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This is to certify that the

thesis entitled

AN ANALYSIS OF LOCAL BENEFITS AND COSTS
ASSOCIATED WITH HOG OPERATIONS IN MICHIGAN

presented by
MARK ABELES-ALLISON

has been accepted towards fulfillment
of the requirements for

MASTER'S AGRICULTURAL ECONOMICS

degree in

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Major professor

Dr. Darrell Fienup
Date June 11, 1990

 

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MSU I. An Affirmative Action/Equal Opportunity Institution
emmht

AN ANALYSIS OF LOCAL BENEFITS AND COSTS

ASSOCIATED WITH ROG OPERATIONS IN MICHIGAN

BY
Mark Abeles-Allison

A THESIS

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

MASTER OF SCIENCE

Department of Agricultural Economics

1990

ABSTRACT
AN ANALYSIS OF LOCAL BENEFITS AND COSTS
ASSOCIATED NITH HOG OPERATIONS IN MICHIGAN
BY
Mark Abeles-Allison

This report examines local benefits and costs
associated with hog operations in Michigan. A regression
analysis approach was used to determine the implicit costs
of hog odors on property values. Property values were
regressed against household and neighborhood characteristics
of residential properties surrounding the hog farms. Local
input purchases by hog operations are compared with
reductions in property tax receipts as recorded by the local
government. The benefit/cost ratios are positive in the
case of 500 and 5000 head operations.

This study investigated over 300 residences surrounding
eight hog operations that received multiple odor complaints.
Results indicate that in a township with an SEV of $20
million, a 500 head hog operation purchases $33,545 in hog
operation inputs locally in one year. That same 500 head
operation will reduce property tax receipts by $5,937. A
5,000 head hog operation will purchase $229,347 locally in

one year, while property tax receipts drop by $59,375.

to Lisa, Linda, Jeanette and Norman

ii

ACKNOWLEDGEMENTS

Many individuals and organizations made substantial
contributions to this thesis. I would like to thank the
members of my committee, Darrell Fienup, Larry Connor and
Lynn Harvey for their support and encouragement.
Conversations with Jerry Schwab, Jim Hilker and John Hoehn
also aided me greatly.

Financial support for this research was provided by the
Animal Manure Management Task Force in the Agricultural
Experiment Station at Michigan State University.

Data for this research was collected with the help from
township officials and county equalization offices. I wish
to thank them all for the time and patience they spent
helping me to collect the data.

The direction of this thesis, examining township
benefits and costs is due in part to the guidance of Lynn
Harvey and George Mansell. My work with them on the Kellogg
Leadership and Local Government Education Project has
provided me with new perspectives and insights.

I wish to thank my parents and sister Linda for their
constant understanding and comfort. Steve Leuty, Steve

Sanislo, Klengolo Traore, Lucy Winchester, Don Hinman, Jeff

iii

Wilson and Aliou Diagne all helped in various ways. Thank
you.

Finally I thank Lisa Chloie Abeles-Allison. A friend,
fellow student and soulmate, her support, enthusiasm and

encouragement pulled me through even the roughest of times.

iv

TABLE OF CONTENTS

LIST OF FIGURES . . . . . . . . . . . . . . . . .

LIST OF TABLES . . . . . . . . . . . . . . . . .

1.3

CHAPTER I

ISSUE IDENTIFICATION

Introduction . . . . . . . . . . . . .
Farming Trends . . . . . . . . . . . .
1.2.1 Livestock Operations . . . . . .
1.2.2 Michigan Livestock Operations .
1.2.3 Urban Sprawl in Michigan . . . .
1.2.4 Environmental Change and citizen

Concerns . . . . . . . . . . . . .
Taking Action . . . . . . . . . . . .
Local Government Role . . . . . . . . .
1.4.1 Local Government Structure . . .
1.4.2 Revenue Sources . . . . . . . .
1.4.3 Township Ordinance Authority . .
Research Problem . . . . . . . . . . .
1.5.1 Hypotheses . . . . . . . . . . .

Thesis Overview . . . . . . . . . . . .

xiii

10
12
13
14
14
16

16

CHAPTER II

CONCEPTUAL APPROACH

2.1 Literature Review . . . . . . .

2.1.1 Perspectives . . . . . .
2.1.2 Parm Studies . . . . . .
2.1.3 Environmental Impacts .
2.1.4 Livestock Parms.. . . .

2.1.5 Air Quality and Hogs . .
Benefit/Cost Techniques . . .

2.2.1 Parm Pollution Controls

2.2.2 Multipliers in the nog Sector

2.3 Finding Prices . . . . . . . . .

2.3.1 Agriculture and the Environment

2.3.2 Hedonio Price Studies . .
2.3.3 Air Pollution Studies . .
2.3.4 noise Pollution Studies

2.3.5 "Other" Pollution Studies

2.4 Study Approach . . . . . . . . .

2.4.1 Benefits . . . . . . . .
2.4.2 Costs . . . . . . . . . .
Hypothesis . . . . . . . . . .

Conclusion . . . . . . . . . .

vi

Eage

17

17

19

19

20

21

22

22

24

25

26

28

29

31

33

34

34

35

35

36

CHAPTER III

REGULATORY ANALYSIS

3.1 Right to Parm Act . . . . . . . . . . . .
3.1.1 Background . . . . . . . . . . . .
3.1.2 Environmental Protection . . . . .
3.1.3 National and State Cases . . . . .

3.2 Ration-wide and Michigan Complaint Cases
3.2.1 Rational Cases . . . . . . . . . .
3.2.2 Michigan Cases . . . . . . . . . .

3.3 Local Government Concerns . . . . . . . .

3.4 Property Value Change Impacts . . . . . .
3.4.1 Local Government Impacts . . . . .

3.4.1.1 Townships: . . . . . . . .
3.4.1.2 Counties: . . . . . . . .
3.4.1.3 School Districts: . . . .
3.4.2 Property Tax Stability . . . . . .

3.5 Regulatory Options . . . . . . . . . . .
3.5.1 Ordinances . . . . . . . . . . . .
3.5.2 Best Management Practices . . . .
3.5.3 Technological Improvements . . . .
3.5.4 Regulatory Alternatives . . . . .

3.58unary.................

vii

38

39

40

40

41

41

42

44

45

47

47

48

48

49

49

50

51

52

53

53

CHAPTER IV

RESEARCH METHODS

4.1 Literature Search . . . . . . . . . . . . .

4.2

4.3

Animal Complaint Survey . . . . . . . . . .
Data Collection . . . . . . . . . . . . . .
4.3.1 Property Value Study . . . . . . . .
4.3.1.1 Identification of Parms . . .
4.3.1.2 Identification of Houses
Surrounding Parms . . . . . . .
4.3.1.3 Housing Characteristics . . .
4.3.1.4 Neighborhood Characteristics
4.3.1.5 Environmental Characteristics
Variables Selected for Property Value Study
4.4.1 Dependent Variables: Property Sale
Price . . . . . . . . . . . . . . . .
4.4.2 Month of Sale . . . . . . . . . . .
4.4.3 Year of Sale . . . . . . . . . . . .
4.4.4 Road . . . . . . . . . . . . . . .
4.4.5 Age of House . . . . . . . . . . . .
4.4.6 Plot . . . . . . . . . . . . . . . .
4.4.7 Brick or Stone Exterior . . . . . .

4.4.3 Air Conditioning . . . .

4.4.9 number of Baths . . . . . . . . . .

viii

54

55

57

57

57

58

59

59

60

6O

61

62

62

62

62

63

63

63

63

4.4.10 number of Fireplaces . . . . . . .
4.4.11 Garage . . . . . . . . . . . . . .
4.4.12 Class Greater Than or Equal to "CB"
4.4.13 Square Footage of House . . . . . .
4.4.14 Mobil Home . . . . . . . . . . . .
4.4.15 Distance to Central Business
District . . . . . . . . . . . . . . .
4.4.16 Distance to Highway . . . . . . . .
4.4.17 Distance to Earn . . . . . . . . .
4.4.18 Wind Direction . . . . . . . . . .
4.4.19 Mind x Distance Interaction . . . .
4.4.20 Animal numbers . . . . . . . . . .
4.5 Model Specification . . . . . . . . . . . .
4.6 Variables Selected for Input Analysis . . .
4.6.1 Hog Input Costs . . . . . . . . . .
4.6.2 Local Spending By Livestock
Operations . . . . . . . . . . . . . .
4.6.2.1 Peed Purchases . . . . . . .
4.6.2.2 Peed Supplement Expenditures
4.6.2.3 Labor Expenditures . . . . .
4.6.2.4 Power and Machinery
Expenditures . . . . . . . . .
4.6.2.5 Improvements . . . . . . . .
4.6.2.6 Livestock Expenditures . . .

4.6.2.7 Land Charges . . . . . . . .

ix

64

64

64

64

64

65
65
65
65
66
66
66
67

67

70

71

72

73

74

75

76

77

4.6.2.8 Other Expenses . . . . . . . . . 78

4.6.2.9 Total Local Expenditures . . . 79

4.7 Comparing Benefits and Costs . . . . . . . . 81
4.7.1 Scenarios . . . . . . . . . . . . . . 81

‘ .8 8m.” 0 O O O O O O O O O O O O O O O O O O 81

CHAPTER V

RESEARCH RESULTS

5.1 Pindings . . . . . . . . . . . . . . . . . . 82

5.2 Livestock Operation Benefits . . . . . . . . 82

5.2.1 Input Purchases . . . . . . . . . 83

5.2.2 Output Multiplier . . . . . . . 84

5.3 Livestock Operation Costs . . . . . . . . . . 87

5.3.1 Property Value Model . . . . . . . . . 87
5.3.1.1 Set #1. Hog Impact on Property

Price: . . . . . . . . . . . . . . 88

5.3.1.2 Set #2. Large vs. Small

Operations: . . . . . . . . . . . 94

5.3.1.3 Set #3. Distance Calculations 99
5.3.1.4 Set #4. Log-Linear Porm . . 105
5.3.2 Local Impact . . . . . . . . . . . . 108
5.3.2.1 Calculating Costs . . . . . . 108
5.3.2.2 Township Cost Sharing: . . . 109

5.4
5.5
5.6
5.7

Appendix
Appendix

Appendix

Livestock Operation Benefit/Cost Ratio . .

Ratio Pormulation . . . . . . . .
Significance of Pindings . . .
Hypotheses Testing . . . . . .

Summary . . . . . . . . . . . . .

CHAPTER VI

CONCLUSION

Conclusion . . . . . . . . . .

6.1.1 Summary of Pindings . . .
6.1.2 Distance and Size Impacts
Regulatory Options . . . . . . .
Existing Conflict Solutions . .
Thesis Strengths . . . . . . . .
Thesis Weaknesses . . . . . . . .

Puture Research Needs . . . . . .

A: Livestock Complaint Tabulation

C

: 0.8. weather Bureau, Climatic
Summary of The 0.8 . . . . . .

: Graph for Mind Plotting. . . .

xi

Procedures.

109
111
112
113

114

116

117

117

117

118

119

120

120

122

124

125

Appendix D:

Bibliography.

Data Used in Regression Analysis .

xii

229E
126

140

List of Figures

Figure 1 Crop vs. Livestock Demand . . . . . . . . . . 8
Figure 2 Crop vs. Livestock Impacts . . . . . . . . . . 9
Figure 3 Environmental Quality and Property Values . . 29
Figure 4 Hog Operation Input Costs For Farms UNDER 200 Sows.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Figure 5 Hog Operation Input Costs For Farms OVER 200 Sows.

. O O O O O C O O C O O O O O O O O O O O O O O O O O 69

Figure 6 Local Corn Purchases . . . . . . . . . . . . 72
Figure 7 Feed Supplement Purchases in Local area. . . 73
Figure 8 Local Labor Expenditures . . . . . . . . . . . 74
Figure 9 Local Power and Machinery Expenditures . . . . 75

Figure 10 Local Improvement Expenditures . . . . . . . 76

Figure 11 Local Livestock Expenditures . . . . . . . . 77
Figure 12 Local Land Charges . . . . . . . . . . . . . 78
Figure 13 Other Local Expenditures. . . . . . . . . . . 79

Figure 14 Estimated Total Local Expenditures Per Hog By Size

of Operation. . . . . . . . . . . . . . . . . . . . . . 80

xiii

BESS

Figure 15 Estimated Local Hog Input Expenditures . . . 85

Figure 16 Estimated Local Input Expenditures with Multiplier
...........86

xiv

Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table
Table

List of Tables

I Property Tax Distribution and Dependence. . . .
II Complaint Tabulation Form. . . . . . . . . . .
III Arkansas Complaint Survey. . . . . . . . . .
IV Operator Grown Grain . . . . . . . . . . . . .
V Local Input Expenditures . . . . . . . . . . .

VI Local Input Expenditures with Multiplier . . .
VII Hog Impact Model . . . . . . . . . . . . . .
VIII Regression #1, Hog Impact Model . . . . . .
IX Wind and Distance Model . . . . . . . . . . .
X Regression #2, WIND and DIST impact model. . .
XI Small Hog Operation Model . . . . . . . . . .
XII Regression #3, Small Hog Operation Model. . .
XIII Large Hog Operation Model . . . . . . . .
XIV Regression #4, Large Hog Operation Model. . .
XV Close Property Model . . . . . . . . . . . .
XVI Intermediate Distance Model . . . . . . . .
XVII Distant Property Model . . . . . . . . . .
XVIII Regression #5, Close Property Model. . .
XIX Regression #6, Medium Distance Property Model.
XX Regression #7, Distant Property Model. . . .

XXI Regression #8, Log-Linear Hog Farm Impact . .

47

56

56

71

84

85

89

91

92

93

95

96

97

98

101

101

102

103

104

105

107

gggg
Table XXII Benefit Cost Ratios . . . . . . . . . . . 110
Table XXIII Benefit Cost Ratio with Multiplier . . . 110
Table XXIV Benefit Cost Ratio with Low Local Purchases 111

Table XXV Benefit Cost Breakeven Levels . . . . . . . 111

xvi

CHAPTER I

ISSUE IDENTIFICATION

1.1 Introduction

As far back as the 14003 in England, neighbors
protested when nearby land owners began to raise pigs.
Controlling odors from wafting across property lines was
difficult. Lawsuits seeking abatement and damages flour-
ishedl. Today the situation has changed only slightly.
While pigs and hogs are still associated with "noxious"
odors, larger operations, urban sprawl, and increasing
environmental awareness have revitalized this age old
dilemma. In addition, local governments, under the
authority of the township zoning act of 19432 and the
Environmental Response, Compensation, and Liability Act3

have begun enacting ordinances to control the size and

 

1Roberts, E. F., The Law and the Preservation of
Ag;igultural_LangL Cornell University, Ithaca, New York, p.

7, 1982.
2Michigan Department of Agriculture, "Preliminary
Report of the Animal Waste Resource Committee", p. 13,
August 1987.
3Also known as Superfund.

1

2
location of hog operations. This thesis examines the
benefits from hog operation purchases on local economies and
the costs measured by impacts on property values. The
information in this thesis may aid local government decision
makers when considering specific zoning and planning
situations involving livestock operations.

This chapter briefly outlines the circumstances
affecting livestock operations in Michigan, including
farming and environmental trends, legal actions and local
government involvement. The chapter concludes with a
statement of the research problem, hypotheses to be tested

and an overview of the remaining chapters.

1.2 Farming Trends

The 19803 was a decade of change. Beginning in the
early 1980s a major deterioration of farm finances occurred
in Midwest Agriculture. Reductions in net farm incomes and
land values led to increases in debt-to-asset ratios and
accelerated farm bankruptcies. As a result of the farm debt
crisis, the number of full time farmers has diminished and
the number of part-time farmers increased. While this trend
in farm numbers has been evident for over 50 years the 1980s
saw unprecedented declines and significant structural
changes in farm production. In the livestock sector, the
emergence of some large, intensified farming operations

characterized primarily by expert management and advanced

3
technologies was the result. Slaughtering and processing
plants have followed this same trend towards
intensification. These larger operations utilize economies

of size to provide expert management‘.

1.2.1 Livestock Operations

Like other farm enterprises, livestock operations have
declined in number and increased in size. Nationwide trends
reflect intensification of livestock farms and greater
resource productivity. Intensification refers to higher
levels and quality of management while increases in resource
productivity suggests more animals per production unit.
These two factors generally result in greater product

standardization and lower costs of production per hog.

1.2.2 Michigan Livestock Operations

Over the five year period 1982-1987, the number of
farms raising hogs and pigs in Michigan has declined from
7,433 to 5,577, a drop of 25%. Farm size has moved in the
opposite direction as number of hogs and pigs increased 20%
over the same five year period from 1,064,073 in 1982 to

1,277,069 in 19875. The shift from smaller operations,

 

4Iowa State University, "The Iowa Pork Industry:
Competitive Situation and Prospects", p. 63, Ames, Iowa,
December 1988.

5U.S. Department of Commerce, 1987 Census of

Agriculture, Michigan State and County Data, Bureau of
Census, Vol. 1, Part 22, p. 30, 1987.

4
with less than 500 head to large operations with over 500
head is important for this study. Since 1982, the percent
of hogs produced from large operations in Michigan has
increased from 60% to 69% of total production.

Larger operations are characterized by confinement
systems. In 1987 there were 636 "large" operations over 500
head. Most are concentrated in the southern portion of the
state, close to slaughtering plants in Detroit (Michigan),
Indiana, Ohio and Pennsylvania.

More intensive land use in all livestock sectors has
raised farm and field waste management issues. Confinement
systems concentrate animal wastes within animal buildings,
storage pits or lagoons instead of on the fields.
Confinement systems use water to clean buildings adding
additional volume to the quantity of manure that must be
stored. Storing animal wastes can cause noxious odors,
flies and the potential for groundwater contaminationG.

Proper disposal of large quantities of livestock wastes
created by large concentrations of animals, requires proper
storage facilities and calculated land applications to avoid
nutrient overdoses and precautions against both water

contamination and odor disturbances.

 

6For more information see: Earth 1984, Fenton 1988,
Hamilton 1989.

5

1.2.3 Urban Sprawl in Michigan

Rural population increases and conversion of farmland
to non-agricultural uses are issues straining local
government revenues. As people move from urban population
centers providing water and sewer, regular road maintenance
and 24 hour police protection, to rural areas, many expect
to receive these same services. Because the population in
rural areas is less dense, economies of size are not
attained. Increased distances between subscribers, further
adds to service costs. The majority of Michigan
townships are zoned. Most rural townships are zoned
primarily agricultural but permit residential dwelling. The
declining number of farms and urban-rural movement accounts
for the loss in farm land. Since 1940, the number of acres
in farm use in Michigan has dropped nearly in half, from 18
to under 10 million acres7. The conversion of farmland in
Michigan to non-agricultural uses is one of the highest in

the nation. Approximately 120,000 acres are removed from

agricultural production annuallys.

1.2.4 Environmental Change and Citizen Concerns
Environmental consciousness affecting land use has

swelled as has consumer product safety over the past decade.

 

7Census of Agriculture, Michigan.

8Michigan Farmland Preservation Project, "Michigan's
:Land and Soil Resources: A Status Report", American Farmland
Trust, October 1985.

6
Scientific findings regularly uncover new threats to health
and safety. Radon gas, hazardous wastes, groundwater
contamination and the use of pesticides and preservatives
have aided in strengthening environmental legislation while
Superfund statutes9 "create powerful tools for local
governments to protect the public and environment from
problems posed by toxic and hazardous substances"1°.

Until the 19705 agriculture was exempt from many
environmental regulations. In the late 1970s and early 805,
Michigan, along with 49 other states in the U.S., adopted
"Right to Farm" legislation protecting agriculture against

nuisance suitsll.

This was intended to reduce the barrage
of complaints against agricultural operations. The act
condones "generally accepted agricultural practices" but
requires that farmers use "best management practices". In
spite of the act, environmentally related agricultural
complaints are increasinglz. Michigan is not a unique

case as environmental farm issues are gaining importance

throughout the Midwest, however, Michigan's non-metro

 

9Superfund statutes are supported by the national
government and provide monies to clean up designated sites.

10Hanson, Kent E., and Babich, Adam, "Taking Charge:
Local Governments and Hazardous Substances", in Zoning and

£1annigg_;aw_fiangbggk‘ edited by Mark S. Dennison, Clark
Boardman Company, New York, NY, p. 560, 1989.

11Bahls, Steve, "Is Your Farm a Nuisance", Hog Farm
.Management, p. 16, March 1989.

