The Geography of Bidder Behavior in Peer-to-Peer Credit markets Garrett T Senne The Ohio State University, Department of Economics Original Draft: November 10, 2014 Current Draft: January 11, 2016 Abstract. Theoretical and empirical research on the traditional credit market finds strong evidence that investors and lenders are sensitive to their distance from the borrower, especially early on in a venture. This is due to the cost of information gathering and monitoring. However, new online platforms could overcome the geographical constraints on investing. Recent empirical work, across many types of crowdfunding, has found mixed results. In this paper, using transaction data from a peer-to-peer lending site, I find that local lenders tend to bid earlier, both chronologically and relatively to other bidders in the auction, and bid larger amounts than nonlocal lenders Additionally, local lenders are more informed in the sense that they are better able to evaluate the underlying risk of borrowers. This is demonstrated by the fact that they bid significantly higher interest rates on loans that ex-post default and lower rates on loans that ex-post pay back in full. Lastly, I develop a simple model of social learning with heterogeneous agents that provides testable predictions. My results are consistent with this model; a listing with more early local bidding activity will attract more lenders, leading to a higher probability of funding and a lower final interest rate, if funded. This work suggests that the behavioral differences between local and nonlocal lenders are driven mostly by informational frictions and not merely preferences. Local lenders are better informed because they have easier and cheaper access to information, and this asymmetry contributes to explaining why geographic-based frictions are still present and relevant in online lending markets Keywords: Geography, Peer-to-Peer Lending, Informational Frictions JEL Classification: DS2, D83, L10, L86 1. Introduction It is an established fact that most of the inefficiencies in the credit market are due to the existence of asymmetric information between lenders and borrowers( Stiglitz and Special thanks to Jason Blevins, Maryam Saeedi, and Stephen Cosslett for their assistance and guidance, Xiang Hui and Robert Munk for their comments and encouragements, and Efraim Berkovich for providing the data Electroniccopyavailableathttp://ssrn.com/abstract=2721756
Electronic copy available at: http://ssrn.com/abstract=2721756 The Geography of Bidder Behavior in Peer-to-Peer Credit Markets∗ Garrett T. Senney The Ohio State University, Department of Economics Original Draft: November 10, 2014 Current Draft: January 11, 2016 Abstract. Theoretical and empirical research on the traditional credit market finds strong evidence that investors and lenders are sensitive to their distance from the borrower, especially early on in a venture. This is due to the cost of information gathering and monitoring. However, new online platforms could overcome the geographical constraints on investing. Recent empirical work, across many types of crowdfunding, has found mixed results. In this paper, using transaction data from a peer-to-peer lending site, I find that local lenders tend to bid earlier, both chronologically and relatively to other bidders in the auction, and bid larger amounts than nonlocal lenders. Additionally, local lenders are more informed in the sense that they are better able to evaluate the underlying risk of borrowers. This is demonstrated by the fact that they bid significantly higher interest rates on loans that ex-post default and lower rates on loans that ex-post pay back in full. Lastly, I develop a simple model of social learning with heterogeneous agents that provides testable predictions. My results are consistent with this model; a listing with more early local bidding activity will attract more lenders, leading to a higher probability of funding and a lower final interest rate, if funded. This work suggests that the behavioral differences between local and nonlocal lenders are driven mostly by informational frictions and not merely preferences. Local lenders are better informed because they have easier and cheaper access to information, and this asymmetry contributes to explaining why geographic-based frictions are still present and relevant in online lending markets. Keywords: Geography, Peer-to-Peer Lending, Informational Frictions. JEL Classification: D82, D83, L10, L86. 1. Introduction It is an established fact that most of the inefficiencies in the credit market are due to the existence of asymmetric information between lenders and borrowers (Stiglitz and ∗Special thanks to Jason Blevins, Maryam Saeedi, and Stephen Cosslett for their assistance and guidance, Xiang Hui and Robert Munk for their comments and encouragements, and Efraim Berkovich for providing the data. 1
Weiss, 1981; Dell'Ariccia and Marquez, 2004). However, the modern financial ecosystem is evolving rapidly, as innovations and technological advancements are radically altering the delivery of financial services worldwide. Increasingly, through use of the internet, people have been able to interact with each other more intensively(sharing more information) and extensively(cheaper to develop larger networks). Empirical work on the economic quences of the internet consistently find two facts: the Internet can overcome g graphic isolation(Balasubramanian, 1998; Forman, Ghose, and Goldfarb, 2009; Choi and Bell, 2011), and search costs are lower online than compared to offline(Bakos, 1997; Baye, Gatii, Kattuman, and John, 2009). Utilizing these new and cheaper online connections and crowdfunding websites, individuals are starting to find novel ways to address and overcome the asymmetry in the credit market. In this paper, I explore the informational frictions, arising due to geography, that have traditionally plagued the offline credit market and evaluate the effectiveness of online peer-to-peer lending to address it. Peer-to-peer (henceforth P2P) lending is a mechanism for groups of investors to lend money directly to individual borrowers without using a bank as an intermediary. This arrangement creates the possibility that borrowers can obtain loans at lower interest rates than they would get on a credit card or a normal loan without collateral. Individual lenders get the opportunity to invest in short-duration assets with higher rates of return than would be available on certificates of deposit, bonds, or money market accounts, all due to the cost savings arising from removing the intermediary. P2P lending has been heralded as an online tool that has the potential to level the playing field in the credit market by providing access to financing to more people in a more approachable way. A potential blend of altruism and the allure of higher yield has attracted investors to lending money online, especially in the aftermath of the 2008 financial crisis, where interest rates and banks' willingness to lend have hit historic lows. Additionally, a survey performed by Zopa. com(a British P2P site) reports that, although most P2P loans are unsecured, borrowers feel a greater responsibility to repay a loan that has been created by individual people instead of a loan from a bank(Cortese, 2014). Compared to traditional banks, online lenders enjoy the advantage that online marketplaces currently face fewer regulatory constraints, albeit the Security Exchange Commission and the Treasury Department are deeply interested their activities. Moreover P2P sites are structured to facilitate faster transfers of capital to the borrowers, and are operated by internet-based companies, which have lower overhead costs than companies with physical locations. Therefore, these companies operate more profitably in a market traditionally viewed as fairly risky with low margins(Cowley, 2015). P2P lending, along with other forms of crowdfunding, has established itself as an emerging alternative source of financing for startups and people with particularly limited access to traditional means Electroniccopyavailableathttp://ssrn.com/abstract=2721756
