IDEAS home Printed from https://ideas.repec.org/p/hig/wpaper/55-fe-2016.html
   My bibliography  Save this paper

Discontinuity in Relative Credit Losses: Evidence from Defaults on Government-Insured Residential Mortgages

Author

Listed:
  • Agata M. Lozinskaia

    (National Research University Higher School of Economics)

  • Evgeniy M. Ozhegov

    (National Research University Higher School of Economics)

  • Alexander M. Karminsky

    (National Research University Higher School of Economics)

Abstract

This paper investigates the distribution of relative credit losses given mortgage default for loans provided by a major government-sponsored creditor in a local area. We use borrower’s individual and loan-level data on residential mortgages originated in the period 2008–2012. Our numerical analysis indicates that mortgages bunching at certain Loan-to-Value ratios (LTV) led to a discontinuity in relative credit loss given mortgage default. Through regression analysis, we demonstrate discrete jumps in the approximated historical credit losses generated by loans with a high LTV ratios and find thresholds allowing the segmentation of loans according their credit risk. In addition, our results suggest that mortgage insurance is a potentially valuable instrument for compensation for expected loss in certain risk segments.

Suggested Citation

  • 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.
    • Handle: RePEc:hig:wpaper:55/fe/2016
    as

    Download full text from publisher

    File URL: https://www.hse.ru/data/2016/07/22/1119215870/55FE2016.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alex Kaufman, 2014. "The Influence of Fannie and Freddie on Mortgage Loan Terms," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 42(2), pages 472-496, June.
    2. 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, July.
    3. Andra C. Ghent & Rubén Hernández-Murillo & Michael T. Owyang, 2015. "Did Affordable Housing Legislation Contribute to the Subprime Securities Boom?," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(4), pages 820-854, November.
    4. Jacobson, Tor & Roszbach, Kasper, 2003. "Bank lending policy, credit scoring and value-at-risk," Journal of Banking & Finance, Elsevier, vol. 27(4), pages 615-633, April.
    5. Michael LaCour-Little, 2007. "The Home Purchase Mortgage Preferences of Low- and Moderate-Income Households," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 35(3), pages 265-290, September.
    6. Qi, Min & Zhao, Xinlei, 2011. "Comparison of modeling methods for Loss Given Default," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2842-2855, November.
    7. Yan Zhang, 2013. "Fair Lending Analysis of Mortgage Pricing: Does Underwriting Matter?," The Journal of Real Estate Finance and Economics, Springer, vol. 46(1), pages 131-151, January.
    8. 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.
    9. Kasper Roszbach, 2004. "Bank Lending Policy, Credit Scoring, and the Survival of Loans," The Review of Economics and Statistics, MIT Press, vol. 86(4), pages 946-958, November.
    10. Loterman, Gert & Brown, Iain & Martens, David & Mues, Christophe & Baesens, Bart, 2012. "Benchmarking regression algorithms for loss given default modeling," International Journal of Forecasting, Elsevier, vol. 28(1), pages 161-170.
    11. Isaias Hazarmabeth Salgado-Ugarte & Makoto Shimizu & Toru Taniuchi, 1994. "Exploring the shape of univariate data using kernel density estimators," Stata Technical Bulletin, StataCorp LP, vol. 3(16).
    12. Marina Khmelnitskaya, 2014. "Russian housing finance policy: state-led institutional evolution," Post-Communist Economies, Taylor & Francis Journals, vol. 26(2), pages 149-175, June.
    13. Sumit Agarwal & Brent W. Ambrose & Yildiray Yildirim, 2015. "The Subprime Virus," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(4), pages 891-915, November.
    14. Anthony A. DeFusco & Andrew Paciorek, 2017. "The Interest Rate Elasticity of Mortgage Demand: Evidence from Bunching at the Conforming Loan Limit," American Economic Journal: Economic Policy, American Economic Association, vol. 9(1), pages 210-240, February.
    15. Vassilis Lekkas & John M. Quigley & Robert Van 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, December.
    16. 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.
    17. Lambrecht, Bart M & Perraudin, William R M & Satchell, Steven, 2003. "Mortgage Default and Possession under Recourse: A Competing Hazards Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 35(3), pages 425-442, June.
    18. 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.
    19. 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.
    20. Hibbeln, Martin & Gürtler, Marc, 2011. "Pitfalls in modeling loss given default of bank loans," Working Papers IF35V1, Technische Universität Braunschweig, Institute of Finance.
    21. Robert Order & Peter Zorn, 2000. "Income, Location and Default: Some Implications for Community Lending," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 28(3), pages 385-404.
    22. Zhenguo Lin & Yingchun Liu & Jia Xie, 2016. "Immigrants and Mortgage Delinquency," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 44(1), pages 198-235, February.
    23. Stephen L Ross, 2000. "Mortgage Lending, Sample Selection and Default," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 28(4), pages 581-621.
    24. David M. Harrison & Thomas G. Noordewier & Abdullah Yavas, 2004. "Do Riskier Borrowers Borrow More?," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 32(3), pages 385-411, September.
    25. Michael G. Bradley & Amy Crews Cutts & Wei Liu, 2015. "Strategic Mortgage Default: The Effect of Neighborhood Factors," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(2), pages 271-299, June.
    26. Larry Cordell & Liang Geng & Laurie S. Goodman & Lidan Yang, 2015. "The Cost of Foreclosure Delay," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(4), pages 916-956, November.
    27. Han, Chulwoo & Jang, Youngmin, 2013. "Effects of debt collection practices on loss given default," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 21-31.
    28. 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.
    29. Dermine, J. & de Carvalho, C. Neto, 2006. "Bank loan losses-given-default: A case study," Journal of Banking & Finance, Elsevier, vol. 30(4), pages 1219-1243, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Magdalena Brygała, 2022. "Consumer Bankruptcy Prediction Using Balanced and Imbalanced Data," Risks, MDPI, vol. 10(2), pages 1-13, January.

