XXV Edition

1-2 December 2016"

High Frequency House Price Indexes with Scarce Data

Hoesli Martin, University of Geneva
Bourassa Steven, Florida Atlantic University

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. The method is also shown to be useful in mitigating index revisions as data for subsequent periods become available.

Area: Other

Keywords: house prices, high-frequency price indexes, repeat sales method, scarce data

Paper file

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