A Hierarchical Bayesian Approach for Residential Property Valuation:Application to Hong Kong Housing Market
Sam K. Hui (),
Alvin Cheung and
Jimmy Pang Additional contact information Sam K. Hui: Stern School of Business of New York University
Alvin Cheung: Massachusetts Institute of Technology
Jimmy Pang: Stanford University
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.
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