ANALYSIS OF SPATIAL AUTOCORRELATION IN-HOUSE PRICES

This article examines spatial autocorrelation in transaction prices of single-family properties in Dallas, Texas. The empirical analysis is conducted using a semilog hedonic house price equation and a spherical autocorrelation function with data for over 5000 transactions of home s sold between 1991:4 and 1993:1. Properties are geocoded and assigned to separate housing submarkets within metropolitan Dallas. Hedonic an d spherical autocorrelation parameters are estimated separately for ea ch submarket using estimated generalized least squares (EGLS). We find strong evidence of spatial autocorrelation in transaction prices with in submarkets. Results for spatially autocorrelated residuals are mixe d. In four of eight submarkets, there is evidence of spatial autocorre lation in the hedonic residuals for single-family properties located w ithin a 1200 meter radius. In two submarkets, the hedonic residuals ar e spatially autocorrelated throughout the submarket, while the hedonic residuals are spatially uncorrelated in the remaining two submarkets. Finally, we compare OLS and kriged EGLS predicted values for properti es sold during 1993:1. Kriged EGLS predictions are more accurate than OLS in six of eight submarkets, while OLS has smaller prediction error s in submarkets where the residuals are spatially uncorrelated and the estimated semivariogram has a large variance.