Electronic copy available at: http://ssrn.com/abstract=2721756 Weiss, 1981; Dell’Ariccia and Marquez, 2004). However, the modern financial ecosystem is evolving rapidly, as innovations and technological advancements are radically altering the delivery of financial services worldwide. Increasingly, through use of the internet, people have been able to interact with each other more intensively (sharing more information) and extensively (cheaper to develop larger networks). Empirical work on the economic consequences of the internet consistently find two facts: the Internet can overcome geographic isolation (Balasubramanian, 1998; Forman, Ghose, and Goldfarb, 2009; Choi and Bell, 2011), and search costs are lower online than compared to offline (Bakos, 1997; Baye, Gatii, Kattuman, and John, 2009). Utilizing these new and cheaper online connections and crowdfunding websites, individuals are starting to find novel ways to address and overcome the asymmetry in the credit market. In this paper, I explore the informational frictions, arising due to geography, that have traditionally plagued the offline credit market and evaluate the effectiveness of online peer-to-peer lending to address it. Peer-to-peer (henceforth P2P) lending is a mechanism for groups of investors to lend money directly to individual borrowers without using a bank as an intermediary. This arrangement creates the possibility that borrowers can obtain loans at lower interest rates than they would get on a credit card or a normal loan without collateral. Individual lenders get the opportunity to invest in short-duration assets with higher rates of return than would be available on certificates of deposit, bonds, or money market accounts, all due to the cost savings arising from removing the intermediary. P2P lending has been heralded as an online tool that has the potential to level the playing field in the credit market by providing access to financing to more people in a more approachable way. A potential blend of altruism and the allure of higher yields has attracted investors to lending money online, especially in the aftermath of the 2008 financial crisis, where interest rates and banks’ willingness to lend have hit historic lows. Additionally, a survey performed by Zopa.com (a British P2P site) reports that, although most P2P loans are unsecured, borrowers feel a greater responsibility to repay a loan that has been created by individual people instead of a loan from a bank (Cortese, 2014). Compared to traditional banks, online lenders enjoy the advantage that online marketplaces currently face fewer regulatory constraints, albeit the Security Exchange Commission and the Treasury Department are deeply interested their activities. Moreover, P2P sites are structured to facilitate faster transfers of capital to the borrowers, and are operated by internet-based companies, which have lower overhead costs than companies with physical locations. Therefore, these companies operate more profitably in a market traditionally viewed as fairly risky with low margins (Cowley, 2015). P2P lending, along with other forms of crowdfunding, has established itself as an emerging alternative source of financing for startups and people with particularly limited access to traditional means 2
Using data from an American P2P lending site, I examine whether the internet has improved the efficiency of the credit market by loosening the geographic constraints on investing. Evidence for this potential reduction in the informational difference between local and nonlocal lenders should be observable in their bidding behavior. If the two types of lenders bid the same way when faced with similar investing options, it is most likely that the internet has removed the geography-based frictions. However, if local and nonlocal lenders behave differently, this might be explained-as pointed out in the empirical literature- by one of two possible channels: asymmetric information or preference. My analysis shows that lenders indeed behave differently based on geography and, while a local preference exists informational differences seem to be a major driver for this behavior Most of the scholarly work on P2P lending has focused on the determinants of a listing being funded and the determinants of the final interest rates. The consensus is that soft factors like demographic and network effects matter; however, they are second order in importance, after hard factors, like the verified financial information of income, debt, and credit score. Because P2P lending is still in its infancy, its full potential as an alternative or supplement to the traditional banking industry is still an open empirical question A long literature documents the importance of distance in social and economic behavior; famously the first law of geography states"all things are related but near things are more elated than far things, "(Tobler, 1970). Additionally, there is an extensive theoretical work on early investing and capital ventures that predicts that investors and lenders are sensitive to their distance from the borrower- especially true in the early stages of the venture when there is little to no observable history. This result is because the cost of gathering and processing information, as well as monitoring, are generally thought to increase as the distance between lender and borrower grows. Empirical evidence supports these finding Explicitly, recent research on angel investing and large-scale acquisition reports that most investors are located within a half day of travel of the entrepreneur that they funding The predicted informational asymmetry between potential local and nonlocal investors may derive from the informational opacity of startups and small firms. Potential investors IBelleflamme, Lambert, and Schwienbacher(2010); Schwienbacher and Larralde(2010): Mollick(2014) Herzenstein, Andrews, Dholakia, and Lyandres(2008); Berger and Gleisner(2009); Pope and Sydnor (2011); Ravina(2012); Duarte, Siegel, and Young(2012); Lin, Prabhala, and Viswanathan(2013) Klafft(2008b, a); Iyer, Khwaja, Luttmer, and Shue(2009); Weiss, Pelger, and Horsch(2010) Arthur(1986, 1988, 1990); David and Rosenbloom(1990); Krugman(1991a, b) t STribus(1970); Florida and Kennedy(1988); French and Poterba(1991); Florida and Smith(1988); Martin, Inley, and Turner(2002); Mason and Harrison(2002): Powell, Koput, Bowie, and Smith-Doerr(2002): Zook 6Sohl(1999); Sorenson and Stuart(2001); Wong, Bhatia, and Freeman(2009)
