THE VALIDITY OF THE ASSESSED VALUE AS AN INDICATOR OF PHYSICAL URBAN DETERIORATION Thesis for the Degree .ofPh. D. MICHIGAN STATE UNIVERSITY RICHARD ALAN ANDERSON 1969 L [B R A R. Y Michigan Seats Univew-‘V ; ‘ iv 4“. This is to certify that the thesis entitled The Validity of the Assessed Value as an Indicator of Physical Urban Deterioration presented by Richard Alan Anderson has been accepted towards fulfillment of the requirements for FILED- degree in Mience rs I I - ‘ > C I W ( \' -Jf,z’4,(/‘zo'j 1:. M/C‘T/ LA/ Major professor Date February 10, 1969 \ 0-169 TA R. Y an 8: ate reK'SI? Y ABSTRACT THE VALIDITY OF THE ASSESSED VALUE AS AN INDICATOR OF PHYSICAL URBAN DETERIORATION BY Richard Alan Anderson This thesis explores the use of assessment data in measuring and predicting physical urban deterioration in single—family residential structures. An examination of current "yardsticks“ or measuring instruments for evaluating and comparing relative housing conditions within different sub-areas of the city indicates that such tools are (l) costly and cumbersome to apply, (2) have little or no way of determining "levels" or degrees of physical deterioration, and (3) have no capacity for gen— erating predictive statements regarding the development of future physical deterioration. The purpose of this study then was to develop new techniques for studying physical deterioration in single- family residences to take care of some of these short— comings. ._. .-__-.n. -. n a. annual-54 _ .-=_._? I I I l ”wee—um? “ ' " EIIIT__________——————————————————————————————————————————————————————————————‘-Ill-'] Richard Alan Anderson Five objectives were set forth for accomplishment in this research effort. They can be stated as follows: 1. To examine the assessed value of single-family residential buildings to determine those variables or housing characteristics that influence it most strongly and directly, 2. To demonstrate the degree of correlation between the behavior of the assessed value of improvements and levels of physical deterioration, 3. To demonstrate a method using assessment data to quantify the extent of relative physical deteri- oration of single—family buildings within various sub-areas of the city, 4. To identify or retrodict the critical stage in the deterioration process in those areas of the city that are physically deteriorated, and 5. To demonstrate a method for predicting possible future physical deterioration in those areas of the city that evidence some of the early charac— teristics of physical deterioration. The study explored each of these objectives in detail and produced the following conclusions: 1. The major variables or housing characteristics in- fluencing the behavior of the assessed value of single—family structures are (1) building class, (2) age, (3) number of stories, and (4) tenure of occupancy, - — “duh..-- -.t-.-.- .u- .. - ___ _ —M-—‘ -..r. -...-;-«._:I m,_-;;_-. -' - ______I Richard Alan Anderson 2. Depreciating rates of assessed valuation correlate very strongly with levels of physical deterioration ._.—__.-_-..._a- - . in single—family residential structures, 3. Differences in slope in percent change in mean square foot assessed value in single—family res— idences can be used to measure levels or relative degrees of physical deterioration in various sub- areas of the city, 4. Differences in slope in percent change in mean square foot assessed value when examined over time can be used to identify the critical stage in the deterioration process of single family structures in those areas of the city that are physically I deteriorated, and 5. Differences in SIOpe in percent change in mean square foot assessed value can be used as a leading : -n-ub . surrogate for predicting possible future physical deterioration in those areas of the city that evi- dence early characteristics of physical deteriora— tion. For the most part the technique of time-series analysis was utilized to analyze the data and to demon— strate the "behavior" or percent change in mean square I foot assessed value in the single—family buildings examined in the study. 1 Richard Alan Anderson The laboratory community selected for developing the study was the city of Ann Arbor, Michigan. The major contributions of the study to the field of urban planning were (1) the exploration of a body of public data, namely the assessed value, to determine its worth and utility in solving urban planning problems, (2) the development of a technique for determining indices of deterioration amongst single—family residential areas throughout the city, and (3) the fashioning of a practical tool for improving local decision-making in regards to the selection of possible urban renewal areas within the city. THE VALIDITY OF THE ASSESSED VALUE AS AN INDICATOR OF PHYSICAL URBAN DETERIORATION BY Richard Alan Anderson A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY College of Social Science 1969 .-.. -.- 2;. _-:._.._._-..-— -u-.1. =.\. 7005 ©Copyright by RICHARD ALAN ANDERSON 1969 This thesis is dedicated to the memory of Reginald Grant Anderson, Jr. ACKNOWLEDGMENTS As in most research efforts of this kind there are ral individuals who have played important roles in ing me to develop this thesis, and I would like to ass my sincere appreciation to them for their encour— ant and assistance. I am very much indebted to Professors Warren Tied of the School of Business Administration of the ersity of Washington and Myles Delano of the College .siness Administration of Michigan State University heir valuable comments and ideas in helping me to ally formulate and organize my thoughts regarding the rch design. I would also like to acknowledge the members of my 3 committee who gave very generously of their time and :ise in guiding my research and keeping me honest—— ssors Stewart Marquis and Richard D. Duke of the Urban ition Laboratory of the University of Michigan, Grafton and Donald Olmsted of the Department of Sociology of Ian State University, and Raleigh Barlowe of the De- :nt of Resource Development of Michigan State sity. iv Of those individuals outside of my committee, I am tedly most indebted to Mr. Torbin Thomsen of the e of Business Administration for his great skills sistance as an astute programmer, statistician, and social scientist. I would like to extend special thanks to my com— chairman, Professor Stewart Marquis, for taking the >ersonal interest that he has in my work. Indeed, much to do with the success of the study. Finally, I would like to express my appreciation 'ife Sari and my daughters Majda and Rena who had to countless dull afternoons and weekends while the was in progress. 4 TABLE OF CONTENTS 7 TABLES . . . . . . . . . . . . . . . . . 7 FIGURES . . . . . . . . . . . . . . . . . 1 D4APS D I I O I O O 0 C I I I O O O C O I ICTION . . . . . . . . . . . . . . . . . . The Research Task . . . . . . . . . . . . A Brief Review of the Current Literature of Urban Blight . . . . . . . . . The Development of the Research . . . . SURROGATES, SOCIAL INDICATORS, AND URBAN DETERIORATION . . . . . . . . . . . Possible Surrogates and Indicators for Physical Deterioration . . . . . . . Yardsticks for Measuring Housing Deterioration . . . . . . . . . AN APPROACH TO THE STUDY OF PHYSICAL DETERIORATION . . . . . . . . . Introduction . . The Development of. a Framework of Study Operational Definitions . . . . . . . . THE ASSESSED VALUE AS A RECORD OF INVESTMENT . . . . . . . . . . . . The Determination of the Assessed Value The Problem of Building Classification vi Page ix xiii xviii (DON l4 19 25 33 33 46 51 52 56 Page Assessment Practices in the City of Ann Arbor . . . . . . . . . . . . . . . . 57 The Assessed Value as Both a Yardstick and Surrogate for Physical Urban Deterioration . . . . . . . . . . . . . . 60 UTILIZING THE ASSESSED VALUE TO MEASURE AND PREDICT PHYSICAL URBAN DETERIORATION . 72 An Overview of the Study . . . . . . . . 72 Assumptions . . . . . . . . . . . . . . . 73 Research Procedures--Part I . . . . . . . 74 Research Procedures——Part II . . . . . . 75 Research Procedures——Part III . . . . . . 78 The Data . . . . . . . . . . . . . . . . 80 The Sample . . . . . . . . . . . . . . . 82 The Selection of Sub—Areas . . . . . . . 86 Base Maps . . . . . . . . . . . . . . . . 9O Hypotheses to be Tested . . . . . . . . . 91 THE FINDINGS . . . . . . . . . . . . . . . 95 Determining Primary Variables Affecting the Behavior of the Assessed Value . . . 95 The Findings . . . . . . . . . . . . . . 99 Lot Area and Assessed Values . . . . . . 120 Age and Assessed Values . . . . . . . . 120 An Examination of the Behavior of the Mean Square Foot Assessed Value in Selected Sub—Areas . . . . . . . . . . 124 Sub—Area Number 1 (Deteriorated Area) . . 126 Sub-Area Number 2 (Transitional Area) . . l3l Sub—Area Number (Transitional Area) . . 137 ALA) Sub-Area Number Urban Renewal (Deteri— orated Area) . . . . . . . . . . . . 143 Sub-Area Number 5 (No Deterioration "Good" Area) . . . . . . . . . . . . . . 149 Sub—Area Number 6 (No Deterioration "Good" Area) . . . . . . . . . . . . . . 153 AN EXAMINATION OF THE FINDINGS . . . . . . 161 Introduction . . . . . . . . . . . . . 161 Objective Number One . . . . . . . . . . 162 Objective Number Two . . . . . . . . . . 163 Objective Number Three . . . . . . . . . 166 Objective Number Four . . . . . . . . . . 168 er Objective Number Five . The Relevance of the Findings to the Field of Urban Planning . CONCLUSIONS DICES )GRAPHY viii Page 172 177 180 185 234 Percent Change in Mean Square Foot Assessed Value: Percent Class Percent Class Percent Class Percent Class ?ercent LIST OF TABLES City of Ann Arbor, Michigan Change and Difference in Slope: A Buildings . . . . . . . . Change and Difference in Slope: B Buildings . . . . . . . . . Change and Difference in Slope: C Buildings . . . . . . . . . Change and Difference in Slope: D Buildings . . . . . . . . . Change and Difference in Slope: Owner—Occupied Buildings . . . ercent Change and Difference in Buildings with Rental Rooms . ercent Change and Difference in Buildings Built Before 1940 I o . Slope: Slope: ercent Change and Differences in Slope: Buildings without Garages . . ercent Change and Difference in Slope: Buildings with Garages . . . . . . . rcent Change and Difference in Slope: Woodframe Houses . . . . . . . . . . rcent Change and Difference in Slope: Brick Dwellings . . . . . . . . . . rcent Change and Difference in Slope: Multi—Story Buildings . . . . . . . ix Page 100 102 103 105 106 107 109 111 113 114 115 117 Percent Change and Difference in Slope: Single— Story Dwellings . . . . . . . Percent Change and Difference in Slope: Single—Family Zoning . . . . . . . . . Difference in Slope of Various Housing Characteristics . . . . . . . . . . . Percent Change Sub—Area No. Percent Change Sub~Area No. 1940 . . . . Percent Change Sub—Area No. Rooms . . . Percent Change Sub—Area No. Percent Change Sub—Area No. Percent Change Sub—Area No. Percent Change Sub—Area No. Percent Change Sub-Area No. 1940 . . . . Percent Change Sub-Area No. Percent Change Sub—Area No. Percent Change Sub-Area No. Rooms . . . Percent Change Sub-Area No. and Difference in Slope: 1, Class D Buildings . . and Difference in Slope: 1, Buildings Built Before a o c n o a o u I o o o u and Difference in Slope: 1, Buildings with Rental 0 o o o o a o o o I o I a and Difference in Slope: l, Multi—Story Buildings and Difference in Slope: 2, Class C Buildings . and Difference in Slope: 2, Class D Buildings . and Difference in Slope: 2, Multi—Story Buildings and Difference in Slope: 2, Buildings Built before and Difference in Slope: 3, Class C Buildings . . and Difference in Slope: 3, Class D Buildings . and Difference in Slope: 3, Buildings with Rental and Difference in Slope: 3, Multi—Story Buildings Page 118 119 122 127 128 131 132 134 135 136 138 139 140 142 Percent Change Sub-Area No. 1940 I l O I Percent Change Sub—Area No. Rooms . . . Percent Change Sub—Area No. Percent Change Sub—Area No. 1940 . . . . Percent Change Sub—Area No. Percent Change Sub—Area No. Percent Change Sub—Area No. (Percent Change Sub-Area No. 1940 . . . Percent Change Sub—Area No. 1940 Percent Change Sub—Area No. ercent Change Sub—Area No. ifferences in ifferences in ifferences in and Difference in Slope: :‘Li ~. A 3, Buildings Built before and Difference in Slope: 4, Buildings with Rental and Difference in Slope: 4, Class D Buildings and Difference in Slope: 4, Buildings Built before and Difference in Slope: 4, Multi—Story Buildings and Difference in Slope: 5, Class B Buildings and Difference in Slope: 5, Multi—Story Buildings and Difference in Slope: 5, Buildings Built before a o o I o n o n and Difference in Slope: 6, Buildings Built before and Difference in Slope: 6, Class B Buildings and Difference in Slope: 6, Multi-Story Buildings Slope Slope Slope otal Differences in No. 1 . . . in Various Sub—Areas in Sub—Area No. in Sub—Area No. Slope in Sub—Area o o o I o a o o 1 4 ighest Value Differences in Slope in Sub—Area No. 4 . . o a a o a o o 0 xi 0 c Page 143 144 146 147 148 150 151 152 154 155 157 158 164 166 169 171 Differences in Slope in Sub-Area No. 2 . . Differences in Slope in Sub—Area No. 3 . . General Housing Characteristics for the City of Ann Arbor . . . . . . . . . . . Locations and Characteristics of Sub-Areas Demographic Characteristics of the Sub- Areas 0 C O I I I O I 0 I O O O I 0 I I Page 173 175 187 190 197 LIST OF FIGURES Page & Schematic Diagram of the Deterioration Process . . . . . . . . . . . . . . . . . 38 Breger's Concept of Physical Deterioration . 39 t Time—Series Analysis for Measuring Changes in Investment . . . . . . . . . . 42 t Time—Series Analysis for Measuring Change in Mean Square Foot Assessed Value . . . . . . . . . . . . . . . . . 43 Difference in Slope Defined . . . . . . . 47 The Assessed Value as a Record of Private Investment . . . . . . . . . . . . . . . . 54 .ssessment Practices in the City of Ann Arbor, Michigan . . . . . . . . . . . . . 58 ssessed Values and Building Improvements: Example Number 1 . . . . . . . . . . . . 61 ssessed Values and Building Improvements: Example Number 2 . . . . . . . . . . . . . 62 ssessed Values and Building Improvements: Example Number 3 . . . . . . . . . . . . . 63 sessed Values and Building Improvements: Example Number 4 . . . . . . . . . . . . . 64 sessed Values and Building Improvements: Example Number 5 . . . . . . . . . . . . . 65 sessed Values and Building Improvements: Example Number 6 . . . . . . . . . . . . . 66 xiii Utilizing the Assessed Value to Predict Future Levels of Physical Deterioration Defining the Critical Stage . . . . Predicting Physical Deterioration The Behavior of the Mean Square Foot Assessed Value . . . . . . . . . . Percent Change in Mean Square Foot Assessed Value: Class A Buildings Percent Change in Mean Square Foot Assessed Value: Class B Buildings Percent Change in Mean Square Foot Assessed Value: Class C Buildings Percent Change in Mean Square Foot Assessed Value: Class D Buildings Percent Change in Mean Square Foot Assessed Value: Owner-Occupied Buildings . . . . . . . . . . . . Percent Change in Mean Square Foot Assessed Value: Buildings with Rental Rooms . . . . . . . . . . . 'ercent Change in Mean Square Foot Assessed Value: Buildings Built before 1940 . . . . . . . . . . . ercent Change in Mean Square Foot Assessed Value: Buildings Without Garages . . . . . . . . . . . . . ercent Change in Mean Square Foot Assessed Value: Build1ngs w1th Garages . . . . . . . . . . . . . ercent Change in Mean Square Foot Assessed Value: Woodframe Houses ercent Change in Mean Square Foot Assessed Value: Brick Dwellings . a Page 68 78 80 100 101 103 104 105 107 108 109 111 112 113 115 Page Percent Change in Mean Square Foot Assessed Value: Multi-Story Buildings . . 116 Percent Change in Mean Square Foot Assessed Value: Single—Story Dwellings . 117 Percent Change in Mean Square Foot Assessed Value: Single—Family Zoning . . 119 Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 1, Class D Buildings . . . . . . . . . . . . . . . . 126 Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 1, Buildings Built before 1940 . . . . . . . 128 Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 1, Buildings with Rental Rooms . . . . . . . 129 Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 1, Multi—Story Buildings . . . . . . . . . . 130 Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 2, Class C Buildings . . . . . . . . . . . . . . . . 132 ’ercent Change in Mean Square Foot Assessed Value: Sub—Area No. 2, Class D Buildings . . . . . . . . . . . . . 133 ercent Change in Mean Square Foot Assessed Value: Sub-Area No. 2, MUlti'Story Buildings 0 I I o I a o I I o 134 ercent Change in Mean Square Foot Assessed Value: Sub—Area No. 2, Buildings Built before 1940 . . . . . . . 136 ercent Change in Mean Square Foot Assessed Value: Sub-Area No. 3, Class C 137 Buildings . . . . . . . . . . . . . . . ercent Change in Mean Square Foot Assessed Value: Sub-Area No. 3, Class D 138 Buildings . . . . . . . . . XV Page Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 3, Buildings with Rental Rooms . . . . . . . 140 Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 3, Multi-Story Buildings . . . . . . . . . . 141 Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 3, Buildings Built before 1940 . . . . . . . 142 Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 4, Buildings with Rental Rooms . . . . . . . 144 Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 4, Class D Buildings . . . . . . . . . . . . . . 145 Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 4, Buildings Built before 1940 . . . . . . . 146 Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 4, Multi-Story Buildings . . . . . . . . . . 148 ?ercent Change in Mean Square Foot Assessed Value: Sub-Area No. 5, Class B Buildings . . . . . . . . . . . . . . . . 149 ’ercent Change in Mean Square Foot Assessed Value: Sub—Area No. 5, Multi—Story Buildings . . . . . . . . . . 151 ercent Change in Mean Square Foot Assessed Value: Sub—Area No. 5, Buildings Built before 1940 . . . . . . . 152 ercent Change in Mean Square Foot Assessed Value: Sub—Area No. 6, Buildings Built before 1940 . . . . . . . 153 ercent Change in Mean Square Foot Assessed Value: Sub-Area No 6, Class B 155 Buildings . . . . . . . . . . . . xvi t—_—'I I Page ercent Change in Mean Square Foot Assessed Value: Sub—Area No. 6, Multi-Story Buildings . . . .'. . . . . . 156 roperty Assessment Record Card City of Ann Arbor, Michigan . . . . . . . . . . . 203 ssessment Data Form . . . . . . . . . . . . 205 xvii :__', [ LIST OF MAPS Page ,se Map for the City of Ann Arbor, Michigan . . . . . . . . . . . . . . . . . 199 tilding Sample for the City of Ann Arbor, Michigan . . . . . . . . . . . . 200 nsus Tract Map for the City of Ann Arbor, Michigan . . . . . . . . . . . . . . . . 201 b— —Area Map for the City of Ann Arbor, Michigan . . . . . . . . . . . . . . . . 202 xviii INTRODUCTION In becoming this giant "Nation of Cities" in an bly short span of time, the United States has in— and developed some of the gravest urban problems befall any country.1 Pollution, congestion, ob— 1ce and deterioration, and large—scale social 2 such as that manifested in organized crime and riots constitute collectively, perhaps, the malaise :entury.2 And, to these general urban crises must added other emerging ills such as increasing rates 1 illness and rising welfare expenditures.3 Solu— these gargantuan problems are indeed neither easy er nor to effect, for not only do they require nt amounts of time and money, but they also demand us personal commitment of human resources from public and private sector. ime, however, seems to be the one resource that rtest supply.4 The growing awareness of the in- in many of our social systems by the lower classes neral urban poor has placed new demands on both 'ticians and administrators to resolve the prob- e city ngw. While long-term solutions will be effect permanent, stable conditions of social 1 are, many short-term ones are now needed to remove the tic edge from the present situation. In an effort to understand and resolve some of 2 urban crises that have worked their way into recent .nence, social scientists and other interested urbanists endeavored to fashion a broad array of analytical tools Investigative techniques.5 One of these methods in cular has centered on the use of social indicators to re and measure the quality of urban life.6 An exam— Dn of the behavior of relevant indicators allows the :igator to achieve considerable insight into a socio— 11 problem before it emerges into a full blown catas— : without the encumbrance and expenditure of costly, .y research. This particular endeavor focuses on the specific m of residential blight or physical deterioration empts to explore the utility of using the assessed (for local property tax purposes) of improvements as indicator for understanding and analyzing it. 1 deterioration or the slum has long been a center n research interest.7 However, an examination of erature of blight indicates that very little in the systematic research has been accomplished to date.8 The general aim of this study is first to determine or not the assessed value constitutes a valid f investment in property (i.e. that there are .ations between assessed values and levels of private .ment); secondly, to ascertain whether or not there rrelations between levels of investment and physical oration, and thirdly, to explore subsequent ways in they might be utilized to analyze and differentiate 1 various levels of deterioration within the city.9 There are several reasons for wanting to undertake ' of this nature. The first is to develop a frame of .ce for an explicit discussion of deterioration—~e.g. t easily lends itself to quantification allowing the her to speak of blight in specific terms. The second is that, as Meier has pointed out, one an delimit parameters of behavior for anticipating social activity if he can "tap” certain primary r 1 ion flows that are contained in many of our on— cial systems.lO Thus, a study of economic activity cted in changes of assessed values might provide t clues to future urban conditions. The third, and perhaps most important reason, is eriorated or blighted areas usually provide the ars" in which many adverse social conditions occur ammunity; hence, any effort to study them or to ays of studying them is in itself an important ion towards the general solution of urban 11 Research Task A considerable amount of research has been focused veloping adequate criteria and/or measuring instru— for evaluating building and environmental conditions n our cities.12 In regards to specific research ef— in urban blight, or, more precisely, physical urban ioration, this has had a strong effect in supporting :neral notion that "blight" is essentially a patholog- rondition of either a building or an area of a city. r, we all know that many areas of cities, and indeed cities (ghost towns, etc.) are “blighted" long they ever manifest any of the physical characteris- f deterioration. How then might one investigate this "lag period" in Ihere exists a potential for blight to occur prior 39 sequent development of an actual state of physical ration? What can we use as a predictor of blight? terms of some of the current sociological research 9 conducted, what might be a suitable "leading" e or proxy for physical deterioration, and how be utilized? The specific charge of this research task is to ate how assessment data for buildings (for tax ) might be used as a "leading" surrogate for phys— erioration. There are five principal objectives research hopes to accomplish. These are listed as To examine a sample of assessment data of single— family residential buildings to ascertain the extent to which particular housing characteristics in— fluence assessed values, To demonstrate the degree of correlation between the behavior of the assessed value of improvements and levels of physical deterioration according to current standards of physical deterioration, To demonstrate a method using assessment data to quantify the extent of relative physical deteriora- tion of single—family residential buildings within various sub—areas of the city, To identify or retrodict the critical stage in the deterioration process in those areas of the city that are "classified" as being physically deteri- orated, and To demonstrate a method in which the assessed value might be used as a leading surrogate for predicting possible future physical deterioration. In general the research problem will be developed stages or phases. The first concerns the develop- 1 random sample for selecting a number of single- sidential buildings and the subsequent examination values to determine the extent to which particular bles (i.e. age, building class, construction type, luence them. The second phase of the study will be to demon- te the degree of correlation between the behavior of assessed value over time and levels of physical deteri- ion as measured by current standards and yardsticks of Lcal deterioration. At this point, the study will also :vor to show how assessment data can be utilized to [re levels of physical deterioration in any sub—area of ity. The final portion of the research will confront the em of identifying the critical stage in the deteriora— process in those areas of the city already designated ing physically deteriorated (census definitions of .oration and dilapidation, etc.) and will also try to LOW future physical deterioration might possibly be ted in those parts of the city that have some of the "earmarks" of blight. E Review of the Current :ure of Urban Blight A review of the literature of urban blight indi- .hat very little to date had been done in the way of ng or quantifying physical deterioration. To verify ther substantiate this initial observation, letters about research efforts in the field of urban blight it to several leading people in the field of housing 14 1 The letters not only inquired about past or t studies for measuring (quantifying) physical deteri- n, but also asked about research efforts that were ed or focused upon blight prediction. The only major hit of research uncovered in this was that of Dr. Frederick Case of UCLA in his studies :hern California and Baltimore, Maryland.15 In his :h of general residential blight Case measured deteri- 1 as the total impact that various factors (housing :eristics given in the U.S. census data) had on indi— census tracts. A second, and somewhat related effort, was that of nislawChamanski of the University of Pennsylvania. study of Baltimore, Maryland, Dr. Czamanski attempted stigate the effect of public investment on urban Lues.l6 As Case had attempted to isolate the major as affecting social and building conditions, :i tried to determine the principal variables affect— .n land values. A third important attempt at investigating urban ation that should be mentioned is that of the San 3 Community Renewal Program study conducted by 17 In their investigation Little and associates. :, deterioration was measured as the extent to 'ticular sub-areas of the city did or did not hold 18 Thus, those areas having al for investment. no potential for investment were ranked as being alighted. One early effort (circa 1946) of quantifying phys— deterioration in commercial and industrial areas was conducted by Harland Bartholomew and Associates in louis, Missouri.19 In this study a point—score system evaluating building conditions and their potential use established and each building was rated accordingly. was an important study in that many of the rating s explored by the Bartholomew technique were later ed over into the U.S. Public Health standards and such rating devices. :yelopment of the Research This research effort hopes to complement some of >using research that has been accomplished to date. ruses on the assessed value of single-family residen— uildings in an effort to identify those variables or g characteristics that have the greatest impact on ad values. It then endeavors to demonstrate a tech— Eor utilizing these findings to measure physical >ration. In that assessed values are strongly related to investment (see Chapter I, Figures 1 through 6) in parts of the city and that blight or deterioration conceived of as varying levels of investment poten- thin the city, this study hopes to provide a small I—‘ a between the research of Case and Czamanski in Los : and Baltimore and that of Arthur D. Little in San co. The importance of this work however lies not in tribution to the general fund of urban theory and h, but rather in its utility as a tool for direct formulation._ As a technique for measuring blight ntifying areas of future physical deterioration, it i that it could eventually be utilized as a device acting specific urban renewal areas within a city. FOOTNOTES 1Prospects for America, The Rockefeller Panel is, New York: Doubleday and Co., 1961. See also 1r, Robert C., "The Urban Frontier, . The Problems before in The Urban Complex, Garden City, New York: Anchor 1966. I 2Weaver, Robert C., The Dilemmas of Urban America, idge: Harvard University Press, 1965. 