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Daily House Price Indexes: Construction, Modeling, and Longer-Run Predictions

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Author Info

  • Tim Bollerslev
  • Andrew J. Patton
  • Wang Wenjing

Abstract

We construct daily house price indexes for ten major U.S. metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat-sales method that closely mimics the procedure used in the construction of the popular monthly Case-Shiller house price indexes. Our new daily house price indexes exhibit similar characteristics to other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity, which are well described by a relatively simple multivariate GARCH type model. The sample and model-implied correlations across house price index returns are low at the daily frequency, but rise monotonically with the return horizon, and are all commensurate with existing empirical evidence for the existing monthly and quarterly house price series. A simple model of daily house price index returns produces forecasts of monthly house price changes that are superior to various alternative forecast procedures based on lower frequency data, underscoring the informational advantages of our new more finely sampled daily price series.

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Bibliographic Info

Paper provided by Duke University, Department of Economics in its series Working Papers with number 13-29.

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Length: 49
Date of creation: 2013
Date of revision:
Handle: RePEc:duk:dukeec:13-29

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Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097
Phone: (919) 660-1800
Fax: (919) 684-8974
Web page: http://econ.duke.edu/

Related research

Keywords: real estate; price indices; repeat sales index; high frequency data;

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Cited by:
  1. Anenberg, Elliot & Laufer, Steven, 2014. "Using Data on Seller Behavior to Forecast Short-run House Price Changes," Finance and Economics Discussion Series 2014-16, Board of Governors of the Federal Reserve System (U.S.).

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