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We find evidence of significant racial disparities in a new type of credit market known as peer-to-peer lending. Loan listings with blacks in the attached picture are 25 to 35 percent less likely to receive funding than those of whites with similar credit profiles. Despite the higher average interest rates charged to blacks, lenders making such loans earn a lower net return compared to loans made to whites with similar credit profiles because blacks have higher relative default rates. These results provide insight into whether the discrimination we find is taste-based or statistical.

For-profit, or proprietary, colleges are the fastest-growing postsecondary schools in the nation, enrolling a disproportionately high share of disadvantaged and minority students and those ill-prepared for college. Because these schools, many of them big national chains, derive most of their revenue from taxpayer-funded student financial aid, they are of interest to policy makers not only for the role they play in the higher education spectrum but also for the value they provide their students. In this article, David Deming, Claudia Goldin, and Lawrence Katz look at the students who attend for-profits, the reasons they choose these schools, and student outcomes on a number of broad measures and draw several conclusions. First, the authors write, the evidence shows that public community colleges may provide an equal or better education at lower cost than for-profits. But budget pressures mean that community colleges and other nonselective public institutions may not be able to meet the demand for higher education. Some students unable to get into desired courses and programs at public institutions may face only two alternatives: attendance at a for-profit or no postsecondary education at all. Second, for-profits appear to be at their best with well-defined programs of short duration that prepare students for a specific occupation. But for-profit completion rates, default rates, and labor market outcomes for students seeking associate's or higher degrees compare unfavorably with those of public postsecondary institutions. In principle, taxpayer investment in student aid should be accompanied by scrutiny concerning whether students complete their course of study and subsequently earn enough to justify the investment and pay back their student loans. Designing appropriate regulations to help students navigate the market for higher education has proven to be a challenge because of the great variation in student goals and types of programs. Ensuring that potential students have complete and objective information about the costs and expected benefits of for-profit programs could improve postsecondary education opportunities for disadvantaged students and counter aggressive and potentially misleading recruitment practices at for-profit colleges, the authors write.

Most employers permit 401(k) plan participants to borrow from their retirement plan assets. Using an administrative dataset tracking over 800 plans for five years, we show that 20 percent of workers borrow at any given time, and almost 40 percent borrow at some point over five years. Also, workers borrow more when a plan permits multiple loans. Ninety percent of loans are repaid, but 86 percent of workers who change jobs with a loan default on the outstanding balance. We estimate that $5 billion per year in defaulted plan loans generate federal revenues of $1 billion annually, more than previously thought.

PurposeThis study investigates factors contributing to residential mortgage loans default by utilizing a unique dataset of borrowers' default data from one of the pioneer lending institutions in Malaysia that provides home financing to the public. Studies on mortgage loan default have been extensively examined, but limited studies utilize the individual borrower's data, as financial institutions generally hesitant to reveal their customers' data due to confidentiality issue.Design/methodology/approachThis study uses logistic regression model to analyze 47,158 housing loan borrowers' data for the year 2016.FindingsThe findings suggest that male borrowers, Malay and other type of ethnicity, guarantor availability, loan original balance, loan tenure, loan interest rate and loan-to-value (LTV) ratio are the significant factors that influence mortgage loans default in Malaysia.Research limitations/implicationsFuture studies may expand the sample by employing data from other types of financial institutions that would give greater insights as findings might vary due to differences in objectives, functions and regulations. In addition, the findings are subjected to the censoring bias where future studies could perform the survival analysis to control for censoring bias and re-validating the findings of the present study.Practical implicationsThe findings provide valuable insights for lending institutions and the government to formulate housing loan policy in Malaysia.Originality/valueTo the best of the authors' knowledge, this is the first study in the context of emerging economies that uses financial institution's internal data to investigate factors of mortgage loan default.

Using data from Lending Club, we analyzed funded loans between 2012 and 2013, the default status of which were mostly known in 2018. Our results showed that both the borrower characteristics and the conditions of the loan were significantly associated with the loan default rate. Results also showed that the sentiment of a user-written loan description influenced the borrower's loan interest rates. It contributes to expanding the scope of peer-to-peer (P2P) loan research by implementing unstructured data as a new model variable. Financial counselors need to consider the growth potential of the P2P loan market using data analysis: This will reveal niche market opportunities, enabling the development of services necessary for the safe supply of small loans at reasonable interest rates.

