Four Filters to Increase Your P2P Lending ROI

When you look at the spread of late payments from the post I published earlier this week it is obvious that some people get a loan from Lending Club or Prosper with no intention of ever paying it off. Wouldn’t it be nice if we could recognize these people before committing our investment dollars to their loan. Well I believe with some simple filtering you can eliminate most of them.

While p2p lending is still new, there are thousands of loans that have reached or are close to maturity and we can do some statistical analysis on these loans. Ken at has been busy this month upgrading his filtering options for his Prosper and Lending Club loan statistics. I have spent several hours this week poring over his changes to work out which criteria have the most impact on p2p investors ROI.

After looking at all the filters available on Lendstats I decided on four filters that I think will make the most impact while still leaving the investor with plenty of loans to choose from. I chose filters that take data from the borrowers credit report because I didn’t want to rely on unverified information. People who are scammers, with no intention of repaying the loan, will likely provide misleading or incorrect information on their loan application. So I didn’t want to rely on any inputted data from the borrower. These filters obviously don’t catch all defaults but I was able to reduce the number of defaults by well over 50%.

Prosper’s Numbers Using the Filters

Below is the table for Prosper loans. Because Prosper has been around since 2005 I was able to select a large pool of loans that have reached maturity.

[table id=3 /]

You can see that the ROI for all loans originated from the very beginning of Prosper until December 2007 was -6.3%. But after applying the first filter the ROI jumps to positive 1.0%. Defaults go down dramatically as well. Each successful filter improves ROI while at the same time reducing defaults. When the last filter is applied you end with an ROI of 3.2%, not stellar by any means but far better than the -6.3% that you started out with. Here is the link for the Prosper loan filters on Lendstats.

Explanation of the Four Filters

As I said these filters are taken from information from the borrowers credit report (apart from the loan size obviously). Here is a brief explanation of the filters:

Inquiries: 0

These are what are known as “hard inquiries” that appear on a person’s credit report whenever they request credit or a loan. So, if a borrower has recently applied for a credit card or an auto loan this will appear on their credit report. In our filter we look for no hard inquiries at all over the past six months. This may seem a little harsh but even when you expand this number to one the ROI goes down. This filter is the easiest way to improve your ROI and so it is the first and most important filter.

Loan Size < $20,000

This one is self-explanatory. We filter out all loans of $20,000 or more. This makes logical sense because the higher the loan, the higher the payment and presumably the more difficult it is to pay back.

Open Credit Lines >= 5

This refers to the number of open lines of credit, namely credit and charge cards. I am not sure why five is the magic number but if you go much higher or lower than five then you start to negatively impact ROI.

Utilization <= 70%

Credit Utilization refers to the amount of credit used in ratio to the total available credit. You might think that the lower the number the better the return for the p2p investor but the sweet spot seems to be a maximum of 70%.

Lending Club’s Numbers Using the Filters

Lending Club started originating loans in June 2007 so if I chose to analyze only loans that had reached maturity then it would have been a very small sample size (589 loans total). So, I took all the data through December 2008; these loans are all at least two years old and should provide enough data for an analysis. Here are Lending Club’s numbers:

[table id=4 /]

You see the same trends as with Prosper’s data. One quick note about Lending Club’s stats. At the time of this writing the Lending Club loan filter was not yet available to the public on Lendstats. Ken indicated that its release was imminent (and he let me play around with the beta – thanks Ken), but if you don’t see a link to it on his site you can try this link here.

How to Implement These Filters on Prosper and Lending Club

Prosper makes it very easy to use filters when choosing loans on their site. They have an advanced search page where you can add all these filters very easily. At the time of this writing there were 32 loans available on Prosper and after applying these four filters there were eight loans to choose from.

On Lending Club it is a little more difficult. Their filtering isn’t as flexible as Prosper’s, so you have to download the “In Funding Loan Data” from their downloads page. You can save the CSV file and then bring it into Excel to do the filtering there. I prefer this method because you get all the loan data in the spreadsheet and you can add many more filters to the data to find the loans worth considering. At the time of this writing applying these four filters left you with 140 loans to chose from out of a total of 426 loans on the site.

A Word of Caution About Using Filters

These four filters that I have mentioned here are only a starting point. I like them because they will tend to catch most of the scammers – those people who have no intention of repaying their loan. Luckily this is a small minority of borrowers but they likely cause a significant percentage of defaults.

I chose these four filters because they provide substantial improvements with both Prosper and Lending Club loans. But now some words of caution. With additional filters you can slice and dice the numbers so narrowly that you can back into a fantastic return. For example, here is a filter that boasts a 20.86% ROI but if you look closely it contains only 73 loans (out of 31,000) so it is not a significant enough sample. I tried to provide filters that left a decent number of loans to choose from, eliminating the probable worst performing loans. The other thing to be careful about is the possibility that future loans may perform differently than those in the past. As more loans mature I will be re-testing these filters to make sure they still perform well.

Having said all that I believe if you use these filters you will get above average returns on your p2p lending investment. But for the serious investor these filters should only be a starting point. You should add your own filters and other criteria when choosing loans. I would like to hear from others – what factors do you look for when deciding which loans to invest in on Lending Club or Prosper?

  • Peter Renton

    Peter Renton is the chairman and co-founder of Fintech Nexus, the world’s largest digital media company focused on fintech. Peter has been writing about fintech since 2010 and he is the author and creator of the Fintech One-on-One Podcast, the first and longest-running fintech interview series.