Credit Risk Model: Assessing Default Probability of Mortgage Loan Borrower
Ergeshidze, Aleksandre. "Credit Risk Model: Assessing Default Probability of Mortgage Loan Borrower." 3rd RSEP Multidisciplinary Conference 5-7 April, 2017: 1-9; ISBN: 978-605-307-583-7
9 Pages Posted: 20 Sep 2017
Date Written: April 5, 2017
Over the past decade, as a result of rapid growth of the loan portfolio and the financial crisis, importance of credit risk analysis has increased worldwide. After the global financial crisis, more attention has been paid to loan granting process by various researchers and financial market participants. New regulations forced commercial banks to improve credit risk management and existing statistical models. This paper, based on data obtained from three major banks of Georgia, develops logit model to examine mortgage loan borrowers’ characteristics that determine their default probability. Similar data is rarely available for developing countries, therefore findings of this study can be useful for those countries as well. According to the research, main characteristics that determine borrowers’ creditworthiness are payment to income ratio, loan to value ratio, credit history and borrower’s type (whether borrower receives income in that bank). Average prediction accuracy of the model within the sample equals to 93.4%. Findings of this study will enable commercial banks in Georgia to improve their credit risk assessment and make decisions on loan approval cost efficiently. In addition, acquired results can be used by the National Bank of Georgia to estimate the adequacy of loan loss provisions, to assess commercial banks credit portfolio used as collateral for monetary operations and to enhance collateral base to support de-dollarization policy.
Keywords: Credit risk; Logit model, Default Probability, PTI, LTV
JEL Classification: C14, G21, G33, E58
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