12Conversations with the Michigan Department of
.Agriculture.

7
population density, at 105 people per square mile is the
highest among the other lake states. This has some effect

on the number of complaints.

1.3 Taking Action

This thesis examines odor complaints about hog
operations. Reports cite periodic odors that are "noxious"
and "severe", causing headaches and tearing, preventing
children from playing outside. Lawyers use the term
"nuisance", property assessors call it "economic obsoles-
cence", economists refer to it as an externality. All three
terms describe a market transaction that has not included or
reimbursed all those affected. For example:

In a market transaction there is a buyer and

a seller. A crop farmer decides to sell his/her

property to a livestock farmer. After bargaining

the two farmers reach a mutually acceptable price.

This agreement is considered "pareto

efficient" if both parties are better off and no

one is worse off and all costs are internalized.

If, however, the buyer builds a facility to raise

50,000 hogs, there may be some external costs

associated with the operation. Noise, odors and

other environmental impacts may be experienced by

neighbors that are reflected in reduced property

values.
In this situation a market transaction's ability to create
social pareto efficient outcomes is in doubt13. The

figures on the following pages illustrate the situation.

 

13Mandelker, Daniel R., Environment and Equity: A

W, McGraw-Hill Inc., United States, p. 6,
1981.

 

CROP VS. LIVESTOCK DEMAND

55

 

SD —

45—

 

 

 

 

 

 

M
u 40
O
O
5
u, 35 -
§ VJ
I—
Z 30
8
_ 25 _
(I
C1

20 —

15 r-

10 1 1 ‘

2D 4D 60 80
LAND CHARACTERISITICS
D LIVESTOCK DEMAND + CROP DEMAND

 

 

 

Figure 1 Crop vs. Livestock Demand

In Figure 1. the vertical supply curve refers to a
fixed plot of land and its characteristics. Dl represents a
livestock farmers demand for the land while DC represents a
crop farmers demand. Since the livestock farmer places a
higher value on the land, he/she is able to purchase the
land.

In Figure 2. demand for residential land, originally

D is now Dr" While the seller gained W by selling to the

r,

9
livestock farmer, neighbors lost Q as a result14. If W is
greater than Q the situation is a "potential" pareto
solution, where an equitable distribution of benefits and

costs would result in a pareto solution for both parties.

 

PPOPEQTY VALUE CHANGE

SS

 

50i-

45_

S

 

 

 

 

 

 

u. 40 4
O
2
<
35 -
é Q
.—
Z 30
8
_ 25 _
r:
a.
20 -
15 —
1o 1 1 '
20 40 60 80
LAND CHARACTERISITICS
D LIVESTOCK DEMAND + CROP DEMAND

 

 

 

Figure 2 Crop vs. Livestock Impacts

There are three common ways of solving this problem;
bargaining, adjudication and government regulation. Because

of the joint impact characteristics of clean air, few people

 

14The township tax base is reduced when this happens.

10
will bargain with the farmer because there is no way to
exclude those who do not participate in the bargaining. If
the farmer was willing to be paid not to raise hogs,
everyone would benefit, especially those who failed to pay.

Lawsuits are an option many are pursuing, although the
process is long, expensive and not very encouraging to
residential property ownersls.

Government regulation is an option that has
traditionally attempted to prevent the problem from
occurring, instead of minimizing it afterwards. In addition
to state regulations for air and water pollution, townships
have established zoning ordinances limiting the location and
size of livestock operations. These ordinances reflect
political pressures from township residents. The township,
however, may be in an awkward situation as responsibility to
respond to citizens demands of quality of life may conflict

with its responsibility to generate revenue and maintain or

improve service delivery.

1.4 Local Government Role
Economic fluctuations caused by an exodus of local
enterprises and technological advances resulting in
intensive management affect not only the sectors in which
they occur, ie: automotive, agriculture, etc. but also the

government jurisdictions where they are located.

 

15See Bahls 1989, p. 34 and Fenton 1988, p. 50-55.

11

A study commissioned by Congress examined agricultural-
ly dependent counties in seven regions in the United States
and concluded that because of declines in the "real value of
the agricultural tax base", tax delinquency rate increases
and "declines in non-farm incomes, employment, and property
values caused by lower farm incomes", local government
revenues will falllé. Declines in revenues pose serious
problems as monies from local government supports the
operation of school districts, fire and police services,
road maintenance, general local government administration
and other local services.

Agricultural dependence is generally defined to be 20%
of net revenues in a county. Michigan has only a few
counties in this category and none in the region of this
study. However, revenues from agriculture and
agriculturally related industries are "Michigan's second
leading industry and contributes approximately $15.5 billion
annually to the state's economy"17.

Understanding the structure and responsibilities of

local governments in Michigan aides in analyzing the impacts

of regulatory changes.

 

16Stinson, Thomas, "Governing the Heartland: Can Rural
Communities Survive the Farm Crisis?", Report of the Sub-
Committee on Intergovernmental Relations, U.S. Government
:Printing Office, Washington D.C., p. 2, 1986.

17Michigan Department of Agriculture, "Michigan
Agricultural Statistics", 1988.

12
1.4.1 Local Government Structure

Within each of Michigan's 83 counties, there exist
township, city, village, school and special districts that
depend on revenues from property taxation. In addition some
units have authorities and special assessment districts that
collect taxes. Within every county a taxpayer is listed as
a resident of either a township or a city. Village
properties are included on township tax rolls. The major
common function of townships is assessing property and
collecting property taxes.

Townships have "limited legal authority relative to
that of cities and villages". Townships do however have
considerable ordinance and service authority. These powers
include: land and building use control, liquor and business
licensing and control and service provision to name a
few13.

Location in a specific city, village, township and
county will affect tax rates based upon the type of services

provided.

 

18Sokolow, Alvin D., "Government Response to
Urbanization", Agricultural Economic Report No. 132,
IMichigan State University, pg. 21, May 1968.

 

13
1.4.2 Revenue Sources

As townships cannot impose taxes on personal
incomelg, they must rely "upon property taxes as a major
source of local income"2°. Townships also receive revenue
from the state in the form of state revenue sharing which is
based upon local property taxation levelsZI. Taxes
collected at the township level are allocated to township
and county government operations, school districts, fire and
special assessment sources.

Maximum operating millage rates are constitutionally
established. The constitution provides a minimum township,
county, intermediate school and K-12 school millage rate
referred to as allocated millage. The combined operating
millage (allocated and voted), for the four units cannot
exceed 50 mills. This thesis focuses on townships as most
agricultural lands come under their jurisdiction. All of
the farms examined are found in townships accountable under

township zoning ordinances.

 

19VerBurg, Ken, Managing The Modern Township. Lifelong
Education Programs, Michigan State University, East Lansing,

Michigan, p. 119, 1981.

20ibid. The amount varies between 20%-50% of a
townships budget .

21The distribution formula for state revenue sharing
rnonies is: population x tax effort x relative income.

14

1.4.3 Township Ordinance Authority

The Township planning Act, P.A. 168 of 195922, the
Township Rural Zoning Act, P.A. 184 of 1943, and the
Michigan County Zoning Act, P.A. of 183 of 1943 "are laws
which authorize local units of government to enact
ordinances regulating land uses"23. Both the U.S. and
Michigan Supreme Courts have upheld their constitutionality.
Particular agricultural practices can be encouraged,
restricted or excluded through the use of ordinances"24.

In the case of livestock operations these laws can be
used to: require conditional or special use permits,
determine setback standards, specify maximum number of

animals per acre and other land use requirementszs.

1.5 Research Problem
Township ordinances affecting land use can influence
quality of life levels measured by property values. These
ordinances may increase or decrease property tax receipts

thus affecting service delivery (unless millage levels are

 

22Cooperative Extension Service, "Administering
Township Zoning: A Basic Guide For Citizens and Local
Officials", Michigan State University, Introduction,
February 1987.

23Michigan Department of Agriculture, "Preliminary
Report of the Animal Waste Resource Committee", pg. 13,
August 1987.

24ibid.

25ibid.

 

15
also changed). In addition, land use decisions can affect
employment, production and/or income opportunities for
community members. This thesis will identify, measure and
aggregate these diverse categories in a benefit/cost
analysis. Comparing the benefits and costs resulting from a
particular land use decision allows decision makers and
community members to make more informed decisions.

The purpose of this study is to provide additional
information to decision makers. This information however,
is not likely to be used by itself. Political and social
considerations must join with economic concerns when making
decisions.

This study examines benefits and costs of hog opera-
tions. Impacts on the communities property values, income,
employment and production are assessed. By regressing
property values with household, neighborhood and farm
characteristics, the impact per animal, if any, is
determined. An estimation of township impacts26 are
provided, examining potential income, output and employment
impacts. The importance of farm purchases such as feed,

building materials, veterinary services, etc. for the

 

26This is bolded to emphasize that benefits and costs
*wdll be assessed from a township government perspective. For
example, if a particular house's property value declines by
$1000, the township impact will be the resultant decline in
property taxes received by the township, not the $1000
property value drop.

 

16
communities surrounding hog operations is included. The

following hypotheses are addressed in this thesis.

1.5.1 Hypotheses

A. Livestock operations have a negative impact on property
values. Damages increase as the size and concentration of
the operation increases.

B. Local employment, income and output increase with the
addition of hog operations.

C. Benefits to local governments from a hog operation
exceed the costs of declining property tax receipts for that

same unit of local government.

1.6 Thesis overview
This chapter, introducing the issue and research
problem is followed by Chapter II, introducing the concep-
tual framework, Chapter III, providing a regulatory
analysis, Chapter IV, describing the methodology, Chapter
V, examining research results and Chapter VI, summarizing

the thesis.

CHAPTER II

CONCEPTUAL APPROACH

This chapter examines several methods that can be used
to evaluate regulatory changes affecting livestock opera-
tions. Methods to value both benefits and costs are
discussed, followed by a presentation of the methods chosen
for this study.

2.1 Literature Review

Benefit/cost analysis can be used to examine benefits
and costs associated with regulatory changes. As opposed to
setting an output goal or expenditure ceiling and letting
regulations develop to meet those limits, it considers
impacts occurring as a result of different choices.
Benefit/cost analysis is a tool to provide information to

decision makers.

2.1.1 Perspectives
Benefit/cost analysis can examine alternative actions
from a local, regional, national or global perspective,

«depending upon the issues involved. Choosing the unit of

17

18
analysis, or perspective, is a crucial part of the study as
it determines which benefits and costs are included.

Benefit/cost analysis challenges decision makers to
examine the status quo and ask, "is it enough to make
everyone better off and noone worse off" or are certain
segments of society more deserving than others? The
analysis raises social efficiency issues and contrasts them
with distributional issues. The question of net gains
versus who receives the gains is key.

This thesis posits, in this situation, that benefits
must be assessed from the area in which costs occur. It em-
phasizes the importance of assessing costs and benefits at a
local or county wide level, when, as in this case, the
decision makers are within that area.

This may be seen in contrast to more common "local
undesirable land uses" such as a hazardous waste sites,
where state or national decision makers choose the location.
Here, state-wide benefits are weighed against local costs.
Since the decision makers represent residents from the
entire state, the net benefit often outweighs localized
costs.

In the case of townships, officers must weigh resident
demands together with economic development in a community.
Both of these categories have political, social and economic

issues to consider.

19

2.1.2 Farm Studies

Benefit/cost studies are often used to assess the
impacts of policy changes. Experimental farm projects are
assessed with this method in order to make recommendations
for project expansion or cancellation. Other studies, such
as this thesis, examine impacts of future or emerging
regulatory changes by comparing present benefits and costs

with those predicted under different ordinances.

2.1.3 Environmental Impacts

The literature concerning agricultural environmental
impact statements is growing. Leaching from chemical
applications, runoff and soil erosion top the list of farm
related environmental studies. In addition, harm to humans
and wildlife are implicated when surface and groundwater
contamination occur.

Past studies (Connor 1971, Forster 1975) focus upon
state and federal air and water regulations. The economic
studies estimate the cost of on farm improvements if more
stringent regulations are enforced. Several of these
studies addressed the possibility of increased regulation

for livestock farms.

20

2.1.4 Livestock Farms

Several livestock studies discuss the concept of an
"acceptably clean" environment27. Choosing a particular
level of "clean" will influence producer costs and thus
consumer prices. Selected pollution control measures
including runoff control and increased manure storage
capacity revealed additional capital outlays and increased
annual costs per head28. Many early studies examined
water pollution rules for livestock operations. Water
quality concerns are not new to farm analysts. Testing and
measurement standards determined by the Environmental
Protection Agency and the Department of Agriculture have set
permissible levels of nitrate and trace mineral content in
watersheds for decades. The Department of Natural Resources
still issues stiff fines and penalties when contamination
occurs. 'Air quality, not a traditional farm issue, has
invoked growing concern especially since livestock
operations continue to expand.

Air and water pollution standards long applied to

industries, where particulate emissions and odors are regu-

 

27Connor, L.J., Maddex, R. L., and Leighty, L.L.
"Environment Quality Legal Considerations For Michigan
Livestock Producers", Michigan State University Extension
Bulletin E—732, p.3, December, 1971.

28Forster, D. L., Connor, L.J., and Johnson, J.B.,
"Economic Impacts of Selected Water Pollution Control Rules
«on Michigan Beef Feedlots of Less Than 1,000—Head Capacity",
liichigan State University Research Report, Agricultural
Experiment Station, East Lansing and Economic Research
Service, U.S.D.A., April, 1985.

21

lated, are now being applied to farming operations.
Agricultural enterprises, once exempt, now face these same
environmental regulationszg.
2.1.5 Air Quality and Hogs

Air emissions resulting from hog operations are well
documented. Miner and Hazen identified "odor-producing
amines... in swine manure"30 over 20 years ago while over
75 other "odorous components of animal manure odor" have
been identified since then. Research into swine odors have
focused on odor transfer through particulate emissions.
Removal of dust from pens resulted in reduced odors in
several studies. Scientifically, particulate concentrations
are measurable, however, "knowing the composition of manure
odor along with the concentration of individual components
does not explain the composite odor, qualitatively, or
quantitatively"31.

Research reveals that management is a key factor in

odor control. The surface area as well as waste management

system, weather and spreading area also affect odors.

 

29Parnell, Calvin, 8., ""Air Pollution Control For
.Agricultural Processing Plants", Agriculture and the
(Emyirgnmgnti American Society of Engineers, St. Joseph,
Michigan, p. 107, 1984.

30Barth, C.L., and Melvin, S.W., "Odor", Agricuiture
v' American Society of Engineers, St.
Joseph, Michigan, 1984.

31ibid, p. 100.

22
Implicit in this research is the relationship of manure
quantity and number of animals.

Some attempts are being made to measure gases present
at livestock operations. One township in Michigan has
incorporated these gas measurements into their zoning
ordinances. The ordinance states that "odors shall not
create a nuisance...so that air quality standards are
maintained of no more than one-half (1/2) part per million
of hydrogen sulfide or ten (10) parts per million air borne
ammonia average per hour at the site perimeters"32. The

state of Iowa also regulates feedlot and lagoon odors.

2.2 Benefit/Cost Techniques
The following studies examine methods of valuing
pollution impacts on the environment. In addition benefits
associated with local expenditures on agricultural inputs

are included.

2.2.1 Farm Pollution Controls

A study of agricultural non-point pollution control in
Vermont investigated the impacts of high phosphorus
concentrations in St. Albans Bay caused by wastewater
treatment plant discharges and dairy farm runoff. These

effects "impaired recreational and other amenity values of

 

32From a township zoning ordinance.

23

the bay"33. The Rural Clean Water Program supported a
manure storage project involving 62 dairy farms to "reduce
the agricultural contribution"34 to Bay pollution. Cost
sharing enabled farmers to build 180-200 day storage
structures. The study investigated farm costs and benefits,
government costs, and water quality benefits. Farm level
impacts of manure storage, including construction costs and
benefits from labor and fertilizer savings were analyzed.
Government costs included project and wastewater treatment.
Benefits included property value increases, recreation
enhancement and reduced water treatments35.

Water quality benefits were measured using two methods.
An hedonic model assessing changes in property values was
used to calculate appreciating properties as water quality
changed. Recreational benefits were found using a travel
cost method.

The hedonic model calculated the reduction in property
value as a result of increased pollution in the lake. From

this estimate property values were re-estimated based upon

lower levels of pollution. The travel cost method

 

33Young, Edwin C. and Shortle, James 8., "Benefits and
¢Costs of Agricultural Non-point-source Pollution Controls:
frhe Case of St. Albans Bay", Journal of Soil and Water
(Conservation, Vol. 44, #1, p. 64, January-February 1989.

34ibid.

35ibid.

24
calculated additional business and park fees generated by
the less polluted lake.
The benefit-cost ratio of 1.2 was calculated over a 50
year period. A three year lag was assumed before project

benefits would begin.

2.2.2 Multipliers in the Hog Sector

Benefits or costs of regulatory decisions that affect
livestock operations must consider the forward and backward
linkages associated with the industry. In the Michigan hog
industry, backward linkages for inputs purchased including,
corn, feed supplements, labor, veterinary services, fuel and
machinery accounted for $153.3 million, 79% of producer
receipts or an average of $87 per hog36. Forward
linkages, including transportation, meat packing and
processing accounted for an estimated 5600 jobs in 1986.

Otto combined farm and non-farm impacts associated with
the hog subsector using input/output models to generate
multipliers examining hog industry impacts on employment,
income and output. He found multipliers in the Michigan hog

industry to be:

Employment multiplier = 1.86
Output multiplier = 1.5
Income multiplier = 1.68

 

36Otto, Daniel, "Economic Importance of Michigan's Pork
iIndustry", Unpublished Paper, Iowa State University, 1987.

25

For every job created in the hog sector, another .86
position is created. For every dollar generated in hog farm
income, 68 cents is created off farm37. For every dollar
of output created, 50 cents is generated outside the hog
sector38. Combined these multipliers result in a total
direct and indirect value to Michigan of $292.7 million39.
This thesis attempts to expand this analysis and estimate

resultant local impacts as operation size changes.

2.3 Finding Prices

Input-output models help find backward and forward
linkages associated with an industry. This aides policy
makers when considering changes affecting the industry. In
cases where market values are not readily determined, cost
and benefit information is difficult to find. When prices
are not available two decision making situations follow.
First, regulatory decisions can be made ad hoc, without the
knowledge of costs or benefits. In the case of environmen-
tal goods, over or under production is likely since

equilibrium production levels cannot be determined from

 

37The income multiplier includes direct and indirect
effects of a one dollar increase in final demand.

38Output is the producers value (what sellers receive
for their goods).

39ibid.

26

prices‘o.

By using enforcement "powers to assign and
direct resources without compensation, the crucial role of
prices as a metering device for efficiency is des-
troyed."41

In a second situation, where output levels (net
benefits or costs) are difficult to measure, policy making
will attempt to control inputs42. This may or may not
have the desired effect depending upon the relationship
between a set of inputs and the final output. For instance,
regulations limiting the size of livestock operations may
not be effective in reducing

complaints if most of the complaints are the result of poor

management practices.

2.3.1 Agriculture and the Environment
Classical economic rent theory supports differences in
agricultural land values based upon environmental

quality43. These quality differences can affect

 

4oSiegan, Bernard, H., Bianning Without Priges,
Lexington Books, Lexington, Massachusetts, 1977.

4libid.

42Kilmer, Richard, L. and Armbruster, Walter, J.,

Eggngmig Ettigigngy in Agricgitgzgl and Food Magkgtigg, Iowa
State University Press, 1987.

43Freeman, Myrick, A., The Benefigs of Envigonmentgi
Imprgygmgng, Resources for the Future, Johns Hopkins

University Press, Baltimore, 1979.

27
productivity, thus net output. Alternate land uses also
affect value. Raup categorizes these two components as:

A) The capitalized valuation of the income flow from

the land, in agricultural use.

B) The value of the land in alternate non-farm

uses44.

An example of this distinction is seen in acts that
entitle farmers to property tax assessments performed at
use, not market value levels. By taxing according to "use"
value it recognizes differences in land quality, and
environmental as well as managerial factors.

Attempts are being made to extend this theory of an
agricultural product's dependence upon environmental
quality, to household properties. An agricultural product's
value is seen as a bundle of goods comprised of seeds,
fertilizers, management and environmental conditions.
Likewise the value of a house is made up of a bundle of
goods including, square footage, number of number of baths
and fireplaces and environmental conditions as well.

