As any follower of the marketplace lending space knows, the primary use case for consumer borrowers today is debt consolidation – presumably revolving credit card debt that generally carries punitively high interest rates. Based on data from the Federal Reserve, these rates average 13% but can be much higher.
Research: P2P Lending Should Focus on Partners, New Opportunities and ... Crowdfund Insider A recent study published by Cognizant addressing the fast growing peer to peer lending space in the United States.
Adweek Peer-to-Peer Digital Banks Are Lending Billions of Dollars Adweek A recent Fitch Ratings report shows that the peer-to-peer (P2P) digital banking space has become a billion-dollar industry and is growing fast.
Four Benefits for P2P Investors. But in this article I want to ... in all 50 states. This does not happen automatically so it will not change overnight but shortly after the IPO Lending Club should be open to investors in every state.
“Marketplace lending is a growing and increasingly complex ecosystem. Making sense of all the various players and the ways they connect can be a challenge. To help, Orchard Platform has created a “Lendscape” that brings the industry into focus.”
International P2P Lending Services - Loan Volumes August 2014 P2P-Banking.com In August Prosper, Bondora and Thincats showed big growth of newly originated loan volume. For most other services it was a slow month.
Cleveland FED: Peer-to-Peer Lending – a Funding Channel Set to Grow Forex Magnates With the business originating in the UK back in 2005, US companies which provide P2P business lending have been limited to providing up to $100,000, with Lending...
In October of 2013, we did a post on Roll Rates for Lending Club. We defined roll rates and analyzed the patterns on Lending Club data. For the purposes of both posts, we’ll consider roll rates to be the probability of a loan going from one state of delinquency to another (e.g. 30 days past due to charge off). Roll rates provide insight into the likelihood of future loss on a loan that has missed just one payment. This can be used for:
1. Modeling: it is best practice to use more recent data as the current vintages are underwritten and aging during a similar period of time. To actually charge off, an account needs at minimum 120 days (although – it usually takes at least 10 months or so.) If you can use an early default predictor like missing 1 payment (i.e. 30 days past due) you can adjust the model with this information.
2. Portfolio Analysis: If you know the roll rates of a population, you can then extrapolate the current performance of your portfolio earlier because you can use early indications as proxy for later credit loss. This allows for adjustments in underwriting strategy prior to actually experiencing losses.
3. Loan Loss Reserve Calculations: If an investor keeps a reserve based on expected losses, roll rate calculations can be useful.
In order to calculate roll rates, it is important to first understand the nature of charge-offs, as the analysis requires some assumptions. These loans amortize over time, which means if the charge off occurs later in the borrower’s tenure, the principal charged off could become de minimis. Below is the average dollar loss and percentage of original principal lost based on when the charge-off occurs in the life of the loan. As expected, the average dollars lost decreases as the loans age, as does the percentage of the original balance that charges off.
As opposed to Lending Club (which only designates past due loans as 31-120 days late), Prosper’s data allows for a more granular assessment of the transitions between the different past due buckets. Prosper provides the actual days past due of every loan at every month. In order to calculate loan movement, we need to pick a starting point. Based on the distribution of 30 days past due (below), 50% of loans that miss one payment do so by month 12, so this will be the starting point.
We analyzed all Prosper loans that have at least 18 months tenure to see the actual change in status from 12 months to 18 months. We used a statistical method called Markov Chains, also known as transition matrices. These are mathematical models that calculate the probability of an object moving from one state to another. In this case, the states are the status of the loan, and the time period we are analyzing is 6 months (from 12 to 18 months). By applying this model to the historical changes in loan status, we are able to extrapolate the likelihood of a loan in any status moving to another status in 6 month increments from the initial 12 month status.
Below are the results of the analysis, in which we show the probability of movement between all of the different status buckets. The likelihood of eventual charge-off on loans that miss just one payment by 12 months is quite high. When predicting out to 30 months, our analysis shows the likelihood to be about 85%.
The Markov Chain is a useful tool, and was easy to use for this analysis. In the case of roll rates, this analysis could be accomplished with any starting point and ending point. In this case, we used 12 months to start and looked out 6 months. If an analysis required even more recent data, then starting at 3 months and looking to 6 would work. Making this change to the analysis would bring in more recent data (including all loans with at least 6 months of tenure, instead of 18), but the tradeoff would be a model that may under predict the probability of charge off because it goes out to 9 months instead of 18 months.
Using statistical methods, both basic and complex, can help yield preliminary results for a loan portfolio. This analysis specifically helps validate the use of early default behavior as an input into a charge-off model. Based on what we've found, an analysis done on a more recent vintage can yield results that are worthwhile. Of course, more data is always better, and actual performance is always the optimal input to a model. In the case where this is not available, statistical methods such as those discussed here are useful.
