Regional
Science
and Urban
ESTIMATING
Economics
15 (1985)
THE DEMAND
77-107.
North-Holland
FOR HOUSING
CHARACTERISTICS:
A Survey and Critique*
James R. FOLLAIN
University of Illinois, Champaign, IL 61820, USA
Emmanuel JIMENEZ
University of Western Ontario, London, Ontario N6A 5C2, Canada
Received
December
1983, final version
received
April
1984
Many
economists
criticize
the concept
of the composite
commodity
‘of housing
that forms the
basis of modern
urban economics.
As a result, much empirical
work has been produced
that
attempts
to estimate
the household
demand
for housing
and locational
characteristics.
The
purpose
of this paper is to take stock of the literature.
The theoretical
foundations
of the
literature
and the econometric
procedures
employed
are analyzed
and critiqued.
In addition,
the
empirical
results
are examined
in order
to identify
any patterns
that exist. The principal
conclusion
of this survey is that the theoretical
basis is sound, but the econometric
applications
leave much to be desired.
One consequence
is that the literature
has produced
few empirical
regularities.
Another
is that more studies using better estimation
procedures
and better data are
needed before it can be safely argued that the composite
commodity
concept
is replaced
by the
characteristics
approach.
1. Introduction
The textbook model of the demand for housing services assumes that there
is an infinite. variety of housing types, so that standard economic analysis can
be applied to a composite commodity whose units are homogeneous [see
Muth (1969) and Mills (1980)]. A more general approach focuses the analysis
on the physical and locational characteristics which determine the level of
services offered by a housing unit. Because it avoids assumptions that are not
*This paper is one of several papers of a research
project sponsored
by the World
Bank and
directed
by Stephen Mayo
(RPO
672-49).
We have benefited
a great deal from conversations
and comments
from Steve Mayo and other members
of the project,
Steve Malpezzi,
Dave Gross
and Sungyong
Kang.
In fact, all members
of the project
helped
assemble
an annotated
bibliography
that contains
citations
and brief abstracts
on over 100 papers on this general topic.
The bibliography
will soon be published
by the World
Bank. Helpful
comments
have also been
made by others outside the project.
These include Jan Brueckner,
Jan Ondrich,
Doug Diamond
and Serena Ng. Of course,
all errors
and opinions
are our own. Esther
Gray,
Stephanie
Waterman,
and Cynthia
Lowe also deserve our thanks for the wonderful
job they did to prepare
this manuscript.
01660462/85/$3.30
0
1985, Elsevier
Science
Publishers
B.V. (North-Holland)
78
J.R. Follain
and E. Jimenez,
Estimating
demand
for
housing
characteristics
intuitively appealing, such as complete divisibility, there has been considerable research utilizing the latter approach. Moreover, the analysis of
characteristics yields important insights regarding the tradeoffs consumers
are willing to make between dwelling unit and location characteristics. Such
information is important in understanding the dynamics of the housing
market and its impact on the urban structure.
The theoretical and empirical developments in the use of the hedonic
technique in the analysis of the demand for housing characteristics have been
extensive. A recent survey by Freeman (1979) provides an excellent summary
of the issues in investigating the demand for one characteristic - environmental benefits. This paper extends that review to more general studies.
More importantly, it focuses on the issues in econometric applications.
Although the theoretical basis for sound analysis has been established,
empirical applications of the theory are of widely varying quality. Significant
problems remain: the data requirements are stringent and the econometric
procedures to implement the theory are quite complex. This paper attempts
to clarify some of these outstanding issues and to give a broad picture of
how recent developments relate to earlier work.
The survey is divided into three sections. First, the theoretical foundations
for an econometric analysis of the demand for housing characteristics are
discussed. Second, the empirical approaches that have been used are summarized and categorized. The final section highlights the major conclusions
of the survey and indicates how future work might proceed. In addition,
some thoughts are offered as to the implications of the hedonic literature
concerning the traditional composite commodity model.
2. Theoretical basis
The theoretical basis for the recent literature is Rosen’s (1974) pioneering
study of a market for a single commodity with many characteristics. This
section outlines Rosen’s model and applies it to housing.
Let z=(zl,..., z,) be a vector of housing characteristics and p(z) be a
hedonic price function defined by some market clearing conditions. Households and firms take this price function as given in a competitive world. In
general, p(z) is non-linear.
The household decision is characterized by the utility function u = u(x,z),
where x is a composite commodity whose price is unity. Households then
maximize utility subject to a non-linear budget constraint y =p(z) +x. Firstorder conditions require that ap/dzi E pi = u&,,
i = 1,. . . , n, under usual
properties of U. An essential ingredient to the Rosen model is the bid-rent
function. Define a bid-rent function 8(zi, U, y, a) as the amount of money a
consumer is willing to pay for alternative values of z at a given utility index
and income:
J.R. Follain
and E. Jimenez,
Estimating
demand for
housing
characteristics
79
where CI is a parameter that differs from household to household (tastes).
Solving the above for 8 will obtain a schedule like fF pictured in the upper
panel of fig. 1. The household represented by O1 is everywhere indifferent
along @. 8 schedules which are lower correspond to higher utility levels. It
can be easily shown that
8i =u,/uX = the additional expenditure a consumer is willing
another unit of zi and be equally well off,
= compensated demand curve.
P(Z)
s2
P(Z).
B
82
81
A
m’
K
=i
'i
1
z. 1
G
Fig.
1
to make on
80
J.R. Follain
and E. Jimenez,
Estimating
If p(z) is given, it denotes the minimum
utility is maximized when
0”;
u*,
Y,
demand for
housing
characteristics
price the household must pay. Thus,
4 = P(z*),
(1)
and
where * denotes optimum quantities. The upper panel of fig. 1 denotes two
such equilibria: A for household O1 and B for household d2.
Since p(z) is determined by the market, the supply side must also be
considered. Like buyers, suppliers accept p(z) as given. Constant returns to
scale are assumed. Each firm’s costs per unit are assumed to be convex and
can be denoted as c(z;/?) where p denotes factor prices and production
function parameters. The firm then maximizes profits per unit rc=p(z) - c(z; p)
which would yield the condition that the additional cost of providing that ith
characteristic, Ci, is equal to the revenue that can be gained so that pi = ci.
To establish a relationship comparable to consumers’ bid-rents, let 4(z; rc,/?)
be an offer function of unit prices at which a firm is willing to sell a given
design at constant profit per unit:
7-c=@-c(z;j3).
4’ corresponds to one such schedule in fig. 1.
It can be easily shown that
ci = the additional price a firm will want to obtain for providing
of a characteristic,
= MC of providing that characteristic,
another unit
which in standard microeconomic
If profits are maximized, then
supply curve.
do*;x*,P)=P(z*),
terms defines the (short-run)
0
(2)
and
h(z”; n*, P) = Pi(Z”).
Fig. 1 also denotes a firm equilibrium.
Equilibrium points are those points where supply equals demand and p(z)
then represents a function consisting of the various tangencies. The basic
empirical problem is to infer preference structures from p(z) and data on
household and (possibly) firm characteristics.
J.R. Follain
3. Estimation
and E. Jimenez,
Estimating
demand for
housing
characteristics
81
techniques
The literature has been primarily concerned with estimates of the determinants of the demand for housing characteristics. The simple hedonic
approach, in which demand parameters are inferred directly from the
coefficients of the estimated hedonic function, works only under certain very
restrictive assumptions. The other approaches are significantly more complicated. The Rosen two-step and the Lancasterian index approaches entail
the estimation of compensated demand curves for characteristics. Another
approach is to estimate the structure of preferences directly through the use
of bid-rents. A fourth approach treats characteristics packages as discrete
consumption bundles. Each of these is discussed below.
3.1. Simple hedonic approach
This straightforward
approach assumes that the coefficients of the estimated hedonic regression are sufficient to reveal the preference structure. In
particular, the marginal willingness to pay for a particular characteristic is
interpreted as the derivative of the hedonic regression with respect to the
characteristic. However, the marginal price derived from the hedonic function
does not measure what a particular household is willing to pay for additional
units of a housing characteristic. Rather, it is the valuation that is the result
of demand and supply interactions of the entire market.
