Title: Estimation of black and white housing services demand elasticities in the United States using a simultaneous model of tenure choice and housing services demand /
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Title: Estimation of black and white housing services demand elasticities in the United States using a simultaneous model of tenure choice and housing services demand /
Alternate Title: Black and white housing services demand elasticities in the United States
Physical Description: ix, 114 leaves : ; 28 cm.
Language: English
Creator: Williams, David Richard, 1958-
Publication Date: 1986
Copyright Date: 1986
 Subjects
Subject: Housing surveys   ( lcsh )
Housing -- Prices -- United States   ( lcsh )
Elasticity (Economics)   ( lcsh )
Economics thesis Ph.D
Dissertations, Academic -- Economics -- UF
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Thesis: Thesis (Ph.D.)--University of Florida, 1986.
Bibliography: Bibliography: leaves 109-113.
Statement of Responsibility: David Richard Williams.
General Note: Typescript.
General Note: Vita.
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Bibliographic ID: UF00099335
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 000931583
notis - AEP2525
oclc - 016243397

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ESTIMATION OF BLACK AND WHITE HOUSING SERVICES DEMAND ELASTICITIES
IN THE UNITED STATES USING A SIMULTANEOUS MODEL OF TENURE CHOICE
AND HOUSING SERVICES DEMAND










By

DAVID RICHARD WILLIAMS


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN
PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY


UNIVERSITY OF FLORIDA





















































Copyright 1986

by

David Richard Williams















ACKNOWLEDGEMENTS


I would like to thank the members of my dissertation committee, Dr.

Lawrence Kenny (Chairman), Dr. Stephen Cosslett, Dr. David Denslow, Dr. Jerome

Milliman and Dr. Gary Lynne, not only for all their advice and continued

support during the preparation of this dissertation, but also for their

guidance during my years at the University of Florida.

My deepest gratitude goes to my father, late mother, auntie Gwyn, and

uncle Bill for their love and continuous understanding over the years.

I would also like to thank Dr. James Frew, who aroused my interest in

applied econometrics while I was at the University of North Carolina at

Greensboro. Also, I want to thank the staff of the Revenue and Economic

Analysis Unit of the Governor's Office of Florida for their encouragement

during the latter stages of this dissertation. Financial assistance was

provided to me during my doctoral studies by the Elizabeth Tuckerman

Scholarship Foundation, Los Angeles, California.

Last, but by no means least, my thanks to DeLayne Redding for typing this

dissertation.
















TABLE OF CONTENTS


ACKNOWLEDGMENTS ...................................

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

ABSTRACT ...........................................

CHAPTERS


I INTRODUCTION ...........................

II REVIEW OF THE LITERATURE ON PREVIOUS
STUDIES ESTIMATING RACIAL HOUSING DEMAND
ELASTICITIES .............................

III IMPROVEMENTS IN THE MEASUREMENT OF
HOUSING VARIABLES ........................

IV THE MODEL ................................

Estimation Technique Theory.............
Specification of the Model and
Construction of Housing Variables.......

V EMPIRICAL RESULTS AND DISCUSSION..........

Notes....................................

VI CONCLUSIONS ..............................


APPENDIX


DATA SOURCES AND DETAILED VARIABLE

DEFINITIONS..............................

BIBLIOGRAPHY ......................................

BIOGRAPHICAL SKETCH...............................


Page



iii

V

viii



1



7


13

20

20

22

42

94

95






99

109

114















LIST OF TABLES


TABLES Page

1 List of Dependent and Independent Variables Used in
the Hedonic Equation Regression Analysis............. 27

2 List of Independent Variables Used in the Permanent
Income Regression Equation Analysis................. 33

3 List of Dependent Variables Used in the Tenure
Choice Regression Equation Analysis................. 36

4 List of Independent Variables Used in the Housing
Services Demand Regression Equation Analysis......... 37

5 1981 Hedonic Equation Regression Coefficients for
Anaheim-Santa Ana-Garden Grove SMSA ................. 51

6 1981 Hedonic Equation Regression Coefficients for
Boston SMSA........................................ 52

7 1981 Hedonic Equation Regression Coefficients for
Dallas SMSA......................................... 53

8 1981 Hedonic Equation Regression Coefficients for
Detroit SMSA......................................... 54

9 1981 Hedonic Equation Regression Coefficients for
Fort Worth SMSA...................................... 55

10 1981 Hedonic Equation Regression Coefficients for
Madison SMSA......................................... 56

11 1981 Hedonic Equation Regression Coefficients for
Minneapolis-St. Paul SMSA........................... 57

12 1981 Hedonic Equation Regression Coefficients for
Newark SMSA ........................................ 58

13 1981 Hedonic Equation Regression Coefficients for
Orlando SMSA ........................................ 59

14 1981 Hedonic Equation Regression Coefficients for
Phoenix SMSA......................................... 60

15 1981 Hedonic Equation Regression Coefficients for
Pittsburgh SMSA..................................... 61









TABLES Page

16 1981 Hedonic Equation Regression Coefficients for
Spokane SMSA......................................... 62

17 1981 Hedonic Equation Regression Coefficients for
Tacoma SMSA......................................... 63

18 1981 Hedonic Equation Regression Coefficients for
Washington, D.C. SMSA............................... 64

19 1981 Hedonic Equation Regression Coefficients for
Wichita SMSA........................................ 65

20 Significance and Mean Effects of Hedonic Equation
Regression Coefficients using the Pearson PX Test... 66

21 Means of Hedonic Housing Variables (X.'s) used in
the Construction of the Hedonic Housing Services
Price Indices....................................... 67

22 1981 Hedonic Housing Services Price Indices for
Black and White Homeowners in each of the 15 SMSAs.. 71

23 1981 Hedonic Housing Services Price Indices for
Black and White Renters in each of the 15 SMSAs..... 72

24 1981 Price Index for all Goods and Services in each
of the 15 SMSAs ..................................... 73

25 1981 Permanent Income Equation Regression
Coefficients for Grouped Sample..................... 75

26 1981 Probit Equation Regression Coefficients........ 76

27 1981 Housing Services Demand Equation Regression
Coefficients for Homeowners ........................ 82

28 1981 Housing Services Demand Equation Regression
Coefficients for Renters............................ 83

29 1981 Housing Services Demand Equation Regression
Coefficients for White Homeowners by Education
Classification...................................... 84

30 1981 Housing Services Demand Equation Regression
Coefficients for Black Homeowners by Education
Classification...................................... 85

31 1981 Housing Services Demand Equation Regression
Coefficients for White Renters by Education
Classification......................... ............. 86









TABLES Page

32 1981 Housing Services Demand Equation Regression
Coefficients for Black Renters by Education
Classification ...................................... 87

33 Final Price and Income Elasticities of Housing
Services Demand for Black and White Homeowners and
Renters............................................. 88

34 Comparison of Homeowner Housing Services Demand
Equations Using (1) AHS Endogenously Constructed and
(2) BLS Price Indices............................... 91

35 Comparison of Renter Housing Services Demand
Equations Using (1) AHS Endogenously Constructed and
(2) BLS Price Indices............................... 92

36 Comparison of Final Price and Income Elasticities of
Housing Services Demand for Black and White Homeown-
ers and Renters Using (1) AHS Endogenously Construct-
ed and (2) BLS Price Indices........................ 93

37 Sample Size of Each Tenure/Race Classification by
SMSA............................................... 101

38 Income Tax Brackets, 1981........................... 102

39 City Specific Features Used in the Permanent Income
Regression Equation................................ 103

40 Non-Housing and Housing Budgets from the 1981 Bureau
of Labor Statistics Urban Family Budget Survey...... 104

41 Capital Gains Rates in the SMSAs in the Annual Hous-
ing Survey......................................... 105

42 Non-Housing and Housing Budgets from the 1978 Bureau
of Labor Statistics Urban Family Budget Survey...... 106















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy


ESTIMATION OF BLACK AND WHITE HOUSING SERVICES DEMAND ELASTICITIES
IN THE UNITED STATES USING A SIMULTANEOUS MODEL OF TENURE CHOICE
AND HOUSING SERVICES DEMAND

By

DAVID RICHARD WILLIAMS

December 1986



Chairman: Dr. L. W. Kenny
Major Department: Economics


This dissertation presents estimates of black and white homeowner

and renter income and price elasticities of housing services demand in

the United States for the year 1981. The econometric model employed in

this analysis takes into account the individual's twofold decision on

(1) the level of housing services to select and (2) whether to own or

rent a housing unit. The data set used in this study is the 1981 SMSA

Annual Housing Survey, the most detailed housing database currently

available.

A measure of permanent income is estimated using an instrumental

variables technique. Endogenously constructed racial housing price

indices are built using hedonic pricing techniques for homeowners and

renters in each of the fifteen Standard Metropolitan Statistical Areas

(SMSAs) in the sample. These income and price terms together with a set

of demographic variables and a variable which captures any simultaneity









between the housing demand and tenure choice decisions are used as

explanatory variables in the final housing demand equations.

The results from these equations show that blacks have higher price

and income elasticities of housing services demand than whites in both

the homeowner and rental markets. The price elasticities are -1.04 and

-1.01 for black and white owners, -0.62 and -0.12 for black and white

renters. The income elasticities are 1.42 and 1.30 for black and white

owners, 0.53 and 0.36 for black and white renters. Evidence of

simultaneity is found between the rent and housing consumption

decisions and also between the own and housing consumption decisions.

On examining any changes in price and income elasticities estimates

caused by using price indices (1) constructed endogenously from the

Annual Housing Survey and (2) from the Bureau of Labor Statistics,

differences in the relevant elasticities ranged between two and

fifty-four percent.















CHAPTER I
INTRODUCTION


The main contribution of this research is that it is the first

paper to use endogenously constructed price indices for black and white

homeowners and renters in housing demand equations. Most housing demand

studies use the time-series or cross-sectional housing price indices

constructed by the Bureau of Labor Statistics (BLS). The time-series

BLS city housing price indices are inappropriate for use in

cross-sectional studies as they do not measure relative differences in

prices or living costs between cities at a point in time. Unfortunately,

Lee and Trost (1978) use these price indices in their housing study.

These indices show how prices change in a particular city over time, not

whether prices are higher in one city than in another. As stated in

U.S. Department of Labor (1984):

The area indices measure the average changes in price for
each area since the base period. They do not measure
differences in the level of prices among cities; that is,
they cannot be used to determine "high living cost" or
"low living cost" cities or regions. (p. 6)

The BLS cross-sectional family budget data measure differences in

living costs between cities for a family comprising a 38-year-old

employed husband, a wife not employed outside the home, an 8-year-old

girl, and a 13-year-old boy a typical 4-person family. For renter

families, the standard unit is an unfurnished five-room apartment in

sound condition with a particular set of neighborhood dwelling

characteristics, such as a complete private bath and a fully equipped

kitchen. For homeowner families, the standard unit is a five- or









six-room, 1- or 12-bath house with the same set of neighborhood and

dwelling characteristics as the standard rental unit. Problems with the

BLS family budget are that it not only looks at a very arbitrarily

specified family composition, but also only keeps quality constant in so

far as looking at arbitrarily and vaguely defined sound rental and

homeowner housing units (see Chapter III for a thorough discussion of

this topic). The price and income elasticities of housing services

demand reported in studies using BLS price indices can be directly

compared to the price and income elasticities obtained in this study

which use endogenously constructed price indices.

Several authors, notably Gillingham (1975), Follain, Ozanne, and

Alburger (1979), and Follain and Malpezzi (1979), do construct owner and

rental price indices using hedonic methods, as in my study. However,

they do not incorporate their indices into housing demand analysis, a

logical place for such housing price indices. This study takes the

current state of the art forward by incorporating endogenously

constructed housing price indices, not BLS price indices, in a study of

the demand for housing.

A second contribution of this study is to apply the econometric

technique developed by Lee and Trost (1978) to the best housing data

available, the Annual Housing Survey (AHS). Other papers using the

Lee-Trost model, namely Lee and Trost (1978) and Rosen (1979a and

1979b), use the Panel Study of Income Dynamics (PSID) from the

University of Michigan. This data set over-represents low-income

households, includes only 2,000 households, and contains few items

concerning housing. The only other paper to use the Lee-Trost model,

namely Gillingham and Hagemann (1983), used the Consumer Expenditure

Survey (CES) which, although it contains more households (approximately









23,000) than the PSID, again has insufficient information on shelter.

Housing was not the focus of the PSID or the CES. However, the Annual

Housing Survey, as the name suggests, emphasizes housing. The 1981

Annual Housing Survey (SMSA file), which I use in this study, is a sur-

vey of about 75,000 individual housing units across 15 SMSAs. Each

household head was asked approximately 500 questions about his previous

and current housing situation. The Annual Housing Survey is by far the

largest and the most detailed source of housing information currently

available.

Before summarizing housing demand elasticities computed by single

equation methods, that generally use BLS price indices, I will briefly

describe the methodology underlying the single equation estimation

methods.

Prior to Lee and Trost (1978), housing services demand elasticities

(income and price) for grouped and micro data were obtained by

estimating single equations of housing demand for homeowners and renters

separately, taking no account of the tenure choice decision. Before

1978, the typical procedure used to derive estimates of housing demand

elasticities started with the following equation:

log qH = 0 + B1 log y + B2 log PH + B3 log Px + u (1-1)

where qH = quantity of housing services

y = current income

PH = price index of housing services

PX = price index of all goods (excluding housing

services)

In equation (1), B1 and 82 were the "true" income and price

elasticities of housing services demand, respectively. The demand









equation is homogeneous of degree zero in prices and income, implying

the constraint B1 + B2 + 83 = 0. Adding log pH and subtracting log pX

from both sides of (1) yielded the following equation, estimable with

micro data:

log (eH/PX) = B0 + 10 log (y/PX) + (1 + B2) log (pH/PX) + e (1-2)

where eH = expenditures on housing services.

A similar procedure was carried out for grouped data (details of

which are discussed in Polinsky and Ellwood (1979)). Equation (1-2) in

many studies also included demographic variables, so that the housing

demand equation estimated was a slight variation of (1-2). An excellent

survey of the pre-1978 literature by Mayo (1981) found micro housing

demand elasticities to lie in the following ranges: renter and owner

income elasticities of demand, 0.08+0.70 and 0.21+0.71; and renter and

owner price elasticities of demand, -0.17+-1.28 and -0.52+-0.89.

Lee and Trost (1978) solved the econometric problem posed in Vaughn

(1976): "A method does not yet exist to include both categories (home-

owners and renters expenditures) within a single regression equation"

(p. 55).

All pre-1978 housing demand studies are limited by not having the

Lee-Trost modelling procedure available. Not incorporating a variable

which takes into account the simultaneity between the housing

consumption and tenure choice decisions renders the elasticities

computed from single equation methods biased and inconsistent. Since

Lee and Trost, three more papers have been published, all using micro

data, which allow for simultaneity between the decision whether to own

or rent and how much to spend on housing services.Lee and Trost

estimated income elasticities of demand to be in the ranges 0.55+0.61









for owners and 0.50+0.51 for renters. Rosen (1979a) estimated owners'

elasticities of demand to be 0.76 for income and -0.97 for price.

Gillingham and Hagemann (1983) estimated income elasticities for owners

and renters in the ranges 0.23+0.54 and 0.29+0.74, respectively, and the

price elasticities for owners and renters to be in the ranges

-0.27+-0.57 and -0.66+-1.17, respectively. A similar study carried out

in the United Kingdom by King (1980) estimated the price elasticities of

housing demand for owner occupied, subsidized rental, and furnished

rental housing at -0.52, -0.49, and -0.65, respectively. From these

four studies the evidence seems to be that the income elasticities are

in the ranges 0.25+0.75 and 0.30+0.75 for owners and renters, while the

price elasticities are in the ranges -0.30+-1.00 and -0.50+-1.20 for

owners and renters, respectively.

Each of the three U.S. studies leaves room for improvement. Lee

and Trost (1978) fail to include the income tax advantages of owning a

home in their price index of housing services for homeowners. Although

Rosen (1979a) and Gillingham and Hagemann (1983) both correct for income

tax effects, Rosen uses for eo (the expenditures on housing services by

homeowners) the market value of the home, while Gillingham and Hagemann

use the respondent's estimate of the rental value of the home net of

utilities. Also Gillingham and Hagemann use current instead of

permanent income and do not include the net imputed rental value of

owning a home in owners' income.

