Managing student loan default risk: Evidence from a privately guaranteed portfolio

This research models default development for a large proprietary dataset of private (nonfederally guaranteed) education loans extended to law school s tudents in the early 1990s, Employing the statistical techniques of surviva l analysis and credit scoring, the study documents a pronounced seasoning e ffect for such loans and demonstrates the robust predictive power of credit bureau scoring of student borrowers. Other constructs found to be statisti cally predictive of default include school-of-attendance (or, alternatively , a measure of perceived school reputation), geographic location of attende d school, and new attorney unemployment rate within certain regions. Althou gh statistically predictive, these last constructs are of far less substant ive importance in assessing credit risk than are the effects of portfolio s easoning and scoring (an ordinal measure of the risk of extending credit to an individual based upon their past credit behavior). The article challeng es the prevailing approach to modeling student loan default (one that searc hes for "institutional" as well as "borrower" explanations) and suggests a return to the older, simpler banking paradigm of borrower willingness and b orrower ability to repay.