In the example depicted in fig. 1, the partial derivative of the hedonic, p(z),
is depicted by pi in the lower panel. In general, the hedonic equation will
overstate the valuation of an additional unit of the characteristic since, to the
right of points a and b, pi > 19: and 0;. This is the basis of the criticism by
Harrison and Rubinfeld (1978), Freeman (1979) and Blomquist and Worley
(1981) of early studies like those of Ridker and Henning (1967). This
difference can sometimes be important if one is doing a benefit-cost analysis.
For example, Harrison and Rubinfeld show that use of the hedonic regression results versus the use of estimates of household marginal rates of
substitution (obtained via other methods discussed below) can overstate the
benefits of air pollution abatement by as much as 30 percent.
Under restrictive conditions, the hedonic equation can reveal the underlying demand parameters. For example, if all households in the sample are
alike in terms of income and socio-economic characteristics, but supplies are
not, p(z) would correspond exactly to 8 in fig. 1. In this case, the hedonic
coefficients will be the marginal willingness to pay.
Many papers, which are too numerous to summarize here, have used the
simple hedonic approach to obtain measures of the market valuation of
particular characteristics. Some of the papers in this group include: Ridker
and Genning (1967), Follain and Malpezzi (1981), Goodwin (1977), Halvorsen and Pollakowski (1981), Jud (1980), Krumm (1980), Lang and Jones
82
J.R. Follain
and E. Jimenez,
Estimating
demand for
housing
characteristics
(1979) Li and Brown (1980), Nelson (1981), Schnare and Struyk (1976),
Wilkinson and Archer (1976), Borukhov, Ginsberg, and Werczberger (1978),
Abelson (1979), Brown and Pollakowski (1977), Dubin and Goodman (1982),
and Diamond (1980a). The applications include the study of household
valuation of shoreline, access measures, neighborhood characteristics like
crime rates and education quality, fuel types, zoning restrictions, and aircraft
noise.
3.2. The two-step approach
Rosen suggests a two-step approach to estimate the compensated demand
curve. First, a hedonic equation, e(z), is estimated. Second, the derivative of
the hedonic with respect to each characteristic, j+(z), is evaluated at a
particular bundle of z and used as the price vector in a system of demand
and supply equations:
$i!z) = OiCz; Y, a),
(3’4
The claim is that the simultaneous estimation of the above equation will
yield the desired parameters of the structural system. This would be
equivalent to tracing the demand and supply curves of fig. 1. Observed
values of zi are combined with the estimated pi’s in the lower panel to yield
points a and b for their households. A simple regression that draws a line
through these points would yield the hedonic equation. A regression that
held constant for demand and supply shifts would theoretically yield the
structure.
There have been many applications of this approach. They can be roughly
divided among those which: (i) investigate one or many housing characteristics; (ii) use micro-level or aggregate data; (iii) use data from one metropolitan market or many markets; (iv) estimate compensated demand (and
supply) equations such as (3) or estimate the parameters of the utility
function to obtain willingness to pay measures; and (v) use various assumptions about the supply side. These distinctions are useful since they will
determine which estimation technique is appropriate for a particular problem
and data set.
Table 1 categorizes the studies which have utilized the two-step procedure,
i.e., those that use hedonic functions to generate prices to be used in the
identification of preference structures. The categories have important implications for estimation that have yet to be fully resolved. Two of the most
important are possible simultaneity biases inherent in the approach and the
identification
of the structural parameters. The rest of this subsection
Table
1
Single
Single
Census
Micro
Micro
Micro
Micro
Single
Single
Multiple
Multiple
Single
Single
Multiple
(10) Nelson (1978)
(11) Witte et al. (1979)
tract
tract
UFP
CD
UFP
CD
CD
Single
Multiple
CD
CD
UFP
CD
CD
CD
Level of
analy&
Single
Single
Single
Single
of
supply
used
one
taken
taken
to generate
Price-taken/only
type of supplier
Simultaneous
Simultaneous
Fixed
Fixed supply
Simultaneous
Price-structure
Various
Fixed supply
Price-structure
Supply side
assumption
“The types of characteristics
included
in each study vary greatly.
See table 2 for a partial list.
“Micro
stands for dwelling-unit
level observations.
‘Multiple
markets
indicate
that various
hedonic
price structures
estimated
in the first step were
marginal
price terms used in the second step.
%D ccompensated
demand estimate;
UFP=
utility function
parameters.
Census
Micro
Single
Micro
Multiple
tract
Census
Multiple
Multiple
Multiple
Micro
Micro
Number
marketsc
Multiple
Single
and year
(1) Bajic (1983)
(2) Bartik (1983)
(3) Blomquist
and
Worley
(1981)
(4) Follain
and
Jimenez (1983)
(5) Harrison
and
Rubinfeld
(1978)
(6) Jud (1982)
(7) Kaufman
and
Quigley
(1982),
Quigley
(1982)
(8) Linneman
(1981)
(9) McMillan
et ~2. (1980)
Author
of datab
applications.
Type
of two-step
Number
of
characteristics”
Categorization
the
84
J.R. Follain
and E. Jimenez,
Estimating
demand for
housing
characteristics
discusses these and other issues. The categories depicted in table 1 determine
whether or not an issue is relevant for the study.
3.2.1. Simultaneity
There has been a considerable amount of intellectual energy expended on
this subject. Aside from brief discussions in most of the empirical work,
papers by Diamond and Smith (1983) Murray (1983), Ohsfeldt (1983) and
Blomquist and Worley (1982) specifically address the issue. However, much
confusion still prevails.
There are two sources of possible simultaneity problems. The first is the
traditional simultaneity problem in which error terms are correlated with
right-hand side variables in either the supply or demand equations because
of the fact that price and quantity are simultaneously determined. The
second simultaneity problem is quite different from the traditional one and
stems from the non-linearity of the price function. The nature of the data
base and the hedonic price function (see table 1) determines whether one or
both of these problems is confronted by the researcher. The resolution of the
problem depends upon assumptions regarding the supply curves in the first
case and estimation techniques in the second case.
The traditional simultaneity problem may arise if one is using aggregate
data. In this case, it is possible that the unit of observation is large enough
to influence the hedonic price function that clears the market. The parameters of the price function used to calculate the marginal prices may be
dependent upon the error terms in either the supply or demand equations
being estimated. Therefore, using OLS to estimate the supply and demand
equations leads to biased and inconsistent parameter estimates.
The problem is well illustrated in fig. 1, where it is assumed that all
observations come from a single market [Nelson (1978), Jud (1980)]. The
goal of the estimation is the structure of tJi and $i in the lower panel. Some
studies [Witte et al. (1979)] attempt to estimate simultaneous systems for
many markets, as in fig: 2.
Another simultaneity problem (of the second kind) may exist even if
micro-level data are being used. Because the price function depends upon the
vector of characteristics consumed by the household, the marginal price
depends upon the choices of the households. In other words, the consumer
faces a given price function, but is free to choose points along the function.
Since the derivative of the function varies as one moves along the function,
so too does the marginal price. In this sense, then, the marginal price paid by
a consumer is simultaneously determined along with the choice of the
quantity of the characteristic consumed. This problem exists if p(z) is nonlinear in z.
Theoretically, the simultaneity problem in the micro-data case of a single
market can be easily seen in fig. 3. In the lower panel, there are two
J.R. Follain
and E. Jimenez,
Estimating
demand for
housing
characteristics
85
k
pi
pi
1
Fig. 2
observation points, a, ($f,z!), and b, (@f,z”), which both lie on the same
price structure pi. These points represent the equilibrium consumption points
and marginal prices faced by two individuals, with preference structures 0: and l$, who are the same in all respects except for one socio-economic
characteristic (say, incomes). If each individual accepted the & that it faced
as given - i.e., the price that rules, no matter how much zi he or she
consumed - a regression of zi on pi, holding constant for income, would
yield the slope of segment ‘ad’. Preference structure 0: would then be
estimated if enough data are available. However, the assumption of pi as
fixed is unjustified. The household accepts the entire price structure pi as
86
J.R. Follain
and E. Jimenez,
Estimating
demand for
housing
characteristics
P(Z)
'i
‘i
given, but can choose where to be on that curve at the same time as
choosing how much zi to consume. Thus, pi and Zi are simultaneously
determined and OLS estimation generates biased results. The regression of Zi
and fii would be biased, not because of any effect of the supply curve, but
because of the non-linear price structure.