of financing.1 Using data from an American P2P lending site, I examine whether the internet has improved the efficiency of the credit market by loosening the geographic constraints on investing. Evidence for this potential reduction in the informational difference between local and nonlocal lenders should be observable in their bidding behavior. If the two types of lenders bid the same way when faced with similar investing options, it is most likely that the internet has removed the geography-based frictions. However, if local and nonlocal lenders behave differently, this might be explained– as pointed out in the empirical literature– by one of two possible channels: asymmetric information or preference. My analysis shows that lenders indeed behave differently based on geography and, while a local preference exists, informational differences seem to be a major driver for this behavior. Most of the scholarly work on P2P lending has focused on the determinants of a listing being funded and the determinants of the final interest rates. The consensus is that soft factors like demographic and network effects matter; however, they are second order in importance,2 after hard factors, like the verified financial information of income, debt, and credit score.3 Because P2P lending is still in its infancy, its full potential as an alternative or supplement to the traditional banking industry is still an open empirical question. A long literature documents the importance of distance in social and economic behavior; famously the first law of geography states "all things are related, but near things are more related than far things," (Tobler, 1970). Additionally, there is an extensive theoretical work on early investing and capital ventures that predicts that investors and lenders are sensitive to their distance from the borrower– especially true in the early stages of the venture when there is little to no observable history.4 This result is because the cost of gathering and processing information, as well as monitoring, are generally thought to increase as the distance between lender and borrower grows. Empirical evidence supports these findings.5 Explicitly, recent research on angel investing and large-scale acquisition reports that most investors are located within a half day of travel of the entrepreneur that they funding.6 The predicted informational asymmetry between potential local and nonlocal investors may derive from the informational opacity of startups and small firms. Potential investors 1Belleflamme, Lambert, and Schwienbacher (2010); Schwienbacher and Larralde (2010); Mollick (2014). 2Herzenstein, Andrews, Dholakia, and Lyandres (2008); Berger and Gleisner (2009); Pope and Sydnor (2011); Ravina (2012); Duarte, Siegel, and Young (2012); Lin, Prabhala, and Viswanathan (2013). 3Klafft (2008b,a); Iyer, Khwaja, Luttmer, and Shue (2009); Weiss, Pelger, and Horsch (2010). 4Arthur (1986, 1988, 1990); David and Rosenbloom (1990); Krugman (1991a,b). 5Tribus (1970); Florida and Kennedy (1988); French and Poterba (1991); Florida and Smith (1988); Martin, Sunley, and Turner (2002); Mason and Harrison (2002); Powell, Koput, Bowie, and Smith-Doerr (2002); Zook (2002); Mason (2007). 6Sohl (1999); Sorenson and Stuart (2001); Wong, Bhatia, and Freeman (2009). 3
llect a sizeable amount of infe ng rate. The geographic proximity of local lenders facilitates cheaper and easier access to this information. Anderson and van Wincoop(2004)find that informational frictions associated with geography, including search costs, communication barriers, and contracting cos contribute to reducing transactional efficiency when parties are physically separated from each other In addition to more standard channels of financing small to medium-sized firms alse rely on relationship-based lending to obtain funds. Relationship lending is when a financial nstitution uses a sustained relationship across multiple interactions with the potential borrower in addition to normal financial information they regularly gather in order to process a loan request. Many studies of relationship lending find that the physical distance between the banks and the borrowers has increased significantly, implying that banks from farther away are able to develop the needed relationship with the borrower that historically only local banks would have. However, other studies find no discernable change in the distance between lenders and borrowers. Elyasiani and Goldberg(2004)suggest that the observed mixed results are most likely caused by the fact that the technological developments that mitigate geographic-based frictions have only been adopted by a small share of banks engaging in relationship lending. As the adoption and use of these new tools become more widespread, it is thought that location should become a less important factor in obtaining financing The informal capital markets, alternative sources of financing outside of the traditional mainstream credit market, have not to date been the focus of much scholarly analysis However, it is generally accepted, as noted by Harrison, Mason, and Robson(2010), that they comprise a series of potentially overlapping local markets rather than one fully integrated national market. With the internet and e-commerce being omnipresent, it is important to examine whether this shift in the commercial paradigm coupled with new online tools has been strong enough to alter or even invalidate the theoretical predictions of the effects that geography has on investing. Recent empirical work has shown that the internet has the potential to allow individuals and firms to overcome many traditional barriers that have fettered offline markets by mitigatil research on online transactions finds that online platforms might reduce some, but not all of the distance-related frictions, 10 'Cyrnak and Hannan(2000); Wolken and Rohde(2000); Petersen and Rajan(2002) Degryse and Ongena(2003): Brevoort and Hannan(2003) Ratchford, Pan, and Shankar(2003); Brynjolfsson, Hu, and Rahman(2009 ); Goldfarb and Tucker(2011) Lendle, Olarreaga, Schropp, and vezina(2013). Blum and Goldfarb(2006); Hortacsu, Martinez-Jerez, and Douglas(2009); Agrawal, Catalini, and Goldfarb (2011); Lin and Viswanathan(2014)
collect a sizeable amount of information before deciding to invest and determining the rate. The geographic proximity of local lenders facilitates cheaper and easier access to this information. Anderson and van Wincoop (2004) find that informational frictions associated with geography, including search costs, communication barriers, and contracting costs, contribute to reducing transactional efficiency when parties are physically separated from each other. In addition to more standard channels of financing, small to medium-sized firms also rely on relationship-based lending to obtain funds. Relationship lending is when a financial institution uses a sustained relationship across multiple interactions with the potential borrower in addition to normal financial information they regularly gather in order to process a loan request. Many studies of relationship lending find that the physical distance between the banks and the borrowers has increased significantly, implying that banks from farther away are able to develop the needed relationship with the borrower that historically only local banks would have.7 However, other studies find no discernable change in the distance between lenders and borrowers.8 Elyasiani and Goldberg (2004) suggest that the observed mixed results are most likely caused by the fact that the technological developments that mitigate geographic-based frictions have only been adopted by a small share of banks engaging in relationship lending. As the adoption and use of these new tools become more widespread, it is