The third major contribution of this study is to fill a void in the

existing housing demand literature. No published papers use Lee and

Trost's simultaneous model of tenure choice and housing services demand

to estimate black and white homeowners and renters' price and income









elasticities of demand for housing services separately. A crux of my

paper is to estimate elasticities for blacks and whites separately.

This study also estimates these elasticities for black and white

homeowners and renters across three major education levels to examine

whether housing demand elasticities vary with educational attainment.

The endogenous construction of racial price indices for owners and

renters in this study also permits me to test for racial price

discrimination with regard to housing across the fifteen SMSAs in my

sample. Although the main questions asked in this thesis are not

related to housing policies, it is possible to use the computed

elasticities to examine whether national housing policies have varying

effects on different racial groups and even across groups with different

education levels in the United States.

In Chapter II the few previous studies on racial housing demand

elasticities are reviewed. Chapter III highlights the deficiencies

of past housing research and the resulting consequences on coefficient

estimation. Chapter IV details the statistical theory behind the

Lee-Trost model of simultaneously looking at the decisions of how much

housing services to consume and the tenure choice decision. Also in

this chapter the housing variables used in this analysis are

constructed and the best functional form of the housing demand and other

relevant equations are found. In Chapter V the empirical results are

tabulated and discussed, the conclusions of the thesis are presented in

Chapter VI. Finally, the appendix details the data used from the Annual

Housing Survey and other sources. The appendix also defines all the

housing variables used in the thesis.















CHAPTER II
A REVIEW OF THE LITERATURE ON PREVIOUS STUDIES
ESTIMATING RACIAL HOUSING SERVICES DEMAND ELASTICITIES


Using single equation methods, three papers using micro data have

looked at racial housing demand elasticities. The first, Kain and

Quigley (1975), used a sample of 1,200 individual housing units (owners

and renters) in St. Louis. The best of their numerous specifications

yielded income elasticities of housing demand of 0.28 for white and 0.07

for black owners. Their best equation for renters produced income

elasticities of 0.25 for white and 0.15 for black renters. They assumed

a linear functional form for owner equations and a semi-logarithmic form

for renter equations. No price elasticity of housing demand was

computed since all individuals in St. Louis face the same price of

housing; therefore, no price term was included in their demand

equations. When calculating a homeowner's income, they did not take

into account the net imputed rental value of a house. Finally, the

sample had only 72 black owners and 266 black renters.

The second study, Mayo (1977), has its results tabulated below:


White Renter-Occupied


Black Renter-Occupied


n Tn
y P

Pittsburgh Phoenix Pittsburgh Phoenix


0.52 0.72 -0.27 -0.55


0.77 0.33 -0.38 -0.81









Mayo used a stock adjustment model on individual rental data from

the Housing Allowance Demand Experiment (HADE), carried out in

Pittsburgh and Phoenix, to examine the feasibility of a percent of rent

payment scheme. Although the price elasticities (n ) are consistently

more elastic for black renters than for white renters, no pattern

emerges concerning the income elasticities (n ). Mayo's results must be

viewed with caution. First, his equations suffer from a high degree of

collinearity between the income and lagged rental expenditure variables.

Second, the data, from an experiment of limited duration, reflect house-

hold reaction to a temporary, not a permanent rental price reduction.

Third, the equations included no demographic variables.

The third paper, Friedman and Weinberg (1982), to use micro data in

order to examine racial housing demand elasticities produced the

following results:



"y np


Pittsburgh Phoenix Pittsburgh Phoenix


White Renter-Occupied 0.39 0.43 -0.21 -0.29


Black Renter-Occupied 0.21 0.22 -0.20 -0.26



Friedman and Weinberg also used data from the Housing Allowance

Demand Experiment (HADE), and consequently their analysis, like Mayo's,

suffers from flaws inherent in using this data, particularly that it is

from a temporary experiment. Unlike Mayo, Friedman and Weinberg did not

use a stock adjustment model but instead a more standard demand equation









approach. Their paper also assumes a semi-logarithmic functional form

for the equation, and has no demographic regressors.

Using pre-1978 techniques, two papers employing grouped data have

looked at racial housing demand elasticities, de Leeuw (1971) and Vaughn

(1976). The results of the two studies are summarized in the table

below:


All Owner-Occupied


OtherI Head, Owner-
Occupied

All Renter-Occupied


OtherI Head, Renter-
Occupied


de Leeuw, Other = Nonwhite

Vaughn, Other = Black

I have little confidence in the de Leeuw and Vaughn estimates. Both

studies use current instead of permanent income in their demand equa-

tions, resulting in a downward bias in their estimates of the permanent

income elasticity of housing demand (Chapter III has a thorough discus-

sion of the consequences of this error). The elasticity ranges given by

Vaughn are far too wide for any meaningful comparisons with other

estimates. Also, neither study includes the net imputed rental value of

a home in homeowners' income, or allows for the income tax advantages of

owning a house. In fact, de Leeuw does not even include a price term in


Ti n
ny "p

de Leeuw Vaughn de Leeuw Vaughn


1.34 1.88 -0.33+-2.70


0.76+2.67 -0.63+-6.25


0.81+0.99 0.32 -0.71+-1.47 -0.48+-2.63


0.79 0.41+0.50 -0.97 -0.35+-2.38









his owner demand equations. Finally, neither study treats blacks and

whites separately.

My study fills a void in the existing racial housing demand

elasticity literature by not only taking into account the omissions and

errors of previous studies, but also by using a simultaneous model of

housing demand and the tenure choice decision. All previous racial

studies have used only single equations of housing demand in their

elasticity estimation.

On examination of the few prior racial housing demand studies,

it is clear that no overall conclusion can be drawn that the price and

income elasticities of housing services demand are higher for blacks

than whites, or vice versa. From a general review of the housing demand

literature it was found that the price and income elasticities of

housing services demand are higher for homeowners than renters. The

price elasticity of housing services demand for homeowners is generally

in the -0.7+-1.0 range, while the income elasticity of housing services

demand for homeowners is generally in the 0.8+1.3 range. As stated

above, the same two elasticities are found to be lower in the rental

market.

There are numerous theories (see King and Mieszkowski (1973)) on

why blacks pay more than whites for structurally identical housing

units. A slight majority of studies (see Yinger (1979) and Follain and

Malpezzi (1979)) support the finding that blacks pay more than whites

for structurally identical housing units. However, there are no

theories detailing why the price and income elasticities of housing

services demand should differ, if at all, between blacks and whites.

There is nothing in the literature to support the following statement









made by de Leeuw (1971): nonwhite households appear to have a slightly

lower income elasticity of demand than all households, perhaps because

of restrictions on choice due to racial discrimination. (p. 8)

A question one could ask of this dissertation is: What is the

motivation and purpose behind wanting to estimate racial housing ser-

vices demand elasticities? In response, I can think of two main areas

where racial housing demand elasticities are of particular interest.

The first area concerns the effectiveness of past, current, and

future federal housing policies and whether there are different

responses from blacks and whites to these policies. During the early

1970s the majority of federal housing policies were generally costly and

inefficient supply oriented programs. Examples of these policies were

public housing which gave a subsidy to local housing authorities for the

construction of rental housing, Section 221 which gave an interest

subsidy to private sponsors, and Section 236 which gave an annual

subsidy to project owners. The National Housing Policy Review (NHPR)

Commission, established by President Nixon in 1973, came out in favor of

turning to more demand oriented housing programs. The higher the price

and income elasticities of housing services demand, the greater the

effectiveness of demand oriented programs. The NHPR study led to the

implementation of Section 8, a demand subsidy program that gave an

indirect payment to renters. Also, the Housing Allowance Demand

Experiment (HADE) was conducted which gave housing allowances to renters

and owners in the form of direct income transfers to both tenure groups.

Programs such as the percent of rent subsidy could be evaluated using

racial price and income elasticities of housing services demand to

examine whether it was acting as a housing subsidy or merely an income









transfer to the participants. The success of such housing programs

depends on the magnitudes of the income and price elasticities of

housing demand whichever elasticity is appropriate to the program in

questions. If the two elasticities are higher for blacks than whites,

then demand oriented housing policies have more of a beneficial effect

on blacks due to their greater responsiveness than whites.

A second application for racial housing services demand

elasticities is in the arena of traditional monocentric model theory and

helps explain why blacks are more concentrated in central cities, while

whites prevail in the suburbs. As stated in Mills (1980):


Suppose that the disutility of a mile of commuting is proportionate
to the wage rate, and that the factor of proportionality is no
greater for high- than for low-income workers. Then, if the income
elasticity of demand for housing exceeds 1.0, high-income workers
live further from the urban center than do low-income workers. If
the income elasticity is less than 1.0, high-income workers
nevertheless live farther out, provided the demand for housing is
not too inelastic with respect to its price. (p. 73).


The income elasticities of housing services demand of blacks and

whites, together with the income levels of the two racial groups, can

help explain the process of white suburbanization over the years.















CHAPTER III
IMPROVEMENTS IN THE MEASUREMENT AND SPECIFICATION OF
HOUSING VARIABLES


The previous literature on housing demand has ignored or

mis-defined one or more of the relevant economic variables from the

theory of consumer demand relating to housing as a durable, as well as

an investment, good. This is the first study to correct for all the

previous shortcomings in the same analysis that builds on the work of

prior authors in the housing area.


i. As discussed in the introduction, this study improves on the

work of previous authors by endogenously constructing racial housing

price indices for black and white owners and renters. Virtually all the

prior literature on housing demand has used cross-sectional housing

price indices from the family budget survey published annually by the

Bureau of Labor Statistics, which can definitely be improved upon. The

standard unit in the BLS survey for renter families is defined in the

U.S. Department of Labor (1984) as: "...an unfurnished five-room unit

in sound condition with a complete private bath, a fully equipped

kitchen, hot and cold running water, electricity, central or other

installed heating, access to public transportation, schools, grocery

stores, play space for children, and location in residential

neighborhoods free from hazards and nuisances" (p.23). The standard

unit in the BLS survey for a homeowner family is defined in the U.S.

Department of Labor (1984) as: "...the same as that for the renter

families insofar as neighborhood and dwelling unit characteristics are









concerned, except that the unit itself was a five- or six-room dwelling

and one or one and one-half baths..." (p. 23).

The hedonic price indices used in this study control for housing

quality in a superior fashion to the BLS price indices. There can be

much variation in the owner and rental housing units described above

that only have the very basic necessities included. The definition of a

"sound" housing unit is also very subjective.

The hedonic price indices in my study have far more detail on the

structural, neighborhood, and locational features of the housing unit in

question (see Table 1). The structural characteristics in this study

take into account the age of the structure, totally ignored in the BLS

indices. Also, the number of floors and the presence of air

conditioning, a fully working electrical system, a telephone, and

rodents in the housing unit. The neighborhood characteristics in the

BLS are equally as vague as those in the AHS. However, the locational

variables are superior in the AHS as the location of the unit within or

outside the central city is available, as well as the distance and time

the household head is from his place of work. Another superior feature

of the indices computed from the hedonic analysis is that separate price

indices are constructed for homeowners and renters, the BLS only has a

composite shelter cost.

Finally, the BLS does not differentiate between blacks and whites

in either tenure category. My housing price indices are constructed for

both racial groups and for both tenure categories in each of the fifteen

SMSAs in my sample. There are more sub-groups available from my AHS

endogenously constructed housing price indices than from the BLS family

budget survey data.









ii. Much of the previous literature uses current (measured) income

instead of permanent (normal) income (see Friedman (1957)). The

problems involved in estimation with current income are shown below:


YC = Yp + YT (identity)

where

YC = Current (Measured) Income

Yp = Permanent (Normal) Income

YT = Transitory Income


The "true" housing demand function can be specified as follows:


Q = Bp Yp + BTYT + rw + U

where

Q = Quantity of Housing Services Demanded

W = a vector of other explanatory variables, including the

constant and a housing price variable

However, many previous studies have a misspecified housing demand

function where current income is used instead of permanent income in the

following form:


Q = CYC + rw + V


Given the above misspecification and that Yp, YT, and W are

uncorrelated, the ordinary least squares (OLS) estimators of BC and r

are

C + C (W'W) V
r r0 (')1W IV






16


EY V
where plim BC = BC + plim -L-


After some manipulation and using the following expressions:


plim --- = o plim -T-- = o2



V = (Bp BC)YP + (T )YT + u



0=
P C

the following is derived:


plim BC = B + (1-8) B


As permanent income is expected to have a greater impact on housing

demand than transitory income (i.e., p > BT and 0 < 0 < 1), the

following is found:


Bp > plim BC > BT


The final conclusion is that if current income is used instead of

permanent income, the result will be an underestimate of the permanent

income elasticity of housing services demand and an overestimate of the

transitory income elasticity of housing services demand. My paper will

use a measure of permanent income derived from an instrumental variable

technique using Milton Friedman's Permanent Income Hypothesis (see

Chapter IV).


iii. Many housing demand studies do not add net imputed rental income of

a house (NI = gross rental value housing expenses) to the permanent









income of homeowners. De Leeuw (1971) defines this net imputed rental

income as: "the nonmonetary income owner-occupants derive from the

rental value of their homes" (p. 3).

Not taking net imputed rental income into account biases the income

elasticity of demand for housing services of homeowners away from one.


iv. Many housing studies use incorrect price deflators, or even no

price deflators at all. The price index of all goods and services

(excluding shelter), px, is used to deflate nominal price and income

variables. The price index of all goods and services should exclude

shelter; unfortunately many housing studies do not take this into

account. The bias introduced by not excluding shelter depends on the

share of the overall index going to housing. The bias is larger, the

greater the share of the good being estimated is in the person's budget.

As individuals usually spend a large share of their income on shelter,

as opposed to say fish, the bias introduced by not excluding shelter

from the price index can be large. The price index of all goods and

services (excluding shelter) used in this study is derived in Chapter

IV.


v. A further point concerns the definition of e (expenditures on

housing services by homeowners) used in the majority of the literature.

As de Leeuw correctly states: "The variable to which the theory of

consumer demand and most empirical demand studies refer is expense per

unit of time (usually per year), not market value (or sales price)"

(1979, p. 2).

My interpretation of e will follow that of de Leeuw by measuring

the annual expenditures on all housing services. One should also









include in e the net imputed rental income (NI) of the home to the

homeowner, which is the opportunity cost of the homeowner's equity.

Rosen's (1979a) rationale for using an incorrect measure (i.e., market

value) is seen where he states:

Since the theory of consumer demand suggests that housing
services is the appropriate variable, it is implicitly assumed
that the flow of housing services is proportional to the value
of the house. This has been the explicit or implicit
assumption in most studies of the demand for owner-occupied
housing and is retained (here) for lack of a better
alternative. (p. 9)

However, de Leeuw has shown that average housing service expense as

a percent of average market value decrease as market value rise (see de

Leeuw (1971), Table 1, p. 2). Rosen and other writers are empirically

incorrect when using house value (or sales price) as a proxy for expen-

ditures on housing services by homeowners.


vi. Many authors fail to adjust P the price index of housing services

for homeowners, for the implicit subsidy given to homeowners under

current federal income tax regulations in the United States.

Under current law, homeowners can deduct property taxes (T) and

mortgage interest payments (MI) from their taxable income. The

adjustment to the price index for homeowners (P ) used in this thesis

incorporates a procedure similar to that reported in Hamilton and Mills

(1984), where



= (1t) + (1-t) + D + M (gr+e) ] P


where poj = homeowner price index for individual j

P = homeowner price index for a race/tenure group in a

certain SMSA










MI = mortgage interest payments

V = value of home

t = marginal tax rate

T = property taxes

D = rate of depreciation

M = rate of maintenance

g = real rate of capital gains

e = expected inflation rate
N
g = nominal rate of capital gains

r + e
= g


The above approach links together the rental price and the value

price in housing markets, where p is a rental equivalence index of an

owner's house price index (P ). The above equation takes into account

that both mortgage interest payments and real estate taxes are currently

deductible from taxable income. Also, depreciation and maintenance are

costs to an owner, hence their positive signs, whereas capital gains,
N
g are a negative cost to an owner, hence its negative sign. This

expression also takes into account that the nominal capital gains rate

is made up of two components, a real capital gains rate (gr) and an

expected inflation rate (ne).