3Recent figures now indicate that a 11f of the total number of hospital be 3w occupied by mental health patients. For further ration into the severity of the situation see Action antal Health, edited by the Joint Commission on Mental ss and Health, New York: Basic Books, Inc., 1961. pproximately ds in the country 4Lowe, Jeanne L., Cities in a Race with Time, New Random House, 1967. In addition to this work there :en a recent spate of literature on the subject. Carson's The Silent Spring points to the dangers of ants in the earth's atmosphere, Lewis Herber's Crisis Cities addresses the full spectrum of urban dangers—— congestion, psychological stress, congestion, pollu— etc., Peter Blake attacks the ugliness of the urban nment in his harangue, God's Own Junkyard, and Paul n focuses on the absurdities of urban living and the n's complacency in going along with them. (Growing 1rd.) 5The systems analysis approach to the solution of and metropolitan planning problems has ushered in a :riety of techniques and analytical tools._ For a axamination of some of them see Wheaton, William L. C. :ions Research for MetrOpolitan Planning," Journal of trican Institute of Planners, Nov. 1963, Goldschalk, '., and William E. Mills, TrA’Collaborative Approach .ning through Urban Activities," Journal of the Amer— stitute of Planners, March 1966, Me1er, Richard L. hard D. Duke, "Gaming Simulation for Urban Planning," of the American Institute of Planners, Jan. 1966, ers, Andrei, "Matrix Methods of Population Analysis," of the American Institute of Planners, Jan. 1966. ct, the list is endless. Considerable numbers of s have appeared in the Proceedings of the Regional Association, the Annals of the Assoc1ation of Amer- Dgraphers, and other such publications. I 11 I 6Bauer, Raymond A., Social Indicators, Cambridge: M.I.T. Press, 1966. See also Maisel, Sherman, "Housing . Obtained from Sampling Public Records," Land Econom— Vol. 31, August 1955, and Hearle, Edward F., and 10nd J. Mason, A Data Processing System for State and ,1 Government, Englewood Cliffs, N. J.: Prentice-Hall, 7Early interest in the slum or blighted areas was It evidenced in the literature in the works of Charles Lens--e.g. Oliver Twist, 1838, Hard Times, 1843, etc. .he United States, Jane Addams was one of the earliest .orers of the lot of the urban poor, being primarily :rested in the European immigrants who were endeavoring .cculturate themselves in the cultural atmosphere of the ,t American cities. See Addams, Jane, The Hull House and Papers (New York: T. Y. Crewell, 1895), and tty Years at Hull House (New York: The Macmillan Co., 3. Jacob Riis, an early American sociologist also .ished a considerable amount of literature-~e.g. H93 Other Half Lives (New York: Charles Scribner & Sons, ), and The Battle with the Slum (New York: The Mac— .an Co., 1892). In the interest of city planning, tezer Howard published a very persuasive book on the .ition of the London industrial environment and the lot .he working man which subsequently led to the develop— of an actual garden city, Letchworth, England. See rd, Ebenezer, Garden Cities of Tomorrow (London: on Univ. Press, 1898). 8McGuire, Joseph W., "Measuring Change in Real te Values," The Appraisal Journal, Volume 23, July . See also Ratcliff, Richard U., "Housing Standards," rban Housing edited by William L. C. Wheaton, Grace rim, and Margy Meyerson, New York: The Free Press, , and Twitchell, Allan A., "An Appraisal Method for lring the Quality of Housing," also in Urban HouSing. 9Considerable interest has been demonstrated in general sphere of research; however, the speCific >ach undertaken in this study has not as yet been fol— I. Sherman Maisel has explored the idea of obtaining tng data from public records (note footnote 6 above) Lorton Isler has examined the specific utility of var- kinds of data to be used in community renewal program- "Selecting Data for Community Renewal Programming, al of the American Institute of Planners, Vol. 33, 1967. szanski explored the effect of ublic invest- On land values in his study of Baltimore in 1964 which 5 focus is perhaps the counterpart to this research. t. However, szanski was not concerned w1th examining te improvements or building values. lOMeier, Richard L., A Communications Theory of 'ban Growth, Cambridge: The Joint Center for Urban Stud— :s, Harvard, M.I.T., 1965. Many urbanists have taken at the slums are major crime and delinquency centers in e city. See especially Jacobs, Jane, The Death and Life Great American Cities, New York: Vintage Books, 1961, rticularly the Introduction and Chapter 1. Also, see eicher, Peggy, "Some Sources of Residential Satisfaction an Urban Slum," Urban Renewal: People, Politics, and inning. Edited by Jewel Bellush and Murray Hausknecht, :den City, New York: Doubleday Anchor Books, 1967, and in, John P., "The Myths of Housing Reform," Urban Housin .ted by William L. C. Wheaton, Grace Milgraim, and Margy .in Meyerson, New York: The Free Press, 1966. issue with this notion 12There has be en a long standing interest in social Iicators, but most n oticeably on the part of economists. ve examined the possibility of devel— social indicators to cover a full ge of behavior. Such a development could be used for hioning feedback systems for monitoring our environment warning us of impending social crises of grave impor— ce and significance. See Bauer, Raymond A., Social icators, Cambridge: The M.I.T. Press, 1966 and The als of the American Academy of Political and Social ance, "Social Goals and Indicators for American Society imes I and II," May and September 1967. ng a broad array of For the most part, single-family residential .dings will be examined. However, there are some dwel— [8 within the sample that have rooms for rent or that Lain small apartments. Single-family reSidential build— I have been selected for tWO major reasons. The first that their assessed values are determined by replacement 1 methods which makes them independent of market condi— s and influences (see Chapter III, Figure 6). Other 5 of residential structures such as duplexes and apart— houses have their assessed values determined largely income capability and market value methpds. Thus," incur many problems in isolating their true worth would extend beyong the scope of this research. .The ad reason is that single—family re51dent1a1 buildings titute the greatest land use in any American City roximately 40 to 50%) and thus comprise a major segment 1e housing problem in the country. .— ass. 13 14Letters were sent to Warren Seyfreid, Dept. of Real Estate in the School of ness Administration at the University of Washington, Glenn H. Beyer, Director of the Center for Housing and ronmental Studies at Cornell University, Dr. John W. nan, Center for Urban Studies, University of California nitoring the environment.4 This should not be mis- ‘ued as a utopian effort to effect a "big brother" y for the close scrutiny of individual citizens, but as a public warning network for impending social . Thus, one could argue that if it is so important ecast impending economic conditions—-e.g. recessions, 16 t money, rising interest rates, etc., why isn't it also rtant to warn of other social dangers of comparable itude and significance? The effectuation of a practical urban feedback 2m to detect and correct deficiencies and dangers in environment would not only require the Herculean task Illing together many existing indicators, but also the oning of many additional ones to fill numerous infor— n gaps. For we often find that the probability of a social phenomenon being represented in some statis— data is usually a direct function of the articulate— and power of the groups who are affected by it.5 In are exact case of housing and the urban poor, this appears to be pretty well substantiated. We know tittle about the quality of life in slum areas and portance of adequate housing in the lives of slum rs. Many difficulties of both a social and psychological seem to arise in the development of information 5. Boulding points out that there is almost a con— effort on the part of the general public to guard from "information overload" which eventually con— s to the unfortunate resultant effect of such ad— ocial behavior as arms races, price wars, schisms, etc.6 Dyckman also states that giant federal data ontaining millions of facts of a private and personal 17 Ire portend threatening situations to many people simply .use there has traditionally been a great lack of con- over the use and regulation of them.7 In utilizing leading surrogates and social indica— to achieve early insight into developing behavioral asses, there are many problems with which the researcher :o contend. Initially there is the selection of proper indices zitable yardsticks for investigating or measuring par— ar social phenomena. For example, if poverty is to be ed as a specific reading on a yardstick such as a par— ar ceiling or level of income, such an arbitrary index well include many families who are not actually suf- J the adverse effects of poverty and exclude many who A second problem is the rudimentary matter of .te data. In many instances, research has to be ted on very nebulous shreds of evidence simply be— there is nothing better to go on. Such research 5 are indeed not worthless or totally invalid, but limited in their scope and utility. Thus, in con— ; any kind of social science research, the question not be, "Is the data accurate?," but rather, "For Lses and levels of research are their accuracy suit- .nd for which are they not?"9 18 A third difficulty that often plagues social scien— .sts in applying surrogates and social indicators to their :search is the matter of conflicting indices. Quite often ends which are seemingly indicated or revealed by one rrogate are emphatically contradicted by those of another. us, in studying trends in religious beliefs, one might be sled into assuming that there is an increasing interest religion simply because a possible indicator such as erh attendance is increasing, for it might also be pos- Ile to find a second indicator to point out that concom- Lnt actions regarded as charitable or "religious" are kedly decreasing.lO A final point of concern that warrants mentioning that the utilization and interpretation of social indi— ors is closely linked to personal values, tastes, pref— ll Hence, what one investigator aces, and the like. It conclude to be of great significance in his partic- : investigation of some social behavior, another indeed It not. A good example, of course, is the proliferation Lhe automobile and the mass media of communication.12 see them only as the wanton pursuit of crass material es, while others View them as the largesse of technol- or the bounty of the free enterprise system. 19 sible Surrogates and Indicators Physical Deterioration Before examining the use of the assessed value of :ovements as an indicator and possible surrogate for :ical urban deterioration in some detail, it would be . to explore briefly the general field of social indi- rs. This would provide some rationale for the specific ction of assessment data and would give this portion of study some additional grounding in sociological litera— as well. Presently social scientists are examining a wide aty of surrogates and social indicators in the hopes of >Ving their general knowledge of urban blight. Among lore prominent items on the general list of possible es that are currently being explored are trends in 1 health, distribution of welfare expenditures, levels come, education, and employment, and rates of crime elinquency. Historically it has been felt that there strong linkage between "location" or the physical :s of the environment in which adverse social behavior >lace and the actual deviancy or social pathology 5.13 Recently this notion has been somewhat rein— ’ by the spate of violent street riots that followed sassination of Martin Luther King, Jr. Most of the Fook place in those parts of the city where conges— rd unemployment rates were the highest, levels of I I 20 income, education, and occupational skills the lowest, and the quality of housing the poorest. In examining the literature of social welfare and clinical psychology, a substantial amount of evidence ap- pears to support the idea that there are fundamental link- ages between various kinds of mental illness and particular geographical areas of the city. In their study of mental illness in the city of New Haven, Hollingshead and Redlich found that there were definite correlations between certain types of mental disorders and social class.14 And, by 10— :ating the incidence of certain mental disorders on a map, :hey found that physically deteriorated areas proffered :ingularly high rates for some diseases. Paris and Dunham further noted that there were striking differences in rates If hospitalization for particular kinds of mental diseases mong specific residential areas in Chicago.15 As had ollingshead and Redlich, they too found that certain dis— ases had higher correlations with "slums" or deteriorated reas than did others. Such findings give rather strong redence to utilizing rates of mental illness as a possible irrogate for physical urban deterioration. As general indicators of urban deterioration, avels of income, education, and occupation have had a fair .are of success.16 For although the Horatio Alger myth es become a reality in a few singular instances in our ciety, it is more often fiction than fact. As a rule, 21 I children of humble parentage do not make it to the top in our society.17 Levels of education, income, and status seem to correlate very strongly with housing quality (those with higher incomes and levels of achievement live in better neighborhoods, etc.). A further important correla— tion regarding housing and personal achievement on the part of children is that not only do deprived individuals achieve less, but they have substantially less of an idea as to what success is or should be.18 This fact is not only evidenced in the literature of housing and urban deteriora— tion, but in the very intentions and actions of many of our present day federal programs. Among the more notable of these, of course, are Operation Headstart, The Job Corps, ‘Federal Aid to Education (adult education, etc.), VISTA and I ICommunity Renewal. Aid to Families with Dependent Children (AFDC) is Ialso an important indicator for housing conditions and a {possible surrogate as well. Since those individuals receiv- Iing financial assistance from this program have housing that is considerably below the national average in quality, AFDC is a good indicator for urban deterioration.19 In Examining the 1960 U. S. Census data, it can be seen that bnly 70% of the number of families receiving AFDC have Housing with hot and cold running water as opposed to 87% 5f the total number of U. S. families. And in addition, I )nly 72% of the total number of AFDC families have housing 22 which affords them the exclusive use of a flush toilet. Again, this figure compares to 87% for the total number of U. S. families.20 Other welfare expenditure programs such as Old Age Assistance, Aid to the Blind, General Assistance, etc., are also indicators and possible surrogates for urban deteriora- tion. In almost all instances the assistance meted out to the low income families in these programs is considerably below that which they need to obtain adequate housing.21 Thus, if one wished to have a quick overview of the poorer quality housing stock in almost any city, he would only have to glance at the distribution of welfare recipients within the city. Crime and delinquency rates have historically been thought of as suitable indicators for measuring slums or fieteriorated areas.22 However, upon closer examination, Hhe causal relationship between sub—standard housing condi— tions and crime and delinquency rates seems to be more myth than reality. Street riots and the like do take place in phe slum, but they also take place on the college campus, in front of the White House, and in numerous other “soc— Ially approved" areas. | If there were any logic to the notion that slums fired criminals, the proponents of such theories would be I ard pressed to explain why a very large number of families 23 .g in such areas where there is substandard housing [2 experience drug addiction, alcoholism, or general .nal behavior. There indeed are many short—comings to almost all :ators, and certainly those that have just been men— :d are no exception. If one were to utilize the index of rates of mental ass as a possible surrogate for urban deterioration, he I have to temper his conclusions or prognoses consid- .y. For example, a glance at the records show that ; of mental illness have increased several hundred fold Le past few decades. Yet, even so this is certainly Idication that mental illness is approaching epidemic irtions.23 I A goodly amount of the patients now being treated I Ispitals as mental patients are old and consequently Lffering from senility and other geriatric diseases Lb as they are mental illness. Thus, by virtue of increases in life expectancy, we should expect a sub— lal increase in the number of mental patients. In .on, there are now considerably more facilities for ‘ng mental patients than there were in previous years. ore, many people suffering from mental illness are Iceiving treatment on both an in—patient and out— It basis (instead of merely staying home and being hey were "odd"), and so have become "statistically" I I ‘I r: -. u I I 24 If one gives close scrutiny to the indicator of personal income as a possible surrogate for urban deteri— oration, it is quite evident that there are many related factors that must be weighed and considered.24 On one hand many people are inclined to overstate their income simply to gain a false sense of economic status, while on the other, some are hesitant to quote too large a figure, and understate their earnings for fear of disqualigying them- selves for receiving certain public benefits--i.e. food stamps, rent supplements, tax benefits, etc. Also, many people now consider their income to be that amount of their earnings on which they pay taxes. {ence, with such things as non—taxable gifts and other 'tax—free" sources of income, it is very difficult to levelop an adequate measure for an individual's real income. Education is also becoming a difficult variable to :ssess in appraising its worth as a possible surrogate for mban deterioration. In previous years when education was pasured principally as the number of years of formal edu- Ition that an individual received, it was quite a simple tter to determine levels of education. And, very often pose inhabitants in the "poorer" areas were the ones who ad achieved the least amount of education in the city. >wever, with the recent spate of training programs that Ive been instigated by both industry and the military, evels of education and training for some individuals are w quite difficult to determine.25 25 In attempting to use an indicator such as the rate and distribution of welfare expenditures within a city as a surrogate for physical deterioration one would have some severe research problems to overcome.26 The most prominent one would concern that of the data itself. In the densely overcrowded sections of the city where more than one family is occupying a single dwelling unit, only one is allowed to receive welfare benefits, and is therefore counted in the statistics. Thus, even though welfare recipients do occupy housing that is for the most part substandard, the diffi- culty of getting an accurate count of them almost precludes any utility that such an indicator might have. Other difficulties that arise in interpreting wel— fare statistics, are the tendency for welfare recipients :0 shift addresses in the city and to "farm out" various hembers of their households to friends and relatives.27 Ilso, in cases where there are large numbers of illegitimate Phildren, it is difficult to determine parentage and family 28 ‘ize. I hrdsticks for Measuring Housing pterioration I Aside from some of the current social indicators mat might be utilized as possible surrogates for physical rban deterioration, there are the actual housing standards ? yardsticks that measure blight. They cover a wide range 26 building and environmental conditions and could well be ilized as indices of physical deterioration. There are presently three principal methods employed urban researchers for measuring housing deterioration. it has already been stated these are (1) American Public alth standards, (2) Urban Renewal criteria, and (3) U. S. nsus definitions of deterioration and dilapidation. Public Health standards examine housing from the sad point of view of health, safety, and welfare and are as concerned with sanitation and safety hazards as well 29 The standards are applied structural deficiencies. trained public health officers in accordance with an :ablished point—score rating system against which various :ts or functions of individual dwelling units are com- red and evaluated. The appraisal items are spread over ee general areas of investigation (facilities, mainten- e, and occupancy) and total 600 points.30 Theoretically, inspected dwelling unit with no penalties would receive tal score of 600 points. Public housing inspections are sometimes carried routinely, but more often they are instigated at some— Is request—-i.e. the complaint of a neighbor, a tenant Jehalf of his landlord, or by a related agency carrying its own inspection (building department, assessor's .ce, etc.). 27 Records of both housing code violations and com— pliances are maintained in the City Health Department's premise files. Aggregate data for the overall city is usually maintained by the County Health Officer and re— corded by census tract. Typical building deficiencies noted in public health inspections include items such as insufficient light and air (sleeping areas), inadequate cooking facilities (kitchens), inadequate toilet facilities, and improperly installed wiring and plumbing. Safety hazards often noted by inspectors are items such as broken windows, dangerous stairs, exposed wiring, and missing safety valves (gas lines and appliances). Housing quality, as defined by Urban Renewal cri— teria, is much less rigorous than that specified by Public Health standards. For the most part only broad guidelines are presented in the Urban Renewal Manuals for defining substandard housing. In specifying criteria for an "eligi- Jle" urban renewal area, the manual states: . . Specifically, at least 20% of the buildings in the area must contain One or more building deficiencies, and the area itself must contain at least two environ— mental deficiencies. t then goes on to list building deficiencies and gives the ollowing criteria: (1) Defects to a point warranting clearance. (2) Deteriorating condition because of a defect not Correctable by normal maintenance. 28 (3) Extensive minor defects which, taken collec— tively, are causing the building to have a deteriorating effect on the surrounding area. (4) Inadequate original construction or alterations. (5) Inadequate or unsafe plumbing, heating, or electrical facilities. (6) Other equally significant building deficiencies.32 In most instances where Urban Renewal criteria is utilized to determine the housing quality in a given area, the agency making the survey develops its own standards for deterioration within the spirit of the broader framework of the manual. In making a housing quality survey, the building inspectors usually make most of their observations from the exterior. 3 Once a criteria for deterioration is developed, inspections are carried out in much the same way as Public Health surveys. A point—score system for particular defic- Iencies is constructed, and every building within the area Is penalized a certain amount for each of its deficiencies. If it contains a sufficient number of them, it is labeled Is deteriorated. I U. S. Census definitions for housing quality specify ‘hterioration in accordance with strict criteria.34 Houses dwelling units) are classified as either “sound," "deteri— iated," or "dilapidated" on the basis of several items of 'aluation——i.e. building condition (major defects in walls, Ioors, roof, foundation, etc.), plumbing (adequaCy of i 29 toilet and kitchen facilities), and other such things as general appearance, painting, etc. Although census enumerators are lay individuals, they are trained to make particular observations in regards to housing to minimize their errors in judgment.35 They also record their data on standard forms that are designed to cover a wide range of building qualities and character— istics. Most yardsticks for measuring housing quality or condition are rather cumbersome and costly to apply.36 Not only do they require large expenditures of time and effort, but a considerable amount of organization and inter—agency coordination and cooperation as well. An important point regarding such measuring in— L'truments, is that their purpose is not to predict future Levels of deterioration, but rather only to assess present :onditions of housing quality. In this respect they can 3e used as indices or indicators of blight, but not as redictors. The real need then is to devise a measure for hysical deterioration that can also be utilized as a eading surrogate. 30 FOOTNOTES lBauer, Raymond, Social Indicators, Cambridge: The M.I.T. Press, 1966. See especially the Introduction. Also see Volumes I and II, "Social Goals and Indicators for American Society," The Annals of the American Academy of Political and Social Science, May and September, 1967. 2Ibid., Bauer, Chapter I. 3Gross, Bertram, "The State of the Nation," in Bauer, Raymond A., Social Indicators, p. 267. 4Op. cit., Bauer, p. 56. 5Likert, R., "The Dual Function of Statistics," Journal of the American Statistical Association, Volume 55, 1960. 6Boulding, Kenneth, "The Ethics of Rational Deci- sion," Management Science, February 1966, pp. 161—69. 7Dyckman, Jack W., "Social Planning, Social Plan— ners, and Planned Societies," Journal of the American In— stitute of Planners, March 1966. 80p. cit., Bauer, p. 80. 9Op. cit., Bauer, p. 82. l00p. cit., Bauer, p. 84. llOp. cit., Bauer, p. 85. 12Op. cit., Bauer, p. 86. 