabilitation projects.
They are:

l. The revitalization process is incremental; change cannot all happen all at
once. (Such a one-shot approach was the essential failing of earlier urban
renewal programs.)

2. Revitalization must be a self-help program, supported and implemented by
members of the downtown community themselves.

3. Accomplishments should be emphasized, rather than problems.

4. As a corollary to the above, focus should be placed on the assets of a
downtown, rather than on its weaknesses.

5. In the first year of a program, the biggest accomplishment may be a change

in attitude, from negative to positive.
6. Quality should be the byword in all physical improvements.

7. The rehabilitation process should be continuous and ongoing. A good
program will always have short-term goals with projects that can easily be
seen and appreciated, along with long term efforts which, though not
always on the front burner, may be more critical in the long term.

When the Main Street Program failed in a community, it was usually for one of the

following reasons:
1. The project manager was not working full-time, and could not follow
through properly on initiatives.
2. Some downtown groups were unhappy with a "new show in town," and
sabotaged efforts of the Main Street project office.

264

3. The Board of Directors tried to accommodate too many groups, and

became large and unwieldy.

BedestdanMalls

A revitalization strategy commonly tried in the 19705 and ‘805 was converting an
existing downtown street into a pedestrian mall. The goal was to make the downtown
retail shopping environment pedestrian friendly by replacing cars with people. The first
pedestrian mall in the U.S. was built in Kalamazoo, Michigan. About 150 have been built
since then. Few have fulfilled the promise of the early advocates of this revitalization
strategy, and it is now generally accepted that this approach has been less than successful.
Knack (1982) found downtown malls have had little impact, and concluded “Just about as
many department stores and first-run movie theaters have closed in towns with malls as
without, and just as many wig stores, fast-food places, and video-game arcades have
opened up.”156 Lorch and Smith (l993)l57 found that enclosed malls tended to promote a
“fortress attitude,” with pedestrians largely staying within the mall area and little spillover
to other downtown businesses from consumer traffic generated by the mall.

Oak Park, Illinois converted their main street, Lake Street, in 1974 to a picturesque
outdoor pedestrian way called Oak Park Mall. Once the major commercial district for
Chicago’s western suburbs, Oak Park’s downtown had suffered from the shopping centers
being built at the time, and attempted to “fight fire with fire.” Oak Park Mall was
beautifully executed, with the planting of mature trees and major streetscape
improvements. Unfortunately, the Mall was not able to continue to draw shoppers.

Merchants and planners had not realized how important traffic was to the success of retail

 

156 Ruth Ecklish Knack. 1982. “Pedestrian Malls: Twenty Years Later.” The Best of
Planning. p. 492

157 Brian J. Lorch and Mark J. Smith. 1993. “Pedestrian Movement and the
Downtown Enclosed Shopping Center.” Journal of the american Planning
Association (Winter 1993). pp. 75-86.

265

businesses. People were not willing to shop unless they could drive by a store, make sure
it’s open, and then look for parking in front. By 1988 the Village Board approved funding
to “restreet” Lake Street.”8 By 1989 they had ripped out the pedestrian mall and let traffic
back in. “The effect was immediate and positive. Retailers began to move back into
vacant storefronts. Sales rose a reported 24 percent.”159

The pedestrian mall concept was tried in hundreds of locations across the country, and
although it was not a bad idea, it has generally been unsuccessful for two reasons. First, it
was too little too late, and attempted to draw back retailers after they had moved out of the
downtown. As explained by a consultant for Mt. Clemens, Michigan, who helped design
the city’s downtown mall in 1979, “a lot of the malls were too late to do what they were
supposed to do. It was an attempt to save something that was already dead.”160

Second, the forces of suburban development and new shopping malls on the urban

fringe were too strong to counter with direct head-to-head competition.

Transit Malls

A5 a follow-up to the pedestrian mall, transit malls were built in a number of cities,
including Minneapolis, Denver and Portland, Oregon. The switch was largely due to the
fact that the federal government was willing to pay 80 percent of the cost if transit systems
were integrated into the mall. Like the earlier pedestrian malls, these transit malls restricted
auto traffic and encouraged pedestrians, but they have generally been more successful
because they have permitted transit vehicles on the streets. The transit vehicles have both
brought shoppers and others directly into the mall and have also given movement and

vitality to the street again.

 

153 Ed Zotti. 1988. “Un-Malling a Downtown.” Inland Architect (July/August 1988).
p. 14.

159 John Gallagher. 1991. “Taking Back the Streets.” Detroit Free Press (September
23, 1991). p. 6F.

160 Gallagher. p. 6F.

APPENDIX 7

THE IMPACT OF WAL-MART STORES
AND OTHER LARGE DEPARTMENT STORES

APPENDIX 7:

THE IMPACT OF WAL-MART
AND OTHER LARGE DISCOUNT STORES

In the past few years, the downtowns of cities across Michigan have been impacted by
the opening of new Wal-Mart discount stores. These all-inclusive discount stores have
found relatively easy market opportunities in smaller urban areas, where they have
specifically targeted new store locations. Using a “down-home” retailing approach which
presents an image of providing everyday goods at discount prices, Wal-Mart has appealed
most to lower middle-class households (40 percent of its customers have household
incomes of $20,000 or lesslél) and has been so successful it has become the nation’s
leading retailer in just a few years. The advantages for Wal-Mart of small town locations
have been numerous—there is virtually no competition for selection or price among small
local retailers; municipalities had few regulations over land development (lot sizes range
from five to 25 acres), and were generally very receptive to the increased tax base created
by a large retailer; and access from surrounding communities has been relatively
convenient via the interstate highway system, which now criss-crosses much of the rural
countryside.

However, the long-term impact of this recent retailing phenomenon is still unclear.
Some downtown businesses in cities where Wal-Marts have located have benefited from
the greater draw of customers. Others have been devastated by the overwhelming new
competition for their customers’ dollars. In a study of the impact of 14 Wal-Marts in

Iowa, Kenneth Stone found that towns within a 20-mile radius of the Wal—Mart stores saw

 

‘61 John Gruidl and Steven Kline. "What Happens When a Large Discount Store
Comes to Town?" Small Town. March-April 1992. pp. 20-23.

266

267

total retail sales drop 25 percent after five years.162 The study clearly showed both the
positive impact in towns where Wal-Mart had located and the negative impact on other
similar towns in the surrounding area.

The purpose of this section is to evaluate the impact new Wal-Mart stores have had on
the retail environment of the 16 study cities. The impact will be evaluated by comparing
sales tax and retail sales data from 1987 (prior to the opening of the first store in Michigan)
and 1992 (when 12 new stores had been Opened, primarily in smaller cities). It looks at
whether there is a significant correlation between the economic status of the study cities and
the proximity of the new Wal-Mart stores to their downtowns. Changes in sales tax and
retail sales data between 1986 and 1992 will represent changes to the economic
environment. In addition, the changing health of the study downtowns will be evaluated

using the author’s “Health Perception Index.”

HISTORY OF WAL-MART IN MICHIGAN

Large discount stores have made important inroads into the market areas of small and
medium sized cities. Initially located in larger urban areas, these all-inclusive stores have
found relatively easy market opportunities in smaller urban areas. The discount chain that
has taken most advantage of this more rural market is Wal-Mart, whose founder, Sam
Walton, specifically targeted small cities for his store locations. Wal-Mart has been able to
locate in cities with populations of 20,000 or less and thrive. In fact, these locations often
have become retail battlegrounds for discounters competing for this new and largely
untapped market.

Maps indicating the locations of large discount department stores in Michigan show

that in 1987 there were no Wal-Mart stores in the state. KMart and Meijers were the two

 

‘62 Edward O. Welles. “When Wal-Mart Comes to Town.” INC. July 1993.. p. 78.

268
discount retailers, and the locations of their stores specifically targeted larger urban centers

for the majority of their store locations.

Shown below are the discount department stores in the lower peninsula which were at

least 60,000 square feet in size.

WebMurt/Sam's
Nelle"
roam/rm
Other

ODD.

 

 

Figure 36
Locations of Discount Department Stores, 1986164

Five years later, the first twelve Wal-Mart stores were opened at strategic locations
across the state, generally in smaller cities that didn’t have direct competition from Meijers

and KMart and where they could easily establish retail dominance in a region.

 

‘64 Store locations taken from Directory of Discount Stores, published by Business
Guides, Inc.

ova.

090.

269

 

 

WIPMIIUSIM'I
Meijers
KMart/Target
Other 'Geylord
A
an .
‘ . !
2M1. ' -' ~ nt
rfi
.Alne
A
Grand flJohnl ..
.A A %m J
a.» g, A .,
a . 1%:
AD I. e‘-e.."
lb Haven$6 ‘-:\!"3‘ "
fi '/. v.31'in'.‘ '
.1 t:., ”3%;
/flow W DA .t'
Figure 37
Locations of Discount Department Stores, 1992
Cheboygan
Wll‘MIfl/SIIII'I
Meijers
KMart/forget Alpem'
Other 0 °

O..Cotllllot:
‘ .
Ludington Mt. ' - 1 1
o .31; Input. A -.-‘ :- 0
wk on . PhA‘ A, C“,

a“ °GnndRapk§A '8‘}; “W‘s

troll-n30 .fim & 1AA

 

 

owell A
A a; {3.3-.5:- ‘
OSouth Haven .2: '1 41$?”
4 A:;.'
“agitate Creek '. .' ;, ZN" $1.-.
. A
O
ot- ” dswrflj

Figure 38

Locations of Discount Department Stores, 1993

270

A year later, a total of 38 Wal-Mart stores had been opened in the state, including a
large number in the larger metropolitan areas where they competed head to head with
Meijers and KMart. This is especially significant growth over such a short time span
because both Meijers and KMart had the distinct advantage of having their national
headquarters located in Michigan, Meijers in Grand Rapids and KMart in Troy, while Wal-
Mart had its corporate decision-making done in distant Bentonville, Arkansas.

