Open AccessPredicting take-up of home loan offers using tree-based ensemble models: A South African case study
Author(s) -
Tanja Verster,
Samistha Harcharan,
Lizette Bezuidenhout,
Bart Baesens
Publication year - 2021
Publication title -
south african journal of science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.317
H-Index - 61
eISSN - 1996-7489
pISSN - 0038-2353
DOI - 10.17159/sajs.2021/7607
Subject(s) - loan , profitability index , interest rate , econometrics , logistic regression , actuarial science , economics , business , computer science , finance , machine learning
We investigated different take-up rates of home loans in cases in which banks offered different interest rates. If a bank can increase its take-up rates, it could possibly improve its market share. In this article, we explore empirical home loan price elasticity, the effect of loan-to-value on the responsiveness of home loan customers and whether it is possible to predict home loan take-up rates. We employed different regression models to predict take-up rates, and tree-based ensemble models (bagging and boosting) were found to outperform logistic regression models on a South African home loan data set. The outcome of the study is that the higher the interest rate offered, the lower the take-up rate (as was expected). In addition, the higher the loan-to-value offered, the higher the take-up rate (but to a much lesser extent than the interest rate). Models were constructed to estimate take-up rates, with various modelling techniques achieving validation Gini values of up to 46.7%. Banks could use these models to positively influence their market share and profitability.