23 Pages Posted: 3 Jun 2016 Last revised: 6 Mar 2017
Date Written: March 7, 2016
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
JEL Classification: R31
Suggested Citation: Suggested Citation
Bourassa, Steven C. and Hoesli, Martin, High Frequency House Price Indexes with Scarce Data (March 7, 2016). Swiss Finance Institute Research Paper No. 16-27. Available at SSRN: https://ssrn.com/abstract=2789585 or http://dx.doi.org/10.2139/ssrn.2789585