13 See pamphlet entitled The Cost of Slums prepared by the Housing Authority of Newark, N. J. Edited by Dr. Jay Rumney and Sara Shuman, Newark, 1946 pp. 32—40. See also Mowrer, E. R., Disorganization: Social and Personal, Philadelphia, 1942, Faris, R. E. L. and H. W. Dunham, Men— tal Disorders in Urban Areas, Chicago, 1939, and Shaw, C. L. and Mackay, Juvenile Delinquency and Urban Areas, Chi— cago, 1942. l4Hollingshed, August B. and F. Redlich, Social Ilass and Mental Illness, New York: Wiley, 1958. 15Paris, Robert E. L. and H. Warren Dunham, Mental fisorders in Urban Areas, Chicago: University of Chicago fress, 1939. —_=.‘=-”-‘—""“ ‘- 31 P l6Eli Ginsberg et a1, Occupational Choice, New York: Columbia University Press, 1955. See also Reynolds, Lloyd and Joseph Shuster, Job Horizons, New York: Harper Rowe & Co., 1949. l7Ibid., Reynolds and Shuster. l8Ibid., Reynolds and Shuster. 19Schorr, Alvin L., "How the Poor Are Housed," in Urban Housin . Edited by William L. C. Wheaton, Grace Milgrim, and Margy Meyerson, New York: The Free Press, 1966. pp. 236—37. 20Ibid., Schorr, p. 236. 2libid., Schorr, p. 239. 22Dean, John P., “The Myths of Housing Reform," in Urban Housin . Edited by William L. C. Wheaton, Grace Milgrim, and Margy Meyerson, New York: The Free Press, 1966. pp. 256-59. 23Action for Mental Health, edited by the Joint I Commission on Mental Health and Illness, New York: Basic Books, Inc., 1961. , 24U. S. Department of Commerce, "Quality Control, A lReporting, and Progress of Enumeration," Principal Data Collection Forms and Procedures, U. S. Censuses of Popula- I tion and Housing, Washington: U. S. Department of Commerce, 1962, pp. - . I 25 . Op. Cit., Bauer, p. 110. ( 26 Orshansky, Mollie, "Who' 5 Who Among the Poor,“ Social SecuritypBulletin, July 1965. See also Lynch, John M., "Trend in Number of AFOC Receipts——l96l to 1965," in pWelfare in Review, May 1967. 27 Op. cit., Schorr, p. 234. 28Schorr, Alvin L., Poor Kids, New York: The Mac— ‘millan Co., 1967. 29American Public Health Association, Committee on the Hygiene of Housing, An Appraisal Method for Measuring the Qualipy of Housing: A Yardstick for Health Officers, Housing Officials, and Planners, New York. American Public Health Association, 1946. pp. vii-viii. 32 30Ibid., American Public Health Association, p. 16. 31Housing and Home Finance Agency, Urban Renewal Manual, Washington: Department of Housing and Urban Devel- opment, 1965. "Eligibility and Delineation of Area," p. 3, Chapter I. 32Ibid., Housing and Home Finance Agency, p. 3. 33Ibid., Housing and Home Finance Agency, Chapter I, Survey and Planning. In the specific case of the pro- posed Urban Renewal Project for the City of Ann Arbor, Michigan (October 1956) the following criteria was used: (1) yard, (2) foundation, (3) structure, (4) roof-~gutters and chimney, (5) walls and windows, (6) porch and stairs, and (7) paint and general appearance. All observations were made from the outside and a point—score method indi- cating that any five of the seven characteristics were negative rated the building as dilapidated or qualified for razing in the project. A score of three characteris- tics defective out of the seven qualified the building for rehabilitation. 34U. S. Census of Housing and Population, Ann Arbor, Michigan, 1960, p. X. 35U. S. Department of Commerce, "Quality Control, LReporting, and Progress of Enumeration," Principal Data ;Collection Forms and Procedures, U. S. Censuses of Popula- gtion and Housing, Washington: U. S. Dept. of Commerce, H962. p. 6. I 36 Chapin, F. Stuart, Land Use Planning. CHAPTER II AN APPROACH TO THE STUDY OF PHYSICAL DETERIORATION [ntroduction Proper research should always begin with a basic inderstanding of the nature and properties of the partic— ilar subject under investigation.1 However, a complex social phenomenon such as urban deterioration or blight >oses severe limitations for the researcher in this respect :ince its definition and characteristic attributes appear 0 encompass a great range of theory and conjecture.2 The ask of study becomes even further complicated when one eflects on the extent of vagueness and ambiguity which as traditionally characterized the literature of residen- ial deterioration.3 Traditional notions and theories of urban deteri- ration (at least from the viewpoint of city planning) have entered primarily on a general consideration of the phys- :al environment——defective housing, conflicting (mixed) nd use, traffic congestion, substandard utilities, etc.4 sentially, blighted properties were those that were vis- ly identifiable as being either physically or functionally solete.5 33 34 With later research however (notably by Vernon, Hoyt, and others), these notions of physical deterioration were broadened to include the dynamic action of various socio-economic forces that were Operative within the city causing it to decay. With the inclusion of these patholog- ical influences into his concept of deterioration, Vernon fashioned his early (circa 1935) construct of the "gray area."6 The central principle in this theory was that as the city began to expand at an increasing rate, and employ— ment centers enlarged or moved in accordance with advance- ments in technology (mainly in transportation and industrial development-—e.g. a slackening in the "tyranny of the site" or a reduction in the "friction of space") subsequent shifts in the housing market took place. This evidenced itself in a sudden migration of the industrial worker from the core Irea to the periphery or suburbs of the city. Such a move— Eent drastically altered land use patterns and locational demands and simultaneously set the stage for large-scale ieterioration to ensue.7 More recent theories of slum formation or incipient ilight have proffered an even wider range of concepts and 'asual factors. Many of our recent federal programs for mproving the quality of the urban environment have tacitly hpported a notion of "blighted people" who, not too unlike pphoid Mary, go from one part of the city to the other DWittingly spreading the "seeds" of blight and 35 deterioration.8 Much of this viewpoint centers on the basic thought that a capacity for good stewardship or citizenship is predicated on having access to certain essential "urban skills" and desirable humanistic values, all of which is indeed not possible for all urban residents. In noting some of the prime ecological considera— tions in some cities, many social scientists have pointed to the severe blighting effects that are caused by inade- quately developed street and highway networks.9 The cen— tral point of their arguments is that as circulation pat— terns are altered causing some residential areas to have less access than others to central land use functions, there are correSponding shifts in local housing demands. (Those areas with less access tend to evidence marked hepreciation which subsequently results in ensuing deteri— oration and blight.) z I Many land economists in focusing on major causes of blight and deterioration in the residential sector of the real estate market, have stressed the importance of sudden phanges in public tastes and preferences.10 This, they éeel, has not only been reflected in demands for new designs Ln subdivision layouts and architectural styles, but in :hanges in life styles as well. The shortened working week that has evolved through increased technology and other (labor—saving" measures has generated considerable leisure jdme with a resultant demand in the residential market for s 1 I 36 housing that offers not only larger lots and three-car garages, but extra rooms for entertainment and other ac— tivities as well. When such new demands take effect in the market, they quickly render older style homes and neighbor- hoods obsolete. Arthur Sporn, a noted law professor and income tax specialist, presents the argument that the present struc— ture of the federal income tax system has much to do with the perpetuation of residential blight.ll Through its own system of rewards and penalties, the federal taxing struc— ture leaves little incentive for property owners to main— tain and improve their property. Martin Anderson even maintains that the great panacea of residential deterioration, urban renewal, has ‘had many counter effects and is indeed contributing to the Further spread of urban blight.12 This he states is not bnly evident in the large number of low—income families who I pre forced to move from their "substandard" housing to even I phabbier surroundings, but also in the high mortality of Small businesses that are forced to close and/or relocate and consequently suffer the loss of much of their good will and income. A more direct consequence and contribution to frban blight can be seen in the commercial sector of the lrban real estate market. As new buildings are constructed In the urban renewal areas (where they receive the benefit 1 f such externalities as additional parking, greater access, 37 improved aesthetic surroundings, etc.) they attract occu- pants and businesses from the older downtown structures . . 13 and cause severe increases in vacancy rates. Yet, even though this great diversity of theory and opinion exists, there g9 seem to be some commonalities regarding the nature and formation of urban residential deterioration. The first is that blight appears to develop from negative forces in the environment or to be the result of certain breakdowns or failures in some key social systems. Examples of such negative effects might be (1) an out- migration of a particular segment of the population, (2) a loss of some critical factor of production, (3) a shift in markets, (4) an alteration of the transportation system, [5) a relocation of principal land use functions, (6) a thange in urban values and public attitudes, (7) a lack of political influence, or (8) a myopic View of impending social dangers resulting from a failure of municipal lead- Irs to "plan." ,. (n single—family residential buildings and areas is that in kst instances the process of deterioration is approximately be same.14 Once certain pre—conditions have been estab— I t fished to set the stage for deterioration to develop, there The second common feature of physical deterioration ppears to be a sequence of related events that occurs in pth the political and economic spheres of the city.15 38 Figure 1.——A Schematic Diagram of the Deterioration Process. EXISTING "BLIGHTING" PRE-CONDITIONS RESULTING FROM FAILURES IN "KEY" SOCIAL PROCESSES POLITICAL DEVELOPMENTS Failure of local residents to generate countervailing political forces to "ward— off" injurious external— ECONOMIC DEVELOPMENTS -—1-—i Reduction in private income on the part of local res- idents. ities. I Lack of access to the political “machinery" al- lowing the local residents little opportunity to permeate the power struc- ture or the decision- making mechanism. Demands for public invest- ment go unheeded, i.e. re— quests for street repair, increased sanitation, etc. Lack of code enforcement ‘ in local area. I Favor—buying in terms of I granting requests for use I permits and variances be- comes prominent. r1 Substandard housing, "jerry—built" construction, etc. begins to appear. #4fl I Area loses its political identity as a bargaining unit. Any remaining po— litical efficacy is read- wowed- \ ACTUAL STATE OR CONDITION OF PHYSIC DETERIORATION —5__. Land use patterns begin to change via such devices as spot zoning and strip 1 zoning. I With the area now "vis— ibly" identifiable as being deteriorated (little poten— tial for attracting capital), there is a marked drop in private investment. / AH. 39 This pattern of events is illustrated in Figure l and is entitled "A Schematic Diagram of the Deterioration Process." The third point is that within the general deteri- orating process there is a particular phase of rapid path- ological development which Breger has termed the critical 16 This is an important E2293 of physical deterioration. period of development because it is during this stage that the area changes from one of potential deterioration to one of actual deterioration. In simple diagramatic form the general thrust of Breger's concept can be illustrated as follows: Figure 2.——Breger's Concept of Physical Deterioration BLIGHT CRITICAL ACTUAL CONDITION POTENTIAL STAGE OF PHYSICAL DETERIORATION LOW —’””—‘“\\\\\\ VARIOUS ‘ LEVELS OF TERIORATION HIGH \ TIME 9 40 This illustration of Breger's concept of the crit- ical stage shows that the physical condition of the area passes from an initial stage of little or no deterioration (blight potential) to a stage of severe rapid deterioration (the critical stage) to an eventual stage of slower but more advanced deterioration (the actual state or condition of physical deterioration). The Development of a Framework of Study If one could accept these commonalities of urban deterioration or blight as actual postulates of urban de— velopment along with the notion that assessed values of single—family residential buildings correlate strongly with levels of private investment (as it will be illustrated in Chapter III), how might one examine the phenomenon of physical residential deterioration in an effort to measure and perhaps predict it? In that levels of private investment are reflected in changes in assessed values of single—family buildings, Fne might expect that considerable insight into the actual hysical condition of a building or a neighborhood could be leaned from an examination of the local city assessor's ecords. It would follow that those buildings in better Lndition would be those having stronger records of invest- ant, hence maintenance and improvement, while those in 4l >orer physical condition would be ones demonstrating >orer histories of investment. The first task in the research then would be the election of an appropriate unit to reflect changes in nvestment. One that comes readily to mind would be the quare foot assessed value. As a standardized unit it Duld be easily utilized in making comparisons of different avels of investment in various single-family residential reas throughout the city. The next task would be the selection of a suitable athematical technique for measuring or illustrating changes 1 levels of investment, or, more precisely, for examining 1e behavior of the square foot assessed value. Such a achnique would not only have to show differences in iso- ted values at various points of time, but would also have 11 L demonstrate rates of change or percent changes in assessed Llues as well. The statistical method or technique of Lm§:§eries analysis would be an apprOpriate device since “ % lends itself very nicely to this problem of comparing F evaluating levels and rates of change. I Since an examination of the percent change in sessed values of single—family buildings at regular in— rvals through a time-series analysis would indicate the ) tent or rate of investment in them for a given period of me, one could indicate or measure the relative condition a building or a neighborhood by demonstrating the extent .i-A. .wd . 42 :0 which its percent change in assessed value differed from :hat of the overall city. Hence, a suitable and very prac- :ical measure for deterioration in the case of physically alighted areas might be one that demonstrates differences in slope in percent changes in assessed values. To get a better grasp of this notion for measuring and comparing rates of investment or percent changes in nean square foot assessed values, the reader should note the following illustration, Figure 3, shown below. ?igure 3.-—A Time—Series Analysis for Measuring Change in Investment Mean Sq. Ft. Assessed Value for Single—Family Buildings in Overall City {// 3° / I 20 > ‘ AY I 10 Y'56 } I '44 '48 '52 '56 '60 '64 1 Time in 4—Yr. Intervals The percent change for the time period 1952-56 1 equals AXEEY%§X§§ times 100 ‘ Y56 - Y52 [ which equals Y52 times 100 43 To show comparisons for different percent changes in mean square foot assessed value and hence rates of in— vestment in various parts of the city, one could determine the differences in slope between two curves at various points in time. As in the preceding example, Figure 3, one curve (the upper one) could represent the percent change in mean square foot assessed value or the norm for investment in single—family buildings in the overall city while the other (the lower one) could represent the percent change in mean square foot assessed value for a particular area or neighborhood within the city. A time-series anal— ysis showing the juxtaposition of the two curves might appear as follows in Figure 4: Figure 4.——A Time—Series Analysis for Measuring Change in Mean Square Foot Assessed Value Mean Sq. Ft. Asses. Value for Single—Pam. Buildings in Overall City 30 arcent Iange in aan Square 20 >ot Assessed tlue for revious 10 Mean Sq. Ft. Asses. >ur Year Value for Single- sriod Family Bldgs. in Parti— 0 cular Area within the City '44 '48 '52 “56 '60 '64 Time in Four-Year Intervals 44 The difference in slope between the top and bottom urve for the interval (time—period) 1952 to 1956 would hen equal (a2 ‘ a1) ' (b2 ‘ b1) Thus, the example shown in Figure 4 indicates that he rate of investment or the percent change in mean square not assessed value for the given area is less than that ar the overall city. Since this is the case, one could ay that the area is relatively physically deteriorated, 1d the measure of this deterioration for the period 1952 3 1956 would be the difference in slope between the two 1rves or (a 2 ‘ a1) ' (b2 ‘ b1) It should also be pointed out that any area within ‘ e city demonstrating a curve or record of investment that ceeded that of the overall city would be one that was ceiving a larger share of private investment, hence main— nance and improvement, than the average for the city. In attempting to utilize the technique of time- ries analysis and assessment data to predict future {sical deterioration, one would first have to demonstrate it the behavior of the mean square foot assessed value 11d be used as a leading surrogate for physical blight. Lin, going back to Breger's concept, one can see that : critical stage of the deterioration process always 45 always precedes the more developed stage of actual physical deterioration. Hence, the initial task for predicting future physical deterioration would be one of identifying and isolating this critical stage. In the example shown in Figure 4 the difference in (slope in the percent change in mean square foot assessed value is greatest from 1952 to 1956. This indicates that 1it was during this period of time that the rate of private investment in single—family homes, in terms of maintenance and improvement, was the lowest. This in turn further implies that it was also during this interval that the buildings deteriorated physically most rapidly. Thus, the period 1952 to 1956 represents the critical stage of the deterioration process for this particular example. The possibility then of predicting physical deteri— oration through an examination of changes in assessed values centers on one being able to locate this critical stage. Therefore, any effort to substantiate Breger's :heory of the critical stage would have to begin with the selection of a sub-area of the city that evidences some of :he early characteristics of physical deterioration-—i.e. hose characteristics listed as blighting pre-conditions n the deterioration process, page 37. 46 pgrational Definitions For purposes of this study it is necessary to perationalize many of the definitions and concepts that ill be used extensively in the study. The first is that f blight potential. This term refers to a capacity for a building or 1 area to become blighted. In essense, something is said > have a potential for blight to occur once it is sub— acted to any or all of the forces illustrated in Figure l, 5 Schematic Diagram of the Deterioration Process." The second term is that of the critical stage of .e general deterioration process. This concept, shown in gure 2, refers to that particular phase or stage of dden pathological development where either a building or area deteriorates most rapidly. The third term is that of physical deterioration. is concept refers to the actual state or condition of terioration. For all practical purposes, physical deteri- ation is a pathological state of a building or an area it can be measured in accordance with various standards criteria for dilapidation and deterioration. In effect, .5 is the level of deterioration that is popularly re- ‘red to as blight. A fourth term, difference in slope, refers to the nge in direction between two curves at different inter— s in the time—series analysis—-e.g. Figure 4. Difference 47 slope can best be illustrated by the following example igure 5): Difference in slope (gure 5.—-Difference in Slope Defined between the top and bottom curve for the interval "X1" {TTTTTTTTTT equals 1 2 X X tions are made at 4—year c___v____1t___\,___4 Since all observa— 1 intervals, all the intervals are equal. A final term, percent change, refers to the extent > which the square foot assessed value differs from one .terval or observation to the next. It was computed by Viding the square foot assessed value for one interval to the square foot assessed value of the next and multi— ing the resulting quotient by 100. At each interval percent change has been added to the previous one so effect the graphic illustration for each sub—area repre- ts a cumulative curve. 48 FOOTNOTES lBeveridge, William I. B., The Art of Scientific Investigation, New York: Norton, 1957. 2Wingo, Lowdon, "Urban Renewal: A Strategy for Information and Analysis," Journal of the American Insti- tute of Planners, May 1966. See also Stokes, Charles J., "A Theory of Slums," Land Economics, Vol. 38, Number 3. Jacobs, Jane, The Death and L1fe of Great American Cities, New York: Vintage Books, 1961, or Seeley, John R., "The Slum: Its Nature, Use, and Users," Urban Housing. Edited by William L. C. Wheaton, Grace Milgram, and Margy Ellin Meyerson, New York: Free Press, 1966. 3Case, Frederick E., "Prediction and the Incidence of Urban Residential Blight," Papers and Proceedings of the Regional Science Association, 1962. In his article Case states ". . . A major problem related to seeking to measure the incidence of urban blight is that the term 'blight' is used loosely and consistently to cover a multitude of un- satisfactory land use problems . . . Furthermore, the apparently widespread incidence of blight and the somewhat complex procedures involved in identifying it in connection with the majority of urban renewal programs argue for a definition of blight which can be universally applied to all urban areas in reasonably consistent quantitative terms. . . ." pp. 211-212. 4Walker, Mabel L., Urban Blight and Slums, Cam— bridge: Harvard University Press, 1938. « 5Wood, Edith E., "A Century of the Housing Problem," in Urban Housing. Edited by William L. C. Wheaton, Grace Milgram, and Margy Ellin Meyerson, New York: The Free Bress, 1966. ‘ 6Frieden, Bernard, The Future of Old Neighborhoods, Zambridge: The M. I. T. Press, 1964. 1 7Hoyt, Homer, "The Structure and Growth of Residen- ;ial Neighborhoods in American Cities," in Urban Housing. Edited by William L. C. Wheaton, Grace Milgrim, and Margy Ellin Meyerson, New York: The Free Press, 1966. See also Seyfried, Warren, "The Centrality of Urban Values," Land Economics, Vol. 39, Number 3, August 1963. 8An examination of almost any federally supported mban program, e.g. Urban Renewal, Community Renewal, Job brps, Head Start, etc., will reveal that citizen education 49 a vital ingredient in the successful development of the ogram. The emphasis of the general "War on Poverty" is milarly a blatant example of this need to bring the urban or and the rural migrant (who will soon join the ranks) to middle class standards and values. See Rein, Martin, ocial Science and the Elimination of Poverty," Journal of e American Institute of Planners, May 1967. See also rloff, Harvey S., "New Directions in Social Planning," urnal of the American Institute of Planners, November, 55. For some historical perspective on this notion of ight transference see also Bauer, Catherine, Social Ques— ons in Housing and Planning, London: Univers1ty of London ess, 1952. ‘ 9Firey, Walter, "Ecological Considerations in Plan- ng for Rurban Fringes," in Cities and Society. Edited by ul K. Hatt and Albert J. Reiss, Jr. See also Horwood, gar, Community Consequences of HighwayiDevelopment, attle: University of Washington Press, 1965, and Duke, chard D., “The Effects of a Depressed Expressway-—A troit Case Study," The Appraisal Journal, Vol. 26, tober 1958. lOBarlowe, Raleigh, Land Resource Economics, Engle— od Cliffs, N. J.: Prentice—Hall, 1958. See also Thomp- n, William R., A Preface to Urban Economics, Washington: sources for the Future, 1963. llSporn, Arthur D., "Some Contributions of the come Tax Law to the Growth and Prevalence of Urban Slums," lumbia Law Review, November 1959. For further discussion this issue, also see Blum, Walter J. and Allison Dunham, come Tax Law and Slums," Columbia Law Review, April 1960. I“ | 12Anderson, Martin, The Federal Bulldozer, Cambridge: é M.I.T. Press, 1964. 1 13There have been many notable examples of this tket displacement effect throughout the country. Partic— fir examples are the construction of the Pontchartrain tel in Detroit which drove several competing facilities to bankruptcy in the early 1960's, the IBM Building in ittle in 1965 which drove office vacancy rates to an all— pe low, and the Standard Insurance Building in Portland, :gon which caused similar repercussions in downtown Port- ld properties. l4Breger, G. E., "The Concepts and Causes of Urban .ght," Land Economics, Vol. 43, Number 4, November 1967. 50 15Banfield, Edward C., Political Influence, A New eory of Urban Politics, New York: The Free Press, 1961. e also Dahl, Robert A., "The Analysis of Influence in cal Communities," in Main Street Politics. Edited by arles Press, East Lansing: Michigan State University ess, 1962. 