IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v28y2012i1p183-195.html
   My bibliography  Save this article

Predicting loss given default (LGD) for residential mortgage loans: A two-stage model and empirical evidence for UK bank data

Author

Listed:
  • Leow, Mindy
  • Mues, Christophe

Abstract

With the implementation of the Basel II regulatory framework, it became increasingly important for financial institutions to develop accurate loss models. This work investigates the loss given default (LGD) of mortgage loans using a large set of recovery data of residential mortgage defaults from a major UK bank. A Probability of Repossession Model and a Haircut Model are developed and then combined to give an expected loss percentage. We find that the Probability of Repossession Model should consist of more than just the commonly used loan-to-value ratio, and that the estimation of LGD benefits from the Haircut Model, which predicts the discount which the sale price of a repossessed property may undergo. This two-stage LGD model is shown to perform better than a single-stage LGD model (which models LGD directly from loan and collateral characteristics), as it achieves a better R2 value and matches the distribution of the observed LGD more accurately.

Suggested Citation

  • Leow, Mindy & Mues, Christophe, 2012. "Predicting loss given default (LGD) for residential mortgage loans: A two-stage model and empirical evidence for UK bank data," International Journal of Forecasting, Elsevier, vol. 28(1), pages 183-195.
    • Handle: RePEc:eee:intfor:v:28:y:2012:i:1:p:183-195
    DOI: 10.1016/j.ijforecast.2011.01.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207011000598
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2011.01.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Qi, Min & Yang, Xiaolong, 2009. "Loss given default of high loan-to-value residential mortgages," Journal of Banking & Finance, Elsevier, vol. 33(5), pages 788-799, May.
    2. Campbell, Tim S & Dietrich, J Kimball, 1983. "The Determinants of Default on Insured Conventional Residential Mortgage Loans," Journal of Finance, American Finance Association, vol. 38(5), pages 1569-1581, December.
    3. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November.
    4. Calem, Paul S. & LaCour-Little, Michael, 2004. "Risk-based capital requirements for mortgage loans," Journal of Banking & Finance, Elsevier, vol. 28(3), pages 647-672, March.
    5. von Furstenberg, George M, 1969. "Default Risk on FHA-Insured Home Mortgages as a Function of the Terms of Financing: A Quantitative Analysis," Journal of Finance, American Finance Association, vol. 24(3), pages 459-477, June.
    6. Esa Jokivuolle & Samu Peura, 2003. "Incorporating Collateral Value Uncertainty in Loss Given Default Estimates and Loan‐to‐value Ratios," European Financial Management, European Financial Management Association, vol. 9(3), pages 299-314, September.
    7. Somers, Mark & Whittaker, Joe, 2007. "Quantile regression for modelling distributions of profit and loss," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1477-1487, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peter-Hendrik Ingermann & Frederik Hesse & Christian Bélorgey & Andreas Pfingsten, 2016. "The recovery rate for retail and commercial customers in Germany: a look at collateral and its adjusted market values," Business Research, Springer;German Academic Association for Business Research, vol. 9(2), pages 179-228, August.
    2. Do, Hung Xuan & Rösch, Daniel & Scheule, Harald, 2018. "Predicting loss severities for residential mortgage loans: A three-step selection approach," European Journal of Operational Research, Elsevier, vol. 270(1), pages 246-259.
    3. Emily Johnston Ross & Lynn Shibut, 2021. "Loss Given Default, Loan Seasoning and Financial Fragility: Evidence from Commercial Real Estate Loans at Failed Banks," The Journal of Real Estate Finance and Economics, Springer, vol. 63(4), pages 630-661, November.
    4. Benjamin Bade & Daniel Rösch & Harald Scheule, 2011. "Default and Recovery Risk Dependencies in a Simple Credit Risk Model," European Financial Management, European Financial Management Association, vol. 17(1), pages 120-144, January.
    5. 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.
    6. Yurchenko, Yurii, 2019. "The impact of macroeconomic factors on collateral value within the framework of expected credit loss calculation," MPRA Paper 97135, University Library of Munich, Germany.
    7. Agata M. Lozinskaia & Evgeniy M. Ozhegov & Alexander M. Karminsky, 2016. "Discontinuity in Relative Credit Losses: Evidence from Defaults on Government-Insured Residential Mortgages," HSE Working papers WP BRP 55/FE/2016, National Research University Higher School of Economics.
    8. Asish Saha & Hock-Eam Lim & Goh-Yeok Siew, 2021. "Housing Loan Repayment Behaviour in Malaysia: An Analytical Insight," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 20(2), pages 1-19, September.
    9. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn, 2013. "A zero-adjusted gamma model for mortgage loan loss given default," International Journal of Forecasting, Elsevier, vol. 29(4), pages 548-562.
    10. Jean-David Fermanian, 2020. "On the Dependence between Default Risk and Recovery Rates in Structural Models," Annals of Economics and Statistics, GENES, issue 140, pages 45-82.
    11. Bank for International Settlements, 2011. "Portfolio and risk management for central banks and sovereign wealth funds," BIS Papers, Bank for International Settlements, number 58.
    12. Andersson, Fredrik & Mayock, Tom, 2014. "Loss severities on residential real estate debt during the Great Recession," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 266-284.
    13. David Harrison & Michael Seiler, 2015. "The Paradox of Judicial Foreclosure: Collateral Value Uncertainty and Mortgage Rates," The Journal of Real Estate Finance and Economics, Springer, vol. 50(3), pages 377-411, April.
    14. Hong Kong Monetary Authority, 2008. "The housing market channel of the monetary transmission mechanism in Hong Kong," BIS Papers chapters, in: Bank for International Settlements (ed.), Transmission mechanisms for monetary policy in emerging market economies, volume 35, pages 221-234, Bank for International Settlements.
    15. Jesús Saurina & Carlos Trucharte, 2007. "An Assessment of Basel II Procyclicality in Mortgage Portfolios," Journal of Financial Services Research, Springer;Western Finance Association, vol. 32(1), pages 81-101, October.
    16. Zhang, Jie & Thomas, Lyn C., 2012. "Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD," International Journal of Forecasting, Elsevier, vol. 28(1), pages 204-215.
    17. Hui, C.H. & Lo, C.F. & Wong, T.C. & Man, P.K., 2006. "Measuring provisions for collateralised retail lending," Journal of Economics and Business, Elsevier, vol. 58(4), pages 343-361.
    18. O. Emre Ergungor, 2009. "Foreclosures in Ohio: does lender type matter?," Proceedings, Federal Reserve Bank of San Francisco, issue Jan.
    19. Betz, Jennifer & Kellner, Ralf & Rösch, Daniel, 2018. "Systematic Effects among Loss Given Defaults and their Implications on Downturn Estimation," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1113-1144.
    20. Matthias Bodenstedt & Daniel R�sch & Harald Scheule, 2013. "The path to impairment: do credit-rating agencies anticipate default events of structured finance transactions?," The European Journal of Finance, Taylor & Francis Journals, vol. 19(9), pages 841-860, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:28:y:2012:i:1:p:183-195. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.