High Frequency House Price Indexes with Scarce Data

Steven Bourassa and Martin Hoesli

No 16-27, Swiss Finance Institute Research Paper Series from Swiss Finance 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.

Keywords: House Prices; High-Frequency Price Indexes; Repeat Sales Method; Scarce Data (search for similar items in EconPapers)
JEL-codes: R31 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2016-03
New Economics Papers: this item is included in nep-ure
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: High-Frequency House Price Indexes with Scarce Data (2017)
Working Paper: High Frequency House Price Indexes with Scarce Data (2016)
Working Paper: High Frequency House Price Indexes with Scarce Data (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp1627

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