This distinction arises because unlike agricultural
land, utilized to produce consumer goods, a house is a
durable good, with no tangible output, whose value was not

thought to be affected by environmental differences.

 

‘-

44Raup, Philip, M., "Structure and Performance of the
ILand Market in the United States", Staff Paper #P86-54,
Department of Agricultural and Applied Economics, University
of Minnesota, December 1986.

28

Contemporary studies refute this claim (Abelson, 1979,
Freeman, 1979, Harris, 1981, Ridker, 1967). Researchers
have found that a change in environmental quality does
affect property values. Environmental quality in this
context does not refer to locational characteristics such as
proximity to a lakefront, park or forest. Instead, changes
in environmental quality are considered. For example, if a
lake became polluted, the change in water quality would
likely have an impact on property bordering the shoreline.
Or, as in this case, odors from livestock operations may

affect property values.

2.3.2 Hedonic Price Studies

Hedonic studies look at the impacts of changes in
environmental quality on property values. Air, water and
noise pollution associated with industrial developments are
the most common subjects of study (see Freeman, 1979, #3,
for a survey of issues). In addition some studies involving
soil conservation practices have attempted to quantify
resultant changes in land values (Palmer, 1989). Figure 3
shows the hypothesized relationship between environmental
quality and property values. As property values increase,

so do environmental amenities.

29

 

Environmental Quality & Property Values

 

11

10

Property Values

 

 

 

 

Environmental Goods

 

 

 

Figure 3 Environmental Quality and Property Values

2.3.3 Air Pollution Studies

In 1967, Ronald Ridker introduced the idea of using
property values to measure the impacts of environmental
quality changes. Ridker looked at property values and air
pollution using cross sectional and time series regressions.
Subsequent studies have included land, water, and noise as
well as air pollution. In all cases the purpose is to
measure the underlying prices associated with environmental

cfirange. When Ridker did his study he experimented with both

30
a time series and cross sectional regression. Today the
cross sectional analysis is used exclusively as property
value fluctuations over extended periods confound
measurements. Ridker's analysis was an urban study for the
city of St. Louis. Except for the relatively few agricul-
tural studies available, (Palmer 1989, Wellner) most hedonic
studies are in concentrated population areas.

Ridker's air quality study examined the impacts of a
factory emitting smoke in downtown St. Louis. He used two
measures of pollution, sulfation levels at various sampling
stations and a measure of suspended particulates gathered by
high-volume air samplers. This method of measurement, using
sophisticated air, water and land samplers is still common
today.

Additional information including characteristics of
household properties near the factory, the exact house
location, type of neighborhood, as well as information on
the occupations, taxes, submarket variables and median
income of the residents were collected.

Results of the cross-sectional analysis indicated that
"property values were adversely affected by pollution"45.
If sulfate levels were reduced by .25 mg of 803/110cm2/day
'the property value would increase at least $83 and up to

$245. This result was for all houses in the metropolitan

 

45Ridker, Ronald., "Property Values: A Cross-Section

AnalysiS" W p. 137 1967.

31
area. The findings revealed that use of a low sulfur fuel
could reduce sulfation levels, saving 10-15 million dollars
in pollution costs45.
Since Ridker's initial work, hedonic price analysis has
gained popularity. Contemporary studies have applied the
technique to evaluate noise pollution impacts in multiple

regions.

2.3.4 Noise Pollution Studies

In 1981, Jon Nelson published an article on aircraft
noise and hedonic prices47. The study examined
residential property values in six U.S. metropolitan cities
surrounding airports to see what affect noise had on
property values.

Nelson obtained noise annoyance index calculation known
as the Noise Exposure Forecast (NEF) from the cities and
overlaid them upon census block maps around the airports.
The author was careful to exclude sample areas that included
other environmental features, major transportation facili-
ties, community developments or other special neighborhood
features.

The housing characteristics examined included physical

accessibility, neighborhood, public sector and alternate use

 

46Drops or increases in property values.

47Nelson, Jon, P., "Measuring Benefits of Environmental
Improvements: Aircraft Noise and Hedonic Prices", Advances
in Applied Microeconomics, Vol 1, p. 51-75, JAI Press, 1981.

32
characteristics. Results indicated that areas exposed to
high NEF levels reported lower property values. Aggregating
the six areas Nelson found that a house in a NEF=45 area, 20
points above normal levels, (the NEF range is 15-55, with
little annoyance reported under 25) "would sell for $2675
less than a house exposed to only NEF=20."48 ceteris
paribus. Implicit prices per decibel ranged from $61 in
Cleveland to $239 in San Diego.

Of particular interest in this study was Nelson's
combination of cities and use of diverse and distant areas
of study. By aggregating data across localities the
assumption is that the markets for noise abatement are
stable. Freeman refers to this as market segmentation. He
cites Straszheim who argues "that the urban housing market
really consist(s) of separate compartmentalized markets with
different hedonic price functions in each". If this is the
case, aggregating data would "provide faulty estimates of
the implicit prices facing subsets of buyers in different
market segments."49

The aircraft noise study by Nelson, in addition to one

of his earlier studies tested for stability across markets.

 

48Nelson, Jon, P., "Measuring Benefits of Environmental
Improvements: Aircraft Noise and Hedonic Prices", Advances
in Applied Microeconomics, Vol. 1, Jai Press, p. 62, 1981.

49Freeman, Myrick, A., The hehegits of Environmentai
IEEIQYEEQDL. Resources For The Future, Johns Hopkins Press,

Baltimore, p. 142-143, 1979.

33
In both cases the hypothesis that the hedonic price

functions were the same in both markets was not rejected.

2.3.5 "Other" Pollution Studies

Air, noise and land studies similar to Ridker and
Nelson's, in addition to water quality studies, predominate
the literature. However, there are a number of other areas
that are not well researched. In Myrick Freeman's book, The
Benefigs g: Ehvigghhehtgi improvemenh, a list of "Types of
Effects" of environmental quality changes is included.

Under the topic heading "other" is listed "odor, visibility,
(and) visual aesthetics." He explains that:
"Since aesthetic effects are often not

associated with direct use of environmental

resources, they pose difficult measurement and

valuation problems. But to the extent that they

involve utility gains and willingness to pay, they

are nevertheless every bit as real, in an economic

sense, as the impairment of health."

One study in this category attempts to value country
hedge rows admired by tourists passing on the road. Wellner
investigated the maintenance and opportunity costs incurred
by a land owner to determine the value, or what a
countryside commission should pay in order to prevent a
farmer from replacing the hedges with farmland. No studies
involving the impacts of odors upon property are identified.
These "aesthetic" effects are difficult to study due to

:measurement problems and the degree of subjectivity involved

‘with measuring the environmental changes. In the past,

34
contingent valuation has been used to evaluate "difficult

:50 areas. Hedonic pricing

measurement and valuation'
techniques can also provide policy information for decision
makers with specific feedback translated into monetary

impacts.

2.4 Study Approach
A benefit/cost analysis is used in this study to
measure the benefits associated with livestock operations on

a local community with the costs imposed by hog odors.

2.4.1 Benefits

Backward and forward linkages, reflected in output,
income and employment multipliers in the hog sector will be
examined to determine the local impact of hog operations of
various sizes. Discussions with industry participants and
observers will generate estimated expenditure and production
levels of two operation sizes. A small operation of 200
sows a year51 will be compared with an operation of over
200 sows. Expected local benefits will be calculated based
upon size of the operation. A regional benefit factor will
also be generated for the statewide impact but this will not

be used in the Benefit/Cost calculation.

 

50Freeman, from above quote.

51This is the state average at present.

35

2.4.2 Costs

Externality costs associated with livestock operations
are reflected in lower property values (environmental
preferences are transferred to property). By recording
residential property values in areas surrounding hog
operations, together with characteristics of the property,
neighborhood and farm/environmental conditions affected by
farm location, implicit prices per hog will be determined.
Cost estimates will be generated using the implicit prices
for a particular farm size, multiplied by property densities
in the farm area. Both the pecuniary costs shown as
property value declines and the impact on township tax
receipts will be presented. The purpose of this study is to
examine annual impacts across an entire community, to do
this the township tax loss will be used in the benefit/cost

calculationsz.

2.5 Hypothesis
Hypothesis # 1: Livestock operations have a negative
impact on surrounding property values. Hedonic studies have
consistently found environmental disamenities to have a
negative impact on property values. This hypothesis will be
supported by examining the coefficients and significance of

two key variables. The distance of the house from the

 

52This does not ignore the pecuniary losses experienced
by individuals but instead focuses upon costs experienced by
township residents as a whole.

36

livestock operation and number of animals should have both
positive and negative signs respectively. Prevailing winds,
should have a positive sign. As distance away from the farm
is increased, impacts should be lessened. In addition, the
number of animals should be positively related to odor
production and thus negatively affect property values.

Hypothesis # 2: Livestock operations provide revenues
to local economies. A large portion of input costs used to
raise and sustain hogs is spent in the communities
surrounding the operation. While the percent of total input
dollars spent locally declines as operations increase in
size, a substantial portion of purchases remain within the
locality.

Hypothesis 0 3: Local benefits of livestock operations
exceed costs. Monies spent within a township(s) by a hog
operation, exceed township tax losses resulting from that

hog operation.

2.6 Conclusion
This chapter reviewed literature concerning
environmental factors affecting agricultural production
practices. The second half of the chapter focused on
various techniques used to determine the extent of
environmental impacts both on and off of the farm.
The following chapter provides an analysis of the legal

framework affecting livestock operations. It examines

37
livestock producer responses, local government concerns and

regulatory alternatives.

CHAPTER III

REGULATORY ANALYSIS

This chapter examines the legal status of livestock

operations and the effects regulations have upon them.

3.1 Right to Farm Act

49 states have adopted Right to Farm legislation as a
response to farmland conversion to non-agricultural use.

The vast majority were enacted after 1978. Nationwide these
statutes can be summarized as helping to ensure that "a farm
cannot be considered a nuisance simply because the use of
surrounding land has changed"53.

The Right to Farm Act in Michigan had the same impetus
as other state's. However, Michigan has added an additional
requirement that certain best management practices be
adopted before protection under the law is assured. For

livestock operations this includes "manure management

 

53Bahls, Steven C. and Bahls, Jane E., "Is Your Farm a
Nuisance?", Hog Farm Management, p. 16, March 1989.

38

39
practices for land applications"54. Specifically, when
responding to a livestock complaint the Environmental
Division of the Michigan Department of Agriculture will
investigate the farm in question to determine whether the
farm is in compliance with best management practices

pursuant to their guidelines.

3.1.1 Background

In 1981 the National Agricultural Land Study reported
that agricultural land was being converted to non-agricul-
tural uses at the rate of three million acres a year55.
Michigan's farmland conversion rate of 120,000 acres a year
is twice the national average. It is contrary to state
plans expanding the role of agriculture and preserving
existing farmland in the Michigan economy. PA 116, the
Farmland Preservation Act, and the state's role in encourag-
ing agriculture is evidence of this. In the early 1980s,
despite opposition, the governor's office recruited large
livestock operations to settle in the state. At the end of
1987, the hog and pig inventory for the state was the second

highest ever, up 8% from 1986. University and livestock

 

54Hamilton, Neil, "Environmental Pressures Are Going to
Increase", Pork '89, p. 12, October 1989.

55Voth, Donald E., McCormic, Linda Grim, and Blair,
Joanne 8., "Right To Farm Legislation in Arkansas, Its
Objectives and Its Applicability", Arkansas Agricultural
Experiment Station, Bulletin 918, p. 1, March 1989.

40
association representatives throughout the state are eager

to continue this upward trend in hog production.

3.1.2 Environmental Protection
The Right to Farm Act legalizes the special status long
granted to agriculture. The following statutes in Michigan
were established to protect the environment:
1. The Michigan Water Resources Act (PA 245) of 1929
2. The Michigan Air Pollution Control Act (PA 348) of 1965
3. The Michigan Environmental Protection Act (PA 127) of
1970
4. The Michigan Solid Waste Management Act (PA 641) of 1978
5. The Michigan Hazardous Waste Management Act (PA 64) of
1979
For many years prior to the passage of the Right to
Farm Act, exemptions were granted and authorities were not
fully exercised. Today, litigation involving livestock
operations are becoming more numerous despite passage of

Right to Farm Acts. According to agricultural law experts

they will be on the rise in the 19905.

3.1.3 National and State Cases

Experts across the nation disagree regarding the
solution to agriculturally related environmental problems.
Some are confident that new technologies and additional
resources directed at the issues will solve the problem.
Others are less optimistic. Neil Hamilton, an agricultural
law professor, reports that:

"The odorless pig is still a creature of the
future. Well-designed facilities, good management

41

and proper waste disposal help, but it i3 hard to
escape the fact that hog manure stinks. "

The following cases disclose the types of complaints

encountered.

3.2 Nation-Wide and Michigan Complaint Cases
Nuisance suits are not uncommon in the United States.
Several cases have attracted national attention due to media
coverage. In response, trade journals suggest ways for
livestock farmers to avoid litigation. A sample of several

cases follows.

3.2.1 National Cases

A case in Colorado involves National Farms of Kansas
City and some influential and wealthy neighbors. National
Farms is constructing a farrow-to-finish operation valued at
40 million dollars for 300,000 to 350,000 head of hogs. A
local conservation organization along with the Farmers Union
object to potential water and odor impacts in addition to
corporate farming in the state. Opponents site environmen-
tal issues as their key concern, despite compliance by
National Farms with state and local laws. The quantity of
liquid waste estimated by opponents is expected to reach 2.5
million gallons a day at full capacity. The Sierra Club,

Audubon Society and neighbors, Colorado billionaire, Philip

 

56Hamilton, Neil, "Environmental Pressures Are Going to
Increase", Pork '89, p. 12, October 1989.

42
Anschutz and beer magnate, Peter Coors have joined op-
ponents. These opponents were unsuccessful in their first
attempt at stopping construction through legal channels.

In Missouri several smaller, family owned operations
are contesting a series of nuisance cases filed against
them. In one case problems began when the hog operators
switched to a confinement system. Five neighbors located
one mile from the operation have complained about "in-
tolerable" odors. The complaints have resulted in litiga-
tion to solve the problem. The Missouri hog operations
range in size from 2500-7000 head. These are smaller than
the one in Colorado but larger than the average hog farm in
Michigan, approximately 200 head.

Missouri farmers have formed a support group called the
Producers Rural Protection Association. Farmers pay
membership dues of 10 dollars a year and 25 cents per hog
sold to assist other farmers with nuisance suits.

In the Colorado and Missouri cases as well as in
Michigan, some of the more vocal opponents include hog
farmers. They insist that confinement systems smell much
worse. Observers suggest that they are worried about

competing with corporate livestock enterprises.

3.2.2 Michigan Cases
Hog operations about which the Michigan Department of

Agriculture has received complaints, range in size from one

43
hog to over 10,000 hogs. As might be expected the number of
odor complaints about operations over 500 head is greater
than for those less than 500 head. Of the 21 complaints
reported in the first six months of 1989, at least ten of
these were for operations over 500 head. There were
approximately 670 large operations in 1987. This converts
to 1.5% of all large operations receiving complaints. The
remaining 11 complaints were directed towards smaller
operations. .18% from a total of over 6200 smaller opera-
tions received complaint557.

At least four of these 21 complaints have resulted in
litigation. Lawsuits brought by community or environmental
organizations are more common than those brought by
individuals. Community interest may be based on the
possibility of operations expanding or new ones moving into
their areas. These groups are becoming more powerful as
they obtain influential positions on township planning
boards. Townships lacking well-documented planning guide-
lines to deal with nuisance abatement or refusing to issue
building permits, risk charges of exclusionary zoning by the
courts. Reluctance on the part of township governments to
become involved in land use decisions is likely to be short
lived, however. Litigation and rising concern often prompts

township officials to establish a track record and basis for

 

57Hog farm numbers are obtained from the 1987 Census of
Michigan Agriculture.

44
opposing incompatible land uses. The result is more

complete land use guidelines.

3.3 Local Government Concerns

As elected representatives, local government officials
respond to citizen concerns. Unlike national and even state
representatives, township officials are very much "in touch"
with their constituents. Critics challenge local govern-
ments with accusations of ineffectivenesssa. They demand
consolidation of county government to do away with service
repetition. Advocates describe "proximity to constituency,
responsiveness, and flexibility" as advantages while noting
that success depends upon "the quality of local leadership
and the leadership agenda itself, volunteer action, multiple
assignment to public employees and ad hoc--but effective--
administration"59.

Examining farming systems from a non-farm, rural
development perspective has involved discussion of the

agricultural recessions and their impacts upon "the

financial health of state and local governments"6°. Oddly

 

58Schuh, Edward G., "Rural Development Issues", p. 8,
University of Minnesota, Minneapolis.

59Sokolow, Alvin D., "Small Local Governments as
Community Builders", National Civic Review, p. 363,
September/October 1989.

60See The Agricultural Recession: Its Impact on the
Finances of State and Local Governments" ACIR, June 1986 and
"Agricultural Land Values an Assessments in Selective
Counties in Michigan" by Lynn Harvey et al., November 1987.

45

enough, the current debate over livestock operations across
the United States has not focused upon the economic impacts,
positive and negative, when assessing the situation.
Instead the focus is on quality of life standards. However,
studies have shown that environmental quality standards
increase with incomes. Vocal individuals, who do not depend
upon the local economy for their employment, may have higher
standards than others. The question becomes one of standard
setting and whose preferences count?

The next section outlines the Benefit/Cost analysis
used in this study. It begins with an introduction to local

government revenue sources.

3.4 Property Value Change Impacts
Property taxes are the largest source of government
revenue in the state of Michigan61. They accounted for
37.3% of "total state and local taxes in fiscal year
1985”62. Property taxes in 1985, accounted for 91.9%
percent of local government tax revenue. Dependence on

property tax revenues to support activities varies from 86%

 

61Harvey, Lynn R., and Knudson, William, "Property
Taxes and Relief in Michigan: Including The P.A. 116
Program", Staff Paper, Department of Agricultural Economics,
Michigan State University, no. 88-98, pp. 1, December 1988.

62Taxation and Economic Policy Office, "Property Taxes
in Michigan, Rates, Revenue and Relief 1986-1987", Depart-
ment of Treasury, p. 1, September 1988.

46

for schools to 35.7% for municipalitiesé3. Remaining
funds are generated through fees and intergovernmental
transfers. Residential real properties are by far the
largest contributor to property tax revenues. They totaled
57.2% of property tax revenues in 198764.

Michigan property taxes are based on the state
equalized value65 multiplied by the township tax rate66.
The total township tax rate is the sum of the millage rates
for community colleges, schools, county and township
(operation and debt, including fire, drain and special
assessments)67 located within the township boundaries. In
1986 the average millage rate was 48 mills. In addition to
determining individual landowner property tax levels the
state equalized value per pupil and millage rates are used

to calculate state aid to school districtssa.

 

63ibid, p. 8.
64ibid, p. 14.

65Equal to 50% of market value as determined by
property tax assessors.

66Not to be confused with the 1 mill allotment to
townships, this instead refers to total taxes paid to the
township office.

67A mill equals ten dollars of taxes for every thousand
dollars of SEV.

68Taxation and Economic Policy Office, "Property Taxes
in Michigan, Rates, Revenue and Relief 1986-1987", Depart-
ment of Treasury, p. 10, September 1988.

47

3.4.1 Local Government Impacts
Since township residents pay township, county,

community college and school taxes, a decline in property
values affects each of these units differently. The average
distribution of property tax revenues amongst these units is
shown below in table I along with the average degree of
dependence on these revenues in 1984-198569.

Table I Property Tax Distribution and Dependence.

Property Tax: Distribution Dependence
Township = 31.1% 48.4%
County = 21.7% 40.4%
School = 57.2% 86.0%

3.4.1.1 Townships: Townships by constitutional provision
generally claim one mill to aid in operational expenses,
however, voters may approve additional millage by a vote of
residents. Additional revenues are obtained through state
revenue sharing and service or user charge fees. Other
township services that receive property tax revenues include
fire districts, drain and other special assessments. Thus a

decline in property value, as measured by the SEV directly

 

69Harvey, Lynn R., and Knudsen, William, "Property
Taxes and Relief in Michigan: Including The P.A. 116
Program", Staff Paper, Department of Agricultural Economics,
Michigan State University, no. 88-98, p. 16, December 1988.

48
impacts the quality and quantity of services offered in a

township.

