Machine Learning Based Housing Loan Default Prediction Using Client and Property Data

K.A Bhavani1, Vijinigiri Vinay Kumar2

1Associate Professor, Department of Computer Applications,

Visakha Institute of Engineering & Technology(A), Narava, Visakhapatnam, India.

2PG Research scholar,  Department of Computer Applications,

Visakha Institute of Engineering & Technology(A), Narava, Visakhapatnam, India.

Abstract - The timely and precise assessment of housing (mortgage) loan default risk is crucial for lenders to be able to minimize their credit losses while, at the same time, maintain fairness and efficiency in loan approval. Conventional underwriting methods are based on the calculation of various ratios and judgment of an underwriter. However, this is an extremely difficult process that can take a long time and does not provide consistency between different underwriters. The goal of this research is to analyze how machine learning can be used as the full process of predicting a housing loan default by means of using client’s demographic information, income, credit and property data. For this study, five classification algorithms were trained: Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and XGBoost. Every numerical feature of the dataset was median-imputed and standardized while every categorical feature was mode-imputed and one-hot encoded. Every model was tuned using RandomizedSearchCV with stratified cross-validation with 5 folds based on F1 score. And the last engineered features, Debt-to-Income Ratio and Loan-to-Value Ratio, were obtained via the use of applicant’s income, existing debts, loan amount, and property value.The XGBoost model produced the best performance in that it got 95.08% of accuracy, 96.62% of precision, 97.54% of recall, and 0.9708 of F1 score while training in 78.8 seconds, which is a record speed compared to Gradient Boosting which took 984.9 seconds and Random Forest requiring 458.0 seconds. The model was deployed through a Flask application with features such as user sign-up, session-based authentication, an explanation engine indicating the main influences of particular predictions but also via a command-line application that runs offline predictions. Overall, it can be concluded that the application of gradient-boosted tree ensembles in combination with good feature engineering and hyperparameter optimization makes it possible for such models to provide an optimal trade-off between accuracy, speed, and explainability when predicting housing loan default.

Keywords: Housing Loan Default Prediction, Credit Risk Modelling, Machine Learning, XGBoost, Random Forest, Gradient Boosting, Debt-to-Income Ratio, Loan-to-Value Ratio, Flask Web Application.