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The recovery rate for retail and commercial customers in Germany: a look at collateral and its adjusted market values

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  • Peter-Hendrik Ingermann

    (Finance Center Muenster, University of Muenster)

  • Frederik Hesse

    (Finance Center Muenster, University of Muenster)

  • Christian Bélorgey

    (Finance Center Muenster, University of Muenster)

  • Andreas Pfingsten

    (Finance Center Muenster, University of Muenster)

Abstract

Based on a unique data set of 909 defaulted retail and commercial (self-employed and SMEs) credit customers in Germany, whose original loans were made by 123 different banks, our article confirms a significant positive influence of collateral, and of amicable agreements between the debtor and the bank (redemption), on the recovery rate [1 − loss given default (LGD)]. In a further analysis of collateral, systematic biases between the realized market price and the expected market values of real estate are revealed, even though the appraisal reports should have already considered all factors influencing the value. Using valuations that were adjusted for these recognized biases, we can increase the explanatory power of the underlying models. Moreover, we compare these models to models that apply, as is common practice in the banking industry, flat haircuts to collateral values and show the superior performance of our proposed approach.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:busres:v:9:y:2016:i:2:d:10.1007_s40685-016-0028-5
    DOI: 10.1007/s40685-016-0028-5
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    1. Grunert Jens, 2009. "Kreditsicherheiten: eine Umfrage zu deren Häufigkeit, den erzielten Verwertungserlösen und der Bewertungsfrequenz," Zeitschrift für Bankrecht und Bankwirtschaft (ZBB) / Journal of Banking Law and Banking (JBB), RWS Verlag, vol. 21(2), pages 126-135, April.
    2. 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.
    3. Gürtler, Marc & Hibbeln, Martin, 2013. "Improvements in loss given default forecasts for bank loans," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2354-2366.
    4. Bastos, João A., 2010. "Forecasting bank loans loss-given-default," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2510-2517, October.
    5. Calabrese, Raffaella & Zenga, Michele, 2010. "Bank loan recovery rates: Measuring and nonparametric density estimation," Journal of Banking & Finance, Elsevier, vol. 34(5), pages 903-911, May.
    6. 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.
    7. Grunert, Jens & Weber, Martin, 2009. "Recovery rates of commercial lending: Empirical evidence for German companies," Journal of Banking & Finance, Elsevier, vol. 33(3), pages 505-513, March.
    8. 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.
    9. repec:bla:jfinan:v:44:y:1989:i:4:p:909-22 is not listed on IDEAS
    10. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn C., 2012. "Mixture cure models in credit scoring: If and when borrowers default," European Journal of Operational Research, Elsevier, vol. 218(1), pages 132-139.
    11. 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.
    12. Khieu, Hinh D. & Mullineaux, Donald J. & Yi, Ha-Chin, 2012. "The determinants of bank loan recovery rates," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 923-933.
    13. Jon Frye, 2000. "Collateral damage detected," Emerging Issues, Federal Reserve Bank of Chicago, issue Sep.
    14. D Rösch & H Scheule, 2014. "Forecasting probabilities of default and loss rates given default in the presence of selection," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 393-407, March.
    15. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    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. 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.
    18. 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.
    19. Renault, Olivier & Scaillet, Olivier, 2004. "On the way to recovery: A nonparametric bias free estimation of recovery rate densities," Journal of Banking & Finance, Elsevier, vol. 28(12), pages 2915-2931, December.
    20. 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.
    21. 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.
    22. John Y. Campbell & Stefano Giglio & Parag Pathak, 2011. "Forced Sales and House Prices," American Economic Review, American Economic Association, vol. 101(5), pages 2108-2131, August.
    23. Thorburn, Karin S., 2000. "Bankruptcy auctions: costs, debt recovery, and firm survival," Journal of Financial Economics, Elsevier, vol. 58(3), pages 337-368, December.
    24. James D. Shilling & John D. Benjamin & C.F. Sirmans, 1990. "Estimating Net Realizable Value for Distressed Real Estate," Journal of Real Estate Research, American Real Estate Society, vol. 5(1), pages 129-140.
    25. Terrence M. Clauretie & Nasser Daneshvary, 2009. "Estimating the House Foreclosure Discount Corrected for Spatial Price Interdependence and Endogeneity of Marketing Time," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 37(1), pages 43-67, March.
    26. Stefano Caselli & Stefano Gatti & Francesca Querci, 2008. "The Sensitivity of the Loss Given Default Rate to Systematic Risk: New Empirical Evidence on Bank Loans," Journal of Financial Services Research, Springer;Western Finance Association, vol. 34(1), pages 1-34, August.
    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. Thomas M. Carroll & Terrence M. Clauretie & Helen R. Neill, 1997. "Effect of Foreclosure Status on Residential Selling Price: Comment," Journal of Real Estate Research, American Real Estate Society, vol. 13(1), pages 95-102.
    29. 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.
    30. Bellotti, Tony & Crook, Jonathan, 2012. "Loss given default models incorporating macroeconomic variables for credit cards," International Journal of Forecasting, Elsevier, vol. 28(1), pages 171-182.
    31. 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.
    32. 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.
    33. Wolter, Marcus & Rösch, Daniel, 2014. "Cure events in default prediction," European Journal of Operational Research, Elsevier, vol. 238(3), pages 846-857.
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    Cited by:

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    2. Nataliya Barasinska & Philipp Haenle & Anne Koban & Alexander Schmidt, 2023. "No Reason to Worry About German Mortgages? An Analysis of Macroeconomic and Individual Drivers of Credit Risk," Journal of Financial Services Research, Springer;Western Finance Association, vol. 64(3), pages 369-399, December.
    3. Barasinska, Nataliya & Haenle, Philipp & Koban, Anne & Schmidt, Alexander, 2019. "Stress testing the German mortgage market," Discussion Papers 17/2019, Deutsche Bundesbank.
    4. Georgescu, Oana-Maria & Ponte Marques, Aurea & Galow, Benjamin, 2024. "Loss-given-default and macroeconomic conditions," Working Paper Series 2954, European Central Bank.
    5. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2021. "Determinants of corporate exposure at default under distressed economic and financial conditions in a developing economy: the case of Zimbabwe," Risk Management, Palgrave Macmillan, vol. 23(1), pages 123-149, June.
    6. 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.
    7. Barasinska, Nataliya & Ludwig, Johannes & Vogel, Edgar, 2021. "The impact of borrower-based instruments on household vulnerability in Germany," Discussion Papers 20/2021, Deutsche Bundesbank.
    8. 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.

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