This paper examines the institutional determinants of federal loan status for a recent cohort of college students. We first set out how institutions influence loan accumulations and repayment rates, with particular focus on for-profit colleges. We then test a set of hypotheses about loan status and repayment using national data on loans, defaults, and repayments merged with college-level data. For all measures of loan status there are significant raw gaps between for-profit colleges and public and not-for-profit colleges. After controlling for student characteristics, measures of college quality, and college practices, large gaps in loan balance per student remain: students in for-profit colleges, especially the 2-year colleges, borrow approximately four times as much as they would have at a 2-year public college. For a student attending the 'average' college, their repayment rate is predicted to be 5 [9] percentage points lower if that college is for-profit compared to public [non-profit]. Repayment rates are also lower for colleges with higher proportions of minority students and with lower graduation rates; contrary to some claims, single-program institutions appear to have higher repayment rates.

Student loans schemes are in operation in more than seventy countries around the world. Most loans schemes benefit from sizeable built-in government subsidies and, in addition, are subject to repayment default and administrative costs that are not passed on to student borrowers. We probe two issues in this paper, for 44 loans schemes in 39 countries: how much of the original loan is an individual student required to repay (the \"repayment ratio\") and what percentage of the total costs of loans schemes can the lending body expect to receive back in repayments (the \"recovery ratio\")? The analysis shows considerable variation in the size of the repayment and recovery ratios across schemes. Moreover, many loans schemes exhibit sizeable built-in subsidies accruing to student borrowers-in over 40% of the schemes examined, the repayment ratio is 40% or less. Overall loans recovery is considerably lower. Policy implications of these findings are discussed together with a consideration of steps that may be taken to improve the financial outcome of loans schemes. (HRK / Abstract übernommen).

The authors present empirical evidence that borrowers, consciously or not, leave traces of their intentions, circumstances, and personality traits in the text they write when applying for a loan. This textual information has a substantial and significant ability to predict whether borrowers will pay back the loan above and beyond the financial and demographic variables commonly used in models predicting default. The authors use text-mining and machine learning tools to automatically process and analyze the raw text in over 120,000 loan requests from Prosper, an online crowdfunding platform. Including in the predictive model the textual information in the loan significantly helps predict loan default and can have substantial financial implications. The authors find that loan requests written by defaulting borrowers are more likely to include words related to their family, mentions of God, the borrower's financial and general hardship, pleading lenders for help, and short-term-focused words. The authors further observe that defaulting loan requests are written in a manner consistent with the writing styles of extroverts and liars.

Some models of loan default are binary, simply modelling the probability of default, while others go further and model the extent of default (eg number of outstanding payments; amount of arrears). The double-hurdle model, originally due to Cragg (Econometrica, 1971), and conventionally applied to household consumption or labour supply decisions, contains two equations, one which determines whether or not a customer is a potential defaulter (the 'first hurdle'), and the other which determines the extent of default. In separating these two processes, the model recognizes that there exists a subset of the observed non-defaulters who would never default whatever their circumstances. A Box-Cox transformation applied to the dependent variable is a useful generalization to the model. Estimation is relatively easy using the Maximum Likelihood routine available in STATA. The model is applied to a sample of 2515 loan applicants for whom loans were approved, a sizeable proportion of whom defaulted in varying degrees. The dependent variables used are amount in arrears and number of days in arrears. The value of the hurdle approach is confirmed by finding that certain key explanatory variables have very different effects between the two equations. Most notably, the effect of loan amount is strongly positive on arrears, while being U-shaped on the probability of default. The former effect is seriously under-estimated when the first hurdle is ignored.

Due to the rise in foreclosure filings, policymakers are increasingly concerned with helping families in financial distress keep their homes. This paper tests the extent to which distressed mortgage borrowers benefit from three types of state foreclosure polices: (1) judicial foreclosure proceedings, (2) statutory rights of redemption, and (3) statewide foreclosure-prevention initiatives. Based on an analysis of borrowers in default who reside in 22 cross-state metropolitan statistical area pairs, state policies generally have weak effects. Both judicial foreclosure proceedings and foreclosure prevention initiatives are associated with modest increases in loan modification rates. Using a matching procedure, a lender's letter promoting mortgage default counseling was associated with increases in loan modifications, decreases in loan cures, and decreases in foreclosure starts. The effects of the letter were also stronger in states with judicial proceedings.