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Local Housing Market Cycle and Loss Given Default

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Author Info

  • Yanan Zhang
  • Lu Ji
  • Fei Liu
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    Abstract

    This paper studies the impact of housing market cycles on loss given default (LGD). Previous studies have shown that the current loan-to-value ratio (CLTV) is the most important determinant of LGD. This paper establishes another linkage which is between the house price cycles before the time of mortgage origination and LGD. The empirical analysis is based on a large loan-level sub-prime residential mortgage loss dataset from 1998 to 2009. Results show that house price history has a long memory in explaining LGD. Its explanatory power far exceeds the original LTV and other loan characteristics. This paper offers a countercyclical view of LGD risk. The model can be combined with a default probability model to serve as a regulatory prudential tool. Such a tool provides a solution to the inherent procyclical bias in BASEL II capital requirements, and can contribute to the safety and soundness of banking institutions.

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    Bibliographic Info

    Paper provided by International Monetary Fund in its series IMF Working Papers with number 10/167.

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    Length: 29
    Date of creation: 01 Jul 2010
    Date of revision:
    Handle: RePEc:imf:imfwpa:10/167

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    Related research

    Keywords: Housing; Loans; Housing prices; Business cycles; Economic models; mortgage; mortgages; foreclosure; residential mortgage; mortgage default; predictions; sensitivity analysis; mortgage loans; residential mortgages; statistics; correlation; explanatory power; equation; probability; mortgage market; housing finance agency; standard deviation; foreclosure laws; housing policy; survey; dummy variable; probability model; foreclosure process; nonlinearity; data analyses; parameter estimation; standard deviations;

    This paper has been announced in the following NEP Reports:

    References

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    1. Edward Altman & Andrea Resti & Andrea Sironi, 2004. "Default Recovery Rates in Credit Risk Modelling: A Review of the Literature and Empirical Evidence," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 33(2), pages 183-208, 07.
    2. Gordy, Michael B. & Howells, Bradley, 2006. "Procyclicality in Basel II: Can we treat the disease without killing the patient?," Journal of Financial Intermediation, Elsevier, vol. 15(3), pages 395-417, July.
    3. Anthony Pennington-Cross, 2006. "The duration of foreclosures in the subprime mortgage market: a competing risks model with mixing," Working Papers 2006-027, Federal Reserve Bank of St. Louis.
    4. Vassilis Lekkas & John M. Quigley & Robert Order, 1993. "Loan Loss Severity and Optimal Mortgage Default," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 21(4), pages 353-371.
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    Cited by:
    1. Been, Vicki & Weselcouch, Mary & Voicu, Ioan & Murff, Scott, 2013. "Determinants of the incidence of U.S. Mortgage Loan Modifications," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3951-3973.
    2. Park, Yun W. & Bang, Doo Won, 2014. "Loss given default of residential mortgages in a low LTV regime: Role of foreclosure auction process and housing market cycles," Journal of Banking & Finance, Elsevier, vol. 39(C), pages 192-210.

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