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

  • Iyer, Rajkamal

    (MIT)

  • Khwaja, Asim Ijaz

    (Harvard University)

  • Luttmer, Erzo F. P.

    (Harvard University)

  • Shue, Kelly

    (Harvard University)

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. This paper evaluates the screening ability of lenders in such peer-to-peer markets. Our methodology takes advantage of the fact that lenders do not observe a borrower's true credit score but only see 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 subjective information 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.

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Paper provided by Harvard University, John F. Kennedy School of Government in its series Working Paper Series with number rwp09-031.

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Date of creation: Aug 2009
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Handle: RePEc:ecl:harjfk:rwp09-031
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  8. Manove, M. & Padilla, A.J. & Pagano, M., 1998. "Collateral vs. Project Screening: a Model of Lazy Banks," Papers 9807, Centro de Estudios Monetarios Y Financieros-.
  9. William Adams & Liran Einav & Jonathan Levin, 2009. "Liquidity Constraints and Imperfect Information in Subprime Lending," American Economic Review, American Economic Association, vol. 99(1), pages 49-84, March.
  10. Seth Freedman & Ginger Zhe Jin, 2008. "Do Social Networks Solve Information Problems for Peer-to-Peer Lending? Evidence from Prosper.com," Working Papers 08-43, NET Institute.
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