# RECORD 973124ea687da38bfcb0d526fb28745dae9e1e90
# TITLE: MCMC Bayesian spatial filtering for hedonic models in real estate markets
# YEAR: 2017.0  VENUE: nan  DOI: 10.1016/j.spasta.2017.07.010
# AUTHORS: P. Gargallo; Jesus Miguel; M. Salvador
# PAGES: 37  CANDIDATE TABLE PAGES (1-based): []
# ABSTRACT: Abstract The traditional hedonic model postulates that housing prices depend on their characteristics and their location. However, this model assumes a constant relationship between the dependent and the independent variables. This assumption is unrealistic because empirical studies have shown that the regression coefficients depend on the housing location. For this reason, it is necessary to use models with spatially varying coefficients. The approaches proposed in the literature used to estimate this type of models do not incorporate the uncertainty associated with the estimation and selection of models and/or are computationally expensive. To improve these aspects, this paper proposes spatial filtering techniques to parsimoniously model the spatial dependencies of the hedonic coefficients and an adaptive MCMC Bayesian algorithm to select the most appropriate filters. The method is illustrated through an application to the real estate market of Zaragoza, and a comparison with alternative procedures is conducted. Our results show that our valuation methodology has better goodness of fit and predictive performance properties than alternative methods. Although our proposal assumes normality and homoscedasticity of the model error distribution, the method is easy to implement and not very computationally demanding, which makes this approach attractive and useful from a practical viewpoint.


===== PAGE 1 (score 5, numeric-paren 0, table-words 0, var-words 5) =====

1 
 
MCMC BAYESIAN SPATIAL FILTERING FOR HEDONIC 
MODELS IN REAL ESTATE MARKETS 
Gargallo, P. ; Miguel, J.A. and  Salvador, M.J. *  
 
emails: pigarga@unizar,es, jamiguel@unizar.es, salvador@unizar.es  
Departamento of Estructura e Historia Económica y Economía Pública 
Facultad de Economía y Empresa. University of Zaragoza (SPAIN) 
 
Abstract 
The traditional hedonic model postulates that housing prices depend on their 
characteristics and their location. However, this model assumes a constant relationship 
between the dependent and the independent variables. This assumption is unrealistic 
because empirical studies have shown that the regression coefficients depend on the 
housing location. For this reason, it is necessary to use models with spatially varying 
coefficients. The approaches proposed in the literature used to estimate this type of 
models do not incorporate the uncertainty associated with the estimation and selection 
of models and/or are computationally expensive. To improve these aspects, this paper 
proposes spatial filtering techniques to parsimoniously model the spatial dependencies 
of the hedonic coefficients and an adaptive MCMC algorithm of Bayesian variable 
selection to select the most appropriate filters. The method is illustrated through an 
application to the real estate market of Zaragoza, and a comparison with alternative 
procedures is conducted. Our results show that our valuation methodology has better 
goodness of fit and predictive performance properties than alternative methods. 
Although our proposal assumes normality and homoscedasticity of the model error 
distribution, the method is easy to implement and not very computationally demanding, 
which makes this approach attractive and useful from a practical viewpoint. 
 
 
Keywords: Bayesian Inference; Variable Selection; Adaptive MCMC; Hedonic 
Models; Real Estate Market; Spatial Filtering; Geographically Weighted Regression. 
 
* This work was supported by grants ECO2012-35029 and ECO2016-79392-P of the Ministerio 
de Economía y Competitividad (Spain) 
 
1. Introduction 
 
Real estate activities are linked to many sectors of the economy, including 
construction, finance, and insurance. Therefore, updated fair market housing values are 
extremely valuable to financial regulators and institutions, municipal assessors, housing 
index compilers, real estate developers, investors, and many others. Hedonic modelling 
is the most widely used method to estimate housing prices.  
The traditional hedonic model postulates that housing prices mainly depend on 
their characteristics and locations. The model accounts for locational attributes (location