Abstract
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.
 Significance:
 
 We attempt to answer the question: What is the optimal offer that a bank could make to a home loan client to ensure that the bank meets the maximum profitability threshold while still taking risk into account? To answer this question, one of the first factors that needs to be understood is take-up rate. We present a case study – with real data from a South African bank – to illustrate that it is indeed possible to predict take-up rates using various modelling techniques.
Highlights
On a daily basis, banks receive home loan applications from potential customers
By understanding the factors that influence the take-up rates of home loans offered, the bank potentially benefits through increased market share and profits
The results indicate that 22% moved due to a similar or worse deal, 11% moved due to a better interest rate, 48% moved due to a better LTV, and 19% moved due to a better interest rate and a better LTV
Summary
Banks receive home loan applications from potential customers. Depending on the customer’s risk profile, affordability and other factors, the bank decides whether or not to offer a home loan to this customer. We build a model to predict the probability of take-up of home loans offered by focusing on interest rate[1] and loanto-value (LTV)[2] This take-up model relates to the responsiveness of a specific customer segment (based on, for example, the risk type of a customer) to a change in the quoted price. We investigate whether it is possible to predict take-up rates of home loans offered by a bank using a combination of LTV and interest rates Both logistic regression and tree-ensemble models were considered. The last aim of this paper is to predict take-up of home loans offered using logistic regression as well as tree-based ensemble models. Similar or worse deal Better (lower) interest rate Better (higher) LTV Better interest rate and better LTV
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