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Opinions and analysis of Marketplace Lending, Online Lending, and Peer to Peer Lending.

Impact of Changes in Lending Club Credit Criteria on Credit Grade - Six Months After

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Lending Club Loans - Positive ROI for Two-third of All Loans

Posted by Anil Gupta | Sunday July 7, 2013, 6:54 pm | Categories: Lending Club

Question: Why do you focus on defaults so much and not enough on returns after default?

Answer: I am a conservative investor and tend to worry about risk first. I believe, the investors who focus on maximizing return first tend to really panic when they encounter adverse environment. Having lived through three market crashes, the lesson I learnt is to focus on minimizing the unpredictability of returns.

In peer to peer lending while the loans with highest interest rate may provide the maximum return, such loans will also cause the maximum variability in returns due to defaults. Focusing on defaults without paying attention to return enables lenders to measure their risk tolerance. Do you freak-out every time a loan goes into grace period? Do you check your portfolio frequently and try to get rid of any suspicious loan as soon as possible? Your risk tolerance may be much lower than you think.

P2P Loan Return on Investment (ROI)

I still haven't found a measurement that accurately measures the return of P2P loans. The performance of amortized loans are difficult to measure without knowing the payment history due to declining non-linear principal with duration and other factors. Well, as the saying goes, sometime an imperfect measurement is better than no measurement. I decided to adopt the ROI (Return on Investment) popularized by Lendstat and NSR until I find better alternative.

The equation below shows how the ROI is calculated. The starting parameters for loss factor are based on Loan Status Migration over 6 Months chart on Lending Club Statistics page. The ROI in charts below are binned into 5% increments, i.e. each bar combines the number of loans with ROI in a 5% interval. For example, ROI bin 0 represents loans with ROI between 0 and 4.99%.

The first chart below shows the ROI bins and corresponding percentage of loan volume for all loans. What's striking in this chart is that about 68% of all loans have positive ROI (greater than 0). About 40% of all loans have ROI between 0 and 10%.

The second chart below shows the similar ROI data but only for loans that either have status of Default or Charged Off. Less than 5% of defaulted loans have positive ROI indicating most likely very few borrowers default after making most of the payments on their loan and very few loans default in final stages of maturity.

Though the charts are not shown, about 100% of the fully paid loans have positive ROI. About 65% of current loans and about 22% of late loans are estimated to have positive ROI.

Key Takeaways

  • Almost two-third of all loans are expected to have positive return. Though, it is still unknown whether the returns from such loans can overcome the negative returns from other one-third of the loans.
  • As a rule of thumb, the buyers on secondary market could target to purchase current loans with 35% discount and late loans with 78% discount (1 - probability of positive return). Of course, there are a lot more factors, including loan age and probability distribution and confidence interval of ROI that should go into making final decisions.

Comments: (4)

Ana | Wednesday July 17, 2013, 11:13 pm
Hi, I am trying to understand what the ROI is trying to capture. What does the denominator of the ROI measure? The numerator are the net gains, but what does the Interest received/ interest rate + Expected losses mean? Thanks,
Rajeev | Monday August 5, 2013, 4:16 am
Hi Anil, Have you built any regression models ? if not which variables from the data provided would you (gien your experience in analyzing the loans) use given that LC already uses FICO scores and we would not want to reuse them OR reruse some of them with the expectation that LC places more importance to these independant variables than FICO. I'm trying to build a regression model to replicate LC ratings and also improving returns from a portfolio of LC loans? thanks, Rajeev
Anil Gupta | Monday August 5, 2013, 1:15 pm
@Rajeev, Yes I have done regression analysis. Since the credit model change past November, I don't believe regression is the way to go. My preliminary analysis indicates the new rating assignment may be more adaptive. If you review old Lending Club SEC filing before Nov 2012, it lists the attributes that LC used to assign rating. I currently use Bad Loan Experience (BLE) Index that tend to pickup a few mis-rated loans but occurrence has been fewer since the credit rating change. @Ana, Interest Received / Interest Rate in numerator is picking up the average principal invested and second term in numerator is subtracting the probability of outstanding principal loss. Hope this clarifies the formal. It is an approximation.
Rajeev | Monday August 5, 2013, 11:21 pm
Thanks Anil. Why do you think regression is no longer relevant? Could you please elaborate on what you mean by "adaptive" - is it that the new method is more sensitive ie does it use more information that earlier to rate a loan application? Also could you please let me know what the BLE is (I think its been developed by you) where could I find out more to understand its importance. I am new to this and apologies if you've already gone over this in your numerous blogs already. thanks, Rajeev

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