Quantifying the Value of Heterogeneous Preference: An Empirical Matching Model of Peer-to-Peer Lending

39 Pages Posted: 7 May 2019 Last revised: 9 Oct 2019

See all articles by Yang Jiang

Yang Jiang

Nanjing University - School of Business

Jinyang Zheng

Purdue University - Krannert School of Management

Yong Tan

University of Washington - Michael G. Foster School of Business

Xiangbin Yan

Harbin Institute of Technology - School of Management

Date Written: April 22, 2019

Abstract

This paper focuses on an essential, but previously underexplored, mechanism that underlies the operation of peer-to-peer (P2P) lending markets: lender-borrower matching. We model the formation of lender-borrower matches as an endogenous matching process in which agents strategically choose their partners based on their preferences. Using data from a large online lending site, we empirically investigate the determinants of lender-borrower matching and explore their importance. Our structural estimates suggest that heterogeneous preference leads to various matching patterns across different types of lenders and borrowers. Experienced, hasty, and male lenders exhibit more tolerance toward borrowers with a lower credit score than do their counterparts. Based on these estimates, we perform counterfactual analyses to quantify the economic value of P2P lending. Note that P2P lending differs from traditional lending by following a decentralized matching process under which heterogeneous preference is maintained. We quantify the matching value from the decentralized matching process in P2P lending and compare it to that of its centralized counterpart for which heterogeneous preference is not respected. We find that P2P lending gains value from heterogeneous preference and that a more centralized matching process hurts large and active markets less than for small and inactive markets. Further, an agent-level analysis shows that more experienced lenders and low credit score borrowers are more vulnerable to a credit-score-ruled centralized matching process. Our findings offer managerial implications for credit scoring and information disclosure policy.

Keywords: peer-to-peer lending, two-sided matching, information disclosure, credit score, maximum score estimation

JEL Classification: G20, C78

Suggested Citation

Jiang, Yang and Zheng, Jinyang and Tan, Yong and Yan, Xiangbin, Quantifying the Value of Heterogeneous Preference: An Empirical Matching Model of Peer-to-Peer Lending (April 22, 2019). Available at SSRN: https://ssrn.com/abstract=3375802 or http://dx.doi.org/10.2139/ssrn.3375802

Yang Jiang (Contact Author)

Nanjing University - School of Business ( email )

Nanjing, Jiangsu 210093
China

Jinyang Zheng

Purdue University - Krannert School of Management ( email )

West Lafayette, IN 47907-1310
United States
7654966221 (Phone)
7654966221 (Fax)

HOME PAGE: http://https://www.krannert.purdue.edu/directory/bio.php?username=zheng221

Yong Tan

University of Washington - Michael G. Foster School of Business ( email )

Box 353226
Seattle, WA 98195-3226
United States

Xiangbin Yan

Harbin Institute of Technology - School of Management ( email )

Heilongjiang
China

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