Using the Spatial Configuration of the Data to Improve Estimation



Using the well-known Harrison and Rubinfeld (1978) hedonic pricing data, this manuscript demonstrates the substantial benefits obtained by modeling the spatial dependence of the errors. Specifically, 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 configuration of the data to increase predictive accuracy and estimation efficiency, has great potential in real estate empirical work.

spatial autocorrelation SAR hedonic pricing 


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Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

    • 1
    • 2
  1. 1.Department of Finance, E.J. Ourso College of Business AdministrationLouisiana State UniversityBaton Rouge
  2. 2.Department of Economics and Finance, College of Administration and BusinessLouisiana Tech UniversityRuston

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