Student loans occupy a significant portion of the federal budget, as well as, the largest financial burden in terms of debt for graduates. This paper explores data-driven approaches towards understanding the repayment of such loans. Using statistical and machine learning models on the College Scorecard Data, this research focuses on extracting and identifying key factors affecting the repayment of a student loan. The specific factors can be used to develop models which provide predictive capability towards repayment rate, detect irregularities non-repayment, and help understand the intricacies of student loans.
This study updates and expands the literature on student loan default. By applying multilevel regression to the Beginning Postsecondary Students survey, four key findings emerge. First, attending proprietary institutions is strongly associated with default, even after accounting for students’ socioeconomic and academic backgrounds. Second, cumulative loan debt has a non-linear relationship to defaulting. Third, minoritized and students from low-income families default at disproportionately high rates; and fourth, unemployment and degree completion are strongly associated with greater default rates. These findings counter the argument that default is a “preexisting condition,” and the discussion is framed around implications for federal financial aid policy.  This article examines debt burden among college graduates and contributes to previous research by incorporating institutional and state characteristics. Utilizing a combination of national datasets and zero-one inflated beta regression, we find several major themes. First, family income and college experiences are strongly associated with the probability of zero debt burden as well as the level of debt burden. Second, graduates from private institutions have a higher level of debt burden than graduates from a public institution. Lastly, state funding of merit-based aid programs play a role in reducing students’ debt burden, but the effect disappears when accounting for the “Georgia effect.” Conclusions and suggestions are made about the roles institutions and government can play in reducing debt burden among college graduates.