Early delinquencies are more likely for higher income borrowers

Andrey Kamenov, Ph.D. Probability and Statistics

Any lending (and especially peer-to-peer) is inherently associated with substantial financial risks.  But sometimes it looks like the borrower didn’t make any serious effort to repay the loan, defaulting just months after the origination date.

Today we will take a look at the number of early delinquencies among all loans issued through Prosper, one of the two major peer-to-peer lending marketplaces in the U.S. For the purpose of this post, we consider a loan to end with an early delinquency if it was charged off less than one year after the origination date.

As we can clearly see on the chart below, the percentage of early delinquencies has been rising steadily along with the explosive growth of the total number of Prosper loans. While the total default rate has been relatively stable, he number of early delinquencies increased from four percent of all loans originated in 2010 to more than 10 percent by late 2013.

early_qoq_total

As evidenced by the map below, there is no clear geographical pattern that potential lenders should look for. At the same time, several states do stand out. Minnesota and New Mexico are the states with the highest early delinquency rates – 47 and 46 percent of all charged off loans were being repaid for less than one year.

The southeastern states (except for Florida), on the other hand, show rates below average, with just over 30 percent of loan delinquencies happening during the first 12 months.

And what about borrower income ranges? People with smaller incomes tend to borrow less, but there are always risks that could prevent them from repaying their loans.

It appears (see the chart below) though that higher-income borrowers are somewhat more susceptible to defaulting on their loans early.

 

Source(s):

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About Andrey Kamenov

Andrey Kamenov, Ph.D. Probability and Statistics

Andrey Kamenov is a data scientist working for Advameg Inc. His background includes teaching statistics, stochastic processes and financial mathematics in Moscow State University and working for a hedge fund. His academic interests range from statistical data analysis to optimal stopping theory. Andrey also enjoys his hobbies of photography, reading and powerlifting.

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