Abstract
We investigate the effect of big bids on uniform price auctions with the use of data from peer-to-peer loan auctions. Following a big bid by a prospective lender, subsequent bidders are less likely to enter the auction, and those that do enter tend to bid higher interest rates. We show that bidders are not reacting to the identity of the big bidder—just to the bigness of the bid. Additionally, we find that auctions with a big bid receive fewer bids overall, but these loan requests are still more likely to get funded and to have higher interest rates. These results have strong implications for maximizing a seller’s revenue (or minimizing a buyer’s cost in a procurement auction) and the allocative efficiency in uniform price auctions.
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Notes
For context, the mean and median bid are $78 and $50, respectively.
This assumption is reasonable given the fact that correlation and Durbin-Wu-Hausman tests cannot reject the null of exogeneity. We discuss this more in Sect. 4.4.
To protect the members’ privacy, their true identity is never revealed.
Prosper deemphasized groups starting in 2011, but this feature still exists on the platform.
This format is similar to what is commonly used in Treasury auctions and multi-unit procurement auctions.
Lenders cannot cancel a bid after it has been submitted.
Recall, loan requests are between $1000 and $25,000.
While the duration can be between 3 and 14 days, 81% of listings are set for one week.
The loan is fully amortized in monthly payments.
Credit grades are constructed as 40-point bands of the borrower’s Experian credit score.
Outcome data were collected on September 21, 2011. 9,099 of the loans had reached maturity, while the 515 remaining loans were still active in good standing.
Being delinquent ranges from being a single day late on a payment to being so incurable that the loan is charged off.
Delinquency rates are substantially higher than default rates. See Federal Reserve Bank of Richmond (2012), which is available via the “Residential Mortgage Delinquency and Foreclosure Rates” link courtesy of the Internet Archive Way Back Machine: http://web.archive.org/web/20121019072734/http://www.richmondfed.org/banking/markets_trends_and_statistics/trends.
Previous research has found that most lenders tend to diversify across listings by pledging small amounts in multiple listings.
The final interest rate is an upper bound on what lenders actually bid.
The p-values for all four of the distributional tests are basically zero.
On average, these lenders have made 54 bids, so the vast majority of their bidding is not with the big bidder.
A Lagrange Multiplier test does not reject the null of homoskedasticity at the 5% level, a Hausman test of the normality of the Tobit does not reject the null at the 5% level, and Wald and Chi-squared tests fail to reject that pooling before and after is appropriate.
We also perform a U-test and a K-S test to examine the equality of the before and after interest rate distributions. The after big bid distribution stochastically dominates the before distribution.
A Lagrange Multiplier test does not reject the null of homoskedasticity at the 5% level; a Hausman test of the normality of the Tobit does not reject the null at the 5% level; and Wald and Chi-squared tests fail to reject that pooling before and after is appropriate.
The exception is for the 100% definition of a big bid, which funds the loan itself.
E listings have a mean request amount of $3295, while AA listings have a mean of $14,770.
These results are available from the authors upon request. Note that for the 100% definition, the Big Bid Pct variable must be removed.
The mean and median differences in the number of other active auctions are 42 and 35, respectively.
References
Ahlers, G. K., Cumming, D., Güunther, C., & Schweizer, D. (2015). Signaling in Equity Crowdfunding. Entrepreneurship Theory and Practice, 39(4), 955–980. https://doi.org/10.1111/etap.12157
Bollen, K., Guilkey, D., & Mroz, T. (1995). Binary outcomes and endogenous explanatory variables: Tests and solutions with an application to the demand for contraceptive use in Tunisia. Demography, 32(1), 111–31. https://doi.org/10.2307/2061900
Bonaldi, P., Hortaçsu, A., & Song, Z. (2015). An empirical test of auction efficiency: Evidence from MBS auctions of the federal reserve (Tech. Rep. No. Finance and Economic Discussion Series: 2015-085). Washington, DC: Board of Governors of the Federal Reserve Board.
Burtch, G., Ghose, A., & Wattal, S. (2014). Cultural difference and geography as determinants of online prosocial lending. Management Information Systems Quarterly, 38(3), 773–794. https://doi.org/10.25300/misq/2014/38.3.07
Chen, X., Zhou, L., & Wan, D. (2016). Group social capital and lending outcomes in the financial credit markets: An empirical study of online peer-to-peer lending. Electonic Commerce Research and Applications, 15, 1–13. https://doi.org/10.1016/j.elerap.2015.11.003
Davidson, R., & MacKinnon. (1993). Estimation and inference in econometrics. Oxford University Press.
Federal Reserve Bank of Richmond (2012). Residential mort- gage delinquency and foreclosure rates (Markets, Trends, and Statistics). Federal Reserve Bank of Richmond. (http://web.archive.org/web/20121019072734/https://richmondfed.org/banking/markets_trends_and_statistics/trends)
Herzenstein, M., Andrews, R., & Dholakia, U. (2011). Strategic herding behavior in peer-to-peer loan auctions. Journal of Interactive Marketing, 25(1), 27–36. https://doi.org/10.1016/j.intmar.2010.07.001
Hortaçsu, A., & Puller, S. (2008). Understanding strategic bidding in multi-unit auctions: A case study of Texas electricity spot markets. RAND Journal of Economics, 39(1), 86–114. https://doi.org/10.1111/j.0741-6261.2008.00005.x
Kagel, J., & Levin, D. (2001). Behavior in multi-unit demand auctions: Experiments with uniform price and dynamic Vickrey auctions. Econometrica, 69(2), 413–54. https://doi.org/10.1111/1468-0262.00197
Klafft, M. (2008). Peer to peer lending: Auctioning microcredits over the internet. In A. Agarwal & R. Khurana (Eds.), International conference on information systems, technology and management. Dubai.
Long, J. S., & Mustillo, S. A. (2021). Using predictions and marginal effects to compare groups in regression models for binary outcomes. Sociological Methods & Research, 50(3), 1284–1320. https://doi.org/10.1177/0049124118799374
Nekipelov, D. (2007). Entry deterrence and learning prevention on eBay. Duke University Working Paper Series.
Senney, G. (2019). The geography of lender behavior in peer-to-peer loan markets. Journal of Industrial Economics, 67(1), 1–36. https://doi.org/10.1111/joie.12195
Senney, G., & Lhost, J. (2023). Balancing attraction and risk revelation: The optimal reservation price in peer-to-peer loan auctions. Review of Industrial Organization. https://doi.org/10.1007/s11151-023-09914-0
Smith, R., & Blundell, R. (1986). An exogeneity test for a simultaneous equation Tobit model with an application to labor supply. Econometrica, 54(3), 679–685. https://doi.org/10.2307/1911314
Zhang, J., & Liu, P. (2012). Rational herding in microloan markets. Management Science, 58(5), 555–63. https://doi.org/10.1287/mnsc.1110.1459
Acknowledgements
We appreciate the comments and suggestions of two anonymous reviewers and the editor, Lawrence White. The authors did not receive funding, grants, or other support for this work, and have no relevant financial or non-financial interests to disclose. The views in this paper are those of the authors and do not reflect those of the Office of the Comptroller of the Currency or the Department of the Treasury.
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Senney, G.T., Lhost, J.R. Big Bids and Bidder Behavior in Uniform Price Auctions: Evidence from Peer-to-Peer Loan Markets. Rev Ind Organ 63, 349–372 (2023). https://doi.org/10.1007/s11151-023-09912-2
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DOI: https://doi.org/10.1007/s11151-023-09912-2