thought that location should become a less important factor in obtaining financing. The informal capital markets, alternative sources of financing outside of the traditional mainstream credit market, have not to date been the focus of much scholarly analysis. However, it is generally accepted, as noted by Harrison, Mason, and Robson (2010), that they comprise a series of potentially overlapping local markets rather than one fully integrated national market. With the internet and e-commerce being omnipresent, it is important to examine whether this shift in the commercial paradigm coupled with new online tools has been strong enough to alter or even invalidate the theoretical predictions of the effects that geography has on investing. Recent empirical work has shown that the internet has the potential to allow individuals and firms to overcome many traditional barriers that have fettered offline markets by mitigating some geographic frictions.9 Other research on online transactions finds that online platforms might reduce some, but not all, of the distance-related frictions.10 7Cyrnak and Hannan (2000); Wolken and Rohde (2000); Petersen and Rajan (2002). 8Degryse and Ongena (2003); Brevoort and Hannan (2003). 9Ratchford, Pan, and Shankar (2003); Brynjolfsson, Hu, and Rahman (2009); Goldfarb and Tucker (2011); Lendle, Olarreaga, Schropp, and Vézina (2013). 10Blum and Goldfarb (2006); Hortaçsu, Martínez-Jerez, and Douglas (2009); Agrawal, Catalini, and Goldfarb (2011); Lin and Viswanathan (2014). 4
Although technology makes it easier and cheaper for individuals to gather and process information about market conditions it is unclear if informational frictions like search costs persist in the online marketplace. I examine the ongoing empirical question of whether geographic frictions exist on the online credit market, and if so what factors contribute to their existence. My analysis centers on how geography affects lender behavior in P2P lending markets by focusing on the issue of whether the new P2P lending sites can loosen the geographic constraints on investing. More explicitly, I ask relative to nonlocal lenders (1)do local lenders bid different amounts, (2)do local lenders evaluate and price the risl of the listings differently, and(3)do local lenders tend to bid at different times during he auction. Additionally, if distance-related frictions do exist, (4)does the difference in behavior arise from informational asymmetry or simply a preference on the part of local lenders, and lastly, (5)how does the presence of local lenders in the market affect other lenders'decisions to enter and their behavior after entering Using bid and listing level transaction data from lending auctions on Prosper. com, I stimate that local lenders tend to bid earlier and larger amounts than nonlocal lenders Furthermore, local lenders also seem better able to evaluate the riskiness of loan requests They tend to ex-ante bid larger interest rates when the loan ex-post defaults and less when the loan ex-post pays back in full. Reconciling theory and previous empirical work, I conclude from my results that there exists a local preference in the demand for loans, but local lenders' interest rate bids demonstrate support for the informational-based explanation of the observed behavioral differences between lenders 2. Overview of Peer-to-Peer Lending Crowdfunding, which P2P is a part of, is a process through which an individual or firm attempts to obtain financing by soliciting for usually small contributions from a large number of online investors. These new financing platforms are the result of a social movement that arose in reaction to the emergence of new technologies that are enabling new and cheaper ways of forming social networks(Adams and Ramos, 2010) The crowdfunding market has quickly grown from its creation in the early 2000s and is predicted to reach $34.4 billion globally in 2015 (Massolution. com, 2015). The movement has bifurcated into donation and financial(debt/equity) based sites both geared towards different market segments. The first American online platform to facilitate debt transactions launched in late 2005. The two current largest domestic players, Lending Club and Prosper, control about 98% of the American P2P credit market. Combined, they issued almost $5 billion in loans in 2014 and are predicted to issue about $8 billion in 2015(NSR Invest 2015). In this paper I focus on the P2P lending site Prosper. com, which has been called the
Although technology makes it easier and cheaper for individuals to gather and process information about market conditions, it is unclear if informational frictions like search costs persist in the online marketplace. I examine the ongoing empirical question of whether geographic frictions exist on the online credit market, and if so what factors contribute to their existence. My analysis centers on how geography affects lender behavior in P2P lending markets by focusing on the issue of whether the new P2P lending sites can loosen the geographic constraints on investing. More explicitly, I ask relative to nonlocal lenders: (1) do local lenders bid different amounts, (2) do local lenders evaluate and price the risk of the listings differently, and (3) do local lenders tend to bid at different times during the auction. Additionally, if distance-related frictions do exist, (4) does the difference in behavior arise from informational asymmetry or simply a preference on the part of local lenders, and lastly, (5) how does the presence of local lenders in the market affect other lenders’ decisions to enter and their behavior after entering. Using bid and listing level transaction data from lending auctions on Prosper.com, I estimate that local lenders tend to bid earlier and larger amounts than nonlocal lenders. Furthermore, local lenders also seem better able to evaluate the riskiness of loan requests. They tend to ex-ante bid larger interest rates when the loan ex-post defaults and less when the loan ex-post pays back in full. Reconciling theory and previous empirical work, I conclude from my results that there exists a local preference in the demand for loans, but local lenders’ interest rate bids demonstrate support for the informational-based explanation of the observed behavioral differences between lenders. 2. Overview of Peer-to-Peer Lending Crowdfunding, which P2P is a part of, is a process through which an individual or firm attempts to obtain financing by soliciting for usually small contributions from a large number of online investors. These new financing platforms are the result of a social movement that arose in reaction to the emergence of new technologies that are enabling new and cheaper ways of forming social networks (Adams and Ramos, 2010). The crowdfunding market has quickly grown from its creation in the early 2000’s and is predicted to reach $34.4 billion globally in 2015 (Massolution.com, 2015). The movement has bifurcated into donation and financial (debt/equity) based sites both geared towards different market segments. The first American online platform to facilitate debt transactions launched in late 2005. The two current largest domestic players, Lending Club and Prosper, control about 98% of the American P2P credit market. Combined, they issued almost $5 billion in loans in 2014 and are predicted to issue about $8 billion in 2015 (NSR Invest, 2015). In this paper I focus on the P2P lending site Prosper.com, which has been called the 5