16Breger, G. E., "The Concepts and Causes of Urban ight," Land Economics, Volume 43, No. 4, 1967. CHAPTER III THE ASSESSED VALUE AS A RECORD OF INVESTMENT An extensive amount of literature has appeared in :ent years to attest to the fact that there is consid— 1ble correlation between the way people behave and the z in which they handle money.l Thus, it seems only fit— Ig that municipal policies regarding changes or altera— >ns of urban development should be centered on the way which people actually behave in regards to personal ex— Lditures rather than the way in which they say they >uld act. This is especially true in regards to the ICess or failure of proposed urban renewal programs when ir funding is partially based on individual property essments. It is therefore quite important that local an renewal agencies have an adequate measure of citizen erest and stewardship within any area prior to embark— Upon a proposed project for its rehabilitation or evelopment. In the previous chapters it was demonstrated that regards to single—family houses there was a definite {age between levels of private investment and corresponding 51 52 ssessed values. In instances where buildings were improved 1d maintained, assessed values appreciated, and in in- ;ances where buildings were neglected and not maintained, :sessed values depreciated. In this respect assessed .1ues form an important source of raw data for local muni— pal authorities and other interested urbanists because ey reflect how individual owners behave in regards to intaining and improving their property. As a record of investment, assessed values offer me very good advantages for urban research purposes. a first lies in the fact that they are public documents 1 a matter of public record. In this sense, they are idily accessible to anyone wishing to use or examine am. The second point in regards to their utility is it they extend over the entire history of the city. arefore, they can provide assessment information for any 'iod of the city's development. In short, they are a {9 term record of private investment. A final advantage centers on the fact that they systematically determined in accordance with some Cific criteria. In this respect they are uniform data. Determination of the essed Value In most of the United States, the assessed value Single—family structures is determined as the cost of 53 aplacing the structure (the cost of labor and materials or some specified base year) minus its depreciation.2 mus, for all practical purposes, it is, by definition, a -gure that is free of any biasing influences that might )ssibly arise from market demands or fluctuations. The .sessed value then represents what a building is wgpph, d therefore what it should sell £g£.3 There are limits though to the validity and cre— nce of assessment data. Even though most assessors like approximate a practice of determining assessments that fair and equitable, they frequently fall short of this rk. Quite often an assessment office is severely under— affed and therefore unable to make frequent examinations i reassessments of property values. Also, it can happen at a city can quite unwittingly be utilizing out—moded :hods for determining assessed values which can result ‘having certain building types appraised unfairly.4 never assessment data.is grossly inequitably determined, affords little utility for urban research purposes. however, assessment practices are assidiously followed values are equitably determined and distributed, as- sed values can present an important source of economic a. Figure 6 illustrates how the assessed value is ated to private investment in the case of single—family idential buildings. 54 ure 6.—-The Assessed Value as A Record of Private Investment Depreciation 1ding Value as Re— :ted in the Behav- Demand of Some Standard— 1 Unit (e.g. the Inflation Ft. Assessed 1e Time —_____> A building begins to depreciate (both physically economically) once its construction is completed and .5 ready for occupancy. To counteract the general ‘e of depreciation (which indeed can be made up of ral sub—forces), three countervailing forces enter in- he economic system. These are (1) demand, (2) infla- (which is very closely related to demand), and (3) tenance and improvement. However, when assessed Values are based on re- ment costs (which is the case of single—family resi- 'a1 buildings in the City of Ann Arbor), they are the tant of only two opposing forces--(1) depreciation (2) maintenance and improvement. For this reason they ct sound records of investment on the part of build— wners . Maintenance & Imp. 55 Inflation cannot have any skewing effects on the termination of assessed values of single—family build- gs because it affects all types of construction uniformly. mand, likewise, cannot have any pulling effects on as- ssed values either (as might possibly be the case with rket values for certain types or locations of single— mily houses) because replacement costs are pegged to a ngle base price of labor and materials of some specified ar.5 An examination of the theoretical quality of the sessed value, in appraising its worth as a suitable rec- d of private investment, indicates that it incorporates 1 increments of value that accrue to a building from both intenance and improvement.6 For if a building is prOperly intained, the appraiser uses a lower rate of depreciation determining its value than he normally would were it Jrly cared for.7 Also, if a building is improved to the \ ent that the proposed construction will exceed $100 in us, the assessor is immediately notified by the building artment and the additional worth of the improvement is ed to the assessed value once the construction is com— ted and inspected.8 In determining the assessed value of a single—family idential building (utilizing the replacement cost minus reciation) for purposes of taxation, three methods of raisal can be used for estimating the replacement cost.9 56 are (1) the summation method, (2) the breakdown of 5 method, and (3) the unit—cost method.10 When using ation method, the appraiser determines the assessed as the sum of all the individual costs incurred in tual construction of the building.11 When using the own of trades method, he calculates the assessed as the total cost of all the individual trades that sed in the construction of the building-—e.g. masonry, ‘try, plumbing, etc.12 When utilizing the unit—cost 1, he determines the replacement cost as the cost of .dual components of the building—-e.g. kitchen, bath— etc.l3 oblem of Building fication Not all single—family residential buildings depre— at the same rate. Therefore, in order to make de— tion rates equitable in determining assessed values, essor categorizes buildings by class in accordance e established state criteria.l4 Determining the appropriate building class for a 1tial structure is indeed no mean task for it re— extensive knowledge and experience on the part of >raiser. This is true for several reasons. The first is that buildings are constructed from nsive variety of materials and therefore often 57 ive an eclectic architectural treatment. Thus, it can en that one segment or story of a house can be of one of construction (brick, frame, etc.) while another of it is of another. Then too, contractors and archi— s (particularly those associated with large-scale de— pers) frequently build and design some parts of houses ne set of building standards and some to others. A second problem centers on the fact that many ldings are built in stages. Sometimes, as is the case . a good portion of the housing stock in Ann Arbor, e are twenty to thirty years between building additions. ith the case of mixed standards and construction methods, too can cause severe discrepancies in building classi— tion. A third classification difficulty lies in the gen- problem of remodeling. Once a building has been ex- Lvely remodeled, its effective age is appreciably :ed. New materials, equipment, and building techniques add to this problem and increase the change of error -assifying buildings. sment Practices in the of Ann Arbor For the period 1944 to 1963 assessed values of e—family residential buildings in the City of Ann were based on 75% of their 1941 replacement cost. 58 .963, however, this practice was altered, and assessed les have since been based on 100% of the fair market 1e of the structure.15 The major reason for the change— ' in assessment practices can be readily seen in the [stration below, Figure 7. 100%L|.l : 75 acement Costs \\ ngle-Family es as Re- 50 \\ ted in Actual \\\ \ ‘t Values ‘~\\‘ I 25 . . 1 U I '44 '48 '52 '56 '60 '64 '63 7e 7.-—Assessment Practices in The City of Ann Arbor, Michigan In 1944 when assessed values of single-family resi— al buildings were determined at 75% of their 1941 re- ment cost, they closely approximated actual market 3. However, as time progressed, these replacement 5 became less realistic in the sense of reflecting Iarket values. Thus, in 1963, 1941 replacement costs, ect, only reflected approximately 25% of the actual arket value of a single-family house. For purposes of determining property taxes such a Lon can become quite critical. For when property is 59 essed at a very low percentage of its actual value, the ace of error in determining its appropriate amount of becomes grossly magnified. Therefore, the assessor ays attempts to base property taxes on assessments that Lect at least 40 to 50% of their actual fair market 1e.l6 Since replacement costs of single—family houses (dropped to 25% of actual fair market values by 1963, iCity of Ann Arbor was forced to make a change in their Lssment practices. Rather than continue on with the :acement cost method and cope with the problem of rede— ping new indices of construction costs, they decided witch to a system of fair market values for determining ssed values. Thus, since 1963, assessed values of le-family houses have been based on 100% of their fair To keep the data uniform within this study, 1964 :sed values were determined by multiplying the figures (e assessor's records by 25%.17 In general, assessment practices within the City of rbor are governed by those procedures specified within ssessor's Manual of 1955 published by the authority of 18 .chigan State Tax Commission. Appraisers from the Assessor's Office re-examine f necessary, reappraise every building within the f Ann Arbor every three years. Building permits on 6O 2w construction are followed up immediately, and adjust— ants are posted on assessment records within three to six )nths after the improvements are completed and inspected.19 Le Assessed Value as Both a .rdstick and a Surrogate for .ysical Urban Deterioration Before any statements can be made to the effect at the assessed value can be utilized as a measure of vestment and thus as either an indicator or surrogate r physical deterioration, it must first be demonstrated at assessed values for single—family residential build- gs d2 in fact relate to levels of private investment. is can best be verified and illustrated by a direct 1mination of the data itself. Particularly good exam— as to support this notion can be seen in numbers 14, 69, 109, 110, and 119 of the study sample.20 The effect private investment on the assessed value of each build- can be illustrated in Figures 8, 9, 10, 11, 12, and The mean square foot assessed value of single— ily residential buildings can be utilized to represent :andardized unit of investment, and, when examined >ugh a time—series analysis, can combine both the func— l of the yardstick and/or indicator and the leading ogate. 61 uucmfim>oumEH mcflpaflsm Una maam> pmmmmmmdll.m ousmflm .oz mamfimxm vmma coma wmma Nmma mvma vama _ come .mmhvw oomv 05Hm> .mmm nonom .>00 1 mmma mmmam u mmma Ba moan ooom . IUSHDmGOU HMHDHCH mnomw. .mfillllll . . . maam> .mmm EH pma so osaws ommmmmm< mmmumo pogomwum oommw Hmma .MEH UQN msfim> pmmmomm< U uwmmn—HO mCHmVHHDm vmma "pHflsm new» #:mfimmmn awe: mfimuwooos )lr’ll .mn0pm mamsflm mmuvoauamnmo "cosumwuomwo "ngfinjz— n 4))7)--. 62 N .02 mHmmem "mucwaw>oumEH msfltaflsm tam m5am> pmmmwmm<11.m whamflm .Hm> Uflmmmmmd vcma ccma mmma Nmma wvma evca 1 __ _ momma on mmoup ooam .mu%lm ncwmw on mommmuo How mecmfim>oumfifl oz IsH .Hm> owmmmmmm wmmumw 3oz .cwma 1 comm unmfim>os EH Dma F comm nccmm Hmwommw on mmmmmnoca u seas ca coanosnpmaoo ooosm . wmmmmw< efltflm ma and so new moom 3oz .mmcH .u. . msam> pmmmwmmm vcmfio>os EH cam _ msam> Ummmmmm< pswfimmMQ shes Moaun o "mmmHo maaeaaam cam ”mascoN mmma .muoum N\H|H "pHHDm Hmww "coflpmfluommo mclmamncmnmc "Hmhfisz m.u0mmmmm¢ umwuvm MOSHE man "mmmHUU< 63 ,;., omnumo BmZ .mmma m .02 memem coma owma wmma _ 1 Nmma wvma vvma . "#cmEm>OHQEH wsfloaflsm one wSHm> Ummmwmmfill.ca mudmflm macmw ow mommmso use mSHw> Ummmwmmd #Gmam>oumEH pmd. chmw OD mmoup m5am> Umwwmmwfl .mummm v Mmm mDSwEw>oMmEfi oz mmmmw ou mommwuocfl wDHm> emmmmmma soapflewa msaeaasm .mmma pame>ou®EH cam ucwfiwmmn QDAB wEmanOOB .>H0#m mco momem n mama GH coapodnpmcoo asap IHQH no wdam> Ummmommfi oomv oomv ooom oommm m5am> commomwd o "mmmao acaeaasm Dam hvma hIfilthIWfiIHD o 403:3: "mcflsoN nuaflsm Hmmw "QOHumHHOme ) 4>))))'II v .02 wammem vmma omma mmma Nmma _ _ “mnsmfiw>oumfia mcataadm can wSam> pommmmw<|1.aa musmam mvma vvma _ mmmvw Op mommwao Isa wSam> twmwwwm< nuomnmo .mmma mmmvm on mmoup asme>0HmEH pm mSam> Ummmwmm< macmfim>oamfia mm 64 mmomw OD mommwnbca m5aw> pmmmommd coapaeem mqaeaasm .mmma acwfim>OH EH DEN “cmfiwmmn chaB mfimnwoooz oovw ocov mmmsm n was ca coma Goaaosupmcoo amaaaca go @DHGNV Umwmwmm< ooomm m5am> Ummmmmmfi o "mmmao maaeaasm 0am "mcacoN mama "uaaom Hmmw ~>hflopm mCD mau)4-J\H:1:1))\I\< 65 m .02 mamfimxm "pcme>0HmEa mcapaasm paw osam> pmmmommmtl.ma mnsmam voma omma mmma Nmma mvma vvma _ ooom comm Nmamw 8. 886 633/ . Noamw H vvca ca Ummmmmmm munch v How mangm>0Hmfia oz scapoduawcoo awapasa ccov AfIIIIIIIILT\\\\\\\l1YlllllllllA co onam> pwwmmmm< Nwmmm 0p mommmnosa .am> m mamm OD mommwuoca . m commmmmm womuwo swz .mmma .mmfi owawposou mmsom cwmw ccvmw lllllllllllllll ucwfio>oumfia tam mmmmwwmmmmwlmmm wsaw> Umwmmwmd Q ”mmmHU mflHUHHHQm Dom .mcasoN ucwfiomMQ huas oEmnmoooz .mnoum MNma.h)mmwm,Hmmw 66 o .02 madsmxm .ucmsm>onasH mcawaasm tam 65am> ommmmmm owmmmmmd mommmuoca mommw 0a mmouo wsam> powwow Im< .mummm a Hmm macwfim>oumfia oz .am> owmmommm coanawwa mcaeaasm .amma #GOEw>OH EH USN A mmmmw OH wmmmeUGH honom can moom 3oz .mmca pamfiw>0u EH cam JI‘V .Hm> U0mm®mm4 usmemmn spas wEmeoooz meHJO ncvmw on mommwuoca .am> memmmm¢ Umawposwu wmsom .cvma pswfiw>onmaa pwa _ _ 0 «Nu mwma .muoam 039 malnmvxmmlmc ...:3.1.1.1.1.. vvma .J oowm ooom oovm oommm osaw> oomwmmma "mamau mcaeaasm "measoN "uaasm Mama “coaamauomwo "Hwhssz m.uannuaz: 67 It has just been demonstrated that the assessed e, in effect, is primarily the result of two interact- forces-—(l) depreciation and maintenance and (2) im— ement. Thus, when buildings are receiving more invest— (in terms of maintenance and improvement) than they being neglected, they are appreciating in value. And, ersely, when they are being more neglected than they being either maintained or improved, they are depre- ing in value. In the case of "blighted" properties where depre— 'ng levels of assessed values should correlate gly with levels of physical deterioration, percent ge in the mean square foot assessed value should serve suitable indicator for physical deterioration. One could also add that such an indicator should rre credible and hence valuable than indicators devel— from either census data or urban renewal criteria be— both of these latter measures are primarily derived exterior observations and evaluations of building :ions. Since it is possible to determine relative levels t maintenance and improvement for various sub-areas city, the behavior of the percent change in mean foot assessed value, when examined over a given of time, should constitute an adequate leading sur- for future physical deterioration. 68 Since assessed values of structures are derived 1y from levels of maintenance and improvement, one expect that there would be a stronger proclivity ysical deterioration to ensue in those areas re- 'ng little history of improvement than in those areas ting just the opposite. This can be graphically il— ted in the following diagram (Figure 14). l4.—-Utilizing the Assessed Value to Predict Future Levels of Physical Deterioration Norm for Overall City / ,t Change . n Square _______________,,_—"’J ssessed _ of Pre— #—_—__——‘——‘—““—~—.1~ 4—year Norm for Particular Sub—area of City / X\\ Time seer In the hypothetical example it can be seen that iven interval of time (X) the difference in slope the two curves is only Y. However, for the same . of time (X1) beyond the base year of 1968, the ce in slope between the two curves is now Yl which derably greater than the past value of Y. 69 This means that the given area is falling further further behind the general leVel of maintenance and :ovement for the city. When the difference in slopes :hes some critical threshold, the probability of some major investment for rehabilitation becomes fairly >te (since it approaches the cost of completely replac- the buildings). 70 FOOTNOTES lMany social commentators have reflected and ex- uded on this subject in recent years, i.e. John Gal— -th, The Affluent Society; Vance Packard, The Status :ers; David Riesman, Individualism Reconsidered, Th2 sly Crowd, etc., John Keats, The Crack in the Picture low, The New Romans, etc.; and numerous others. More 5? writings include George Katona, The Powerful Con— _£, The Mass Consumption Society, etc.; and James Mor— The Productive American. 2Wagner, Percy, "The Appraisal of Single-Family s," The Appraisal Journal, Volume 26, July 1958. 3This is primarily true for single-family houses, in a rather strict sense. When a single—family resi- e is converted into a rooming house or even a duplex, ssessed value is still largely based on its replace— cost less depreciation. However, if the owner is Ving a considerable amount of rent from the building tive to its replacement cost and risk factor of owner- , the assessed value is adjusted to make it more table with other residential income properties-—e.g. axes, apartment houses, etc. See Chapter IV, "The iisal of Single—Family Residences," Assessor's Manual 255, published by the authority of the Michigan State Iommission, 1955. 4Lahde, Walter, "Practical Application of Residen— Building Cost Schedules," A Short Course for Municipal (sing Officers, Papers in Public Administration, No. 3, .rbor: Bureau of Government, University of Michigan , 1949. 5There are some perhaps who may disagree with this ment. However, if one were to determine the replace- cost of any building at various points in time, he find that there would only be minimal differences in of labor eXpended for certain units of work, i.e. g, plumbing, etc. and in types of equipment utilized, the replacement cost of a dishwasher or disposal in when such items were not available in 1941 or even The major costs of single-family residences center :h items as basements and foundations, framing, floor- :oofing, etc. These items account for approximately 90% of the actual construction cost of the building. 71 6Michigan State Tax Commission, The Assessor's ial of 1955, Lansing, Michigan: Michigan State Tax nission, 1955. 7Ibid., Assessor's Manual, p. 69. 8Interview with Mr. Wayne Johnson, Deputy City essor for the City of Ann Arbor, Michigan, April 17, I. At this time Mr. Johnson stated that the time lag assessing new improvements on existing structures :ly exceeded six months, and that subsequent changes .he tax rolls for buildings with new improvements were for the following year's assessment. 9Barlowe, Raleigh, Land Resource Economics, Engle— Cliffs, New Jersey: Prentice-Hall, 1958. lOIbid., Barlowe. llIbid., Barlowe. 12Ibid., Barlowe. l3Ibid., Barlowe. 14Op. Cit., Michigan State Tax Commission. 15Interview with Mr. Lahde, City Assessor for the of Ann Arbor, March 1, 1968. 16The Michigan constitution calls for equilization e 50% level. 17Interview with Mr. Lahde, City Assessor for the of Ann Arbor, March 1, 1968. 18Op. Cit., Michigan State Tax Commission. lglnterview with Mr. Wayne Johnson, Deputy City sor for the City of Ann Arbor, Michigan, April 17, 20The study sample is described in full detail in :r IV, pages 66 through 68. CHAPTER IV UTILIZING THE ASSESSED VALUE TO MEASURE AND PREDICT PHYSICAL DETERIORATION rerview of the Study For the most part, the empirical research involved 1e study will follow the outline presented in the open- ection of the dissertation. The research will begin [deavoring to show the extent to which assessed values .e to levels of maintenance and improvement. It will try to demonstrate the degree to which depreciating of assessed valuation correlate with levels of phy- deterioration, and, in this respect, will attempt to how the behavior of the assessed value can be utilized asure physical deterioration. The next phase of the study will endeavor to dem— lte a method using the assessed value to identify the tal stage in the deterioration process in areas desig— as "physically deteriorated." For purposes of the such areas will be those that meet present accepted rds for physical deterioration--census definitions, renewal criteria, public housing standards, etc. 72 73 The final portion of the study will explore the >ility of using the behavior of the assessed value Leading surrogate for physical deterioration. ations In developing this dissertation concerning assessed :ion and physical deterioration, certain assumptions ling assessment practices within the City of Ann Arbor :o be stated. Such assumptions lend credence to the and provide an appropriate framework for the research. ssumptions may be stated as follows: The assessment data obtained from the Ann Arbor City Assessor's Office is generally accurate. a. The City Assessor adheres to the rules and regulations governing assessment practices and procedures within the city, b. Assessments are made by trained appraisers who assess properties in accordance with the established municipal and state regulations, c. Assessments are regularly re—examined and re—evaluated on a scheduled basis every three years, and d. The assessor's office is notified by the building department every time a building permit is issued for new construction so that adjustments in assessments may be made to those buildings being improved. The general range of problems inherent in estab— g and maintaining assessment practices in the City Arbor, Michigan are appreciably no different than in any other Michigan city. 74 The City of Ann Arbor while unique in its own phy— sical and cultural resources, does not have any singular characteristics that might influence re- placement costs on assessments for single—family residential buildings. :ch Procedures——Part I :I a The procedures for accomplishing the first portion research may be stated as follows: Develop a random sample for selecting a number of assessed values of single—family residential buildings. For every building in the sample record its as- sessed value and floor area in four—year intervals from 1940 to 1964. Also record the added infor— mation for each of the buildings that is indicated on the Assessed Value Data Format shown in Appen— dix B. Examine the assessed values of the single—family buildings within the sample to determine the ex- tent to which particular housing characteristics (i.e. age, building class, construction type, etc.) influence them. Determine the mean square foot assessed value for the total number of buildings sampled for each of the four—year intervals. 75 Determine the percent change in mean square foot assessed value for each of the four—year intervals. Construct a curve representing the percent changes in mean square foot assessed value for the period 1940 to 1964. This curve should represent the general behavior of the mean square foot assessed value for the City of Ann Arbor for the time pe- riod 1940 to 1964. arch Procedures——Part II 1. Select a number of assessed values of single—family residential buildings that in accordance with cur— rent standards of blight are physically deteriorated. Record the assessed value and floor area of these buildings for each four—year interval from 1940 to 1964. Determine the mean square foot assessed value for the buildings for each of the four-year intervals. Determine the percent change in mean square foot assessed value for each of the four—year intervals. Construct a curve representing the percent change in mean square foot assessed value for the total number of buildings in the deteriorated area for the period 1940 to 1964. This curve should rep- resent the general behavior of the mean square foot assessed value for the physically deteriorated buildings. 76 Note the direction of the curve. This should in- dicate the extent to which changes in assessed values correlate with levels of physical deterio- ration. Develop an arbitrary grid for the base map of the selected city (the City of Ann Arbor). The size of the grid (scale) can be of any suitable dimen- sion; however, to be most practical it would be best to keep it to the approximate size of an eight or ten block sub—area since this is approximately the minimum allowable urban renewal project as per current federal policies. Compare the direction of the curve representing the behavior of the mean square foot assessed value of the deteriorated buildings from the phy- sically deteriorated sub—area (selected from the arbitrary grid) with that of the behavior of the mean square foot assessed value for the overall city by placing both of the curves on one graph as shown in Chapter II page 43. Note the differ— ence in direction (lepe) between the two curves for the various time intervals. These differences represent the degree to which the behavior of the mean square foot assessed value of the selected sub—area (in this case, the physically deteriorated area) differs from that for the overall city. 77 Thus, the juxtaposition of the two cruves, in effect, represents a measure or level of physi— cal deterioration at various stages of time (each four—year interval) for the physically deterio— rated sub—area. The technique of comparing the behavior of mean square foot assessed value does not have to be limited to deteriorated properties. Indeed, any sub-area of the city can be measured to ascertain the extent to which its behavior differs from that of the overall city. Hence, through the de— velOpment of an arbitrary grid (as outlined in procedure No. 7) and indicator of physical deteri- oration can be developed for any sub-area of the city. Note the particular time interval at which the difference in slope between the Egg curves (the one representing the percent change in mean square foot assessed value for the physically deteriorated area and the one representing the percent change in mean square foot assessed value for the overall city) is the greatest. This time interval can be identified as the critical EEEEE in the deteriora- tion process, for it is during this period that the area has deteriorated most rapidly and gone from one of blight potential to one of actual phy- sical deterioration. 78 Lgure 15.-—Defining the Critical Stage 20 greent 15 City of Ann Arbor lange 1 Mean 10 1. Ft. ssessed 5 alue for :evious , ~ __. _.m )ur—Year ' Critical Stage ariod -5 Deterio— rated Area I F” l l l '44 '48 '52 '56 '60 '64 '68 Time The critical stage is actually defined by two 1rves. The top curve represents the normal behavior for \ e mean square foot assessed value for the whole city. e lower one represents the mean square foot assessed lue for the deteriorated area. The critical E3333 in e above diagram (Figure 15) is from 1956 to 1960. For is during this time that the area has gone from one blight potential to one of actual physical deteriora- on. It is also during this interval of time that the ea has deteriorated most greatly and most rapidly. earch Procedures—-Part III The procedures for accomplishing the final portion the research may be stated as follows: 79 Locate a sub—area of the city that has some actual "earmarks" of physical deterioration on the Sub— Area Map. Such an area conceivably would appear as having a potential for becoming physically de— teriorated. Such buildings or areas could have physically identifiable characteristics (i.e. peel— ing paint, broken windows, sagging roofs, etc.) as well as some of the other conditions or circum- stances mentioned in Chapter II, page 37, as "Blighting Pre-Conditions." Select an appropriate number of assessed values of single—family residential buildings utilizing either a random or cluster sampling technique from the sub—area. Determine the behavior of the mean square foot as— sessed value for the area and note its direction in comparison with the mean square foot assessed value for the overall city. Project the two curves as indicated in Figure 16. If there appears to be an increasing separation between the two curves (an increase in change in slope), one could expect that the sub-area would be approaching an actual state of physical deterio- ration. The reason for such a judgment or opinion would be that the sub—area had received a dispro- portionate or lesser amount of private investment 80 relative to the rest of the city, and consequently would manifest less of a potential for attracting future capital or investment. igure l6.——Predicting Physical Deterioration 20 15 ercent City of Ann Arbor a—'”‘T 1ange in 10 /,//’fi aan Sq. :. Assessed 5 L————————~—J”"’T?”’// Y [#1 Y2 tlue of :evious a__. __1___u_n___Lm.