There is one important difference to note between the first and second
problems in terms of estimation. The single-market micro studies focus upon
the behavior of individuals; thus, it is safely assumed that the market hedonic
J.R. Follain
and E. Jimenez,
Estimating
demandfor
housing
charhcteristics
87
price function and its parameters are independent of the error terms
associated with individual demand equations. Therefore, it is not necessary to
incorporate supply-side variables into the estimation procedure.
Given this view of the simultaneity problem, the empirical literature can be
criticized on several points. First of all, studies by Witte et al. (1979) of
several housing characteristics and by Jud (1982) of access to schools correct
for a problem they do not have. The papers use micro data but employ
estimation techniques appropriate for a conventional simultaneous system.
The studies thus imply that the error terms in an individual demand
equation are correlated with right-hand side variables because of the supply
equation. This is misleading since an error term in an individual consumer’s
demand equation should not influence the price function that clears the
market. Surely the sum of all demands would affect it, but not the demand of
an individual consumer. This does not mean that a simultaneity problem of
the second kind does not exist. Consequently, the use of an instrumental
variables estimator like two-stage least squares may be quite valid, although
even this is not at all obvious since the model that makes two-stage least
squares a valid instrumental variables estimator is not the one analyzed by
Witte et al. (1979). Thus, the estimation of simultaneous demand and supply
systems is inappropriate in micro data sets.
A second criticism pertains to the assumption regarding the nature of the
supply function. As evident in table 1, both micro [Bajic (1983)] and
aggregate [Harrison and Rubinfeld (1978)] studies have utilized the assumption of fixed supply to circumvent the simultaneity issue. This assumption is
inappropriate for micro data since individual households cannot affect the
price structure. For aggregate data, the situation is depicted in fig. 4. Fixed
supply at each location enables the researcher to use demand shifters to
estimate the preference structure 8i in an unbiased way.
Harrison and Rubinfeld (1978) are technically correct when they argue that
a simultaneous equations estimator like two-stage least squares is not
necessary if it is assumed that supply is fixed at each location. In terms of the
system of equations in (2), since zi is fixed, it remains on the right-hand side
of the estimating equation. (In the lower panel of fig. 4, a regression of I on
zi with zi fixed would yield segment “ac” of Qf.) While the assumption may
be valid for the particular attribute, air quality, that they analyze, its general
applicability is largely an empirical issue that should be examined whenever
possible, as appropriately suggested by Blomquist and Worley (1982). They
analyze the severity and existence of the simultaneity bias via empirical
methods developed by Hausman (1978).
Of course, suggesting that two-stage least squares should be used when
aggregate data are used begs the difficult question of where to find good
variables that affect the supply side of the market. Typical supply-side
variables like input prices do not vary within a metropolitan area, except for
88
J.R. Follbin
d2
r
and E. Jimenez,
Estimating
demand for
housing
characteristics
P(Z)
B
82
01
A
ml
c
pi
61
pi
A2
pi
'i
Fig. 4
a
land prices, and land-price data are quite difficult to obtain. What variables
should then be included as instruments? More research needs to be done to
explore the question and future data collection be done to include more
supply-side variables, such as landlord specific information.
A third criticism deals with the correct econometric treatment of the
simultaneity problem that arises as a result of the non-linearity in the price
term. The standard procedure is to replace the marginal price by an
instrumental variable. This has been used by Quigley (1982), Linneman
(1981), and Follain and Jimenez (1983). The instrumental variable is obtained
J.R. Follain
and E. Jimenez,
Estimating
demandfor
housing
characteristics
89
via regression of the marginal price against a set of variables thought to be
correlated with the marginal price but not correlated with the error term in
the individual consumer’s demand equation. For example, Follain and
Jimenez (1983) use household current income, permanent income and. household size.
An alternative is to replace the marginal price by its definition in terms of
the hedonic price function parameters and the characteristics. Then, the
system (3) reduces to one of m endogenous variables, the m ,q’s. The
estimation of this simultaneous system is difficult because it is likely to be
nonlinear and consist of a set of implicit functions. Estimation of a nonlinear set of simultaneous and implicit ‘equations is discussed by Gallant
(1977). Another approach is to use non-linear three-stage least squares with a
linearized version of the demand system. One obvious problem with this
approach is the lack of good instruments. Ohsfeldt (1983) conducts some
interesting Monte Carlo experiments aimed at improving our understanding
of the small sample properties of various estimators that have been used to
estimate systems of demand for housing characteristics. Although his work is
not definitive, making use of Monte Carlo methods to study the properties of
the various estimators seems to be a good idea because of the complexity of
the equation system.
A final word is in order regarding this view of the simultaneity problem.
That is, it excludes the possibility of a third type1 of simultaneity. This third
type has to do with the relationship between the total number of dwelling
units and the aggregate quantities of the characteristics. For example, the
price structure associated with a market with two dwellings each with 50
rooms would likely be different than the structure in a market with 100 units
each with one room. An examination of this simultaneity problem is likely to
be quite complex. Because of this complexity and the fact that studies that
examine the aggregate quantity of dwellings and its characteristics are rare,
further discussion of it brings us beyond the scope of this paper. It should be
added, though, that if such aggregate studies are attempted in the future, this
third simultaneity problem should be addressed.
3.2.2. Identification
The problem is raised by Brown and Rosen (1981). If j&(z), which is the
dependent variable in the system (3), is a function of all the variables z on
the right-hand side and comes from the same data base, then the variation
that enables the system’s estimation is caused by the functional forms of the
behavioral relationships. For some functional forms (a quadratic hedonic and
a linear demand equation, for example), the coefficients of the behavioral
demand relationships between jii and zi can be derived exactly from the
‘This
point
was brought
to out attention
by Douglas
Diamond.
90
J.R. Follain
arid E. Jimenez,
Estimating
demand for
housing
characteristics
hedonic coefficients. Thus, estimation of (3a) and (3b) is unnecessary. One
way to surmount this problem is to ensure that the observations in (3) come
from different markets (see fig. 2). The estimation of the behavioral relationships in (3) would thus use new information provided by the inter-market
price variation.
A simple example demonstrates the point. Consider a hedonic model with
one characteristic,
and a demand equation for zr,
Then OLS estimates of a, and a0 are exactly equal to the hedonic
coefficients estimated in step one. The OLS estimator of cur when the data
are in deviation form is
2&z;z,
&(OLS) =-------
4%
- 28,.
The OLS estimator of a, is
B,(OLS) = 8, + 2&)
- 2&& = 8,.
Obviously, then, the two-step procedure yields nonsensical results in the case
in which all data are from the same market. If, however, the data are from
several markets, then the result obtained above does not occur because the
dependent variable in step two is no longer a linear transformation of the
right-hand side variable. Keep in mind this is a simple and unrealistic
example, it does seem to capture the essence of the problem identified by
* J
Brown and Rosen (1982).
There is one other way of estimating the parameters of a demand or
supply system using data from only one market. That is, the researcher can
specify a functional form -for the hedonic equation that is different and,
generally more complex, than the functional forms of the demand and supply
equations, For example, Quigley (1982) and Follain and Jimenez (1983) have
used a Box-Cox framework to derive a form for the hedonic equation. This,
combined with knowledge of the functional form of the compensated demand
system, enables the identification of the demand structure.
3.2.3. Other issues
The literature
has left virtually
unexplored
an important
issue. That is,
J.R. Follain
and E. Jimenez,
Estimating
demand
for
housing
characteristics
91
what should be the appropriate specification of the error terms in the
hedonic and the behavioral equations. Epple (1982) provides the only paper
that even raises the issue. He explores the implications of placing error terms
in the hedonic equation versus the behavioral equations. The main lessons of
the paper are that the error specification can make quite a difference in the
selection of an estimation procedure and that some of the error specifications
(the most general ones) lead to the need of- employing estimators that are
quite complex. However, he presents no empirical results and offers little
guidance as to how the error specification should be made.
Finally, it should be noted that the two-step studies done to -date do little
to analyze the demand for discrete characteristics. Quigley (1982) and Follain
and Jimenez (1983) try to derive estimates consistent with the bid-rent
approach, but the methods are largely ad hoc and not altogether successful.
