Screening in New Credit Markets: Can Individual Lenders Infer Borrower Creditworthiness in Peer-to-Peer Lending?

42 Pages Posted: 15 Mar 2010

See all articles by Rajkamal Iyer

Rajkamal Iyer

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Asim Ijaz Khwaja

Harvard University - Harvard Kennedy School (HKS); Center for Research on Pensions and Welfare Policies (CeRP); Bureau for Research and Economic Analysis of Development (BREAD); National Bureau of Economic Research (NBER)

Erzo F. P. Luttmer

Dartmouth College; National Bureau of Economic Research (NBER); IZA Institute of Labor Economics

Kelly Shue

Yale School of Management; National Bureau of Economic Research (NBER)

Date Written: August 2009

Abstract

The current banking crisis highlights the challenges faced in the traditional lending model, particularly in terms of screening smaller borrowers. The recent growth in online peer-to-peer lending marketplaces offers opportunities to examine different lending models that rely on screening by multiple peers. While these market-based, non-hierarchical structures potentially offer screening advantages, especially in utilizing soft information, individual lenders likely lack financial expertise and lending experience. This paper evaluates whether lenders in such peer-to-peer markets are able to use borrower information to infer creditworthiness. We examine this ability in one such online market using a methodology that takes advantage of lenders not observing a borrower’s true credit score but only seeing an aggregate credit category. We find that lenders are able to use available information to infer a third of the variation in creditworthiness that is captured by a borrower’s credit score. This inference is economically significant and allows lenders to lend at a 140-basis-points lower rate for borrowers with (unobserved to lenders) better credit scores within a credit category. While lenders infer the most from standard banking “hard” information, they also use non-standard (subjective) information. Our methodology shows, without needing to code information contained in the pictures or personal descriptions posted by borrowers, that lenders learn even from such “softer” information, particularly when it is likely to provide credible signals regarding borrower creditworthiness. Our findings highlight the screening ability of peer-to-peer markets and suggest that these emerging markets may provide a viable complement to traditional lending markets, especially for smaller borrowers.

Keywords: Peer-to-peer credit markets, Market-based Lending, Screening, Market Inference, Information and Hierarchies.

JEL Classification: D53, D8, G21, L81

Suggested Citation

Iyer, Rajkamal and Khwaja, Asim Ijaz and Luttmer, Erzo F.P. and Shue, Kelly, Screening in New Credit Markets: Can Individual Lenders Infer Borrower Creditworthiness in Peer-to-Peer Lending? (August 2009). AFA 2011 Denver Meetings Paper. Available at SSRN: https://ssrn.com/abstract=1570115 or http://dx.doi.org/10.2139/ssrn.1570115

Rajkamal Iyer

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-416
Cambridge, MA 02142
United States

Asim Ijaz Khwaja

Harvard University - Harvard Kennedy School (HKS) ( email )

79 John F. Kennedy Street
Cambridge, MA 02138
United States
617-384-7790 (Phone)
617-496-5960 (Fax)

Center for Research on Pensions and Welfare Policies (CeRP) ( email )

Via Real Collegio, 30
Moncalieri, Turin 10024
Italy

Bureau for Research and Economic Analysis of Development (BREAD) ( email )

Duke University
Durham, NC 90097
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Erzo F.P. Luttmer

Dartmouth College ( email )

Department of Economics
Hanover, NH 03755
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

Kelly Shue (Contact Author)

Yale School of Management ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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