City Research Online

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.

Publication Type:	Article
Additional Information:	Copyright © 2021 ARPG & Author This work is licensed under the Creative Commons Attribution Internationa Licence.
Publisher Keywords:	Commercial mortgages; Probability of default; Loan charasteristics; Probit regression; linear discriminant analysis.
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HG Finance
Departments:	Bayes Business School > Finance
SWORD Depositor:	Symplectic Administrator

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