There is a wholly different approach that has been used to estimate the
demand for housing characteristics in which the entire choice process is
treated as a problem of choosing from among discrete alternatives. This
approach is discussed below.
3.3. Bid-rent approach
The bid-rent approach involves direct estimation of the bid-rent function
rather than first-order conditions or compensated demand equations associated with a particular bid-rent function. The procedure is as follows.
First, the bid-rent function is calculated for a particular utility function
that shows p(z) as a function of household income, the characteristics, the
utility function parameters, and the exogenous level of utility. Second, the
households are divided into groups in which each member of the group is
assumed to receive the same level of utility from the bundle being consumed.
Third, the bid-rent function is estimated via non-linear least squares for each
group. The dependent variable is the rent or value of the household’s
housing unit and the independent variables are income and the various
housing and location characteristics. The output of the estimation is a set of
estimates of the utility function parameters.
The relationship between this and the two-step approach is easily shown.
In the upper panel of fig. 1, suppose that O1 and 8’ represent the preference
structure of two types of individuals in the sample. If the analyst can
distinguish between these types then the estimation is straightforward. If they
are the only types, O1 will correspond exactly to p(z) over the range where
type 1 individuals consume Zi. All households of type 1 are alike and are of
equal utility. The observed price differentials compensate individuals for
variations in the consumption of zi. A regression of observed p(z) on zi for
all type 1 households in the sample identifies the structure of tI1 if its
functional form is presumed. There is no need to involve first-order
conditions.
92
J.R. Follain and E. Jimenez, Estimating demand for housing characteristics
This approach has been used by Wheaton (1977) to estimate household
willingness to pay for various characteristics for a sample of households from
the San Francisco Bay Area. He used a variety of utility functions (e.g.,
Cobb-Douglas and CES) and a variety of variables to define groups of equal
utility (e.g., income class, race, household size and age). In a very
interesting application of the same methodology, Galster (1977) studied the
willingness to pay for housing characteristics of black versus white households in the St. Louis area. Follain et al. (1982) analyze the willingness to
pay of Korean households for various measures of living space. Diamond
(1980a) uses a variant of this approach in his analysis of Chicago households.
He ranks households by income and makes use of the assumption that
households of similar income (ranked next to one another) must enjoy the
same level of utility to estimate a function similar to the bid-rent function.
In theory, the bid-rent approach is a very convenient and straightforward
way to estimate the household parameters for housing and locational
characteristics. It does, however, suffer from a serious, if not critical, flaw.
How does one identify groups of households who receive the same level of
utility? If one has access to a very large data set - Wheaton’s data set
includes over 50,000 households - then perhaps the method has some merit.
However, as the data set decreases in size, so does the possibility of
identifying groups that are at least comparable in utility and large enough to
permit estimation. In an attempt to deal with this problem, Wheaton (1977),
Galster (1977), and Follain et al. (1982), allow the intercept in the regression
(i.e., the exogenous level of utility) to depend upon household income, or
some function of it. This may improve the quality of the results produced by
the method, but it is difficult to say by how much without further analysis.
3.4. Index approach
A fourth approach in studying the demand for housing characteristics is
based on the Lancaster household production model of consumer behavior
[e.g., King (1976)]. However, to the extent it is based upon the use of
hedonic regressions, this approach is closely akin to the Rosen model. The
name is used to signify that hedonic regressions are used to create indexes of
various housing and locational characteristics; these indexes are then used to
conduct traditional demand analysis.
The method generally consists of three steps. First, a hedonic regression is
estimated. Second, the coefficients of the hedonic are used to construct
weighted sums of various subsets of characteristics. For example, a living
space index might be constructed by using the coefficients of indoor and
outdoor living space. The inside space coefficient is multiplied by the amount
of inside space occupied by a household. The same is done for the outside
living space. The sum of the two products is computed to constitute an index
J.R. Follain and E. Jimenez, Estimating demand for housing characteristics
93
of the variable, SPACE. Several indexes of this type are constructed. Third,
the various indexes are used as measures of the dependent variable on each
category of characteristics to conduct standard demand analysis. A price
variable is usually not used. If one is, it is constructed as a weighted average
of the hedonic price coefficients (fixed weights). The number of price indexes
constructed equals the number of categories of characteristics.
Several authors employ this approach. Barnett and Noland (1981) use the
approach to study the demand for living space versus housing quality using
experimental housing allowance data. Only two equations, a hedonic and a
demand equation for space, are estimated. More elaborate applications
include King (1976), who estimates a full-system of demand equations and
then uses the estimates to test assumptions regarding household preferences,
e.g., homogeneity of demand, additivity, homotheticity, etc. One of the
techniques used in the paper by Follain et al. (1982), employs an approach
that is categorized for the purposes of this paper as an index approach, but it
is not an obvious example of the approach. They have available to them two
indexes - a land price index and a construction cost index - and they use
these as price variables to estimate the demand for living space and lot size
for Korean households.
The primary criticism of this approach is that the indexes are necessarily
arbitrary and dependent upon the data available and the ideas of the
researchers as to how characteristics should be grouped. Consequently,
application of the approach yields estimates that are difficult to compare
among studies and difficult to use for policy analysis.
A second criticism applies to its ability to estimate price elasticities. If the
data are from one metropolitan area, then price indexes cannot be defined
since weighted averages of the hedonic coefficients are the same for all
households. Therefore, multiple market data are essential to this approach if
price elasticities are to be obtained [or the local market must be segmented,
as in King (1976)]. Even if multiple market data are available, using a
weighted average of the hedonic coefficients as a price index introduces
measurement error since the true marginal price is not a simple weighted
average of the coefficients, but is rather the derivative of the hedonic price
function evaluated at the bundle consumed by the household. The seriousness of the measurement error is unknown.
3.5. Discrete choice approaches
Implicit in the analyses presented so far is the assumption that the
characteristics in question are continuous and the consumer is able to
purchase as many as he or she wants given the hedonic price function. Of
course, there are some types of characteristics that are not continuous and
are inherently discrete, such as the availability of types of water supply.
R.S.U.E.-
D
94
J.R. Follain
and E. Jimenez,
Estimating
demand for
housing
characteristics
Moreover, some believe the housing market is inherently a discrete choice
problem. That is, a given number of households are bidding against one
another for a given stock of housing that is not changeable, or at least not
very easily changed. In this sense, then, the problem is one of matching a
finite number of households with a finite number of housing units. The
problem facing the consumer is one of choosing the single best unit for itself
given the existing housing stock.
The approach has its roots in the econometric literature devoted to the
development of logit, probit and other discrete choice models of consumer
choice. The consumer chooses from among a set of discrete characteristic
bundles to maximize utility. Since the comparisons are not continuous, the
estimation procedure attempts to identify the parameters that determine the
probability that a consumer will choose one bundle versus another.
McFadden has presented numerous papers that discuss the general problem of consumer choice in a discrete framework. For an example of his
work, see Domencich and McFadden (1975). A very good discussion of the
discrete choice approach as it applies to housing is presented by Ellickson
(1981), who shows that the problem can be addressed in one of two ways.
The problem can be viewed as a large number of distinct consumers (distinct,
say, due to income differences) choosing a finite number of housing unit
types, or as a small number of household types choosing from a large
number of housing units. The first approach leads to the estimation of the
probability that a consumer will choose to live in a house of a particular
type, e.g., more than two bedrooms. The second approach leads to the
estimation of the probability that a house will be occupied by a household of
a particular type. Another excellent aspect of the Ellickson paper is that he
presents the approach in the context of the Rosen bid-rent framework.
In addition to Ellickson, Quigley (1981) has applied a methodology
developed by McFadden (1978) to study consumer choice in a discrete choice
framework. He is interested in two questions. First, how does one solve the
domputational problem of analyzing consumer choice when the number of
alternatives is quite large? Second, how does one test one of the quite
restrictive assumptions associated with the most popular discrete choice
model - the logit estimator - that is, independence of irrelevant alternatives? Two others who have done work of this type include Anas (1983)
and Zorn (1985). Anas analyzes consumer bid-rents in Chicago to see how
they are influenced by transportation
alternatives. Zorn analyzes how
consumers choose between the central city and the suburban areas.