3.4.1.2 Counties: An average Michigan county collected
6.14 mills in 1986. In return township residents receive a
variety of county services including road maintenance, vital
statistic registration, law enforcement, court services and
register of deeds. Fees, intergovernmental transfers and
additional services provided make up the remainder of the

counties' funding.

3.4.1.3 School Districts: School districts are extremely
dependent upon property value revenues. Eighty six percent
of school revenues comes from property taxes. In an attempt
to protect townships against extreme fluctuations in school
funding the state guarantees a minimum funding support per
pupil. A school district receiving funding via the state
(membership aid) is known as "in-formula" while a district
whose SEV/pupil ratio is greater than the state average is
designated as an ”out-of-formula" district. When "in-
formula", a district receives state membership aid based
upon the millage rate and the SEV per pupil. Property tax
losses impact more heavily upon "out-of—formula" districts
since they are above the state aid level, therefore bear
100% of the lost revenue. In a study of 29 counties in

Michigan, declines in SEV per pupil have resulted in an

49
increase of state membership aid from $381,356 in 1985-86 to

$1.3 million in the 1987-88 school year7o.

3.4.2 Property Tax Stability

While property values are subject to substantial
fluctuations depending upon the economy, safety mechanisms
have been incorporated. Property values are stabilized
because "hggggggg" values lag behind actual drops in land
prices. Equalization offices use "sales studies" over a
twelve to eighteen month period to lessen dramatic changes.
In addition, certain taxation methods such as the "use"
value assessment of farm land, are less vulnerable to
economic downturns. Finally, in the short run, millage
rates can be increased in order to offset property tax
revenue resulting from declining agricultural land

values71.

3.5 Regulatory Options
Property tax revenues are important to support local
services. Local governments are expected to protect the

property values of its residents. In response to situations

 

7oHarvey, Lynn R., House, Al e., Cybulski, Karen K.,
and Walker, David R., "Agricultural Land Values and
Assessments in Selective Counties in Michigan", Agricultural
Economics Report, p. 32-33, No. 503, November 1987.

71Advisory Commission on Intergovernmental Relations,
"The Agricultural Recession: Its Impact on the Finances of
State and Local Governments", p. 17, Staff Information
IReport, Washington D.C., June 1986.

50
involving agriculture there are several mechanisms available
to prevent declines in property values. These include
ordinances, best management practices, technological
improvements and regulatory alternatives described in the

next four sections.

3.5.1 Ordinances

Zoning ordinances attempt to separate incompatible land
uses. "Uses of land are typically incompatible if the
characteristics of the use of the land may create negative
consequences on adjacent land uses"72. Even though zoning
ordinances are "legal, enforceable document(s)", they cannot
predict future conflicts. The result is that new ordinances
are adopted with the hope of preventing future conflicts.

Anne Fenton summarized a variety of these new ordinan-
ces in Michigan. Site requirements, involving "setbacks,
(animal) density and prevailing winds are the typical
criteria targeted" for livestock operations73. Fenton's
summary cited conditional use permits distributed by
townships requiring a minimum number of acres per operation,

setbacks from residences and roads, direct access to county

 

72Cooperative Extension Service, "Administering
Township Zoning: A Basic Guide For Citizens and Local
Officials", Michigan State University Extension Bulletin E-
1408, p. 1, February 1987.

73Fenton, Anne M., "The Future of Intensive Swine
lOperations in Michigan" Plan B. Master's Paper, Department
of Resource Development, Michigan State University, p. 46,
March 1988 .

51
or state roads and animal densities74. With the exception
of maximum number of animals per farm restrictions,
discussed next, compliance with these new ordinances is
easily obtained. Fenton's summary cites two types of animal
density requirements. One, adopted by the Michigan Animal
Waste Resource Committee, sets a maximum number of animal
units permissible per acre. Whether the owner uses a
pasture or confinement feeding system is not important,
instead the number of acres available where manure can be
spread is the limiting factor. In the second case, limita-
tions on the number of animals, regardless of acreage are

imposed. The specific limits in the second case are:

300 cattle, horses, mules, or donkeys
600 swine, goad, sheep, ponies, or veal calves

75
3.5.2 Best Management Practices

Best management practices have long been described as
the most effective tools in preventing local conflicts.
Careful thought given to barn location and timing of barn
cleanup and manure spreading solve many of the problems.
Agricultural engineers suggest moisture reduction to halt
anaerobic biological decomposition, covered manure storage
systems that "inhibit the interchange of odorous compound

between the liquid surface and overlying atmosphere". They

 

74ibid, p. 47.

75ibid, p. 48.

52

also discuss sprays that scrub odors from the air and
finally barriers which aid in the dilution of odors76. A
variety of other practices have been adopted by hog farmers
in order to reduce the chances of nuisance suits. These
include:

1. No spreading on Fridays, weekends or holidays

2. Informing neighbors before spreading

3. Spreading before it rains when possible to pull

down odors
These practices involve extra time and consideration which

may not be available to producers or thought of as necessary

by them.

3.5.3 Technological Improvements

Technology exists to minimize, not eliminate odors.
Most larger operations use anaerobic lagoons (as opposed to
pits) with recirculating waste to cut down water usage.
Lagoons with six month storage capacities allow for
spreading only twice a year. This not only reduces the
odors associated with spreading but also saves labor.

In some areas industrial odor management practices,
such as floating lagoon aerators, are recommended. Costs
for these technologies, when borne by the operator alone,

often exceed the operator's perceived or realized benefits.

 

76Miner, Ronald, J. and Barth, Clyde L., "Controlling
Odors from Swine Buildings", p. 2, Pork Industry Handbook,
Michigan State University Extension Bulletin E-1158, July
1979.

53
3.5.4 Regulatory Alternatives

Like commercial or industrial enterprises, livestock
enterprises construct buildings, purchase inputs and spend
money in their communities. Encouraging commercial or
industrial enterprises to settle in an area sometimes
involves tax easements and other incentives. If the
benefits are great enough, townships might be able to use
some of these same measures to encourage investment in both
best management practices and technology which reduces odors
and other environmental impacts.

Specifically, 12 year industrial tax easements,
property tax waivers and local economic development
associations can help secure low interest loans. Other more
deliberate options include paving and plowing roads77.
Looking at a livestock operation as an enterprise, important
to the viability of the community, may perk community
interest.

3.6 summary

Accounting for the impact of environmental damages, be
they cleanup costs from water contamination or lost
revenues, is appropriate for decision makers. Regulatory
options are one way of dealing with externalities created by
livestock production. The next chapter presents the

research methodology.

 

77Discussion with Ken VerBurg, Michigan State Univer-
sity, Department of Continuing Education.

CHAPTER IV

RESEARCH METHODS

This chapter outlines the methods used in the thesis.
The literature search, waste complaint survey, data
collection and variables selected for the model are

described.

4.1 Literature Search

The study began with a literature search focusing on
state Right to Farm laws. The role of federal, state and
local governmental units was explored in terms of interest
and jurisdiction in addressing animal waste issues. A study
of local ordinances affecting livestock farmers in Michigan
led to an examination of local government decision making
processes, waste management issues and finally a number of
benefit/cost techniques used to evaluate environmental

regulations.

54

55
4.2 Animal Complaint Survey

A study was performed to tabulate animal complaints
filed with the environmental division of the Michigan
Department of Agriculture. The purpose of the tabulation
was to determine how livestock complaints were distributed
across operation types and what kind of complaints were
being made.

The tabulation categorized complaints by type of
operation, (hog, dairy, beef, poultry and other), year
(1986-1989), and type of complaint (air, water, other).
Preliminary reporting in September of 1989 indicated a
predominance of hog related complaints. 7.2 complaints per
thousand farms were recorded for the hog sector as opposed
to 4.3 for the next highest level in dairy cattle78. The
tabulation is below in Table 1179 (see tabulation procedu-
res in Appendix A).

The number of water related complaints across livestock
types was nearly equal. The decision to focus on hog odor
related cases was made based upon the preponderance of
complaints in this category.

While this research focuses upon environmental concerns

in Michigan, odor complaints are not unique to Michigan. In

 

78Michigan Agricultural Statistics for 1988 report
26,000 cattle operations and 7000 hog farms.

79Abeles-Allison, Mark, "A Tabulation of Livestock
Complaints in Michigan", Unpublished report, Department of
.Agricultural Economics, MSU, East Lansing, MI., September
1989.

56

Table II Complaint Tabulation Form.
—

 

 

COMPLAINT9 TYPE OF FARM
TYPE 9
9 Dairy Cattle Hogs Poultry Other 9Totals
Odor 9 9
1986 9 0 0 2 0 0 9 2
1987 9 2 l q 8 0 O 9 11
1988 9 2 l 3 1 l 9 8
1989 9 5 6 18 4 6 9 39
Water 9 9
1986 9 6 l 1 O O 9 8
1987 9 2 0 2 1 0 9 5
1988 9 4 O . 5 0 O 9 9
1989 9 ll 4 ll 2 3 9 31
Other 9 9
1986 9 O O 0 O 0 9 0
1987 9 0 O 0 1 0 9 1
1988 9 O 0 0 O O 9 0
1989 9 O l 0 0 0 9 l
eeeeeeeeeeeeeeeeeeseeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee
TOTAL 0 32 14 so 9 10 c 115

t of Tot 9 27.83% 12.17% 43.48% 7.839 8.70t9
lIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII-IIIIIIIIIIIIIIIIIIII

Arkansas a mail survey recorded complaints against farmers
using a random sample of 462 farmers in three counties over
a five year period. Findings revealed that 10% of farmers
reported complaints against them. The major complaint
categories in Arkansas were chemical, odor and water. The
top four complaint categories are shown below in

Table 11180.

Table III Arkansas Complaint Survey.
—

Chemical Use : 36%
Odors : 18%
Water Use : 16%

Water Pollution: 10%
—

 

80 Donald E., McCormick, Linda G., Blair, Joanne 8.,
"Right to Farm Legislation in Arkansas, Its Objectives and
Its Applicability", Arkansas Agricultural Experiment
Station, University of Arkansas, March 1989.

57
4.3 Data Collection
There were five phases of data collection: 1)
identification of farms; 2) houses and; 3) neighborhood,
including environmental, characteristics for the property
value study was followed by: 4) discussions with industry
representatives and: 5) a literature search investigating

local expenditures.

4.3.1 Property Value Study

This part of the study was intended to be a worst case
scenario. This approach was used because community groups
campaigning against undesirable land uses expect the worst.
In order to incorporate this into the study, residential
properties surrounding farms with odor complaints were used.
In addition the farms in this study had received multiple
complaints registered with the Michigan Department of

Agriculture.

4.3.1.1 Identification of Farms: A total of 21 odor
related complaints were registered for hog operations in the
first nine months of 1989. Fourteen farms were selected
using the criteria below. Eight of the fourteen were used
in the final study. The other six were deleted because of
insufficient data and/or lack of access to the data.

Farm selection was based on the following criteria:

1. Multiple complaints: Only farms with multiple

58

complaints against them were considered in this study. This
included farms receiving more than one complaint in 1989 or
several complaints over the three year period, 1986-1989
with at least one complaint in 1989. This criteria was
designed to exclude situations involving neighborhood feuds,
minor nuisances and/or individuals who complained in order
to prevent the future expansion of an operation.

2. Availability of residential sales. Due to both PA
116 and poor recording procedures, several cases were
removed because of insufficient residential sales or
incomplete parcel card reporting.

3. Information on size of operation. When farm size
information was not reported on the complaint forms, the

farm was excluded from the survey.

4.3.1.2 Identification of Houses SurrOunding Farms:
Residential properties were selected based upon several
criteria. First, odor complaints are based upon concern for
quality of life. Valuation of changes in quality of life
can best be determined in areas where people live. Commer-
cial or industrial properties do not function primarily as
consumers of quality of life factors. Finally, in the case
of agricultural properties, where both residences and
production facilities coexist, several problems exist.
First, relatively few farm sales take place as compared to

residential sales. Second, valuation of a production unit

59
is different than a residential unit. Including farm sales
that have both might confound the results.

In order to insure that standard or "good" sales were
used, "sales studies" provided by county equalization
offices were used to identify recently sold houses and their
prices. Houses on these studies are researched by the
equalization office for use in their own studies to
determine whether: personal property was included in the
sale price, it was amongst family members and whether

creative financing was used which could affect sale price.

4.3.1.3 Housing Characteristics: Structural characteris-
tics of all buildings in townships are recorded on parcel
cards. Maintaining these is the responsibility of the
township supervisor. Once particular houses were identified
from sales studies and their parcel numbers were recorded,
trips to township offices were made to record the house

characteristics used in this study.

4.3.1.4 Neighborhood Characteristics: Two locational
variables were used in this study. Distance to the central
business district was measured from the sale house to the
nearest town with over 2500 people. In addition distance to
'the nearest highway from the house was recorded. Plat maps
were used to make these measurements. The hypothesis is

‘that proximity to a city or major highway allows for ease of

60
access for rural residents who are likely to have urban
jobs. Hedonic studies of urban areas support this claim as

well.

4.3.1.5 Environmental Characteristics: Three environmental
proxies were used to examine hog odors. These variables
measure intensity and direction of odors. The variables are
the number of hogs, wind direction and distance to the hog

operation from the sale house.

4.4 Variables Selected for Property Value Study
Hedonic studies using property values to determine
implicit prices utilize a wide range and number of vari-
ables. Freeman suggests that while house and neighborhood
categories are almost always selected, the actual choice of
variables within this grouping appears to be "haphazard".
This recognizes the restrictions imposed by data availabil-
ity. Some of the property and neighborhood variables used

in previous studies are listed below.

61

WM '000 ac '0
Square footage Distances to school, business
Number of rooms areas and highways.

Type of construction Income levels

Year of Construction Tax rates

Heating system Area Amenities

Number of baths Education level

Fireplace Crime rate

Classification Zoning

Roof quality Population Density
Improvements

4.4.1 Dependent Variables: Property Sale Price

The dependent variable is the sale price of the house.
Three years of sales were used in this study in order to
obtain a large enough sample size. Use of the state
equalized value (SEV) of the house prior to selling,
normally 50% of the value of the house, was considered for
the dependent variable, but due to variations in assessing
methods across townships and within and between counties,
sale prices were considered more consistent. It was
interesting to find that even though equalization ratios81
of individual plots varied between 30% and 70% of the sale
price, when regressed against sale price, the coefficient of
the independent variable, equalized values, was 49%, very
close to the 50% state requirement.

It is hypothesized that property values are influenced

by certain independent variables. Data on 19 independent

 

81State equalized valuation divided by sale price.

62
variables were collected for this study. Descriptions of

these variables are below.

4.4.2 Month of Sale

The month of sale, from 1-12, is included to capture
the potential for lower sale prices in months associated
with potent odors. Early spring and fall are the most

common times for cleaning out lagoons and spreading manure.

4.4.3 Year of Sale

Three years of sales are recorded, March of 1986
through March of 1989. This should capture changes in
interest rates and inflation. Expected sign of the

coefficient is positive.

4.4.4 Road
This is a dummy variable for road surfaces. "One" =
paved. "Zero" = unpaved. Expected sign of the coefficient

is positive.

4.4.5 Age of House
Assessors depreciate houses year by year up to 35
years. This variable lists the age of the house in years.

Expected sign of the coefficient is negative.

63

4.4.6 Plot

A large number of residential plots are vacant in rural
areas. In order to include these plots in the study without
having large numbers of observations with missing data, this
variable is added allowing "zeros" to be entered on the
remaining housing variables. "Ones" were recorded when
there was just a plot of land with no building present.

Expected sign of the coefficient is negative.

4.4.7 Brick or Stone Exterior

A dummy variable is designed to distinguish between
brick and other types of houses. Any stone or brick on the
house was recorded as a "one". Expected sign of the coeffi-

cient is positive.

4.4.8 Air Conditioning
A dummy variable is included for air conditioning. A
"one" was recorded when it is present. Expected sign of the

coefficient is positive.

4.4.9 Number of Baths
Quantity of full bathrooms is a variable. A full
bathroom included a shower or bathtub, toilet and sink. Ex-

jpected sign of the coefficient is positive.

64
4.4.10 Number of Fireplaces
Quantity of fireplaces is the variable. Expected sign

of the coefficient is positive.

4.4.11 Garage
If a garage is present, attached or otherwise, it is
recorded as a "one". Expected sign of the coefficient is

positive.

4.4.12 Class Greater Than or Equal to "CB"

The state assessing system classifies houses by ranking
them A through D. The classes reflect quality levels. A is
an architecturally designed house while D is a below average
house. C is average. All houses CB or better are recorded

as a "one". Expected sign of the coefficient is positive.

4.4.13 Square Footage of House
The base footage of the house, not including porches,
garages or terraces is recorded in square feet. Expected

sign of the coefficient is positive.

4.4.14 Mobil Home

A dummy variable is included for a mobile home that
sold with the property. "One" was recorded when a mobile
lhome was present. Expected sign of the coefficient is

negative .

65
4.4.15 Distance to Central Business District
Distance in one fifth mile segments is measured from
the house to the nearest town over 2500 people. Expected

sign of the coefficient is negative.

4.4.16 Distance to Highway
Distance in one fifth mile segments is measured from
the house to the nearest four lane highway. Expected sign

of the coefficient is negative.

4.4.17 Distance to Farm
Distance from the house to the hog operation is
measured in fifth mile segments. Expected sign of the

coefficient is positive.

4.4.18 Wind Direction

Wind direction is recorded to include any climate
affects. A polar, 180 degree graph was used to assign
measurements to houses in relation to the farm. Zero is
directly upwind and 180 is directly downwind. Expected sign
of the coefficient is negative. Prevailing wind directions
were obtained at the Michigan Weather Service. Appendix 2

shows the map used for this study.

66

4.4.19 Wind x Distance Interaction

A wind and distance interaction variable is created to
balance confounding distance measurements. This is included
because there may be a relationship between the wind and
distance variables. As distance from the operation changes,
this should affect the dependent variable. However, this is
dependent upon wind direction. For example, an upwind
property, very close to the operation, may experience no

impact. Expected sign of the coefficient is positive.

4.4.20 Animal Numbers
The number of animals, as recorded on complaint forms
is recorded for each operation. Expected sign of the

coefficient is negative.

4.5 Model Specification

PV = c + MON + er + ROADX + YEARX4 + ACREXS + pLo'rx6+ +
BRICKX7 + AI THX + IREX + GARX + CLASSX
FEETX + MOBI + c133):15 + HWAYQIG + DI§Tx17 + WIN0§1+8 +
INTE 19 + ANIMALit20

PV = Property sale value in nominal dollars
C = Constant

MON = Month of year house sold

Yr = Year house sold, 1986-1989

ROAD = Dummy, 1=paved road, 0=unpaved
YEAR = Age of house in years

ACRE = Acreage of plot, in acres

PLOT = Dummy, 1=just plot, 0=house

BRICK = Dummy, 1=brick or stone, 0=other

AIR = Dummy, 1=air conditioning, 0=none

BATH Number of baths

FIRE Number of fireplaces

GAR = Dummy, 1=garage, 0=no garage

CLASS = Dummy, =Class CB or better, O=less than CB
FEET = Square footage of main house

67
MOBIL a Dummy, 1=mobile home, 0=not mobile home
CBD - Distance from house to central business district
HWAY - Distance from house to highway
DIST 8 Distance from farm to house
WIND s A vector reflecting odor intensity when downwind.
The scale is 0-180. 0 = directly upwind, 180 = directly
downwind.

INTER = WIND x DIST
ANIMAL = Number of animals at farm.

4.6 Variables Selected for Input Analysis

Calculations gathered from reports on hog farm input
costs are integrated with expectations as to the degree of

local spending.

4.6.1 Hog Input Costs

Backward linkages associated with raising pigs and hogs
from birth to slaughter weight of 230 pounds represents
expenditures of over $100 million dollars in Michiganaz.
Local communities benefit most from input purchases due to
the structure of the livestock industry. Outputs are sold
to slaughterer/processors which are more centrally located.

The amount spent to produce each hog goes towards feed,
feed supplements, feed processing, labor, power and
machinery, improvements, livestock expenses, land charges
and other miscellaneous items. The figures below show total

input expenses for hog operations under 200 and over 200

sows. The figures show that feed and feed supplement are

 

82Otto, Daniel, "Economic Importance of Michigan's Pork
IIndustry", Iowa State University, 1987.

68
the two largest input items. Although total input costs are
less for larger operations ($96.46 vs. $104.44), the

distribution of expenses is similar.

