GP versus GLS Spatial Index Models to Forecast
Single-Family Home Prices
New Mathematics and Natural Computation, 4 (2):
143--163 (July 2008)DOI: doi:10.1142/S1793005708001021
Abstract
This paper investigates use of genetic programming
regression models to forecast home values.
Neighbourhood 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 neighbourhood 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 generalised least squares. Out-of-sample genetic
programming predictions of home prices obtained using
spatial index models deliver reasonable forecasts of
home prices.