CHAPTER IV
THE MODEL


Estimation Technique Theory

The model used in this paper is the one developed by Lee and Trost

(1978) which allows for simultaneity between the decision whether to own

or rent (the tenure choice equation) and the decision on how much to

spend on housing consumption (the housing demand equation). The

theoretical model can be set up as follows:


e = h(X ) + e e = e if I > 0

e= h(X ) + e (4-1) e = e otherwise
r r r r
I = g (Z) + t

where

e = expenditures on housing services if household head owns

er = expenditures on housing services if household head rents

Xo, X Z = explanatory variables in each equation

I = unobservable index determines tenure choice


It is a switching regression model where the index function

determines whether e or e is observed. The Lee-Trost model
o r
incorporates the correlations between o and c, and between Er and E

into the estimation process. Unless E and E are both independent of E
o r

(i.e., no simultaneity), traditional simultaneous equation estimation

yields biased and inconsistent estimates of the parameters in system

(4-1). Lee and Trost suggest two methods of estimating the above









system of equations which avoid inconsistency, a maximum likelihood

procedure and a two-stage procedure. I will use the two-stage procedure

(as have Rosen, 1979a and Gillingham and Hagemann, 1983) because it is

computationally easier to implement.

The first stage is to estimate the tenure choice equation. I

specify this as a Probit model (see McFadden, 1976) and use the esti-

mated parameters to construct the following variables, which are used as

explanatory variables in the demand equation in the second stage:



A f(I) A -f(I )
0 F(I ) r (1-F(I))


where Ao, A = expected value of errors in Probit equation,

conditional upon owning and renting, respectively

f(") = standard normal density function

F(') = standard normal cumulative density function

I = g(Z)

F(I ) = estimated owner-occupant probability

(1-F(I )) = estimated renter probability


The coefficient on A in the final homeowner demand equation equals

(p*o), where p is the correlation between E and E, and a is the

standard error of E Similarly, the coefficient on A in the final
o r
renter demand equation equals (p*o), where p is the correlation between

E and c, and o is the standard error of E ,

Given the Probit estimates and the linearity of the conditional

expectations of e and e given e, the expected errors in the Probit
o r
equation are proportional to the expected errors in the owner and renter

demand equations taken separately.









The second stage is to estimate the demand equations by ordinary

least squares (OLS), including the constructed variables A and A to

take into account the possible correlation between the errors in the

demand and tenure choice equations. If the A's are statistically

significant, the demand and tenure choice decisions are simultaneous.


Specification of the Model and Construction of Housing Variables

Having discussed the econometric theory behind the model's

estimation in the first section of Chapter IV, I will now explain the

stratification of the data by Standard Metropolitan Statistical Area

(SMSA), race, and tenure. Also I describe in this section the

explanatory variables and show how some of them were constructed and

incorporated in the tenure choice and housing services demand equations.

The first step in my statistical analysis is to stratify the 1981

Annual Housing Survey SMSA data into four sub-groups by tenure choice

and by race of household head. I use Lee and Trost's model in (4-1) on

blacks and whites separately as follows (where the subscripts o, r, w,

and b refer to owner, renter, white, and black):


WHITE BLACK


e = h(X ) + e e h(X ) +
o,w o,w o,w o,b o,b o,b
e = h(X ) + E e h(X ) + E
r,w r,w r,w r,b r,b r,b
I = g(z) + E I = g(z) + Eb


where e = ew if I>0 where e = eo,b if I>0
o,w o,b
e = e otherwise e = e otherwise
r,w r,b

The model specification and explanatory variables I use are the

following:









WHITE BLACK


e = h(Pow/Pxo,w/ x,d) + EO eo,b = h(Po,b/ /Yp,b ,d) + eo,b

r,w h(Pr,w x ,r,w/xd) + cr,w er,b = h(Pr,b/PxYr,b/Pd) + Er,b
I = g(y w/pxP ,w/Pr,wd) + w I = g(yb/Px Po,b/Pr,bd) + Eb

where

e = expenditures on housing services

p = price index of housing services

px = price index of all goods, excluding shelter

y = permanent income of household head

d = a vector of demographic variables


As can be seen, the above analysis requires price indices of

housing services for black and white homeowners and renters, namely

Po,w' o,b' r,w, and pr,b As was noted in the introduction, there

are major problems encountered when using either time-series or

cross-sectional Bureau of Labor Statistics price indices. These indices

also do not attempt to capture any price differentials between blacks

and whites. Fortunately, with the rich data set used in this study, it

is possible to construct racial/tenure housing price indices in the

fifteen cities in my sample and permit estimation of racial housing

demand price elasticities.

By stratifying the data set by tenure choice and by race for each

of the fifteen SMSAs in the survey, it is possible to construct a

housing service price index number for each tenure/racial classification

in each SMSA, i.e., sixty price indices in all. This is accomplished

using hedonic pricing techniques, first developed by Rosen (1974) and









applied to housing demand studies by numerous authors including Linneman

(1980) and Goodman and Kawai (1984), as follows:


V -1
Ri = j(Si, N., Li)


where

V = property value of owner-occupied house

R = annual contract rent

S = a vector of structural characteristics of the housing unit

N = a vector of neighborhood characteristics of the housing unit

L = a vector of locational characteristics of the housing unit


In the separate regressions of (i) V on S, N, and L in the

homeowner market, and (ii) R on S, N, and L in the rental market, each

regression coefficient denotes the implicit dollar value (or marginal

trait price) associated with each structural, neighborhood, or

locational characteristic embodied in the housing unit.

Before discussing the theory behind and actual construction of the

hedonic housing price indices, let us discuss the functional form of the

hedonic regression equation.

Some of the previous literature utilizing hedonic price theory has

rationalized using a particular functional form for the equation,

usually either the linear or semi-logarithmic forms. This study,

however, will select the functional form using a Box-Cox transformation

(Box and Cox, 1964) of the dependent variable as follows:


V(-l = 0+ 1X + 2 2 + ... + et (4-2)
X









where

V )-1 V if X=l (linear)
AX inV if X=0 (semi-logarithmic)


The above assumes that there exists a value of A such that in

(4-2) et is i.i.d. and N(0,o2). The log of the likelihood function of

(4-2) can be written in vector form as follows:


N M(V -XB)'(V -XB) N
L = constant- ln2 (+ (X-l) E InV (4-3)
2 o2 i=1


where N = sample size.

The concentrated likelihood function of (4-2), excluding the

constant, is as follows:


N 2 N
L(A) = ln 2A) + (A-I) Z lnV (4-4)
i=1


The computational burden is simplified by substituting in values

for A of 0 and 1 into (4-2), separately, then using ordinary least

squares to find 02 (estimated value of the sample variance) for both

values of A. The final step is to select the value of A for which the

concentrated likelihood function (4-4) is maximized. The above

procedure is employed to avoid, a priori, assuming either the linear or

the semi-logarithmic functional form, but instead to empirically

determine the best functional form for each of the sixty hedonic

equations.

The next step in the analysis is to use the hedonic coefficients to

construct a cross-sectional Laspeyres-type (fixed weight, as opposed to

a fixed price Paasche index) housing price index for each of the four

race/tenure sub-groups in each of the fifteen SMSAs. The housing price









index (Pjk) for the race group j in city k when the functional form of
jk
the hedonic equation is linear is as follows:




P = jk x 100
jk k
WjkP

where city k = 1,...,15

characteristic i = 1,...,15

race j = 1,2


15
Pjk = bi,j,k X
i=1

15 15 2
P = E E b.,, X.
k=l i=1 j=l l k


X. = means of housing variables (characteristics) across entire
1
sample.

Wjk = weight of the racial/tenure group j in city k relative to

the entire owner or renter samples (N).

b. k = hedonic equation regression coefficient for

characteristics i for race group j in city k.



[w == EW =11
[jk N jk 1]



The idea behind the index is to price the characteristics of a

particular racial group in a certain city relative to the price

paid for the same bundle of housing characteristics (note that X. is in

the numerator and denominator of the price index) in the entire owner or

renter samples. An analogous procedure is followed on the above index










TABLE 1: LIST OF DEPENDENT AND INDEPENDENT VARIABLES USED IN THE
HEDONIC EQUATION REGRESSION ANALYSIS



DEPENDENT VARIABLES

V PROPERTY VALUE OF OWNER-OCCUPIED HOUSE ($'000)

R ANNUAL CONTRACT RENT ($)

INDEPENDENT VARIABLES

AGEST AGE OF STRUCTURE

BEDR NUMBER OF BEDROOMS IN HOUSING UNIT
S
T FLOORS NUMBER OF FLOORS IN BUILDING
R
U AIRCN 1 IF AIR CONDITIONING IN HOUSING UNIT
C
T WORKELEC 1 IF WORKING ELECTRICAL WALL OUTLETS IN EVERY ROOM
U
R RATS 1 IF SIGN OF RATS OR MICE IN LAST THREE MONTHS
A
L TELEP 1 IF HAVE USE OF A TELEPHONE

BATH* NUMBER OF BATHROOMS IN HOUSING UNIT


N
E
I PBTRANS 1 IF ADEQUATE PUBLIC TRANSPORTATION IN NEIGHBORHOOD
G
H SCHOOL 1 IF ADEQUATE PUBLIC SCHOOLS IN NEIGHBORHOOD
B
0 POLICE 1 IF ADEQUATE POLICE PROTECTION IN NEIGHBORHOOD
R
H RECR 1 IF ADEQUATE OUTDOOR RECREATIONAL FACILITIES IN
0 NEIGHBORHOOD
0
D


L
0 INOUTCC 1 IF HOUSING UNIT LOCATED INSIDE CENTRAL OR SECOND
C CENTRAL CITY IN SMSA
A DIST* MILES FROM HOUSEHOLDER'S HOUSING UNIT TO WORK
T
I TIMWORK* MINUTES FOR HOUSEHOLDER TO TRAVEL FROM HOUSING UNIT
0 TO WORK
N


*indicates quantitative variable









when the functional form of the hedonic equation is semi-logarithmic.

This part of the analysis will prodt*f two sets of indices, one for

renters and one for homeowners. Each index has 30 values, corresponding

to the fifteen SMSAs and the two racial groups.

The fifteen variables that are used in this study are listed in the

following table. As can be seen, eight are structural, four neighbor-

hood, and three locational characteristics. Though the Annual Housing

Survey provides many more housing characteristics, these fifteen out

perform the others in explanatory power. Unfortunately, the neighbor-

hood characteristics are all qualitative in nature. Also, square

footage of the housing unit was an unavailable structural characteristic

together with the locational variable distance of the housing unit to

the central business district.

The final step in the construction of the housing services price

indices for individual homeowners, namely po,w (white owners) and po,b

(black owners), is to adjust the owner SMSA/race indices just calculated

to take into account the implicit subsidy given to homeowners under the

current income tax laws (see Chapter III), such that



p oj [ V(l-t) + (l1-t) + D + M (grne)] p
V 0


where

poj = individual housing price index for jth homeowner,

P = homeowner housing services price index for the relevant

owner SMSA/race group computed from the hedonic regression

analysis.


Prior to this final step, every white homeowner in Anaheim, for

example, had exactly the same housing price index value. This final









adjustment for homeowners introduces price differentials within each

group. That is, the price varies within Anaheim to reflect tax rate and

equity differences across households. The nominal capital gains rate,

gN(=gr+je), used in this expression uses data from the U.S. Department

of Housing and Urban Development (see Table 41) on the average property

values in the selected SMSAs between 1980 and 1981. That rationale

behind calculating a historical capital gains rate is that it is the

best guess available for an estimate of the future capital gains rate.

The final step in the construction of the housing services price

indices for individual renters, namely pr,w (white renters) and r,

(black renters), examines whether the renter price indices calculated

should take into account discounts given to renters for length of

tenure. In a survey article of the rent discount literature, Marshall

and Guasch (1983) concluded that landlords offer sitting tenants

discounts which average approximately 1% for each year of renter

occupancy, after the first year, as follows:


Prj = Pr (.01)*(n-1)*(Pr)

where

prj = individual housing price index for jth renter,

n-1 = years of renter residency at present unit 1,

P = renter housing services price index for relevant

SMSA/race group computed from the hedonic regression

analysis.


The final demand equations are estimated with and without the adjustment

to the renter price indices that takes into account any discounts given

to renters for length of tenure.









The price index for all goods and services, excluding shelter (px)

for each SMSA used in this study is constructed based on the relation-

ship between the non-housing and housing budgets for a 4-person family

in the United States obtained from the U.S. Department of Labor (1982).

The purpose of this annual budget survey is to determine for each of the

24 metropolitan areas in the survey the cost of purchasing a specific

market basket of goods that reflects the usual purchasing patterns of a

typical 4-person family. Each item priced in the budget is placed in

one of the following five categories:

1) food,

2) housing,

3) medical care,

4) transportation,

5) all other categories.

The price index of all goods and services (excluding shelter) used

in this thesis relies on the relationship between the non-housing and

housing budgets in the 24 metropolitan areas in the 1981 family budget

survey holding true for the 15 SMSAs in my 1981 Annual Housing Survey

dataset (see Appendix, Table 35). The regression of the non-housing

indices on the housing indices from the BLS budget survey quantifies how

much variation in the non-housing price index is explained by the

housing component


(Non-housing index) = a + b (Housing index)


An ordinary least squares regression of the above equation yielded the

following results:









Non-housing Index = 43.9781 + 0.5602 (Housing Index) (4-5)

standard errors (9.0256) (0.0897)

t-statistics (4.8776) (6.2418)

R2 = 0.6391
N = 24


Variation in the housing price index explains two-thirds of the

variation in the non-housing price index in the 24 metropolitan areas

included in the family budget survey. Both the constant term and single

regressor are highly significant. My study uses the above relationship

to construct the overall price indices of all goods and services,

excluding shelter (px) in each of the fifteen SMSAs in my sample.

The next stage in the construction of the price index of all

non-housing goods and services in, say, Anaheim is to weight each of the

four housing price indices in Anaheim calculated from the hedonic

equations by population, as follows:


n1 white n2 black n3 white n4 black
nl whit) + (Po+ (p, )+(p )+ (Pr )
PH N o' owner N o' owner N r' renter N r renter


where
PH = housing price index for Anaheim

n, = number of white owners in Anaheim

n2 = number of black owners in Anaheim

n3 = number of white renters in Anaheim

n4 = number of black renters in Anaheim

N = n1 + n2 + n3 + n4









The next step is to substitute in the pH index for Anaheim on the

right-hand side of equation (4-5) in order to calculate the price index

of all goods and services (excluding shelter) in Anaheim. I repeat the

procedure for the remaining fourteen SMSAs in my sample.

The next variable which has to be constructed is permanent income,

y Numerous methods have been proposed for estimating this unobser-

vable variable, including (1) grouping data, (2) income averaging over

an arbitrary number of years, and (3) instrumental variable methods.

There is no widely accepted technique in the literature of calculating

unobservable permanent income. As my data set is cross-sectional, it

was not possible to average income over an arbitrary number of years.

However, Mayo (1977) has shown that permanent income elasticities

obtained by income averaging and by instrumental variable techniques are

on the same order of magnitude. In Chapter III the problems encountered

in accurate coefficient estimation were discussed when current income is

used as a proxy for permanent income. This study will utilize Milton

Friedman's (1957) Permanent Income Hypothesis in the construction of a

measure of permanent income:


YC = Yp + YT (identity)

where

YC = Current Income

Yp = Permanent Income

Y = Transitory Income


The Permanent Income Hypothesis assumes that permanent income

depends on human and non-human wealth, H and NH, so that:

Y = f(H,NH) (4-6)









On substituting (4-6) into the identity we get

YC = f(H,NH) + YT


(4-7)


On the assumption that permanent income is encompassed by the human

and non-human wealth components, the systematic part in the regression

of current income on the human and non-human wealth variables represents

an estimate of permanent income. The non-systematic part, the residual,

represents an estimate of transitory income. The following table lists

the variables chosen in the regression to estimate permanent income.


TABLE 2: LIST OF INDEPENDENT VARIABLES USED IN THE
PERMANENT INCOME REGRESSION EQUATION ANALYSIS


EDUC Years of education of household head

AGE Age of household head

SEXHH 1 if household head is female

MARHH 1 if household head is presently married

CHIL Number of children (under 18 years of age)

RACE 1 if household head is black


SMSA SPECIFIC FEATURES


P Price index of all goods and services (excluding
x shelter) in SMSA where housing unit located

COOL Average number of cooling degree days per annum in
SMSA where housing unit located


See appendix for a complete definition of this variable.


