Screening Peers Softly: Inferring the Quality of Small Borrowers

Rajkamal Iyer (), Asim Ijaz Khwaja (), Erzo Luttmer and Kelly Shue ()
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Rajkamal Iyer: MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142
Asim Ijaz Khwaja: Harvard Kennedy School, Harvard University, Cambridge, Massachusetts 02138; and National Bureau of Economic Research, Cambridge, Massachusetts 02138
Kelly Shue: Booth School of Business, University of Chicago, Chicago, Illinois 60601; and National Bureau of Economic Research, Cambridge, Massachusetts 02138

Management Science, 2016, vol. 62, issue 6, 1554-1577

Abstract: This paper examines the performance of new online lending markets that rely on nonexpert individuals to screen their peers’ creditworthiness. We find that these peer lenders predict an individual’s likelihood of defaulting on a loan with 45% greater accuracy than the borrower’s exact credit score (unobserved by the lenders, who only see a credit category). Moreover, peer lenders achieve 87% of the predictive power of an econometrician who observes all standard financial information about borrowers. Screening through soft or nonstandard information is relatively more important when evaluating lower-quality borrowers. Our results highlight how aggregating over the views of peers and leveraging nonstandard information can enhance lending efficiency. This paper was accepted by Amit Seru, finance.

Keywords: peer-to-peer credit markets; market-based lending; crowd sourcing; screening; market inference; information and hierarchies; soft information (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (123)

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http://dx.doi.org/10.1287/mnsc.2015.2181 (application/pdf)

Related works:
Working Paper: Screening Peers Softly: Inferring the Quality of Small Borrowers (2013)
Working Paper: Screening Peers Softly: Inferring the Quality of Small Borrowers (2013)
Working Paper: Screening Peers Softly: Inferring the Quality of Small Borrowers (2009)
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