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.