This paper develops a new methodology for constructing a real estate price index that utilizes all transaction price information, encompassing both single-sales and repeat-sales. The method is less susceptible to specification error than standard hedonic methods and is not subject to the sample selection bias involved in indexes that rely only on repeat sales. The methodology employs a model design that uses a sale pairing process based on the individual building level, rather than the individual house level as is used in the repeat-sales method. The approach extends ideas from repeat-sales methodology in a way that accommodates much wider datasets. In an empirical analysis of the methodology, we fit the model to the private residential property market in Singapore between Q1 1995 and Q2 2014, covering several periods of major price fluctuation and changes in government macroprudential policy. The index is found to perform much better in out-of-sample prediction exercises than either the S&P/Case-Shiller index or the index based on standard hedonic methods. In a further empirical application, the recursive dating method of Phillips et al. (2015a,b) is used to detect explosive behavior in the Singapore real estate market. Explosive behavior in the new index is found to arise two quarters earlier than in the other indices.
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