000071190 001__ 71190 000071190 005__ 20190709135705.0 000071190 0247_ $$2doi$$a10.1016/j.spasta.2017.07.010 000071190 0248_ $$2sideral$$a103242 000071190 037__ $$aART-2017-103242 000071190 041__ $$aeng 000071190 100__ $$0(orcid)0000-0003-1205-1756$$aGargallo Valero, Pilar$$uUniversidad de Zaragoza 000071190 245__ $$aMCMC Bayesian spatial filtering for hedonic models in real estate markets 000071190 260__ $$c2017 000071190 5060_ $$aAccess copy available to the general public$$fUnrestricted 000071190 5203_ $$aThe 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. 000071190 536__ $$9info:eu-repo/grantAgreement/ES/MICINN-FEDER/ECO2012-35029$$9info:eu-repo/grantAgreement/ES/MINECO/ECO2016-79392-P$$9info:eu-repo/grantAgreement/ES/MINECO/SO2014-55780-C3-2-P 000071190 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000071190 590__ $$a1.026$$b2017 000071190 591__ $$aSTATISTICS & PROBABILITY$$b61 / 123 = 0.496$$c2017$$dQ2$$eT2 000071190 591__ $$aMATHEMATICS, INTERDISCIPLINARY APPLICATIONS$$b66 / 103 = 0.641$$c2017$$dQ3$$eT2 000071190 591__ $$aGEOSCIENCES, MULTIDISCIPLINARY$$b156 / 189 = 0.825$$c2017$$dQ4$$eT3 000071190 591__ $$aREMOTE SENSING$$b27 / 30 = 0.9$$c2017$$dQ4$$eT3 000071190 592__ $$a1.488$$b2017 000071190 593__ $$aComputers in Earth Sciences$$c2017$$dQ1 000071190 593__ $$aStatistics and Probability$$c2017$$dQ1 000071190 593__ $$aManagement, Monitoring, Policy and Law$$c2017$$dQ1 000071190 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000071190 700__ $$0(orcid)0000-0003-1394-9816$$aMiguel Álvarez, Jesús Ángel$$uUniversidad de Zaragoza 000071190 700__ $$0(orcid)0000-0002-5788-6661$$aSalvador Figueras, Manuel$$uUniversidad de Zaragoza 000071190 7102_ $$14008$$2623$$aUniversidad de Zaragoza$$bDpto. Estruc.Hª Econ.y Eco.Pb.$$cÁrea Métodos Cuant.Econ.Empres 000071190 773__ $$g22, 1 (2017), 47-67$$pSpat. stat.$$tSpatial statistics$$x2211-6753 000071190 8564_ $$s1911508$$uhttps://zaguan.unizar.es/record/71190/files/texto_completo.pdf$$yPostprint 000071190 8564_ $$s94815$$uhttps://zaguan.unizar.es/record/71190/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000071190 909CO $$ooai:zaguan.unizar.es:71190$$particulos$$pdriver 000071190 951__ $$a2019-07-09-12:51:56 000071190 980__ $$aARTICLE