Working Paper

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


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.

Keywords: Exposure at Default; Macro-sensitivity; Commercial Real Estate (CRE); Machine Learning; EAD Conversion Measures; Construction; Credit Risk;

JEL Classification: G28; G21;

https://doi.org/10.24149/wp2007

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Bibliographic Information

Provider: Federal Reserve Bank of Dallas

Part of Series: Working Papers

Publication Date: 2020-03-17

Number: 2007

Pages: 30