__4:7_v—dk—‘__J :l>_..————— )ur-Year -__T X X1 C_V_;3 :riod -5 X2 Transitional Area I | l l _ .- '44 '48 '52 '56 '60 '64 '68 Time The data utilized in this study concerns single- mily residential buildings. This category of land use s been selected for two reasons. The first is that ngle-family residential buildings presently occupy about % of the land use in most American Cities. Consequently, JOOdly share of the housing problem in the country cen— Cs on this type of housing stock. The second reason is it the type of analysis utilized in this study is best ted to this type of residential land use. As mentioned lier, assessments on single—family residential buildings 81 :e primarily based on replacement costs which in turn are ased on established prices of labor and materials for >me base year. For other types of residential structures, :sessments are based on replacement costs, but they also 1c1ude substantial adjustment factors for such things as 1come capability, location, adaptability for other uses, Co In this study, housing data was obtained directly om the records of the Ann Arbor City Assessor's Office. e data was obtained from both file cards and assessment cord books. (A sample file card illustrating the extent assessment data on each piece of property is shown in pendix B.) Assessed values for single—family buildings were {en directly from the assessment records for the years 14, 1948, 1952, 1956, and 1960. Nineteen Hundred and {ty-Four data was obtained by multiplying the 1964 mar— : value prices by 25%.1 .Nineteen Hundred and Forty fig- es were obtained by dividing the 1944 replacement costs the building cost index numbers for the Detroit area ' 1940.2 This is a fairly reliable technique and in the cific case of Ann Arbor only a matter of small impor— ce. Since 1944 building Values are based on 1941 prices labor and material, 1940 figures actually reflect only Year's difference in building costs. 82 Floor areas for each of the buildings sampled were puted by multiplying the plan area of the building by given number of stories. Basement areas were not nted in the total floor areas. Quarter and half stories e counted as such since they were given in the records both drawings and photographs for each building. The mean square foot assessed value for each in— val of time was determined by dividing the total as- sed value for each four-year period by the total floor a for each 4—year period. Sample In developing the random sample for establishing behavior of the mean square foot assessed value for period 1940 to 1964 for the City of Ann Arbor, a spe— l sampling technique was utilized. A random number unique was employed for the selection process, and perties were identified according to their location on 1960 census map for the city. In building the sample certain parameters regard— the census map were noted and subsequently adhered to :he actual selection process. These parameters can be :ed as follows: 1. Census tracts were numbered 1 through 19. 2. Blocks in the various tracts ranged from 1 through 99. (No tracts had more than 99 blocks.) 83 3. Lots within each block ranged from 1 through 40 in some extreme cases, but in most cases only 1 through 20. Each building within the sample could have been :cted directly from a table of random numbers without .ng any modification in the selection process (e.g. the t two digits could represent the tract number, the two the block number, and so on). However, this would required a lengthy exercise in getting an appropriate er of suitable buildings. To short—cut the selection time, the following fication was utilized in the random number selection nique: 1. The first building was selected by having the first single digit represent the tract number; the second digit, the block number, and the third single digit, the lot number. . The second building was selected by having the first single digit plpg Egg represent the tract number; the second single digit, the block number; and third single digit, the lot number. The third building was selected by having the first single digit represent the tract number; the second and third digits, the block number; and the next (fourth) digit, the lot number. 84 The fourth building was selected by having the first single digit plgg Egg represent the tract number; the second ggg EgEEg digits, the block number; and the next (fourth) digit, the lot number. The fifth building was selected by having the first single digit represent the tract number; the second single digit, the block number; and the third single digit pEgg Egg, the lot number. The sixth building was selected by having the first single digit plgg Egg represent the tract number; the second single digit, the block number; and the third single digit pEgg Egg, the lot number. The seventh building was selected by having the first single digit represent the tract number; the second ggg EgEEg digits represent the block number; and the next (fourth) digit pEgg Egg, the lot number. The eighth building was selected by having the first single digit pEgg Egg represent the tract number; the second ggg EgEEg digits, the block number; and the next (fourth) digit pEgg Egg, the lot number. The ninth building was selected by utilizing the ion criteria for the first building; the tenth 85 uilding was selected by utilizing the selection criteria or the second building; and so on until the sample was ampleted. Whenever a building or area was selected that did at meet the requirements of the sample (i.e. a service :ation, school yard, cemetery, etc.), the random number is passed over and the next one utilized.3 To obtain some notion as to how large the sample .ze should be, a pre—sample (1964 values) was run on 31 Iildings. A crude mean of these sample values indicated lat the estimated value per square foot for the whole -ty was $1.93.4 It was then decided that a sample size with a 95% >nfidence interval of plus or minus $0.30 per square foot r the whole city would afford a reasonable level of ac— racy for the research. Using the formula ere t = 1.96 (for a confidence interval of 95%) S = 1.93 (initial square foot value), and d — 0.30 was indicated that the sample size (N) should be 82. To be on the safe side, an additional 50% was added the sample bringing the total number of observations or 'ldings to 124. 86 In regards to the appropriateness of this sample ze, a subsequent analysis showed that its actual degree variation was even better than what had initially been ped for, for one standard deviation of the value per ;uare foot equaled only 0.89. At a confidence interval 95%, this would bring the value per square foot within .us or minus $0.18 (as opposed to plus or minus $0.30) of 1e true value for the whole city.5 1e Selection of Sub—Areas The selection of particular sub—areas for purposes 5 examination and comparison was made directly from the 1b—Area (grid) Map. The selection of specific buildings Lthin each of the sub—areas was tempered somewhat by the imitations of the laboratory community and by the peculiar quirements of the research itself. Six separate areas re selected for examination, and the selection of indi- dual buildings within each sub—area was treated separately. e areas selected were as follows: fea No. 1, Deteriorated—Dilapidated Housing: Houses within this area were principally identified om the 1960 Housing Census Maps. The buildings selected re those meeting the census definitions of dilapidation d deterioration. In addition to the U.S. Census criteria deterioration, the dwellings were also checked for 87 .uilding and sanitation code violations. Of the four >1ocks examined, there were 26 code violations listed in :he premise files of the Ann Arbor City Health Officer. In respect to other areas of the city this was quite an . . 6 Lnordinate amount. Area No. 2, Semi—Deteriorated, Transitional Area: This sub-area of the city was identified by both the city assessor and the planning director as one that was in a possible state of semi—deterioration or one un— dergoing a physical transition from sound to unsound. Most of the dwellings within this area were in the E299 U.S. Housing Census category of 20 to 40 percent deterio- rated. An examination of the City Health Officer's premise files also indicated that there were 13 code violations for this area. rea No. 3, Semi—Deteriorated, Transitional Area: This sub-area was also identified by both the city ssessor and the planning director as being in a possible ransitional state. As had the buildings within Area No. L, most of these also fell within the 1960 U.S. Housing iensus category of 20 to 40 percent deteriorated. In ad— iition to this general measure of deterioration it was found that there were 22 building and sanitation code vio- .ations for this area. 88 Area No. 4, Ann Arbor Urban Renewal (Deteriorated) Area: The dwellings within this area were defined as deteriorated and dilapidated by the 1956 U.S. Urban Re— newal Criteria. In addition, an examination of the Premise Files of the City Health Officer indicated that there had been a considerable number of Building and Sanitation Code violations within the area (60 such violations). For the most part, the buildings within the area fell within the 1960 U.S. Housing Census category of 40 to 60 percent deteriorated. Area No. 5, Sound Housing (No Deterioration): The buildings within this area had no indications of being physically deteriorated. According to the 1960 ‘U.S. Census of Housing definitions of deterioration and dilapidation gEE of the structures were sound. There were only 3 building and sanitation code violations mentioned for this area in the City Health Officer's Premise Files, and these were all of a very minor nature——note the list- ing of these violations in Appendix C. The area was se— lected because it was a "good" area within walking distance of the central area of the city. Area No. 6, Sound Housing (No Deterioration): As with Area No. 5, none of the buildings within this area were either deteriorated or dilapidated. An examination of the 1960 U.S. Census of Housing indicates 89 that all of the structures were in sound condition. Fur— ther, the Premise Files in the City Health Office indicated that gg violations had been reported in this area ever. The area was selected because it was a "good" area that was within the city, yet not within walking distance of the central area. The buildings in Area No. 1 were selected by tak- ing every building within Grids 10-12 and 11—12 that were defined by the 1960 U.S. Census of Housing as being either deteriorated or dilapidated. Since there was only a limited amount of these buildings within the city, it was decided to combine all of the blocks into one area——even though in a strict geographic sense, this was not actually the case. Twenty-seven buildings were selected. The buildings in Area No. 2 were selected by tak— ing all of the houses within the grid. Sixty—eight build— ings were selected. The houses in Area No. 3 were again selected by including all of the dwellings within the grid. Fifty—five buildings were selected. The buildings in Area No. 4 were selected on the basis of the city‘s urban renewal maps. In sampling this area it was decided to select all of the houses from the best and worst blocks within the area. That is, to select dwellings from the block that was defined as having the 90 greatest amount of physical deterioration and the block that was defined as having the least amount of physical deterioration. Forty—four buildings were selected. The buildings in Area No. 5 were selected by tak— ing all of the buildings within the grid. Since these dwellings were built on much larger lots there were far fewer of them in the grid. Seventeen buildings were selected. The buildings in Area No. 6 were selected by in- cluding all of the dwellings within the grid. Again, the large lots precluded there being very many houses within the grid. Twenty buildings were selected. Base Maps In developing the study several base maps were de— veloped and utilized. They are listed here and exhibited in Appendix B as well. 1. Base Mapgfor the City of Ann Arbor, Michigan. This map was used as the basic reference map for all studies within the city. 2. Building Sample Map for the Citygof Ann Arbor, Michigan. This map was developed to locate the various single—family dwellings selected in the housing sample. 3. Census Tract Map for the City of Ann Arbor. This map indicates the various census tracts for the City of Ann Arbor, Michigan. 91 4. Sub—Area (Arbitrary Grid) Map for the City of Ann Arbor, Michigan. This map was developed to sub- divide the city into a number of sub—areas of equal size. Each grid or sub—area is approximately 1200 feet square. Eypotheses to be Tested In developing this dissertation several hypotheses have been advanced for testing. They are stated along with each research objective that is to be accomplished in the study. OBJECTIVE NUMBER 1: To demonstrate the degree to which the assessed value of single—family residential buildings varies in ac— cordance with specific housing characteristics. Major Hypothesis: The assessed value of single-family residential buildings varies appre— ciably in accordance with the parti- cular housing characteristics. Sub-Hypotheses: (1) Assessed values of single—family residential buildings vary ac- cording to differences in build— Egg class. (2) Assessed values of single—family residences vary according to dif— ferences in building construction. (3) Assessed values of single—family residences vary according to dif— ferences in building age. (4) Assessed values of single-family residences vary according to dif— ferences in their number gE stories. OBJECTIVE NUMBER 2: Major Hypothesis: OBJECTIVE NUMBER Major Hypothesis: )BJECTIVE NUMBER IajOr Hypothesis: L») A 92 (5 V Assessed values of single—family residences vary according to dif— ferences in lot areas. (6 V Assessed values of single-family reSidences vary according to dif— ferences in zoning. (7 V Assessed values of single-family residences vary according to dif— ferences in tenure of occupancy. (8 V Assessed values of single—family residences vary according to dif— ferences in regards to the presence or absence of a garage. ——___—__— To demonstrate that levels of physical deterioration correlate with depreciat— ing rates of assessed valuation in single—family residential dwellings. Levels of physical deterioration cor— relate with depreciating rates of assessed valuation in single—family residential buildings. To demonstrate a method using assess- ment data to quantify the extent of relative physical deterioration of single—family residential buildings within various sub—areas of the city. Percent changes in mean square foot assessed value can be utilized as a measure of building condition, and hence as a measure of physical deterioration. To demonstrate that assessment data for single—family residential build- ings can be utilized to identify the critical stage in the deterioration process in those areas of the City that are physically deteriorated. In those single-family residential areas of the city that are phys1cally OBJECTIVE NUMBER 5: Major Hypothesis: 93 deteriorated, the level of physical deterioration will be greatest and most rapid where the rate of depre- ciation in assessed valuation is the greatest. To demonstrate that the percent change in assessed valuation can be used as a leading surrogate for future physical deterioration. The percent change in assessed valua— tion can be used as a leading surrogate for predicting possible future physical deterioration. 94 FOOTNOTES 1Interview with Mr. Wayne Johnson, Deputy City Assessor for the City of Ann Arbor, Michigan. Mr. Johnson stated that one could obtain the 1964 replacement cost figure for any single—family residential building in the city by merely multiplying the 1964 assessment figure (which was based on market value) by 25%. March 5, 1968. 2Building Costs. Edited by T. L. Ball and E. H. Boechk and Associates, Washington: 1956. 3The table of random numbers utilized in the study were taken from Arkin, H. A., Handbook of Sampling for Auditors and Accountants, New York: Wiley, 1965. The ran— dom number selection began with row 122, column 4, page 246. 4Similar results hold for the values for other years. 5Cochran, William G., Sampling Techniques, New York: Wiley, 1963. According to the author this technique for estimating the sample size obtains whenever the follow— ing conditions are met: (1) When the sample is greater than 30, and (2) When the coefficient of variation of both variables (e.g. valuation and square foot- age) is less than 10%, p. 157. 6A listing of Health and Building Code violations is given in Appendix C. CHAPTER V THE FINDINGS Determinigg Primary Variables Affecting the Behavior of the AsseSSed Value Even the most cursory examination of the litera- ture of urban deterioration and assessment practices will alert one to look for particular variables that might have possible effects on the behavior of assessed values. Case noted in his work that occupany and tenure of occupancy were important factors related to physical blight.l Czamanski's efforts uncovered such blight—related vari— ables as building age and lot area.2 Walker's earlier research led her to believe that physical deterioration resulted from the interplay of many key factors not the least of which was lot size (overcrowdedness) and build- ing age.3 Walker also mentioned that strong relationships existed between physically deteriorated structures and de— preciating assessed values.4 Fisher mentions in his paper on urban blight and zoning that zoning itself (single-family versus multiple usage) is a highly related variable to physical deterioration 95 —1 96 and that changes in municipal zoning (spot zoning, etc.) often precede and perpetuate the spread and development of incipient blight.5 As well as noting many general factors (i.e. demo— graphic characteristics, etc.) that one ordinarily asso— ciates with slums and slum development, Raymond Vernon also states that tenure of occupancy (rental versus owner-oc- cupied housing) is a major factor related to physical deterioration.6 For the most part Vernon found that owner— occupied housing deteriorated less rapidly than did rental housing. Harland Bartholomew found in his investigation of physical deterioration of non—residential construction that standards and classes of building construction were important factors in both the physical and economic life of a building.7 In an examination of housing quality utilizing erican Public Health Standards, Johnson, Williams, and cCaldin noted that residential building condition varied in accordance with such factors as tenure of occupany, ccupancy, and building construction.8 The general literature of assessment practices nd procedures also indicates that particular housing haracteristics have strong linkages to the behavior of ssessed values. 97 McDonald states in his article concerning the prob- lem of depreciation in single-family residences that physi- cal deterioration appears to be closely correlated with construction quality (type of construction and perhaps building class).9 Albert gives primary importance to neighborhood or environmental conditions and demographic characteristics of the inhabitants, but also mentions such factors as zoning and building age.lo Percy Wagner simi- larly gives considerable credence to environmental condi— tions, yet notes such physical characteristics as construc- tion type, and building age.ll From the weight of the findings and evidence of these past research efforts one would be fairly constrained to examine his data quite closely to note the effect of such variables or housing characteristics upon it. Thus, in examining the data for this particular study of single- family housing in the City of Ann Arbor, Michigan, the ex- tent of influence of particular variables has been noted. The variables selected for examination were (1) building assessment class, (2) tenure gE occupancy (owner—occupied versus rental housing), (3) number gE stories (single-story versus multiple stories), (4) building age, (5) presence 9E garages (houses with garages versus houses without garages), (6) building construction (wood frame versus brick construc— tion), (7) zoning (single-family versus multiple zoning), and (8) lot area. 98 To test for the effect of such variables on the behavior of the assessed value of single—family buildings, the percent change in mean square foot assess value of all the buildings in the sample was determined for each four— year interval from 1940 to 1964. The data was then plotted graphically in a time-series analysis as illustrated in Figure 17. This graph then illustrated the general beha— vior of the mean square foot assessed value of single— family residential buildings for the whole city of Ann Arbor for the time period 1940 to 1964. Against this graph in subsequent illustrations (Figures 18 through 31) the effect of each particular variable was shown. Thus, for example, when one wished to test for the effect of building age on the sample, the behavior of the mean square foot assessed value of build— ings built before 1940 was compared with the behavior of the mean square foot assessed value for all the buildings in the sample. The utilization of this technique of time—series analysis allows the researcher to determine the rate of change in mean square foot assessed value. When two curves are compared on the same graph, it is then possible to de— termine differences in slope (hence acceleration or decel— eration) which, in turn, demonstrates the extent to which a particular variable influences assessed values. A posi— tive difference in slope indicates that the mean square 99 foot assessed value of single—family residences throughout the city is appreciating more than that for those buildings characterized by the particular variable or housing charac— teristic under examination. A negative difference in slope indicates just the reverse. The magnitude of the effect of each variable on the sample is, of course, measured by the value of each difference in slope. Thus, if we are to an- swer the specific question of whether or not a particular variable has an effect on the mean square foot as— sessed value of single—family residences, we have only to note the direction and the value or magnitude of the dif— ferences in slope in each particular case or illustration. The Findings The initial part of the empirical research con- cerned the determination of the behavior (percent change) of the mean square foot assessed value of single—family residential buildings for the City of Ann Arbor, Michigan (the sample) for the period 1940 to 1964. Presented in the form of a time—series analysis (Figure 17) the percent change for each four—year period can be seen as follows: 100 ?igure l7.-—The Behavior of the Mean Square Foot Assessed Value Single-Family Residences City of Ann Arbor 20 a 15 Percent Change in Mean 10 rc”‘ Square Foot Assessed 5 r///J Value of Previous 4— Year Period 0_L__1__.._n_.___..___ ____ . 1 '44 '48 '52 '56 '60 '64 Time Specifically, the findings indicated that the mean square foot assessed value had the following values and percent changes for the following years: Table l.——Percent change in mean square foot assessed value: City of Ann Arbor, Michigan Value Percent Change 1940 $1.85 1944 1.92 1940 to 1944 2.6 1948 2.08 1944 to 1948 8.3 1952 2.13 1948 to 1952 2.4 1956 2.26 1952 to 1956 6.1 1960 2.39 1956 to 1960 5.3 1964 2.31 1960 to 1964 -2.9 101 For the most part the percent change in mean square foot assessed value appears to be appreciating with the one exception of the last four—year interval (1960 to 1964). One possible explanation for this aberrant behavior in per- cent change might lie in the changeover in assessment pro- cedures that were effected in January of 1963. After 1963, assessments on single—family buildings were based on fair market value rather than replacement costs. A second pos— sible reason might be that of the Egg buildings sampled, only E3 had improvements made between 1960 and 1964. In examining the data to determine the effect or importance of the variable building gEggg the following differences (Figures 18 through 21) were noted: Figure l8.--Percent Change in Mean Square Foot Assessed Value: Class A Buildings Single—Family Residences City of Ann Arbor 25 20 15 Percent Change in Mean Square Foot 10 Assessed Value of Difference P - _ 5 I . Ppgyégus 4 Year ‘ 1n slope O ‘ , , __L_. -——-‘———' \ 5 Class A Building City of Ann Arbor‘\\‘\ -10 | l I l l , .44 '48 '52 '56 '60 64 Time 102 Specifically, the findings indicated that the mean square foot assessed value for Eléii g buildings had the following values, percent changes, and differences in slope for the following years: Table 2.——Percent change and difference in slope: Class A buildings Difference Value Percent Change in Slope I 1940 $2.74 1944 2.72 1940 to 1944 —0.7 1948 2.79 1944 to 1948 2.7 5.6 1952 2.67 1948 to 1952 -4.4 6.5 1956 2.67 1952 to 1956 0.0 6.1 ‘ 1960 2.97 1956 to 1960 11.3 -6.0 1964 3.21 1960 to 1964 8.2 —ll.1 In examining the data to determine the effect or importance of the variable of building class for gEggg 2 buildings, the differences were noted in Figure 19 (next page). Specifically, the findings indicated that the mean square foot assessed value for giggg E buildings had the values, percent changes, and differences in slope for the Years shown in Table 3 (next page). 103 Figure l9.--Percent Change in Mean Square Feet Assessed Value: Class B Buildings 9 9 12 l4 14 Single-Family Dwellings 25 City of Ann Arbor 2o / Percent Change in 15 (///// /\Difference Mean Square Foot / \in Slope Assessed Value of 10 <’,,/‘ Previous 4—Year ’////A / \ Period 5 \ / \ . \ /» , O k\\\\4 \ /’ -5 Class B/Dwellingj . ‘10 City qf AnnlArbor l l l '44 '48 '52 '56 '60 '64 Table 3.——Percent change and difference in slope: Class B buildings Difference Value Percent Change in Slope 1940 $2.32 1944 2.35 1950 to 1944 1.4 1948 2.29 1944 to 1948 —2.6 11.9 1952 2.49 1948 to 1952 8.9 —8.8 1956 2.78 1952 to 1956 11.5 3.2 1960 2.80 1956 to 1960 0.7 16.5 1964 2.63 1650 to 1964 —5.1 3.3 104 In examining the data to determine the effect or importance of the variable of building class for Class C buildings the following differences were noted (Figure 20): Figure 20.--Percent Change in Mean Square Foot Assessed Value: Class C Buildings Number of Buildings 59 64 74 86 Single—family Dwellings 25 City of Ann Arbor | l 87 20 § l Percent Change in 15 . Class C Dwellings Mean Square FOOt :::::j City of Ann Arbor Assessed Value of 10 Previous 4—Year Period 5 » ‘ / / o ._-l-__ "If -5 Difference in Slope —10 '44 ‘48 '52 '56 '60 '64 Time The findings indicated that the mean square foot assessed value for Class E buildings had the values, per— cent changes, and differences in slope for the years shown in Table 4 (next page). 105 Table 4.--Percent change and difference in slope: Class C buildings Difference Value Percent Change in Slope 1940 $1.80 1944 1.85 1940 to 1944 2.8 1948 2.04 1944 to 1948 10.2 0.4 1952 2.10 1948 to 1952 2.9 0.6 1956 2.22 1952 to 1956 5.7 0.1 1960 2.34 1956 to 1960 5.8 0.1 1964 2.22 1960 to 1964 -4.8 4.8 In examining the data to determine the effect or importance of the variable of building class for Class 2 bUildingS, the following differences were noted (Figure 21): Figure 21.-—Percent Change in Mean Square Foot Assessed Value: Class D Buildings Number of Buildings 17 17 l7 17 25 20 Class 2 Dwellings City of Ann rbor Percent Change in 15 I Mean Square FOOt Single—family Dwellings Assessed Value of 10 City of Ann Arbor Previous 4—Year 1 \\ 0 Difference \ \ in Slope \ -5 \ \ D -10 L—‘fi— I I .44 I18 '52 '56 '60 '64 106 The findings indicated that the mean square foot assessed value for giggg 2 buildings had the following values, percent changes, and differences in slope for the following years: Table 5.