DEP VAR = LOG YC (TOTAL FAMILY INCOME)


HUMAN AND NON-HUMAN WEALTH VARIABLES









In the above specification, the AGE variable is a proxy for

experience and the EDUC variable picks up human capital and skills,

while the (AGE)2 and (EDUC)2 terms are experimented with to determine if

there are diminishing or increasing marginal returns associated with

increased age and education. The (AGE)2 variable picks up any falling

investment in human capital as age increases (see Mincer (1974)). The

AGE, MARHH, and CHIL variables all determine the position of the

household head in his life cycle and how this affects his income. The

MARHH variable picks up differences in human capital formation by

married as opposed to single heads of household. It is expected that a

married head of household will have accumulated more human capital than

a single head in order to support his spouse and children, hence a

positive sign on this variable. Unfortunately, the data set has no

detail available on the labor force participation rate within the

household, which is correlated with total family income.The RACE

variable picks up any differences in human capital formation between

blacks and whites with regard to earnings. Similarly, the SEXHH

variable picks up differences in human capital accumulation between

males and females with regard to earnings. Women generally have less

human capital accumulated than men because they work fewer years due to

child rearing responsibilities.

As can be noted in the previous table there are also SMSA specific

features in the permanent income regression. As this is one regression

on the entire sample, one can add such variables for each SMSA to cap-

ture any inter-city differentials in earnings. These variables capture

cost of living and amenity wage differentials between the SMSAs in the

study. The SMSA specific variables are estimated in this equation in

both linear and logarithmic form.










Heckman and Polachek (1974) conclude that there is overwhelming

evidence that the best functional form relating current income and the

wealth variables is semi-logarithmic, with the dependent variable in

logarithmic form. However, there is no, a priori, evidence on the

functional form of the SMSA specific variables. Therefore, these

variables are tried in linear and logarithmic form using ordinary least

squares on the equation.

Having constructed the housing price indices and the permanent

income estimates as described above, I next use the price and income

variables, along with other variables, in the tenure choice and then

finally the housing demand equations.

The variables selected for the tenure choice decision equation

specification are shown in Table 3. The chosen variables give insight

into why a household head would own, rather than rent, a housing unit,

and vice versa. Two of these variables, namely AGEHH and MARHH,

indicate where the household head is and how he is faring on his life

cycle. The AGEHH variable picks up the household head's accumulated net

assets and savings, while the MARHH variable picks up income stability.

There is likely to be less risk of default on a mortgage when the

household head is married as there are two potential wage earners in the

household. The probability of owning is expected to be positively

correlated with both variables.


___j









TABLE 3: LIST OF INDEPENDENT VARIABLES USED IN THE TENURE CHOICE
REGRESSION EQUATION ANALYSIS



DEP VAR = I(INDEX FUNCTION)

LOG(AGEHH) Age of household head

MARHH 1 if household head is married or widowed

MOVER 1 if household head moved to present residence within
the past 12 months

LOG(poi /rj ) Relative price of owning versus renting




The MOVER variable examines how the recent mobility of the head of

household affects the probability of owning. It is anticipated that the

expected probability of owning is negatively correlated with this

variable. Persons constantly on the move are less likely to want to

incur the high fixed costs (e.g., appraisals, origination fees, and

points) associated with buying and selling a house if they are not going

to be living at that location for very long.

The tenure choice equation also has a relative price term

(p oi/p .). It is expected that as the relative cost of owning, as

opposed to renting, rises in an SMSA, the expected probability of a

household head renting increases, and vice versa. Therefore, the

expected sign on this variable is negative. To construct this relative

price term for a household unit there has to be an owner price (poi) as

well as rental price (p .). If the housing unit is a white owner in

Anaheim, the owner price is the calculated price (p .oi) for a white

homeowner in Anaheim, and the rental price is assumed to be the price

for a white renter in Anaheim, namely prj, and vice versa.










The tenure choice equation is estimated on the entire sample,

excluding housing price indices constructed from sample sizes less than

100, using the Probit procedure. The continuous variables in the

equation are experimented with in linear and logarithmic form. Housing

price indices constructed from samples less than 100 are excluded from

the tenure choice regression (and all the subsequent housing demand

regressions) in order to avoid any false inferences drawn from using

housing price indices constructed using small sample sizes. Consequent-

ly, a number of black owner and renter groups in certain SMSAs are

excluded from the remainder of this analysis.

The final step in my study is to bring in all the constructed

variables from the previous steps in the analysis and estimate the

housing services demand equations using the following variables:


TABLE 4: LIST OF INDEPENDENT VARIABLES USED IN THE
HOUSING SERVICES DEMAND REGRESSION EQUATION ANALYSIS


DEP VAR = e/P (DEFLATED EXPENDITURES ON HOUSING SERVICES)
x


Y /P Permanent income, deflated by the price index of all
S goods and services

Po/ /Px Price index of housing services, deflated by the
price index of all goods and services

A Expected value of errors in the tenure choice
equation

SEXHH 1 if household head is female

MARHH 1 if household head is presently married

AGE Age of household head









These are standard explanatory variables that should appear on the

right hand side of any housing demand equation; a price, income, and a

set of demographic variables. The one exception is the A variable,

which if statistically significant, shows that there is simultaneity

between the tenure choice decision (own or rent) and the housing demand

decision, i.e., how much housing services to consume. To tie things

together, A is obtained from the tenure choice equation, permanent

income (y ) from the permanent income hypothesis instrumental variable

equation, and the owner price indices (p ) and the renter price indices

(pr) from the hedonic price equations.

The housing services demand equations have housing expenditures as

the dependent variables, and use the same manipulations described in

Chapter I to derive the relevant housing demand elasticities. Using the

white owner housing services demand equation as an example

log(qw) = 0 + 8 log(p O) + 62 log( w ) + 3 log(px) + Aow +

7
E B.D. + E (4-8)
j=5 3 j,o,w o,w

Here, B1 and 82 are the price and permanent income elasticities of

housing services demand, respectively, for white homeowners in the

entire sample. Using the same equation transformations found in Chapter

I, one can derive the following estimable logarithmic housing demand

equation:


log(eow/P ) = + (1+Bl)log(pow/P ) + 82 log(yo w/Px) + 4Aow +

7
Z 8. D. + (4-9)
j=5 j',o,w o,w









The estimation of equation (4-9) using ordinary least squares

yields the price and income elasticities of housing services demand for

white owners in the entire sample. By stratifying the entire sample

into three further groups, namely black owners, white renters, and black

renters, and following the same procedure described above, the price and

income elasticities of housing services demand for these three race/tenure

groups can also be estimated. The main purpose of this dissertation is

to estimate these eight racial price and income housing services demand

elasticities. The demand equations are run with and without the vari-

ables measuring the expected value of the errors in the Probit equation

(A or A ) to examine how sensitive the price and income elasticities of
o r
housing services demand are to the exclusion of these variables.

A further piece of empirical work is to stratify each of the four

race/tenure groups into three educational attainment levels:

i) elementary education: no school, kindergarten, or did not

graduate from high school.

ii) high school education: graduated from high school.

iii) college education: attended college for any amount of time.

The purpose of this piece of analysis is to examine whether the price

elasticity of housing services demand varies by educational attainment of

the household head. An F-test is conducted on each of the four

race/tenure groups to see whether there are statistically significant

differences in the price elasticity of housing services demand across the

three levels of educational attainment. By holding the level of education

constant, there should be less variation in permanent income, which makes

it very hard to estimate an income elasticity. The idea behind this final

section is to compare, say, white homeowners in Dallas to white homeowners









in Boston with college educations, and compare their price elasticity of

demand to that of white homeowners in the same two SMSAs with high school

levels of education.

The final piece of empirical work is to compare the price and income

elasticities of housing services demand obtained using the endogenously

constructed hedonic price indices from the Annual Housing Survey to those

obtained from the Bureau of Labor Statistics family budget survey.

Although other authors, namely Gillingham (1975) and Follain, Ozanne and

Alburger (1979), have looked at differences between hedonic housing price

indices and housing price indices from the family budget survey, nobody

has looked at the consequences of using the two types of indices in a

housing demand study and their consequences, if any, on estimates of price

and income housing demand elasticities.

The SMSAs covered in the 1981 Annual Housing Survey and the Bureau of

Labor Statistics family budget survey in any year are not identical.

However, using the 1978 family budget survey when the BLS last conducted

its survey in 39 cities (since 1978 it surveys 24 cities) resulted in

eight common SMSAs between the Annual Housing and the BLS family budget

surveys. These cities were Boston, Dallas, Detroit, Minneapolis, Orlando,

Pittsburgh, Washington D.C., and Wichita. Housing and non-housing price

indices were computed for these eight SMSAs from the BLS budget data (see

Table 42) and used in the final housing demand equations to compute

housing demand elasticities. These elasticities were compared to those

generated using endogenously constructed hedonic housing and non-housing

price indices from the 1981 Annual Housing Survey.






41


The Mills ratio variable (Ao/r) was dropped from this set of

regressions because in its construction back in the Probit analysis, one

of the variables used in the Probit regression is a relative price of

owning versus renting variable (poi /Prj). As the BLS housing index does

not differentiate between owning and renting costs, it is an overall

shelter cost, it was decided that adding the Mills ratio variable would

cause serious methodological problems in the BLS set of regressions.

Hence, for consistency, it was dropped from both the BLS and AHS set of

demand equation regressions.















CHAPTER V
EMPIRICAL RESULTS AND DISCUSSION


This chapter analyzes the results from the intermediate steps on

the way to obtaining the price and income elasticities of housing

demand. The order of discussion of the regression results will be as

follows:

1. hedonic equations used in the construction of the price

indices,

2. instrumental variable equation used to construct an estimate

of permanent income,

3. tenure choice decision equation,

4. the housing demand equations from which the housing demand

elasticities are computed.

Tables 5 through 19 tabulate the results from the hedonic

regression analysis used to compute the Laspayre-type housing services

price indices. It is not practical to individually discuss every

regression coefficient in these tables as for each of the four

race/tenure groups examined in each of the fifteen SMSAs there are

fifteen regression coefficients (excluding the constant term) to

explain, a total of 9001 regression coefficients. Therefore, the Pearson

P test (see Rao (1952)) is employed to determine whether the individual

explanatory variables are generally significant. This test assumes that

each hedonic regression is independent from every other one within a

tenure category. Table 20 presents the results from this test, together









with the mean effects of each explanatory variable in the sample. Using

this test, all the explanatory variables used in the hedonic equations

were significant overall, except POLICE for renters and TIMWORK for

homeowners.

The independent variables used in the hedonic regressions also

performed well in explaining the variation in the dependent variables,

as can be seen from the magnitudes of the coefficients of determination

(R2). The mean R2 was 0.48 in the twenty-nine owner regressions and

0.37 in the thirty renter regressions. The owner and renter equations

are not comparable by looking at R2's, as the dependent variables

differ, logarithmic house value for homeowners and linear annual

contract rent for renters. It should be noted that cross-sectional data

are used in this study, and that the R 's are not comparable to the

generally higher R2's obtained when using time-series data.

Another result from the hedonic equation regression analysis is the

best functional form chosen for the owner and renter equations from the

Box-Cox transformations. All the owner regressions resulted in an

optimal value of A of zero, implying a semi-logarithmic form for all the
2
owner regressions. All but one of the renter regressions resulted in

an optimal value of A of one, implying a linear form for these

equations.

The above results are consistent with the notion that a renter pays

for specific housing accessories through explicit extra rental payments

to a landlord. The coefficients in a linear model measure the extra

dollar cost in the dependent variable (in this case annual contract

rent) associated with a one unit change in an explanatory variable. An

example is where a renter pays $x in rent when he moves to an apartment









complex with a swimming pool, when he was paying $y when he used to live

in an apartment complex without a pool, the two apartment complexes

being virtually identical in all other attributes. The $(x-y) is the

amount of money the renter is willing to pay in order to have pool

facilities. Theoretically, introducing a dummy variable for pool

facilities into a linear hedonic equation for renters would produce a

coefficient on the pool variable of (x-y).

The semi-logarithmic form for the owner hedonic regressions

measures the percentage change in the dependent variable (in this case

value of the owner's house) associated with a one unit change in an

explanatory variable. Therefore, the semi-logarithmic form allows the

value added by an extra unit of an explanatory variable to vary with the

value of the owner's house. An additional bathroom adds z% to the value

of a house, avoiding the linear form inference that an additional

bathroom adds the same extra $k to a $10,000 house as a $300,000 house.

A plausible explanation for this is that there is more heterogeneity in

terms of quality and quantity in owner-occupied housing than in rental

housing. The mean effects of individual housing characteristics in

owner and rental housing markets are contained in Table 20.

One thing that becomes evident from this analysis is that certain

housing characteristics play differing roles across the SMSAs in the

sample. Numerous characteristics are significant in one SMSA with, say,

a positive sign, and are significant in another SMSA with the opposite

sign. It is clear from this study that some housing characteristics

have indeterminate signs and that the role of the housing characteristic

is determined partly by the SMSA in which it is located.









The analysis now turns to focus in more detail on the magnitudes of

the variables in the hedonic equations and the expected effects of the

variables in terms of economic theory.

One expects the sign of AGEST to be negative, as housing stock

depreciates with age. As Table 20 shows, this was the case overall for

renters and homeowners. However, one can think of instances in certain

downtown neighborhoods (e.g., Baltimore and Jacksonville) where

tremendous incentives have been given by city councils to individuals

who renovate old houses in downtown neighborhoods. The success of this

type of program on a wide scale would lead to the AGEST variable having

a positive sign. Another scenario for the AGEST having a positive sign

is if there are some unaccounted for locational features positively

correlated with the age of the unit. Such features include, for

example, proximity to a lake or a nature trail. In this study, the

AGEST variable was significant and positive for white owners in

Washington D.C., as well as black renters in Minneapolis, Newark, and

Phoenix. For each year a housing unit ages, the value falls by i% for

an owner-occupied home and by $11 in annual contract rent for an

apartment.

Both BEDR and AIRCN had positive signs, reflecting that the more

bedrooms and the presence of air conditioning units both add value or

rent to a housing unit. Each bedroom adds nearly 9% in value to a

homeowner's house and $238 in annual contract rent to a rental unit.

AIRCN was significant, not surprisingly, across all four race/tenure

groups in the following SMSAs with high mean annual temperatures, namely

Dallas, Fort Worth, Orlando, and Phoenix. The structural characteristic

that performed best in terms of overall significance was BATH. This









characteristic had a positive sign for renters and owners, reflecting

that additional bathrooms add value or rent to a housing unit. The

presence of rats or mice in a housing unit did not add dollars in value

or rent to a housing unit as in no case was this variable significant

and positive! The presence of these rodents reduces home values by

approximately 6% while reducing annual rent for a renter by $155.

The presence of a telephone (TELEP) and working electrical wall

outlets in every room (WORKELEC) of a housing unit both entered the

hedonic regression equations, as expected, with positive signs.

The last structural characteristic, FLOORS (the number of floors in

the housing unit) entered the owner equation with a positive sign,

reflecting that the more floors in the house, the larger it probably is

and hence has a higher value. The sign of this variable in the renter

equations was also positive. However, it was not clear before the

regressions were run whether skyscraper rental apartments should command

higher rents than, say, two-story rental housing complexes. The overall

result from the regression analysis supports the former hypothesis.

On examining the four neighborhood variables employed in the

analysis, all four are specified as dummy variables with a value of one

for an adequate neighborhood characteristic and zero otherwise. The

RECR variable asks whether there are adequate outdoor recreational

facilities in the neighborhood. This variable had a positive effect on

housing values and rent, since land near recreational facilities is more

valued than other land. The presence of adequate outdoor recreational

facilities close to a housing unit adds just over 2% to the value of a

home and $145 in annual contract rent for a rental unit.









The POLICE variable asks the household head whether he considers

there is adequate police protection in the neighborhood. At first glance,

one might expect this variable to have a positive sign, reflecting high

values and rent in locations with high standards of police protection.

However, a convincing argument could be made for this variable having a

negative sign if crime and burglaries occur more frequently in areas of

high income households, i.e., criminals are more likely to be operating in

areas where the rich are their targets, not the poor. The POLICE vari-

able came in negative overall for homeowners, bearing out the latter

hypothesis, while for renters it was one of only two variables not

generally significant at the 10% level.

