GP VERSUS GLS SPATIAL INDEX MODELS TO FORECAST SINGLE-FAMILY HOME PRICES

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

New Mathematics and Natural Computation (NMNC), 2008, vol. 04, issue 02, 143-163

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.

Keywords: Genetic programming; generalized least squares; hedonic model; spatial index; home prices (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:nmncxx:v:04:y:2008:i:02:n:s1793005708001021

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DOI: 10.1142/S1793005708001021

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