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Analyzing Real Estate Data Problems Using the Gibbs Sampler

Author

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  • John R. Knight
  • C.F. Sirmans
  • Alan E. Gelfand
  • Sujit K. Ghosh

Abstract

Real estate data are often characterized by data irregularities: missing data, censoring or truncation, measurement error, etc. Practitioners often discard missing‐ or censored‐data cases and ignore measurement error. We argue here that an attractive remedy for these irregularity problems is simulation‐based model fitting using the Gibbs sampler. The style of the paper is primarily pedagogic, employing a simple illustration to convey the essential ideas, unobscured by implementation complications. Focusing on the missing‐data problem, we show dramatic improvement in inference by retaining rather than deleting cases of partially observed data. We also detail Gibbs‐sampler usage for other data problems.

Suggested Citation

  • John R. Knight & C.F. Sirmans & Alan E. Gelfand & Sujit K. Ghosh, 1998. "Analyzing Real Estate Data Problems Using the Gibbs Sampler," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 26(3), pages 469-492, September.
    • Handle: RePEc:bla:reesec:v:26:y:1998:i:3:p:469-492
    DOI: 10.1111/1540-6229.00753
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    Cited by:

    1. Griffiths, William E. & Newton, Lisa S. & O'Donnell, Christopher J., 2010. "Predictive densities for models with stochastic regressors and inequality constraints: Forecasting local-area wheat yield," International Journal of Forecasting, Elsevier, vol. 26(2), pages 397-412, April.
    2. Sampagnaro, Gabriele & Battaglia, Francesca, 2010. "Reliability and Heterogeneity of Real Estate Indexes and their Impact on the Predictability of Returns," MPRA Paper 23378, University Library of Munich, Germany.
    3. Yong Liu & A. Ford Ramsey, 2023. "Incorporating historical weather information in crop insurance rating," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(2), pages 546-575, March.
    4. Lyubov Doroshenko & Brunero Liseo, 2023. "Generalized linear mixed model with bayesian rank likelihood," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 425-446, June.

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