One criticism of the earlier literature using the discrete approach is that it
does not produce estimates of willingness to pay for various attributes;
rather, it produces estimates of probabilities. This makes the results a little
more difficult to use from a policy point of view since an important question
to many policymakers concerns cost recovery; that is, how much can be
Table
Income
and price
2
elasticities
reported
Income
elasticity
Characteristic
Price
elasticity
.
Size
Living
-0.94
-1.61
0.35 (owners)
0.26 (renters)
1.20
0.64
Rooms
Author
Approach
Follain et al.
Witte et al.
Index
Two-step
Barnett
Index
space measures
0.45
-0.05
Space
in the literature.
0.03
0.31
0.30
and Noland
- 3.41
-0.14
McMillan
King
Index
Index
-0.77
-0.87
Linneman
Follain et al.
Awan et al.
Blomquist
and
Worley
Two-step
Index
Index
Two-step
-0.26
Site
2.62
0.54
-6.56
-0.82
McMillan
King
Index
Index
Lotsize
0.32
-0.40
Witte
Two-step
Quality
1.65 (owners)
2.32 (renters)
1.09
1.72
et al.
Structural
Dwelling
quality
Age and dwelling
unit type
Structure
Barnett
- 1.61
- 0.20
2.50
3.80
-
2.04
-0.31
-0.15
3.89
Neighborhood
Existence
of amenities
and Noland
Index
Witte
King
et al.
Awan
et al.
Index
Awan
et al.
Index
Index
King
McMillan
Index
Index
quality
15.7
Awan
et al.
Index
Good social
neighborhood
0.2
Awan
et al.
Index
Low stability
of
neighborhood
7.80
Awan
et al.
Index
Public
2.36
Diamond
safety
Homogeneity
(probability)
Quiet
-0.01
0.29
- 1.42
Access
-0.29
Distance
to nearby
highway
Access
to rail
2.88
Access
to CBD
2.11
Access
Low
0.01
accessibility
- 3.40
\
-0.79
(198Oa)
Index
Linneman
Two-step
McMillan
Index
Blomquist
Worley
and
Two-step
Diamond
(198Oa)
Index
Diamond
(1980a)
Index
Linneman
Awan
et al.
table continued
Two-step
Index
on next
page
table 2 continued
Others
All attributes
0.4
-
Reduction
in:
Air pollution
4.3
-0.87
Air quality
1.0
- 1.22
Table
Description
Awan
of variables
Awan
et al.
Index
Harrison
and
Rubinfield
Nelson (1978)
Two-step
Two-step
3
in elasticity
measures.
et al.
Barnett
and Noland
Space
-interior
Quality
-aggregate
Blomquist
Rooms
Distance
Distance
Diamond
Public
Access
Access
living area
dwelling
quality
and Worley
to Highway
to Highway
(1980a)
safety
to rail
to CBD
-number
66 -accessibility
55 -accessibility
-incidents
of crimes against
-miles
to nearest commuter
-miles
to CBD along major
Follain
et al.
Size
-inside
Rooms
-number
living area, measured
of rooms
Harrison
and Rubinfield
Reduction
in air pollution
King
Space
Site
Quality
Structural
Linneman
Rooms
Homogeneity
Accessibility
McMillan
Space
Site
Structure
Quiet
Nelson
Air quality
of rooms
measures
measures
-measured
persons, by municipality
rail station
roadways
(cases
per thousand)
in pyongs
as a reduction
in the level of nitrogen
-interior
square
feet, presence
of small
special
characteristics,
number-of
stories in home
-lot
size, distance
to CBD, perceived
neighborhood
garbage removal
and sewers
-interior
and exterior
quality measure
-presence
of full insulation,
number
of garages,
laundry
facilities
I
oxide
purpose
rooms,
basement
quality,
provision
of public
of baths,
basement
number
-number
of rooms in dwelling
unit, excluding
bathrooms
-dummy
variable
equal to one if the highest density structure
category
equals
the lowest density structure
category
-variable
equal to one if the dwelling
unit is less than 5 miles to the nearest
city center; to 0.5 if 5 to 14.9 miles; to 0.33 if 15 to 29.9 miles; to 0.25 if 30 to
49.9 miles; to 0.20 if 50 miles or greater
-floor
area, number
of four piece bathrooms,
number
of bedrooms,
developed
basement
-lot
size, distance to CBD, local zoning laws
-age
of house, presence
of fireplace,
garage with property,
brick exterior,
style of
house
-measure
of freedom from local airport
noise
-Inverse
of air pollution
concentration
level,
as measured
by the average
monthly
particulate
J.R. Follain and E. Jimenez, Estimating demand for housing characteristics
97
extracted from the tenants in rent for a particular dwelling type. A recent
paper by Lerner and Kern (1983) shows how willingness to pay estimates can
be extracted from a discrete choice model, although we know of no
applications of their approach.
The most significant criticism of the discrete choice approach is that
computational considerations force the model to be quite restrictive in terms
of the number of choices available to the consumer. For example, Ellickson
(1981) assumes that there are about twenty different types of households in
the housing market. The twenty different types of households emerge from
assuming that households are either high or low income, black or white,
large or small family, married or not. This approach masks one of the most
important variables in economics, income. The approach taken by Quigley
(1981) is designed to combat this problem in that it allows, at least in theory,
for the consumer to face a large number of choices, or alternatively, allows
the number of consumer types to be quite large. In order to apply the
Quigley approach, though, some additional assumptions are required regarding the possible choices a household considers. The restrictiveness of these
assumptions requires more analysis.
4. Empirical applications: Results
The focus thus far has been on the analysis of the techniques used in the
various empirical studies of the demand for housing characteristics. What
have they taught us about consumer demand? To summarize the results of
the research, attention is focused on income and price elasticity estimates,
and willingness to pay estimates.
The results are presented in two tables. Table 2 contains a listing of all the
income and price elasticity estimates that could be gleaned from the
empirical studies. Table 4 contains estimates of the various willingness to pay
measures as well as willingness to pay for a particular characteristic as a
percent of rent and income. Both tables present the results in terms of groups
of housing and locational characteristics. The groups are: living space,
structural quality, neighborhood quality, access, and other. Tables 3 and 5
provide detailed descriptions of the various variables referred to in tables 2
and 4.
Consider first the estimates of the income and price elasticities of the
demand for living space. The exact measure of living space varies from study
to study, but a consistent pattern does emerge. That is, the income elasticity
of the demand for living space seems quite inelastic. Only the estimates by
McMillan exceed unity, but none of the others exceed 0.64. Although it is
difficult to compare these to estimates of the income elasticity of the demand
for the composite housing good, housing services, the numbers suggest that
the income elasticity of the demand for living space is less than that for the
Lot
Living
Living
area ( 10 m’)
area (10 m’)
size (10 m’)
Rooms
Characteristic
i
0.053
0.024”
0.110”
0.117”
0.165”
5.73
15.27
39.28
41.69
2.45
0.123”
0.361”
0.539”
8.53 (Cali renters)
52.06 (Seoul renters)
53.35 (Busan renters)
(Bogota
owners)
(Cali owners)
(Seoul owners)
(Busan owners)
0.040”
renters)
3.24 (Bogota
0.017”
0.089”
0.045”
0.084”
0.097”
0.077”
Q.268”
0.352”
0.150”
0.003”
space measures
0.066”
As a
percent of
rent/value
0.13
(Bogota
owners)
(Cali owners)
(Seoul owners)
(Busan owners)
(Davao
owners)
4.00
12.27
15.24
21.24
2.12
4
as reported
2.01
(Cali renters)
(Seoul renters)
(Busan renters)
(Davao
renters)
renters)
5.30
38.73
34.89
3.54
94.63
5.41
0.21 (Bogota
Living
to pay measures
1983 U.S. dollars
(monthly)
Willingness
Table
literature.
0.009
0.008
0.029
0.019
0.062
0.021
0.086
0.101
0.008
0.006
0.023
0.019
0.031
0.009
0.022
0.013
0.064
0.066
0.017
0.029
5.1 x 1o-4
As a
percent
of income
by previous
Follain
and
Jimenez
Quigley
Follain
et al.
