Bayesian Spatial Modeling of Housing Prices Subject to a Localized Externality

• Published in: Communications in statistics. Theory and methods Vol. 37; no. 13; pp. 2066 - 2078
• Main Authors: Ecker, Mark D., Oliveira, Victor De
• Format: Journal Article
• Language: English
• Published: Philadelphia, PA Taylor & Francis Group 09.05.2008; Taylor & Francis
• Subjects: Exact sciences and technology; General topics; Geostatistics; Hedonic regression; Inference from stochastic processes; time series analysis; Markov processes; Mathematics; Monte Carlo; Probability and statistics; Probability theory and stochastic processes; Random field; Real estate data; Sciences and techniques of general use; Statistics; Stochastic processes; Covariance function; Statistical distribution; Stochastic method; Stochastic process; Markov chain; Random field; Sales; Monte Carlo; Correlation function; Approximation theory; 62M40; Bayes estimation; Mixed distribution; Monte Carlo method; Real estate data; Geostatistics; Statistical estimation; Algorithm; Mean estimation; Statistical method; Statistical regression; Image analysis; Numerical analysis; Hedonic regression; Price; Random function; 62F15; 62M30
• ISSN: 0361-0926; 1532-415X
• DOI: 10.1080/03610920701858404

This work proposes a non stationary random field model to describe the spatial variability of housing prices that are affected by a localized externality. The model allows for the effect of the localized externality on house prices to be represented in the mean function and/or the covariance function of the random field. The correlation function of the proposed model is a mixture of an isotropic correlation function and a correlation function that depends on the distances between home sales and the localized externality. The model is fit using a Bayesian approach via a Markov chain Monte Carlo algorithm. A dataset of 437 single family home sales during 2001 in the city of Cedar Falls, Iowa, is used to illustrate the model.