Assuming that marketplace lending offers borrowers a good deal and a positive customer experience, we would expect to see repeat business. In a post last October, we first analyzed repeat borrowers on Prosper. That analysis found that 10% of loans were issued to those who had previously taken out a Prosper loan. In Oct. 2013, Prosper originated $50 million in loans. Since then, Prosper has issued an additional $726 million, including $145 million in June 2014 alone. Given the massive growth in the marketplace, we wanted to see how much of this growth was due to repeat borrowers, how these individuals have performed, and what we can learn from borrowers’ prior experience.
Prior Borrower Market Share
The charts below show monthly loan originations on Prosper as well as the share comprised by repeat borrowers.
As we can see, Prosper’s origination volume has grown significantly over the past 8 months. Existing borrowers, while growing in absolute number, have comprised a smaller share of monthly issued loans over time. This pattern is not surprising given the platform’s high rate of growth. In order for Prosper to expand, it has presumably needed to cast a broader net in acquisition marketing to source borrowers from new channels.
When Borrowers Return
In the first half of 2014, Prosper made 3,455 loans to previous borrowers, totaling $40,185,826 in originations. Using the data made available by Prosper, we can explore the characteristics of this population. In the chart below, we group these borrowers by the number of previous loans they had taken prior to the new loan’s date of origination. While the vast majority had taken only 1 prior loan, 35% had 2 or greater!
For the borrowers with one prior loan, one has to wonder how much time had passed between the issuance of the prior loan and the new one. In the histogram below, we show the distribution of time between loans, divided into 3-month intervals and broken out by the term of the previous loan.
Interestingly, a very large proportion of new loans were granted to borrowers who had already received Prosper loans under a year ago. The vast majority of repeat loans were granted within 2 years of the prior origination, perhaps a surprising finding given that Prosper’s minimum loan term is now 3 years (a small number of 1-year loans were issued in the past, but this practice stopped in early 2013). If so many borrowers are getting new loans prior to the initial maturity date of an existing loan on the same platform, there are various potential reasons to consider.
1. The borrower is current on the existing loan and has sufficiently good credit to qualify for an additional loan (potentially to refinance the first one) 2. The borrower has paid off the existing loan and now qualifies for an additional loan 3. The borrower defaulted on the existing loan (note – this scenario would not occur within Prosper’s underwriting policy, as prior defaulters are restricted from obtaining new credit)
Understanding the frequency and details of the above scenarios is an analysis unto itself and perhaps will be the subject of a future post.
Creditworthiness of Prior Borrowers
Presumably, if Prosper has decided to give an additional loan to a prior borrower, its own credit rating system has favorably evaluated this individual. Therefore, to get an externally consistent view of the creditworthiness of repeat vs. first-time borrowers, we must use an externally-developed model: in this case, FICO. In the graph below, we show the distribution of loans by FICO score, split out by the number of prior Prosper loans at the time of origination. The graph reveals a relatively stable distribution between new and prior borrowers, with some rightward-skew for those with a previous loan. Perhaps the higher scores for these individuals reflect good performance on the earlier loan.
Conclusion – Relationship Lending
The concept of “relationship banking” has existed for decades in traditional financial services. The idea was that customers who were already doing business with a given institution would prefer to come back to that same institution as repeat customers and for additional services. Embedded in this concept was the assumption that this would allow a bank to make more accurate credit decisions, aided by a wealth of information on existing customers and the ability to consider a customer’s lifetime value. With this idea in mind, retail banks across the country expanded into a wide variety of financial services, such as checking, savings, credit cards, mortgages, auto loans, student loans, and personal loans. For a time, this was quite successful. However, the internet has essentially driven switching costs to zero, and consumers increasingly decide to do business with whichever firm is able to provide the best cost for the best service. Clearly, this unbundling has been to the benefit of marketplace lenders, who have been able to deliver a positive customer experience without the burden of legacy technologies, and whose operating efficiency has allowed them to offer favorable rates. As this new wave of loan origination platforms grows to comprise a larger share of overall lending, it will be interesting to observe if they are able to use relationships and data to their advantage to make better decisions and win the loyalty that had been so highly sought by traditional banks.
P2P lending suffers from the same risks associated with any other credit provision institution, which include: identity theft, money laundering, terrorism financing, consumer privacy, and data protection violations.
After months of speculation about the when, Lending Club Corp. has started down the path to going public, tapping banks including Morgan Stanley and Goldman (Lending Club Going Public: The P2P lending platform connects investors to individual borr...
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