“Do social networks solve information problems for peer-to-peer lending? Evidence from Prosper.com” by Ginger Zhe Jin
Ginger Zhe Jin
University of Maryland
This paper studies peer-to-peer (p2p) lending on the Internet. Prosper.com, the first p2p lending website in the US, matches individual lenders and borrowers for unsecured consumer loans. Using transaction data from June 1, 2006 to July 31, 2008, we examine what information problems exist on Prosper and whether social networks help alleviate the information problems. As we expect, data identifies three information problems on Prosper.com. First, Prosper lenders face extra adverse selection because they observe categories of credit grades rather than the actual credit scores. This selection is partially offset when Prosper posts more detailed credit information on the website. Second, many Prosper lenders have made mistakes in loan selection but they learn vigorously over time. Third, as Stiglitz and Weiss (1981) predict, a higher interest rate can imply lower rate of return because higher interest attracts lower quality borrowers. Micro-finance theories argue that social networks may identify good risks either because friends and colleagues observe the intrinsic type of borrowers ex ante or because the monitoring within social networks provides a stronger incentive to pay off loans ex post. We find evidence both for and against this argument. For example, loans with friend endorsements and friend bids have fewer missed payments and yield significantly higher rates of return than other loans. On the other hand, the estimated returns of group loans are significantly lower than those of non-group loans. That being said, the return gap between group and non-group loans is closing over time. This convergence is partially due to lender learning and partially due to Prosper eliminating group leader rewards which motivated leaders to fund lower quality loans in order to earn the rewards.