The PBTRANS variable asks household heads whether their

neighborhoods have adequate public transportation, namely bus routes.

At first glance this variable might be expected to have a positive sign

for owners and renters. However, in owner regressions it was generally

significant but negative. A rationale for the negative sign in owner

hedonic regressions is that bus routes do not usually go through high-

income neighborhoods, but instead provide poorer districts of an SMSA

with the means of transportation to the downtown and shopping malls.

The final neighborhood variable included in the hedonic analysis is

SCHOOL, which asks the household head whether there are adequate public

schools in the neighborhood. One associates good neighborhoods with

high quality schools in the suburbs of an SMSA. For owners, this

variable was generally significant and positive, supporting the previous

notion. However, it was negative and generally significant for renters.

A rationale for the negative signs is that the question asked of the

household head related to adequate public schools, with no mention of









private schools. This could be the premiere schooling choice of the

siblings of the household heads in the high-income neighborhoods.

The final set of variables included in the hedonic equations are

three locational variables, namely INOUTCC, DIST, and TIMWORK. The

variable that I really wanted is the distance of the housing unit from

the central business district. These three locational variables act as

proxies for this unavailable variable. INOUTCC has a value of one if

the housing unit is within the central or second central city of the

SMSA, zero if outside the central city. For owners and renters, it was

generally significant and negative. The location of the housing unit

within the boundary of the central city reduced, on average, the value

of the owner's home by nearly 12% while reducing annual rent payments by

$375 for renters. From the traditional monocentric model theory of

Mills and Muth, one expects the sign of this variable to be negative.

The assumptions underlying such traditional models include: (1) the

SMSA is located on a flat, featureless plain, and (2) all the production

and economic activity in the SMSA takes place in a single area in the

center, the central business district (CBD). The rationale for this

sign is that as income rises, individuals will trade off access to the

central business district (CBD) for more housing and land (space), which

is more available in the suburbs outside the central city. The

traditional theory incorporates the notion that the income elasticity of

demand for space (land) exceeds the income elasticity of the marginal

cost of commuting. A supporting argument relies on high-income families

having shallower sloped bid-rent curves than low-income families. As a

result, high income families buy large lots on relatively cheap land on

the outskirts of the SMSA. Also, central cities are generally more run









down and decaying gradually due to flight to the suburbs, this would be

particularly true for some northern cities. A negative sign can also be

expected if there is an inappropriate housing stock in the central city

of an SMSA, an example is the older cities that were built for downtown

rail transit. The INOUTCC variable will also have a positive sign if

the structural and neighborhood characteristics in the hedonic equation

capture all the quality and quantity aspects of the housing unit. If

this is the case, the remaining locational variables should measure the

price of land on which the housing unit sits. As the price per parcel

of land decreases as you move away from the CBD, the INOUTCC variable

should have a positive sign by this line of thought. However, the

structural and neighborhood variables in the hedonic analysis might not

capture all the quantity and quality aspects of the housing unit. As a

result of unmeasured housing quantity and quality measures, the sign of

INOUTCC can be expected to be positive. INOUTCC was negative and

significant for all four race/tenure groups in Detroit, Minneapolis-St.

Paul, and Newark, appearing to support the view concerning decaying

central cities within certain northern cities. However, this variable

was significant and positive in three cases for both owners and renters,

in particular in Dallas. In Dallas, this positive and significant sign

was the case for black and white owners, along with white renters. An

argument for the positive sign is relying on the traditional monocentric

model theory no longer holding for newer cities, such as Dallas.

The expected signs on the coefficients of the two remaining

locational variables, DIST (miles to work) and TIMWORK (time to work),

follow an analogous line of reasoning to INOUTCC. If the structural






50


and neighborhhod characteristics capture all the quantity and quality

aspects of the housing unit then the signs of these two variables will

be negative. However, if there are housing attributes not captured in

the hedonic equation, the signs on these variables will be positive. As

it turns out, these variables produce a mixed bag of overall positive

and negative significant coefficients in the hedonic regression

analysis.









TABLE 5: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
ANAHEIM-SANTA ANA-GARDEN GROVE SMSA



HOMEOWNERS: RENTERS:
V -1 RX-1
DEP VAR -DEP VAR --

WHITE BLACK WHITEBLACK
WHITE BLACK WHITE BLACK


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK



x
R
N


10.83***
(0.16)
0.0004
(0.0007)
0.0576***
(0.0107)
0.0507***
(0.0170)
-0.0503***
(0.0160)
0.1019
(0.1275)
0.0425
(0.0290)
0.0133
(0.0850)
0.3266***
(0.0162)
-0.0338**
(0.0165)
0.0193
0.0187
-0.0014
(0.0242)
0.0017
(0.0269)
-0.1845***
(0.0197)
0.0039***
(0.0016)
-0.0023**
(0.0011)


10.98***
(0.42)
-0.0013
(0.0074)
0.0169
(0.0707)
-0.0353
(0.1362)
-0.2773**
(0.1097)

NA

NA

NA
0.3862***
(0.1107)
-0.0173
(0.1068)
0.1770
(0.1306)
-0.3651
(0.2260)
0.3354*
(0.1678)
-0.3956**
(0.1484)
0.0094
(0.0096)
0.0004
(0.0055)


1706.46***
(657.32)
-0.402
(3.069)
531.691***
(63.604)
12.177
(41.372)
182.600**
(89.105)
-113.588
(595.233)
-154.966
(173.513)
142.709
(176.764)
1138.435***
(100.909)
-286.925***
(92.656)
64.529
(92.193)
52.999
(112.809)
500.971***
(123.520)
-492.222***
(94.235)
-2.285
(9.329)
5.919
(6.206)


4788.456*
(2523.281)
-4.527
(11.862)
954.725**
(410.578)
-95.464
(721.022)
294.498
(488.084)
-589.986
(1294.244)
-1397.370
(1159.442)
1081.681
(1193.806)
-325.152
(694.979)
-569.903
(545.716)
-332.197
(676.789)
-566.881
(759.258)
-1155.930
(767.277)
-747.876
(558.046)
-10.995
(57.468)
59.948
(42.566)


0 0 1 1
0.39 0.92 0.33 0.75
2,103 22 1,360 28

(where standard errors are in parentheses)


*** indicates significant at
** indicates significant at
* indicates significant at


1% level
5% level
10% level









TABLE 6: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
BOSTON SMSA



HOMEOWNERS: RENTERS:
V -l R1-1
DEP VAR DEP VAR -
SA

WHITE BLACK WHITE BLACK


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK



x2
R
N


10.22***
(0.14)
-0.0028***
(0.0008)
0.0560***
(0.0114)
0.0607***
(0.0119)
0.0453**
(0.0201)
0.1461
(0.1025)
-0.1255***
(0.0401)
0.0405
(0.0824)
0.3472***
(0.0177)
-0.0617***
(0.0206)
0.0085
(0.0239)
0.0285
(0.0289)
0.0633***
(0.0248)
-0.2556***
(0.0298)
-0.0021
(0.0020)
0.0008
(0.0012)


10.92"**
(0.72)
-0.0253***
(0.0079)
-0.0572
(0.0756)
0.3827***
(0.1515)
0.0541
(0.1430)
0.0307
(0.4674)
-0.1568
(0.1840)
-0.1458
(0.1756)
0.0899
(0.1222)
-0.0242
(0.1576)
0.0523
(0.1558)
-0.0479
(0.1747)
0.1251
(0.1465)
-0.2528
(0.2232)
0.0078
(0.0125)
0.0026
(0.0055)


48.331
(457.785)
-3.054
(3.245)
203.424***
(49.341)
101.818***
(15.912)
264.158***
(85.041)
253.935
(350.829)
-57.836
(119.452)
181.820
(140.406)
1901.052***
(183.066)
-44.678
(92.537)
-440.972***
(84.610)
264.271***
(92.378)
365.731***
(88.753)
-180.999**
(86.918)
-26.486***
(8.456)
8.291*
(4.450)


689.409
(916.322)
-9.456
(6.878)
52.598
(100.724)
-109.791***
(36.873)
468.268***
(201.494)
1176.538**
(576.177)
-151.423
(178.159)
143.494
(200.268)
1409.611***
(437.811)
7.913
(198.890)
-294.673
(184.239)
-57.251
(188.842)
354.574*
(183.480)
-104.649
(343.974)
-59.322***
(22.313)
7.088
(5.183)


0 0 1 1
0.40 0.58 0.18 0.19
1,510 43 1,727 268

(where standard errors are in parentheses)


*** indicates significant at
** indicates significant at
* indicates significant at


1% level
5% level
10% level









TABLE 7: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
DALLAS SMSA



HOMEOWNERS: RENTERS:
VX-1 R_-1
DEP VAR = DEP VAR =

WHITE BLACK WHITE BLACK
WHITE BLACK WHITE BLACK


9.54***
(0.17)
0.0004
(0.0009)
0.0844***
(0.0175)
0.0767***
(0.0182)
0.2397***
(0.0656)
0.0184
(0.1178)
-0.0482
(0.0351)
-0.0470
(0.0906)
0.5650***
(0.0192)
-0.0452**
(0.0222)
-0.0655**
(0.0274)
-0.0111
(0.0320)
0.0526*
(0.0297)
0.0844***
(0.0239)
0.0003
(0.0023)
-0.0023
(0.0016)


9.94***
(0.61)
-0.0038
(0.0032)
0.1181*
(0.0631)
0.0819
(0.1545)
0.2616**
(0.1166)
-0.7319
(0.5291)
-0.1602*
(0.0838)
-0.2252
(0.2108)
0.5457***
(0.0895)
-0.1137
(0.1023)
0.2404*
(0.1270)
-0.2757***
(0.0987)
-0.0183
(0.0955)
0.3487***
(0.1103)
0.0020
(0.0078)
-0.0024
(0.0051)


-4.57
(533.40)
-10.42***
(3.33)
147.11*
(66.62)
262.67***
(39.34)
1021.468***
(168.61)
-292.04
(465.50)
-75.56
(134.83)
148.49
(150.02)
1626.21***
(107.19)
-196.44**
(91.56)
-124.61
(94.81)
-244.72**
(122.22)
516.09**
(105.66)
177.10*
(93.40)
-12.96
(8.44)
0.90
(5.17)


1328.95**
(555.94)
-30.92***
(5.39)
64.40
(94.92)
454.00***
(135.94)
857.09***
(179.30)
-253.56
(403.81)
-368.95***
(133.81)
31.41
(153.90)
1266.65**
(172.92)
-315.44*
(173.05)
-360.68**
(158.67)
-24.09
(144.77)
227.53*
(131.22)
-394.49**
(193.68)
10.75
(10.74)
-6.82
(6.89)


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK



K2
R
N


*** indicates significant at
** indicates significant at
* indicates significant at


1% level
5% level
10% level


0 0 1 1
0.52 0.45 0.40 0.59
1,826 217 977 271

(where standard errors are in parentheses)









TABLE 8: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
DETROIT SMSA



HOMEOWNERS: RENTERS:
V -1 R -1
DEP VAR= DEP VAR -
ITEBLACKWHITE BLACK
WHITE BLACK WHITE BLACK


9.77***
(0.15)
-0.0083***
(0.0008)
0.0571***
(0.0128)
0.0545***
(0.0155)
0.0319*
(0.0164)
0.0763
(0.0929)
-0.1556***
(0.0339)
0.3589***
(0.1106)
0.3632***
(0.0156)
-0.0610***
(0.0169)
0.0518***
(0.0194)
0.0837***
(0.0245)
0.0394*
(0.0214)
-0.5768***
(0.0245)
0.0023
(0.0020)
0.00005
(0.00140)


10.06***
(0.21)
-0.0116***
(0.0021)
-0.0009
(0.0287)
0.0391
(0.0457)
0.1891***
(0.0401)
0.2093*
(0.1223)
-0.1097**
(0.0517)
-0.0809
(0.1014)
0.2984***
(0.0429)
0.0146
(0.0438)
0.0058
(0.0444)
-0.0131
(0.0429)
0.0870**
(0.0419)
-0.2161***
(0.0661)
0.0045
(0.0039)
-0.0008
(0.0026)


1366.86***
(481.47)
-11.22***
(3.60)
154.33**
(65.39)
16.39
(21.39)
467.49***
(106.83)
438.30
(330.31)
-96.70
(156.89)
64.47
(164.13)
676.37***
(177.34)
-135.99
(94.96)
-93.94
(95.06)
217.10*
(125.06)
240.51**
(111.22)
-551.19***
(119.48)
-12.34
(12.34)
6.91
(7.15)


2319.13"**
(328.58)
-7.06**
(3.29)
63.77
(41.32)
-47.53***
(11.76)
433.12***
(83.31)
53.45
(156.12)
16.12
(76.96)
-64.03
(91.83)
383.86***
(188.15)
-94.17
(84.15)
18.54
(73.68)
15.78
(75.09)
169.25**
(74.77)
-662.49***
(140.23)
3.42
(7.79)
-2.97
(3.37)


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK



A
R
N


*** indicates significant at
** indicates significant at
* indicates significant at


1% level
5% level
10% level


0 0 1 1
0.60 0.25 0.25 0.24
2,207 570 665 447

(where standard errors are in parentheses)









TABLE 9: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
FORT WORTH SMSA



HOMEOWNERS: RENTERS:
V_-1 RR-1
DEP VAR =- DEP VAR -


WHITE BLACK WHITE BLACK


9.27***
(0.17)
-0.0020**
(0.0008)
0.0705***
(0.0150)
0.0765**
(0.0322)
0.3630***
(0.0477)
-0.0736
(0.1195)
-0.0566**
(0.0259)
0.1482*
(0.0920)
0.5499***
(0.0181)
-0.0375*
(0.0199)
-0.0292
(0.0231)
0.0128
(0.0250)
0.0725***
(0.0215)
-0.0118
(0.0209)
0.0004
(0.0017)
-0.0003
(0.0013)


8.36***
(0.45)
-0.0163***
(0.0031)
0.0669
(0.0552)
0.4068*
(0.2465)
0.2201**
(0.1050)
0.4569
(0.2882)
-0.0785
(0.0738)
0.3401**
(0.1507)
0.4363***
(0.0850)
-0.0105
(0.0983)
0.2330**
(0.1170)
-0.0761
(0.0817)
0.1390*
(0.0744)
-0.0799
(0.1162)
0.0084
(0.0072)
0.0007
(0.0047)


239.39
(561.66)
-15.19***
(3.65)
178.61**
(68.80)
176.94***
(48.02)
739.48***
(157.91)
157.60
(514.25)
-277.36**
(130.37)
-17.32
(140.39)
1337.97"**
(118.46)
-388.29***
(100.00)
-53.62
(104.23)
-7.49
(123.70)
314.91***
(111.66)
100.62
(101.51)
-2.91
(8.97)
1.33
(6.71)


-1101.91
(868.37)
-17.06***
(5.41)
-12.44
(122.32)
209.36
(184.32)
420.96**
(194.15)
1985.67***
(762.06)
111.54
(164.82)
-220.87
(181.55)
1350.81***
(248.56)
12.46
(212.77)
67.44
(222.04)
271.63
(190.66)
189.09
(183.25)
-920.81***
(283.31)
5.58
(15.55)
1.00
(9.32)


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK




R
N


0 0 1 1
0.50 0.49 0.34 0.42
2,262 202 881 173

(where standard errors are in parentheses)

*** indicates significant at 1% level
** indicates significant at 5% level
* indicates significant at 10% level









TABLE 10: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
MADISON SMSA



HOMEOWNERS: RENTERS:
V -1 R-1
DEP VAR = --- DEP VAR -

WHITE BLACK WHITE BLACK
WHITE BLACK WHITE BLACK


9.98***
(0.12)
-0.0041***
(0.0005)
0.0738***
(0.0098)
0.0381***
(0.0130)
0.0447***
(0.0134)
0.2411***
(0.0621)
0.0145
(0.0215)
0.2202**
(0.0961)
0.2784***
(0.0115)
0.0231
(0.0147)
0.0260
(0.0194)
-0.0133
(0.0240)
0.0005
(0.0247)

NA
-0.0012
(0.0016)
0.0003
(0.0011)


