USING THE SPATIAL CONFIGURATION OF THE DATA TO IMPROVE ESTIMATION

Using the well-known Harrison and Rubinfeld (1978) hedonic pricing dat a, this manuscript demonstrates the substantial benefits obtained by m odeling the spatial dependence of the errors. Specifically, the estima ted errors on the spatial autoregression fell by 44% relative to OLS. The spatial autoregression corrects predicted values by a nonparametri c estimate of the error on nearby observations and thus mimics the beh avior of appraisers. The spatial autoregression, by formally incorpora ting the areal configuration of the data to increase predictive accura cy and estimation efficiency, has great potential in real estate empir ical work.