Prosper provides unsecured, 36-month, fixed-rate personal loans ranging from $1,000 to $25,000. Borrowers and lenders must be legal U.S. residents with a valid domestic address and bank account. The members true identities, addresses, and other contact information are never publicly disclosed by the site. For privacy, the borrower is prohibited from releasing that information to lenders. However, the borrower's state of residence is displayed on the listing Borrowers create a listing requesting a loan for a specified amount and a maximum interest rate they are willing to accept(borrower's max rate). They set the duration of the listing(up to 14 days), specify the category of use(Debt Consolidation, Home Improvement, Business, etc. ) write a brief description, and, optionally, include an image of themselves. Each listing displays financial information about the borrower including debt-to-income ratio, income, occupation, employment status, credit grade(40-point bands of the borrowers Experian credit score), total credit lines open, number of credit inquiries in the last six months, current delinquencies, and home ownership status. Prosper posts aggregate historical data on the default and interest rates grouped by credit grade After some institutional and legal restructuring in 2008, Prosper started to collaborate with a national bank, who became the legal originator for all Prosper loans. This arrange ment makes it possible for the site to utilize a 1978 Supreme Court decision allowing all Prosper borrowers to avoid their individual state's usury law and face a uniform fixed Lenders(bidders)search through the listings for loan requests that they want to bid n,see Figure 1. The funding mechanism is a descending price uniform share auct Lenders'bids are made up of two components: amount of bid($50 minimum) and the lowest interest rate that they are willing to accept. The bidding process is proxy bidding, similar to that on eBay. Each bid is considered independent, so a lender may bid multiple times with potentially different interest rates. These price-quantity pairs form the supply curve of available funds for this loan request. The auction is partially open; lenders always see the number of bids and the quantity of money pledged of each bid. The interest rate submitted by the lenders is only shown for losing bids; accordingly, before the loan is fully funded, lenders only see the borrower's maximum rate Successfully submitted bids cannot be rescinded. Given that this platform is a collection of individualized markets for each individual loan each market clears when the amount of pledged funds is at least as large as the requested amount, with the interest rate The market description in this section refers to market conditions and mechanisms that were in place when the data was collected. Some policy and regulation changes have since occurred, making the current market slightly different. 6
eBay of loans.11 Prosper provides unsecured, 36-month, fixed-rate personal loans ranging from $1,000 to $25,000. Borrowers and lenders must be legal U.S. residents with a valid domestic address and bank account. The members’ true identities, addresses, and other contact information are never publicly disclosed by the site. For privacy, the borrower is prohibited from releasing that information to lenders. However, the borrower’s state of residence is displayed on the listing. Borrowers create a listing requesting a loan for a specified amount and a maximum interest rate they are willing to accept (borrower’s max rate). They set the duration of the listing (up to 14 days), specify the category of use (Debt Consolidation, Home Improvement, Business, etc.), write a brief description, and, optionally, include an image of themselves. Each listing displays financial information about the borrower including debt-to-income ratio, income, occupation, employment status, credit grade (40-point bands of the borrower’s Experian credit score), total credit lines open, number of credit inquiries in the last six months, current delinquencies, and home ownership status. Prosper posts aggregate historical data on the default and interest rates grouped by credit grade. After some institutional and legal restructuring in 2008, Prosper started to collaborate with a national bank, who became the legal originator for all Prosper loans. This arrangement makes it possible for the site to utilize a 1978 Supreme Court decision allowing all Prosper borrowers to avoid their individual state’s usury law and face a uniform fixed legal maximum borrower rate of 36%. Lenders (bidders) search through the listings for loan requests that they want to bid on, see Figure 1. The funding mechanism is a descending price uniform share auction. Lenders’ bids are made up of two components: amount of bid ($50 minimum) and the lowest interest rate that they are willing to accept. The bidding process is proxy bidding, similar to that on eBay. Each bid is considered independent, so a lender may bid multiple times with potentially different interest rates. These price-quantity pairs form the supply curve of available funds for this loan request. The auction is partially open; lenders always see the number of bids and the quantity of money pledged of each bid. The interest rate submitted by the lenders is only shown for losing bids; accordingly, before the loan is fully funded, lenders only see the borrower’s maximum rate. Successfully submitted bids cannot be rescinded. Given that this platform is a collection of individualized markets for each individual loan, each market clears when the amount of pledged funds is at least as large as the requested amount, with the interest rate 11The market description in this section refers to market conditions and mechanisms that were in place when the data was collected. Some policy and regulation changes have since occurred, making the current market slightly different. 6
determined by the auction. In the case of ties, bids placed earlier take precedence over later bids. When the auction closes, the bids are sorted by bid interest rate. The bids with the lowest rates are bundled until the total loan amount has been reached and are then combined into a single loan. Each winning lender receives the same interest rate, which is determined by the marginal losing or last winning bid, depending on the auction Winning bids are either fully or partially participating in the loan; a partially participating bid means that the lender is allocated a smaller share of loan than his or her quantity bid If the loan request is not fully funded by the listings close date, the listing is closed and dismissed. Successful loan requests get further review by the site to initiate the needed legal documentation for the loan to be originated. a borrower who defaults on his or her Prosper loan is barred from using the site again 3. Data This paper uses publicly released data containing all loan requests, with their accompa nying bids, using the open auction format posted by borrowers with FICO credit scores greater than or equal to 560 that were active from January to October 2008. Prior to late 2008, any legal resident of the United States could be a lender on the site and legal residents of every state, except South Dakota, could be borrowers. During the period in my sample, 42, 657 loan requests and 2,022,910 bids were posted. A total of 9, 624 listings and 1,688, 531 bids were for listings that were