Higher education capacity, quality, and availability has driven more countries to turn to student loan schemes in order to assist students whose families are unable to meet their university costs. Ideally, all students seeking university education should be able to access these loans. It is also expected that student loan applicants pay back the entire loan in the stipulated time frame to allow other needy students joining university to utilize the repaid amounts. In this study, we seek to perform a quantitative analysis of loan applications by computing the probability of default of a given applicant using the qualitative information provided in the application forms. We apply multiple logistic regression with the binomial nominal variable defined either as defaulter or re-payer. Further, we treated different factors affecting default probability of the student as independent variables. The main objective was to find out the effect that the independent variables have on the dependent variable. We then validated the resulting model by comparing its results to observed data from the Kenyan Higher Education Loans Board. Results show the amount of loan reimbursed as the main factor affecting default. This can be an eye-opener for policy makers in their effort to mitigate non-repayment.