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

Author

Listed:
  • Tim Bollerslev

    (Duke University, NBER and CREATES)

  • Andrew J. Patton

    (Duke University, NBER and CREATES)

  • Wenjing Wang

    (Moody’s Analytics, Inc.)

Abstract
We construct daily house price indices 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 methodology of the popular monthly Case-Shiller house price indices. Our new daily house price indices exhibit dynamic features similar to those of other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity of the corresponding daily returns. A relatively simple multivariate time series model for the daily house price index returns, explicitly allowing for commonalities across cities and GARCH effects, produces forecasts of monthly house price changes that are superior to various alternative forecast procedures based on lower frequency data.

Suggested Citation

  • Tim Bollerslev & Andrew J. Patton & Wenjing Wang, 2015. "Daily House Price Indices: Construction, Modeling, and Longer-Run Predictions," CREATES Research Papers 2015-02, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2015-02
    as

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    File URL: https://repec.econ.au.dk/repec/creates/rp/15/rp15_02.pdf
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    References listed on IDEAS

    as
    1. Holly, Sean & Pesaran, M. Hashem & Yamagata, Takashi, 2010. "A spatio-temporal model of house prices in the USA," Journal of Econometrics, Elsevier, vol. 158(1), pages 160-173, September.
    2. John P. Owens & Douglas G. Steigerwald, 2006. "Noise reduced realized volatility: a kalman filter approach," Advances in Econometrics, in: Econometric Analysis of Financial and Economic Time Series, pages 211-227, Emerald Group Publishing Limited.
    3. Fulvio Corsi & Stefano Peluso & Francesco Audrino, 2015. "Missing in Asynchronicity: A Kalman‐em Approach for Multivariate Realized Covariance Estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(3), pages 377-397, April.
    4. Karl E. Case & Robert J. Shiller, 1987. "Prices of single-family homes since 1970: new indexes for four cities," New England Economic Review, Federal Reserve Bank of Boston, issue Sep, pages 45-56.
    5. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    6. 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.
    7. Badi H. Baltagi & Georges Bresson & Jean‐Michel Etienne, 2015. "Hedonic Housing Prices in Paris: An Unbalanced Spatial Lag Pseudo‐Panel Model with Nested Random Effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(3), pages 509-528, April.
    8. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    9. 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.
    10. Tian, Jing & Anderson, Heather M., 2014. "Forecast combinations under structural break uncertainty," International Journal of Forecasting, Elsevier, vol. 30(1), pages 161-175.
    11. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    12. Robert J. Shiller, 1991. "Arithmetic Repeat Sales Price Estimators," Cowles Foundation Discussion Papers 971, Cowles Foundation for Research in Economics, Yale University.
    13. Ghysels, Eric & Plazzi, Alberto & Valkanov, Rossen & Torous, Walter, 2013. "Forecasting Real Estate Prices," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 509-580, Elsevier.
    14. 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 339-350, July.
    15. 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 357-374, December.
    16. 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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    More about this item

    Keywords

    Data aggregation; Real estate prices; Forecasting; Time-varying volatility;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand

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