--Percent change and difference in slope: Class D buildings. Difference Value Percent Change in Slope 1940 $1.39 1944 1.50 1940 to 1944 7.9 1948 1.76 1944 to 1948 17.3 10.0 1952 1.74 1948 to 1952 —l.l 3.4 1956 1.77 1952 to 1956 1.1 5.1 1960 1.86 1956 to 1960 5.0 0.1 1964 1.95 1960 to 1964 4.8 -7.1 In examining the data to determine the extent or importance of the variable of tenure gE occupancy (owner- occupied versus rental housing), the differences were noted in Figure 22 (next page). The findings indicated that the mean square foot as— sessed value for owner—occupEgg buildings had the values, Percent changes, and differences in slope for the years shown in Table 6 (next page)- Figure 22.-—Percent Change in Mean Square Foot Assessed Value: Owner—Occupied Buildings 95 Buildings Owner—occupied Dwellings 25 City of Ann Arbor 20 Percent Change in 15 Mean Square Foot _ _ _ Assessed Value of 10 Single—family Dwellings Previous 4—Year City Of Ann Arbor Period 5 ‘ G 0 ',_._____ —_— —— ———. —5 Difference in Slope I l I l | l '44 '48 '52 '56 '60 '64 Time Table 6.——Percent change and difference in slope: owner— occupied buildings Difference Value Percent Change in Slope 1940 $1.96 1944 2.01 1940 to 1944 3.0 1948 2.18 1944 to 1948 8.3 —0.1 1952 2.26 1948 to 1952 3.6 -l.l 1956 2.42 1952 to 1956 7.3 —l.2 1960 2.55 1956 to 1960 5.4 —0.3 1964 2.48 1960 to 1964 —3.4 0.6 108 In examining the data to determine the extent or importance of the variable of occupancy in regards to owner—occupied dwellings with rented rooms, the following differences were noted (Figure 23): Figure 23.--Percent Change in Mean Square Foot Value: Buildings with Rented Rooms 16 Buildings Single-family Dwellings City of Ann Arbor 25 20 Percent Change 15 in Mean Square r’///A FOOt Assessed 10 §= , Owner—occupied Dwellings Value of Pres r””A with Rented Rooms vious 4—Year 5 , Period , I /,/ O ' F-——0/ Difference in Slope —5 ‘ —10 I I '14 '48 '52 '56 '60 '64 Time The findings indicated that the mean square foot assessed value for owner—occupied dwelliggg gipg £32332 rooms had the values, percent changes, and differences in slepe for-the years shown in Table 7 (next page). 109 Table 7.——Percent change and difference in slope: Build- ings with rental rooms Difference Value Percent Change in Slope 1940 $1.74 1944 1.75 1940 to 1944 1.0 1948 1.88 1944 to 1948 7.6 0.7 1952 1.91 1948 to 1952 1.7 0.7 1956 1.94 1952 to 1956 1.0 5.0 1960 1.96 1956 to 1960 1.5 3.6 1964 1.81 1960 to 1964 -7.6 4.7 In examining the data to determine the effect or importance of the variable of age (those buildings built before 1940), the following differences were noted (Figure 24): Figure 24.—-Percent Change in Mean Square Foot Assessed Value: Buildings Built before 1940 82 Buildings 25 Single—family Dwellings City of Ann Arbor 20 15 Buildings Built Percent Change in before 1940 Mean Square Foot 10 Assessed Value of Previous 4-Year 5 , Period I 0 Difference in Slope —5 -10 —_T———-_ I '44 '48 '52 '56 '60 '64 Time 110 The findings indicated that the mean square foot assessed value for those buildings built before 1940 had the following values, percent changes, and differences in slope for the following years: Table 8.—-Percent change and difference in slope: Build— ings built before 1940 Difference Value Percent Change in Slope 1940 $1.87 ‘ 1944 1.90 1940 to 1944 1.1 I 1948 2.01 1944 to 1948 5.8 2.5 1952 1.97 1948 to 1952 -0.2 2.6 ‘ 1956 1.99 1952 to 1956 1.0 5.1 ‘ 1960 2.02 1956 to 1960 1.5 3.6 1964 1.95 1960 to 1964 —3.5 6.4 In examining the data to determine the effect or importance of the variable of garages (houses without garages versus houses with garages), the differences were noted for those dwellings without garages in Figure 25 (next page) , The findings indicated that the mean square foot assessed value for those buildings without garages had the and differences in slope for the values, percent changes, years shown in Table 9 (next page). 111 Figure 25.--Percent Change in Mean Square Foot Assessed Value: I I I Dwellings without Buildings without Garages 38 Buildings 30 Garages 25 20 \4 Percent Change Single—fami y Dwellings 1n Mean Square 15 City of Ann Arbor Foot Assessed r’///A Value of Pre— 10 vious 4—Year I\ Period 5 ‘ \ N. O \ //// \\ __ _5 V . . Difference in Slope -10 l I l I '44 '48 '52 '56 '60 '64 Time Table 9.-—Percent change and difference in slope: Build— ings without garages Difference Value Percent Change in Slope 1940 $1.66 1944 1.72 1940 to 1944 4.1 1948 1.99 1944 to 1948 15.8 7.4 1952 1.98 1948 to 1952 -1.5 -3.9 1956 2.15 1952 to 1956 8.8 2.7 1960 2.22 1956 to 1960 3.7 —1.4 1964 2.10 1960 to 1964 —5.4 -1.5 112 In examining the data to determine the effect or importance of the variable of garages for those houses gEEg garages, the following differences were noted (Figure 26): Figure 26. --Percent Change in Mean Square Foot Assessed Value: Buildings with Garages 87 Buildings I 25 Dwellings with Garages 20 15 Single-family Dwellings City of Ann Arbor 10 ’///// 5‘/ mo Monssz omoam ca mosmnmmmao ”Wan—IJnnfiuHDJ 36463.3 maa4fljoaa 3.33.4” 123 five of the fourteen class B buildings received any improve- ments during the entire twenty—year period. Ten of the seventeen class D buildings received improvements during the overall period of analysis while approximately half (41 out of 87) of the class C buildings were improved over the same span of time. ggg, as demonstrated by those buildings built be- fore 1940, also appears to be a major variable. Newer buildings depreciate much more rapidly than do older build— ings (Figure 24). Once a building reaches a particular age, it seems to reach a plateau in regards to the influence that this particular variable has upon it. Tenure gE Occupancy is similarly an important var— iable that influences the behavior of the square foot as— sessed value. The principal reason for this is that once a building is used for income purposes, its assessed value ‘is increased because of its income capabilities. As men— Itioned earlier this is not as nearly significant in regards Ito single—family dwellings as it is with duplexes and ‘apartment houses which the assessor and the state tend to view as commercial properties.12 Number gE stories, as indicated in Figure 29, is also an important building characteristic that influences the behavior of the square foot assessed value. An exami— nation of the data in regards to single-story buildings indicates that 23 of the 41 buildings in the sample had 124 improvements between 1940 and 1964 and 18 did not. A possible reason for this might be that single—story resi- dences are less expensive to improve than are multi-story residences (i.e. it is much cheaper to build a one-story addition than a two-story one). An Examination of the Behavior or the Mean Square Foot Assessed Value in Selected Sub-Areas The second portion of the research involves a com— parison of the behavior of the mean square foot assessed value of single-family residences in particular sub—areas of the city with certain segments of single-family housing stock throughout the city. In other words, the behavior of the mean square foot assessed value of single—family buildings in particular sub—areas will be compared with Ithe general behavior of the mean square foot assessed value iof single—family residences throughout the city that share Isimilar characteristics. For example, a certain sub—area comprised primarily of deteriorated buildings which has _characteristics such as class D structures, older buildings, Iand rental rooms will be compared with single-family hous— \ing stock throughout the city that has the same character- istics. A time series-analysis technique will again be utilized to demonstrate differences in slope between per— I I I cent changes in mean square foot assessed values. 125 As it has just been stated in Part I, four vari- ables or housing characteristics in particular seem to have the greatest influence on the behavior of the as— sessed value of single—family residences. These are (1) building class, (2) ggg, (3) tenure gE occupancy, and (4) number 9E stories. To get a more accurate picture as to how single— family residential buildings in particular sub-areas of the city vary from the overall development of the city in regards to percent change in mean square foot assessed value, each sub-area will be examined several times in accordance with the above variables.l3 Six sub—areas will be examined as indicated in Chapter IV.14 These sub-areas are: Sub-area No. l, (Grids 10-12 and 11—12), 81 to 100% De— teriorated in accordance with the 1960 I U.S. Census of Housing. ISub—area No. 2, (Grid 8—11), Transitional Area showing some evidence or pre—conditions for nggg— EEgE future physical deterioration. Sub—area No. 3, (Grid 9—12), Transitional Area showing some evidence or pre—conditions for REESE? EEgE future physical deterioration. Sub-area No. 4, (Grid 10—14), North Central Urban Renewal Area, declared deteriorated in accordance with U.S. Urban Renewal criteria. 126 Sub—area No. 5, (Grid 15—21), Physically sound area "good housing" with gg deterioration. Sub-area No. 6, (Grid 17—23), Physically sound area "good housing" with gg deterioration. Sub—Area Number 1 (Deteriorated Area) In examining the data to determine the difference in slope in percent change in mean square foot assessed value between Sub-area No. l and the overall city, it was noted that the sub—area contained structures that were primarily in building class D, were predominately two- story, had rented rooms, and were older (all were built before 1940). Differences in slope due to building gEggg were noted as follows (Figure 32): Figure 32.——Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 1, Class D Buildings 18 Buildings 35 I I I Class D Buildings 30 City of Ann Arbor I 25 »._i_d IPercent Change in 20 {Mean Square Foot IAssessed Value of 15 I ‘ _ IPreVious 4—Year \ Difference in Slope IPeriod 10 \ \ 5 FT‘TN \ O Sub—Area No. l —5 (Deteriorated) I i r———- '44 ‘48 '52 '56 ‘60 '64 I I I . I L 127 The findings indicated that the mean square foot assessed value in regards to gEggg 2 buildings in Sub-area No. 1 had the following values, percent changes, and dif— ferences in slope for the following years: Table l7.--Percent change and difference in slope: Sub- area No. 1, Class D buildings Difference Value Percent Change in Slope 1940 $1.31 1944 1.39 1940 to 1944 6.1 1948 1.39 1944 to 1948 O 17.3 1952 1.38 1948 to 1952 —l.0 0.0 1956 1.35 1952 to 1956 —2.0 3.0 1960 1.34 1956 to 1960 -l.0 2.1 1964 1.26 1960 to 1964 —6.1 10.6 Differences in slope due to ggg (buildings built before 1940) in Sub—area No. l were noted in Figure 33 (next page). The findings indicated that the mean square foot assessed value in regards to buildings ggEEE before E239 in Sub—area No. 1 had the values, percent changes, and differences in slope for the years shown in Table 18 (next page). ... .u.I. Figure 33.——Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 1, Buildings Built be— fore 1940 27 Buildings 25 20 Percent Mean Square Foot Assessed Value of 10 Previous 4-Year ‘ 5 775 1 Period Buildings Built before 1940 City of Ann Arbor Change in 15 0 . Difference in Slope -5 Sub—Area #1 (Deteriorated) -10 I I ‘ I l | '— '44 '48 '52 '56 '60 '64 Time Table 18.-—Percent change and difference in slope: Sub— area No. 1, buildings built before 1940 ————; Difference Value Percent Change in Slope 1940 $1.31 1944 1.39 1940 to 1944 6.1 1948 1.39 1944 to 1948 0 5.8 1952 1.38 1948 to 1952 —l.0 0.5 1956 1.35 1952 to 1956 -2.0 3.3 1960 1.34 1956 to 1960 —1.0 2.1 L964 1.26 1960 to 1964 -6.1 3.0 129 Differences in slope due to the variable of rental rooms in Sub-area No. l were noted as follows (Figure 34): Figure 34.--Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 1, Buildings with Rental Rooms 7 Buildings 25 I I I Single—Family Residences 20 with Rental Rooms City of Ann Arbor Percent Change in 15 Mean Square Foot Assessed Value of 10 ::/"d Previous 4—Year 5 Period (//l \v-—- Di ference O \\ in Slope Sub—Area #1 —5 (Deteriorated) —10 l l j '44 '48 '52 '56 '60 '64 Time The findings indicated that the mean square foot assessed value in regards to owner-occupied dwellings with rental rooms in Sub—area No. 1 had the values, percent changes, and differences in slope for the years shown in Table 19 (next page). 130 Table l9.—-Percent change and difference in slope: Sub— area No. 1, buildings with rental rooms ' Difference Value Percent Change in Slope 1940 $1.31 1944 1.39 1940 to 1944 6.1 1948 1.39 1944 to 1948 0 7.6 1952 1.38 1948 to 1952 —l.0 2.7 1956 1.35 1952 to 1956 —2.0 3.0 1960 1.34 1956 to 1960 -l.0 2.5 1964 1.26 1960 to 1964 —6.1 —1.6 Differences in slope due to the variable of multiple— stories (houses with two or more stories) in Sub-area No. 1 were noted as follows (Figure 35): Figure 35.—-Percent Change in Mean Square Foot Assessed Value: Sub—Area No. l, Multi—Story Buildings 7 Buildings 25 Single-Family Residences 20 with Multiple StoriesI City of Ann Arbor Percent Change in 15 wean Square Foot 10 A f r————a _ 9:23:83: Ziigiro I >~___s Difference in Slope Period 5 \\ EN, I ‘\ 0 Sub-Area #1 ’ 5 (Deteriorated) -10 I l . '44 '48 '52 '56 '60 '64 Time 131 The findings indicated that the mean square foot assessed value in regards to houses gEEg Egg 2E gggg .stories in Sub-area No. 1 had the following values, per- cent changes, and differences in slope for the following years: Table 20.—-Percent change and difference in slope: Sub— area No. 1, multi-story buildings Difference Value Percent Change in Slope 1940 $1.31 1944 1.39 1940 to 1944 6.1 1948 1.39 1944 to 1948 0 8.3 1952 1.38 1948 to 1952 —l.0 1.0 1956 1.35 1952 to 1956 —2.0 3.2 1960 1.34 1956 to 1960 —l.0 4.0 1964 1.26 1960 to 1964 —6.1 2.3 Sub—Area Number 2 (Transitional Area) In examining the data to determine the difference in slope in percent change in mean square foot assessed value between Sub-area No. 2 and the overall city, it was noted that the sub—area contained structures that were almost evenly divided between class C and class D, were Predominantly two—story, and were older (all were built before 1940), Only 12% of the buildings had rental rooms. 132 Differences in slope due to building class g were noted as follows (Figure 36): Figure 36.--Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 2, Class C Buildings I I | Class C Buildings City of Ann Arbor 32 Buildings 25 20 Percent Change in 15 Mean Square Foot Assessed Value of 10 Previous 4—Year ( ransitional) Period 5 ‘ . \ o \ ' Difference in Slope —5 I I' I I '44 '48 '52 '56 '60 '64 Time The findings indicated that the mean square foot assessed value in regards to class 9 buildings in Sub—area No. 2 had the following values, percent changes, and dif— ferences in slope for the following years: Table 21.——Percent change and difference in slope: Sub— area No. 2, class C buildings Difference Value Percent Change in Slope 1940 $1.36 1944 1.39 1940 to 1944 2.2 1948 1.52 1944 to 1948 11.8 —2.6 1952 1.56 1948 to 1952 14.0 0.8 1956 1.55 1952 to 1956 13.6 6.4 1960 1.51 1956 to 1960 11.9 6.7 1964 1.46 1960 to 1964 8.9 -4.7 133 Difference in slope in regards to structures in building class 2 in Sub-area No. 2 were noted as follows (Figure 37): Figure 37.-—Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 2, Class D Buildings 28 Buildings 35 Clais D Bindings /’ 30 City of Ann Arbor 25 \/ Percent Change in 20 Mean Square Foot Assessed Value of 15 Previous 4-Year Va”’d I“‘~a~\\\\\ Sub-Area #2 (Transitional) "4 Period 10 D \ /,/” 5 \ / /I>/ \ Are 0 \ /// \K' Difference in Slope —5 l I '14 '48 '52 '56 '60 '64 Time The findings indicated that the mean square foot assessed value in regards to claSS 2 buildings in Sub-area No. 2 had the values, percent changes, and differences in slope for the years shown in Table 22 (next page). 134 Table 22.——Percent change and difference in slope: Sub- area No. 2, class D buildings Difference Value Percent Change in Slope 1940 $1.36 1944 1.39 1940 to 1944 2.2 1948 1.52 1944 to 1948 11.8 7.7 1952 1.56 1948 to 1952 14.0 —3.2 1956 1.55 1952 to 1956 13.6 1.4 1960 1.51 1956 to 1960 11.9 3.3 1964 1.46 1960 to 1964 8.9 7.6 Differences in slope in regards to structures with multiple stories in Sub—area No. 2 were noted as follows (Figure 38): Figure 38.——Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 2, Multi-Story Buildings 53 Buildings 20 Sub-Area No. 2 (Transitional) 15 Percent Change in Mean Square Foot 10 Assessed Value of Buildings with Multiple Stories Previous 4—Year 5 ///,\\ Period 1 / \\ 0 K_~ // -5 Difference in Slope —lO '44 '48 '52 '56 '60 '64 135 The findings indicated that the mean square foot assessed value in regards to buildings with multiple stories in Sub—area No. 2 had the following values, percent changes, and differences in slop for the following years: Table 23.—-Percent change and difference in slope: Sub- area No. 2, multi—story buildings Difference Value Percent Change in Slope 1940 $1.36 1944 1.39 1940 to 1944 2.2 1948 1.52 1944 to 1948 11.8 —1.3 1952 1.56 1948 to 1952 14.0 -2.2 1956 1.55 1952 to 1956 13.6 1.4 1960 1.51 1956 to 1960 11.9 4.7 1964 1.46 1960 to 1964 8.9 -l.7 Difference in slope in regards to age (buildings built before 1940) in Sub—area No. 2 were noted in Figure 39 (next page). The findings indicated that the mean square foot assessed value in regards to the variable ggg (older build- ings) in Sub-area No. 2 had the values, percent changes, and differences in slope for the years shown in Table 24 (next page). 136 Figure 39. ——Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 2, Buildings Built be— fore 1940 60 Buildings 25 20 Sub—Area #2 (Transitional) Percent Change in 15 Mean Square Foot Assessed Value of 10 Previous 4—Year , Period 5 //////Buildings Constructed before 1940 _: III Diffelrence lin Slope —10 '44 '48 '52 '56 '60 '64 Time Table 24.-—Percent change and difference in slope: Sub— area No. 2, buildings built before 1940 Difference Value Percent Change in Slope 1940 $1 36 1944 1.39 1940 to 1944 2.2 1948 1.52 1944 to 1948 11.8 ~3.8 1952 1.56 1948 to 1952 14.0 -l.4 1956 1.55 1952 to 1956 13.6 1.4 1960 1.51 1956 to 1960 11.9 3.2 1964 1.46 1960 to 1964 8.9 -0-5 137 Sub-Area Number 3 (Transitional Area) In examining the data to determine the difference in slope in percent change in mean square foot assessed value between Sub—area No. 3 and the overall city, it was noted that the sub—area contained structures that were al— most evenly divided between class C and class D (45% class D, 55% class C), were predominantly older (all built be— fore 1940), had rental rooms, and were predominantly two— story. Differences in slope due to building class C were noted as follows (Figure 40): Figure 40.—-Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 3, Class C Buildings 30 Buildings 25 Class C Buildings 2 City of Ann Arbor Percent Change in 15 Mean Square Foot Assessed Value of 10 Previous 4—Year Period 5 Difference in Slope The findings indicated that the mean square foot assessed value in regards to glass 9 buildings in Sub—area N0. 3 had the values, percent changes, and differences in Slope for the years shown in Table 25 (next page). I 138 Table 25.--Percent change and difference in slope: Sub— area No. 3, class C buildings Difference Value Percent Change in Slope 1940 $1.36 1944 1.38 1940 to 1944 1.6 i 1948 1.43 1944 to 1948 3.6 5.2 g 1952 1.41 1948 to 1952 —l.0 4.2 E 1956 1.50 1952 to 1956 0.0 6.0 1960 1.37 1956 to 1960 -1.7 6.7 1964 1.30 1960 to 1964 —4.4 -3.3 Differences in slope in regards to structures in building class D in Sub-area No. 3 were noted as follows (Figure 41): Figure 41.—-Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 3, Class D Build1ngs 25 Buildings 35 I I | > Class D Buildings 30 City of Ann Arbor 25 t--r,,/«'“ Percent Change in 20 Mean Square Foot Assessed Value of 15 Previous 4—Year \ Difference in Slope Period ,0 10 \\ /// 5 \ ,4// O \b/ / f) D Sub—Area No. 3 .5L1 (Transitiopal) ——————L-‘“ "-_—__F* l .44 '48 '52 '56 '60 '64 139 The findings indicated that the mean square foot assessed value in regards to glass D buildings in Sub—area No. 3 had the following values, percent changes, and dif- ferences in slope for the following years: Table 26.-—Percent change and difference in slope: Sub— area No. 3, class D buildings Difference Value Percent Change in Slope 1940 $1.36 1944 1.38 1940 to 1944 1.6 1948 1.43 1944 to 1948 3.6 13.7 1952 1.41 1948 to 1952 —1.0 0.0 1956 1.40 1952 to 1956 0.0 1.0 1960 1.37 1956 to 1960 —1.7 6.9 1964 1.30 1960 to 1964 —4.4 9.8 Differences in slope in regards to single—family dwellings with rental rooms in Sub—area No. 3 were noted in Figure 42 (next page). The findings indicated that the mean square foot assessed value in regards to dwellings with rental rooms in Sub-area No. 3 had the values, percent changes, and differences in lepe for the years shown in Table 27 (next page). .mm ‘ '“l ’ 140 Figure 42.--Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 3, Buildings with Rental Rooms 20 Buildings I l l | 20 Single—Family Residences with Rental Rooms 15 Cit of Ann Arb r Percent Change in Mean Square Foot 10 Assessed Value of Sub-Area #3 Trans'tional) Previous 4—Year 5 »_.\ ’ Period ‘ 0 Difference in Slope \\ I -5 III III '44 '18 '52 '56 '60 '64 Time Table 27.——Percent change and difference in slope: Sub— area No. 3, buildings with rental rooms Difference Value Percent Change in Slope 1940 $1.36 1944 1.38 1940 to 1944 1.6 1948 1.43 1944 to 1948 3.6 4.0 1952 1.41 1948 to 1952 —1.0 2.7 1956 1.40 1952 to 1956 0.0 1.3 1960 1.37 1956 to 1960 —l.7 3.2 1964 1.30 1960 to 1964 -4.4 -l.9 141 Differences in slope in regards to dwellings with multiple stories in Sub—area No. 3 were noted as follows (Figure 43): Figure 43.--Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 3, Multi-Story Buildings 46 Buildings 25 Single-Family Residences 20 with Multiple Stories Percent Change in 15 City Of Ann Arbor Mean Square Foot Assessed Value of 10 Previous 4—Year Sub—Area #3 Period 5 K\ /‘/ (TranSitional) \\ \ 0 Difference in lope _5 1 I I I '44 '48 '52 '56 '60 '64 The findings indicated that the mean square foot assessed value in regards to dwellings with multiple stories in Sub-area No. 3 had the values, percent changes, and dif- ferences in slope for the years shown in Table 28 (next page). 142 Table 28.——Percent change and difference in slope: Sub— area No. 3, multi-story buildings Difference Value Percent Change in Slope 1940 $1.36 1944 1.38 1940 to 1944 1.6 1948 1.43 1944 to 1948 3.6 4.7 1952 1.41 1948 to 1952 —1.0 1.0 1956 1.40 1952 to 1956 0.0 1.2 1960 1.37 1956 to 1960 —1.7 4.9 1964 1.30 1960 to 1964 —4.4 0.7 Differences in slope in regards to age (those structures built before 1940) in Sub—area No. 3 were noted as follows (Figure 44): Figure 44.——Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 3, Buildings Built Before 1940 55 Buildings 25 20 Single-Fami y ReSidences Built Before 1940 Percent Change in 15 City of Ann Arbor Mean Square Foot Assessed Value of 10 Previous 4—Year ' Period 5 - ‘ h‘—{£———— \\1’ 0 Difference in Slope ’ Su —Area #3 “5 l (Transitional) I l '44 '28 '22 '56 '60 ' 4 Time 143 The findings indicated that the mean square foot assessed value in regards to age 9: structures in Sub—area No. 3 had the following values, perCent changes, and dif— ferences in slope for the following years: Table 29.--Percent change and difference in slope: Sub— area No. 3, buildings built before 1940 Difference Value Percent Change in Slope 1940 $1.36 1944 1.38 1940 to 1944 1.6 1948 1.43 1944 to 1948 3.6 2.2 1952 1.41 1948 to 1952 —1.0 1.2 1956 1.40 1952 to 1956 0.0 1.2 1960 1.37 1956 to 1960 —1.7 3.2 1964 1.30 1960 to 1964 —4.4 1.4 Sub-Area Number 4--Urban Renewal (Deteriorated Area) In examining the data to determine the difference in slope in percent change in mean square foot assessed value between Sub-area No. 4 and the overall city, it was noted that the sub—area contained structures that were pri- marily class D buildings, had multiple stories, had rental rooms, and were older (all were built before 1940). Dif— ferences in slope due to the presence of £2233; {9923 were noted in Figure 45 (neXt page). 144 Figure 45.——Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 4, Buildings with Rental Rooms 17 Buildings 20 Single—Family Residences with Rental Rooms 15 City of Ann Arbor ), Percent Change in 10 ,/ Mean Square Foot ‘S-A4/ Assessed Value of 5 K. (DetZEioieieg) ' Previous 4—Year \ /,/” Period 0 \ g,/ _5 Difference in Slope —10 D —15 I l '44 '48 '52 '56 '60 '64 Time The findings indicated that the mean square foot assessed value in regards to rental rooms in dwellings in Sub—area No. 4 had the following values, percent changes, and differences in slope for the following years: Table 30.--Percent change and difference in slope: Sub— area No. 4, buildings with rental rooms Difference Value Percent Change in Slope 1940 $1.18 1944 1.21 1940 to 1944 2.7 1948 1.24 1944 to 1948 3.0 4.6 1952 1.28 1948 to 1952 3.7 -2.1 1956 1.29 1952 to 1956 1.5 -0.4 1960 1.27 1956 to 1960 -1.5 3.0 1964 0.99 1960 to 1964 -21.7 14.1 145 Differences in slope in regards to structures in building class D in Sub-area No. 4 were noted as follows (Figure 46): Figure 46.——Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 4, Class D Buildings 34 Buildings 35 Class B Buifidings ’ City of Ann Arbor 30 P / 20 Difference in / Percent Change in Mean Square Foot 15 \ Assessed Value of Previous 4—Year 10 \ r””4 Period r/X”A Slope \ 0 \ // V Sub-Area #4 ‘5 (Deteriorated) -1o , —l5 l '44 '48 '52 '56 '60 '64 The findings indicated that the mean square foot assessed value in regards to class D structures in Sub— area No. 4 had the values, percent changes, and differences in slope for the years shown in Table 31 (next page). 146 Table 31.-—Percent change and difference in slope: Sub- area No. 4, class D buildings Value Percent Change Dififgiggge 1940 $1.18 1944 1.21 1940 to 1944 2.7 1948 1.24 1944 to 1948 3.0 14.3 1952 1.28 1948 to 1952 3.7 —4.7 1956 1.29 1952 to 1956 1.5 -0.5 1960 1.27 1956 to 1960 1.5 6.5 1964 0.99 1960 to 1964 —21.7 26.5 Differences in slope structures in Sub—area No. 4 47): in regards to the age gf were noted as follows (Figure Figure 47.-—Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 4, Buildings Built be— fore 1940 44 Buildings 25 20 15 Percent Change in 10 Mean Square Foot Assessed Value of 5 Previous 4—Year Period 0 Si '44 ngle-Family Residences }’ Built Before 1940 / City of Ann Arbor / / Difference in Slope Sub-Area #4 (Deteriorated) l I I l I :48 -52 '56 '60 '64 Time ”31‘?“ (v5 147 The findings indicated that the mean square foot assessed value in regards to the age g: structures in Sub-area No. 4 had the following values, percent changes, and differences in slope for the following years: Table 32.--Percent change and difference in slope: Sub- area No. 4, buildings built before 1940 Difference Value Percent Change in Slope 1940 $1.18 1944 1.21 1940 to 1944 2.7 1948 1.24 1944 to 1948 3.0 2.8 1952 1.28 1948 to 1952 3.7 3.9 1956 1.29 1952 to 1956 1.5 0.3 1960 1.27 1956 to 1960 —1.5 3.5 1964 0.99 1960 to 1964 —21.7 18.7 Differences in slope in regards to dwellings with ‘multiple stories in Sub—area No. 4 were noted in Figure 48 (next page). The findings indicated that the mean square foot assessed value in regards to dwellings with mglgiglg §Eg£ig§ in Sub-area No. 4 had the values: percent changes, and dif— ferences in slope for the years shown in Table 33 (next page). 148 Figure 48.