69

 

40

HOG OPERATION INPUT COSTS

OPERATIONS UNDER 200 SOVS

 

 

 

35'-

15r-

CDLLARS SPENT FER ANIMAL
T

10'-

 

 

 

 

Figure 4 Hog Operation Input Costs For Farms UNDER 200
Sows. Total input costs equal $104.44 per hog for this
size operation (Schwab 1985).

 

35

3O

15

COLLARS SPENT FER ANIMAL

‘IO

 

HOG OPERATION INPUT COSTS

OPERATIONS OVER 200 sows

 

 

 

eeeeeeeeeeeee
eeeeeeeeeeeee
eeeeee

   

 

 

III-INV-
' = " '9 "' LIVEST” M 0m

 

Figure 5 Hog Operation Input Costs For Operations OVER 200
Sows. Total input costs equal $96.46 per hog for this
size operation (Schwab 1985).

 

70
4.6.2 Local Spending By Livestock Operations

Benefits associated with local (township or neighboring
township) purchases by livestock operations are more
relevant to land use decisions than are state wide livestock
operation impacts. In part this is because land use
decisions are decided by township governments. Because
township ordinances are concerned with livestock densities,
this section looks at the effects of larger operations on
local input purchases.

As hog operations increase in size local spending per
hog is hypothesized to decline. The following figures are
based upon articles about hog farm purchasing practices and
discussions with hog industry participantss3. The figures
show the relationship between size of operation and
hypothesized local purchases. Eight categories of input
purchases will be discussed. These categories are feed,
feed supplements, labor, power and machinery, improvements,
livestock expenses, land charges and other miscellaneous
items. The categories are the same as those shown in
Figures 4 and 5.

While little data is available documenting the location
of purchases by livestock operations, purchasing practices
and production information can be used to estimate the

amount of local purchases. For example, the amount of own

 

83Various articles in PORK magazine, 1981-86 and
(discussions with Dr. Jerry Schwab and Dr. Andy Thullen of
Michigan State University.

71
grain grown by a hog operation may be a proxy for feed

purchases foregone.

4.6.2.1 Feed Purchases
Hog operations grow a substantial portion of their own
feed grain needs. However, Table IV shows that feed grain
purchases increase substantially as the size of the
84

operation increases .

Table IV Operator Grown Grain
—

FEED GRAIN NEEDS GROWN BY THE OPERATION

Size of Operation National Average
1,000-1,999 81%
2,000-2,999 69%
3,000-4,999 59%
5,000-9,999 45%

10,000-100,000 25%

In Figure 6, local feed grain purchases are
hypothesized to rise as operations increase in size.
Because top grain producing counties in Michigan are in the
southern portion of the state where the majority of hog
operations are located, most corn purchases will be local so
as to minimize transportation costs. At present consumption
of corn by hogs accounts for 11% of the corn produced in
Michigan. Total feed purchases for small and large

operations are $34.17 and $32.72 per head. Estimated local

 

84Rhodes, V.J., et al., "Basic Data on U.S. Mid/Large
Size Hog Operations 1986-87", AEWP 1987-17, 1987.

72
feed purchases range between $5 and $15 for operations 500-

5000 head in size.

 

LOCAL CORN PURCHASES

BY SIZE OF OPERATION
17

16
15
14
13
12
11

10

mmucnlo

DOLLARS SPENT PER ANIMAL LOCALLLY
A

row

 

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

NUMBER OF HOGS

 

 

 

Figure 6 Local Corn Purchases

4.6.2.2 Feed Supplement Expenditures

Figure 7 shows local feed supplement purchases
declining dramatically as operations grow. Feed supplement
is a major portion of total expenses. For large operations,
economies of size are obtained by purchasing truckloads from
regional, often out of state manufacturers instead of the
local elevator. Total supplement purchases for small and

Ilarge operations are $27.78 and $26.17 per head. Estimated

73
local supplement purchases range between $27 and $4 for

operations 500-5000 head in size.

 

LOCAL FEED SUPPLEMENT PURCHASES

BY SIZE OF OPERATION
30

 

28

25 N

24 \

22 T6\

20 \

1. \

.5 \

14 \

.2 \
.0 \

 

 

 

 

 

 

 

 

 

 

 

DOLLARS SPENT PER ANIMAL LOCALLY

 

 

U

 

 

2 l l l l l l l l l l l
0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

 

NWBER OF l-DGS

 

 

 

Figure 7 Feed Supplement Purchases in Local area.

4.6.2.3 Labor Expenditures

Labor expenses include, operator, family and hired
labor. The amount spent locally per hog declines slightly
with larger and larger operations as more specialized labor
is brought in. Farm family labor is used primarily on
smaller operations. Larger operations may have more staff,
in addition to expert management, but it is likely that

these individuals will live in the vicinity of the operation

74

 

LOCAL LABOR EXPENDITURES

BY SIZE OF OPERATION
14

 

 

13

1. MI.,
M \

 

 

 

 

 

 

DOLLARS SPENT PER ANIMAL LOCALLY
(D

 

\

S l l l l l l l l l l
500 1000 1500 2000 2500 3000 3500 4000 4500 5000

 

 

 

NUMBER OF HOGS

 

 

 

Figure 8 Local Labor Expenditures

as well. Total labor purchases for small and large
operations are $13.1 and $9.49 per head. Estimated local
labor expenditures as shown in Figure 8, range between $13

and $6 for operations 500-5000 head in size.

4.6.2.4 Power and Machinery Expenditures

Power and Machinery expenditures include repairs,
fuels, depreciation and interest. Repairs and depreciation
are the major categories. Most repairs will involve local
labor and in some cases local parts. Depreciation on

machinery is estimated to be over 50% local. Total power

75

 

POWER AND MACHINERY

BY SIZE OF OPERATION
15

 

14

 

13

 

12

 

11

 

10

 

 

 

 

 

 

 

 

DOLLARS SPENT PER ANIMAL LOCALLY

 

 

 

 

 

0 144* 1 1 I l l l 1 l l
500 1000 1500 2000 2500 3000 3500 4000 4500 5000

 

NUMBER OF HOGS

 

 

 

Figure 9 Local Power and Machinery Expenditures

and machinery purchases for small and large operations are
$7.98 and $6.18 per head. Estimated local power and
machinery purchases as shown in Figure 9, range between $6
and $5 for operations 500-5000 head in size.
4.6.2.5 Improvements

Improvements include repairs, insurance, depreciation
and interest. Total improvement purchases for small and
large operations are $8.74 and $8.64 per head. Estimated
local improvement purchases as shown in Figure 10, are $8

for operations 500-5000 head in size.

76

 

IMPROVEMENTS

BY SIZE OF OPERATION
15

 

 

14

13

 

12

 

M

 

10

 

 

 

c
m
CH
m
m

5
o

 

 

 

 

DOLLARS SPENT PER ANIMAL LOCALLY

 

 

 

 

 

 

0 l l l l l l l l l l
500 1000 1500 2000 2500 3000 3500 4000 4500 5000

NUMBER OF HOGS

 

 

 

Figure 10 Local Improvement Expenditures

4.6.2.6 Livestock Expenditures

Livestock expenditures include semen and breeding,
veterinary care, marketing, livestock supplies, other and
interest expenses. Many medicines and drugs are produced
outside the area while interest on loans will be paid
outside the area as well. Total livestock expenditures for
small and large operations are $7.56 and $8.57 per head.
Estimated local livestock expense purchases as shown in
Figure 11, range between slightly less than $4 and slightly

more than $4 for operations 500-5000 head in size.

77

 

LIVESTOCK EXPENSES

BY SIZE OF OPERATION

DOLLARS SPENT PER ANIMAL LOCALLY

 

500 1000 1500 2000 2500 3000 3500 4000 4500 5000

NUMBER OF LOGS

 

 

 

Figure 11 Local Livestock Expenditures

4.6.2.7 Land Charges

Land charges are primarily taxes which are returned to
the local area. Total land charges for small and large
operations are $.82 per head. Estimated local land
expenditures as shown in Figure 12, are $.78 for operations

500-5000 head in size.

78

 

LAND CHARGE

BY SIZE OF OPERATION
15

 

M

 

13

 

12

 

 

11

10

 

 

 

 

 

 

 

DOLLARS SPENT PER ANIMAL LOCALLY

 

 

 

 

 

 

E} B 3 E} 8* {3 8 G G D
0 1 1 1 1 l 1 1 1 1 l
500 1000 1500 2000 2500 3000 3500 4000 4500 5000

 

NUMBER OF HOGS

 

 

 

Figure 12 Local Land Charges

4.6.2.8 Other Expenses

Other local expenses include utilities and
miscellaneous items. Most all of these are local expenses.
Total other expenditures for small and large operations are

$4.23 and $3.81 per head. Estimated local "other" expense

79

 

OTHER

BY SIZE OF OPERATION

DOLLARS SPENT PER ANIMAL LOCALLY

 

500 1000 1500 2000 2500 3000 3500 4000 4500 5000

NUMBER OF HOGS

 

 

 

rigure 13 other Local Expenditures.

purchases as shown in Figure 13, range between slightly more

than $3 and $3 for operations SOD-5000 head in size85.

4.6.2.9 Total Local Expenditures
As operations increase in size, local expenditures
steadily decline. This is especially true in the feed

supplement category as economies of size make it profitable

 

85These eight sections derived "local" purchases by
multiplying the total cost by a percentage of purchases that
were made locally. The 500 and 5000 head operation
percentages are corn,14 & 47: supplement,98 & 15: labor,98 &
60; power,80 & 80; improvement,90 & 90; livestock,50 & 50;
land,95 & 95; other,80 & 80.

80

 

TOTAL LOCAL EXPENDITURES

By SIZG of operation
70

 

68

66 B—_—_—*3\\\\\f}

64 \6\

52 \

50 ‘\\\\fiKL

s. \

56 \

s. \

.2 \

so \

4. \
4s \

44 l l l l l l l l l l
500 1000 1500 2000 2500 3000 3500 4000 4500 5000

 

 

 

 

 

 

 

 

 

 

DOLLARS SPENT PER ANIMAL LOCALLY

 

 

 

 

 

Number of Hogs

 

 

 

Figure 14 Estimated Total Local Expenditures Per Hog By
Size of Operation.

to buy entire truck loads direct from manufacturers not
located in the local area. Figure 14 shows estimated local
expenditures to vary between $67 per hog for a 500 head
operation, and $46 per hog for a 5000 head operation.
Because of limited data, the findings of this thesis
will include a sensitivity analysis of local hog input
purchases at a lower and higher level than that estimated

above.

81

4.7 Comparing Benefits and Costs

In the following chapter benefits and costs are
compared. Property tax receipt impacts86 generated by the
hedonic model will be determined. Property tax receipts are
a percentage of the dependent variable, property value.
Declines in property tax receipts are considered costs.

Local expenditures by hog operations are considered
benefits. Benefit/cost ratios will be computed using two

sizes of hog operations.

4.7.1 Scenarios

In Chapter V two scenarios are presented examining 500
and 5000 head hog farms. Benefits from local hog operation
input purchases will be divided by costs measured as
declines in property values.

Costs per property will be multiplied by property
density in the affected region to generate total cost
calculations. In order to compute local government impacts,
the total cost calculation will be multiplied by an average

township tax rate.

4.3 summary

This chapter evaluated methods used for environmental
benefit/cost analysis. The method used in this study

combines regression analysis findings with local spending

practices. The next chapter presents the research results.

 

86The change in SEV is due to hog operations.

CHAPTER V

RESEARCH RESULTS

5.1 Findings

Hog operations benefit the communities surrounding them
by purchasing inputs in the local area. Local purchases per
hog, however, decrease as an operation increases in size.
This chapter compares input purchases with the results of
the regression analysis outlined in Chapter IV. An
examination of the procedures used in determining benefits
and costs, the benefit/cost ratio, significance of the

findings, and a testing of hypotheses are below.

5.2 Livestock Operation Benefits
In 1986 hog operations in Michigan purchased over $153
million in inputs, employed 5,600 meat packing workers in
pork related activities and generated $292.7 million in

direct and indirect outputs87.

 

87Otto, Daniel, "Economic Importance of Michigan's Pork
Industry", Iowa State University, 1987.

82

83

Revenues from processing hogs and selling pork do not
usually benefit the community in which they are raised.
However, a portion of input purchases, in addition to
producer income, are returned to the communities where the
animals originate. Because of odor and other problems
associated with hog operations, hog producers make a
conscious effort to purchase inputs locally.
5.2.1 Input Purchases

In 1985 input costs to raise a hog from birth to 230
lbs. was $104.44 for operations with under 200 sows and
$96.46 for operations with over 200 sows88. The estimate
of local purchases as a percent of these input costs are 64%
and 46% respectively for a 500 and 5000 head operationsg.
Local expenditure estimates per hog are less for larger
operations as economies of management and size are attained.
The total dollar amount however is greater. Local
expenditures per hog are estimated at $67.09 for small
operations and $45.87 for larger operationsgo. Local

expenditures for small and large hog operations are shown in

Table V.

 

88Schwab, Gerald D., "Business Analysis Summary For
Swine Farms", Agricultural Economics Report, #485,
Department of Agricultural Economics, Michigan State
University, 1985.

89 See Chapter IV of this thesis.

90 See Chapter IV.

84

Table V Local Input Expenditures

Local Input Expenditures
By Size of Operation

# Of Head Expenditures

500 $33,545
5000 $229,357

5.2.2 Output Multiplier

Otto's paper, "The Economic Importance of Michigan's
Pork Industry", went beyond actual purchases in the hog
sector to include indirect affects. He used an output
multiplier to generate direct and indirect effects from all
sectors needed to produce the output of the hog
industrygl. This output multiplier can be used to
calculate the added impact of hog producer expenditures in
local areas92. Using the producer output multiplier of

1.5, the additional revenues generated by small and large

operations, are shown in Table VI.

 

91Schmid, Allan, Benefit Cost Analysis: A Political
Economy_Apngggh, Westview Press, 1990.

92In a discussion with Dr. Otto, December 1989, it was
determined that the multiplier used was from the producer
level.

85

Table VI Local Input Expenditures with Multiplier

Local Input Expenditures with Multiplier
By Size of Operation

# of Head Expenditures

500 $50,317
5000 $344,035

These calculations include the income and employment
multipliers generated by producer spending on non-agricul-
tural products. Figures 15 and 16 show the local input

expenditures without and with the output multiplier.

 

LOCAL HOG INPUT EXPENDITURES

BY SIZE OF OPERATION
240

230
220
210
200
190
180
170
160
150
140
130
120
110
100
90
80
70
60
SO
40
30

LOCAL INPUT EXPENDITLRES IN 8
(Thousands)

 

500 1000 1500 2000 2500 3000 3500 4000 4500 5000

SIZE OF OPERATION IN 8 OF HEAD

 

 

 

Figure 15 Estimated Local Hog Input Expenditures in
thousands of dollars. For operations up to 5000 head.

In Figure 15 the slope of the expenditure line levels
off somewhat as local expenditures, per hog, decline. In

the Figure 16 the slope of the line is identical but the

86

levels of expenditure are greater due to the output

 

 

O
multiplier.
LOCAL EXPENDITURES WITH MULT I PL l ER
BY $125 or onemnou
360
340
320
300
"‘ 290
E
w 260
8:”
3 240
PM
68 220
2 C
3‘ 3 200
X 3
"’3 190
50-
U
‘21 160
.1 140
6
O 120
.J
100
so
so
40
500 1000 1500 2000 2500 3000 3500 4000 4500 soon
SIZE OF OPERATION IN 8 OF HEAD

 

 

 

Figure 16 Estimated local input expenditures with output
multiplier for operations up to 5000 head.

87
5.3 Livestock Operation Costs

An hedonic property value model is used to estimate
losses in property tax receipts at the township level due to
livestock operations. The regressions use property sale
price as a dependent variable and property, neighborhood and
environmental characteristics as independent variables.
Linear and log-linear functional forms are used. The
regression coefficients are used to calculate property value
changes. This section discusses how these calculations were

made.

5.3.1 Property Value Model

Four sets of regression equations are used in this
analysis. The first set includes two regressions using the
entire data set of 288 observations. The second set divides
the dataset into small and large operations and examines
each individually. In the third set, the data is sorted
according to distance from the farm. Three regressions are
then run for properties of close, medium and lengthy
distances from the hog operations. The fourth and final
regression set used a log-linear form with the entire
dataset to determine how the independent variables are
valued as a percent of property price. These four

regression sets are described in detail below.

88

5.3.1.1 Set #1. Eog Impact on Property Price: The first
regression in this set is used to generate the implicit cost
of an additional hog on property value. The coefficient of
the ANIMAL variable is the change in property value for
every additional hog.

Several variables were removed from the original model
specification93 due to lack of significance.
Specifically, these include month of sale, year of sale,
type of road, age of house, whether the land has a house on
it, air conditioning and property classification. The
coefficients and statistical measures for SET 1., are shown
in Table VII. Variables with significant t-tests at the 95%
level are bolded. The key for the variable names follows

the regression.

 

93 See Chapter IV.

89

Table VII Hog Impact Model
_

Regression #1. Eog Impact Model
288 Observations

PV = 5,505 + 297 acnzx1 + 17 rssrxz + 1,855 mucxx3 +
12,761 BATEX‘ + 10,409 rInzx5 + 9,746 snnxs -20,184
nosrnx, + 215 csnxa - 62 0151019 + 67 WINDXlo — 2.62
INTERX11 -.43 anrxanxlz

R2 = .65 Adj. R2 = .64 SER = 19,230 F-Statistic=
44.1

PV = Property sale value in nominal dollars

C = Constant

ACRE = Acreage of plot, in acres

BRICK = Dummy, 1=brick or stone, =other

BATH = Number of baths

FIRE = Number of fireplaces

GAR = Dummy, 1=garage, 0=no garage

FEET = Square footage of main house

MOBIL = Dummy, 1=mobile home, =not mobile home
CBD = Distance from house to central business
district

DIST = Distance from farm to house

WIND = A vector reflecting odor intensity when
downwind. The scale is 0-180. 0 = directly upwind,
180 = directly downwind.

INTER = WIND X DIST
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII-I-I-I-I-I-I-I-I-I-I-

Strengths *An R2 of .65 compares well with other cross-
sectional hedonic studies.
*The F-Statistic is significant at .01 level.
*The first eight variables are of the correct
sign and significant at the .05 level.
*The animal variable is significant at the .025
level.

Weaknesses *The standard error of the regression is 40%.
*Wind, distance and central business district
variables have unexpected signs.

90

The important explanatory variable in this model is
ANIMAL. The negative .43 coefficient means that for every
hog added to the area, value for each property in the entire
survey area94 declines 43 cents. Using this approach one
thousand hogs results in a drop of $430 in property value on
a single property.

Several other variables in the model are included for
explanatory purposes. DIST and WIND are variables
representing the distance from the property to the
operation, and the prevailing wind direction. They are
included because of their hypothesized importance in
relation to odor transfer.

The coefficient of the variable DIST is expected to be
positive. Property owners who live farther away should have
higher property values, ceteris paribus. The coefficient
for WIND is expected to be negative. Property owners
located directly downwind of a hog operation are expected to
have lower property values, ceteris paribus, than those
upwind. Properties directly downwind were assigned a value
of 180°, while those directly upwind were assigned 0°.

In this first regression both DIST and WIND had
unexpected coefficient signs. These unexpected signs and

insignificant t-statistics suggest that the relationship

 

94A five mile block surrounding the farm.

91

between property value, distance from the farm and wind

direction are not as important as earlier expected. The

regression is shown in Table VIII.

Table VIII Regression #1, Hog Impact Model

 

 

 

VARIABLE COEFFICIENT STD. ERROR T-STAT. Z-TAIL SIG.