-308.41
(387.04)
-1.57
(2.12)
448.00***
(37.13)
140.33***
(22.32)
346.18***
(68.89)
1141.11***
(293.95)
-36.40
(101.95)
173.14
(169.41)
552.55***
(112.47)
325.64***
(69.74)
-89.96
(61.77)
-140.28*
(85.96)
16.74
(91.43)

NA
-4.00
(6.55)
3.37
(3.75)


196.48
(1427.52)
6.04
(10.24)
266.86
(246.48)
86.93
(156.44)
1062.12**
(406.08)

NA
-962.15
(757.43)
-283.83
(558.47)
647.81
(690.92)
-333.88
(549.70)
353.05
(440.83)
30.37
(515.05)
83.94
(501.45)

NA
17.47
(76.64)
45.69
(35.79)


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK



1
R
N


*** indicates significant at 1% level
** indicates significant at 5% level
* indicates significant at 10% level


0 NA 1 1
0.42 NA 0.20 0.52
1,936 8 1,311 43

(where standard errors are in parentheses)









TABLE 11: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
MINNEAPOLIS-ST. PAUL SMSA


HOMEOWNERS: RENTERS:
V X-1I RX-1
DEP VAR = -- DEP VAR =-


WHITE BLACK WHITE BLACK

10.75 11.13*** 951.54** 323.71
(0.14) (0.91) (488.71) (1345.46)
-0.0020*** 0.0108 -2.12 30.26*
(0.0006) (0.0074) (3.06) (15.65)
0.0832*** -0.0386 474.67*** 561.04**
(0.0084) (0.0744) (58.55) (273.31)
0.0422*** -0.1436 -94.92*** 238.95***
(0.0076) (0.1232) (14.77) (79.77)
0.0536*** 0.2703* 565.94*** 15.05
(0.0145) (0.1553) (94.36) (406.04)
-0.0310 296.61
(0.0580) NA (362.66) NA
0.0131 0.0970 159.82 -283.31
(0.0223) (0.1402) (131.11) (460.68)
-0.1994* -0.1994 276.91 -245.92
(0.1173) (0.3096) (222.90) (637.79)
0.2868*** 0.4371** 944.33***
(0.0113) (0.1599) (140.73) NA
-0.0328** -0.2970* 86.52 766.38
(0.0153) (0.1676) (109.22) (552.95)
-0.0190 0.1418 -223.62*** -404.64
(0.0184) (0.2138) (84.38) (457.17)
0.0185 -0.1016 57.92 1719.43***
(0.0238) (0.1650) (116.10) (584.77)
0.0052 -0.0178 78.93 -882.19*
(0.0225) (0.3834) (121.38) (495.05)
-0.0870*** -0.6879** -264.38*** -2374.58***
(0.0182) (0.2403) (94.95) (696.16)
0.0054*** -0.0219** 7.95 -51.77
(0.0017) (0.0104) (10.24) (58.49)
-0.0039*** 0.0098 -1.50 50.82*
(0.0011) (0.0071) (6.46) (30.45)


0 0 1 1
0.43 0.79 0.29 0.52
2,275 28 1,005 50

(where standard errors are in parentheses)


*** indicates significant
** indicates significant
* indicates significant


at 1% level
at 5% level
at 10% level


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK



X
S
R
N









TABLE 12: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
NEWARK SMSA



HOMEOWNERS: RENTERS:
V _- R-_1
DEP VAR DEP VAR -


WHITE BLACK WHITE BLACK


10.71***
(0.16)
-0.0059***
(0.0008)
0.1081***
(0.0120)
-0.0431**
(0.0180)
0.0112
(0.0207)
0.0085
(0.1216)
-0.0378
(0.0445)
-0.0902
(0.0956)
0.2922***
(0.0139)
-0.0111
(0.0188)
0.0366
(0.0234)
0.0692*
(0.0363)
0.0603**
(0.0256)
-0.5598***
(0.0772)
0.0032*
(0.0017)
-0.0009
(0.0009)


10.67"**
(0.42)
-0.0163***
(0.0044)
-0.0613
(0.0497)
0.1932**
(0.0945)
-0.0327
(0.0843)
0.0639
(0.2626)
0.0092
(0.1242)
0.1446
(0.2009)
0.2809***
(0.0710)
-0.0182
(0.0942)
-0.0007
(0.1044)
-0.0099
(0.1257)
-0.0773
(0.1048)
-0.3172**
(0.1503)
0.0090
(0.0070)
-0.0015
(0.0051)


2366.79***
(521.77)
-25.02***
(3.99)
296.64***
(67.36)
8.00
(19.19)
263.53**
(107.03)
-413.08
(395.69)
-173.71
(170.50)
367.92*
(196.20)
974.61"**
(173.94)
-18.25
(103.27)
-116.32
(100.99)
24.25
(136.66)
221.64**
(108.33)
-817.72***
(151.52)
16.88*
(10.35)
0.36
(5.24)


1482.36***
(446.61)
9.60**
(4.32)
246.00***
(59.28)
-22.71
(15.29)
716.29***
(111.49)
-145.88
(220.27)
-131.32
(110.18)
130.47
(138.42)
482.57*
(273.48)
-180.86
(133.20)
-97.97
(109.48)
-12.28
(123.10)
-51.29
(116.36)
-549.44***
(116.90)
6.19
(9.79)
4.44
(5.24)


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK



2
R
N


*** indicates significant at
** indicates significant at
* indicates significant at


1% level
5% level
10% level


0 0 1 1
0.49 0.40 0.22 0.24
1,446 118 835 429

(where standard errors are in parentheses)









TABLE 13: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
ORLANDO SMSA



HOMEOWNERS: RENTERS:
V_-1 R -1
X A
ITDEP VAR = ITDEP VAR BLACK

WHITE BLACK WHITE BLACK


9.42***
(0.21)
0.0002
(0.0007)
0.1191***
(0.0135)
0.0501***
(0.0153)
0.2402***
(0.0391)
-0.0940
(0.1954)
-0.0904*
(0.0468)
0.1807***
(0.0634)
0.4601***
(0.0176)
-0.0214
(0.0190)
-0.0280
(0.0195)
0.0008
(0.0233)
0.0881**
(0.0190)

NA
-0.0013
(0.0022)
-0.0008
(0.0017)


9.40***
(0.31)
-0.0048*
(0.0025)
0.2234***
(0.0511)
0.0812
(0.1604)
0.2143***
(0.0696)
-0.3094*
(0.1933)
-0.0354
(0.0743)
-0.0103
(0.1089)
0.3380***
(0.0748)
0.0426
(0.0680)
-0.1950**
(0.0820)
0.1594**
(0.0710)
0.0247
(0.0641)

NA
0.0041
(0.0065)
0.0008
(0.0032)


869.95*
(464.39)
-10.63***
(2.90)
342.04***
(57.05)
-43.42**
(19.30)
655.07***
(139.70)
25.29
(407.99)
-373.59**
(175.76)
19.44
(106.78)
800.11**
(96.14)
-78.01
(82.77)
-32.41
(80.36)
-122.30
(92.85)
201.35**
(88.32)

NA
5.22
(9.16)
3.15
(6.35)


-96.60
(516.34)
-8.78*
(5.17)
103.66
(80.19)
149.02
(142.56)
612.75**
(156.50)
292.29
(286.44)
-520.18***
(157.92)
-255.50*
(134.10)
1404.07**
(207.61)
-280.44*
(147.51)
88.24
(141.35)
96.22
(154.60)
-155.90
(132.83)

NA
15.04
(15.82)
0.55
(9.87)


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK



x
R2
N


*** indicates significant
** indicates significant
* indicates significant


at 1% level
at 5% level
at 10% level


0 0 1 1
0.46 0.38 0.31 0.52
2,037 230 879 193

(where standard errors are in parentheses)









TABLE 14: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
PHOENIX SMSA



HOMEOWNERS: RENTERS:
V -1 R -1
DEP VAR DEP VAR --

HITE BLACK WHITE BLACK
WHITE BLACK WHITE BLACK


9.71"**
(0.14)
-0.0016**
(0.0006)
0.1233***
(0.0112)
0.0134
(0.0160)
0.3060***
(0.0222)
0.2405*
(0.1273)
0.0035
(0.0364)
-0.0130
(0.0501)
0.3545***
(0.0176)
-0.0449***
(0.0169)
0.0087
(0.0179)
0.0191
(0.0215)
0.0063
(0.0183)
-0.0274*
(0.0162)
0.0013
(0.0020)
-0.0018
(0.0015)


9.47***
(0.88)
0.0022
(0.0039)
0.1491*
(0.0826)
-0.3069
(0.3526)
0.3849***
(0.1369)
-0.2903
(0.3702)
-0.2144
(0.1576)
1.0349*
(0.5712)
0.0614
(0.1028)
-0.1264
(0.1011)
0.0003
(0.1312)
0.0608
(0.1199)
0.0571
(0.1062)
0.0546
(0.1547)
0.0150
(0.0131)
-0.0044
(0.0058)


311.79
(435.02)
-8.89***
(3.47)
236.09***
(57.45)
228.34***
(55.44)
831.73***
(110.17)
393.59
(387.01)
-305.87*
(166.43)
66.67
(116.33)
997.20***
(107.87)
86.09
(83.72)
-202.45**
(84.54)
9.21
(91.92)
208.16**
(96.41)
-146.29*
(84.69)
-15.33*
(8.68)
9.36
(6.20)


123.98
(1256.60)
25.96*
(15.02)
98.16
(195.99)
-447.45
(407.08)
2476.42***
(444.82)
-37.39
(839.21)
412.11
(545.20)
-164.72
(412.61)
1056.82**
(477.16)
198.23
(381.12)
-300.86
(342.39)
-429.97
(379.48)
649.55*
(359.62)
145.06
(403.80)
3.44
(24.82)
-24.94*
(15.39)


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK



12
R
N


0 0 1 1
0.41 0.54 0.34 0.69
2,491 58 997 51

(where standard errors are in parentheses)

** indicates significant at 1% level
* indicates significant at 5% level
indicates significant at 10% level









TABLE 15: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
PITTSBURGH SMSA



HOMEOWNERS: RENTERS:
V -1 RA-1
DEP VAR DEP VAR -
WHITE BLACK WHITE BLACK
WHITE BLACK WHITE BLACK


9.95***
(0.16)
-0.0101***
(0.0009)
0.1239***
(0.0134)
0.0349**
(0.0151)
0.0823***
(0.0192)
0.2604***
(0.0839)
0.0089
(0.0388)
0.0526
(0.1273)
0.2715***
(0.0175)
-0.0290
(0.0201)
0.0051
(0.0245)
-0.0138
(0.0259)
0.0545***
(0.0204)
-0.1500***
(0.0299)
0.0004
(0.0022)
-0.0012
(0.0012)


10.56***
(0.51)
-0.0090*
(0.0048)
-0.0310
(0.0702)
-0.0616
(0.1145)
0.3729***
(0.1355)
-0.5174
(0.3291)
-0.1734
(0.1553)
0.6067**
(0.2872)
0.1004
(0.1000)
0.0017
(0.1271)
0.2476*
(0.1380)
-0.3104**
(0.1386)
-0.0457
(0.1187)
-0.3288**
(0.1344)
0.0133
(0.0181)
0.0064
(0.0071)


443.56
(471.64)
-25.71***
(3.58)
114.98**
(56.44)
99.82***
(21.19)
333.97***
(94.51)
82.38
(345.45)
-121.51
(191.11)
543.79***
(202.01)
903.00***
(142.35)
355.51***
(102.08)
-88.78
(97.71)
60.80
(111.25)
268.26***
(93.05)
402.30***
(111.85)
-7.10
(10.35)
13.38**
(5.47)


1548.00
(976.95)
-12.03*
(7.40)
-71.86
(121.26)
20.21
(38.98)
374.98
(241.43)
-41.35
(637.91)
-492.43
(353.64)
424.97*
(252.20)
1003.63**
(479.20)
15.48
(211.95)
51.24
(223.31)
90.39
(237.01)
87.12
(198.07)
-94.25
(195.55)
-119.54***
(33.18)
6.04
(5.80)


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK



A2
R
N


*** indicates significant at 1% level
** indicates significant at 5% level
* indicates significant at 10% level


0 0 1 1
0.38 0.31 0.32 0.27
2,084 103 770 136

(where standard errors are in parentheses)










TABLE 16: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
SPOKANE SMSA


*** indicates significant at 1% level
** indicates significant at 5% level
* indicates significant at 10% level


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK



x2
R
N









TABLE 17: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
TACOMA SMSA


HOMEOWNERS: RENTERS:
VX-1 R -1
DEP VAR DEP VAR -
x x

WHITE BLACK WHITE BLACK

10.15*** 10.53*** 1146.93*** 885.26
(0.10) (0.35) (297.89) (1019.26)
-0.0044*** -0.0044** -14.44*** -25.52***
(0.0007) (0.0022) (2.23) (4.90)
0.0761*** 0.1028* 373.57*** 351.98***
(0.0115) (0.0537) (39.28) (112.83)
0.0342*** -0.0759 -5.99 56.30
(0.0132) (0.0647) (24.00) (136.69)
0.2135*** -0.1518 -61.64
(0.0376) (0.1377) (229.48) NA
0.1688** -0.0128 515.85** 813.64
(0.0693) (0.1962) (243.34) (842.75)
-0.0109 -0.0608 -281.82** 450.49
(0.0326) (0.1531) (121.82) (612.61)
0.1343** 0.0267 -132.51 111.91
(0.0592) (0.1673) (86.39) (174.37)
0.3171*** 0.2588*** 648.99*** 789.71**
(0.0154) (0.0550) (101.43) (355.61)
-0.0682*** -0.1036 -64.51 -350.17
(0.0188) (0.0720) (69.72) (232.54)
0.0185 -0.0850 20.38 271.86
(0.0220) (0.0828) (66.45) (182.82)
-0.0287 0.0399 16.22 55.94
(0.0197) (0.0718) (73.46) (186.82)
0.0222 0.0257 121.87* -47.88
(0.0214) (0.0812) (74.40) (201.75)

NA NA NA NA
0.0035** 0.0073 8.56 32.50*
(0.0017) (0.0069) (7.15) (17.58)
-0.0013 -0.0026 -1.47 -10.72
(0.0013) (0.0058) (5.31) (8.43)


0 0 1 1
0.37 0.52 0.25 0.43
2,158 79 1,043 119

(where standard errors are in parentheses)


*** indicates significant at 1% level
** indicates significant at 5% level
* indicates significant at 10% level


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK



A
R
N









TABLE 18: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
WASHINGTON, D.C. SMSA



HOMEOWNERS: RENTERS:
V _- R -1
DEP VAR DEP VAR -
WHITE BLACK WHITE BLACK
WHITE BLACK WHITE BLACK


10.66***
(0.18)
0.0021**
(0.0008)
0.1475***
(0.0117)
0.0158***
(0.0048)
0.0581
(0.0409)
-0.2068
(0.1433)
0.0271
(0.0372)
-0.0054
(0.0829)
0.2510***
(0.0144)
0.0202
(0.0202)
-0.0121
(0.0215)
-0.0024
(0.0293)
0.0008
(0.0269)
0.3156***
(0.0305)
-0.0065***
(0.0018)
-0.0011
(0.0010)


10.60***
(0.27)
0.0005
(0.0015)
0.0822***
(0.0207)
0.0212
(0.0167)
0.1096***
(0.0403)
-0.2768
(0.2115)
-0.0611*
(0.0380)
0.1437
(0.1379)
0.2216"**
(0.0243)
0.0522
(0.0390)
-0.0184
(0.0362)
0.0031
(0.0387)
-0.0471
(0.0364)
0.0098
(0.0426)
-0.0070**
(0.0032)
0.0003
(0.0015)


-1046.95
(1034.45)
-2.34
(4.66)
509.74***
(72.50)
48.59***
(14.59)
795.81***
(162.78)
1504.52*
(935.09)
-233.46
(181.67)
546.21**
(240.71)
894.56***
(130.81)
345.10***
(133.84)
-441.70***
(110.04)
-233.78*
(135.40)
422.74***
(128.45)
486.08***
(138.23)
-10.85
(10.42)
4.73
(5.09)


2044.08***
(345.37)
-2.74
(3.01)
224.60***
(43.45)
51.28"**
(13.78)
411.97***
(78.27)
298.88
(242.42)
-340.70***
(76.43)
-209.21
(136.35)
700.18"**
(122.61)
11.55
(102.50)
-92.19
(78.02)
6.98
(83.18)
61.23
(76.55)
-889.35***
(96.95)
-9.60
(6.56)
1.05
(2.45)


