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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
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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. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Robert J. Shiller, 1991. "Arithmetic Repeat Sales Price Estimators," Cowles Foundation Discussion Papers 971, Cowles Foundation for Research in Economics, Yale University.
    4. 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.
    5. 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.
    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. 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.
    8. Owens, John & Steigerwald, Douglas G, 2009. "Noise Reduced Realized Volatility: A Kalman Filter Approach," University of California at Santa Barbara, Economics Working Paper Series qt4n80536m, Department of Economics, UC Santa Barbara.
    9. Tian, Jing & Anderson, Heather M., 2014. "Forecast combinations under structural break uncertainty," International Journal of Forecasting, Elsevier, vol. 30(1), pages 161-175.
    10. 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.
    11. 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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    Citations

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    Cited by:

    1. Wendy Nyakabawo & Rangan Gupta & Hardik A. Marfatia, 2018. "High-Frequency Impact of Monetary Policy and Macroeconomic Surprises on US MSAs and Aggregate US Housing Returns and Volatility: A GJR-GARCH Approach," Working Papers 201817, University of Pretoria, Department of Economics.
    2. Bremus, Franziska & Krause, Thomas & Noth, Felix, 2017. "Bank-specific shocks and house price growth in the U.S," IWH Discussion Papers 3/2017, Halle Institute for Economic Research (IWH).
    3. Robert J. Hill & Alicia N. Rambaldi & Michael Scholz, 2018. "Higher Frequency Hedonic Property Price Indices: A State Space Approach," Graz Economics Papers 2018-04, University of Graz, Department of Economics.
    4. Badarinza, Cristian & Ramadorai, Tarun, 2013. "Home Away From Home? Safe Haven Effects and London House Prices," CEPR Discussion Papers 9786, C.E.P.R. Discussion Papers.
    5. Yongheng Deng & Eric Girardin & Roselyne Joyeux, 2015. "Fundamentals and the Volatility of Real Estate Prices in China: A Sequential Modelling Strategy," Working Papers 222015, Hong Kong Institute for Monetary Research.
    6. repec:eee:chieco:v:48:y:2018:i:c:p:205-222 is not listed on IDEAS
    7. 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.).

    More about this item

    Keywords

    Data aggregation; Real estate prices; Forecasting; Time-varying volatility;

    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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