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High-Frequency House Price Indexes with Scarce Data

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  • Steven C. Bourassa
  • Martin Hoesli

Abstract

We show how a method that has been applied to commercial real estate markets can be used to produce high-frequency house price indexes for a city and for submarkets within a city. Our application of this method involves estimating a set of annual robust repeat sales regressions staggered by start date and then undertaking an annual-to-monthly (ATM) transformation with a generalized inverse estimator. Using transactions data for Louisville, Kentucky, we show that the method substantially reduces the volatility of high-frequency indexes at the city and submarket levels. We define submarkets in terms of both ZIP Codes and groups of contiguous ZIP Codes that approximate areas defined by the local multiple listing service. Focusing on ZIP Codes, we demonstrate that both volatility and the benefits from using the ATM method are related to sample size. Our method is clearly useful for constructing house price indexes for small areas with relatively scarce data.

Suggested Citation

  • Steven C. Bourassa & Martin Hoesli, 2017. "High-Frequency House Price Indexes with Scarce Data," Journal of Real Estate Literature, Taylor & Francis Journals, vol. 25(1), pages 207-220, January.
    • Handle: RePEc:taf:rjelxx:v:25:y:2017:i:1:p:207-220
    DOI: 10.1080/10835547.2017.12090448
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