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK



S2
R
N


*** indicates significant at
** indicates significant at
* indicates significant at


1% level
5% level
10% level


0 0 1 1
0.46 0.27 0.28 0.29
1,466 569 960 1,004

(where standard errors are in parentheses)









TABLE 19: 1981 HEDONIC EQUATION REGRESSION COEFFICIENTS FOR
WICHITA SMSA



HOMEOWNERS: RENTERS:
V _- R_-1
DEP VAR = DEP VAR = --


WHITE BLACK WHITE BLACK


9.31***
(0.13)
-0.0065***
(0.0007)
0.0943***
(0.0116)
0.0694***
(0.0162)
0.1516***
(0.0446)
0.2521***
(0.0904)
-0.0366
(0.0278)
0.4506***
(0.0916)
0.3683***
(0.0140)
0.0218
(0.0162)
-0.0104
(0.0208)
-0.0168
(0.0244)
0.0085
(0.0199)

NA
0.0020
(0.0022)
-0.0022
(0.0016)


9.18***
(0.73)
-0.0081
(0.0054)
0.1588**
(0.0678)
-0.0474
(0.1445)
0.5512***
(0.2092)
0.5238
(0.3807)
-0.0940
(0.1327)
-0.3379
(0.3685)
0.4103***
(0.0970)
-0.1291
(0.1464)
-0.1157
(0.1337)
0.0985
(0.1198)
0.1060
(0.1291)

NA
-0.0373*
(0.0221)
0.0103
(0.0100)


1949.48***
(488.47)
-25.42***
(2.76)
300.13***
(47.61)
-19.14
(38.99)
263.62**
(126.93)
-137.18
(394.69)
-301.28***
(112.85)
52.52
(141.57)
695.26***
(121.76)
21.92
(77.34)
-80.00
(77.55)
5.39
(98.10)
-42.87
(91.99)

NA
-17.08*
(10.35)
14.40**
(6.73)


1617.81
(1123.88)
-28.18***
(9.09)
17.10
(151.16)
-179.64**
(91.97)
220.41
(293.50)
733.79
(854.50)
-337.02
(270.53)
12.72
(362.31)
591.14
(423.04)
239.79
(302.30)
44.80
(250.10)
88.29
(285.80)
-126.70
(246.53)

NA
25.07
(30.98)
-9.73
(15.77)


CONSTANT

AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH

PBTRANS

SCHOOL

POLICE

RECR

INOUTCC

DIST

TIMWORK



A
R
N


*** indicates significant at 1% level
** indicates significant at 5% level
* indicates significant at 10% level


0 0 1 1
0.47 0.46 0.24 0.21
2,124 98 938 116

(where standard errors are in parentheses)










TABLE 20: SIGNIFICANCE AND MEAN EFFECTS OF HEDONIC EQUATION REGRESSION
COEFFICIENTS USING THE PEARSON P TEST
A


AGEST

BEDR

FLOORS

AIRCN

WORKELEC

RATS

TELEP

BATH



PBTRANS

SCHOOL

POLICE

RECR



INOUTCC

DIST

TIMWORK


*** indicates
** indicates
* indicates


significant at
significant at
significant at


1% level
5% level
10% level


k
P = -2* loge i
i=1

P 2
A X2k
where k = number of tests
p = statistical significance of test


HOMEOWNERS RENTERS


-0.0048*** -11.1753***

0.0863*** 238.1041***

0.0372*** 48.3248***

0.1584*** 534.0739***

0.0400*** 333.5762**

-0.0573*** -155.1904***

0.1254*** 81.6752**

0.3316*** 969.1369***



-0.0338*** 1.7156***

0.0129** -109.1637***

-0.0075** 66.0267

0.0236*** 144.7370***



-0.1166*** -375.3105***

0.0013*** -8.0522***

-0.0004 3.1007*











TABLE 21: MEANS OF HEDONIC HOUSING VARIABLES (X.'s)
1
USED IN THE CONSTRUCTION OF THE HEDONIC
HOUSING SERVICES PRICE INDICES




1



AGEST 21.7500

BEDR 3.1047

FLOORS 1.4720

AIRCN 0.6733

WORKELEC 0.9927

RATS 0.0865

TELEP 0.9877

BATH 1.7816



PBTRANS 0.5334

SCHOOL 0.8005

POLICE 0.8427

RECR 0.8007



INOUTCC 0.5105

DIST 10.2642

TIMWORK 21.0202









The next step is to construct Laspeyres housing services price

indices for black and white homeowners and renters using the hedonic

equation regression coefficients and the methodology detailed in Chapter

IV. These hedonic indices are the right-hand side price variables used

in the tenure choice and final housing demand equations. Price indices

constructed from sample sizes less than 100 are ignored in this analysis

to avoid any false inferences drawn from small samples.

The Laspeyres housing services price indices for homeowners in the

sample are found in Table 22. For white homeowners, the highest price

index of 175.0 was found in Newark (with Anaheim a close second) and the

lowest price index of 40.3 was found in Fort Worth. For black

homeowners, the highest price index of 152.2 was found in Newark, while

the lowest price index of 40.2 was found in Fort Worth. The seven SMSAs

where the price index for whites is greater than 100 are Anaheim,

Boston, Detroit, Minneapolis, Newark, Pittsburgh, and Washington, D.C.,

while the two SMSAs where the price index for blacks is greater than 100

are Newark and Washington, D.C. The relatively more expensive SMSAs for

homeowners are located in the northern part of the United States.

Black owners pay more than white owners for the same bundle of

housing services in two out of seven SMSAs in the sample, namely Dallas

and Washington, D.C. In Dallas, black owners pay on average 77% higher

than their white counterparts for the same bundle of owner housing

services. However, white owners pay more than black owners for the same

bundle of housing services in five out of seven SMSAs in the sample,

namely Detroit, Fort Worth, Newark, Orlando, and Pittsburgh. The

largest mark-up, in percentage terms, is for white owners in Pittsburgh

who, on average, pay 123% more for the same bundle of owner housing

services than their black counterparts.










The Laspeyres housing services price indices for renters in the

sample are found in Table 23. For white renters, the highest price

index of 151.6 was found in Washington, D.C., 92% higher than the lowest

price index of 79.1 found in Spokane. For black renters, the highest

price index of 103.5 was found in Fort Worth, while the lowest price

index of 61.4 was found in Pittsburgh. The five SMSAs where the price

index for white renters is greater than 100 are Anaheim, Boston,

Minneapolis, Newark, and Washington, D.C., all in the northern section

of the country. The three SMSAs where the price index for black renters

is greater than 100 are Boston, Fort Worth, and Tacoma.

Black renters pay more than their white counterparts for the same

bundle of rental housing services in two out of the ten SMSAs in the

sample, namely Fort Worth and Tacoma. In Tacoma, black renters pay on

average 22% more than white renters for an equivalent bundle of housing

services. However, white renters pay more than black renters for the

same bundle of housing services in eight out of ten SMSAs in the sample,

namely Boston, Dallas, Detroit, Newark, Orlando, Pittsburgh, Washington,

D.C., and Wichita. The largest mark-up, in percentage terms, is for

white renters in Washington, D.C. who, on average, pay 63% more for the

same bundle of rental housing services than their black counterparts.

The next stage in the analysis is to compute the price index of all

goods and services (excluding shelter), px, for each of the fifteen

SMSAs in the sample using the metholodogy discussed in Chapter IV. This

variable enters the permanent income regression as a variable capturing

wage differentials between SMSAs. It also enters the final housing

demand equations as a deflator for the nominal housing expenditures,

price, and permanent income variables.









On examining the price indices of all goods and services, excluding

shelter (px) for each SMSA (see Table 24), one finds the highest index

in Anaheim of 133.6 and the lowest index of 74.9 in Wichita. These two

indices reflect a 78% higher cost of living in Anaheim relative to

Wichita. All six SMSAs with a price index greater than 100 (excluding

Anaheim) are to be found in the northern section of the United States;

these SMSAs are Boston, Detroit, Minneapolis, Newark, Pittsburgh, and

Washington D.C. The three fast growing "sunbelt" SMSAs in the sample,

namely Dallas, Fort Worth, and Orlando, all had price indices less than

100. This is more than likely due to the fact that labor used in

housing construction is generally less expensive in the South.

The next step is to use an instrumental variable technique to

produce an estimate of permanent income which is used as an explanatory

variable in the final housing demand equations. The results from the

permanent income regression of current income on the human and non-human

wealth variables, as well as px, the SMSA specific price index of all

goods and services (excluding shelter), and an SMSA specific climatic

variable, are shown in Table 25. Whereas diminishing marginal returns

are associated with age, denoted by the negative and statistically
2
significant coefficient on (AGE) increasing marginal returns are

associated with education, denoted by the positive and statistically
2
significant coefficient on (EDUC)2. The experience (AGE) variable in

the linear form is highly significant and positive in determining the

permanent income of a family. Marriage and the number of children also

have strong positive effects. The high coefficient of 0.41 on the MARHH

variable suggests the possibility of two-wage earners in a number of the

households surveyed. Unfortunately, the data set has no information on

the number of paid workers in a family. It is quite probable that the

MARHH variable acts as a proxy for labor force participation.










TABLE 22: 1981 HEDONIC HOUSING SERVICES PRICE INDICES FOR
BLACK AND WHITE HOMEOWNERS IN EACH OF THE 15 SMSAS



P
o


WHITE BLACK


ANAHEIM 173.2 180.1

BOSTON 143.0 177.0

DALLAS 53.4 94.3

DETROIT 120.3 91.9

FORT WORTH 40.3 40.2

MADISON 70.2

MINNEAPOLIS 154.5 178.5

NEWARK 175.0 152.2

ORLANDO 58.2 52.9

PHOENIX 52.0 52.3

PITTSBURGH 159.5 71.3

SPOKANE 70.2 40.1

TACOMA 90.4 173.5

WASHINGTON D.C. 104.4 132.6

WICHITA 42.5 41.7


The homeowner price indices in the above table
do not take into account the implicit subsidy
given to homeowners under the present tax system
(see p. 26 for the individual homeowner adjustment).









TABLE 23: 1981 HEDONIC HOUSING SERVICES PRICE INDICES FOR
BLACK AND WHITE RENTERS IN EACH OF THE 15 SMSAS



P
r


WHITE BLACK


ANAHEIM 139.8 153.5

BOSTON 114.8 100.1

DALLAS 98.3 87.3

DETROIT 87.9 73.4

FORT WORTH 91.9 103.5

MADISON 87.8 103.3

MINNEAPOLIS 116.6 119.6

NEWARK 109.0 82.6

ORLANDO 83.6 73.3

PHOENIX 97.1 81.3

PITTSBURGH 88.6 61.4

SPOKANE 79.1 60.5

TACOMA 83.9 102.1

WASHINGTON D.C. 151.6 92.8

WICHITA 84.2 65.8


The renter price indices in the above table
do not take into account any rent discounts
given to renters by landlords (see p. 26 for the
individual renter adjustment).










TABLE 24: 1981


ANAHEIM

BOSTON

DALLAS

DETROIT

FORT WORTH

MADISON

MINNEAPOLIS

NEWARK

ORLANDO

PHOENIX

PITTSBURGH

SPOKANE

TACOMA

WASHINGTON D.C

WICHITA


PRICE INDEX FOR ALL GOODS AND SERVICES
IN EACH OF THE 15 SMSAS


P
x


133.6

114.8

84.4

102.9

75.5

87.4

124.0

122.7

80.6

80.3

119.4

84.5

94.8

109.5

74.9









The statistically significant and negative coefficients on the

SEXHH and RACE variables could be interpreted as showing that females

and blacks may suffer from discrimination in the labor market. However,

it is more likely that the SEXHH and RACE variables are merely acting as

proxies for other excluded variables. The results from using the two

SMSA specific features were very encouraging. These two variables were

included to capture any premiums paid to individuals in terms of

increased wages for living and working in "high cost" cities and

different geographical location. The px variables, constructed in

Chapter III, came in positive and statistically significant showing that

in "high cost" cities workers are paid more than workers in "low cost"

cities. In preliminary work, three climate variables were experimented

with, namely COOL (number of cooling degree days per annum), HEAT

(number of heating degree days per annum), and TEMP (mean annual

temperature). The final permanent income equation includes COOL, the

positive and statistically significant coefficient on this variable

shows that premiums are paid to workers for living and working in colder

regions of the United States, namely the North Central and Northeast.

The results from the tenure choice regression, which are used in

the construction of the A and A variables, are presented in Table 26.
o r
This is the final step prior to estiamtion of the housing demand

equations. All the included variables had the hypothesized signs and

were significant at 1% level. The expected probability of a household

head owning, rather than renting, increases if he is or has been

married, and decreases if he has moved within the past twelve months to

his current residence. The relative price term, LOG(poi /prj), appears

to be the dominant explanatory variable in the tenure choice decision.









TABLE 25: 1981 PERMANENT INCOME EQUATION REGRESSION
COEFFICIENTS FOR GROUPED SAMPLE



DEP VAR = LOGYC


CONSTANT 7.2035***
(0.2971)
EDUC 0.0298
(0.0280)
(EDUC) 0.0013*
(0.0008)
AGE 0.0583***
(0.0058)
(AGE)2 -0.0006***
(0.0001)
(EDUC* AGE) 0.0001
(0.0001)
SEXHH -0.3050***
(0.0352)
MARHH 0.4116***
(0.0360)
CHIL 0.0410***
(0.0097)
RACE -0.3267***
(0.0394)
COOL 0.00003**
(0.00001)
p 0.0029***
x (0.0007)


R2 0.25

N1 6,134


(where standard errors are in parentheses)

*** indicates significant at 1% level
** indicates significant at 5% level
indicates significant at 10% level


approximately a 10% sample of the entire
grouped population of 73,173









TABLE 26: 1981 PROFIT EQUATION REGRESSION COEFFICIENTS1


CONSTANT

MARHH

LOG(AGEHH)

MOVER

LOG(p i/prj)



Sample size
Convergence
Criterion
Significance
Probability
Maximum Likelihood
Converged at
Iteration

(whe


2.6614***
(0.4210)
0.9763***
(0.0873)
0.2582***
(0.1146)
-1.1447***
(0.1089)
-0.2892***
(0.0430)


1,369

0.00011

0.404


7

ire standard errors are in parentheses)


*** indicates
** indicates
* indicates
1


significant at 1% level
significant at 5% level
significant at 10% level


with housing price indices from SMSA
race/tenure groups greater than 100









tenure choice decision. As anticipated, as the relative price of owning

relative to renting increases, the expected probability of an individual

owning falls.

The conclusion of the empirical work is to bring together the

variables constructed in the prior steps of this analysis into the

housing services demand regression equations. Tables 27 and 28 show the

results from the housing services demand regressions using the four

race/tenure groups, with and without the A term. Tables 29 through 32

show the results from a further breakdown into the three educational

attainment levels within those groups. Table 33 summarizes the final

price and income elasticities of housing services demand from Tables 27

through 32. It should be noted that in the rental housing services

demand equations the unadjusted price index of renters was used in all

the regressions. In preliminary work, the regression including the

price index adjusted for rent discounts produced numerous instances of

statistically insignificant price elasticities.

Tables 22 and 23, the most important tables in the entire study,

show the results from the regressions of housing services expenditures

on permanent income, price, and a set of demographic variables for both

black and white homeowners and renters. Looking first at owners, for

black and white owners the income elasticities of housing services

demand are an elastic 1.42 and 1.30, respectively, signifying that a 10%

increase in income would lead to approximately a 13%-14% increase in

housing services consumption by both black and white owners. Turning to

the price elasticity, black owners are slightly more responsive to price

changes than their white counterparts. The price elasticities of

housing services demand are -1.04 and -1.01 for white and black owners,

respectively, both elastic.