fully funded. The publicly available characteristics of the borrowers are: debt-to-income ratio(henceforth DIR, the variable is top coded at 10.1), credit grade, homeowner status, whether the borrower is in a Prosper group, and state of residence Klafft(2008b)confirm that the rules that apply in the traditional banking system apply to P2P lending as well; credit grade and the dir are the two most important hard financial variables in determining the financial outcomes. The site provides members the ability to join groups that are designed to develop and foster a community of lenders and borrowers akin to what occurs in relationship lending, contract enforcement via reputation, and peer effects. Agrawal et al. (2011)and Lin et al. (2013)find that social connections seem to reduce market frictions. Therefore, i collect data on these on-site social networks. In the loan-level analysis, the In group variable indicates whether a borrower is in a Prosper group; and in the bid-level analysis, the In group variable indicate whether that particular lender is in the borrower's specific In addition to the variables collected directly from Prosper, I construct two variables Total Competition and Credit grade Competition -to measure the competition that each 12This Prosper group never quite took out and are not widely used
determined by the auction. In the case of ties, bids placed earlier take precedence over later bids. When the auction closes, the bids are sorted by bid interest rate. The bids with the lowest rates are bundled until the total loan amount has been reached and are then combined into a single loan. Each winning lender receives the same interest rate, which is determined by the marginal losing or last winning bid, depending on the auction. Winning bids are either fully or partially participating in the loan; a partially participating bid means that the lender is allocated a smaller share of loan than his or her quantity bid. If the loan request is not fully funded by the listing’s close date, the listing is closed and dismissed. Successful loan requests get further review by the site to initiate the needed legal documentation for the loan to be originated. A borrower who defaults on his or her Prosper loan is barred from using the site again. 3. Data This paper uses publicly released data containing all loan requests, with their accompanying bids, using the open auction format posted by borrowers with FICO credit scores greater than or equal to 560 that were active from January to October 2008. Prior to late 2008, any legal resident of the United States could be a lender on the site and legal residents of every state, except South Dakota, could be borrowers. During the period in my sample, 42,657 loan requests and 2,022,910 bids were posted. A total of 9,624 listings and 1,688,531 bids were for listings that were fully funded. The publicly available characteristics of the borrowers are: debt-to-income ratio (henceforth DIR, the variable is top coded at 10.1), credit grade, homeowner status, whether the borrower is in a Prosper group, and state of residence. Klafft (2008b) confirm that the rules that apply in the traditional banking system apply to P2P lending as well; credit grade and the DIR are the two most important hard financial variables in determining the financial outcomes. The site provides members the ability to join groups that are designed to develop and foster a community of lenders and borrowers akin to what occurs in relationship lending, contract enforcement via reputation, and peer effects.12 Agrawal et al. (2011) and Lin et al. (2013) find that social connections seem to reduce market frictions. Therefore, I collect data on these on-site social networks. In the loan-level analysis, the In Group variable indicates whether a borrower is in a Prosper group; and in the bid-level analysis, the In Group variable indicate whether that particular lender is in the borrower’s specific Prosper group. In addition to the variables collected directly from Prosper, I construct two variables –Total Competition and Credit Grade Competition –to measure the competition that each 12This Prosper group never quite took out and are not widely used. 7
listing faces. The competition measures are the number of current listings that are active for at least one full day at the same time that the particular listing is also active. Tota Competition is the number of total listings, regardless of credit grade, while Credit Grade ompetition is the number of listings from the same credit grade. At the bid level, I recreate the auction to determine the current standing interest rate, money pledged, and bid count that exist in the auction at the exact moment that each lender bids on a particular listing. This process allows me to observe the current state of the listing as each lender sees it before he or she bids Table 1 shows the summary statistics of the borrower's characteristics for all listings The maximum listing request amount is $25,000, but most listings request significantly less(around $6,000-9,000). Intuitively, the mean request amount increases as the credit grade improves as more credit-worthy borrowers have the ability to support larger loans Although borrowers can select the duration of their listing(3-14 days), around 80% of listings are active for one week. The borrower's max rate is the reservation price for the auction and, as one would expect, it tends to increase as the credit grade worsens However, across all credit grades, over 22% of borrowers select max rates greater than or equal to 35%. Table 2 displays that breakdown of the listings and completed loans by credit grade; over half of all of the listing requests are in the bottom credit grades. The simple funding rate decreases strictly monotonically as the credit grade worsens I also collect data on the ex-post loan outcomes, paid back or defaulted for the listing that are actually originated. However, I do not observe when in the cycle a borrower defaulted or how much of the loans principle was paid back, but only the discrete outcomes of any kind of default or not. The loan outcome data comes from a different Prosper data release which contains only outcomes of the loans that were settled by early September 2011. Out of the 9, 624 completed listings in my sample, I am able to match 9, 099 of them to their final outcomes in 2011. The last column of Table 2 displays the simple default rate by credit grade for this sample. As one would expect, the default rate has a clear positive trend as the credit grade worsens. While these default rates seem ather, it is important to recall that this is a measure of any kind of default. However, these magnitudes are well in line with national residential mortgage delinquency rates from the same time period. 13 The top half of Table 3 presents the frequency distribution of the different categorie e by credit grade. Regardless of credit grade, the three most commonly chosen categories are debt consolidation, business, and personal. The bottom half of Table 3 displays the ompletion rate of loan requests by credit grade and category of use. while the Other https://www.richmondfed.org_/-/media/richmondfedorg/banking/markets-trends_-and statistics/trends/pdf /delinquency_and- foreclosure_rates. pdf
