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A Hierarchical Bayesian Approach for Residential Property Valuation:Application to Hong Kong Housing Market

Author

Listed:
  • Sam K. Hui

    (Stern School of Business of New York University)

  • Alvin Cheung

    (Massachusetts Institute of Technology)

  • Jimmy Pang

    (Stanford University)

Abstract

We have developed a statistical method for the valuation of residential properties using a hierarchical Bayesian approach, which takes into consideration the unique structure of the Hong Kong property market. Our model is calibrated on a dataset that covers all residential real estate transactions in ten major Hong Kong residential complexes from February 2008 to February 2009. Although parsimonious, our model outperforms other valuation methods that are based on average price-per-square- feet or expert assessments. By providing our model-based valuations online without charge, we hope to improve transparency in the Hong Kong housing market, thus enabling consumers to make better investment decisions.

Suggested Citation

  • Sam K. Hui & Alvin Cheung & Jimmy Pang, 2010. "A Hierarchical Bayesian Approach for Residential Property Valuation:Application to Hong Kong Housing Market," International Real Estate Review, Global Social Science Institute, vol. 13(1), pages 1-29.
    • Handle: RePEc:ire:issued:v:13:n:01:2010:p:1-29
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    References listed on IDEAS

    as
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    Cited by:

    1. Simon K.C. Cheung, 2017. "A Localized Model for Residential Property Valuation: Nearest Neighbor with Attribute Differences," International Real Estate Review, Global Social Science Institute, vol. 20(2), pages 221-250.
    2. Anthony C Constantinou & Norman Fenton, 2017. "The future of the London Buy-To-Let property market: Simulation with temporal Bayesian Networks," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-30, June.
    3. Yunxi Bai & Jusheng Song & Shanshan Wu & Wei Wang & Jacqueline T. Y. Lo & S. M. Lo, 2020. "Comparing the Impacts of Location Attributes on Residents’ Preferences and Residential Values in Compact Cities: A Case Study of Hong Kong," Sustainability, MDPI, vol. 12(12), pages 1-23, June.
    4. Daikun Wang & Victor Jing Li, 2019. "Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review," Sustainability, MDPI, vol. 11(24), pages 1-14, December.
    5. Daikun Wang & Victor Jing Li & Huayi Yu, 2020. "Mass Appraisal Modeling of Real Estate in Urban Centers by Geographically and Temporally Weighted Regression: A Case Study of Beijing’s Core Area," Land, MDPI, vol. 9(5), pages 1-18, May.
    6. Volkan Sevinç, 2022. "Determining the Flat Sales Prices by Flat Characteristics Using Bayesian Network Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 549-577, February.
    7. Ying-Hui Chiang & Yuan Ku & Feng Liu & Chin-Oh Chang, 2019. "House Price Dispersion in Taipei Residential Communities," International Real Estate Review, Global Social Science Institute, vol. 22(1), pages 109-129.
    8. Tommy Wu & Michael Cheng & Ken Wong, 2017. "Bayesian analysis of Hong Kong's housing price dynamics," Pacific Economic Review, Wiley Blackwell, vol. 22(3), pages 312-331, August.

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    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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