On the rental side, black renters are more responsive than white

renters to income changes with regard to their future housing

consumption. The income elasticities for the black and white rental

groups as a whole are 0.53 and 0.36, respectively, both inelastic. As

in the owner market, black renters are more responsive to changes in the

price of housing services than white renters. The price elasticity of

housing services demand are -0.62 and -0.12 for black and white renters,

respectively, both inelastic. On further examination of Tables 27 and

28, one sees that the demographic variables are virtually all

significant at the 1% level. Also from Tables 27 and 28 there is strong

evidence of simultaneity between the rent and housing consumption

decisions, but inclusive evidence of any simultaneity between the own

and housing consumption decisions as witnessed by the significance of

the A variable. A major focus of this study emphasized the estimation

of the relevant housing services demand elasticities taking into account

any simultaneity between the tenure choice and housing services demand

decisions. Although the A variable looks at the simultaneity between

the tenure choice and housing consumption decisions, it could well be

just capturing unobservable variables in the two decision processes.

The generally positive signs on the A and A variables indicate that
o r
the expected errors in the Probit equation are positively correlated

with the epxected errors in the owner and renter demand equations taken

separately.

The exclusion of the A variable in the homeowner market had a

minimal effect on the income elasticities of housing services demand for

blacks and whites. Omitting this variable changed the price elastici-

ties of housing services demand by less than two percent for black and









white homeowners. However, the exclusion of the A variable in the

rental market had more of an effect on the relevant housing services

demand elasticities. By excluding the A variable, the income elasticity

of housing demand rose by nearly three percent for white renters and by

nearly five percent for black renters. Omitting this variable increased

the price elasticity of housing demand by 36% for white renters and by

nearly five percent for black renters.

The three demographic variables, AGEHH, SEXHH, and MARHH, were in

the most part significant with the hypothesized signs. The age of the

household head was positively correlated with housing expenditures in

the owner market, but negatively correlated with housing expenditures in

the rental market. In the homeowner market, as people age they

generally have larger asset accumulation, move into larger homes and

thus incur larger housing expenses than younger families in smaller

homes. In the rental market, as people age their rental expenditures

decrease. The coefficient on SEXHH shows that the owner and rental

housing service expenditures of female headed households is higher than

that of male headed households. The MARHH variable was generally

statistically insignificant in the rental market, but statistically

significant and negative in the homeowner market.

Turning now to Tables 29 through 32, the four race/tenure groups

are each broken down into three levels of educational attainment, namely

(1) no schooling through eleventh grade (did not graduate from

high school),

(2) graduated high school, and

(3) attended college.

Table 28 again summarizes the price elasticity estimates of housing

services demand from these tables.









The motivation behind this section is to see how the price

elasticity of housing services demand varies, if at all, with

educational attainment. The income elasticity of housing demand is not

reported here because educational attainment, which is highly correlated

with income, is held constant in this set of regressions, and hence the

income elasticity derived is not meaningful.

The price elasticities of housing services demand for white owners

who did not graduate from high school, graduated from high school, and

attended college are -0.981, -0.986, and -0.994, respectively. Although

the price elasticity increases with the level of educational attainment,

no significant difference was found to exist between these three

elasticities. The price elasticities of housing services demand for

black owners who did not graduate from high school, graduated from high

school, and attended college are -1.020, -1.012, and -1.004. No

significant difference was found to exist between these three price

elasticities of housing services demand which diminished with the

increased education of the household head.

For white renters who did not graduate from high school and

attended college there were obtained statistically significant price

elasticities of -0.390 and -0.056. The remaining price elasticity for

white renters that graduated from high school was statistically

insignificant. However, the results obtained for black renters show

that the housing price responsiveness decreased with the household

head's level of schooling. However, there was no statistically

significant differences found between these three elasticities. The

price elasticities for black renters who did not graduate from high

school, graduated from high school, and attended college are -0.737,






81


-0.650, and -0.489, respectively. The set of demographic variables used

in the black and white owner and renter demand equations were generally

statistically significant with the hypothesized signs. The A variable

was statistically significant in four out of the six owner demand

equations and also in four out of the six renter demand equations

broken down by educational attainment. It appears that there is

fairly strong evidence of simultaneity between the decisions to rent and

of how much housing services to consume, and the decisions to own and of

how much housing services to consume as was the case from the results

on the four race/tenure groups not broken down by level of educational

attainment.










TABLE 27: 1981 HOUSING SERVICES DEMAND EQUATION REGRESSION
COEFFICIENTS FOR HOMEOWNERS


DEPENDENT VARIABLE = LOG(HOUSING SERVICE EXPENDITURES)


HOMEOWNERS


WHITE BLACK


-2.2936*** -2.3335*** -3.1491*** -2.8080***
(0.1236) (0.1016) (0.2039) (0.1587)

0.0048*** 0.0050*** 0.0077*** 0.0066***
(0.0005) (0.0004) (0.0009) (0.0008)

0.2186*** 0.2207*** 0.1940*** 0.1850***
(0.0218) (0.0215) (0.0306) (0.0305)

-0.3534*** -0.3468*** -0.3509*** -0.3958***
(0.0239) (0.0208) (0.0356) (0.0314)

1.2995*** 1.3001*** 1.4163*** 1.4051***
(0.0182) (0.0182) (0.0302) (0.0300)

-0.0073 -0.0098** -0.0430*** -0.0240***
(0.0068) (0.0051) (0.0105) (0.0077)

-0.0108 0.0811***
(0.0191) (0.0306)


0.70 0.70 0.70 0.69

2,717 2,717 1,128 1,128


(where

**
**
*


standard errors are in parentheses)

indicates significant at 1% level
indicates significant at 5% level
indicates significant at 10% level


CONSTANT


AGEHH


SEXHH


MARHH


LOG(Y /p )


LOG(p/p )


A



R2

N









TABLE 28: 1981 HOUSING SERVICES DEMAND EQUATION REGRESSION
COEFFICIENTS FOR RENTERS



DEPENDENT VARIABLE = LOG(HOUSING SERVICE EXPENDITURES)


RENTERS


WHITE


1.9114***
(0.1658)

-0.0067***
(0.0007)

0.1538***
(0.0269)

0.0093
(0.0315)

0.3631***
(0.0328)

0.8848***
(0.0733)

0.3643***
(0.0751)


1.9116***
(0.1660)

-0.0088***
(0.0006)

0.1477***
(0.0269)

-0.0634**
(0.0277)

0.3725***
(0.0328)

0.8431***
(0.0729)


BLACK


1.1157"**
(0.2615)

-0.0047***
(0.0011)

0.1380***
(0.0354)

0.0848*
(0.0507)

0.5288***
(0.0543)

0.3771***
(0.0689)

0.5564***
(0.1175)


1.0674***
(0.2622)

-0.0076***
(0.0009)

0.1301***
(0.0355)

-0.0396
(0.0435)

0.5522***
(0.0542)

0.3417***
(0.0687)


0.08 0.08 0.13 0.12

9,184 9,184 3,007 3,007

(where standard errors are in parentheses)


*** indicates significant at 1% level
** indicates significant at 5% level
* indicates significant at 10% level


CONSTANT


AGEHH


SEXHH


MARHH


LOG(Y /p )


LOG(p/p )


A


I


i









TABLE 29: 1981 HOUSING SERVICES DEMAND EQUATION REGRESSION
COEFFICIENTS FOR WHITE HOMEOWNERS BY EDUCATION CLASSIFICATION


DEPENDENT VARIABLE = LOG(HOUSING SERVICE EXPENDITURES)


11th GRADE OR GRADUATED HIGH COLLEGE
LESS SCHOOL EDUCATION

-3.6814*** -3.7650*** -3.2283***
(0.1768) (0.1219) (0.0914)

1.5086*** 1.5731*** 1.4721***
(0.0270) (0.0205) (0.0146)

0.0190** 0.0136** 0.0059*
(0.0080) (0.0056) (0.0035)

0.0128*** 0.0043*** 0.0030***
(0.0006) (0.0004) (0.0003)

0.2685*** 0.3050*** 0.2755***
(0.0240) (0.0171) (0.0154)

-0.3546*** -0.3871*** -0.3806***
(0.0261) (0.0190) (0.0165)

0.0005 0.0609*** 0.0119
(0.0285) (0.0163) (0.0123)


0.66 0.67 0.71

1,975 3,632 5,494

(where standard errors are in parentheses)


*** indicates
** indicates
* indicates


significant at 1% level
significant at 5% level
significant at 10% level


CONSTANT


LOG(Y /p )
p x

LOG(p/p )


AGEHH


SEXHH


MARHH


A









TABLE 30: 1981 HOUSING SERVICES DEMAND EQUATION REGRESSION
COEFFICIENTS FOR BLACK HOMEOWNERS BY EDUCATION CLASSIFICATION


DEPENDENT VARIABLE = LOG(HOUSING SERVICE EXPENDITURES)


llth GRADE OR GRADUATED HIGH COLLEGE
LESS SCHOOL EDUCATION

-4.1092*** -3.8727*** -3.7203***
(0.2889) (0.2547) (0.2499)

1.5803*** 1.6282*** 1.5837***
(0.0415) (0.0434) (0.0424)

-0.0197* -0.0118* -0.0039
(0.0101) (0.0070) (0.0124)

0.0142*** 0.0020 0.0016*
(0.0012) (0.0009) (0.0010)

0.1454*** 0.2714*** 0.2621***
(0.0384) (0.0319) (0.0349)

-0.4550*** -0.3913*** -0.3592***
(0.0434) (0.0390) (0.0408)

0.0915** 0.0817** 0.0696**
(0.0465) (0.0335) (0.0314)


0.70 0.73 0.72

728 769 709

(where standard errors are in parentheses)

*** indicates significant at 1% level
** indicates significant at 5% level
indicates significant at 10% level


CONSTANT


LOG(Y /p )


LOG(p/p )


AGEHH


SEXHH


MARIRH









TABLE 31: 1981 HOUSING SERVICES DEMAND EQUATION REGRESSION
COEFFICIENTS FOR WHITE RENTERS BY EDUCATION CLASSIFICATION


DEPENDENT VARIABLE = LOG(HOUSING SERVICE EXPENDITURES)


11th GRADE OR GRADUATED HIGH COLLEGE
LESS SCHOOL EDUCATION

2.2984*** 2.0839*** 2.8283***
(0.6014) (0.4910) (0.3680)

0.2645** 0.3243*** 0.1726**
(0.1205) (0.1035) (0.0743)

0.6104** 1.2081 0.9445***
(0.2590) (0.9850) (0.1366)

-0.0089*** -0.0063*** -0.0060***
(0.0024) (0.0018) (0.0015)

0.1517 0.0754 0.1098**
(0.1021) (0.0670) (0.0497)

0.1291 -0.0396 -0.0477
(0.1181) (0.0855) (0.0635)

0.4002* 0.2432* -0.0772
(0.2372) (0.1505) (0.1510)


0.08 0.06 0.03

1,023 1,936 2,517

(where standard errors are in parentheses)


*** indicates
** indicates
* indicates


significant at 1% level
significant at 5% level
significant at 10% level


CONSTANT


LOG(Yp/Px)


LOG(p/p )


AGEHH


SEXHH


MARHH


A









TABLE 32: 1981 HOUSING SERVICES DEMAND EQUATION REGRESSION
COEFFICIENTS FOR BLACK RENTERS BY EDUCATION CLASSIFICATION


DEPENDENT VARIABLE = LOG(HOUSING SERVICE EXPENDITURES)


11th GRADE OR GRADUATED HIGH COLLEGE
LESS SCHOOL EDUCATION

0.8612** 1.1821** 0.9221*
(0.4457) (0.4669) (0.5032)

0.5545*** 0.5354*** 0.5801***
(0.0922) (0.1062) (0.1110)

0.2635** 0.3504*** 0.5111***
(0.1172) (0.0985) (0.1159)

-0.0043** -0.0061*** -0.0039*
(0.0018) (0.0018) (0.0023)

0.1274** 0.1086** 0.2022***
(0.0592) (0.0535) (0.0605)

0.1087 0.0718 0.0642
(0.0820) (0.0798) (0.0852)

0.2294 0.6622*** 0.8093***
(0.1664) (0.1779) (0.2216)


0.13 0.08 0.10

1,114 1,495 1,025

(where standard errors are in parentheses)

*** indicates significant at 1% level
** indicates significant at 5% level
indicates significant at 10% level


CONSTANT


LOG(Y /p )
p x

LOG(p/px)


AGEHH


SEXHH


MARHH


A



R2

N






88


TABLE 33: FINAL PRICE AND INCOME ELASTICITIES OF HOUSING SERVICES
DEMAND FOR BLACK AND WHITE HOMEOWNERS AND RENTERS



HOMEOWNERS


WHITE BLACK

With Without Withy Without With Without Withy Without
A A A A A A A A

ALL -1.007 -1.010* 1.300* 1.300* -1.043* -1.024* 1.416* 1.405*

LITTLE H.S. -0.981* -1.020* -

GRAD H.S. -0.986* -1.012* -

COLLEGE -0.994* -1.004 -


F(2,2749) = 0.51 F(2,1170) = 0.55
(not significant at 10% level) (not significant at 10% level)






RENTERS


WHITE BLACK
np ny p
With Without With Without With P Without With y Without
A A A A A A A A

ALL -0.115* -0.157* 0.363* 0.373* -0.623* -0.658* 0.529* 0.552*

LITTLE H.S. -0.390* -0.737* -

GRAD H.S. 0.208 -0.650* -

COLLEGE -0.056* -0.489* -


F(2,1085) = 0.94 F(2,2986) = 0.49
(not significant at 10% level) (not significant at 10% level)


indicates elasticity coefficient significant in
housing expenditures demand equations









The final piece of empirical work in this dissertation examines how

sensitive, if at all, the price and income elasticities of housing

services demand are to using (1) endogenously constructed and then (2)

Bureau of Labor Statistics family budget data price indices. This

dissertation constructed the three following set of price indices, p

(housing price index for owners), pr (housing price index for renters),

and px (price index for all goods, excluding shelter) from the Annual

Housing Survey. The equivalent set of indices are computed from the BLS

family budget data. It should be noted that the BLS price indexes have

shelter costs as a component which does not break down into owner and

renter costs within a city. The results of this exercise are presented

in Tables 34 through 36.

In the owner regressions, using the BLS price indices resulted in a

decrease in the price elasticity of housing services demand for white

owners by 2%, while increasing the same elasticity for black owners by

2%. The income elasticity of housing services demand using BLS price

indices increased by nearly 4% for white owners and increased by nearly

5% for black owners. In the homeowner regressions, all the demographic

variables were statistically significant and had the hypothesized signs.

Although using the two sets of price indices did not produce

startlingly different elasticity estimates in the homeowner market, the

results were more divergent in the rental market. The black price

elasticity of housing services demand using the BLS price index was

statistically insignificant at the 10% level. The same elasticity using

the price index endogenously constructed from the Annual Housing Survey

was -0.45. The price elasticity of housing services demand for white

renters when using the BLS price indices relative to the AHS price









indices was decreased by nearly 30%. The income elasticity of housing

services demand when using the BLS price indices relative to the AHS

price indices was reduced by 54% and by 5% for white and black renters,

respectively. In the renter demand equations the R 2's were higher in

the AHS than in the BLS regressions. The demographic variables were

generally statistically significant with the hypothesized signs.









TABLE 34: COMPARISON OF HOMEOWNER HOUSING SERVICES DEMAND EQUATIONS
USING (1) AHS ENDOGENOUSLY CONSTRUCTED AND (2) BLS PRICE INDICES


WHITE BLACK

AHS BLS AHS BLS

-2.3121*** -2.6175*** -2.6082*** -2.9386***
(0.1340) (0.1445) (0.1429) (0.1628)

1.2887*** 1.3362*** 1.3574*** 1.4191***
(0.0242) (0.0259) (0.0274) (0.0313)

0.0020 -0.0169** -0.0296*** -0.0509***
(0.0072) (0.0075) (0.0071) (0.0078)

0.0060*** 0.0059*** 0.0068*** 0.0066***
(0.0006) (0.0006) (0.0007) (0.0008)

0.2523*** 0.2539*** 0.2009*** 0.1355***
(0.0297) (0.0298) (0.0270) (0.0289)

-0.3135*** -0.3635*** -0.3935*** -0.4746***
(0.0293) (0.0285) (0.0278) (0.0298)


0.70 0.69 0.74 0.72

1,467 1,437 1,066 1,076

(where standard errors are in parentheses)

*** indicates significant at 1% level
** indicates significant at 5% level
indicates significant at 10% level


CONSTANT


LOG(Yp/px)


LOG(p/p )


AGEHH


SEXHH


MARHH



R2

N




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