listing faces. The competition measures are the number of current listings that are active for at least one full day at the same time that the particular listing is also active. Total Competition is the number of total listings, regardless of credit grade, while Credit Grade Competition is the number of listings from the same credit grade. At the bid level, I recreate the auction to determine the current standing interest rate, money pledged, and bid count that exist in the auction at the exact moment that each lender bids on a particular listing. This process allows me to observe the current state of the listing as each lender sees it before he or she bids. Table 1 shows the summary statistics of the borrower’s characteristics for all listings. The maximum listing request amount is $25,000, but most listings request significantly less (around $6,000–9,000). Intuitively, the mean request amount increases as the credit grade improves as more credit-worthy borrowers have the ability to support larger loans. Although borrowers can select the duration of their listing (3–14 days), around 80% of listings are active for one week. The borrower’s max rate is the reservation price for the auction and, as one would expect, it tends to increase as the credit grade worsens. However, across all credit grades, over 22% of borrowers select max rates greater than or equal to 35%. Table 2 displays that breakdown of the listings and completed loans by credit grade; over half of all of the listing requests are in the bottom credit grades. The simple funding rate decreases strictly monotonically as the credit grade worsens. I also collect data on the ex-post loan outcomes, paid back or defaulted, for the listings that are actually originated. However, I do not observe when in the cycle a borrower defaulted or how much of the loan’s principle was paid back, but only the discrete outcomes of any kind of default or not. The loan outcome data comes from a different Prosper data release, which contains only outcomes of the loans that were settled by early September 2011. Out of the 9,624 completed listings in my sample, I am able to match 9,099 of them to their final outcomes in 2011. The last column of Table 2 displays the simple default rate by credit grade for this sample. As one would expect, the default rate has a clear positive trend as the credit grade worsens. While these default rates seem rather, it is important to recall that this is a measure of any kind of default. However, these magnitudes are well in line with national residential mortgage delinquency rates from the same time period.13 The top half of Table 3 presents the frequency distribution of the different categories of use by credit grade. Regardless of credit grade, the three most commonly chosen categories are debt consolidation, business, and personal. The bottom half of Table 3 displays the completion rate of loan requests by credit grade and category of use. While the Other 13Source: https://www.richmondfed.org/-/media/richmondfedorg/banking/markets_trends_and_ statistics/trends/pdf/delinquency_and_foreclosure_rates.pdf 8
category is the fourth most common request type, it has the highest simple completion rate across all credit grades. It is well established that business loan requests have noticeably more difficulty being fully funded on P2P lending sites than other types of crowdfunding, especially for lower credit grades(Lin and Viswanathan, 2014). 4 The sizeable share of listings that are business loan requests across all credit grade is a contributing factor in explaining the rather low completion rate for the bottom credit grades Table 4 presents the descriptive statistics of the bid amounts and bid interest rates, grouped by credit grade. The median bid amount, regardless of credit grade, is the minimum bid (S50). This result is in-line with previous Prosper research that most lenders tend to diversify across listings by pledging small amounts in any one particular listing The mean bid amount is non-monotonic across credit grades; lenders in the better credit grades tend to be bid larger amounts, but bidding in E listings has the largest mean However, when bid amount is viewed as a share of the loan request amount, the mean and median become significantly closer to being monotonically increasing as the credit grade worsens. This is a function of the loan request amounts generally becoming smaller as the credit grade worsens. One immediate question that might arise, given the size of an individual bid, is why a lender would invest in this unsecured market. It has been observed that most lenders commit to invest around $50-100 across dozens of loans Aggregated, a portfolio of an individual lender on a P2P lending site resembles a new asset lass, different from the traditional ones. If diversified correctly, they can offer lenders returns that do not directly follow the motions of stocks and bonds (Lieber, 2011) The nature of the auction mechanism does not allow me to observe the bid interest winning bids. Similarly to how eBay operates, the bid are displayed for losing bids, while only bid amount is shown for winning bids. The current standing interest rate is als hich is either the borrower's max rate o interest rate of the first losing bid. The final interest rate sets an upper bound on what the actually bid interest rates may be for the winning bids. Unless otherwise noted, following Bajari and Hortassu(2003)and the rest of the literature, I assume that winning bids equal the final interest rate. Not surprisingly, the mean and median bid interest rate increase as the credit grade worsens. Additionally, the amount of variation in bid interest rates appears to increase as the credit grade worsens(the correlation between credit grade and standard deviation of bid interest rate is 0. 833) a recent industry survey, it was found that 22% of all funds obtained by startups from crowdfunding sites came via debt-based platforms(Massolution. com, 2013)
category is the fourth most common request type, it has the highest simple completion rate across all credit grades. It is well established that business loan requests have noticeably more difficulty being fully funded on P2P lending sites than other types of crowdfunding, especially for lower credit grades (Lin and Viswanathan, 2014).14 The sizeable share of listings that are business loan requests across all credit grade is a contributing factor in explaining the rather low completion rate for the bottom credit grades. Table 4 presents the descriptive statistics of the bid amounts and bid interest rates, grouped by credit grade. The median bid amount, regardless of credit grade, is the minimum bid ($50). This result is in-line with previous Prosper research that most lenders tend to diversify across listings by pledging small amounts in any one particular listing. The mean bid amount is non-monotonic across credit grades; lenders in the better credit grades tend to be bid larger amounts, but bidding in E listings has the largest mean. However, when bid amount is viewed as a share of the loan request amount, the mean and median become significantly closer to being monotonically increasing as the credit grade worsens. This is a function of the loan request amounts generally becoming smaller as the credit grade worsens. One immediate question that might arise, given the size of an individual bid, is why a lender would invest in this unsecured market. It has been observed that most lenders commit to invest around $50–100 across dozens of loans. Aggregated, a portfolio of an individual lender on a P2P lending site resembles a new asset class, different from the traditional ones. If diversified correctly, they can offer lenders returns that do not directly follow the motions of stocks and bonds (Lieber, 2011). The nature of the auction mechanism does not allow me to observe the bid interest rates for winning bids. Similarly to how eBay operates, the interest rate and amount of a bid are displayed for losing bids, while only bid amount is shown for winning bids. The current standing interest rate is also shown, which is either the borrower’s max rate or interest rate of the first losing bid. The final interest rate sets an upper bound on what the actually bid interest rates may be for the winning bids. Unless otherwise noted, following Bajari and Hortaçsu (2003) and the rest of the literature, I assume that winning bids equal the final interest rate. Not surprisingly, the mean and median bid interest rate increase as the credit grade worsens. Additionally, the amount of variation in bid interest rates appears to increase as the credit grade worsens (the correlation between credit grade and standard deviation of bid interest rate is 0.833). 