Abstract
A special type of spatial dependence model has been developed for the valuation of residential property by using a Bayesian approach which takes into account the unique attributes of Hong Kong property. Inspired by an approach that bases transactions on nearest neighbor estimation, the current model has fine-tuned the spatial dependence method of deriving value from the transactions of the real estate closest in proximity by factoring in differences in structural and auxiliary attributes for more precise property valuation. The model is established on a dataset that covers all residential real estate transactions in a new town in Hong Kong during a given period of time. This model excels other valuation methods with its unique reference to attribute differences, which may impact the valuation results of properties in close vicinity. With this calibration method, we hope to improve the accuracy of real estate appraisals, thus enabling potential homeowners, lenders and investors to make better informed decisions.
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