-—Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 4, Multi-Story Buildings 39 Buildings 25 l l 20 Single-Family Residences with Multiple Stories ’ 15 City of Ann Arbor // Percent Change in 10 Mean Square Foot Assessed Value of 5 Previous 4-Year Period 0 ‘5 Difference in Slope —10 Sub-Area #4 . (Deteriorated) -15 ! I Table 33.-—Percent change and difference in slope: Sub— area No. 4, multi-story buildings Difference Value Percent Change in Slope 1940 $1.18 1944 1.21 1940 to 1944 2.7 1948 1.24 1944 to 1948 3.0 5.3 1952 1.28 1948 to 1952 3.7 —3.7 1956 1.29 1952 to 1956 1.5 0.3 1960 1.27 1956 to 1960 —1.5 4.5 1964 0.99 1960 to 1964 —21.7 17.6 149 Sub-Area Number 5 (No Deterioration, "Good" Area) In examining the data to determine the difference in slope in percent change in mean square foot assessed value between Sub—area No. 5 and the overall city, it was noted that the sub-area contained primarily class B build— ings, had mostly two—story structures, and was comprised of older housing stock. Differences in slope due to glggg B structures were noted as follows (Figure 49): Figure 49.——Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 5, Class B Buildings 10 Buildings 25 20 Sub—Area #5 (No Deterioration) 15 Percent Change in 10 Mean Square Foot Assessed Value of 5 Previous 4—Year , Period Class B Buildings City of Ann Arbor /\ 0 -5 / Difference \\ ’ _10 in Slope l \ -15 . . '44 '48 '52 '56 '60 '64 Time The findings indicated that the mean square foot assessed value in regards to 31355 B buildings in Sub-area No. 5 had the values, percent changes, and differences in Slope for the years shown in Table 34 (next page). 150 Table 34.—-Percent change and difference in slope: Sub- area No. 5, Class B buildings Difference Value Percent Change in Slope 1940 $2.00 1944 2.05 1940 to 1944 2.5 1948 2.13 1944 to 1948 4.0 -7.6 1952 2.19 1948 to 1952 3.1 8.0 1956 2.26 1952 to 1956 3.1 —0.1 1960 2.34 1956 to 1960 3.7 -15.1 1964 2.54 1960 to 1964 9.0 ~15.l Differences in slope in regards to dwellings with multiple stories in Sub—area No. 5 were noted in Figure 50 (next page). The findings indicated that the mean square foot assessed value in regards to dwellings with multiple stories in Sub-area No. 5 had the values, percent changes, and differences in slope for the years shown in Table 35 (next page). Differences in slope in regards to glggr dwellings 5 were noted (structures built before 1940) in sub—area NO- in Figure 51 (page 152)- 151 Figure 50.—-Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 5, Multi—Story Buildings 16 Buildings 30 25 ' Sub—Area #5 20 (No Deterioration 15 Percent Change in 10 r’ I Mean Square FOOt r’///4 Single—Family Residences Assessed Value of 5 with Multiple Stories Previous 4-Year : \\ Period 0 \ ,.—A\ \t’ ’ ’D" \ —5 \ 1 Difference \ ' 0 in Slope \ , —15 —20 '44 '48 '52 '56 '60 '64 Time The findings indicated that the mean square foot assessed value in regards to dwellings with multiple stories in Sub—area No. 5 had the following values, percent changes, and differences in slope for the following years: Table 35.——Percent change and difference in slope: Sub— area No. 5, multi—story buildings Difference Value Percent Change in Slope 1940 $2.00 1944 2.05 1940 to 1944 2.5 1948 2.13 1944 to 1948 4.0 4.3 1952 2.19 1948 to 1952 3.1 —3.1 1956 2.26 1952 to 1956 3.1 —1.9 1960 2.34 1956 to 1960 3.7 —0.7 1964 2.54 1960 to 1964 9.0 —12.7 53‘“, -*— hw—J-t— . 152 Figure 51.--Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 5, Buildings Built be- fore 1940 16 Buildings 25 20 Sub—Area #5 (No Deterioration) 15 Percent Change in 10 Mean Square Foot p74::: ‘ I Assessed Value of 5 577/45 Buildings Built Previous 4—Year \\\ before 1940 Period 0 \ — \P'” - -5 \ Difference \ -10 in Slope \ I —15 l I l '44 '48 '52 '56 '60 '64 Time The findings indicated that the mean square foot assessed value in regards to older dwellings in Sub-area No. 5 had the following values, percent changes, and dif— ferences in slope for the following years: Table 36.--Percent change and difference in slope: Sub- area No. 5, buildings built before 1940 ' Difference Value Percent Change in Slope 1940 $2.00 1944 2.05 1940 to 1944 1948 2.13 1944 to 1948 4.0 1.8 1952 2.19 1948 to 1952 3.1 —3.3 1956 2.26 1952 to 1956 3.1 —l.9 1960 2.34 1956 to 1960 3.7 —2.2 1964 2.54 1960 to 1964 9.0 -12.0 153 Sub—Area Number 6 (No Deterioration, "Good" Area) In examining the data to determine differences in slope in percent change in mean square foot assessed value between Sub—area No. 6 and the overall city, it was noted that the sub-area contained mainly class B buildings, had mostly 2—story houses, and was comprised of older build— ings. Differences in slope due to building ggg were noted as follows (Figure 52): Figure 52.-—Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 6, Buildings Built be- fore 1940 16 Buildings 40 Sub—Area #6 35 (No Deterioration) 30 25 ////// Percent Change in 20 Mean Square Foot Assessed Value of 15 BUildingslgzglt Previous 4—Year Before Period 10 K\\\\‘ 5 / ——-—0 0 ./ /, \ / \ -5 /0 _ ’// Difference \ _10 / in Slope \ | I ‘r -15 I I '44 '48 '52 '56 '60 -e4 Time 154 The findings indicated that the mean square foot :essed value in regards to building age in Sub—area No. Lad the following values, percent changes, and differ- :es in slope for the following years: Ile 37.——Percent change and difference in slope: Sub- area No. 6, buildings built before 1940. Difference Value Percent Change in Slope 0 $2.10 4 2.25 1940 to 1944 7.5 8 2.61 1944 to 1948 16.1 -10.3 2 2.74 1948 to 1952 5.2 —5.4 6 2.79 1952 to 1956 2.0 —0.8 0 2.84 1956 to 1960 2.1 -0.5 4 3.09 1960 to 1964 8.9 —12.9 Differences in slope in regards to class B dwellings Sub—area No. 6 were noted in Figure 53 (next page). The findings indicated that the mean square foot essed value in regards to class B dwellings in Sub— a No. 6 had the values, percent changes, and differences slope for the years shown in Table 38 (next page). 155 gure 53.--Percent Change in Mean Square Foot Assessed Value: Sub-Area No. 6, Class B Buildings Buildings 40 | l I I Sub—Area #6 35 (No Deterioration) 30 25 / 20 rcent Change in 15 an Square Foot sessed Value of Class B Dwellings evious 4—Year 10 r’///4 City of Ann Arbor riod 5 N\\\ o \\ \ // \ / \ / -5 ' Difference —10 in Slope \L\ \\ -15 / \II -20; r '44 '48 '52 '56 '60 '64 Time ale 38.-—Percent change and difference in slope: Sub— area No. 6, class B buildings Difference Value Percent Change in Slope l0 $2.10 14 2.25 1940 to 1944 7.5 =8 2.61 1944 to 1948 16.1 -19.7 #2 2.74 1948 to 1952 5.2 6.1 ‘6 2.79 1952 to 1956 2.0 1.0 0 2.84 1956 to 1960 2.1 ~11.4 4 3.09 1960 to 1964 8.9 -15.1 156 Differences in slope in regards to dwellings with iple stories in Sub—area No. 6 were noted as follows ure 54): ,re 54.—-Percent Change in Mean Square Foot Assessed Value: Sub—Area No. 6, Multi—Story Buildings suildings 4o Lub-Area #6 35 (No Deterioration) 30 25 ////// lent Change in 20 .Square Foot Single—Family Residences with Multiple Stories :ssed Value of 15 ‘ious 4—Year 0d 10 v I 5 { / 0 /," \ // \ -5 //1 \ I’ . 'fference in 810 e -10 131' I p \ '44 '48 '52 '56 '60 '64 Time The findings indicated that the mean square foot ssed value in regards to dwellings with multiple ies in Sub—area No. 6 had the values, percent changes, differences i n slope for the years shown in Table 39 t page). ble 39.--Percent change and difference in slope: Sub— area No. 6, multi—story buildings Difference Value Percent Change in Slope 40 $2.10 44 2.25 1940 to 1944 7.5 48 2.61 1944 to 1948 16.1 —7.8 52 2.74 1948 to 1952 5.2 -5.2 56 2.79 1952 to 1956 2.0 -0.8 60 2.84 1956 to 1960 2.1 1.0 64 3.09 1960 to 1964 8.9 -12.6 158 Table 40.--Differences in slope in various sub—areas ~Differences in Slope Variable (HOUSIng CharaCter') '44—'48 '48-'52 ‘52—'56 '56-'60 '60—'64 Sub—Area Number One Building Class D 17.3 0.0 3.0 2.1 10.6 Bldgs._Bui1t before 1940 5.8 0.5 3.3 2.1 3.0 Bldgs. With Rental 7 6 2 7 3.0 2 5 —1.6 Rooms Bldgs. with 2 Stories 8.3 1.0 . . 2.3 Sub-Area Number Two Building Class C —2.6 0.8 6.4 6.7 —4.7 Building Class D 7.7 —3.2 1.4 3.3 7.6 B13326 Enllt before —3.8 —1.4 1.4 3.2 -o.5 Bldgs. with 2 Stories —l.3 —2.2 1.4 4.7 —1.7 Sub-Area Number Three Building Class C 5.2 4.2 6.0 6.7 -3.3 Building Class D 13.7 0.0 1.0 6.9 9.8 Bldgs. with Rental 4.0 2.7 1.3 3.2 _ . Rooms Bldgs. Built before 2 2 2 .2 3 2 .4 1940 Bldgs. with 2 Stories 4. . . . . Sub—Area Number Four 26 5 Building Class D 14.3 —4.7 -O.5 . Bldgs. Built before 2.8 3.9 0.3 18.7 1940 Bldgs. with Rental ,6 -2,1 —0.4 3.0 14.1 Rooms Bldgs. with 2 Stories .3 —3.7 —0.3 4.5 17.6 Sub—Area Number Five Building Class B -7.6 8.0 —0.1 —15.1 ~15-l Bldgs. Built before 1 8 _3_3 —1.9 -2.2 —12.0 1940 ' _ Bldgs. with 2 Stories 4.3 —3.1 —1.9 —O.7 12.7 Sub-Area Number Six _ _ 1 Building Class B —l9.7 6.1 1.0 11.4 15. Bldgs. Built bwfore —10.3 _5‘4 -0.8 —o,5 —12.9 1940 _ - 2.6 Bldgs. with 2 Stories —7.8 -5-2 0-8 1'0 l -~ V 159 FOOTNOTES lCase, Frederick C., "Prediction of the Incidence of Urban Residential Blight," Papers and Proceedings of the Regional Science Association, Volume 11, 1963. 2Czamanski, Stanislaw, "The Effects of Public In- vestment on Urban Land Values," Journal of the American Institute of Planners, July 1966. 3Walker, Mabel L., Urban Blight and Slums, Cam— bridge: Harvard University Press, 1938. 41bid., Walker. 5Fisher, Ernest M., "Economic Aspects of Zoning, Blighted Areas, and Rehabilitation Laws," American Econo— mic Review, Volume 32, No. 1, Part 2, Supplement, July 1942. 6Vernon, Raymond, "Some Reflections on Urban De— cay," Confluence, Volume 7, 1958, pp. 128—40. 7Bartholomew, Harland, The Measurement of Non— Residential Blight in St. Louis, Missouri, St. Louis: Harland Bartholomew and Associates, 1946. 8Johnson, Ralph, J., Huntington, Williams, and Roy 0. McCaldin, "The Quality of Housing 'Before' and . 'After' Rehabilitation," Urban Housing. Edited by William 1.c. Wheaton, Grace Milgrim, and Margy Ellin Meyerson, New York: The Free Press, 1966. 9McDonald, A.M., "A Study of Depreciation in Resi— dences," The Appraisal Journal, October 1958 pp. 581-88. loAlbert, Sterling H., "Neighborhood Factors Affect- ing Residential Value," The Appraisal Journal, January 1960 pp. 81—98. "The Appraisal of Single-Family 11 wagner' Percy' y 1958 pp. 40—43. Homes," The Appraisal Journal, Januar 160 Michigan State Tax Commission, The Appraiser's Hanual of 1955. 13In that Sub-Areas Five and Six are relatively "sound" areas containing no deterioration, they will only be compared against three criteria——age, number of stories, and building class. 14A description of each of the six sub—areas is given in some detail in Appendix A. ( yer-ml 1 EH » *7 1.3M. —stz.-I' CHAPTER VI AN EXAMINATION OF THE FINDINGS Introduction Perhaps the most important part of any thesis is that portion of it that interprets the findings of the research and demonstrates their relevance or importance to the general field of study. The bulk of this research endeavor focuses on as— sessed valuation and urban deterioration; however, a good share of it also deals with the general problem of measuring housing condition. Thus, many of the findings and implica— tions will extend beyond the initial charge of the study as previously stated in the five principal objectives. These objectives appear in the Introduction and Chapter IV and are repeated here as follows: 1. To examine the assessed value of single—family residential buildings to determine those variables or housing characteristics that influence it most strongly and directly, 2. To demonstrate the degree of correlation between the behavior of the assessed value of improvements 161 162 and levels of physical deterioration according to current standards of physical deterioration, 3. To demonstrate a method using assessment data to quantify the extent of relative physical deteriora— tion of single—family residential buildings within various sub-areas of the city, 4. To identify the critical stage in the deterioration process in those areas of the city that are phys- ically deteriorated, and 5. To demonstrate a method for predicting possible future physical deterioration in various sub-areas of the city. Each of these objectives will now be discussed within the framework of these objectives. Objective No. 1 To examine the assessed value of single— family residential buildings to deter— mine those variables or housing charac— teristics that influence it most strongly or directly. Major Hypothesis: Certain variables or housing character- istics comprising the assessed value of single—family residential buildings. exert more influence upon its behaVlor than do others. In examining the findings in the earlier portions Of the research (Figures 18 through 31 in Chapter V), one Can note that housing characteristics such as ppilgipg ' have class, age, tenure of occupancy, and number of stories much greater influence or difference in slope in regards to the behavior of the mean square foot assessed value of 163 single—family dwellings than do others such as zoning, the presence or absence of a garage, lot size, or construction type when scrutinizing them through time—series analyses. In this respect one would have to conclude that the stated hypothesis above would have to be accepted. Objective No. 2 To demonstrate the degree of correlation between the behavior (percent change) of the mean square foot assessed value of single-family residential buildings and levels of physical deterioration. Major Hypothesis: Depreciating assessed values of single— family residential buildings correlate with rates of physical deterioration. Sub—Area No. 1 (Grids 10—12 and 11-12) is considered to be a deteriorated area. Eighty—one to 100% of all the single-family dwellings within this sub-area fall within the 1960 U. S. Census of Housing as being either deteriorated or dilapidated. In addition, this area (which in effect is comprised of only three and one half blocks) contains the highest per capita rate of public health and building code violations of any area within the city. An investigation of the premise files of the Ann Arbor City Health Officer indicates that the area has 27 such violations for the period 1948 to 1964.1 (See Appendix C for a specific list of these code violations.) An examination of the specific housing characteris- tics of the single—family dwellings within this area indi— cates that they are (1) primarily class D structures, (2) two or more stories, (3) older (all built before 1940), A; ~11 I. ..*§¢2’5=“.‘i'—it ‘zzrr'erv’u'rw '.,—r .1 —.-—_:_es_r__ . 1,1, :yb‘n 164 and (4) have rental rooms. Thus, when comparing the beha- vior of the mean square foot assessed value with gggh of these specific variables in a time-series analysis for the period 1944 to 1964, it appears that in 211 cases the dif- ference in slope is consistently positive (with the one exception of the interval 1960 to 1964 in regards to the one variable of tenure of occupancy or dwellings with rental rooms). Note Table 41. Table 41.-—Differences in Slope in Sub-Area No. l Differences in Slope Variable 1944— 1948— 1952— 1956— 1960— (Bldg. Character) 1948 1952 1956 1960 1964 Building Class D 17.3 0.0 3.1 6.0 10.6 Age (Buildings built before 1940) 5.8 0.5 3.3 2.5 3.0 Dwellings with rental rooms 7.6 2.7 3.1 2.5 -1.6 Dwellings with 2 or more stories 8.3 1.0 3.2 4.0 2.3 Sub—Area No. 4 (Grid 10—14) is also considered to be a deteriorated area. All of the buildings within this area were classified as deteriorated structures in accor- dance with the 1956 U. S. Urban Renewal Criteria and were scheduled for either rehabilitation or demolition upon the approval of the urban renewal project by the Ann Arbor City 165 Council.2 In addition, an investigation of the premise files of the Ann Arbor City Health Officer indicated that the area (the entire Project area) contained 69 public health and building code violations which further attested to its general pathological condition.3 (See Appendix C for a specific list of these code violations.) An examination of the specific housing characteris— tics of the single-family dwellings within this area indi— cates that they are (1) primarily class D structures, (2) two or more stories, (3) older (all were built before 1940), and (4) have rental rooms. Except for the one interval of 1948 to 1952, 311 the other periods indicate that the dif— ferences in slope in regards to the various housing charac— teristics examined in the time-series analyses were pggi: Eiyg. This is particularly noticeable in intervals 1956 to 1960 and 1960 to 1964. There were three cases during the interval 1952 to 1956 when the differences in slope were minimumly negative (—O.5, —0.3, and -O.4); however, these indeed are not considered to be significant indica— tions that the sub—area was experiencing a euphoric period of growth and development, but rather that it was approach— ing that point where it would begin to deteriorate most rapidly. Table 42 indicates the differences in slope for each variable of housing characteristic for each interval of time. -_ 1-1 - 9- 166 Table 42.--Differences in Slope in Sub—Area No. 4 Differences in Slope Variable 1944— 1948— 1952— 1956— 1960- i I (Bldg. Character.) 1948 1952 1956 1960 1964 Building Class D 14.3 —4.7 —0.5 6.5 26.5 Age (Buildings built before 1940) 2.8 3.9 0.3 3.5 18.7 Dwellings with 2 or more stories 5.7 -3.7 -O.3 4.5 17.6 Dwellings with rental rooms 4.6 -2.1 —0.4 3.0 14.1 In that these findings indicate that decreasing rates of assessed valuation do in fact correlate with levels of physical deterioration in single-family residen— tial buildings in both of these sub—areas, the stated I hypothesis holds. I Objective No. 3 To demonstrate a method using assess— ment data to quantify the extent of I relative physical deterioration of ' single—family residential buildings within various sub—areas of the city. Major Hypothesis: Percent changes in mean square foot assessed values can be utilized as a measure of building condition, and, hence, as a measure of physical deteri— oration. The findings in Chapter IV indicate that differences in slope can be computed for each of the sub—areas at var— ious points in time by means of time—series analyses. These differences in slope represent the degree to which 167 the percent change in mean square foot assessed value ip each of the sub-areas varies from that of the overall city. That is, the difference in slope demonstrates the rate or extent to which the mean square foot assessed value of single-family residences within a particular sub-area is either appreciating or depreciating relative to the overall city. Thus, when the difference in slope is positive, the percent change in mean square foot assessed value in a particular sub-area is depreciating relative to the city as a whole, and when it is negative, the percent change in mean square foot assessed value is appreciating relative to the city as a whole. When the difference in slope is 3339, the mean square foot assessed value of single—family residences in pgph the sub—area and the city are depreciat- ing or appreciating at the same rate. Since the findings indicated that the mean square 1(foot assessed value in sub-areas with deteriorated housing 'stock depreciated over time when compared with that of the 7overall city (Figures 32 through 35), and appreciated in Lsub—areas with no deterioration or sound housing stock ‘(Figures 49 through 54), it seems apparent that the assessed :value of single—family houses is closely linked with phys- ical building condition and can be used to measure it. In this respect, the stated hypothesis holds. m . _ 168 Objective No. 4 To demonstrate that assessment data for single-family residential buildings can be utilized to identify the critical stage in the deterioration process for those areas of the city that are phys- ically deteriorated. Major Hypothesis: In those single-family residential areas of the city that EEE physically deteriorated (in accordance with cur- rent standards of physical deteriora— tion) the change in physical condition (going from sound to deteriorated) will be most severe where the difference in slope in the percent change in mean square foot assessed value is the greatest. In that we have demonstrated that depreciating assessed values of single-family residential buildings correlate very strongly with rates of physical deteriora— tion (Objective No. 1) and that changes in assessed values correSpond with building improvements in general (Chapter I), we can only assume that buildings meeting the criteria for deterioration in 1960 and 1956 that have histories of depreciating assessed values would meet criteria for deteri- Ioration in 1950. From this premise we should then be able Ito assume that Sub—Area No. 1 has at least been a deteri— ‘orated area from the period 1952 to 1964. Ichange in mean square foot assessed value in the first I ideteriorated area, Sub-Area No. 1, we can note the follow— In examining the difference in slope in the percent ing (Table 43): 169 m.m 33:33 m.o $3-33 m.m Same 80me pins 23333 SS mm< m.> Nmmanmvma c.a memaleema m.m “macs no my mmfluoum h.h vwmalomma w.HI wvmalvvma H.w AmEoom Hmucomv Nocmema W: 3.3133 0.0 33:33 T: 81 mmuao .mgm uwmmww as @038 macaw as 6033 86$ sa Tamposumso .938 MMHQ mocoMQMMHQ mosmsmmmao magmaum> HBDOB m5am> mSHm> #mmsoq pmmsmflm H .02 mmH¢IQSm ca mmon as mmocmanMHo Hmuoenl.mv magmfi 170 In identifying the critical stage in the deteriora— ;ion process in Sub-Area No. 1, we could say that it would Ie either (1) that period of time in which the most critical 'ariable in the time—series analyses had its greatest dif- ference in slope, or (2) that interval in which most of the Variables examined had their greatest difference in slope. 1150, we would expect that the point where the difference .n slope was the least to be that interval immediately following the critical stage (if we were to replicate sreger's notion of the critical stage somewhat schematic— Illy). Again, an examination of Table 43 indicates that :1) the greatest difference in slope of the most critical rariable (building class) occurs in the interval 1944 to .948, and (2) all of the variables have their greatest lifference in slope in this ggmg interval. In addition, Iith the one exception of the variable of tenancy (build— .ngs with rental rooms), all of the variables have their .east difference in slope in the interval 1948 to 1952. Thus, in regards to this particular sub—area the critical Eggg in the deterioration process occurs during the period .944 to 1948. In this respect, we would have to say that :he stated hypothesis holds (e.g. the critical stage in the leterioration process can be identified by the difference .n slope in the percent change in mean square foot assessed 'alue). 171 ‘ Utilizing our same stream of logic in our examina— tion of Sub—Area No. 4 as to its degree of deterioration after 1956 where it was considered to be deteriorated in accordance with U. S. Urban Renewal Criteria and 1960 where it was considered to be 61 to 80% deteriorated in accor— dance with U. S. Census of Housing definitions of deteri— oration and dilapidation, we should be able to assume that the area was at least deteriorated from 1956 to 1964. In examining the difference in slope in the percent change in mean square foot assessed value in the second deteriorated area, Sub-Area No. 4, we can note the follow— ing (Table 44): Table 44.—-Highest Value Differences in Slope in Sub—Area NO. Highest Value . Variable Difference 1 (Bldg. Character.) in Slope Period I Bldg. Class (D) 26.5 1960—1964 Tenancy (Rental Rooms) 14.1 1960-1964 Stories (2 or more) 17.6 1960-1964 Age (Buildings built before 1940) 18.7 1960—1964 In that the greatest difference in slope consistently occurs in the last interval of the time—series analyses, we have to assume that this indeed is the critical stage in the 172 deterioration process for this particular sub—area. If the time—series analyses were to extend beyond 1964, we would expect to find the least difference in slope in most of the variables (e.g. the "plateauing" effect in Breger's concept) to occur during the period 1964 to 1968. However, in that we do not have this information, we can only state this as an assumption. Again using our criteria of the critical stage as being either (1) that period of time where the difference in slope of the most critical variable is the greatest or (2) that period in the time-series analyses in which most of the variables have their greatest difference in slope, we find that the area is consistent with both of these requirements. The greatest difference in slope of the most critical variable (building class) occurs in the period 1960 to 1964, and all of the variables have their greatest difference in slope similarly in this same period. Thus, the critical stage in regards to this particular sub—area is the period 1960 to 1964. We would therefore have to conclude that again the stated hypothesis holds. Objective No. 5 To demonstrate a method for predicting possible future physical deterioration in various sub-areas of the city that contain certain blighting pre-conditions. Major Hypothesis: The percent change in mean square foot assessed values can be utilized as a leading surrogate for physical deteri— oration in those areas of the city that have certain blighting pre—conditions. i- #7:. rs" ;»x~..e_e—_a_.=_..,. ,1 Q. —r__ . ”My”; 173 If the behavior of the mean square foot assessed value is to have any utility as a leading surrogate for the possible prediction of future physical deterioration, it would have to be demonstrated that in those areas of the city possessing some of the blighting pre—conditions that were mentioned in Chapter II there would have to be an indication that the difference in slope was increasing in the latter intervals of the time—series analyses. This would indicate that the buildings in the sub—areas were not appreciating in assessed value and hence were receiving a disproportionate (lesser) amount of private investment in terms of maintenance and imprOVement than were other single— family structures throughout the city. In examining the difference in slope in the percent change in mean square foot assessed value in the first transitional area, Sub—Area No. 2, we can note the follow- ing (Table 45): I (I Table 45.—-Differences in Slope in Sub-Area No. 2 Differences in Slope Variable 1944- 1948- 1952- 1956- 1960- (Bldg. Character) 1948 1952 1956 1960 1964 Building Class C -2.6 0.8 6.4 6.7 -4.7 Building Class D 7.7 -3.2 1.4 3.3 7.6 2 or More Stories —1.3 —2.2 1.4 4.7 -l.7 Age (Buildings built before 1940) —3.8 -l.4 1.4 3.2 -0.5 174 In most instances the difference in slope in mean square foot assessed value appears to be increasing except for the final period of 1960 to 1964. Assuming that depre- ciating rates of assessed valuation correlate with levels of physical deterioration, this would indicate that the area was initially heading in a direction of physical de— terioration or blight, but then somehow managed to counter this trend. Any or all of several things could have hap— pened to bring about this effect. One might have been that the changeover in assess— ment practices from the replacement cost method to the fair market value method on the part of the City of Ann Arbor had some spurious macro effect on single—family residential assessments. A second could be that a majority of the buildings in the sample did in fact receive less investment in terms of improvements during this particular interval than they had in prior ones as it has already been mentioned in Chapter V. Still a third reason could be that the owners of the dwellings in the sub-area actually had been investing proportionally more in their homes than had home owners in general throughout the city. Since building class is a critical variable and half of the buildings are class D structures (which, in turn, reflects a difference in slope for the interval 1960 to 1964 of 7.6), and the general trend of assessed value ii in the direction of depreciation, it seems very likely that m n. '6} fix.:_~."a.