Journal of Real Estate Finance and Economics, 14: 3, 333±340 (1997)
# 1997 Kluwer Academic Publishers
Using the Spatial Con®guration of the Data to
R. KELLEY PACE
Department of Finance, E.J. Ourso College of Business Administration, Louisiana State University,
Baton Rouge, LA 70803
OTIS W. GILLEY
Department of Economics and Finance, College of Administration and Business,
Louisiana Tech University, Ruston, Louisiana 71272
Using the well-known Harrison and Rubinfeld (1978) hedonic pricing data, this manuscript demonstrates the
substantial bene®ts obtained by modeling the spatial dependence of the errors. Speci®cally, the estimated errors
on the spatial autoregression fell by 44% relative to OLS. The spatial autoregression corrects predicted values by
a nonparametric estimate of the error on nearby observations and thus mimics the behavior of appraisers. The
spatial autoregression, by formally incorporating the areal con®guration of the data to increase predictive
accuracy and estimation ef®ciency, has great potential in real estate empirical work.
Key Words: spatial autocorrelation, SAR, hedonic pricing
In a well-known paper, Harrison and Rubinfeld (1978) investigated various
methodological issues related to the use of housing data to estimate the demand for
clean air. They illustrated their procedures using data from the Boston SMSA with 506
observations (one observation per census tract) on 14 nonconstant independent variables.
These variables include proxies for pollution, crime, distance to various centers,
geographical features, accessibility, housing size, age, race, status, tax burden,
educational quality, zoning, and industrial externalities.
Despite the inclusion of a wide variety of important economic variables, the Harrison
and Rubinfeld model and data exhibit various problems common to many hedonic pricing
or mass appraisal models.
For example, not all variables exhibit the proper sign.
Speci®cally, the AGE variable is insigni®cant and positive. In addition, the residuals
display a pattern across space, a result incompatible with the assumed independent and
identically distributed (iid) error structure.
To resolve these empirical problems, this paper explicitly allows for the areal
con®guration of the observations through a spatial autoregression. By appropriate
differencing of the observations, the spatial autoregression re-creates a more iid
error structure, which greatly improves the results. Speci®cally, the estimated spatial
autoregression yields a negative and signi®cant coef®cient for AGE while vastly
improving the sample goodness of ®t. The estimated sum-of-squares errors falls by 44%
relative to the original ordinary least squares (OLS) results.