A study by Gruidl and Kline (1992) of Illinois towns indicated that the infusion of a
large discounter store in a community dramatically increased the retail market area and draw
for that particular town. As shown in the graph below, which uses two years before the
opening of Wal-Mart stores as a base period, for the year prior to the opening there is

minimal growth in retail sales, but after the large store opens the retail base increases 14 to

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

15 percent almost immediately.
16 _
14 C7
12 - —
10 ~ _
Percent " '7
Change 8 — 1 —
6
4 C I I
2 .
0 44.—Z?

-2 -l O 1 2

Years Before and After Store Opening

Figure 39
Changes in Total Retail Sales in Illinois Towns
Before and After Opening of a Wal-Mart Store165

 

‘55 John Gruidl and Steven Kline. 1992. “What Happens When a Large Discount
Store Comes to Town?” Small Town. (March-April):22.

271
Kenneth Stone of Iowa State University studied 14 Iowa towns where Wal-Mart
opened stores during the late 19805. The chart below shows the impact on the host town
and illustrates that the gain in sales of general merchandise is divided unevenly between the

gain for Wal-Mart and the loss for other host city businesses.

 

Total Sales Increase -

q
WalMart Sales, Est. lj

. I
Sales Loss by Others H
4!

I j I I 1

-10 -5 0 5 10 15

 

 

 

 

 

 

 

 

 

Figure 40
Average New Sales Change After 3 Years
In Wal-Mart Host Town166

Its impact on traditional downtown retail was startling. “As Wal-Mart rolled out its
franchises, it sucked commerce off Main Streets, destroying traditional retailers that had
served their communities for generations. But in the face of the abundance Wal-Mart
produced in the form of more jobs, consumer savings, and expanded trade, the loss of
Main Street life seemed an incidental price to pay.”167

Stone also found that surrounding towns within a 20-mile radius of the 14 Wal-Mart
stores studied saw total retail sales dr0p 25 percent after five years.168 The study clearly

showed the positive impact in towns where Wal-Mart has located and the almost universal

 

‘66 Kenneth E. Stone. 1990. “The Impact of Wal-Mart Stores on Retail Trade Areas in
Iowa: Executive Summary.” Published by author. 2.

167 Edward O. Welles. 1993. “When Wal-Mart Comes to Town.” INC. (July):78.
163 Welles. 78.

272
negative impact (except for food stores, a market for which Wal-Mart doesn’t compete) on
other similar towns in the surrounding area. As shown in the following illustration, only
the towns where Wal-Mart stores located were able to benefit from the regional draw of the
large discounter.

Table 32

Sales Change in Wal-Mart Towns vs.
Other Same Size Towns (in percent)”9

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Will-Mart Same Size

Towns Other Towns
Business Type Afteuears After years

1 3 5 1 3 5

Building -6.3 -6.5 -5.1 -4.7 -7.1 -
Materials 10. 4
Gene-ml . 29.1 39.5 58.8 -0.6 -42 -19-
Merchandise
Food -4.7 -4.1 12.1 1.6 5.5 7.8
Apparel -2.7 -6.2 -5.1 -3.5 -5.8 1 1.5
Home 2.9 5.2 4.2 -5.1 - -
Furnishings 12.2 18.91
Eating& 0.8 -0.8 2.4 -0.7 -1.5 -0.8
Drinking
Specialty -5.7 12.1 19.7 0.1 -5.4 -9.9
Services -5.6 -7.9 -6.8 -3.5 -9.5 14.2
TOTAL SALES 2.3 3.1 8.1 .0.7 -35 -491

 

 

 

 

 

 

 

= 0.1....-

From his study Stone developed two primary conclusions.

- Businesses that sell goods or services other than what Wal-Mart sells tend to
experience high sales because of the spillover effect. The additional traffic
attracted to town by Wal-Mart will shop at these stores.

- Businesses that sell the same goods as Wal-Mart tend to experience
reductions in sales after Wal-Mart opens.

 

169 Kenneth E. Stone. 1990. “The Impact of Wal-Mart Stores on Retail Trade Areas in
Iowa: Executive Summary.” Published by author. 2.

273

Some business types are severely impacted. The decline in the furniture and household
categories is very significant, as well as in hardware. In response, Stone has offered a

number of what he calls "Prescriptive Measures for Merchants”:

- Be prepared to make changes.

0 Take proactive action, anticipate a period of declinig sales and prepare both a
short- and long-term business plan.

- Identify market niches that aren't currently filled.
° Improve your level of customer service.

0 Carefully evaluate your ability to compete on price.

Sam Walton responded to the concerns of small-city retailers who criticized the Wal-

Mart strategies as being unfair and destructive to traditional downtowns in this way:

Quite a few smaller stores have gone out of business during the time of Wal-
Mart‘s growth. Some people have tried to turn it into this big controversy, sort
of a "Save the Small Town Merchants” deal, like they were whales or
whopping cranes or something that has the right to be protected.

Of all the notions I've heard about Wal-Mart, none has ever baffled me more
than this idea that we are somehow the enemy of small-town America.
Nothing could be further form the truth: Wal-Mart has actually kept quite a
number of small towns from becoming extinct by saving literally billions of
dollars for the people who live in them, as well as by creating hundreds of
thousands of jobs in our stores. I believe millions of people are better off today
than they would have been if Wal-Mart had never existed.

I don't want to be too critical of small-town merchants, but the truth is that a
lot of these folks just weren't doing a very good job of taking care of their
customers. Whenever we put a Wal-Mart store into a town, customers would
just flock to us from the variety stores. With our low prices, we ended an era
of 45% markups and limited selection. We shut the door on variety-store
thinking.169

 

“59 "Sam Walton Recounts the Life of a Salesman." TIME. June 15, 1992. p. 59.

274
The adjustment to downtown retailing when Wal-Mart arrives is illustrated by the
activities of downtown merchants in Viroqua, Wisconsin:

This small community of 3,500 successfully weathered the competitive
forces of Wal-Mart with good planning. Fred Nelson, owner of the local True
Value Hardware store, organized a downtown revitalization association before
he and other local merchants would otherwise have been forced to close their
doors. Nelson organized what he called a ‘reconnaissance mission’ in which he
and his store employees went to a Wal-Mart in Anamosa, Iowa, to check out its
floor plan, level of service, and stock. With the information they gathered,
Nelson was able to restructure his store and provide goods and services that he

knew Wal-Mart would not have. He also cut out parts of his old inventory that
he knew Wal-Mart could offer at better prices.”0

Viroqua also became designated a “Main Street” community, a program of downtown
revitalization supported by the National Trust for Historic Preservation. This led to an
emphasis on organizational aspects, and a full-time Main Street program coordinator was
hired to implement revitalization strategies. As described in a Smithsonian article, “even as
Wal-Mart’s registers sucked in cash, like a mercantile version of a white shark, the rest of
Main Street Viroqua was hanging on economically and even inching forward....Wal-Mart,
it now appeared, had been a wake-up call for Viroqua, a benefactor-motivator in wolf’s

clothing.”17l

ase St —Wa- ' w w w

The typical scenario for a newly arriving Wal-Mart or other discounter is for the

company to look for an inexpensive piece of agricultural property in the township just

 

‘70 Michelle Gregory. 1993. “Doing Business with Big Box Retailers.” Zoning News
(October 1993). p. 3.

‘71 Michael Melford. 1992. “It’s Wake-up Time for Main Street When Wal-Mart
Comes to Town.” Smithsonian (October 1992). p. 46.

275
outside of a city's boundaries. This frees them from the more restrictive regulations of the
city, but still allows them to compete for the downtown customer.

In Carroll, Iowa, however, the city had made a significant investment in their
downtown and wanted to protect that investment. They developed a strategy of trying to
attract Wal-Mart to locate its proposed new store in the central business district rather than
on the city’s fringe. They had built a downtown mall, using urban renewal funds, and

wanted the store to contribute to its continues success.

...the town convinced Wal-Mart to accept the city center location and agree
to build the new store to complement the existing mall. As importantly, the
community convinced itself that such a move was desirable to protect and
enhance its investment in the central buisness district and that it is possible to
stand fast and force a national chain to recognize local goals.

"...Carroll persuaded Wal-Mart to pay for 50 percent of the cost of a large
new parking lot, with the agreement that everyone, not just Wal-Mart
customers, could use it.

Several reasons explain Carroll's ability to deal with a national volume chain
on a more or less equal footing. First, the community had made major
investments in the downtown, and business and government leaders alike
understood and supported maintaining the investment. Then, Carroll had a high
degree of community consciousness or agreement.

...The wisdom of Carroll's decision to retain and strengthen a strong city core
is borne out by a recent announcement. KMart, Payless Shoe Source, Fashion
Bug, Burger King and Hy-Vee (a major Iowa-based food store) have all
decided to build in Carroll's central business district.”2

 

172 Jerry Knox. 1991. "Dealing with a Volume Chain Store: Carroll Iowas, Guides
Development and Protects Its Downtown." Small Town (September-October
1991). pp.21-23.

