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

Tim Bollerslev, Andrew Patton and Wenjing Wang ()
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Wenjing Wang: Moody’s Analytics, Inc., Postal: Quantitative Research Group, Moody’s Analytics, Inc., San Francisco, CA 94105, USA

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

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.

Keywords: Data aggregation; Real estate prices; Forecasting; Time-varying volatility (search for similar items in EconPapers)
JEL-codes: C22 C32 C53 G17 R21 (search for similar items in EconPapers)
Pages: 51
Date: 2015-01-12
New Economics Papers: this item is included in nep-for and nep-ure
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Citations: View citations in EconPapers (4)

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Related works:
Working Paper: Daily House Price Indexes: Construction, Modeling, and Longer-Run Predictions (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2015-02

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