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The Subprime Crisis and House Price Appreciation

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Abstract

This paper argues that econometric analysis of housing price indexes before 2006 generated forecasts of future long-term price growth and low estimated probabilities of extreme price decreases. These forecasts of future increases in home-loan collateral values may have affected both the demand and the supply of mortgages. Standard time series models using repeat-sales indexes suggest that positive trends had a long half-life. Expectations based on such models could lead to an asset bubble. Analysis of data from the HMDA loan database and LoanPerformance.com at the MSA level and at the loan level substantiates the effects of past price trends on the demand and supply of subprime mortgages. On the demand side, at the MSA level, past home price increases are associated with more subprime applications, higher loan to income ratios and lower loan to value ratios of applications for both prime and subprime mortgages. This is consistent with the notion that households not only borrowed more but also invested more in home equity conditional on greater past house price increases. On the supply side, past home price appreciation had a significantly greater impact on the approval rate of subprime applications than the approval rate of prime applications. Loan level analysis indicates that past home price appreciation increased the approval rate of subprime applications but did not affect the approval rate of prime applications. Further, approved HMDA subprime loans had higher loan to income ratios in MSAs with greater past house price trends.

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Notes

  1. cf. Case and Shiller (2003), Brunnermeier and Julliard (2008) and Glaeser et al. (2005).

  2. For example, the conventional housing tenure choice models, such as Henderson and Ioannides (1983), indicate that owning is more attractive than renting with higher expected capital gain of home values. Further, in a standard mortgage default decision model, changes in home prices are an important explanatory variable. Deng et al. (2000) provide a survey of the literature on these models.

  3. C.f. Goetzmann and Spiegel (2002)

  4. c.f. Doms et al. (2007)

  5. For example, a positive shift in demand may bring less creditworthy investors to the mortgage market, which would result in a lower approval rate if underwriting standards do not change. However supply effects might cause a loosening of credit standards, leaving the approval rate unchanged or higher.

  6. Their findings are consistent with the considerable evidence that home prices are important co-determinants of default. See, for example Downing et al. (2005) and Crews Cutts and Van Order (2005).

  7. The policy implications of the subprime mortgage market are further explored in Wachter et al. (2005) and Calem et al. (2003).

  8. C.f. Bailey et al. (1963).

  9. C.f. Geltner (1997).

  10. C.f. Lin and Vandell (2007), Goetzmann (1992).

  11. C.f. Gatzlaff and Haurin (1997), Goetzmann and Peng (2006), Clayton et al. (2008)

  12. http://www2.standardandpoors.com/portal/site/sp/en/us/page.topic/indexes_csmahp/0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0.html

  13. http://www.ofheo.gov/hpi.aspx

  14. Fisher et al. (2003) address this issue for an index of commercial properties. Goetzmann and Peng (2006) develop methods for adjusting residential indices for time-varying liquidity.

  15. These intersect by construction, in 1995.

  16. Caplin et al. (1999), Goetzmann and Spiegel (2002).

  17. c.f. Capozza et al. (1997), Downing et al. (2005) among others.

  18. The details of the estimation may be found in the documentation for the R statistical language. Shumway and Stoffer (2006) point out inconsistencies in the use of the R ARIMA code for differenced series estimation, and offer a modified code to overcome these issues. This paper used their code available at http://www.stat.pitt.edu/stoffer/tsa2/index.html. The prediction uses a Kalman filtering method, and does not take into account parameter uncertainty associated with the time-series model.

  19. Eight MSAs were estimated with ARIMA(18,1,3), five with ARIMA(12,1,3), one with ARIMA(6,1,3), two with ARIMA(3,1,3) and four with ARIMA(3,1,0).

  20. The time series for Dallas is too short to estimate the ARIMA model.

  21. See Brunnermeier and Julliard (2008) and Goetzmann and Valaitis (2006) for examples of robust modeling of housing trends.

  22. Available from www.ffiec.gov/hmda/hmdarawdata.htm#by_msa. Avery et al. (2007) estimate that 80% of all home lending in the U.S. is covered by the 2006 HMDA data set. All data in this paper uses loans marked for home purchase. We also define “approved” loans as those that are ultimately originated to avoid double counting loans approved by multiple institutions.

  23. Available from www.huduser.org/datasets/manu.html

  24. Susan Wachter also documents this correlation for housing and mortgage data in 2002 in her presentation to the Evolving Housing Finance Marketplace Roundtable, January 16, 2008.

  25. An important exception to this is the triple A standard for money market funds and the investment grade standard for regulated institutional portfolios such as banks and insurance companies.

  26. Cf. Calhoun (1996)

  27. Some readers will not count themselves among these market observers. There were certainly contrarian views.

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Acknowledgements

Thanks to Justin Haaheim for research support. We thank Yan Chang, Donald Haurin, C.F. Sirmans, Kerry Vandell and participants of the 2009 Summer Real Estate Symposium, the 2010 AREUEA annual conference, and the 2010 Florida State University Symposium for their constructive comments.

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Authors and Affiliations

  1. Yale School of Management, New Haven, CT, USA

    William N. Goetzmann & Jacqueline Yen

  2. Leeds School of Business, University of Colorado, Boulder, CO, USA

    Liang Peng

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  1. William N. Goetzmann
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Correspondence to William N. Goetzmann.

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Goetzmann, W.N., Peng, L. & Yen, J. The Subprime Crisis and House Price Appreciation. J Real Estate Finan Econ 44, 36–66 (2012). https://doi.org/10.1007/s11146-011-9321-4

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