276

DOWNTOWN HEALTH AS SHOWN THROUGH LOCAL ECONOMIC INDICATORS

The most commonly used indicators of downtown health and vitality are standard
economic indicators. Two standard data sources were used to compare the economic
vitality of the 16 study cities—sales tax distribution figures from the State and retail sales
figures from the U.S. Census of Retail Trade.174 Sales tax revenue figures for 1987 and
1992 were obtained from the Michigan Database, administered by the Michigan State
University Institute for Public Policy and Social Research. The Census of Retail Trade is
published every five years by the U.S. Department of Commerce. Its Geographic Area
Series includes summary statistics for counties and for places with 2,500 inhabitants or
more. Included in that information is annual retail sales. Although both the sales tax and
retail sales figures are available for each of the cities, they are not specific to the
downtowns in those cities. However, they represent the only consistently collected
economic data available, and must be used to represent the economic vitality of the study

city downtowns.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 33
Sales Tax and Retail Sales for Study Cities, 1987 and 1992
P Julatton Sales Tax Retail Sales (in 3M1]. )

T g 1990 1987 (adj) 19921111191 cmtm 1 1992 . Chang;
Owosso__~ _ ‘ . _______16,322 $601,184 $727, 840 $126, 657 $173: 52321 $66
1151239195 fl_ 12, 6031 _s_524, 570 _,5556 665 532, 09531518 +__ $164, $56
AlEna ___ _11, 354 5446.026 $491, 574 545, 555 $1231 $173 $50
gagge_ _g 10,130 5124: $150 $26
Cadillac _ _,10 104 $372, 329 $449,387. $77,059 $148 , $176- $28
A1999...» ---___.. _,10 066 £91922 - 5433, 538 $36,610 569; 575; $6
C_ol_dw_at£r_fi_ W 9,607 $539,909 _,5425 2031 $80,204 $114, $164,“ $50
Howell _.... ___'_8,184 5253 845 $384,200 $130, 355 __ $88: $161w ,_ $73
Hillsdale ‘ g g 8,170 _ q _,_$375 787 597‘ $117 $21
Lapeer 5--- 7,759 5219, 575 $349, 457 $129.88? 5229; 5255+. $26
Tecumseh . . ___, 7,462 $267 .4375 , $336,071? $68,634 $60 s69} 59
Tylarshall 6,891: $261,335 $243,183' $18,152 $66; $98, $32
Manistee g 6,734 $279,895 $281,463: 51,569 $78; $93; $15
Dowagiacm 6, 409‘ . _ $228312:_ ‘ _ $63*____ $94 __$_31
Ionia 5 .935“ $208 .045 _5261, 649 $53604 $63;*__ $1031 _n_s,3g
SouthHaven 5, 563 $217,128 $257, 394 $40,267 $74

 

 

 

 

'74 United States Department of Commerce. 1987, 1992. Census of Retail Trade.
Washington, DC.

277

The sales tax and retail trade figures were compared over a five year period175 and the
16 cities ranked according to the relative improvement in the economic status of each
downtown. The percentage change between 1987 and 1992 for both sales tax and retail

sales are shown , along with a “Combined” figure which represents the mean of the two.

Table 34
Ranking of Study Cities
by Change in Economic Indicators, 1987-1992176

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

fig fiwwgsgrm. ', 11286111.;th %Ch.'l‘ax1 %Ch.5a|os %Chen£<
._.-.., _ Pop 87 L Pop 92 j Pop.87-92 87.92151) 2 968116 87-92 Combined
LAlbiorl -- 10,3646 9,867, __ -5% 9%; 8% 9%
Alpena 11,612g 11,183; 4% 10%, 41%6 26%
sgaepgdsw 13,1301 12,251; -7%- 6% 52% 29%
'gedrrragmfl 10,131 10,085,: 0% 21% 19% 20%
Coldwater 9.563 9.6367 1% 23%; 44%L 34%
_gowagiae g 6,378 6,429: 1%. i 50%: 50%
gnisdaie __ 7,949, 8,318 5% . 22%' 22%
_l-lgwell____ 7,822 8,426 ___ 8% 51% 83% 67%
Ionia ”___- 5,931, 5,938 0% 26% 62% 44%
Lapeer 7,291 8,07L 11% 59%, 11%: 35%
Manistee __g 6,984 6,568 -6% 1% 19%? 10%
Marshal_17_ .. ___; 6,984, 6.829;” -2% -7%g 49%; 21%
Owosso 16,362 16,295 0% 21 %A 38%- 30%
SguthfiHaven 5,677 5,487 ._.__ -3% 19% g 19%
FS_turgi_s gggggg 9,931 10,262 3% 21% 10%
Tecumseh 7,419 7,490 1% 26% 15% 20%

 

 

 

As shown, the change in the Combined economic indicators varies widely for the study

cities, from a 67 percent increase over the five year period in Howell to only a 9 percent

 

‘75 Although it was originally intended that 1987 and 1992 sales tax figures be used,
significant discrepancies were seen in the Michigan data files for some cities from
1986 to 1987 and 1992 to 1993. Therefore, the mean of 1986 and 1987 figures
and 1992 and 1993 sales tax figures were used to more accurately represent the
general economic health during that time period, and they are shown simply as
1987 and 1992 (adjusted).

‘76 Notes: Sales tax figures were not available for 1992 for Marshall and South Haven
and 1993 figures were substituted; sales tax figures were not available for
Dowagiac, Hillsdale and Sturgis and no figures were available to be substituted.
For cities where either sales tax or retail sales figures were not available, combined
figures were derived from either sales tax or retail sales figures only.

 

1.- -.-1-. .7

 

278
increase in Albion, which is less than the normal increase due to inflation, and thus is
effectively negative growth.

Correlation analysis was used to examine each of the following hypotheses:
- Population, 1990: Did cities with greater population growth have greater
growth in economic indicators?
- Percent change in Sales Tax and Retail Sales, 1987-1992: Did
growth in Sales Tax correlate with growth in Retail Sales?

The correlation coefficients are shown on the following chart:

Table 35

Correlation Coefficients for
Population and Change in Sales Tax and Retail Sales

87 92 Ch. % Ch. Tax Ch. Sales Combined
87 l.
92 0 l

% Ch. . -0.25 -0.1 l.

% Ch. Tax -0.17 0.92 1.
% Ch. Sales -0.05 -0.05 0.05 0.11
Combined -0. 1 -0.08 0.55 0.68

 

When Sales Tax and Retail Sales are compared against population growth, the change
in Sales Tax has a very high correlation with Population Growth (r=0.92), while the
coefficient for Retail Sales is inconsequential (0.05). These discrepancies strongly suggest
that Retail Sales data (collected by the federal census) and Sales Tax data (collected by the
state) are not interchangeable variables, and represent very different perspectives on the
economic condition of cities.

Figure 11 also shows there is virtually no correlation between city size and economic
growth. In other words, larger cities did not benefit from greater economic growth during
the period when Wal-Mart came to the state of Michigan, and smaller cities were at no
relative disadvantage overall.

The above chart also shows no significant correlation between changes in Sales Tax

and Retail Sales (r=0.11), which indicates these two economic indicators are not

279
interchangeable. This leads to the difficulty of deciding which is the more reliable indicator
of economic health, which cannot be determined based on the information presented to this
point.

The question then becomes—Which of these variables is more reliable? Because retail
sales data is based directly on sales, and sales tax data is one step removed, being based on
the tax collected on sales, retail sales data is assumed to be the more accurate of the two
economic indicators. It also benefits from having more complete data available for the
study cities for the years being studied. The Combined variable should not be considered
reliable because of the large discrepancies between the Sales Tax and Retail Sales data on
which it is based. For these reasons, Retail Sales will be used as the indicator of economic

health in the rest of this analysis.

CORRELATION OF ECONOMIC INDICATORS TO "HEALTH PERCEPTION INDEX (HPI)"

Thus far the health and vitality of the study downtowns has been analyzed using the
commonly available economic indicators of Sales Tax Distribution and Retail Sales
figures. However, downtown health can and should be defined in a more comprehensive
sense, and include, in addition to economic factors, an evaluation of the physical and
functional condition of downtowns. Downtown health may be seen in terms of physical
factors, such as streetscape improvements, restored facades, one-way streets, and
improved parking. Functional factors can be more wide-ranging, and may include the
ability of a downtown to serve as a focus of the community, the activities of organizations
connected with the downtown (e.g., merchants association, chamber of commerce), or
even issues of changing lifestyles and shopping patterns. Even economic factors can be
more inclusive than sales tax and retail sales data, and may include such things as public

expenditures in the downtown or the level of employment in the overall community.

280

CORRELATION OF THE HEALTH PERCEPTION INDEX AND THE ECONOMIC INDICATOR

The Health Perception Index values for each of the cities was compared with the Retail
Sales economic indicator, with the correlations shown below:

Table 36

Correlation Coefficients of HPI
to Population and Economic Indicators

HPI Ch. % Ch. Sales
HPI 1 .

% Ch. 0. 1.
% Ch. Sales 0.21 0.05

 

The chart indicates no significant relationship between the Health Perception Index and
changes in Retail Sales. Since a minimum coefficient of 0.50 is needed to indicate a
statistically significant correlation, the r=0.21 represents simply an insignificant “tendency”
toward correlation, but not sufficient to be considered as reliable.