Follain
and
Wheaton
Quigley
Follain and
Jimenez
Author
Bid-rent
Two-step
Two-step
Bid-rent
Two-step
Approach
iP
3
“Signifies
%ignities
Aggregate
Sanitary
Air quality
Quiet
Access
Distance
WTP
WTP
quality
quality
to CBD
Age of dwelling
Structural
quality
owners)
7.15 (Davao
0.328”
quality
0.007”
0.202”
renters)
as a percentage
as a percentage
1.220
1.023
5531.21
179.63
of rent.
of value.
6.38 (Cali renters)
1 x 1Or5 (Cah owners)
0.025”
owners)
0.18 (Davao
51.05
2.58 (Bogota
0.079”
Other
0.067”
Neighbourhood
1.98”
0.092”
7.22 x 10rZa
0.058”
0.032”
0.023”
Access
Structural
renters)
0.55 (Davao
764.05
renters)
16.58
4.77 (Davao
Quigley
Quigley
0.020
Harrison
and
Rubinfeld
McMillan
et al.
Wheaton
Follain and
Jimenez
Diamond
(1980b)
Follain and
Jimenez
Wheaton
Follain and
Jimenez
0.016
0.19
0.016
1.89 x 10”
0.016
0.006
0.001
0.003
0.011
0.031
0.002
0.023
Bid-rent
Two-step
Two-step
Bid-rent
Bid-rent
Two-step
100
J.R. Follain
and E. Jimenez,
Estimating
Table
Description
Diamond
Distance
1980(b)
to CBD
Follain et al.
Living area -an
of variables
to pay measure.
-distance
to CBD along major roadways,
in miles;
mile, for household
income
=$27,100
(1970 $)
-willingness
-willingness
-measured
-measure
expressed
to pay for one additional
square meter of living
to pay for an additional
room
by index
of accessibility
to concentrations
of workplace
et al.
-noise
exposure
forecast
contour
by the Canadian
Air Transport
with average quiet and non-quiet
Sanitary
Aggregate
characteristics
5
in willingness
Harrison
and Rubinfeld
Air quality
-marginal
willingness
to pay
of concentration
of nitrogen
Quigley
Rooms
Living
housing
as WTP
per
additional
pyong
(3.3 square
meters)
of inside living
space. The figures
presented
in table B-3 have been calculated
for 10 square meters, evaluated
for
family size of 4 and monthly
consumption
of 50,000 won
Follain and Jimenez
Living area
Rooms
Structure
quality
Access
McMillan
Quiet
demand for
area
quality
for an improvement
oxides, at an income
corresponding
Administration;
services
area
in air quality
at a ‘high’
level of $11,500
to location
evaluated
level
of property,
as calculated
for the average price house
-average
willingness
to pay for an additional
room
-willingness
to pay for an additional
10 square
meters
area
- 14 index of sanitary
quality in dwelling unit
-1-9
index of aggregate
dwelling
quality
of inside
Wheaton
[all values evaluated
for family size 34, income
10,00~15,000
(1965 US $), household
age 31-551
-value
of one more room (evaluated
at 7 rooms)
Rooms
Age of dwelling
-willingness
to pay for unit one year newer, evaluated
at 10 years old
-compensation
for one unit change in access index
Access
living
head
I
composite good if one takes the consensus estimate of housing income
elasticity as being between 0.5 and 1.0 [see Mayo (1981), Mayo et al. (n.d.)
Polinsky (1977) and Goodman and Kawai (1982)].
“
The pattern for price elasticity also exists, although it is not as pronounced.
Demand seems slightly inelastic although the range is large and the estimates
seem sensitive to the type of living space, i.e., lot size is more elastic than
inside living space.
The second panel of table 2 also reveals a pattern. The income elasticity of
the demand for quality seems quite elastic. Every study suggests the income
elasticity is in excess of one, and most suggest it is quite above unity.
Unfortunately, the price elasticity estimates are few.
Neither does analysis of the other variable types reveal any interesting
patterns. The only one might be a negative one. That is, the range of
J.R. Follain and E. Jimenez, Estimating demand for housing characteristics
101
estimates is so wide one is tempted to conclude that the econometric
analyses have taught us very little about the demand for characteristics like
access and neighborhood quality.
Table 4 is more diflicult to interpret. Since willingness to pay obviously
varies depending upon the income and price level of the household, there is
substantial variation in the estimates obtained from the literature. In an
effort to lessen these differences, the estimates have all been put in 1983 U.S.
dollars. In addition, the estimates have been expressed as a percent of
household income and the rent paid for the unit (all in monthly terms).
Despite all of these attempts to standardize the results, they remain rather
scattered. For example, willingness to pay for a room of about 10 square
meters as a percent of income ranges from about 3 percent of income to
almost 9 percent of income.
5. Conclusions
5.1. Summary of the issues
This paper has reviewed the different methods which have been used to
determine empirically the parameters of the demand for housing characteristics. They include the following approaches: simple hedonic, two-step,
bid-rent, index, and discrete. Which approach is the most appropriate
depends very much on the economic issues that are being addressed in a
particular study as well as the nature of the available data base. For
example, being able to confidently segment a micro-sample into subsamples
of households of homogeneous characteristics allows one to estimate bid-rent
functions directly.
The most popular method in the recent literature, and the one which is
closest to Rosen’s elegant theoretical model of the implicit market for
characteristics, is the two-step approach. This study concludes that, while
significant advances have been made, the empirical applications have yet to
match the rigor of the analytical framework. The issues which seem to have
caused the most problems are related: simultaneity and identification.
The confusion regarding simultaneity in the estimation of demand parameters stems from the fact that there are several types of simultaneity bias.
This paper categorizes these into three: the ‘garden-variety’ demand-supply
simultaneity bias to which Rosen refers in his original work; the bias
caused in estimating compensated demand equations when prices are determined endogenously in the system; and the relationship between the total
number of dwelling units in a market and the aggregate quantities of the
characteristics. The presence of any one or all the types of bias depends on
the data base (for example, whether it is a micro or an aggregate data set)
and the structure of the system being estimated.
R.S.U.E.-E
102
J.R. Follain
and E. Jimenez,
Estimating
demand for
housing
characteristics
The other issue is identification
and it arises because the dependent
variable used in the hedonic regression, the first step, is an explicit function
of #. It is thus generally difficult to distinguish between the estimated
parameters of an ad hoc demand system and those of a hedonic regression,
particularly if all of the data come from a unified market in which all
observations face the same hedonic price structure. Indeed, as Brown and
Rosen (1982) have shown, under certain functional forms (quadratic for the
hedonic and linear for demand), the coefficients of the characteristics in the
second-step (demand equation) regression are exact functions of the coefficients of the hedonic equation. In general, either a rich multi-market data
base or the imposition of a prior structure on the estimated system is needed
to surmount this problem.
5.2. Recommended procedures
Given the difficulties mentioned above, what procedure can be recommended for future work? The ideal estimation procedure depends partly upon the
data as well as the objective of the researcher. Three different types of models
can be specified. The first consists of a system of m demand equations, the
second consists of a system of m compensated. offer functions, and the third
consists of 2m equations, m supply and m demand equations. Each system is
likely to be non-linear, simultaneous and implicit given reasonably general
utility and production functions. The parameters of the system are the
parameters of utility and production. The endogenous variables are the m
quantities of the characteristics and the exogenous variables are the shift
variables for either supply or demand, i.e., income, household size, input
prices. The parameters of the hedonic price function unique to each market
also enter the system, but they are assumed known.
How does one estimate any of these rather complex systems? Estimation is
undoubtedly going to be difficult; however, some recent work by Gallant
suggests that it is at least possible. In a series of papers in which he has
collaborated it has been shown how a system of this type can be estimated
via maximum likelihood, non-linear three-stage least squares and instrumental variables. Gallant (1977), Gallant and Holly (1980), and Gallant and
Jorgenson (1981), although application of any of these procedures is
undoubtedly a difficult and time-consuming task, a very useful area for
further research would be to apply one or all of these techniques.
Given the complexity of the ideal model specification and estimation
procedure, simpler and more ad hoc procedures are likely to remain popular.
Among the methods surveyed in this paper, the Rosen two-step seems the
best. It is well-rooted in theory, and relatively simple to apply. There are,
however, several aspects of this approach that should receive more attention.
