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Using Data on Seller Behavior to Forecast Short-run House Price Changes

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Abstract

We construct a new \"list-price index\" that accurately reveals trends in house prices several months before existing sales price indices like Case-Shiller. Our index is based on the repeat-sales approach but for recent months uses listings data, which are available essentially in real time, instead of transactions data, which become available with signiffcant lags. Our index methodology is motivated by a simple model of the home-selling problem that shows how listings variables such as the list price and marketing time help predict the final sales price. In a sample of three large MSAs over the years 2008-2012, our index (i) accurately forecasts the Case-Shiller index several months in advance, (ii) outperforms forecasting models that do not use listings data, and (iii) outperforms the market's expectation as inferred from prices on Case-Shiller future contracts.

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  • Elliot Anenberg & Steven Laufer, 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.).
    • Handle: RePEc:fip:fedgfe:2014-16
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    Cited by:

    1. Cardella, Eric & Seiler, Michael J., 2016. "The effect of listing price strategy on real estate negotiations: An experimental study," Journal of Economic Psychology, Elsevier, vol. 52(C), pages 71-90.

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    Keywords

    House price indices; house price forecasting; housing market microstructure;
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