This analysis indicates it remains unclear whether the health of a downtown can best be
depicted through standard economic indicators or through the more perceptual and broader
based Health Perception Index. Therefore, both will be used for the remainder of the
analysis, which looks at the impact of the opening of new Wal-Mart stores on the health of

Michigan downtowns.

IMPACT OF WAL-MART STORES ON STUDY CITIES AND THEIR DOWNTOWNS

To study the impact of the rapid infusion of Wal-Mart stores on smaller cities in
Michigan, the 16 study cities were used as a representative sampling. By 1993, seven of
the cities had new Wal-Mart stores located in the community; the nine other cities had

access to Wal-Mart stores only from varying distances.

281

To look at the impact on cities of having Wal-Mart stores located there, the changes in
economic indicators and the Health Index were grouped for Wal-Mart versus non-Wal-
Mart cities, as shown in Figure IV-70. A comparison of the mean values for the two
groups indicates clearly that Wal-Mart cities had higher growth in Sales Taxes, Retail Sales
and the Combined Economic Indicators. This growth had nothing to do with population
growth, since neither group had an increase in population.

It may be assumed that the Wal-Mart company consciously looked for sites where
there was already economic growth, but it is much more likely that locating a new Wal-
Mart store in a community created the economic growth shown. What is also seen is that
there is no significant difference in the downtown Health Index ratings for Wal-Mart
versus non-Wal-Mart cities, with the non-Wal-Mart cities even having a slightly higher
Index rating. This can possibly be explained by the fact that although a new discount store
brings in more retail sales overall, the increased sales is typically in direct competition with
downtown businesses, and merchants don’t perceive any overall improvement in general

downtown health, even though sales and tax figures are higher.

 

282

Table 37

Comparison of Wal-Mart vs. non-Wal-Mart Cities

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

: ;%Clumgcf %Ch£ge %Chauggj%Chaug

. HPI j Pop. 87-92 ‘ Tax 87-92(atlj) Sales 87-92 1 Comb.Eoon.
WalMart Cities i I j I
Howell 1 565:: 8% 51%, 83%: 67%
ngRapids 1 34.3, -7%, 6%1 52%; 29%
Cadillac ; s42? 0%,E 21%1 19% 20%
Coldwater ~' 465 1%;; 23%6 44%1 - 34%
Owosso % 49.1 : 0% 1 21 % 38%A 30%
SouthHaven ; 46.6; 3%; 19% 1 19%
sluran . L 56.8 3%;' ’ 21% 21%
Mean for Group T 49.1., 0%1 24% 43% 31%
Non-WalMart Cities . 1 1 1
Tecumseh 70.8 1%? 26%: 15% 20%
Marshall i 70.6 2% -7%' 49% 21%
Albion 15.9 , -5%; 9%; 8%: 9%
Hillsdale p 44.1; 5%, 1 22%; 22%
Lapeer p 52.8 11% 59% 11% ___. 35%
Ionia -_ - 49.8 0% 1 26% 62% 4 44%
Dowagc ‘ 66.0 1%1 g 50% 50%
Manistee 45.4 -6% 1%: 19%, 10%
Alpena __r 41.8 High; 10% 41% 26%
Mean for Group 50.8 0% 18% 31%; 26%

 

Illeinlpastslfdislance

To test the impact of distance on downtown health, using both economic and perceptual

table below, and the correlation coefficients below it.

indicators, a correlation was done of distance to Wal-Mart and downtown health and how it

had changed in the five years since the first Wal-Marts opened. The data is shown in the

 

 

283

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 38
Distance to Wal-Mart (in miles)
A Estim.Ch. i % Ch. Sales 2' Wal-Mart ; Durance (mi.)
(T HPI Pop.87-92 1 1987-92 3 Size (100051?) 1 toWal-Mart
Albion 1 15.9 T -5% I 8% ' 120 , 17
Alpena 7T 41.8 4% p 41% ‘ 80 T 72
Macias 1 343g -7% 52% 93 - 3
Cadillac T542 0% g 19% 80 3
Coldwater T 46.5 1% j 44% . 80 3
_Dowagiac 4159+ 1% g 50% 100 j___ 40
_Hl_’llsdale 1 44.1 5% 22% 4‘ 80 - 20
iglogcjlw 565g 8% 83% p 119 __ 3
:Ionia 49.8 g 0% __ 62% 100 33
Lapeer 7 52.8 11% 11% 100 25
Manistee ; 45.4 -6% . 19% 80 49
fiarshall T 70.6 -2% _fi 49% , 135 14 _fi
Owosso 49.1 0% 38% 93 3
SouthHaven? 46.6 -3% 80 3
suggls“ M_g.§_+__3% h“ 21% 98 3
Tecumseh 70.8 1% 15% 127 12

 

 

 

A distance of three miles was given to Wal-Mart stores that were within a given
community. This represented an assumed average distance for residents in the surrounding
area to drive to the store.

Table 39

Correlation Coefficients for
Distance to Wal-Mart (in miles) and Size of Store

HPI Ch. % Ch. Sales Dismnce S
HPI 1.
% Ch. . 0. l

% Ch. Sales 0.21 0.05 1.
Distance -0.07 -0 -0.04 1 .
Size 0.33 0.1 0.15 -0.21 1.

 

Using distance in miles as a variable, no correlation is shown to either the downtown
Health Index (—0.07) or Retail Sales (-0.04). However, this analysis does not incorporate
the study of Stone, described previously, who found that cities with a Wal-Mart benefited

from its presence, but that cities in a 20 to 30 mile perimeter of Wal-Mart cities suffered

 

284
the most from the competition. To study this effect for the 16 Michigan study cities, the
distance variable was redefined. The sixteen cities were ranked from 1 to 16, with the
lowest values given to the cities with the least negative competition. Since the seven cities
with Wal-Mart stores were seen as being impacted the least negatively, they were ranked
from 1 to 7 based on how many other discount stores were located there, and how big they
were. From that, the next lowest ranking was given to the city which was the furthest
distance from a Wal-Mart location (Alpena at 72 miles), continuing on to the city closest to
a Wal-Mart city (Tecumseh at 12 miles), where the competition was predicted to be
greatest. The distances and rankings and their correlations to the Health Index and

Economic Indicators are shown below.

Table 40
Distance Ranking to Wal-Mart (according to Stone)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Estim.Ch. %Ch.Sales W Will-Mart 1 Distance (1111.) Stone Rev.
' _ __ I HPI roam-9; 1987-92 ; Size (10008101 toWal-MartWDist. Rank
”06656 49.1 0% ‘ _3_8% W 93 j 3 ‘ 1
Cadillac 54.2, 0% 19% W 80 3 _ Wg___
Eigfijgids - 343: -7% g 52% 1 93 3 - 3
534311 56.5 8% “_N _8_3% _,_-___ 119 3 _ 4
Coldwater 46.5 1% 2.“... 44% 80 , _3_ _ _. W5
§£tltsi§ .563 __.3_%__-_ 2.L%_ -- _ 98 3 6
SouthHaven 46.6 -3% ___ _W 80 3 7
Xmas 4143 ___-13;-“ 412 __ .- 80 _.__ 72 ‘_ ___3- ,
Manistee 45.4 -6_%_W W_____ 19% ___ W 80 ,4_9__ ---L... 9
pom“ 66.0 1% .. ___ 50_%_____+ 100 40 __-__ 10
Ionia 49.8; O%__- _ W___62%___W .,.._ 100 33 11
Lapee_r_ 52.8 11%____ - __.. 11% -- 100 .-_..._ WW25 _-_. .-__. 12
Hillsdale 44.1 5%__ w “313% _ _. 80 __ _g_ __ _29____ ___W___l}__
_Albion 15.96 -5%__ -_...__§.‘_7-;_,,__---_-_ 120 ___- __ IL. ‘ 14
Marshall 70.6 -2% - J%~__ g __ 135 -_-___ l4 __ 15
Tecumseh 70.8 1% 15% 127 12 16

 

 

 

.f‘.‘

285

Table 41
Correlation Coefficients for Distance Ranking

Health Sales Tax Retail Distance Distance
Index 87 87-92 Econ. to WMart

Health Index 1.

Tax 87- 0

Sales 87-92 0.21 1.
Comb.Econ. 0.33 0.
Dist. to WMart -0.07 -0.05
Dist. ' 0.15 . 3

 

The chart indicates there is no significant correlation between the distance factor as
described by Stone and either economic growth or the Health Index. Instead, it indicates
there may be a statistical “tendency” (l=—-0.33) between the distance rankings and growth in
retail sales which is counter to Stone; that is, the non-Wal-Mart study cities closest to the 20

mile perimeter had greater growth than other study cities.

THE IMPACT OF ALL COMPETITOR DISCOUNT DEPARTMENT STORES

Other major discount department stores were next included in the analysis to see if there
was a significant impact on downtowns of all discounters, rather than just the more recent
Wal-Mart stores. However, an analysis of Total Square Feet of All Discount Department
Stores in each community to both Economic Indicators and the Health Index once again
showed no significant correlation.

