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.
One of the first efforts to apply methods on a standardized dataset was in the research conducted by Kelchen and Li (2017) @cite_16 , by using the College Scorecard data. In their study, the dataset was used to test associations between institutional characteristics and one year loan repayment rates. The study also aimed to analyze the effects on the repayment based ond the number of years after a student has left college. The goal of the study is to develop multiple linear regressions capable of analyzing repayment using predictor variables. While the study choose a fixed list of features (based on prior research) to be their predictor variables, a better model of establishing such variables would be to conduct an exhaustive search of all available features in the dataset. The research presented in the paper aims to identify such features using a multi-model approach, where the models are able to suggest the best performing predictors in the dataset.
@cite_16
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The federal government holds colleges accountable if too many of their students default on loan repayment, but the default measure traditionally used captures only a fraction of students who are struggling to repay their loans. The 2015 College Scorecard dataset introduced a new loan repayment metric, showing that the percentage of students who have not reduced the principal balance of their loans by at least $1 over a given period of time far outstrips the traditional loan default measure. Using a sample of 3,595 colleges, we test the extent to which student demographics, institutional characteristics, and state-level economic factors are associated with repayment rates and default rates. We also examine whether factors associated with loan repayment rates change between one and seven years after students begin repayment. We find that characteristics traditionally associated with economic disadvantage, including being a first-generation college student or a member of an underrepresented minority group, t...