    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. Lozinskaia Agata & Ozhegov Evgeniy, 2016. "Key Determinants of Demand, Credit Underwriting, and Performance on Government-Insured Mortgage Loans in Russia," EERC Working Paper Series 16/03e, EERC Research Network, Russia and CIS.
    2. 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.
    3. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    4. Xia, Yufei & Zhao, Junhao & He, Lingyun & Li, Yinguo & Yang, Xiaoli, 2021. "Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1590-1613.
    5. 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.
    6. 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.
    7. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Corporate Loan Recovery Rates under Downturn Conditions in a Developing Economy: Evidence from Zimbabwe," Risks, MDPI, vol. 10(10), pages 1-24, October.
    8. 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.
    9. Kaposty, Florian & Kriebel, Johannes & Löderbusch, Matthias, 2020. "Predicting loss given default in leasing: A closer look at models and variable selection," International Journal of Forecasting, Elsevier, vol. 36(2), pages 248-266.
    10. Ruey-Ching Hwang & Chih-Kang Chu & Kaizhi Yu, 2021. "Predicting the Loss Given Default Distribution with the Zero-Inflated Censored Beta-Mixture Regression that Allows Probability Masses and Bimodality," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(3), pages 143-172, June.
    11. Nithi Sopitpongstorn & Param Silvapulle & Jiti Gao, 2017. "Local logit regression for recovery rate," Monash Econometrics and Business Statistics Working Papers 19/17, Monash University, Department of Econometrics and Business Statistics.
    12. 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.
    13. Chen, Xiaowei & Wang, Gang & Zhang, Xiangting, 2019. "Modeling recovery rate for leveraged loans," Economic Modelling, Elsevier, vol. 81(C), pages 231-241.
    14. Wang, Hong & Forbes, Catherine S. & Fenech, Jean-Pierre & Vaz, John, 2020. "The determinants of bank loan recovery rates in good times and bad – New evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 875-897.
    15. Thamayanthi Chellathurai, 2017. "Probability Density Of Recovery Rate Given Default Of A Firm’S Debt And Its Constituent Tranches," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(04), pages 1-34, June.
    16. 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.
    17. Sopitpongstorn, Nithi & Silvapulle, Param & Gao, Jiti & Fenech, Jean-Pierre, 2021. "Local logit regression for loan recovery rate," Journal of Banking & Finance, Elsevier, vol. 126(C).
    18. Johannes Kriebel & Kevin Yam, 2020. "Forecasting recoveries in debt collection: Debt collectors and information production," European Financial Management, European Financial Management Association, vol. 26(3), pages 537-559, June.
    19. Nazemi, Abdolreza & Fatemi Pour, Farnoosh & Heidenreich, Konstantin & Fabozzi, Frank J., 2017. "Fuzzy decision fusion approach for loss-given-default modeling," European Journal of Operational Research, Elsevier, vol. 262(2), pages 780-791.
    20. Jennifer Betz & Ralf Kellner & Daniel Rösch, 2021. "Time matters: How default resolution times impact final loss rates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 619-644, June.

    More about this item

    Keywords

    discontinuity; credit risk; mortgage default; government mortgage lending programs; loss evaluation.;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • R20 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - General
    • R58 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Regional Development Planning and Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:hig:wpaper:55/fe/2016. 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: Shamil Abdulaev or Shamil Abdulaev (email available below). General contact details of provider: https://edirc.repec.org/data/hsecoru.html .

    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.