To give a more inclusive look at this factor, a gravity model was used to derive for each
city a value based on the competitive draw of all discounters within a 30 mile radius of each
study city. The 30 mile radius includes the area Stone had found was most impacted by
new Wal-Malt stores, and was assumed to spatially represent most of the competitive draw

from the study city downtowns. The gravity model used was simply:

 

286

Competition for each city = 2 SE
2
d
where SF = Square footage of a discounter within 30 mile radius
and d = distance to retailer

(Note: discounters located in the community are
assumed to be at a distance of 3 miles)

Because of the potential for vast differences in competition ratings from squaring the
distance factor, the formula was also calculated simply using distance (unsquared). Both
the “(12 Ratings” and “d Ratings” are shown.177

The values derived from the gravity models for for each of the study cities are as

follows: ‘73

 

Table 42
Gravity Models Values ,
Name of Store Commtitor City Sq. Ft. Dist,(mi.) dzfiating 513311118
Albion '
KMart Albion 55,000 3 6,1 1 1 18,333
Meijer Jackson 225 ,000 17 779 13,235
Quality Farm and Fleet Jackson 60,000 17 208 3,529
Sam’s Club Jackson 120,000 17 415 7,059
Target Jackson 100,000 17 346 5,882
7,859 48,038
Alpena
KMart Alpena 7 1,000 3 17,750 23,667
Big Rapids
KMart Big Rapids 50,000 3 5,556 16,667
Quality Farm and Fleet Big Rapids 30,000 3 3,333 10,000
Wal-Mart Big Rapids 93,000 3 10,333 11.9.0.0
19,222 57,667

 

177 If a large city within the 30 mile radius had more than one store of a certain

discounter, only one store of that discounter was included, with the square footage
of ltS largest store considered as the competitor store.

178 Information taken from Chain Store Guides. 1994. Business Guides, Inc.

Cadillac
KMart
Quality Farm and Fleet
Wal-Mart
Meijer

Coldwater
KMart
Quality Farm and Fleet
Wal-Mart

Dowagiac
KMart
Meijer
NA. 1 30

Hillsdale
Ames Dept Store
Fisher Big Wheel
KMart
Quality Farm and Fleet
Wal-Malt

Howell
Quality Farm and Fleet
Wal-Mart
KMart
Meijers

Ionia
County Post
KMart
Mei jers
Target
Sam’s Club

 

287

Table 42 (cont’d).
Cadillac 68,000
Cadillac 75,000
Cadillac 80,000

Traverse City 200,000

Coldwater 40,000
Coldwater 20,000
Coldwater 80,000
Niles 110,000
Benton Harbor 200,000
South Bend 100,000
Hillsdale 52,000
Hillsdale 45,000
Coldwater 40,000
Coldwater 20,000
Coldwater 80,000
Howell 42,000
Howell 119,000
Brighton 71,000
Brighton 84,000
Ionia 12,000
Ionia 96,000
Ionia 90,000

Grand Rapids 100,000
Kentwood 100,000

WWW

17
22

25

OOOOMUJ

Mb)
OOWWW

22,667
25.000
2,963

$1.082
54,712

13.333
6,667

2.6.5.61
46,667

6,471
9,091

5.999
19,562

17,333
15,000
1,538
769

_LQZZ
37,717

14.000
39,666

8.875
1.11.5!!!
73.071

4,000
32.000
30.000

3,333

.1123
72.667

179 Although Traverse City is beyond the 30 mile radius, a number of survey
respondents from Cadillac indicated they traveled regularly to Traverse City for
shopping, 50 this competitor is included.

'30 N 0 information available for Indiana competitors, but according to surveys South
Bend does attract Dowagiac shoppers, so assumption of size made.

 

Lapeer
KMart
Meijer
Sam’s Club
T.J. Maxx
Target

Manistee
KMart
Quality Farm and Fleet

Marshall
KMart
Meijer
Quality Farm and Fleet
Sam’s Club '
Target

Owosso
Quality Farm and Fleet
Wal-Mart
KMart
Meijer
Target

South Haven
Wal-Mart
KMart
Meijer
Target

Sturgis
KMart
Wal-Mart

Tecumseh
KMart
Meijer
Quality Farm and Fleet
Wal-Mart
Target

288

Table 42 (cont’d).

Lapeer
Flint
Flint
Flint
Flint

Manistee
Ludington

Marshall

Battle Creek
Battle Creek
Battle Creek
Battle Creek

Owosso
Owosso
Corunna
Corunna

Flint

South Haven
Holland
Holland
Holland

Sturgis
Sturgis

Adrian
Adrian
Adrian
Adrian
Ann Arbor

72,000
190,000
100,000

27,000
100.000

80,000
30,000

80,000
225,000
30,000
135,000
100,000

90,000
93,000
1 00,000
1 90,000
100.000

80.000
80,000
80,000
100,000

40,000
98,000

86,000
109,000
30,000
127,000
100,000

17
22
17
17

12
12
12
12
27

8.889
1,148
153
689

_SJQ
11.389

10,000
10,333

6,250
11,875
, 128
38.586

8,889
118
118

__118

9,273

4,444

1.0.88.2
15,333

597
757
208
882

.131
2,581

24,000
1 1,176
4,545
1,588

.1832
47,191
26.667
.1421.
27,821

26,667
16,071
2,143
9,643
1.153
61 ,667

30,000
31,000
259000
47,500
.1511
137,071

26.667
3,077
3,077

.1845

36,667

13,333

3.2.6.61
46,000

7,167
9,083
2,500
10,583

.3104.
33,037

289

The ratings from the gravity model calculations for each study city are shown in the
following chart. As expected, the ratings using “d2” have extreme deviations from high to
low, and the ratings using “(1” suggest a better representation of the relative strength of

competitor discount stores for each city.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 43
Gravity Model Ratings
l Gravity Gravity
l Estim.Ch. % co. Sales Stone Rev. Model Model

HPI l Pop.87-92 1987-92 Dist. Rank Ratifl («12) Rating (11)
Albion 15.91 —5% 8% 14 7,859 48,038
Alpena 41.8T -4% 41% 8 17,750 23,667
_B_igRapids 3433 -7% 52% 3 19,222 57,667
95111ng 54.2%, _____. 0%,“ _ 19% 2 24,861 54,712
Coldwater 46.53 1% 44% 5 15,555 46,667
Dowagiac 66.0; 1% 50% 10 954 19,562
Hillsdale 44.1; 5% 22% 13 10,985 37,717
Howell 56.51 8% 83% 4 20,310 73,071
Ionia 49.81 0% 62% 1 1 22,222 72,667
Lapeer 52.8l 11% 11% 12 9,303 47,191
Manistee 45.4 -6% 19% 9 8.933 27,821
Marshall 70.6 - -2%1 49% 15 11,389 61,667
Owosso 49.11 0% 38% 1 38,586 137,071
SouthHaven 46.61 - ”5:35.70“ _._ _ 7 9,273 36,667
Sturgis 56.8l 3% 21 % 6 15,333 46,000
Tecumseh 70.81 1% 15% 16 2,581 33,037

Table44

 

Correlation Coefficients for Gravity Model Ratings

HPI
HPI 1.
% Ch. 0. 1.
% Ch. Sales 0.21 0.05 1.
Rankin 0.15 0.08 -0.33
Rati -0.1 -0.02 0.3
Ratin -0.03 0.1 0.

Ch. . % Ch. Sales d2

d2

 

Correlation analysis shows there is a strong correlation between the Gravity Model
ratings and the Distance Rankings suggested by Stone (-O.73 and -0.45). This gives

credence to both techniques for evaluating the relative power of competition. In other

 

290
words, distance to competitor stores and size of competitor stores is a good way to evaluate
the level of competition as presented by Stone.

However, there is no significant correlation shown between these competitor
evaluations and either the downtown Health Index (I=-0.03) or the Retail Sales Indicator
(r=0.30), although the Retail Sales coefficient is much stronger than the HPI coefficient.
Overall, this indicates that competition may not be a significant explanatory variable for
evaluating downtown health, and there is no support for concluding that the level of
competition directly impacts downtown health, even if evaluated in terms of of either retail

sales or the perceptions of downtowners.

SUMMARY OF THE IMPACT OF WAL-MART AND OTHER LARGE DISCOUNT

STORES

This section has looked at various techniques for modeling the health of downtowns.
Economic indicators were compared over a five-year period, from 1987 to 1992, for 16
smaller Michigan cities, including changes in sales tax distribution and retail sales. Also,
Tyler’s downtown Health Perception Index, developed from surveys in the same cities,
was used as a broader evaluation based on input from merchants and business owners.
Finally, the strength of retail competition from large discount department stores was
derived for each study city from use of a gravity model.

Correlation analysis was used to compare the relationships between the three
techniques. The lack of correlation between the study’s three primary variables—the
downtown Health Index, Retail Sales, and the Competitors’ Ratings—suggests there is no
reliable way to establish a model for predicting downtown health. Each has a degree of
validity within its own right, but each is left without the reinforcement of other techniques

of analysis.

 

 

 

APPENDIX 8

RECENT PUBLICITY ON THIS STUDY

 

FOCUS EMU

Tuesday. Jan. 23. I996 3

 

'Focus ON FACULTY

e

"1

Tyler researches the ‘health’ of Michigan’s small downtowns

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291

 

IN. THIS ISSUE:

Stunning Decline of
Michigan’s Democratic Party

Sucker Fishermen's Lament
Terrific Travel Ttps
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CITIES

   

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The Health of Michigan’s Small Downtowns

Many efforts to revitalize Michigan downtowns are ineffectual,
researcher Norm Tyler believes. Some have even been harmful.

ack in the 19703. as many Michipn downtowns
languished and declined under the growing pres-
:ure from retail competition ofmalls and com
mercial strips. civic leaders looked for cure; of

L - I III I. - A I L I -

I r 9
firm: for help. All toocften. theae expem' prescription: for

 

. . . ,
I. unvmv-

furl m”, II III... LI-I- In
I- l ‘ L '1 I L I I
one-way hypaues. thus draining tralfie from in downtown
core. The idea was to malt: downtown more automobile
(IIIIAIV -- . r
I I . ~ I I I . I I

Lapeer wu fat from the onWMichipn eity Io‘fIght
I h. I I- L - - I
.

