 Since we’re constantly thinking of ways to help others understand Peer Lending returns, we’ve simplified our platform performance calculations to be more easily understood, more comparable to widely reported bond and stock market returns, and devoid of forecasts and estimations. Essentially, we find an adjusted net yield each month: $\frac{\sum{(interest \ – \ fees\ -\ charge-offs)}}{\sum{beginning\ outstanding\ principal}}$ and annualize it for an Annual Percentage Yield (APY), the adjustment being a standardization for how much new outstanding principal is being issued each month so that the effects of older notes charging-off are not washed away by growth in loan origination volumes.

As stated above, this new methodology should be simpler to understand. There’s no need to estimate default rates and hazard curves, predict future returns, or assume the ability to liquidate on the secondary market; one only needs to keep track of the interest earned (net charge-offs and fees) on the period’s beginning outstanding principal. The calculation equation is more similar to how stock and bond returns are calculated (a period’s return on investment rather than internal rate of return) and thus make it more directly comparable to popularly reported stock and bond returns.

Let us preface the explanation of the performance calculation with the framework for adjusting returns by loan origination volume. Each month, there are loans that are paying that were issued 5 months ago, 3 months ago, 20 months ago, etc. We’ll call these different groups batches, with the commonality within the batch being the age of the loans. Because these loans are amortizing, these batches have different percentages of remaining outstanding principal based on the age of the batch. We keep track of this percentage for each batch in each month to properly weight each batch’s contribution to the overall return that month. A quick thought experiment: Say you’re looking to calculate returns for a platform next month. The returns from batches that are young have a lot more remaining outstanding principal than batches that are old, and thus should have a larger impact to next month’s return calculation than the old loan batches. However, we only want to capture the larger impact due to the age, rather than a larger impact due to the platforms increasing the number of loans they are originating each month. Thus, our adjustment has the same effect as assuming the same dollar amounts are being issued in each successive month. Let us now walk through an example calculation for a theoretical platform that is 3 months old, with summary table for the third month’s return calculation below. Numbers may be slightly off due to rounding:

Batch Original Outstanding Principal $()$ Period’s Beginning Remaining Outstanding Principal$()$ Percent Remaining Outstanding Principal (Weights) $(\%)$ Interest $()$ Return $(\%)$ Weighted Return $(\%^{2})$
1 100 93 93 0.90 .9677 90.00
2 100 97 97 0.60 .6186 60.00
3 100 100 100 1.00 1.0000 100.00
1. Identify all loans ongoing in month and batch them by age: Our theoretical platform has 3 batches by the end of the 3rd month; batch 1 is 3 months old, batch 2 is 2 months old, and batch 3 is 1 month old.
2. Track the percentage of remaining outstanding principal for each batch: We’re only interested in the percentage of remaining outstanding principal each month and not actual dollar amounts, so we can scale dollar amounts to be numbers that are easy to work with. In this case, lets use $100$. Based on how the loans amortize, at the start of the 3rd month, batches 1, 2, and 3 have $93$, $97$, and $100$ beginning outstanding principal respectively. This translates to $93\%$, $97\%$, and $100\%$ of beginning outstanding principal remaining, and the weights we will multiply the returns by.
3. For each batch we calculate the return: Over the 3rd month, batch 1 generated $0.90$ in interest on $93$ of remaining outstanding principal for a return of $\frac{0.90}{93.00} = .9677\%$, batch 2 generated $0.60$ in interest on $97$ of remaining outstanding principal for a return of $\frac{0.60}{97.00} = .6186\%$, and batch 3 generated $1.00$ in interest on $100$ of remaining outstanding principal for a return of $\frac{1.00}{100.00} = 1.0000\%$
4. Calculate the weighted return of each batch: Batch 1: $.9677\% * 93\% = 90.00\%^{2}$, batch 2: $.6186\% * 97\% = 60.00\%^{2}$, batch 3: $1.0000\% * 100\% = 100.00\%^{2}$
5. Sum the weighted returns and divide it by the sum of the weights: $\frac{\sum{Weighted\ Return}}{\sum{Weights}} = \frac{90 + 60 + 100}{93 + 97 + 100} = .862\%$
6. Annualize the month’s return: $(1 + .00862)^{12}-1 = .10849 = 10.862\%$

New performance graphs can be seen here. Compared to our previous performance methodology, the new graphs capture more of the variation between returns each month. This is largely due to the previous methodology relying on projected returns and expected default rates for returns closer to present day. Another benefit, specifically for our Lending Club users, is that the new methodology calculation is more similar to how Lending Club’s Net Annualized Return is calculated. While not a direct translation, the underlying basis of the calculations is similar enough to get a sense for what the NAR for each platform is. The returns in the new performance graphs also lie closer to the returns reported by the Lending Club and Prosper themselves over their respective reported time periods, which should make it more cohesive when trying to digest the reported returns from various sources.