First, attention should be given to the problem of selecting good instru-
J.R. Follain
and
E. Jimenez, Estimating demand for housing characteristics
103
ments for the two-stage least squares or instrumental variables approach. No
one would deny that the procedure is a reasonable one in theory, but one
must question how good can the approach be in practice given the paucity
of good instruments. One way this might be investigated is via Monte Carlo
studies. The work begun by Ohsfeldt (1983) seems well worth pursuing.
Second, more attention needs to be given to the specification of the error
terms in the model. Epple (1982) has done an excellent job of initiating this
research. Further work would involve actual estimation of systems under the
various error specifications suggested by Epple to determine the sensitivity of
estimates to the error specification.
Third, work should be done to determine how best to estimate simultaneously characteristics that are both discrete and continuous. Currently, as
mentioned above, the practice is to do either one or the other. However, the
reality of the world is such that some characteristics are continuous and
some are discrete. Our models should reflect this fact. The recent
econometric work that explores how systems of discrete and continuous
variables should be estimated is likely to be quite helpful [e.g., see Heckman
(1978)].
Comments are also in order regarding the quality of data available for this
type of analysis. The ideal data set is a multiple market data set that includes
variables on individual housing units, the neighborhood in which the unit is
located, the landlord, and the household and infrastructure associated with
the neighborhood. Also, information regarding input prices for each market
is needed. Unfortunately, data sets of this type do not exist. The best data set
in the U.S. for this type of analysis is the Annual Housing Survey. However,
it contains little information regarding the neighborhoods of the housing
units or the landlords. This needs to be improved. In other countries, the
situation seems to be worse. Canada has no national housing survey, and the
United Kingdom has a one time survey that is not nearly adequate. Other
data sets have been discovered in several developing countries, but these are
not standardized or always adequate. Recently, however, the World Bank
has initiated efforts to develop adequate data bases for this type of analysis
in Korea, ‘Philippines, Egypt, Kenya, and Columbia. Future analysis of these
data bases can contribute substantially to lessening the purely data-related
problems of many earlier analyses. Then, perhaps, this literature can begin to
produce results that are of substantial help to policymakers because no
matter how fancy the model or estimation technique, the old adage remains
true - garbage in, garbage out.
5.3. Broader implications
The standard urban economic (SUE) model can be viewed as a special
case of the Rosen characteristics model. The SUE model focuses on two
104
J.R. Follain
and E. Jimenez,
Estimating
demand for
housing
characteristics
characteristics, access to CBD, k, and everything else about a house and its
neighborhood that generates utility, 4. The SUE model has been a mainstay
of the field of urban economics for more than a decade. Its strengths lie in its
simplicity, elegance and ability to produce numerous testable hypotheses and
policy implications about the process of urbanization. The question addressed here is whether the empirical analyses of the more general hedonic
model shed any light on the validity of the SUE model and its conventional
treatment of housing as a composite good. Owing to the diversity of the
econometric results surveyed, it is necessary to be quite humble regarding
any answers presented. Nonetheless, several points do seem valid.
First, if stability and consistency of empirical results are acceptable criteria,
much can be said in favor of the SUE model. Surveys of income and price
elasticity estimates of the demand for the composite good 4 yield a range of
estimates that is much smaller than those observed for subcategories of 4.
This suggests that the concept of an aggregate commodity 4 is a good one
that should not be readily abandoned.
Second, the focus of the SUE model on the characteristic access to CBD
(k) seems unwarranted. Such a focus is warranted if the income and price
elasticities of the demand for CBD access are found to be consistently
different than those for other characteristics. Such is not the case. Little
consistent evidence exists to show the income and price elasticities of the
demand for CBD access are so quantitatively different as to warrant special
attention. This may be due to the decline in the importance of the CBD or it
might simply reflect consumer preferences. Whichever explanation is correct,
the fact remains that the empirical literature on the demand for characteristics does not indicate CBD access is a particularly significant one. Given
that many of the important results in the SUE model are derived under the
assumption that the income elasticity of the demand for 4 is greater than the
demand for CBD access, it is necessary to question those results.
A third and related point is that the only empirical regularity in the
literature on characteristic demand suggests an important alternative to the
SUE model - the Blight Flight Model - may be more appropriate. ,The
Blight Flight Model is a term coined to explain U.S. suburbanization as a
process caused by a deterioration of the quality of life inside many of the
large U.S. cities. The SUE model deemphasizes this aspect of the U.S.
experience and emphasizes instead the income growth and the decrease in
transportation costs enjoyed during the 20th century. The empirical results
on characteristic demand indicate the income elasticity of the demand for
living space is less than the income elasticity of the demand for amenities. If
this is true, then the role played by the amenities of city life is probably more
important than the SUE model indicates. This suggests the application of the
characteristic demand approach to the study of suburbanization
offers
opportunities for insights not possible within the SUE. Only Diamond
J.R. Follain
and E. Jimenez,
Estimating
demandfor
housing
characteristics
105
(1980b), Diamond and Tolley (1982), and Linneman (1982) seem to have
pursued this in any great detail. More work along these lines could be quite
productive.
References
Abelson,
Peter N., 1979, Property
prices
and the value of urban
amenities,
Journal
of
Environmental
Economics
and Management
6, 1 l-28.
Anas, Alex,
1983, Residential
location
markets
and urban
transportation
economic
theory,
econometrics,
and policy analysis with discrete choice models (Academic
Press, New York).
Awan,
K., J.C. Odling-Smee
and Christine
Whitehead,
1982, Household
attributes
and the
demand for private rental housing,
Economica,
183-200.
Bajic, Vladimir,
1983, Housing
market
segmentation
and the demand
for housing
attributes:
Some empirical
findings, Presented
at the 1983 Canadian
Economic
Association
Meeting.
Barnett,
C. Lance and Charles
W. Noland,
1981, The demand
for housing
space and quality,
Working
paper, (Rand Corporation,
Santa Monica,
CA) July.
Bartik,
T.J., 1983, The estimation
of demand
parameters
in hedonic
price models,
Working
paper no. 82-W03
(Vanderbilt
University,
Department
of Economics
and Business
Administration, Nashville,
TN).
Blomquist,
Glenn
and Lawrence
Worley,
1981, Hedonic
price, demand
for urban
housing
amenities,
and benefit estimates,
Journal
of Urban
Economics
9, 212-221.
Blomquist,
Glenn and Lawrence
Worley,
1982, Specifying
the demand
for housing
characteristics: The exogeneity
issue, in: Douglas
B. Diamond,
Jr. and George
S. Tolley,
eds., The
economics
of urban amenities
(Academic
Press, New York) 89-102.
Borukhov,
Eli, Yona Ginsberg
and Elia Werczberger,
1978, Housing
prices
and housing
preferences
in Israel, Urban
Studies, 187-200.
Brown,
Gardner
and Henry
0. Pollakowski,
1977, Economic
valuation
of shoreline,
Review of
Economics
and Statistics,
272-278.
Brown,
James and Harvey
Rosen, 1982, On the estimation
of structural
hedonic
price models,
Econometrica,
May.
Diamond,
Douglas
B., Jr., 1980a, Income and residential
location:
Muth revisited,
Urban Studies,
1-12.
Diamond,
Douglas
B., Jr., 1980b, The relationship
between
amenities
and urban
land prices,
Land Economics,
21-32.
Diamond,
Douglas
B., Jr. and Barton
A. Smith, 1985, Simultaneity
in the market
for housing
characteristics,
Journal
of Urban
Economics
17, March.
Diamond,
Douglas
B., Jr. and George
Tolley,
eds., 1982, The economics
of urban
amenities
(Academic
Press, New York).
Domencich,
Thomas
A. and McFadden,
Daniel,
1975, Urban
travel
demand:
A behavioral
analysis (North
Holland,
Amsterdam).
.
Dubin,
Robin
A. and Allen
C. Goodman,
1982, Valuation
of education
and crime
in
neighborhood
characteristics
through
hedonic prices, Population
and Environment.
Edelfson,
Lee E., 1981, The comparative
statics of hedonic
price functions
and other nonlinear
constraints,
Econometrica,
1501-1520.
Ellickson,
Bryan, 1981, An alternative
test of the hedonic
theory of housing markets,
Journal
of
Urban Economics
9, 5679.