C 5505.3277 5991.3188 0.9188841 0.358

ACRE 297.81103 167.10812 1.7821458 0.075

FEET 17.373255 3.7394666 4.6459179 0.000

BRICK 1855.3025 1117.8127 1.6597615 0.097

BATH 12761.338 2885.4824 4.4226012 0.000

FIRE 10409.632 2627.8144 3.9613268 0.000

GAR 9746.9120 3111.1214 3.1329256 0.002

MOBIL -20184.337 6025.8750 -3.3496110 0.001

CBD 215.34303 86.254317 2.4966058 0.013

DIST -62.703255 224.25020 -0.2796129 0.780

WIND 67.347925 65.370178 1.0302546 0.303

INTER -2.6285560 2.5191474 -1.0434308 0.297

ANIMAL -0.4322518 0.1923493 -2.2472237 0.025
R-squared 0.658532 Mean of dependent var 46506.55
Adjusted R-squared 0.643631 5.0. of dependent var 32214.51
S.E. of regression 19230.98 Sum of squared resid 1.02E+11
Durbin-Watson stat 1.951999 F-statistic 44.19547

Log likelihood -3242.915

 

In order to re-test these relationships, a second
regression included two additional variables, INTER and
WINDZ. INTER is an interaction variable created by
multiplying WIND times DIST. The combined effect of wind
direction and distance from the farm can thus be measured.
This variable is included because both wind direction and
distance from the farm can affect odor transfer. In
addition they can also affect the relation between odors and
property value loss. The expected sign of the coefficient
of INTER is negative. If the coefficient of INTER is

negative, the effect of being more downwind wind on property

92
price will decrease as distance increases or increase as
distance decreases.

WIND2 is the square of the wind variable. This
emphasizes locations downwind. Properties downwind of the
operation were assigned measures closer to 180°, while
upwind locations were nearer to 0°. A negative sign is
expected for this variable as property values should
decrease for properties more downwind of hog operations
because of odors. The regression is below in Table IX.
Variables with significant t-tests at the 95% level are
bolded.

Table I! Wind and Distance Model
_

FIGURE 21. Regression #2. Wind and Distance Impact
Model
288 Observations
PV = 5,129 + 298 AGREE; + 17 FEEsz + 1,868 BRICKX3 +
12,762 BATEX‘ + 10,443 FIREXS + 9,763 GARXG - 20,273
MOBILE, + 214 csnx8 - 64 DISTX9 + 81 WINDXlO
- 1 - -

.08 WINDZX 1 2.59 INTERx12 .42 ANIMAL112

R2 =.65 Adj. R2 =.64 SER =19,264 F-Statistic=40.6
_

Results of Regression #2 in Table X, show negative signs for
both variables. Neither variable however has a significant

t-test at the 95% level.

93

Table x Regression #2, WIND and DIST impact model.

 

 

 

VARIABLE COEFFICIENT STD. ERROR T-STAT. 2-TAIL SIG.

C 5129.0170 6330.4682 0.8102113 0.418

ACRE 298.67460 167.46584 1.7834956 0.075

FEET 17.384605 3.7465374 4.6401793 0.000

ERICK 1868.9946 1122.1723 1.6655149 0.096

BATH 12762.70? 2890.5681 4.4152938 0.000

FIRE 10443.255 2638.5753 3.9579143 0.000

GAR 9763.1389 3117.8035 3.1314158 0.002

MOBIL -20273.112 6055.1282 -3.3480896 0.001

CBD 214.28935 86.589721 2.4747666 0.013

DIST -64.407856 224.82972 -0.2864739 0.775

WIND 81.057826 98.322385 0.8244087 0.410

WINDZ -0.0855147 0.4574634 -0.1869323 0.852

INTER -2.5918655 2.5312007 -1.0239668 0.306

ANIMAL -0.4286842 0.1936305 -2.2139287 0.027
R-squared 0.658575 Mean of dependent var 46506.55
Adjusted R-squared 0.642376 S.D. of dependent var 32214.51
8.8. of regression 19264.81 Sum of squared resid 1.02E+11
Durbin-Watson stat 1.952058 F-statistic 40.65534

Log likelihood -3242.897

 

The dependent variable is PRICE.
observations is 288.

The number of

—

94

Regression #1 supports the hypotheses that hogs are
correlated with reduced property values. Regression #2
shows that no significant correlation exists between
property value and the variables INTER and DISTZ.
5.3.1.2 set #2. Large vs. Small Operations: As mentioned
earlier in this thesis, odor complaints about hogs involve
operations of all sizes. Many of the multiple complaint
cases involving litigation are for larger operations. The
eight cases in this thesis include three small operations,
under 150 head, and five larger operations, over 3000 head.
The regressions in Set #1 combine large and small operations
together. While livestock specialists argue that poor
management on any size of farm can cause environmental
damages, the next section will examine whether small and
large farm impacts on property value are the same.

Of the 288 observations, 67 were from small operations
and 221 were from larger operations. Using the same
variables as those used in Regression #1, Regressions #3 and
#4 shown in Tables XI - XVI were run for the small and large
operations. Variables with significant t-tests at the 95%

level are bolded.

95

Table XI Small Hog Operation Model

Regression #3. Small Hog Operations
67 Observations

PV = 7,071 + 466 ACREx1 + 31 rssrxz + 12,778 BRICKX3 +
5,140 BATHX4 - 20 FIREXS + 3,083 GARx6 + 317 CBDX7 -
179 DISTX8 + 2 WINDX9 - .8 INTERXIO - .55 ANIMALX11

R2 =.66 Adj. R2 =.59 SER =19,458 F-Statistic =9.9

96

Table XII Regression #3, Small Hog Operation Model.

 

 

 

VARIABLE COEFFICIENT STD. ERROR T-STAT. 2-TAIL SIG.

C 7071.4985 37995.318 0.1861150 0.853

ACRE 466.41338 613.25547 0.7605532 0.450

FEET 31.163358 9.6411987 3.2323115 0.002

BRICK 12778.035 8496.0899 1.5039901 0.138

BATH 5140.5256 7603.0097 0.6761172 0.502

FIRE -20.5694l8 6400.1497 -0.0032139 0.997

GAR 3083.6083 6848.1847 0.4502811 0.654

CBD 317.70723 180.97921 1.7554902 0.085

DIST -179.72210 1337.6046 -0.1343612 0.894

WIND 2.3588291 463.07368 0.0050939 0.996

ANIMAL -0.5582432 0.3639765 -l.5337342 0.131

INTER -0.8334508 16.509932 -0.0504818 0.960
R-squared 0.666568 Mean of dependent var 42733.78
Adjusted R-squared 0.599882 S.D. of dependent var 30761.84
S.E. of regression 19458.38 Sum of squared resid 2.08E+10
Durbin-Watson stat 1.632711 F-statistic 9.995563

Log likelihood -750.1515

 

The dependent variable is PRICE.
observations is 67.

The number of

97

Several significant changes occur when examining only
the small operation areas. First, ACRE, BATH, FIRE, GAR and~
CBD variables are no longer significant. This is surprising
because BATH, FIRE, GAR and CBD were robust variables that
had significant t-test levels in all of the other regres—
sions. Most important is the loss in significance of the
t-test for the ANIMAL variable. The t-test dropped just
below the 95% level significance level to -1.5. In addition
the ANIMAL variable coefficient was unexpectedly large. The
coefficient of .55 exceeds earlier estimates by .11. In
this case the coefficient was expected to drop. The large
operation regression is below. Variables with significant
t-tests at the 95% level are bolded.

Table XIII Large Hog Operation Model

Regression 4. Large Hog Operations
221 Observations

PV = 5,433 + 307 AGREE: + 14 FEETXZ + 1,451 BRICKXa +
13,322 BATEX4 + 12,752 FIREXS + 12,556 GARXS - 16,946
MOBILX7 + 205 CBDXB - .42 DISTX9 + 74 WINDXlo -3

INTERXll - .48 mxmxn

98

Table XIV Regression #4, Large Hog Operation Model.

 

 

 

VARIABLE COEFFICIENT STD. ERROR T-STAT. 2-TAIL SIG.

C 5433.9196 6314.1740 0.8605907 0.389

ACRE 307.49570 180.48823 1.7036883 0.088

FEET 14.071166 4.3462261 3.2375597 0.001

BRICK 1451.8969 1154.6557 1.2574284 0.209

BATH 13322.437 3310.4066 4.0244109 0.000

FIRE 12752.374 3099.9669 4.1137129 0.000

GAR 12556.568 3641.5930 3.4480976 0.001

MOBIL -16946.545 8041.7698 -2.1073154 0.035

CBD 174.7712? 103.69530 1.6854309 0.092

DIST -0.4267359 241.96962 -0.0017636 0.999

WIND 74.811294 68.013649 1.0999453 0.271

INTER -3.0794351 2.6939970 -1.l430730 0.253

ANIMAL -0.4859769 0.2372246 -2.0485938 0.041
R-squared 0.659417 Mean of dependent var 47650.33
Adjusted R-squared 0.639768 S.D. of dependent var 32623.43
S.E. of regression 19580.36 Sum of squared resid 7.97E+10
Durbin-Watson stat 1.892945 F-statistic 33.55984

Log likelihood -2490.871

 

The Dependent variable is PRICE.
observations is 221.

The number of

99

Results of the large operation model are similar to the
model with 288 observations presented in Regression #1.
Differences include a drop in t-test significance levels for
ACRE, FEET, BRICK, BATH, MOBIL, CBD, WIND and ANIMAL, a
larger standard error of the regression and a drop in the F-
Statistic. No coefficient signs changed. The ANIMAL
coefficient of this sample is also higher than the total

regression sample by .03.

5.3.1.3 Set #3. Distance Calculations: Based on the fact
that the majority of hog odor complaints come from neigh-
bors, the negative coefficient on the DIST variable is
confusing. In order to re-test the importance of distance
from the farm in determining damages, the 288 observations
were sorted by DIST in ascending order. This permitted
three regressions to be performed on the first, second and
third group of observations corresponding to progressively
greater distances from the farm.

Three regressions using the same variables as in
Regression #1 were run, excluding distance since the
observations were already sorted by distance. The first
regression included properties up to 1.6 miles away from the
farm, the second regression included properties between 1.6
and 2.3 miles away and the last regression included

properties between 2.3 and 3.5 miles away.

100

The results confirmed the importance of distance in
measuring impacts on property values. The three regression
equations are below. The coefficient of the ANIMAL variable
in Regression #5 (close properties) is -1.74. This is
nearly four times the ANIMAL coefficient in Regression #1.
The coefficients for ANIMAL in Regressions #6 (medium
distance) and #7 (farthest), were -.53 and -.13,
respectively. The reduction of the coefficients suggests
that properties farther away from the hog operation are less
affected than those nearby. Of particular interest is the
progressive decrease in the ANIMAL coefficient significance
level as distance increases. The significance level of the
ANIMAL coefficient in Regression #5 is -2.7, #6 is -1.6, #7
is -.59. These steady declines in t-test levels are
evidence of the localized impacts of hog operations.
Variables with significant t-tests at the 95% level are

bolded. Regressions #5 - 7 are shown in Tables XV - xx.

101

Table XV Close Property Model
_

Regression #5. Close Properties
91 Observations

PV = 10,115 - 1,040 ACREXI + 25 FEETXz + 1,588 BRICKX3
+ 9,263 EATEX‘ + 14,276 FIREXS + 11,393 GARX6 - 6,097
MOEILX7 + 551 CEDXB - 69 WINDXQ - 1.7 ANIMALXIZ

R2 =.69 Adj. R2 =.65 SER =23,229 F-Statistic =17.8

Table XVI Intermediate Distance Model
—

Regression #6. Intermediate Distance Properties
96 Observations

PV = 7,467 - 455 ACRExl + 20 FEEsz + 12,326 BRICKX3 +
5,432 BATHX4 + 6,133 FIREXS + 7,717 GARXS - 16,197
MOEILX7 + 278 CBDXB - 39 WINDX9 - .53 ANIMALXlz

R2 =.66 Adj. R2 =.62 SER =17,389 F-Statistic =16.5

102

Table XVII Distant Property Model

Regression #7. Distant Properties
101 Observations

PV = 7,968 - 529 ACREXI + 12 FEETXZ + 1,345 BRICKX3 +
16,130 EATEX4 + 9,268 FIREXS + 1,090 GARX‘ - 22,823
MOBILX7 + 36 CBDXB - 6.9 WINDX9 - .13 ANIMALXIZ

R2 =.74 Adj. R2 =.71 SER =15,116 F-Statistic =26.7

Table XVIII Regression #5, Close Property Model.

103

 

 

 

VARIABLE COEFFICIENT STD. ERROR T-STAT. 2-TAIL SIG.

C 168.44071 7076.7234 0.0238021 0.981

ACRE -1040.2219 540.49163 -1.9245848 0.058

FEET 25.644116 7.9784435 3.2141753 0.002

BRICK 1588.2631 9141.4121 0.1737437 0.863

BATH 9263.3730 5383.2678 1.7207713 0.090

FIRE 14276.892 6413.6231 2.2260261 0.029

GAR 11393.100 6804.2531 1.6744086 0.099

MOBIL -6097.7873 18386.600 -0.3316430 0.741

CBD 551.24463 229.88759 2.3978877 0.019

WIND 69.470317 60.029378 1.1572720 0.251

ANIMAL -1.7457668 0.6289852 -2.7755293 0.007
R-squared 0.691015 Mean of dependent var 47363.15
Adjusted R-squared 0.652391 S.D. of dependent var 39399.13
8.8. of regression 23229.07 Sum of squared resid 4.328+10
Durbin-Watson stat 1.927806 F-statistic 17.89119

Log likelihood -1038.099

 

The dependent variable is PRICE.
observations is 91.

The number of

—

104

Table XIX Regression #6, Medium Distance Property Model.
—

 

 

 

VARIABLE COEFFICIENT STD. ERROR T-STAT. 2-TAIL SIG.

C 7467.8171 5794.3811 1.2888032 0.202

ACRE 455.81950 194.35564 2.3452858 0.022

FEET 20.727822 6.5947381 3.1430850 0.002

BRICK 12326.464 5791.1098 2.1285150 0.037

BATH 5432.8119 5057.0837 1.0742974 0.286

FIRE 6133.2720 4235.9860 1.4478971 0.152

GAR 7717.4892 4896.6610 1.5760718 0.119

MOBIL -16l97.565 8616.7126 -l.8797847 0.064

CBD 278.75874 168.21429 1.6571644 0.102

WIND -39.486028 49.788374 -0.7930773 0.430

ANIMAL -0.5334500 0.3286039 -1.6233831 0.109
R-squared 0.660743 Mean of dependent var 44206.56
Adjusted R-squared 0.620831 S.D. of dependent var 28240.55
S.E. of regression 17389.61 Sum of squared resid 2.57E+10
Durbin-Watson stat 2.240214 F-statistic 16.55476

Log likelihood -1067.685

 

The dependent variable is PRICE.

observations is 96.

The number of

105

Table XX Regression #7, Distant Property Model.

 

 

 

VARIABLE COEFFICIENT STD. ERROR T-STAT. 2-TAIL SIG.

C 7968.0359 5190.3444 1.5351652 0.129

ACRE 529.18792 283.02653 1.8697467 0.065

FEET 12.674263 5.3038512 2.3896340 0.019

BRICK 1345.8306 922.43371 1.4589998 0.149

BATH 16130.486 4535.8226 3.5562427 0.001

FIRE 9268.2839 3613.7263 2.5647443 0.012

GAR 10920.632 4557.1711 2.3963621 0.019

MOBIL -22823.l96 7543.9374 -3.0253692 0.003

CBD 36.303783 104.26429 0.3481900 0.729

WIND 6.9677968 33.700055 0.2067592 0.837

ANIMAL -0.1382784 0.2317851 -0.5965804 0.553
R-squared 0.747942 Mean of dependent var 47920.87
Adjusted R-squared 0.719935 S.D. of dependent var 28564.54
S.E. of regression 15116.68 Sum of squared resid 2.06E+10
Durbin-Watson stat 1.598483 F-statistic 26.70603

Log likelihood -1109.469

 

5.3.1.4 set #4. Log-Linear Form: In Regression #1, the

coefficient on ANIMAL was -.43. For every additional hog,
property values decline 43 cents. This assumes that impacts
are constant across all properties regardless of the value

of the property. Figure 3. in Chapter II however, suggests

that as income increases so does willingness to pay. This

106
means that people with lower property values would discount
property near a hog farm less (in dollar amounts) than would
people with greater property values.

The log-linear form allows for the property, neighbor-
hood and environmental characteristics to be valued as a
percent of property price. While the percent is fixed,
losses vary depending upon the value of the property. In
the log-linear Regression #8 in Table XXI, the variables
significant in Regression #1 stay significant. The only
change is a slight drop in the adjusted R2.

The coefficient for the ANIMAL variable is
-.0000171. This is a .00171% percent impact of an addition-

al hog on a property.

107

Table XXI Regression #8, Log-Linear Hog Farm Impact Model.
—

 

 

 

VARIABLE COEFFICIENT STD. ERROR T-STAT. 2-TAIL SIG.

C 8.9075200 0.2094069 42.536908 0.000

ACRE 0.0118804 0.0058407 2.0340688 0.042

FEET 0.0008264 0.0001307 6.3226835 0.000

BRICK 0.0508212 0.0390695 1.3007905 0.193

BATH 0.3538208 0.1008526 3.5082975 0.000

FIRE 0.1010718 0.0918466 1.1004410 0.271

GAR 0.3395627 0.1087390 3.1227310 0.002

MOBIL -0.4663255 0.2106146 -2.2141169 0.027

CBD 0.0059982 0.0030147 1.9896169 0.047

WIND 0.0010349 0.0022848 0.4529537 0.651

DIST 0.0007884 0.0078379 0.1005914 0.920

INTER -5.359E-05 8.805E-05 -0.6086654 0.543

ANIMAL -l.710E-05 6.723E-06 -2.5435662 0.011
R-squared 0.634597 Mean of dependent var 10.35054
Adjusted R-squared 0.618652 S.D. of dependent var 1.088452
S.E. of regression 0.672156 Sum of squared resid 124.2431
Durbin-Watson stat 1.917618 F-statistic 39.79945

Log likelihood -287.5906

 

The dependent variable is LOPRIC.
observations is 288.

The number of

108

5.3.2 Local Impact

Community impacts from declines in property values vary.
Property owners who sell properties may experience a
reduction in the amount they receive. For an individual
this is a one time loss. Local governments, however, rely
heavily upon property values to support services and other
functions. The next section determines the loss due to a

hog operation on township tax receipts.

5.3.2.1 Calculating Costs: In order to determine property
tax losses, residential SEVs95 in a township are
multiplied by the log-linear ANIMAL coefficient, .0000171.
In this case the residential SEV for the township is divided
by the number of sections in the township and multiplied by
25 sections, the survey area in this study. This averages
the residential SEV for each section based on the total
residential SEV in the township.

For example, the impact of a hog operation with 500 hogs
in a 36 section township with a 20 million dollar SEV would
be calculated as follows.

$29,990,909 X 25 sections X 500 hogs X -.0000171
36 sections

This produces a loss in residential SEV of $118,750. This
must then be multiplied by the township tax rate, 50 mills.

The resultant impact is $5,937 in lost revenues annually for

 

95Chapter 3 explains why residential properties were
chosen.

109
the local government. A 5000 head operation in this same
township would cause an annual loss in tax revenue of
$59,375.

While these losses would occur annually, it would take
several years for the full losses to affect equalized
valuations. The pecuniary losses to the property owner are
presented in the 500 animal case above but are not used in
the benefit/cost calculations. The pecuniary losses would
occur when a property owner sells the property. This study
focuses upon annual costs as experienced by the township.
5.3.2.2 Township Cost Sharing: Because livestock opera-
tions do not always position their operations in the middle
of a township, costs may be shared by as many as four
townships near the operation. This reduces the costs an

individual township must bear.

5.4 Livestock Operation Benefit/Cost Ratio

Table XXII summarizes the findings above by presenting
the benefit/cost ratios of small and large hog operations.
Calculations are for a 36 section township with an SEV of
$20 million and tax rate of 50 mills.

The benefit/cost ratio means (in the case of a 500 head
operation) that for every five dollars and sixty four cents
in community benefits, one dollar of lost property tax
receipts is incurred. The net dollar effect is the benefits

minus costs.

110

Table XXII Benefit Cost Ratios
—

BENEFIT/COST RATIO FOR SMALL AND LARGE OPERATIONS

SIZE OF PROPERTY TAX BEN/COST NET 8
OPERATION BENEFITS LOSS RATIO EFFECT

500 $33,545 $5,937 5.64 $27,607
5000 $229,347 $59,375 3.86 $169,982

Costs are determined by the number of animals and the
amount of real property in the township. In Table XXIII
Otto's output multiplier is used to examine the indirect
effects of an operation on input purchases.