14In a recent industry survey, it was found that 22% of all funds obtained by startups from crowdfunding sites came via debt-based platforms (Massolution.com, 2013). 9
4. Empirical analysis Although existing theory states that distance between investors and borrowers is important, recent empirical work has been inconclusive on this issue. Intuitively, since the internet makes it cheaper and easier to connect and share information with more people, online platforms could reduce informational frictions and improve the efficiency of the credit market. Additionally, several features of this market make the presence of geography- related frictions less plausible: loans are unsecured, lenders have little legal recourse other than the standard collection process and reporting the loan default to all credit reporting bureaus. These constraints minimize the ability of lenders to individually monitor and nforce the contract. Therefore, physical proximity should be less important online as compared to offline lending. Moreover, given this is an online market, participants have to possess at least a minimum level of computer competency. Therefore, it is highly probable that these lenders have the ability to research general local conditions like population, demographics, median household income, unemployment rate, and housing starts the above, it has yet to be determined if there exist a meaningful ful differenc in lender behavior based on geography. If the differences are driven strictly by information, hen these P2P lending sites might be able to eliminate these geographic frictions, and the behavior of local and nonlocal lenders should be observationally equivalent. However, if there are differences between behavior based upon the location of the lender relative to the borrower, it might be caused by two different mechanisms. One channel, as predicted by theory, is an informational asymmetry story where distance-related frictions still matter The other channel is a preference story: local lenders are not any better informed than nonlocal lenders, but they simply prefer local projects Following the literature on online markets(Wolf, 2000; Hillberry and Hummels, 2003; Hortacsu et al. 2009), I define localness to be when the lender and the borrower reside in the same state. Admittedly, a smaller unit measure is preferred; however, Prosper does not require individuals to publicly post their city, and they actually discourage it to prevent borrowers from personally identifying themselves. If the actual effect of information asymmetry is limited to a smaller physical proximity, then my state definition of localness is counting a significant amount of nonlocal lenders as local. Therefore, I am making the two groups of lenders more similar and weakening the potential differences that I can measure. Thus this data limitation mean that my estimates are actually a lower bound on the true effect of localness. As a robustness check, I also run my analysis using the smaller, restricted sample where local status is determined by the lender living within the same city as the borrower. The results are qualitatively the same but suffer from power issues due to the small sample size; thus for brevity, the tables can be found in the appendix
4. Empirical Analysis Although existing theory states that distance between investors and borrowers is important, recent empirical work has been inconclusive on this issue. Intuitively, since the internet makes it cheaper and easier to connect and share information with more people, online platforms could reduce informational frictions and improve the efficiency of the credit market. Additionally, several features of this market make the presence of geographyrelated frictions less plausible: loans are unsecured, lenders have little legal recourse other than the standard collection process and reporting the loan default to all credit reporting bureaus. These constraints minimize the ability of lenders to individually monitor and enforce the contract. Therefore, physical proximity should be less important online as compared to offline lending. Moreover, given this is an online market, participants have to possess at least a minimum level of computer competency. Therefore, it is highly probable that these lenders have the ability to research general local conditions like population, demographics, median household income, unemployment rate, and housing starts. Considering the above, it has yet to be determined if there exist a meaningful difference in lender behavior based on geography. If the differences are driven strictly by information, then these P2P lending sites might be able to eliminate these geographic frictions, and the behavior of local and nonlocal lenders should be observationally equivalent. However, if there are differences between behavior based upon the location of the lender relative to the borrower, it might be caused by two different mechanisms. One channel, as predicted by theory, is an informational asymmetry story where distance-related frictions still matter. The other channel is a preference story: local lenders are not any better informed than nonlocal lenders, but they simply prefer local projects. Following the literature on online markets (Wolf, 2000; Hillberry and Hummels, 2003; Hortaçsu et al., 2009), I define localness to be when the lender and the borrower reside in the same state. Admittedly, a smaller unit measure is preferred; however, Prosper does not require individuals to publicly post their city, and they actually discourage it to prevent borrowers from personally identifying themselves. If the actual effect of information asymmetry is limited to a smaller physical proximity, then my state definition of localness is counting a significant amount of nonlocal lenders as local. Therefore, I am making the two groups of lenders more similar and weakening the potential differences that I can measure. Thus this data limitation mean that my estimates are actually a lower bound on the true effect of localness. As a robustness check, I also run my analysis using the smaller, restricted sample where local status is determined by the lender living within the same city as the borrower. The results are qualitatively the same but suffer from power issues due to the small sample size; thus for brevity, the tables can be found in the appendix. 10