Date on Honors Thesis

Spring 4-30-2025

Major

Applied Math and Economics

Examining Committee Member

Dr. Beau Sauley, Advisor

Examining Committee Member

Dr. Narine Badasyan, Committee Member

Examining Committee Member

Dr. Ayesha Jamal, Committee Member

Abstract/Description

The financial crisis of the early 2000’s is a prime example of the severe consequences that mortgage default and borrower insolvency can have on economies at large. Mortgage default specifically is a prime case with the popularization of mortgage backed securities and the commonality of this loan structure. Multiple hypotheses and models have been formed to understand the reasons, causes, and consequences of mortgage default. This paper uses both machine learning and statistical classification models to inform an understanding of the variables most significant and impactful to the default outcome of mortgages. Consideration is given to both loan-level microeconomic variables and broader macroeconomic variable trends. Model diagnostics and an analysis of prediction performance specify the value of covariates in purely segmenting default and paid outcomes. The chosen variables are thus quantified by their importance, statistical significance, and impact on default probability. Establishing a better understanding of how both micro and macro economic conditions impact the default risk of this unique financial instrument.

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