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Like all investments, returns from P2P lending are a function of risk

by Prosper on 03/4/11

This is the second in a series of posts about risk management. See the previous installment here.

In part two of our series on risk management, we take a deeper look at the two biggest players in the peer-to-peer lending industry. Both companies employ models reliant on internal risk teams to fairly balance interest rates with strong returns for lenders.

Over the last couple of years, Prosper has assembled the best risk team, models, and capabilities in the business, supported by the fact that we accurately matched our expectations of risk with actual performance, as noted in our last blog post.

In contrast, Lending Club’s losses are running as much as four times higher than their original expectations – seriously dampening their actual lender returns. See below for an example of a D-rated loan on Lending Club’s platform. Lending Club estimates that this loan and others like it will have an average annualized loss rate of 2.8% (see Footnote #1 below to see how to find these numbers):

Because the actual losses on this type of loan are really closer to 10% (per Lending Club’s own numbers), actual annual returns are likely to be under 5%, or not quite half of what was predicted. (For more information about the relationship between cumulative and annualized loss rates, see Footnote #2 below.)
Compare that with our ‘D’ rated loans where we forecast an 11.9% loss rate that actually came in at 10.5%:

Also of note is how close Lending Club’s actual losses on D-rated loans are to our actual losses on similar loans. The issue isn’t that these loans shouldn’t be issued – these are reasonable loss rates in consumer credit. The real issue is that it underscores the importance of accurate predictive risk models. At the end of the day, our losses were about 12% under expectation, while Lending Club’s were off by more than 300% – and in the wrong direction.

We feel that customers from both companies need to have access to all the information they can in order to make informed decisions. That’s why we have always made our data widely available. We also want to help you understand those numbers so that you can make informed decisions about your money, whether getting a loan or investing in one. After all, lenders bear the cost of credit losses in the peer to peer industry, not the companies themselves.

Notes:

1. To see the Expected Default Rate for a particular sub-grade at Lending Club, you’ll need to create an Order for a single Note of that sub-grade. Once you have the order created, proceed to the View Order page:

We’ve circled the Default Rate in red. It’s a bit onerous to aggregate these one at a time, so we’ve done it for you in the Excel attachment to our previous post. You can find the figures on the “LC Expectation Calcs” tab.

As an aside, we also thought it was interesting that Lending Club’s Expected Default Rate “is an annualized rate based on industry-wide performance data collected by TransUnion”. We found that out by clicking on the 3% link from our order:

Given the unique nature of peer-to-peer lending and its imperfect correlation with industry-wide measures, we suspect this is one of the primary reasons that their loss expectations have not been very accurate to date.

2. The relationship between cumulative principal losses and annualized default rates can be a bit confusing. In the attachment to our previous post, we provided a breakdown of how to visualize the cumulative loss curves forecast for a given annualized loss rate, but we wanted to provide some more context on that.

For a given set of loans the only way to have a perfect annualized rate is to wait until all the loans have completed – either by paying in full or defaulting. If we waited that long, though, we would be ignoring a wealth of useful information that the loans provide as they age. One of the most effective tools that we have is the cumulative loss chart: on the x-axis we have the age of the loans (i.e., the number of payments or “cycles” that have come due so far); on the y-axis we have the total principal losses as a percentage of what was originally lent. Using actual losses as of a certain cycle, we can extrapolate an estimated annualized default rate.

Because of the varying effect of individual defaults, we would expect Lending Club’s “D”-notes, with a 2.82% weighted average expected default rate, to experience 3.50% principal losses in aggregate over their 36-month life – and 1.62% of that would occur within the first 12 months! So when we see that actual losses within the first 12 months are closer to 6%, we can estimate that the annualized loss rate – when all is said and done and the loans are all paid or defaulted – will be closer to 10% than 3%.

Because the loss curve for a particular vintage may be somewhat skewed in either direction, it’s important to remember that this conversion is only an estimate. The actual annualized loss rate actually may be closer to 9% or 11%. That said, with losses over expectation by 300% or more after a year of data, it’s safe to say that there is a problem.

Again, if you’re interested in the math, we encourage you to check out the data appendix on our previous blog post – there’s lots of detailed commentary there as well as the math that we’re doing to evaluate these relationships. And if you have questions, we encourage you to submit them!


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