Table XXIII Benefit Cost Ratio with Multiplier
—

BENEFIT/COST RATIO FOR SMALL AND LARGE OPERATIONS
USING AN OUTPUT MULTIPLIER OF 1.5

SIZE OF PROPERTY TAX BEN/COST NET 8
OPERATION BENEFITS LOSS RATIO EFFECT

500 $50,317 $5,937 8.47 $44,380
5000 $344,020 $59,375 5.79 $284,645

In order to see how sensitive the ratio is to declines
in local input purchases by hog farmers, table XXIV examines
what would happen if local input purchases dropped to 30
percent of total input purchases. When input purchases
decline to 30% the benefit/cost ratio declines but remains
positive.

The point where benefits equal costs (where the
benefit/cost ratio = 1) occurs when property values in a

township are high enough so that property tax losses surpass

111

Table XXIV Benefit Cost Ratio with Low Local Purchases
—

BENEFIT/COST RATIO ASSUMING 30% LOCAL PURCHASES

SIZE OF PROPERTY TAX BEN/COST NET $
OPERATION BENEFITS LOSS RATIO EFFECT

 

500 $15,666 $5,937 2.63 $9,728
5000 $144,693 $59,375 2.43 $85,318

local input purchases. Table XXV shows this occurring at
SEV levels far above what would typically be found in an
agriculturally oriented township. Calculations are for a 36
section township with an SEV of $112,000,000 and $77,000,000
and a tax rate of 50 mills.

Table XXV Benefit Cost Breakeven Levels
—

BENEFIT/COST BREAKEVEN LEVELS

 

TAX LOSS
SIZE OF EQUALS BEN/COST NET $
OPERATION BENEFITS SEV RATIO EFFECT
500 $33,545 $112 MILLION 1 0
5000 $229,875 $77 MILLION 1 0

The decreasing benefit/cost ratio as residential SEVs
increase, supports locating hog operations in townships with
low SEVs.

5.5 Ratio Formulation

An important part of Benefit/Cost analysis is to have
common units to compare. Both benefits and costs to a local
community are calculated in dollar amounts. Benefits are

the total dollars spent on inputs locally. Costs are the

112
total dollars lost in property tax receipts. Benefits are
reflected in private consumption while costs are public
services foregone. Both are important considerations in
township decision making.

Alternate formulations of benefits might include:
regional or statewide benefits or hog operation contribu-
tions to local tax receipts. The first alternative gets
away from the local decision maker orientation stressed in
this thesis. The second alternative presents technical
problems, mainly data availability.

Alternate formulations of costs might include: declines
in property value (as opposed to property tax receipt
declines), or impacts on health or personal property.

96 and

Because very few parcels are sold in any one year
because people have the opportunity to move away from an
operation, property tax receipts better measure the annual
impact. Impacts on health or personal property are expected
to be reflected in lower property values, thus lower tax
receipts.
5.6 Significance of Findings
Estimates of benefits from local hog operation input

purchases described in table XXII exceed property tax

receipt costs by at least three and as many as five

 

96Between 1% and 4% of parcels are sold in rural areas
each year.

113

timesg7. As distance of houses from a hog operation

increase, costs decline98. Larger operations appear to be
more strongly correlated with property value costs than do
smaller ones.

Township planners, officials and community members make
deliberate decisions that affect the composition and success
of their community when establishing informal policies or
passing ordinances. These findings may aid a governmental

unit in locating a new operation, or justifying the presence

of an existing one.

5.7 Hypotheses Testing

Each one of the three hypotheses was affirmed. A brief
explanation of each is below.

Hypothesis 8 1: Hog operations have a negative impact
on surrounding property values. The negative coefficient on
the ANIMAL variable representing number of animals confirms
this hypothesis. Validating this outcome is the finding
that distance from the operation and number of animals also
affects property price.

Odors are assumed to be the cause of declining property
values. Lack of significance in the prevailing wind

variable suggests that prevailing winds are not as important

 

97In a township with 36 sections and an SEV of $20
million.

98Regressions #5-7.

114
as hypothesized or that odors may not be the primary cause
of lower property values surrounding hog operations.

Hypothesis 8 2: Hog operations provide revenues to
local economies. Between 64% and 46% of input spending for
hog operations is spent locally. Because hog operation
owners make a concerted effort to buy locally and support
the local economy when possible99, hog enterprises may
spend more locally than other types of enterprises. Even as
operations increase in size and local purchases per hog
decrease, local feed purchases, a substantial portion of
total input costs, increase with the size of operation.

This increase in local feed purchases helps balance
diminishing local supplement and labor purchases by larger
operations.

Hypothesis # 3: Local benefits of livestock operations
exceed local property tax receipt costs. In a township
with an SEV of $20,000,000 the benefit/cost ratios for a 500
head operation range between 2.63 and 8.47. The
benefit/cost ratio for a 5000 head operation ranges between
2.43 and 5.79.

5.9 Summary

This chapter presents the results of the procedures

described in Chapter III. An individual examination of both

benefits and costs preceded the benefit/cost ratio section.

 

99Conversation with Dr. Andy Thullen, Dept. of Animal
Science, Michigan State University.

115
The significance of the findings and hypotheses testing
followed. The final chapter comments on the study's
strengths and weaknesses, presents future research needs and

conclusions.

CHAPTER VI

CONCLUSION

This chapter presents the conclusions of this research,
its strengths and weaknesses, in addition to future research
needs.

6.1 Conclusions

The convergence of urban sprawl, growing environmental
concerns and the intensification of livestock operations has
the potential to create deep rifts between the agricultural
and non-agricultural residents within a rural community.
This thesis illustrates there are also economic
ramifications.

As decisions are made affecting land use, local
government tax receipts are affected. The purpose of this
thesis is to provide additional information to local
officials to aid in decision making. In addition the
information should be valuable to livestock operators when

considering expansion or a new location.

116

117
6.1.1 Summary of Findings

This thesis examined local input purchases and property
tax losses from hog operations in Michigan. The study
investigated eight livestock operations which received odor
related complaints in the first 9 months of 1989.

Results indicate that the benefit/cost ratio of hog
operations is positive until SEV levels within a township
exceed $70,000,000. In addition the benefit/cost ratio
increases as state equalized valuations (SEV) decrease.

Potential damages are greater when SEVs are higher.

6.1.2 Distance and Size Impacts

Results also show that as distance from an operation
increased property value losses declined. This supports the
hypotheses that odors are a localized occurrence. The
research also shows that while all sizes of livestock
operations have a negative impact on property values, larger
operations have a greater impact on property values than do

smaller ones.

6.2 Regulatory Options
As the livestock sector continues to change, land use
decisions will become more important. Livestock groups in
the state of Michigan are preparing for a 1 million head
increase in hog production in the next decade. This will

come principally from an expansion of existing operations

118
and new "large" operations. Being aware of benefits and
costs allows local governments to make informed decisions on
whether to "invest" or not in livestock operations.

Local zoning and land use ordinances are particularly
crucial in minimizing potential conflicts between hog
farmers and their farm and non-farm neighbors. They may
prevent potential conflicts by limiting or minimizing the
contacts and interactions between these groups. As
restrictions increase however, ordinance effects on
communities should be examined. Information from this
thesis allows a township to examine the impacts of an
ordinance prohibiting operations over a certain size from
entering their community. It is the communities
responsibility to evaluate and act on these impacts. While
zoning can be used as a preventative measure for resolving

potential future conflicts.

6.3 Existing Conflict Solutions
Existing local conflicts pertaining to either operating

hog farms, operations desiring to expand, or to new
operations usually have to be resolved using other measures.
These may include:

1. Referring hog farmers to the Cooperative Extension

Service and/or the Soil Conservation Service for advice

and council in better management practices and

facilities.

2. Negotiate community support for hog operations in

exchange for appropriate manure handling technology
adoption by farmers.

119
3. Serve as a moderator and facilitator in getting hog
farm operators and complainants together to solve their
problems.

4. Help producers secure low interest loans for
adopting manure handling technology.

5. Application for the Air Pollution Control Tax
Exemption Certificate (Act no. 250, P.A. 1965) to aid
in financing pollution control equipment.

6. Referring the problems to the state level (Michigan
Department of Agriculture) for review and handling.

The resolution of manure management problems of hog
farmers will impact the vitality of both the Michigan hog
industry and of townships where they are located.

6.4 Thesis Strengths

This thesis used results from an input-output model of
the pork sector together with an hedonic pricing method to
determine the benefit/cost ratio of hog operations for local
units of government. This in itself is not unique. Using
the hedonic pricing method in a rural area is, however, a
strength. Most hedonic studies occur in urban areas with
sophisticated particle samplers. This study shows that
where these measurements are not available, proxies can be
used to determine environmental impacts.

A second strength of this thesis is its applicability
for local government officials. Changes in the type of
houses, tax base, size of operation and local input
expenditures can be entered into the hedonic model to

generate township specific results.

120
6.5 Thesis Weaknesses

Rural information collection is extremely time consuming
and difficult. Multiple visits to county and township
equalization and assessing offices revealed different
reporting procedures in addition to large amounts of missing
data. The result is limited observations in certain areas.
Better information is also needed on local purchases of hog
operations.

6.6 Future Research Needs

The following research needs should be explored.
1. An annual tabulation of livestock complaints in the
state. This would permit an early identification of
livestock complaints and their cause and origin. In
addition it would allow individuals involved with the
livestock sector to spot trends and react appropriately.
2. An examination of the political costs involved when
making decisions affecting land use controls.
3. A study of purchasing practices by hog farmers.
4. A study of the impacts of good management practices on

property value losses.

 

APPENDIX

Appendix A: Livestock Complaint Tabulation Procedures.

Livestock Complaint Tabulation

The Livestock Complaint Tabulation consisted of a review of
livestock complaints on file with the Michigan Department of
Agriculture (MDA). Information for the years 1986-1989 was
available. Up until 1988 complaints were handled by the
Department of Natural Resources (DNR). Since then MDA has
handled all environmental complaints involving livestock.
This transfer of authority should be considered when
considering the increase in complaints over the four year
period.

At present complaints come to the MDA from citizens, the
DNR, The Soil Conservation Service and other public agencies
including electrical companies and the local and state
police.

Methods

An attempt was made to determine 1) the region 2) the year
of the complaint 3) the animal species and 4) the type of
complaint made.

1. The MDA regional breakdown was used to indicate where
complaints were made. MDA breaks the state into seven
regions.

2. The year is taken from the most recent complaint. In
some cases complaints for the same livestock operation were
made each of the four years. The most recent one was used to
date the case.

3. The animals involved were listed as dairy, cattle, hogs,
poultry (including turkeys) and other. In the "other"
category, horses made up the bulk of complaints. Where more
than one animal was raised on a farm, an attempt was made to
determine which animal was in question. This was possible
in most cases. One farm however, with over 10,000 hens and
3000 cattle was listed twice.

4. The type of complaint made was categorized as odor,
water or flies. Some liberty was taken to fit complaints
into the two major categories of odor and water. For
example, a frequent complaint was un—disced manure spread on
a field. If the spreading was not next to a stream or
ditch, this was considered to be an odor complaint.

Gathering information on two other sections was
attempted but not very successful. The first was location
of the complaint, whether it was in the field or the farm

122

lot. Any complaint involving manure that had been or was in
the process of mechanical movement, such as spillage on a
road, seepage into a ditch after spreading or odors from un-
disced manure was considered field. Complaints based around
the farm operation, lagoon or storage facility were
considered lot. 55 lot, 43 field and 17 unknown complaints
were recorded.

The second unsuccessful area was number of animals.
Inconsistent and insufficient data on this prevents any

discussion.

123

 

Appendix B: U.S. Weather Bureau Climatic Summary of The U.S.

 

U.S. Weathe:_suresu
Climatic Summary of the 0.8.
Washington: Government Printing Office

1932 - 1948
Supplement for 1931 - 1952

Science Library QC 983 .A35
*‘ * """'""‘.'X"

 

 

 

 

I
1 I '7
“\J 1-:~' i- ‘\ ‘
‘0." ' 9' I“ -‘~ : 77'" -~...‘;’.‘.’ I
.k I _’—:O l. I (7‘ ‘» it I
'9“ 1 N1)” \Québww !
e ' Q (3‘ 0. ' /Y-"?"“:)
! 7’ '3‘ LJ ‘2 Mi; .‘\ .
( ' '
L? ,f/ y x; . 1...} I
1 j, ' -‘ \ N o ;
‘3 Q (‘4 (in. r '7 _“ ' ::"\ I
a / 1‘ I‘ ...... I o . @N
J". )5 I O-o- , '
I I ‘. nr~nr-- ---—-
05-. cu ... : 2 . ...
“.73 m, 3.3.1: ’- °" 1
m? sac-mt: . , “" “'6’- o-'-' I
1113:1011. mama 2mm DIPSIJTION a” . .’ 0‘ 1 . .4»
8.1520 0:111; 57171011 RECORDS to 1930. Inc ft... m: . .. . . ‘ . . f’
(1 I '0’ ' I ' - I I
Average period for all stations 31.? m‘. ' °__ . ° 6" _-° ' ("J’- ‘\\
Shortest per‘od of observations 10 psi/M: ' :.. - / . 1 ~_..»_ .7 3 .’ V' 5 .
Lent-est period of observations 60. yes!" ° , / I. . l ' J ,". .- ’ . E
(The information shown on this chart 1_ ' ° ' °- ° " "' '° 2 '
was obtained from the 'Climatic SW for "-3 ‘2' o/,’- ' J "a" " '
the United States'. for Michigan) \ ~ -,- ° / - o I
0— ~ "‘ O - - “I I
I. .. \ _ 1' o ’ _ . ‘ o / . !
‘, Q — o / a o - f a —- a' :
tn. , . / v' i
'i"“ 0’ i <0 4' l l’ I
‘9, o .. :1.— ° ° :2 35.7 I
. ~d( ‘ l
o .— ° ' / ‘42,?" 1 2 , I
. 0 0° 0 '
3 3.3. Weather Bureau .1. - , . y. - . ' i
, .' - ;. '/ ~,- 1
0 Observation Station ‘_ o\ ’ a ° .° 0° I
A. -- .. _ _ o’_ 3
I
.' - 1
4.101111. 1
I

PWAIIIIB H310

01:75.02 . --' ,

 

124

Appendix C: Graph for Wind Plotting

 

125

Appendix D:

 

Data Used in Regression Analysis

 

PRICE MON.Yr. Road ACRE YEAR PLOT BRICK AIR BATH FIRE GAR
59900 10 87 1 3.5 29 0 0 0 1 1 1
95000 5 88 1 0.1 0 0 0 1 1 1
59000 8 88 1 0.1 74 0 0 0 1 0 0
74000 4 88 1 31 0 0 0 1 0 1

122500 9 88 l 13 0 0 0 1.5 0 1
59400 9 86 1 1.5 51 0 0 0 1 0 1
135000 12 88 1 0.4 37 0 0 0 2 1 1
10000 5 88 1 10. 1 0 0 0 0 0
45900 9 87 1 0.4 49 0 0 0 1 0 0
12500 6 88 1 10. 1 0 0 0 3 0 1
130000 6 86 1 23 0 0 0 2 2 1
135000 9 88 1 0.4 13 0 1 0 2 1 1
68000 7 86 1 0.5 18 0 0 0 1.5 1 1
62000 6 88 1 0.7 9 0 0 0 1 0 1
65900 8 86 1 0.4 29 0 O 0 1.5 1 1
69900 6 87 1 0.5 29 0 0 0 1 1 1
48500 8 86 1 0.6 13 O 0 0 1 1 1
63000 8 86 1 0.7 34 0 0 0 1 1 1
68000 5 88 1 0.7 25 0 0 0 1.5 1 1
78500 3 89 l 0.4 11 0 0 0 1 1 1
9500 6 86 1 1 0 0 0 0 1
79500 9 86 1 0 1 0 0 1 0
59900 9 86 1 0.3 13 0 1 0 1 0 1
60500 10 87 1 0.3 12 0 1 0 2 1 1
66000 3 89 1 0.3 13 O 0 O 1.5 0 1
44900 5 86 1 0.2 0 0 0 1 0 0
42500 10 87 1 0.2 0 0 0 1 0 0
56000 6 88 1 0.2 34 0 0 0 1.5 0 1
26700 12 86 1 0.2 14 0 0 0 1 0 1
8500 8 87 1 0.7 1 0 0 0 0 0
48000 8 86 1 0.5 0 0 0 1 1 1
11000 7 86 1 10 1 0 0 1 2.5 0 1
165000 5 87 1 0.4 12 0 0 0 2 1 1
119500 5 88 1 0.5 12 0 0 0 2 1 1
7000 11 87 1 3 1 0 0 0 0 0
62500 3 88 1 6.2 12 0 0 0 1 0 0
76000 8 86 1 2 14 0 1 0 2 1 1
89900 4 88 1 23 12 0 0 O 1.5 1 1
86000 12 86 1 5 18 0 0 0 2 1 1
11900 5 88 1 10. 1 0 0 O 2 0 0
52675 10 86 1 8.5 0 0 0 1 0 1
1000 10 88 1 6 0 0 0 0 0 0
55900 5 87 1 1.3 26 0 O 0 1.5 0 0
114900 10 86 1 5.5 0 0 0 2 0 1
34900 7 87 1 0.2 0 0 0 1 0 1
2000 4 87 1 0.3 0 0 0 1 0 0
18900 4 87 1 0.2 0 0 0 1 0 1
30500 8 88 1 0.2 0 0 0 1 0 1

126

127

Road ACRE YEAR PLOT BRICK AIR BATH FIRE GAR

MON.Yr.

PRICE

 

 

101100111111101111011111110010101.011011111001111

000000100001001110000100110010010001100210111001

111111511111115521111521151052151012215555121101

1 11 1 1 1 nw 1121

000000000000000000000000010010000000000000000000

000000000000000100000100100010000000000001010000

000000000000000000000000000000000000000000000000

:9 95 1 11.. 970 10290822 52449 939109067828 2
222322—J27m1123158870736 . 6362022111022 655521 .05235
00000 as see-1e0024ee 1 eeeeegeeel 61eee
nU.00000000.0.00nw 24.40 0002101 000.000001000 1 0000

1111111111111111111111111111111111111111111.111qu

767787687888668668887688776776678676768787888896
888888888888888888888888888888888888888888888888
775082819184.017283286192847979686571596528818838
l l 11 1.. 1 1 .1. 1 l
0000000000000000000000001000000000000000000000000
000005000000000000000000000000000000000000000000
050903551890000905905050905005000000950900005000
673729859426133758968034.903558852545830535057375
4223415324 333478434357556 1882264585566066585 4
.1.

