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High Frequency House Price Indexes with Scarce Data

Author

Listed:
  • Steven C. Bourassa

    (Florida Atlantic University)

  • Martin Hoesli

    (University of Geneva - Geneva School of Economics and Management (GSEM); University of Aberdeen - Business School; Swiss Finance Institute; University of Geneva - Geneva Finance Research Institute)

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 demonstrate that both volatility and the benefits from using the ATM method are related to sample size.

Suggested Citation

  • Steven C. Bourassa & Martin Hoesli, 2016. "High Frequency House Price Indexes with Scarce Data," Swiss Finance Institute Research Paper Series 16-27, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp1627
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    Cited by:

    1. Robert J. Hill & Alicia N. Rambaldi & Michael Scholz, 2021. "Higher frequency hedonic property price indices: a state-space approach," Empirical Economics, Springer, vol. 61(1), pages 417-441, July.

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    Keywords

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    JEL classification:

    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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