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Using Housing Futures in Mortgage Research


Expectations of housing prices play an important role in real estate research. Despite their importance, obtaining a reasonable proxy for such expectations is a challenge. The existing literature on mortgage research either does not include housing expectation proxies in empirical models, or uses “backward-looking” proxies such as past housing appreciation or time series forecasts based on past housing appreciation. This paper proposes to use the transaction prices of Case-Shiller housing futures as an alternative “forward-looking” proxy. As an example, we compare the performances of four different expectation proxies in explaining mortgage default behavior. The loan level analysis shows that the futures based expectation proxy outperforms other proxies by having the highest regression model fit and being the only proxy that shows a significant negative effect on mortgage default behavior, as theory suggests. Out of sample predictions also show that futures have better prediction accuracy than other proxies. In addition, the paper shows that futures contain additional information that is not present in the backward-looking proxies.

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  1. 1.

    For simplicity, we use the term appreciation for both price increases or declines.

  2. 2.

    Financial futures are viewed as the market expectation of underlying product price movements in the financial derivatives literature. For example, the Federal funds futures are widely used as the market expectations of future monetary policies (e.g., Krueger and Kuttner 1996; Gürkaynak et al. 2007).

  3. 3.

    BBx data is similar to Loan Performance data. BBx data information is available at

  4. 4.

    Since our data is from privately securitized loans, the results may apply only to this set of mortgages.

  5. 5.

    Although (Case et al. 1993) have long been advocating a derivative market for housing in US, it was not until May 2006 that such a market was established.

  6. 6.

    After 2007, because of the mortgage crisis, very few newly originated loans were added into the dataset.

  7. 7.

    Loan status other than default or prepaid is considered censored which includes uninformative censoring and current status.

  8. 8.

    Lagged two months HPI is used since the release of CSI is lagged by two months and that represents the information available at the transaction time.

  9. 9.

    However, the high transactions costs in real estate do not imply that the predictability leads to arbitrage opportunities.

  10. 10.

    In order to calculate the survival probability, we also estimate the baseline hazard function after estimating the coefficient β.


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The authors would like to thank the helpful comments from James Kau, Carlos Slawson, and other participants in LSU seminar, UGA seminar and AREUEA annual conference. We would like to thank the very helpful comments from one anonymous referee. We appreciate the support from Blackbox Logic, LSU High Performance Computing Center, and LSU Finance department. We thank Cihan Uzmanoglu for help with the data. All errors are our own.

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Correspondence to Shuang Zhu.

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Zhu, S., Pace, R.K. & Morales, W.A. Using Housing Futures in Mortgage Research. J Real Estate Finan Econ 48, 1–15 (2014).

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  • Housing futures
  • Real estate futures
  • Housing expectation
  • House price expectation
  • Mortgage default