This blog details my ongoing efforts to use computer modeling to assist me with loan investment choices at Lending Club.
For those who don’t know, Lending Club is a peer-to-peer lending site. Individual investors can offer to partially fund loans, and individual borrowers can advertise their loan needs on the site. Say you need $10,000 to install solar panels on your roof. You fill out an application at Lending Club as a borrower, and Lending Club gives a rating for your loan application (A loans are lowest risk, G loans are highest risk). Your loan is assigned an interest rate based on the credit letter rating. Then investors choose to invest a minimum of $25 in your loan and are paid proportionally as you make loan payments. Loans are either 3 or 5 years, fixed rate.
Since no bank acts as the lender, Lending Club can offer borrowers lower rates, while offering investors a higher rate of return. (Currently, Lending Club is advertising an average of 9.65% returns. I will explain why that is likely optimistic in a later post.)
From a modeling perspective, Lending Club offers an unparalleled set of lending data statistics. Most importantly, their website contains links to download their entire loan data set as .csv or .xml files. You can download past loan history as well as prospective loans available for investing. This data is what I use to develop my models to hopefully assist with loan investing choices.
Future posts will discuss various aspects of the models and the loan data. I am learning as I go, and I don’t claim any special insights into the proper way to model financial loan data. I don’t have an economics degree and you should probably do your own research if you plan to invest in P2P lending.
All of the modeling is done using R, a free statistical software program.
Header image NASA/courtesy of nasaimages.org