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Using the Spatial Configuration of the Data to Improve Estimation

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  • Pace, R Kelley
  • Gilley, Otis W

Abstract

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. Copyright 1997 by Kluwer Academic Publishers

Suggested Citation

  • Pace, R Kelley & Gilley, Otis W, 1997. "Using the Spatial Configuration of the Data to Improve Estimation," The Journal of Real Estate Finance and Economics, Springer, vol. 14(3), pages 333-340, May.
  • Handle: RePEc:kap:jrefec:v:14:y:1997:i:3:p:333-40
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    References listed on IDEAS

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    1. Ling, David C & Naranjo, Andy, 1997. "Economic Risk Factors and Commercial Real Estate Returns," The Journal of Real Estate Finance and Economics, Springer, vol. 14(3), pages 283-307, May.
    2. Patrick J. Wilson & John Okunev, 1999. "Long-Term Dependencies and Long Run non-Periodic Co-Cycles: Real Estate and Stock Markets," Journal of Real Estate Research, American Real Estate Society, vol. 18(2), pages 257-278.
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