 

l .
In“. I J u L ng

I 'I
meet. ltt Ypsilanti. planner: converted Huron Street to a
one-way feeder. treating I mini-hi5hway that Iliecd in half
I-III--. I-IIIIINflM

 

 

 

 

 

 

DouHun

 

TYLER'S DOWNTOWN
HEALTH INDEX

Tecumseh.................... 70.8
Marshall..................... 70.6
Dowagiac.................... 66.0
Sturgis........................ 56.8
Howell........................ 56.5
Cadillac...................... 54.2
hpeer......................... 52.8
lonia........................... 49.9
Owosso....................... 49,]
South Haven.............. 46.6
Coldwater................... 46.5
Manistee..................... 45.4
Hillsdale..................... 44.1
Alpena........................ (1.8
Big Rapids.................. 34.3

 

 

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andpedatmu.‘ lay-Tyler. ‘it'thata eanbinationafpeo—
pleudnnfltbatnnkuadowntownmalinanlyou

 

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k ' j A k I . I r ., .. Albion........................ 15.9
I r
realisiueinl- ereitiulilteAnnArboIIButT raa 'that
D‘P""r°“"'"“. ” l _ I . MrM.buedalTNa . L .. 'I__ I I. .. .. .. y'le. .1:
I II I
1992 mm of W , L _ _ L ‘ '
(“n1 . a. Tvln L‘ Ill MIMI... h each d h d.- “...... I—qu " I III-
. I . .
for the past I 1 year: has been researching small Michigan"u v L I ‘
downtown; T'yler I considerable research .0" Mi hi Inga “hm ‘ M m‘ TY‘" 5"“ n ‘ ' I I ' n
_ . I I“. I I I I I -
W I, L I L rr 1' t
. I . . l I I I . . .
'l I r r. I L ' ..n-.w.y I I l I n l L J. I . ‘ eh.
I wII I I I . I. I I I’m . . I r _ ' I... 5"“
L J , , l .. . , I I I I . I I -I I- .
I v I I
streets. 'lfanythingquuvnnl motecon estion. mminefi- '1 I. "I I I . - I I . "he" “MK“ Md". ‘1 ‘ t - l - I
de nt. slowL movingtrafiii: downtown: hemneludu. "l'halt ,' . J I- l. " ‘ "7' I" I r l u. n I
treeI. flown :‘llnlm but... 1 I . I L I . 'II l I II .—
ac- ':, 'l I v I mm“ (... I l II-IIII- -r I L, II’I u ' I' l I T
h .I. I . ..IIII. I w I ' l downtown!" uys'l'vl" " l ‘ ‘ " " ' 7‘ L J 'L L ‘ L . . III I
TV." I I I I - I I- I - - . ‘ I I .,
, . . I -
- I- u “2.“. I...I....I.'.... 't - ,. 1 wt - - Whyhmwahgtnattan
- I I IIIII LT I. l I H ‘ ' ‘ J Sueh‘bmminphopa'mnotthe-neaamm-
n IIIIIM- -- I I I I AIM}... u r m 45.-....“ - - II.
directly JUtn‘lMll adowntown Ivitality. MM ' v. I (M A” ' "m‘ J H "I . J I r
I - I I I- . I I I I I I - . “I,
. v I

293

May 1995 MICHIGAN MONTHLY m

 

CITIES

fill IImn up. mlh any kind ofhiuincu. says

I

-\ L L' l What

 

LOvPInNIIrA

 

m. u- .
Iowa‘s fulum
. . I I

r w
. l .

 

luml of humans rally reinlurc: whaI w:
ham Io do wnh dowIIIuwn?" In“ ‘6‘ clung III
lIaIIsIIIonII husinmn Sikh as \«ighl lilting
parlors III ‘ '
daunluwn s II:.IlIh In III: long IIIIL

In III: Imd 1930:. 'I Iyl:r crawl a
mtllmd' III :valualiug III: IIuIlh HuIIII:

IIa.I:d such» Iiunnas mix. Parking. Hhori:
dun-Ia :nuxipcun‘on nulo c.

I-IunI Ih:w Tykr hA\ gran-mi raIinp
ol' I986 and
I991 H: lll:ll um his Ihwnlovrn Hulth
IIIdu to find {anon anucuml with heaIIhy
downlawns as In” as IIIIn: Wand wilh

 

small-c ,

I L I J ' L
.r
In III: hm on pag: IS nu: dmvul from a
IW.’ cumpnlknuv: surv:y of III:r:IIanIs
‘ ‘ ' ‘ ’L ‘III: sludy
cilm. liq-"Inc: I‘ll!“ IIwII ammnls III
III: conipanliv: slunglhs of ll’kl' awn
downtowns. mlwtnul III .I I] III 100 scalc.
III: Indu Lump um: Ilcmzd from
(our qualII-ns:
.. I I I I 3......»
Iown In oIIIu (“In of umihr sin.
' I):pIcI how you I::l III: huIIII ofywr

'

I I I I J l
ycats. Tyler has hecn Ilvlu. lu u: IIramaIic
change III sunk. lbwaguc. lot :nnlplc.

“Mum' " 'rinI min
In: 1930 Iurvq In a much high" rating in
III: IWZ study. WhaI bnup ahoul Mich du-
nIaIIc Improwmmb In Iluwnluwn hullh.’

ans swrl 'I h": a hunch mmhing
lungn around allilwlr I an I pm a numbu'
(aux: Ihings In Inlhappm.
As an :umpk. h: pomIs Io III: :nIhusi-

 

VA

A MISTAKEN ASSUMPTION

 

 

 

 

 

 

 

 

 

 

 

.w-u. , _ _
Imam . " ' v ' '
shuts. LII. many. ‘ , , “

ISSOSW _ ,
u..— I nun-u , , ‘ '
mauanabrmauonwoudbu

- r '1"1 L . u A .I‘ ‘
II Inc 1950:. *6: , . Iu mu
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D n).- ’ I ‘ l4
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294

IIIII dlim IIIIM MCCIIII

III: Chain: VIIIII In Tecumuh In 19”.
'Wha Ihry mud thrir busines.‘ any:
Tylu. 'Ihcy weal upand down III: Iidnull
ullinnhcit Iuiflbon'n; buinm‘lsn'l

Mam followed. This isn't numbing-
:in can inn pmp'am in. BIII a dynamic
ptmunrpcmmunhuheuulyu Io
tum IniIudas around.

Fred Malhnnol'Dova’u: has abbccn

helpd madam a slumbcrin. downm
iIIIo a much mot: vibrant phc: Iha: days
Thccivinniviunolflrian Velvet-{hula
similar «(fun In link Three Gab. Bill

In denim Denoil. Pam Duvall on: Io
Elinlxih's gift shop sham muons": uiIh

“I really feel
what leads the
way, what caum
things to happen,
is attitude,”

says Tyler.

other Ip:o'ally shops by producing cooper-
aliv: brochma and "an: in he: span
rink. (By day she's I probation omen.)
Financing downtown impmmam
across Michigan has hecom: much Inor:
diflicull bccausc of commercial properly In

cialions. So il s a good thing that :nli‘hl-
med :nrhusiasm counts formor:

physical unpm: mmnu in iIanovin‘ III:
health of dowmwm Nam

Icaurch Ihould allu :nwuraganmr Io
Michigan’s maker downlowns nu damlop
mar: pooiIiv: mimics as llky go aboII
findInl ways to Ilrcngllun III¢Ir «nlrll
cilia .

 

Measuring and evaluating your community's quality

URBAN QUAL IIY"

 

 

Retail
Quality

Indicators

Measuring the Availability of
Retail in Central Cities

Introduction

There is general agreement among
grocery store industry experts and
researchers that U.S. Central Cit-
Ies have experienced substantial
declines in the availability of food
alIernatives over the last four de-
cades. The result has been that
central city residents often face high
costs for low quality food in “mom
zmd pop" stores characterized by
limited choices. Some experts con-
tend that this trend has stopped.
with some supermarkets returning
to declining central cities. There
has been little systematic documen-
tation ofthese trends. and even less
explanatory work, This article re—
ports on a recent research project
Intended to document these trends
and pI‘OVIde some InSIghts into their
causes.

Scope of Research

This research focuses on grocery
store trends In declining central Cit-
ies ie. those that haIe small
populations and IOVI incomes rela-
the to their surrounding
metropolitan areas. A multi- stage
screening process resulted in selec-
tion 0 twenty- eight declining
central Cities. listed on page- ‘7.

 

In Cleveland it's “rein-
vestmcnt;" in Atlanta. the
“suburban shift" and in
Denver, a “dc fucto“ trend
in grocery store supply.

By Ron Donahue

The research tracked population.
income and retail sales in the cen-
tral city. the Metroplitan Statistical
Area IMSA) and the suburbs for
these areas over the period 1958-
1992. Data were drawn from the
Census of Population. the Census
of Retail Trade and the Survey of
Consumer Expenditures.

In order to test the abandonment
and reinvestment trends, it is nec-
essary to develop some measure of
how well or poorly population needs
are being met. The research used
St'rt‘lt't' lei-e1, defined as the ratio
of actual sales to expected sales. for
this purpose. Grocery shopping
decismns are heavily influenced by
location. such that shopping gener-
ally occurs close to the residence.
It follows that the ratio of actual
sales to expected sales should be
close to one. ifthe market is In bal-
ance. Serious departures from one
suggest kl disequilibrium between
supply and demand.

