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Gp Versus Gls Spatial Index Models To Forecast Single-Family Home Prices

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  • MAK KABOUDAN

    (School of Business, University of Redlands, 1200 E. Colton Ave., Redlands, CA 92373, USA)

Abstract

This paper investigates use of genetic programming regression models to forecast home values. Neighborhood prices in a city are represented by a quarterly index. Index values are ratios of each local neighborhood to the global city average real price of homes sold. Relative average neighborhood home attributes, local socioeconomic characteristics, spatial measures, and real mortgage rates explain spatiotemporal variations in the index. To examine efficacy of model estimation, forecasts obtained using genetic programming are compared with those obtained using generalized least squares. Out-of-sample genetic programming predictions of home prices obtained using spatial index models deliver reasonable forecasts of home prices.

Suggested Citation

  • Mak Kaboudan, 2008. "Gp Versus Gls Spatial Index Models To Forecast Single-Family Home Prices," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 4(02), pages 143-163.
    • Handle: RePEc:wsi:nmncxx:v:04:y:2008:i:02:n:s1793005708001021
    DOI: 10.1142/S1793005708001021
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