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Bayesian Spatial Modeling for Housing Data in South Africa

Author

Listed:
  • Bingling Wang

    (Department of Biostatistics, University of California, Los Angeles)

  • Sudipto Banerjee

    (Department of Biostatistics, University of California, Los Angeles)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, South Africa)

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.

Suggested Citation

  • Bingling Wang & Sudipto Banerjee & Rangan Gupta, 2018. "Bayesian Spatial Modeling for Housing Data in South Africa," Working Papers 201837, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201837
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