Abstract
In recent years, the increasing availability of large databases on real estate transaction has opened up new research possibilities using revealed preference method. Therefore, the aim of this paper is to investigate the capability of revealed preference method of ordinary least square and rank transformation regression models in valuing shophouse heritage property. This paper has provided the first application that consider the thin market effects by comparing the ordinary least square and rank transformation regression in obtaining the market value of shophouse heritage property. The original dataset consists of 893 commercial properties transacted from 2004 to 2014 in Kota Bharu, Kelantan Malaysia. After filtration process, only 25 units of shophouse heritage property were available and valid to be used. The findings suggest that rank transformation regression model performs better than the ordinary least square model with double-log as the best model. This suggests that rank transformation regression is capable for heritage property valuation in thin market situation.
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