Bayesian Spatial Modeling for Housing Data in South Africa

Bingling Wang (), Sudipto Banerjee () and Rangan Gupta
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Bingling Wang: Department of Biostatistics, University of California, Los Angeles
Sudipto Banerjee: Department of Biostatistics, University of California, Los Angeles

No 201837, Working Papers from University of Pretoria, Department of Economics

Abstract: Spatial process models are increasingly getting attention as data have become available at geocoded locations. In this paper, we build a hierarchical framework with multivariate spatial processes. The hierarchical models are implemented through Markov chain Monte Carlo methods. And Bayesian inference is carried out for parameter estimation and interpretation. The proposed models are illustrated using housing data collected in the Walmer district of Port Elizabeth, South Africa. Our interest is to evaluate the spatial dependencies of dependent outcomes and associations with other independent variables. Comparison across different models confirm that the selling price of a house in our data set is relatively better modeled by incorporating spatial processes.

Keywords: Bayesian inference; Hierarchical models; Multivariate spatial models; Point-referenced data; Spatial processes (search for similar items in EconPapers)
Pages: 18 pages
Date: 2018-06
New Economics Papers: this item is included in nep-ecm and nep-ure
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Journal Article: Bayesian Spatial Modeling for Housing Data in South Africa (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:201837

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