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Loan Characteristics as Predictors of Default in Commercial Mortgage Portfolios

Author

Listed:
  • Nicole Lux

    (Project Director- Real Estate Lending Research, London)

  • Sotiris Tsolacos

    (Real Estate Lending Research, London)

Abstract

This paper examines the role of loan characteristics in mortgage default probability for different mortgage lenders in the UK. The accuracy of default prediction is tested with two statistical methods, a probit model and linear discriminant analysis, using a unique dataset of defaulted commercial loan portfolios provided by sixty-six financial institutions. Both models establish that the attributes of the underlying real estate asset and the lender are significant factors in determining default probability for commercial mortgages. In addition to traditional risk factors such as loan-to-value and debt servicing coverage ratio lenders and regulators should consider loan characteristics to assess more accurately probabilities of default.

Suggested Citation

  • Nicole Lux & Sotiris Tsolacos, 2021. "Loan Characteristics as Predictors of Default in Commercial Mortgage Portfolios," International Journal of Economics and Financial Research, Academic Research Publishing Group, vol. 7(1), pages 1-4, 03-2021.
    • Handle: RePEc:arp:ijefrr:2021:p:1-4
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