‘.‘ - ‘1, . -11, .711 1 1 11h _ -- 1- H;¢x»,~.:fiW 175 the buildings in the sub-area are deteriorating and that the assessed value could be a suitable leading surrogate for physical deterioration. However, since the other dif— ferences in slope for the other variables are negative and a reversal of the direction of the previous time periods, one would have to reject the hypothesis in regards to this particular sub—area. In examining the difference in slope in the percent change in mean square foot assessed value in the second transitional area, Sub—Area No. 3, we can note the follow— ing (Table 46): Table 46.-—Differences in Slope in Sub—Area No. 3 Differences in Slope g Variable 1944— 1948— 1952— 1956— 1960- I (Bldg. Character.) 1948 1952 1956 1960 1964 , Building Class C 3.4 4.2 6.0 6.7 —3.3 I I Building Class D 13.7 0.0 1.0 6.9 9.8 I Dwellings with Rental Rooms 4.0 2.7 1.3 3.2 -3.2 Dwellings with 2 or more stories 4.7 1.0 1.2 4.9 0.7 . «44“ “.kr ~._-_gj_i Age (Buildings built before 1940) 2.2 1.2 1.2 3.2 1.4 An examination of Table 46 indicates that the sub— area has been doing several things in regards to the values of the different variables or housing characteristics. The .11 "-* f“‘ 1 " ‘ '7 ' , -d,1agg=t;’.~ 176 dwellings in the sub—area are almost evenly divided between class C and class D structures. The variable of building class C indicates that the dwellings within the sub—area were following a trend towards deterioration, but then as was characteristic of Sub—Area No. 2 managed to reverse this direction in the final period, 1960 to 1964. Building class D indicates that except for the drop during the period 1948 to 1952 there was a strong and steady increase in the difference in slope. This would mean that the buildings within the sub—area were clearly headed in a direction of physical deterioration. The variable of ten— ancy (dwellings with rental rooms) demonstrates a pattern very much like that of building class C. The other var— iables, however with the one exception of the first inter— val, show an increasing tenanCy towards physical blight with a reversal in rate only during the last period. Therefore, using the criteria of (1) one of the most critical variables (building class D) having a strong and increasing difference in slope throughout the time— series analysis, and (2) almost all of the variables having positive differences in lepe, one could conclude that the dwellings within the sub—area were tending in a direction of physical deterioration or blight. In this respect one would have to accept the stated hypothesis that the percent change in mean square foot assessed value could be used as a leading surrogate for predicting possible future physical deterioration. ‘wa 177 The Relevance of the Findings to the Field of Urban Planning There are several important contributions of the study to the general field of planning and urban develop— ment. Some of the more salient of these can be listed as follows: 1. By combining the technique of the time—series anal— ysis with an arbitrary grid or sub-area map, the study demonstrates a technique for indexing single— family housing condition throughout the city. In this respect it provides a technique for developing indices of relative building condition that are based on investment rather than health or construc— tion standards. 2. It illustrates a method for obtaining a rapid, in— I expensive appraisal (overview) of levels of physical deterioration within any given city. An examination I of the difference in slope at various points in I time allows the researcher an immediate opportunity I to ascertain the relative degree of investment or maintenance and improvement that an area has re— ceived at any given period of time. When the dif— ference in slope is positive, the area under exam— ination is receiving less in terms of investment than the city is in general. When it is negative, just the opposite obtains. When the slope is equal 178 to zero, both the sub—area under examination and the city are receiving proportionally the same amount of investment. The yglpg of the difference in slope determines the magnitude of the relative increase or decrease in investment. In regards to urban redevelopment the study demon- strates an important tool for improving local deci— sion—making. This is especially true in the case of urban renewal conservation or rehabilitation projects. Quite often the success or failure of such efforts hinges upon the willingness and support that the impacted groups are inclined to give them. What better measure of citizenship or stewardship could a renewal agency have for a potential project than-an index based on the actual record of main— tenance and improvement that an area had received? 179 FOOTNOTES 1An examination of the Premise Files in the Ann Arbor Public Health Office indicated that those dwellings in Sub—Area No. 1 had incurred far more violations than single—family dwellings in any other area of the city. There were no aggregate statistics available to verify this observation mathematically or statistically. However, personal observation of the files and an interview with Dr. Bowler, the Ann Arbor City Health Officer, attested to this fact quite emphatically. 2It should be pointed out that even though the buildings within the project area (the North Central Urban Renewal Area) were qualified and classified as deteriorated, the project did not materialize. The reason for this is not spelled out in any of the information included in the urban renewal application. However, both the City Planning Director and the City Assessor felt it was a matter of political concern rather than anything else. 3To give further credence to this notion of numbers of health and building code violations in the different sub—areas, each of the areas were ranked according to their number of violations. The ordinal ranking of the areas showed that the deteriorated areas, Sub—Areas One and Four, had the highest number of violations; the transitional areas, Sub—Areas Two and Three, the next highest number, and the "sound" areas with no deterioration, Sub-Areas Five and Six, the least number of violations. CHAPTER VII CONCLUSIONS Undoubtedly there are many points of criticism that could be raised in regards to a research endeavor of this nature. However, before exploring some of these in detail, a few broad comments generic to the overall purpose and execution of the study should be made. First of all the prime concern of the dissertation centered on the examination of a body of public data to appraise its worth in (l) municipal decision—making, and (2) urban sociological research. Secondly, it attempted to demonstrate a method or technique in which this information could be utilized to practical advantage, and in this respect, narrowed its focus to the specific problem of measuring and predicting physical urban deterioration. Finally, it was an exploratory effort in which little or no previous work of a similar nature had been done. Consequently, many of the mechanical facets of the study had to be developed and utilized on the spot as var- ious research problems occurred. If such a study were to be replicated, it would be well to make many refinements 180 181 of the research techniques to sharpen the quality of some of the measurements and predictive statements. Potentially, there indeed are limitations to a study of this kind. In general, assessment data is fraught with discrepancies, and unfortunately much of the success of this type of research is dependent on "good" data.1 Undoubtedly, the City of Ann Arbor presented a rather sin— gular opportunity as the laboratory for the study. Had the assessment data there merely been an accumulation of "last year's figures in this year's book," little of much value could have been derived from the research. An examination of the Michigan Tax Study Staff Papers of 1958 reveals the presence of some substantial problem areas in regards to assessment practices and as- sessment data in the state of Michigan.2 One rudimentary one centers on the general notion of assessing property at its "true cash value" as specified in the state constitu— tion. For the most part, the State Tax Commission accepts an appraisal standard of 50% of the current true cash value.3 However, in actual practice appraisal levels range anywhere from 20 to 50%. This can allow severe inequities in assess- ments since lower ratios tend to magnify errors in appraised values. Assessors who appraise properties at high propor- tions of their true cash value do a more equitable job than those who use lower assessment levels because they work within a much broader range of tolerance.4 182 In examining the lack of uniformity in assessment levels in regards to counties, townships, and local assess— ment districts, the State Tax Commission also noted that discrepancies in assessed values were higher at the local level than they were at either the county or township level.5 However, the data developed in this study of as- sessed values and physical urban deterioration in the City of Ann Arbor present a much different picture. Assessments were developed on the basis of sound, SOphisticated appraisal techniques. They were re—examined regularly at scheduled periods of time, and there was little time lag in making adjustments for depreciation and building improvements. What then should one conclude from a research effort of this nature? On the surface it appears as though some very significant findings regarding assessment data and physical urban deterioration have been developed in a rather atypical community. However, two important consid- erations seem to extend beyond this initial observation. The first is that the study did show that there was a definite linkage between depreciating assessed values and physical urban deterioration in regards to single—family residential buildings. The second is that it demonstrated various methods and techniques in which assessment data could be applied and utilized in studying urban problems and in improving 183 local decision-making. In this respect it showed that there is considerable value and potential utility in assess- ment data. This fact alone is a substantial contribution in the field of urban studies. If the study were to be replicated to emphasize its broader worth in the solution of problems involving physical urban deterioration, it would have to be applied to a much larger urban community and perhaps even to several such communities. Only then would one be able to demonstrate its full promise and utility as a planning tool. 184 FOOTNOTES lPealy, Robert H., Barlowe, Raleigh, Taylor, Clarence B., and Claude R. Tharp, "The General Property Tax," Mich- igan Tax Study Staff Papers of 1958, Ann Arbor: Institute of Public Administration, University of Michigan Press, 1959. 2Ibid. 3Ibid., Barlowe, p. 214. 4Ibid., Barlowe, p. 215. 5Ibid., Barlowe, pp. 216-19. APPENDIX A THE CITY OF ANN ARBOR Introduction The City of Ann Arbor, Michigan, founded in 1824 is located approximately 40 miles west of the City of Detroit in the Huron River Valley. It has an area of nine- teen square miles and an estimated 1967 population of 92,000 inhabitants.1 The surrounding topography of the city can best be described as gently rolling hills covered with hardwood forests. The city is served by two major highway routes-—Interstate 94 and U. S. 23. In addition, it is also served by the New York Central Railroad. As well as being the county seat of Washtenaw County, the city is also the home of the University of Michigan. As a result, it has become a prominent center in the Midwest for learning and research. An examination of the 1960 U. S. Census of Popula— Eigp reveals several important facts worth noting. Some of these have been listed as follows: 1. The percent foreign born for the city is approx— imately 25. The preponderence of foreign stock originates from western Europe (Germany, U.S.S.R., and Ireland) and from Canada. 185 186 2. The percent of non—white inhabitants is approximately 6.3. 3. The median family income is $7,750. 4. The median number of school years completed for the inhabitants of the city is 13.7. ngsing Characteristics In addition to some of the general housing charac- teristics for the city that are expressed in Table 47, cer- tain other related facts such as the following should be mentioned: Housholds: Household Size: Housing Supply: Age of Dwellings: Housing Condition: There are approximately 26,650 house— holds (occupied housing units) in the City of Ann Arbor. Student households represent approximately 18% of the total number of households. The average household size in the City of Ann Arbor is 2.84. In 1950 the fig— ure was 3.05. A major factor influencing this trend towards smaller household size is the increase in student house- holds in the city.3 Currently there are 27,350 housing units in the City of Ann Arbor. This figure represents a net increase of 6,600 units (or 32%) over the 1960 figure of 20,750.4 Approximately 42% of the housing stock within the City of Ann Arbor was built before 1939.5 Aggregate statistics on the condition of housing in Washtenaw County reveal the following: Of the 59,900 housing units in Washtenaw County (the Ann Arbor Housing Market Area), approximately 4,750 units or 8% Housing Condition: (Cont'd) Housing Demolition: Sales Market: Table 47.——Genera1 187 are either dilapidated or deteriorated (lacking one or more plumbing facilities). These findings indicate an improvement in the housing market area since 1960 when 5,250 units or 11% of the total number of units were either dilapidated or deteriorated. Since April 1, 1960, approximately 200 housing units within the City of Ann Arbor have been demolished.7 The market for new sales housing in Ann Arbor is quite strong. Presently, the most popularly priced housing in single- family dwellings seems to be in the $23,000 to $25,000 price bracket. Within Ann Arbor subdivision activity has cen— tered in areas south of Packard Road and in the vicinity of the intersection of Plymouth and Nixon Roads.8 Housing Characteristics for the City of Ann Arbor Ann Arbor Michigan Total Dwellings 20,752 2,548,792 % in One Housing Unit Structures 82.6 53.7 % Sound with All Plumbing Facilities 86.3 78.6 % Occupied by Non—white 06.3 08.4 % Owner Occupied 51.3 74.4 Median Value of Owner Occupied Units $18,000 $12,000 Median Gross Rent $99-00 $77-00 Vacancy Rate (Owners Occupied Units) 1.7% 1.5% Med. No. of Persons in Occupied Units 2.5* 3.1 *Lowest in the state .2.-. .... - 188 Physical and Locational Characteristics In examining the city for any physical or locational characteristics which might possibly influence the determina— tion or skew the distribution of assessed values of single- family residences, none of any consequence could be found. The University of Michigan, the major employer in the city, is situated approximately in the center of the city and has equal access from most residential areas. The internal road and street system very closely approximates a classical "spoke" or radial system with all major streets and avenues converging on the central area. The freeway system extends around the periphery of the city, and, except for a small missing segment on the northern boundary, all but encloses the entire city. There are no extreme topographical features with the city in the way of hills or low areas. The Huron River runs through the center of the city; however, and, on some occasions, has been known to flood its banks. The Selection of the City as the Laboratogy Community There are two major reasons for selecting the City of Ann Arbor as the laboratory community for the study. The first is that the assessment practices within the city are regarded by many authorities in the field of real prop— erty appraisal and assessment to be among the best in the 189 state.10 Records are kept up to date with minimum lag time, are well organized, and are readily available to the public. Reassessments are made on a regular basis. Approximately every three years each piece of residential property in the city is re-examined. Also, as a matter of mere office routine, building permits for new construction (any improve- ments to the building of $100 or more in value) are for- warded directly from the building department to the acces- sor's office so that adjustments in appraisals and assessed values can be made immediately upon the completion of the work. This gives a close scrutiny to building activity on the part of the city accessor and gives substantial credence to building value assessments as reflecting current worths. The second important reason is that a considerable portion of the housing stock in the city is old (approx- imately 42%) as it has just been mentioned. In that the bulk of the nation's housing problems are in older cities and older parts of cities, this gives the research an appropriate setting for development. DESCRIPTIONS OF THE SUB-AREAS SELECTED FOR STUDY Each of the sub-areas selected for examination in this study will be described from three general vantage points. The first concerns a description of each from the personal viewpoints of the City Planning Director and the City Assessor. The second considers each area from the point of View of its objective physical characteristics and appearance. The third method of description considers each sub—area in light of its major demographic character- istics and statistics. The six sub—areas selected for study were located within the city in the following demographic areas (Table 48): Table 48.—-Locations and Characteristics of Sub-Areas Sub—Area General Description Location One Deteriorated Cen. Tract 7 Two Transitional Cen. Tract 7 Three Transitional Cen. Tract 6 Four Deteriorated Cen. Tract 7 Five No Deterioration Cen. Tract 10 Six No Deterioration Cen. Tract 10 190 »__e::./ ”mt? 191 Both the City Planning Director and the City As- sessor stated that they considered the first sub—area to be deteriorated. They stated that most of the dwellings reflected little maintenance and improvement and had been in a general state of disrepair for an extended period of time. Sub—Area Number Two, however, constituted a resi- dential portion of the city that was somewhat questionable. In their opinion, it had been a fairly viable area of the city that had commanded good rents and had attracted some prospective homeowners. However, there appeared to be little interest in building activity in terms of private rehabilitation efforts. Sub-Area Number Three similarly reflected mixed opinions concerning its viability in the Ann Arbor housing market. Again, both the City Planning Director and the City Assessor considered it to be a marginal area in terms of its ability to attract investment and renewal interest. Sub-Area Number Four was thought to be a deteri— orated residential area. Many of the structures in the area had been replaced by commercial establishments and new multi—family dwellings. For the most part, interest in maintaining the remaining single-family dwellings had apparently been rapidly waning. 192 Sub—Area Number Five, in the opinion of both the assessor and planning director, was a very competitive, upper middle-class, housing market area. Most of the dwel— lings within it commanded high sales prices and inordinately high rents ($300 or more per month). Sub-Area Number Six was similarly an "expensive" residential area, and, as Sub—Area Number Five, had a long history as a "prestige" area within the city. From an aesthetic or Visual point of View the areas are fairly distinctive. Sub—Area Number One can be charac— terized as being an older housing area within the city with late 19th century residential architecture. The lots are narrow and many of the blocks have rear alleys. Curbs are often found broken with parking strips overgrown with weeds and bushes. Sub—Area Number Two is also an older residential area within the city. It contains some houses that are well kept, yet has others that show a definite lack of maintenance-—dirty paint, missing shingles, unkempt yards, Ietc. As in the first sub-area most of the houses are late Inineteenth century architecture (steep roofs, ornamental I,cornices, odd—shaped windows, front porches, etc.) and are “constructed on small, narrow lots. For the most part the Jarea is quite clean with little trash and other debris Istrewn about. Men-em --$ -— 193 Sub-Area Number Three is more closely located to the central part of the city. The houses are older two- story structures. Many of the lots are larger than those in the first two sub—areas, and consequently, there is considerably more space between buildings on some blocks. IA few buildings reflect a lack of maintenance and are in disrepair. Most of the yards are well kept. Sidewalks, curbs, and gutters are well taken care of, and very few parking strips are overgrown. Sub—Area Number Four contains some unfortunate blighting influences. A large junk yard abutts the northern edge of the area while several marginal commercial estab- 1ishments—-sma11 stores, cleaners, service stations, etc. within it have been left to deteriorate. Almost all of the houses are old—-several over 100 years old. Most new con- struction has taken the form of jerry-built additions and shacks. The lots are very narrow. Sidewalks, curbs, and gutters are in very poor condition and appear to have been ‘so for many years. Many of the parking strips and yards are overgrown with weeds, vines, and bushes. Several buildings contain major building defects. Sub—Area Number Five contains very large houses on large lots. The houses are well set back from the streets giving them a very elegant appearance. Most of the dwel— lings are two and three stories and reflect very conserva- tive architectural styles. The area is somewhat hilly, and 194 almost all of the houses are well landscaped and nicely sited on their lots to take full advantage of slopes, trees, and views. Yards are well attended, yet do not give a "manicured" appearance as one notices in newer suburban areas. The buildings are older structures and are mostly of pre World War I vintage. One or two of the houses have been converted to fraternity houses. Sub—Area Number Six is also a bit hilly. The houses are quite large, but much newer than those in Sub— Area Five. The lots and yards are huge. There are no sidewalks in the area so lawns extend over 100 feet in some cases from the front of the buildings to the street. The street pattern is irregular. Large, older trees are decidedly missing giving the area a bit of a "suburban" feel. Many of the dwellings are single—story and one and a half stories. The architecture is fairly "modern" with a variety of styles and tastes~—some houses are predominantly ‘brick while others are of redwood, half-timber, etc. A .Frank Lloyd Wright house abutts the area on the west. In examining some of the census figures for each of the sub—areas (or tracts in which the sub-areas are located) one can note some major distinctions. In the case of mobility (as measured by changes in residence since 1955) it seems as though the percentage of persons living in the same house was approximately the same in all areas or census tracts (43%). However, in noting _ .m'r‘t ark: /..4.~3r:2=:9 :if‘ .; 195 the place of residence in 1955 in the central city, it appears that more people migrated to Tracts 7 and 6 (Sub— Areas 1 through 4) than to Tract 10 (Sub-Areas 5 and 6). In noting the percent living in Michigan, yet outside the SMSA in 1955, it seems that substantially more (26.7% ver- sus 18.3%) migrated to Tract 10 (Sub-Areas 5 and 6) than to either Tracts 7 or 6 (Sub—Areas 1 through 4). In the case of general demographic characteristics Ione can note that the major percentage of non—whites in the City of Ann Arbor lives in Tract 7 (28.5% versus 4.2% in Tract 6 and 0.5% in Tract 10). Median family income also indicates very high differences. In Tract 7 the median family income is $5,500 (Sub-Areas One, Two, and Four). In Tract 6 the median family income is $6,292 (Sub—Area Three). In Tract 10 the median family income is $18,292 (Sub—Areas 5 and 6). Differences in educational level are also significant, but are fairly high. In Tract 7 (Sub- 1Areas One, Two, and Four) the educational level is 10.9. In Tract 6 (Sub-Area 6) the educational level is 12.0. In Tract 10 (Sub—Areas 5 and 6) the educational level is 16+. Differences in employment characteristics are also quite interesting. In Tract 7 (Sub-Areas One, Two, and Four) the percent employed as service workers is three times that in Tract 10 (Sub-Areas 5 and 6). In regards to the percent employed in technical, professional, and kindred 196 work and managerial positions, less than 20% are so employed Tract 7 (Sub—Areas One, Two, and Four) while over 75% are so employed in Tract 10 (Sub-Areas 5 and 6). A detailed listing of differences in demographic characteristics is presented in Table 49. 197 Table 49.—-Demographic Characteristics of the Sub—Areas Residential Residence in 1955 Percent Percent Percent Same Central Outside Sub—Area Tract Population House City SMSA One 7 3,209 43.7 20.3 12.9 Two 7 3,209 43.7 20.3 12.9 Three 6 4,993 44.4 19.8 18.3 Four 7 3,209 43.7 20.3 12.9 Five 10 3,365 43.3 17.3 26.7 Six 10 3,365 43.3 17.3 26.7 General Characteristics Percent Median Non-White Family Educational Sub—Area Tract Population Ann Arbor Income Level One 7 3,209 28.5 $ 5,500 10.9 Two 7 3,209 28.5 $ 5,500 10.9 Three 6 4,993 4.2 $ 6,292 12.0 Four 7 3,209 28.5 $ 5,500 10.9 Five 10 3,365 0.5 $18,292 16+ Six 10 3,365 0.5 $18,292 16+ Employment Characteristics Percent Employed Prof. ' and Educational Sub—Area Tract Population Tech. Manager. Service Service One 7 3,209 15.2 4.7 9.9 18.4 Two 7 3,209 15.2 4.7 9.9 18.4 Three 6 4,993 22.9 6.8 4.9 20.3 Four 7 3,209 15.2 4.7 9.9 18.4 Five 10 3,365 56.4 18.4 3.7 40.6 Six 10 3,365 56.4 18.4 3.7 40.6 198 FOOTNOTES 1Ann Arbor City Planning Commission Report, The Regional Setting of the City of Ann Arbor, Michigan,_AHn Arbor: City Planning Office, 1967, pp. 1-2. 2Archambault, Ronald T., Damiani, Joseph A., Mandeville, Thomas D., Richardson, James S., and Reinhardt Van Dyke, Housing for People of Limited Means in Ann Arbor: A Modest Proposal, Ann Arbor: University of Michigan School of Social Work (mimeo), April 15, 1968. 3 Ibid., p. 39. 4Ibid., p. 39. 51bid., p. 40. 6Ibid., p. 40. 7Ibid., p. 40. 8Ibid., p. 41. 9 Michigan. U. S. Census of Housing and Population, Ann Arbor, loInterview with Dr. Raleigh Barlowe, Chairman of the Department of Resource Development, Michigan State University, East Lansing, Michigan, April 3, 1968. Dr. Barlowe stated that many of the techniques and methods for appraising and assessing real property that were pioneered and developed by the City of Ann Arbor were later adopted as standard assessment practices by many Cities throughout the State of Michigan. APPENDIX B 199 ANN ARBOR. MICHIGAN Map l.-—Base Map for the City of Ann Arbor, Michigan 29041 ANN ARBOR. MICHIGAN Map 2.——Building Sample for the City of Ann Arbor, Michigan II/ // ‘I\/ I" r—Kax 200 ANN ARBOR. MICHIGAN Map 2.—-Building Sample for the City of Ann Arbor, Michigan 201 <1 2 5 3 : - E 0) Si 2 I— I x U 5 . <1 I § (I I— g E 0) II E D a ( <1) < » -JI Z :1 UJ 0 Map 3.--Census Tract Map for the City of Ann Arbor, Michigan 201 ANN ARBOR. NICHIGAN 2’ fl— . . . n Map 3 --Census Tract Map for the City of Ann Arbor, Michiga I.ll.llll' Ii]... I: II II I 2(2303 .con: 28( :1: 1 a a .II. I I 13.. a a d Ifl. 1.. g _ 1 .m. g 7%» c 1. .1 ._ M 111 as: «casino . .1." r 11 b __1.. 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