Abstract
Auction theory states that setting a non-trivial reservation price will generally reduce efficiency. However, new research has shown that reservation prices can have strong signaling effects on equilibrium outcomes. Using data from online P2P loan auctions, we show that the optimal reservation price varies nonlinearly in borrower quality (credit score and debt-to-income) as it balances the tradeoff between offering higher rates of return to attract bidders against potentially signaling low quality and causing the borrower to pay higher interest rates.
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Notes
This is unsurprising since the informational demands for computing optimal reservation prices are substantial (Bulow and Klemperer, 1996).
This format is similar to what is commonly used in Treasury auctions and multi-unit procurement auctions.
Surveys of P2P borrowers consistently find that most borrowers request very close to what they need to satisfy their current budgetary constraint.
To protect the member’s privacy, their true identity is never revealed.
Prosper deemphasized groups starting in 2011, but this feature still exists on the platform.
Lenders cannot cancel a bid after it has been submitted.
While duration can be between 3 and 14 days, 81% of listings are set for one week.
The loan is fully amortized in monthly payments.
This time period simply coincides with when the data was originally collected. Shortly after this period, Prosper changed the funding mechanism.
Experian credit bureau data were used to confirm this fact.
We average the CBOE Daily Volatility Index (VIX) over the duration of the auction to measure the mean value of volatility that existed while the auction was active.
We use Home Mortgage Disclosure Act (HDMA) data that contains information on virtually every real estate secured loan in the United States.
As of September 21, 2011, when the data were collected, 9099 of the listings had matured while the remaining 525 listings were still on-going in good standing.
Refer to 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 on Prosper 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.
For example, when we examine the interquartile range (IQR) in BMR across credit grade-DTI bins, the IQR values range from 4 percentage points (for credit grade AA and DTI < 15%) up to 17 percentage points (for credit grade E and DTI \(\ge\) 60%).
The cubic specification gives rise to very similar results.
For this regression table and those that follow, only key variables of interest are displayed. A full list of control variables is included in the footnote to each table.
Higher order polynomials and log were also tried, and various goodness of fit measures suggest that quadratic is the correct specification.
We define high DTI as \(\ge 60\%\) and low DTI as \(\le 15\%\) (which are conventional retail mortgage standards).
The probability of funding is concave and monotonic for AA and A borrowers, but it begins to decline for E/High at very high BMRs.
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Senney, G.T., Lhost, J.R. Balancing Attraction and Risk Revelation: The Optimal Reservation Price in Peer-to-Peer Loan Auctions. Rev Ind Organ 64, 289–314 (2024). https://doi.org/10.1007/s11151-023-09914-0
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DOI: https://doi.org/10.1007/s11151-023-09914-0