Epple, Dennis,
1982, Hedonic
prices and implicit
markets:
Estimating
demand
and supply
functions
for differential
products,
Working
paper (Graduate
School of Industrial
Administration,
Carnegie-Mellon
University,
Pittsburgh,
PA).
Follain,
James R., Jr. and Emmanuel
Jimenez,
1983, Demand
for housing
characteristics
in
developing
countries,
World
Bank discussion
paper WUDD
43 (Washington,
DC).
Follain,
James R., Jr. and Stephen Malpezzi,
1981, Flight to the suburbs:
Insights
gained from
an analysis
of central city vs. suburban
price differentials,
Journal
of Urban
Economics,
381398.
106
hng
J.R. Follain
and E. Jimenez,
Estimating
demand for
housing
characteristics
in
developing
countries
and willingness
to pay for additional
space, Journal
of Development
Economics
11,249-212.
Freeman,
A. Myrick,
1979, Hedonic
prices, property
values,
and measuring
environmental
benefits: A survey of the issues, Scandinavian
Journal
of Economics,
158-173.
Gallant,
A.R., 1977, Three-stage
least squares estimation
for a system of simultaneous,
nonlinear,
implicit
equations,
Journal
of Econometrics
17, 71-88.
Gallant,
A.R. and A. Holly,
1980, Statistical
inference
in an implicit,
nonlinear,
simultaneous
equation
model in the context
of maximum
likelihood
estimation,
Econometrica,
6977720.
Gallant,
A.R. and D.W. Jorgenson,
1979, Statistical
inference
for a system
of simultaneous,
nonlinear
implicit
equations
in the context
of instrumental
variable
estimation,
Journal
of
Econometrics,
275-302.
Galster,
G.C., 1977, A bid-rent
analysis
of housing
market
discrimination,
American
Economic
Review,
144155.
Goodman,
Allen C. and Masahiro
Kawai,
1982, Permanent
income,
hedonic
prices and the
demand for housing:
New evidence, Journal
of Urban
Economics,
214-237.
Goodwin,
Susan Ann, 1977, Measuring
the value of housing quality:
A note, Journal
of Regional
Science, 107-115.
Halvorsen,
Robert and Henry 0. Pollakowski,
1981, Choice of functional
form for hedonic price
equations,
Journal
of Urban Economics,
3749.
Harrison,
David,
Jr. and Daniel L. Rubinfeld,
1978, Hedonic
housing
prices and the demand for
clean air, Journal
of Environmental
Economics
and Management,
81-102.
Hausman,
J.A., 1978, Specification
tests in econometrics,
Econometrica,
1251-1270.
Heckman,
James,
1978, Dummy
endogenous
variables
in a simultaneous
equations
system,
Econometrica,
931-960.
Jud, G. Donald,
1980, The effects of zoning
on single family
residential
property
values in
Charlotte,
North
Carolina,
Land Economics,
142-154.
Jud, G. Donald,
1982, Schools and housing values, Land Economics,
4599470.
Kaufman,
D. and John M. Quigley,
1982, The consumption
benefits
of investment
in urban
infrastructure:
An application
to sites and service
projects
in LDC
(The World
Bank,
Washington,
DC).
King, A. Thomas,
1976, The demand for housing:
A Lancasterian
approach,
Southern
Economic
Journal,
1077-1087.
Krumm,
Ronald
J., 1980, Neighborhood
amenities:
An economic
analysis,
Journal
of Urban
Economics,
2088224.
Lang, James R. and Wesley H. Jones, 1979, Hedonic
property
valuation
markets:
Are subjective
measures
of neighborhood
amenities needed?, AREUEA
Journal,
344361.
Lerman,
Steven R. and Clifford
R. Kern, 1983, Hedonic
theory, bid-rents,
and willingness-to-pay:
Some extensions
of Ellickson’s
results, Journal
of Urban
Economics,
358-363.
Li, Mingche
M. and H. James Brown,
1980, Micro-neighborhood
externalities
and hedonic
housing
prices, Land Economics,
125-141.
Linneman,
Peter,
1981, The demand
for residence
site characteristics,
Journal
of Urban
(Y
Economics,
129-148.
Linneman,
Peter, 1982, Hedonic
prices and residential
location,
in: Douglas
B. Diamond,
Jr. and
George
S. Tolley,
eds., The economics
of urban
amenities
(Academic
Press, New York)
68-88.
MacLennan,
Duncan,
1977, Some thoughts
on the nature and purpose
of house price studies,
Urban Studies, 59-72.
Mayo, Stephen K., 1981, Theory and estimation
in the economics
of housing demand, Journal
of
Urban Economics,
95-116.
Mayo,
Stephen K., Steve Malpezzi
and Sungyong
Kang, n.d., Housing
demand
in developing
countries:
A cross-country
comparison,
World
Bank working
paper (Washington,
DC).
McFadden,
Daniel,
1978, Modelling
the choice
of residential
location,
in: A. Karlquest,
L.
Lundquist,
F. Snickars
and J. W. Weibull,
eds., Spatial interaction
theory
and planning
models (North-Holland,
Amsterdam)
75-96.
McMillan,
Melville
L., 1979, Estimates
of households’
preferences
for environmental
quality
and
other housing
characteristics
from a system of demand
equations,
Scandinavian
Journal
of
Economics
81, 174-187.
J.R. Follain
and E. Jimenez,
Estimating
demand for
housing
characteristics
107
McMillan,
Melville
L., Bradford
G. Reid and David
W. Dilen,
1980, An extension
of the
hedonic approach
for estimating
the value of quiet, Land Economics,
315-328.
Mills, Edwin, 1980, Urban
economics
(Scott Foresman,
London,
Glenview,
IL).
Murray,
Michael
P., 1983, Mythical
demands
and mythical
supplies
for proper
estimation
of
Rosen’s hedonic price model, Unpublished
mimeo.
Muth, Richard,
1969, Cities and housing (University
of Chicago
Press, Chicago,
IL).
Nelson,
Jon P., 1978, Residential
choice, hedonic
prices, and the demand for urban air quality,
Journal
of Urban
Economics,
3577369.
Nelson,
Jon P., 1981, Estimating
demand
functions
for product
characteristics:
A comment,
Mimeo.
Ohsfeldt,
Robert,
1983, The demand for housing
characteristics:
A test of the theory
of implicit
markets,
Unpublished
mimeo.
Polinsky,
A. Mitchell,
1977, The demand
for housing:
A study in specification
and grouping,
Econometrica,
447-467.
Pollak,
Robert
A. and Terence
J. Wales,
1978, Estimation
of complete
demand
systems
from
household
budget data: The linear and quadratic
expenditure
systems,
American
Economic
Review, 348-360.
Quigley,
John M., 1981, Estimates
of a more general model of consumer
choice in the housing
market
(Institute
of Business
and Economic
Research,
University
of California
at Berkeley,
CA).
Quigley,
John M., 1982, Non-linear
budget constraints
and consumer
demands:
An application
to public programs
for residential
housing,
Journal
of Urban
Economics,
177-201.
Ridker,
R.G. and John A. Henning,
1967, The determinants
of residential
property
value with
special references
to air pollution,
Review of Economics
and Statistics,
246-255.
Rosen,
Sherwin,
1974, Hedonic
prices and implicit
markets:
Product
differentiation
in pure
competition,
Journal
of Political
Economy,
3455.
Schnare, Ann B. and Raymond
J. Struyk,
1976, Segmentation
in urban housing markets,
Journal
of Urban
Economics,
146-166.
Wheaton,
William
C. 1977, A bid-rent
approach
to housing
demand,
Journal
of Urban
Economics,
200-217.
Wilkinson,
R.K. and C.A. Archer,
1976, The quality
of housing
and the measurement
of long
term changes in house prices, Urban
Studies, 273-283.
Witte, A.D., H. Sumka and J. Erekson,
1979, An estimate of a structural
hedonic
price model of
the housing
market:
An application
of Rosen’s
theory
of implicit
marketing,
Econometrica,
1151-1172.
Zorn,
Peter,
1985, Capitalization,
population
movement,
and the local public
sector:
A
probabilistic
analysis, Journal
of Urban
Economics
17, Jan.