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Understanding the Exposure at Default Risk of Commercial Real Estate Construction and Land Development Loans

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  • Shan Luo
  • Anthony Murphy

Abstract

We study and model the determinants of exposure at default (EAD) for large U.S. construction and land development loans from 2010 to 2017. EAD is an important component of credit risk, and commercial real estate (CRE) construction loans are more risky than income producing loans. This is the first study modeling the EAD of construction loans. The underlying EAD data come from a large, confidential supervisory dataset used in the U.S. Federal Reserve’s annual Comprehensive Capital Assessment Review (CCAR) stress tests. EAD reflects the relative bargaining ability and information sets of banks and obligors. We construct OLS and Tobit regression models, as well as several other machine-learning models, of EAD conversion measures, using a four-quarter horizon. The popular LEQ and CCF conversion measure is unstable, so we focus on EADF and AUF measures. Property type, the lagged utilization rate and loan size are important drivers of EAD. Changing local and national economic conditions also matter, so EAD is sensitive to macro-economic conditions. Even though default and EAD risk are negatively correlated, a conservative assumption is that all undrawn construction commitments will be fully drawn in default.

Suggested Citation

  • Shan Luo & Anthony Murphy, 2020. "Understanding the Exposure at Default Risk of Commercial Real Estate Construction and Land Development Loans," Working Papers 2007, Federal Reserve Bank of Dallas.
    • Handle: RePEc:fip:feddwp:87677
    DOI: 10.24149/wp2007
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    References listed on IDEAS

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    6. Michael Jacobs, Jr. & Pinaki Bag, 2011. "What Do We Know About Exposure At Default On Contingent Credit Lines A Survey Of The Literature Empirical Analysis And Models," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 2(1), pages 26-46.
    7. Tong, Edward N.C. & Mues, Christophe & Brown, Iain & Thomas, Lyn C., 2016. "Exposure at default models with and without the credit conversion factor," European Journal of Operational Research, Elsevier, vol. 252(3), pages 910-920.
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    Cited by:

    1. 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.
    2. Dainelli, Francesco & Bet, Gianmarco & Fabrizi, Eugenio, 2024. "The financial health of a company and the risk of its default: Back to the future," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    3. Wattanawongwan, Suttisak & Mues, Christophe & Okhrati, Ramin & Choudhry, Taufiq & So, Mee Chi, 2023. "A mixture model for credit card exposure at default using the GAMLSS framework," International Journal of Forecasting, Elsevier, vol. 39(1), pages 503-518.

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    More about this item

    Keywords

    Credit Risk; Commercial Real Estate (CRE); Construction; Exposure at Default; EAD Conversion Measures; Macro-sensitivity; Machine Learning;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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