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Do Social Networks Solve Information Problems for Peer-to-Peer Lending? Evidence from Prosper.com

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Abstract

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.

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File URL: http://www.netinst.org/Freedman_Jin_08-43.pdf
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Bibliographic Info

Paper provided by NET Institute in its series Working Papers with number 08-43.

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Length: 62 pages
Date of creation: 14 Nov 2008
Date of revision:
Handle: RePEc:net:wpaper:0843

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Web page: http://www.NETinst.org/

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Keywords: peer-to-peer lending; e-commerce; adverse selection; information asymmetry; social networks.;

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  1. Akerlof, George A, 1970. "The Market for 'Lemons': Quality Uncertainty and the Market Mechanism," The Quarterly Journal of Economics, MIT Press, vol. 84(3), pages 488-500, August.
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  3. Ginger Zhe Jin & Andrew Kato, 2007. "Dividing Online and Offline: A Case Study," Review of Economic Studies, Oxford University Press, vol. 74(3), pages 981-1004.
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Cited by:
  1. Rajkamal Iyer & Asim Ijaz Khwaja & Erzo F.P. Luttmer & Kelly Shue, 2009. "Screening Peers Softly: Inferring the Quality of Small Borrowers," NBER Working Papers 15242, National Bureau of Economic Research, Inc.
  2. Ejaz Ghani & William R. Kerr & Christopher T. Stanton, 2012. "Diasporas and Outsourcing: Evidence from oDesk and India," NBER Working Papers 18474, National Bureau of Economic Research, Inc.
  3. Iyer, Rajkamal & Khwaja, Asim Ijaz & Luttmer, Erzo F. P. & Shue, Kelly, 2009. "Screening in New Credit Markets: Can Individual Lenders Infer Borrower Creditworthiness in Peer-to-Peer Lending?," Working Paper Series rwp09-031, Harvard University, John F. Kennedy School of Government.
  4. Nataliya Barasinska & Dorothea Schäfer, 2010. "Are Women More Credit-Constrained than Men?: Evidence from a Rising Credit Market," Working Paper / FINESS 6.3, DIW Berlin, German Institute for Economic Research.

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