Every year around this time I share my investing strategies here for everyone to see. These are the filters I use every day to invest on both Lending Cluband Prosper. These strategies have been tweaked a little over the years but my overall approach has not changed since 2011. I invest in high interest loans at both companies and I try to minimize the risks by only choosing certain subsets of these high interest loans.
You should keep in mind that this article does not contain investment advice and I do not recommend you follow my strategies. I adhere to an aggressive strategy that includes plenty of the higher risk loans which leads to many defaults. Every investor needs to determine the level of risk they are comfortable with and invest accordingly.
Some Background on Filtering
It can feel a little overwhelming when you first start investing. You wonder how you should go about choosing loans. When I first started I did it kind of randomly with very little strategy. Then, for a while I let Lending Club choose the loans for me. Finally, I decided to get serious. What did I do? I spent dozens of hours analyzing the loan history of both Lending Club and Prosper.
The original goto site for this kind of analysis was Lendstats. Today, Nickel Steamroller is the leader and that is the place where investors should go to analyze the p2p lending loan history. Prosper and Lending Club make their entire history available for public analysis and Nickel Steamroller has produced an excellent tool to easily inquire on this history. You can get very detailed with this analysis with dozens of data fields to choose from. I encourage all serious investors to spend some time studying to see what has produced above average returns in the past.
Before I go any further I need to stress this point. My investment decisions are based on historical analysis. There is no guarantee that the loans will perform in a similar way going forward. And if the economy tanks as it did in 2008-09 then it is likely that future returns will look quite different.
One final important note about filtering before I show you how I am investing. You need to make sure you are choosing a broad enough cross section of loans when doing your analysis. If you run your filters and you see only 200 loans, for example, out of a 250,000 loan universe then you can not read much into your results. The rule of thumb I use today is that any set of filters must produce at least 1,000 loans (preferably more) to be considered a relevant result.
If you want to compare my filters today to previous years here are links to my 2012 and 2013 strategies.
Loan Grade: D, E, F, G
Inquiries = 0
DTI% <= 20%
Open credit lines >= 8
2 Yr Delinquencies = 0
Public records = 0
Monthly income >= $3,000 and < $7,500
Loan purpose: All except other, small business and vacation
Term = 36 months
Loan Grade: C, D, E, F, G
Inquiries = 0
Monthly income >= $4,000
Loan purpose: credit card, debt consolidation
The first point to note about these four filters is that filters 1, 2 and 3 are all mutually exclusive. You can deploy them across multiple accounts as I do and be assured that you will not get any duplicate notes. As you can see my two favorite filters are income and number of inquiries. I have found these two fields have some of the most significant impact on returns and I like to use them everywhere.
I have used filter 4, which I call Super Simple, when I need a broad filter to deploy more cash. The Super Simple filter will capture more than 10% of the loans hitting the platform although many of these loans are popular and will disappear quickly. But if you are using the API or even if you are investing manually you can usually find plenty of loans to invest in using this filter.
Long time readers will also notice that I am no longer filtering by state. In previous years I have excluded California and other states but I have decided to stop that practice. I have done this for a couple of reasons. One, by using good filters you are reducing the negative impact of including a “bad” state. Two, I want the largest possible universe of loans to invest in with the filters I have chosen. And I just don’t think it makes a big enough difference to your ultimate returns any more.
I should also point out that I supplement these filters with the P2P-Picks credit model. I do this by logging in at feeding time and frantically selecting the loans to invest in my main Lending Club account.
Loan Grade: B, C, D, E, HR
Inquiries = 0
Open Credit Lines >= 10
The first thing that regular readers will notice is that my Prosper filters are more broad now than in previous years. This was done out of necessity. Even with investing through the API it has been hard to keep my cash fully deployed. Having said that, with the changes that I wrote about earlier this week I may be able to refine some of these filters going forward. My cash is fully deployed now at Prosper for the first time in over 12 months.
For a number of years my favorite filter at Prosper was repeat borrowers. Back in 2010 and 2011 these loans were overpriced and they produced some excellent returns for investors. I still like investing in repeat borrowers because a good payment history is an excellent predictor of future behavior but these loans are relatively hard to come by. In the last two weeks I have invested in just three loans with my repeat borrower filter and I am investing through the API.
So, there you have it. These are the criteria I am using to invest today. There are literally millions of other ways to invest and my selections are certainly not the only way to go. I am happy for you to critique them and provide your own suggestions in the comments.
Peter Renton is the chairman and co-founder of LendIt Fintech, the world’s first and largest digital media and events 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. Peter has been interviewed by the Wall Street Journal, Bloomberg, The New York Times, CNBC, CNN, Fortune, NPR, Fox Business News, the Financial Times, and dozens of other publications.