Supply is measured by actual sales.
drawn from the Census of Retail
Trade. Demand Is measured using
Income distributions and expendi-
ture by Income. The ratio of supply
to demand Is the Sl‘erCt‘ level. Ser-
\'ICO level ratios were calculated for
1958, l963.1967. 1972. 1977,1982.
1987 and 1992. coInCIding With the
Census of Business. Service levels

295

 

Chicago
an Gloria
1mm..m ”"...... 7,428.06

1997 pm. Intro * _______ 7,7‘9199
1997 m, Chum Cay .-_.s._.1,731 .76

INSIDE THIS ISSUE ~

WDOthown Health *-
Index-x . ‘ t.

2 pioneer cities In
sustainability .3 J

I ' ' . ‘ '
Chicago City Culture L,-

Best small cities in the R‘
11.8. 7t ‘ -.
Hancock's Hamlin r .
Mime/Index»

 

 

 

Tyler’s Downtown Health Index for Small Cities

Norman Tyler. now a faculty member of Eastern Michigan University.
recognized the growing importance of lmall cities in the 1970: and
19803 when he set out to do his dissertation (“An Evaluation of the Health
of the Downtowns in Eight Michigan Cities”) at the University of Michi-
gan next door. This growing importance resulted from the desire of
both businesses and the general public to move away from big cities.
But sometimes the smaller cities were not quite ready for this influx of
newcomers. Tyler also found that most of the research on downtown

health had been limited to large cities. Therefore. he established his

own systematic framework for evaluating the health of downtowns in
small cities based on exploratory interviews with merchants and city
officials. but no shoppers or residents. This work identified and exam-
ined factors which contributed to the health of such downtowns and
determined the relative significance of each.

Tyler selected eight cities as representative of small cities in Michigan.
ranging in population from 5.000 to just over 10,000. interviews and
questionnaires were administered in each city to a total of 182 people.
and addressed two questions: (1) What are the beat criteria for evaluat-
ing the 'health' of a downtown? and (2) What factors have had the most
impact in changing the health of downtowns over time? Respondents
were asked to rate their downtown‘s current and future health. how its
health had changed over time. and how it compared to other downtowns.
They were also asked to evaluate twenty specific

characteristics of their downtown (see table to the

 

right ). Their ratings on a four-point scale from “very W "filth Index
good" to “poor" yielded scores on a 100-point scale (ll-M)
for each downtown on each of the 20 Health Index M m
Items. mm;",“' m" IWWW
Findings clearly showed that the one factor most Mmd mp
strongly associated with downtown health was “busi- Midn‘w m
ness mix" (i.e.. the numbers and types of businesses
found in a downtown). In healthy downtowns some WWW “MW
types of businesses were consistently present in
greater numbers than in downtowns with poor wumd‘i FWWWW
health. Most significantly. "browsing shopping"
stores were shown to be closely associated with mum Lowcnmerale
healthier downtowns. M
pagination mine

On the other hand. low evaluations of downtown Whalin- :3“. M
health were most closely associated with two fac-
tors-unemployment. especially from degree of WMUW mmm:m::r”:m
recent loss of jobs: and competition chiefly from ”film ,mw K
shopping centers and malls. Since that study. Tyler
has completed a second Ph.D. dissertation 11996). MM “5m

Gunman-tum WM (wan-y Mont

using the same 8 Michigan cities plus 8 more. Those
findings were pretty consistent with the earlier
study. And this summer he will begin a similar study A . -
of downtowns in large cities across the US. Norman
Tyler. Ph.D.. can be reached at the Dept. of Geogra~
phy and Geology. Eastern Michigan University.
Ypsilanti. Ml 48197. ph 734-487-8656. fax 734-487-
6979, and email: geo_tyle@ online.emich.edu.

296

Qua-”mm

“mmwtupw Source Normanva 1957

Malt]

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SOUTH BEND TRIBUNE

 

 

 

 

memwmm.m.mmmmom

WJbulWhvoNedhmcommflymtu'lubdo
W'IWWNM.

Attitude revives ‘dead’ downtown
yNEVLE “MIX ‘ '

 

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nomenc-umu. mug n1986.tmumudehorowu menu-tumult»: '
mm- : hm'Dowagiaciandeedtown. mwmmmno
mmmeNnNd There'snowayto “M. whwm
“nae-mm ma mmw‘m'mww “W MWMMWHS
wwmhm zone.Halfollhostonsm Whmmwm hll
olthedty‘lbowmun Authw- vacuuendboudedup! mmhmmm
“mu-Mu wind. :1»qu FNW ”maxim-nan: Mum
amhmnmeflfl WWW £11kathth
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I lulu
pie ”- WMWMmu Ihemhuedunuwydhalm
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- m‘l'hernm hrhg m m I} W Ind—n .mw
m'"mmunfldm W ugmmummum ”Altm'l'hewwnnfllfld
email: we new. booth-hum.
who-dean)!” were ”magnum-unbound“ “Amen

'1

Sturgis town cente

%
a O

 

 

w WWW Hiya-pained "Int-ml lheGnuerSouhHlvenClm
M w _ Waller! and Inn-n driving herd .
DOWAGIAC—Jdlingban- l with mums-magnum Mubemnornlstmc
flumludw mthwu-k on m‘fielmeenumemd mum"
chlun mun mm wilh mum mad min damn-1| math-m "m to unmwmcmmmun
downtownhninen ' m Shenldlhe all! "ma men. wine to
mm.nwmswmmdus.nww mmmwummmm
. . bu Hum melee: ”mmwmmde
.mlouflbemStJueph mum-made Soulhlllvendependshuvfly velopednddemolinhin‘ old
CW.WMMW . mmmbrm m“
ce behind Douche. while "We've done when WWW . underuhen by
. Hannah-bellman: me one done.” re .T'hedwnm on mmmmml
mv Communal ‘GeryMAyer. a! unflvumharbor.wiu1m “V“ the
Inca the to communities mm lanur» mmmw» “WWW down
[it“Wetned plantain! WmMye-rm “hm-“WWW
“l'mehledloseeom' loukthemmumhyearinhm The MIMI-n1 m in Wmmwfl'wmmln
ghlhehind'l‘ecmneh. of ‘nghukeu‘lnphcedme mkamflldcoapenuonbe- WWW!“
Ind Dongle I" with viable plank-mm limb~ buli- heal the buinen mum ty mm
downtowns. said Kim neat: are going back to the and government. to promoting residential and in-
uecufivefirectarolthe quflntoldnyleolyeueryur" [any mmu ' t

 

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298

 

 

 

 

The Wal-Mart Invasion

Five years ago, there wasn’t a Wal-Mart to be found in Michigan. But
like a blitzkrieg, the Arkansas corporation is fast blanketing the state,
leaving downtown retailers scrambling to stay afloat.

By Norman "Len

 

lthmIt-h ' ‘ L
(all) located In riuyor metro areas. the Wal-

V:I.ltun specifically targeted cittes with pop-
ulations 01 20 “00 or less lot his store I
stores have appealed largely to lower and middle- class
hols-st holds—forty percent of Its customers have house-
N’D- ”‘

mate-g). Wal- Matt has quickly become the biggest dit-
count retailer In the country In I994 Wal- Mart sold
311) billion in merchandise out of Its 2.500 stores. l r
outdistancing the $34 billion sold by Kmart. with its
3. Nut) store

Wal- Marts have thrived in small towns for manyI rea-
sonsA They have laccd'
retailers. Small cities and townships don't usually regu-
late land development very strictly. and most wanted he
increased tax base Wal-M rt would create. The stores'
attractive prices lure customers from surrounding corn-
munities. creating an economic impact well beyond the
cities where they are located.

The first Michigan WalMart was built in Alma. Now.
live years later. there are 53 across the state. and they

All hnII:h WI

.. c - a 1 ~ c
Meiier or a Kmart and could easily establish reia'il domi-
nance. the company has more recently moved In nto larg-
er rnetro areas tto take on Its rivals directly
Not surprisingly. WalMarts have had a startling Impact
nrinetatl many downtown n.s Writes researcher Edward
Welles. "As WalMart rolled out its franchises. it sucked
commerce oil Main Streets. destroying traditional retail-
en that had served their communitiet for generations."
But in a study of the impact ol' It WalMarts In Iowa.
Kenneth Stone found posIIIve impact. in towns where

 

 

ts'small-mtndedness’—merchants are trying to com»
pete directly with \Val-Marl, when they need to "urg-
nIte new customer markets.

WalMart's Impact hasn't been that great in Howell.
according to a local offictal there. because the down-
town's up scale s penalty stores have her-I a niche that Is
separate from the products the large dIscounlersc arry
One specialty shop merchant saId WalMart has actually
helped their business by drawing shoppers in [nun a
larger area. A new business owner said he didn't feel the
threat. tor he saw WalMart and Kmart catering to elderly

 

‘M-Irntles customers. thle downtowns are still appealing to
away that the Wall Marts hurt. Towns withinI a ZO‘mile younger customers.
(I... It- ... ...... c .
Iadius In use ,
min-d' ... “mu“...

average of 25 percent after five years.
Th... ' ‘ '

about the impact of the WalMart invasion on down

town retailers Merchants in Cadillac directly tie their

downtown' s vacancies with the arrival of a WalMart In

the area. must ul

 

 

 

;.h ‘ . hi -'- n thle
sesadiusted to a new retail mrx. But a local
olficial indicated the downtown at that time had
army of stores. and WalMart actually filled a large void
t -I -

 

 

 

 

L ~ r I t a .I
, , we...

