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

  • Tim Bollerslev
  • Andrew J. Patton
  • Wang Wenjing

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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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
Contact details of provider: Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097
Phone: (919) 660-1800
Fax: (919) 684-8974
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  1. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
  2. Holly, Sean & Pesaran, M. Hashem & Yamagata, Takashi, 2006. "A Spatio-Temporal Model of House Prices in the US," IZA Discussion Papers 2338, Institute for the Study of Labor (IZA).
  3. Pesaran, M. Hashem & Timmermann, Allan, 2007. "Selection of estimation window in the presence of breaks," Journal of Econometrics, Elsevier, vol. 137(1), pages 134-161, March.
  4. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
  5. Tian, Jing & Anderson, Heather M., 2014. "Forecast combinations under structural break uncertainty," International Journal of Forecasting, Elsevier, vol. 30(1), pages 161-175.
  6. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 7(2), pages 174-196, Spring.
  7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  8. Robert J. Shiller, 1991. "Arithmetic Repeat Sales Price Estimators," Cowles Foundation Discussion Papers 971, Cowles Foundation for Research in Economics, Yale University.
  9. Clapp, John M & Giaccotto, Carmelo, 1992. "Estimating Price Trends for Residential Property: A Comparison of Repeat Sales and Assessed Value Methods," The Journal of Real Estate Finance and Economics, Springer, vol. 5(4), pages 357-74, December.
  10. Meese, Richard A & Wallace, Nancy E, 1997. "The Construction of Residential Housing Price Indices: A Comparison of Repeat-Sales, Hedonic-Regression and Hybrid Approaches," The Journal of Real Estate Finance and Economics, Springer, vol. 14(1-2), pages 51-73, Jan.-Marc.
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