128

 

PRICE MON.Yr. Road ACRE YEAR PLOT BRICK AIR BATH FIRE GAR
50000 6 86 1 0.0 0 0 0 0 0 O
4000 5 86 1 0.2 35 0 0 0 0.5 0 1
89900 11 88 1 0.3 12 0 1 0 1.5 1 1
49800 7 88 1 0.4 O 0 0 2 0 0
75000 5 87 1 0.6 15 0 0 0 1.5 1 1
61500 4 86 1 0.7 11 0 0 0 1 0 1
65000 9 87 1 6.4 17 0 0 0 1 1 1
53500 11 86 1 1.8 20 0 1 1 2 0 1
55500 9 86 1 4 13 0 0 1 1.5 0 1
67000 11 86 l 5 31 0 0 0 1.5 1 1
42900 4 87 1 0.6 19 0 0 0 1 0 1
9000 12 87 0 10 7 0 0 0 1 0 0
77500 5 87 0 12. 16 0 1 1 2 3 1
36000 7 87 1 0.6 22 0 0 0 1 0 1
33900 9 87 1 0.3 39 0 O 0 1 O 1
35000 11 88 0 0.7 0 0 0 2 0 1
44000 9 87 1 9.7 2 0 0 0 l 0 0
22500 11 87 1 1.5 9 0 0 0 1 0 0
14000 7 87 0 2.8 1 0 0 0 0 0
13000 10 87 1 0.9 19 0 0 0 1 0 0
59900 12 87 1 1.5 15 0 0 0 1 0 1
8500 7 87 1 6.4 1 0 0 O 0 0
27000 5 87 0 1.4 19 0 0 0 1 0 0
47000 7 87 1 1.3 16 0 1 0 1 0 1
49500 12 87 1 0.9 28 0 0 0 1 0 1
7000 6 87 1 8 0 1 0 0 0 0 0
27500 11 88 1 0.8 49 0 1 0 1 1 0
46000 2 88 0 0.9 20 0 1 0 1 1 1
63000 6 88 1 3.3 1 0 0 0 0 0
23500 9 88 1 0.6 79 0 0 0 1 0 0
89900 8 88 1 1.6 29 0 l 0 2 1 1
7500 7 87 0 0.0 1 0 0 0 0 0
4000 11 87 0 0.1 1 0 0 0 0 0
72000 9 88 1 1.5 0 0 0 2 1
95000 8 88 0.0 10 0 0 0 2 1
88000 7 87 0 5 17 0 0 0 1 0 1
71000 8 88 l 10 39 0 1 0 1 0 1
15000 11 87 1 0.2 15 0 0 0 1 0 0
35000 10 88 1 14 0 O 0 1 1 0
2900 6 88 1 0.3 1 0 0 0 O 0
53500 10 87 1 4.5 7 0 1 0 1 1 1
3800 9 87 1 0.5 1 0 0 0 0 0
73750 8 87 1 9.0 89 0 0 O 1 1 1
2200 12 87 0 0.3 1 0 0 0 0 0
13000 5 87 1 5 1 0 0 O 0 0
7000 8 87 1 3.9 1 0 0 0 0 0
5500 5 87 1 1.2 1 0 0 0 0 0
49000 8 87 0 0.1 39 0 0 0 O 0 1

129

PRICE MON.Yr. Road ACRE YEAR PLOT BRICK AIR BATH FIRE GAR

 

 

46000 9 87 0.4 39 0 1 0 0 0 1

8000 4 87 0 3.5 0 0 0 0 0 0
14000 9 88 0 3 1 0 0 0 0 0
70000 11 88 1 6 24 0 0 0 1.5 0 1

2000 11 86 1 0.4 1 0 0 0 0 0
94000 4 87 1 1.5 21 0 1 0 2 1 1

9000 5 87 0 10. 1 0 0 0 0 0
15000 10 87 0 14. 1 0 0 0 0 0
41000 8 88 0 10. 139 0 1 0 1 0 1
17000 10 88 0 25 1 0 0 0 0 0
12000 6 88 0 4.5 0 1 0 0 0 0 0
67900 6 87 10 11 0 0 0 1.5 0 0
20000 7 88 0 0.8 0 0 0 1 0 1

7000 3 87 1 2 1 0 0 0 0 0
73500 11 87 1 1.9 O 0 0 1 0 0
34000 7 86 0 1 0 0 0 1 0 0
129000 5 89 0 5.9 10 0 0 0 2 0 1
66000 8 88 0 1 31 O 0 0 1 0 1
135000 7 88 1 3.2 11 0 0 0 2 0 1
86000 7 88 1 3.2 12 0 1 0 1.5 1 0
62500 1 88 0 10 9 0 0 0 2 0 0
76000 8 88 0 10 11 0 O 0 1.5 1 1
78250 6 86 0 10 10 0 0 0 2 0 1
15000 6 87 0 10 0 1 0 0 0 0 0
10000 12 87 0 10 1 0 0 0 0 0
14900 5 87 0 10 1 0 0 0 0 0

7000 4 88 0 4 1 0 0 0 0 0
17500 3 88 0 10 1 0 0 0 0 0
50000 9 86 0 5 14 0 0 0 1.5 0 0
47000 9 86 0 10 16 0 0 0 1.5 0 1
94000 4 87 1 5 27 0 1 0 1 1 1
37500 1 87 1 0.9 0 0 0 1 0 1
78000 8 87 1 4.2 16 0 1 0 2.5 2 1
60010 8 87 1 4.2 10 0 0 0 1.5 0 0
14500 10 87 0 10 1 0 0 0 0 0
17500 4 88 0 10 1 0 0 0 0 0
72000 3 89 0 3.5 10 0 1 0 1.5 1 0
63900 12 87 0 2 10 0 1 0 1.5 0 1
63900 11 87 0 10 0 0 0 1 0 1

8000 4 87 0 2.4 l 0 0 0 0 0

6000 7 88 0 2.4 1 0 0 0 0 0
71000 5 87 0 2.4 17 0 1 0 1 0 1
82500 7 87 0 2.4 4 O 0 0 2 0 0

7800 1 88 0 2.4 1 0 0 0 0 0
92900 6 89 0 2.4 10 0 0 0 1.5 0 1
73500 7 86 0 2.4 15 0 0 0 2.5 1 1

9000 11 88 1 0 0 0 0 0

7000 11 87 1 0 0 0 0 0

 

130

Road ACRE YEAR PLOT BRICK AIR BATH FIRE GAR

MON.Yr.

PRICE

 

 

101100011110100000000100000000000100101001011100

100100000000000000000000000000000000000000000000

502211111110111110001110000000000111111101112111
2

000000000000000000000000000000000000000000000000

000100000000000000000000000000000100000000000000

0100000000010000011100011.11111111000000010000000

9 119 60 099 0860 O 18
1 114 12 128 2212 .1. 11

103
109

o .76216475551 1112425443333322438226232212310 .92

000. one... ooooooooooooo co 0.00.00.10.-

00 .0
114.4. 000025 000001000000000000000000000 100
00001111111111110011100000000011000000111111000.1

686698877898668878886878987897667667898676868787
888888888888888888888888888888888888888888888888
084766405010107696760255248027466216512555725739
l 1 1 111 11 1 11 1

000000000000000000000000050000000000000000000000
000000000000000000000000000000000000000000000000
090090005500005005002002061003000000000550505090
0620767381142863387672621.. 1211790888760640678240
81563 11245 21131 113 1543522 1 15751

131

PRICE MON.Yr. Road ACRE YEAR PLOT BRICK AIR BATH FIRE GAR

 

53500 3 88 1 1.3 0 1 0 2 0 1
8000 12 88 1 0.4 1 0 0 0 0 0
8000 9 87 1 0.4 2 0 0 0 1 0 0

21500 1 89 1 4.2 1 0 0 0 0 0
7144 3 89 1 0.6 1 0 0 0 0 0

29288 10 88 1 0.5 0 0 0 1 0 0

20000 3 88 1 3.0 0 0 0 1 0 1

22000 1 87 1 0.2 0 0 0 1 0 0
7500 9 88 1 0.8 1 0 0 0 0 0
5000 1 89 1 7.6 1 0 0 0 0 0

35000 5 88 0 1.0 0 1 0 1 1 0

30500 4 88 1 1.2 45 0 0 0 1 0 0

28130 12 87 0 0.5 0 0 0 1 0 0

89000 4 87 1 1.1 16 0 1 0 1.5 1 1

24500 6 87 1 0.8 49 0 0 0 1 0 1

63500 9 86 1 2.5 11 0 0 0 2 0 1

70000 9 86 1 1.7 19 0 0 0 1 1 1

55300 9 87 0 1.7 27 0 0 0 1.5 1 1

18000 10 87 1 10 1 0 0 0 0 0

79000 6 88 1 2 0 0 0 1 1 1

19258 1 89 1 10. 1 0 0 0 0 0
9000 12 88 1 22. 1 0 0 0 0 0

45500 9 88 1 1.1 70 0 0 0 1 1 1

56000 12 87 1 36 39 0 0 0 1 1 1

95000 8 86 1 13. 17 0 0 0 1.5 1 1

16000 9 86 1 9.2 1 0 O 0 0 0

72000 8 87 1 0.9 25 0 0 0 1.5 0 1

74000 11 88 1 89 0 0 0 1 0 0

50000 6 87 1 0.9 37 0 0 0 1 0 1

68000 1 89 1 1.8 12 0 0 1 l 0 1

78000 4 86 1 0.3 11 0 0 0 2 1 1

15000 9 87 1 1.4 1 0 0 0 1 1 1

82000 10 87 11. 10 0 0 1 2 1 1

56875 6 88 1 0.2 17 0 0 0 1 1 1

70000 7 86 1 0.3 17 0 0 0 2 1 1

159000 12 86 1 6 25 0 1 0 2 2 1

64500 3 87 1 0.4 24 0 1 0 2 2 1

78000 10 87 1 0.3 19 0 0 0 1.5 0 1

74900 5 86 1 0.3 20 0 1 0 1.5 0 1

73900 10 86 1 0.5 16 0 1 0 1.5 0 1

89000 9 87 1.3 14 0 0 0 1.5 1 1

90000 5 88 1 0 0 1.5 1 1

73000 6 86 0.3 17 0 1 0 1 1 1

88000 6 88 1 0.4 16 0 0 0 1.5 1 1

76000 9 86 1 0.4 0 0 1 1 1 1

69000 11 86 1 1.2 14 0 1 0 1.5 0 1

76300 11 88 1 0.5 18 0 0 1 1.5 1 1

78000 3 88 1 0.5 12 0 0 0 1 1 1

 

132

 

PRICE MON.Yr. Road ACRE YEAR PLOT BRICK AIR BATH FIRE GAR
69000 7 87 1 4 26 0 0 0 1 0 1
37500 3 88 1 1.4 33 0 0 0 1 0 0
64500 11 87 1 6.3 53 0 0 0 1 1 0

117500 9 88 1 16 12 0 1 0 2 1 1
51000 6 88 1 1.5 120 0 0 0 1 1 0
133500 5 88 1 10. 5 0 0 0 1 1 1
56000 8 88 0 1.2 109 0 0 0 1 0 1
12000 5 88 1 2.5 1 0 0 0 0 0
82500 10 88 1 4 30 0 1 0 1.5 2 1
55000 10 88 1 7.8 18 0 0 0 1 0 0
63000 8 87 1 2 6 0 0 0 2 0 1
66900 6 87 1 2.4 17 0 1 0 2 1 1
89750 12 88 0 9.9 0 0 0 2.5 1 1
89900 3 88 l 1 20 0 0 0 1.5 0 0

 

 

133
= CB FEET MOBIL CBD DIST HWAY WIND ANIMAL
0 1096 0 51 25 123. 1140 1500
0 1080 0 27 17 110. 113 1500
0 1039 0 27 17 110. 113 1500
0 704 0 43 10 102 102 1500
0 1252 ‘ 0 43 10 102 102 1500
0 956 O 39 13 110 122 1500
0 1120 0 39 13 110 122 1500
0 0 O 28 18 93.5 55 1500
0 0 0 32 16 99.5 71 1500
1 1062 0 25 16 93.5 45 1500
1 1569 0 25 16 93.5 45 1500
0 1272 0 25 16 93.5 45 1500
1 1470 0 27 23 87.5 42 1500
0 1040 0 21 25 81.5 25 1500
1 1480 0 14 24 77 10 1500
1 1344 O 14 24 77 10 1500
1 1428 0 14 24 77 10 1500
0 1220 0 14 24 77 10 1500
1 1218 0 14 24 77 10 1500
0 1144 O 9 24 77 2 1500
0 1522 0 9 24 77 2 1500
0 1368 0 9 24 77 2 1500
1 1092 0 9 24 77 2 1500
0 1092 0 9 24 77 2 1500
0 1092 0 9 24 77 2 1500
0 960 0 9 24 77 2 1500
0 864 0 9 24 77 2 1500
0 1104 0 9 24 77 2 1500
0 960 0 9 24 77 2 1500
0 0 0 20 12 16 20 1500
0 1377 0 20 12 16 20 1500
0 992 0 20 12 16 20 1500
0 1200 0 24 8 95 40 1500
1 1344 0 24 8 95 40 1500
0 0 0 30 5 102 95 1500
0 1196 0 36 8 108 148 1500
0 1292 0 34 7 108 170 1500
0 1264 0 56 34 133 157 1500
0 1524 0 56 34 133 157 1500
0 1320 0 45 23 120 145 1500
0 1071 0 45 23 120 145 1500
0 0 O 14 14 90 27 1500
0 872 0 14 14 90 27 1500
0 1409. 0 20 15 97 70 1500
0 1375 0 3 24 78 19 1500
0 858 0 3 24 78 19 1500
0 1404 0 3 24 78 19 1500
0 900 0 9 22 84 36 1500

1',

 

A

134

 

= CB FEET MOBIL CBD DIST HWAY WIND ANIMAL
0 1080 0 9 22 84 36 1500
0 1157. 0 9 22 84 36 1500
0 1246 0 9 22 84 36 1500
0 1428 0 9 22 84 36 1500
0 648 0 9 22 84 36 1500
0 960 O 9 22 84 36 1500
0 1724 0 9 22 84 36 1500
0 900 0 11 31 78 40 1500
0 792 O 11 31 78 40 1500
0 884 0 11 31 78 40 1500
0 0 0 11 31 78 40 1500
0 696 0 11 31 78 40 1500
0 832 0 11 31 78 40 1500
0 882 0 13 27 88 47 1500
0 1407 O 17 23 90 60 1500
1 1248 0 17 23 90 60 1500
1 1668 0 17 23 90 60 1500
O 1259 0 25 26 97 78 1500
0 964 0 29 20 90 92 1500
0 1008 0 37 36 90 50 1500
0 1008 0 37 36 90 50 1500
0 1560 0 37 36 90 50 1500
0 1464 0 37 36 90 50 1500
0 768 0 37 36 90 50 1500
0 1304 0 32 32 90 43 1500
0 1092 0 38 23 90 78 1500
0 864 0 38 23 90 78 1500
0 0 0 38 23 90 78 1500
0 1144 0 47 28 85 92 1500
0 1504 0 47 28 85 92 1500
0 944 0 47 28 85 92 1500
0 432 0 47 28 85 92 1500
0 760 0 47 28 85 92 1500
0 0 0 47 28 85 92 1500
0 816 0 47 28 85 92 1500
0 1008 0 47 28 85 92 1500
0 918 0 47 28 85 92 1500
0 1120 0 47 28 85 92 1500
0 879 0 47 28 85 92 1500
0 1176 0 50 28 85 102 1500
1 2547 0 50 28 85 102 1500
0 960 0 50 28 85 102 1500
0 616 0 50 28 85 102 1500
0 1064 0 50 28 85 102 1500
0 1380 0 50 28 85 102 1500
0 1040 0 42 23 90 92 1500
0 0 0 42 23 90 92 1500
0 1380 0 45 23 90 105 1500

 

13S

 

= CB FEET MOBIL CBD DIST HWAY WIND ANIMAL
0 0 0 45 23 90 105 1500
0 780 0 45 23 90 105 1500
0 1368 0 45 23 90 105 1500
0 1064 0 45 23 90 105 1500
0 875 0 63 37 85 134 1500
0 1242 0 63 37 85 134 1500
0 1040 0 63 37 85 134 1500
0 1104 0 68 41 85 138 1500
0 888 0 68 41 85 138 1500
0 1344 0 68 41 85 138 1500
0 1232 0 57 35 48 175 25000
0 784 1 58 27 101 155 25000
0 2392 0 58 27 101 155 25000
0 2088 0 71 30 39 74 25000
0 1008 0 63 20 39 90 25000
0 1400 0 63 20 39 90 25000
0 960 0 50 30 42 179 25000
0 924 1 50 34 40 172 25000
0 0 0 39 23 29 155 25000
0 672 1 51 15 21 30 25000
0 1620 0 25 28 16 128 25000
0 0 0 20 29 10 116 25000
0 728 0 11 31 1 90 25000
0 960 0 20 22 7 90 25000
0 1092 0 22 24 3 70 25000
0 0 0 22 24 3 70 25000
0 800 0 52 30 40 1 25000
0 1200 0 44 37 9 24 25000
0 0 0 38 30 4 30 25000
0 1536 0 38 30 4 30 25000
0 1657 0 25 25 8 110 15000
0 0 0 49 8 9 20 15000
0 0 0 49 8 9 20 15000
0 1050 0 25 25 8 110 15000
0 980 0 13 32 21 88 15000
1650 0 73 28 34 83 15000

0 1000 0 71 27 28 65 15000
0 1440 1 73 34 28 44 15000
0 1050 0 73 34 28 44 15000
0 0 0 73 34 28 44 15000
0 950 0 70 28 26 47 15000
0 0 0 64 18 21 62 15000
0 1300 0 64 18 21 62 15000
0 0 0 51 14 9 21 15000
0 0 0 58 34 4 0 15000
O 0 0 58 34 4 0 15000
0 0 0 58 34 4 0 15000
0 950 0 42 18 4 20 15000

 

OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

136

FEET MOBIL CBD DIST HWAY WIND ANIMAL

650
240
0
1100
0
1450
0

0
1150
0

0
800
850
0
1050
1000
1950
1300
1150
864
1200
1050
1400

OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO

42
42
42
40
40
32
51
51
49
17
17
17

3
16
11
11
11
25
20
20
21
21
21
21
24
24
24
25
25
27
33
27
27
27
27
27
37
38
38
38
38
38
38
38
38
38
38
38

18
18
18
62
62
34
40
40
42
30
30
30
22
30
25
13

102
108
108
108
108
108
108
108
108
108
108
108

20
20
20
30
30
42
15
15
22
113
113
113
142
166
171
180
180
42
70
70
110
110
110
110
120
120
120
138
138
122
125
45
45
45
45
45

28
28
42
42
42
42
42
42
42
42
42

15000
15000
15000
15000
15000
15000
15000
15000
15000
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90
90

 

137

 

= CB FEET MOBIL CBD DIST HWAY WIND ANIMAL
0 1100 0 43 28 102 85 90
0 0 0 43 28 102 85 90
0 1005 0 43 28 102 85 90
0 1050 0 43 28 102 85 90
0 1000 O 50 37 102 85 90
0 700 O 0 14 48 25 8400
0 0 0 0 14 48 25 8400
0 720 1 0 14 48 25 8400
0 970 0 0 14 48 25 8400
0 1620 0 36 22 23 172 8400
0 950 0 32 23 16 125 8400
0 0 0 27 18 21 122 8400
O 950 1 31 26 17 111 8400
0 1250 0 10 17 39 51 8400
0 1300 0 18 31 48 28 8400
0 950 0 25 22 57 75 8400
0 1026 O 30 22 52 110 8400
0 0 0 35 23 20 165 8400
0 0 0 35 23 20 165 8400
0 0 0 35 23 20 165 8400
0 624 1 29 25 20 97 8400
0 840 1 29 25 20 97 8400
1 1400 1 29 25 20 97 8400
0 0 0 29 25 20 97 8400
0 0 0 29 25 20 97 8400
0 0 0 29 25 20 97 8400
0 0 0 29 25 20 97 8400
0 0 0 29 25 20 97 8400
0 0 0 29 25 20 97 8400
0 0 0 29 25 20 97 8400
0 0 0 34 42 16 88 8400
0 0 0 34 42 16 88 8400
0 0 0 34 42 16 88 8400
0 1092 0 34 42 16 88 8400
0 960 0 34 42 16 88 8400
0 950 0 34 42 16 88 8400
0 1450 0 34 42 16 88 8400
1 1344 1 34 42 16 88 8400
0 1020 1 34 42 16 88 8400
1 700 1 34 42 16 88 8400
0 0 0 34 42 16 88 8400
0 778 0 8400
0 999 0 8400
0 1150 0 8400
0 1450 0 16 10 9 179 100
0 1250 0 6 10 9 100 100
0 700 0 10 8 9 135 100
0 600 0 2 7 3 65 100

 

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a!

138

FEET MOBIL CBD DIST HWAY WIND ANIMAL

 

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1200
0
960
0

0
1100
1016
1150
0

0
701
1050
1150
1850
850
1144
1248
1218
0
1334
0

0
1248
1344
1446
0
1768
1008
966
768
760
1228
1510
1040
936
2592
1274
1100
788
1144
1040
946
1140
1248
1232
576
1100
816

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mmummmmmmmmmwmmmmocoopmmmmmmbmbqmwuummmbmpmmwwmw

N
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HHHHHHHHHHHH-bmmOOOGbUIUIQQUQUIUIO‘t-‘mmm

65
65
65
65
80
80
55
55
64
30
18
11
11
122
90
175
115
172
169
172
175
175
155
170
80
80
46
46
10
10
10
8

8
52
52
30
10
10
10
10
10
10
10
10
10
10
10
10

100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
60
so
so
so
60
60
60
60
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000
3000

 

FEET MOBIL

139

 

>= CB CBD DIST HWAY WIND ANIMAL
0 832 0 57 30 40 154 3000
0 1056 0 27 20 35 85 3000
0 955 0 27 20 35 85 3000
0 1672 0 56 29 42 144 3000
0 1120 0 48 25 46 142 3000
0 1671 0 30 24 38 95 3000
0 826 0 27 20 35 85 3000
0 0 0 27 _ 20 35 85 3000
1 1310 0 42 13 53 150 3000
0 1152 0 36 28 39 104 3000
0 1345 0 58 30 40 162 3000
0 1060 0 49 23 52 175 3000
1 1850 0 60 40 38 170 3000
0 1200 0 58 30 40 162 3000

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