-hil...‘ I c u t u s Nona-m" . . ’. -. .. ’

.. s. .. u. .... . ~ .- ..v- ., ,_.. .. .. ..

Ill up“. , a .
wholesale for what WalMart was selling them retail. oIMichrgan'r smaller Ibwnrowns
Mn...» ‘ L ‘ '1 L u
.

 

299

m 1995 MlCHK‘sAN MIN n

 

 

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313

INDEX

INDEX

Alberta, Canada 232

Albion, Michigan 45, 83, 116, 119, 124, 125, 170, 278
Albion College 45

Alpena, Michigan 177

American Institute of Architects 50

Andrews, Frank 33, 127, 182

Anson, J. 36, 127

Apparel and Accessory Businesses 162

Architectural Group 146, 149

Battle Creek 47

Battle Creek Mall 46

Berger, Mark C. 27, 181

Berry, Brian J.L. 9, 14, 181, 241

Big Rapids, Michigan 64

Blomquist, et.al. 27

Boyer and Savageau 26, 181

Browsing shopping 14], 145, 150, 151, 152, 163, 170, 174
Bumell and Galster 27

Business category 168

Business mix 145, 151, 152, 156, 157, 170, 184

Cadillac, Michigan 66, 120

Cardono, Girolomo 34

Carroll, Iowa 275

Carter, Pres. Jimmy 248

Category killers 233

Cazes 126

Census of Retail Trade 154, 242, 276

Central Place Theory 8, 180

Certified Historic District 253

Certified Historic Structure 255

Chamber of Commerce 48, 53, 55, 57, 60, 66, 72, 73, 103
Chicago, Illinois 232, 264

Christaller, Walter 8, 10, 180

Clark and Rushton 12, 181

Clawson and Hall 18

Coldwater, Michigan 68, 120

Community Conservation Guidance (CCG) Policy 248
Community Development Block Grants (CDBG) Program 31, 62, 250
Competition 10], 176

Competitors 290

Convenience shopping 232

Correlation analysis 24, 128

Conelation Proximity Chart 185

Country Club Plaza 230

County seat 135

Craycroft and Fazio 20, 181

314

Crystal Market 231
Cutter, Susan L. 35

Dallas, Texas 231

DeBlij and Muller 16

Delphi technique 190

Denman, Clayton 20

Denver, Colorado 265

Dillman, Donald 88

Discount department stores 233, 285

Dowagiac, Michigan 49, 83, 110, 116, 120, 125, 142

Downtown Albion Inc. 46

Downtown characteristics 117, 136, 151

Downtown Development Authorities Act 257

Downtown Development Authority 46, 49, 50, 53, 55, 57, 61 , 63, 65, 66, 69, 73, 75,
189, 247, 257

Easement 254

Eastern Michigan University 89

Economic deve10pment 255

Edmonton Mall 232

Employment 121, 136, 139, 146, 152, 153, 156

Factory outlets 234

Ferris State University 64

Four Point Approach 261
Francis, Walton 33, 34, 126, 182
Frank, Richard 59

Friedrichs, Goodman et al. 240
Fuguitt, Glenn 19, 135
Functional characteristics 121

Galleria 232

Gehrmann, F. 29

Ghosh and Craig 14

Ghosh and McLafferty 12
Goodness Index 24, 181
Gottman, Jean 21

Government Accounting Office 254
Grand Rapids, Michigan 57, 270
Gravity model 11, 285
Greenwood and Hunt 28

Gruidl and Kline 270

Gus Macker tournament 55

Harborside Mall 62

Harris and Ullman 240

Health Perception Index 103, 108, 114, 117, 122, 126, 127, 131, 136, 164, 175, 180,
280

Henderson, J.V. 18

Highland Park, Dallas, Texas 231

Hillsdale, Michigan 70, 170

Hillsdale College 70

315

Historic character 15, 92, 121, 136, 146, 152, 171
Historic districts 31

Holden and Deller 130
Homart 236

Horn, Robert V. 33

Hoselitz, B.F. 17

Household income 132
Houston, Texas 232

Howard, Ebenezer 17
Howell, Michigan 50, 83, 278
Hudson’s 229

Huff, D.L. 12

Huff Model 12

Human Ecology 10

Hutter, Mark 228
Hyatt-Palma 58

Index of Retail Saturation 13, 181

Industrial Revolution 16, 229, 244

Institute for Public Policy and Social Research 276
International Council of Shopping Centers 249
Ionia, Michigan 52, 120, 189

Israel 36

Jackson, Michigan 47
Johnson, Johnson and Roy 59

Kalamazoo, Michigan 264
Kansas City, Missouri 230
KMart 46, 53, 64, 67, 75, 267
Knack, Ruth Ecklish 264

Lalonde, B. 13, 181

Law of Retail Gravitation 11, 130, 181
Lessinger, Jack 19

Liu, Ben-Chieh 19, 29

Local Cooperation Group 145, 153, 155
Lord and Guy 240

Losch, August 10, 181

Ludington, Michigan 57

Lynch, Kevin 17

Main Street Program 46, 51, 75, 247, 260, 263, 274
Malisz, B. 18

Malthus 35

Manistee, Michigan 56, I42, I77

Marans and Rodgers 34, 182

Marcus, M. 15

Marshall, Michigan 59, 83, 124, 125

Marx, Karl 16

Mason, Mayer and Ezell 239

Meijers 55, 267

Merchants association 92, 103, 121, 136, I45, I72

316

Michigan Database 276

Michigan Department of Commerce 54
Michigan Department of Transportation 46
Michigan State University 89, 99, 276
Minneapolis, Minnesota 265
Mookherjee, Harsha N. 35

Mt. Clemens, Michigan 265

Murdie 9

Murphy and Vance 242

Myers, D. 23

Myrdal, G. 16

National Main Street Center 262

National Main Street Program 69

National Register of Historic Places 56
National Retail Merchants' Association 249
National Trust for Historic Preservation 247, 260, 274
Nearest Neighbor Model 12, 181

New Towns 17

New York, New York 29

Nixon, Pres. Richard 250

Normative scaling 106

Northgate Center 232

Oak Park Mall 264
Oak Park, Illinois 264
Optimal size 18
Optimism 114
Owosso, Michigan 72

Palm, Risa 243

Park, Robert 20

Park, Burgess and McKenzie 10
Parking 31, 47. 52.121.136.150,170,171,236
Pedestrian mall 264
Penney’s 57

Penturbia 19

Perceptual input 126
Physical characteristics 121
Pierce, Robert M. 29
Places Rated Almanac 181
Plato 17

Population size 130, 135
Porell, F.W. 29

Portland. Oregon 265
Poverty 132

Preston 9

Pretest 98

Promotions 236

Proxy data 153, 185
Pueblo, Colorado 29

317

Quality of life 20
“Question 1” 105, 107
Quick-stop shopping 141, 149, 152

Reagan, Pres. Ronald 249

Regional megamall 233

Regionalization 245

Rehabilitation Investment Tax Credit 252
Reilly, W.J. 11, 130, 181

Retail group 145, 149

Retail sales 92, 121, 130, 136, 137, 145, 151, 152, 154, I70, 278, 283, 290
Revolving loan fund 52

Richardson, Harry W. 17

River Oaks Center 232

Riverde 57

Robertson, Kent 22, 30, 181

Rogerson, et.al. 35

Rosen, Sherwin 27

Sales tax 278

Sales tax figures 185

San Francisco, California 231
Savageau, David 26
Schumacher, ER 22, 33, 182
Schwartz, Gail Garfield 14, 181
Seattle, Washington 232

Segal, David 18

Shopping center/mall 235
Shopping group 153

Small Towns Institute 20

South Haven, Michigan 74, 170
Southdale Mall 232
Spatial-interaction model 12
Stanford Research Center 34
Stone, Kenneth E. 266, 270, 285, 289
Streetscape 92, 120, 121, I36, I42, I49, 17]
Strip centers 234, 238

Suburban malls 31
Suburbanization 244

Suburbs 19

Survey bias 84

Survey of Professionals 98

Taaffe and Gauthier 11

Tax abatements 255

Tax Act of 1987 254

Tax credit 252

Tax Increment Financing 46, 61, 63, 65, 66, 69, 71, 76, 247, 255
Tecumseh, Michigan 61, 98, I20, 124, I25, 142

Telematics 21

Theissen polygon 13

Thorndike, ER 23

Total Design Method 88

318

Tourism 150, 163, 175
Trachtenberg, Alan 228
Transportation 244
Traverse City, Michigan 57
Tyler, Norman 5, 242

U.S. Census of Retail Trade 185

U.S. Department of Commerce 276

U.S. Department of Housing and Urban Development 40, 181, 249, 250
Units of analysis 101

University of Chicago 10

Urban Development Action Grant 31, 73, 250

Urban Development Action Team 50

Urban Renewal 30

Veblen, Thorsten I8
Vilican-Leman and Associates 68
Viroqua, Wisconsin 274

Von Thunen, Johann Heinrich 8

WaI-Mart 1, 46, 51, 53, 55, 64, 67, 71, 73, 75, 237, 240, 266
Walton, Sam 237, 267, 273

Warner and Fleisch 28

Water Tower Place 232

Waterfront Village 74

Zoning 257

319

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