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Housing price prediction: parametric versus semi-parametric spatial hedonic models

José-María Montero (), Román Mínguez and Gema Fernández-Avilés
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José-María Montero: University of Castilla-La Mancha
Román Mínguez: University of Castilla-La Mancha
Gema Fernández-Avilés: University of Castilla-La Mancha

Journal of Geographical Systems, 2018, vol. 20, issue 1, 27-55

Abstract: Abstract House price prediction is a hot topic in the economic literature. House price prediction has traditionally been approached using a-spatial linear (or intrinsically linear) hedonic models. It has been shown, however, that spatial effects are inherent in house pricing. This article considers parametric and semi-parametric spatial hedonic model variants that account for spatial autocorrelation, spatial heterogeneity and (smooth and nonparametrically specified) nonlinearities using penalized splines methodology. The models are represented as a mixed model that allow for the estimation of the smoothing parameters along with the other parameters of the model. To assess the out-of-sample performance of the models, the paper uses a database containing the price and characteristics of 10,512 homes in Madrid, Spain (Q1 2010). The results obtained suggest that the nonlinear models accounting for spatial heterogeneity and flexible nonlinear relationships between some of the individual or areal characteristics of the houses and their prices are the best strategies for house price prediction.

Keywords: Housing prices; Semi-parametric spatial hedonic models; Generalized additive models; Penalized splines; Mixed models (search for similar items in EconPapers)
JEL-codes: C21 R10 R31 (search for similar items in EconPapers)
Date: 2018
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