The Borrowing Cost of Peer-to-peer Lending in China: An ...

The Borrowing Cost of Peer-to-peer Lending in China:

An Empirical Study

University of Amsterdam

Economics MSc

Track Industrial Organization, Regulation and Competition Policy

Master Thesis

Name: Zhang Yiming

Student number: 10652108

Student email: [email protected]

Supervisor: He Simin

Master thesis The borrowing cost of peer-to-peer lending in China: an empirical study Zhang Yiming

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Abstract

This paper studies the borrowing costs in the peer-to-peer (P2P) lending market in China. Different modes of P2P platforms are identified, and the fee schedules vary among them. The distinction between the actual borrowing costs and the interest rates posted online is emphasized, because large differences are observed. The main reason is that there is not a well-established national credit scoring system available in China, and P2P platforms have to conduct most parts of credit grading and provide some kinds of safeguard from lenders. Data from two P2P platforms is analyzed and the results show that hard information (credit grade, financial conditions, etc.) is given more weight while the effect soft information (gender, loan purpose, etc.) is rather ambiguous in the determination of borrowing costs. Key words: P2P, credit grade, borrowing costs

I would like to thank my supervisor, He Simin, from University of Amsterdam, for her comments and assistance.


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Table of Contents 1 Introduction ..................................................................................................................... 3

2 Literature review ............................................................................................................. 4

3 Background: P2P lending in China ............................................................................... 8

3.1. Market overview ....................................................................................................... 8

3.2. Operation modes of P2P platforms in China ......................................................... 8

3.2.1. Basic P2P modes in Western countries ........................................................... 8

3.2.2. P2P modes in China ........................................................................................ 11

3.2.3. More suitable modes in China ....................................................................... 13

4. Data and Methodology .................................................................................................. 15

4.1. Data sample ............................................................................................................. 15

4.2. Methodology ........................................................................................................... 18

5. Analysis ........................................................................................................................... 20

5.1. Summary statistics ................................................................................................. 20

5.2. Regression analysis ................................................................................................. 24

5.2.1. Anaylsis of the pooled sample ........................................................................ 25

5.2.2. Anaylsis of Renrendai .................................................................................... 27

5.2.3. Anaylsis of Kaikaidai...................................................................................... 29

5.2.4. Anaylsis of Kaikaidai’s default rate .............................................................. 31

5.3. Discussion ................................................................................................................ 32

6. Conclusion ...................................................................................................................... 34

7. Reference ........................................................................................................................ 36


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1 Introduction Peer-to-peer (P2P) lending has emerged as one of the most notable financial innovations in recent years. In this new mode of finance, borrowers post their loan requests on P2P platforms and lenders can choose to fund them after judging their information disclosed online. Those who are in urgent need of money would find P2P lending extremely convenient: on the British platform Zopa1, for example, borrowers can receive loans within two working days if everything goes smoothly. With the help of P2P platforms, lenders and borrowers can match each other without traditional financial intermediaries such as banks. Not only does it provide extra source of finance for borrowers, but it also creates a new way of investment for lenders. A major P2P platform in the US, Lending Club, is planning its process of going public recently. This news has made people dream of the prosperity of this new area. In the wake of the global financial crisis, as banks are extremely careful about making loans, peer-to-peer lending has seen explosive growth as an easier way to acquire small loans. This is just the case in China. Hundreds of platforms have been launched which provide liquidity to those who are turned down by banks. Now that the financial markets in China are not as mature as in western countries, it would be interesting to see how P2P lending, which is only ten years from its birth, has developed in China. This paper looks into the Chinese P2P lending market, with an emphasis on the financing costs of borrowers. Different modes of P2P platforms are identified based on what kind of safeguard they provide to lenders and how they approach borrowers. Those platforms tend to have varied fee schedules. An interesting phenomenon is observed that the actual borrowing costs of peer-to-peer lending are much higher than the interest rates posted online, which suggests that the P2P platforms in China are acting more than information intermediaries. Data manually collected from two P2P platforms is analyzed and it turns out that the determination of borrowing costs is different from the determination of interest rates. Furthermore, it is the hard information that exerts more influence on the borrowing costs, and the role of soft information is still not clearly understood by P2P platforms in China. The structure of the paper is as follows: the next section reviews relevant literature on P2P lending; section 3 introduces the basic information about P2P lending in China and distinguishes several different operation modes of P2P platforms; section 4 introduces the manually collected dataset; section 5 analyzes the data, with the emphasis on the distinctions between the two platforms as well as the difference between borrowing costs and interest rates. Finally, section 6 concludes. 1 www.zopa.com


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2 Literature review When it comes to an innovative financial mode such as peer-to-peer lending, one of the first question to ask is: what role does it play in the current financial system? Berger and Gleisner (2009) studied the self-formed lending groups on the platform Prosper1. They found that the group leaders act as financial intermediaries by filtering out potential borrowers with lower credit scores, so that they reduce the information asymmetry prevalent in the electronic marketplace. Recommending loan listing by group leaders significantly increases the average borrowers' credit conditions and reduces the interest rate spread. Weiss et al. (2010) presented empirical evidence that P2P platforms are successful in limiting adverse selection from bad borrowers. Their results show that P2P lending platforms can act as direct financial intermediaries and reduce information asymmetry via the screening of potential borrowers. Khwaja et al. (2013) found that lenders on the online peer-to-peer lending market can effectively use soft information, and such information is more important in screening out borrowers with lower credit scores. Higher dependence on soft information enables peer-to-peer markets to complement traditional sources of finance and improve access to credit. When the role of peer-to-peer lending is clear, the focus of mainstream research turns to the determinants of P2P lending, e.g., what affects the funding rate (probability of being fully funded), interest rate, and default rate of a certain loan listing? The financial conditions of borrowers are among the most important determinants. Herzenstein et al. (2008) found that borrowers with a credit rating of AA or A (rating with the lowest risk assigned by the platform Prosper, based on individual credit history) are almost ten times as likely to be funded for a loan as borrowers with HR rating (representing the highest risk). Lin et al. (2009) provided further evidence that borrowers with lower credit grades are less likely to get the loan successfully. Furthermore, Klafft (2008) showed that the borrower’s credit rating and debt-to-income ratio have the greatest impact on the interest rate of the loan, and this is supported by Collier and Hampshire (2010). In addition, Puro et al. (2010) discovered that the loan amount requested have a negative impact on the funding rate and interest rate of a listing. But the story does not end here, researchers keep looking for other factors that may play a role in peer-to-peer lending. The research on the effect of soft information in 1 www.prosper.com


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P2P lending market is quite abundant. Information such as race, gender, friendship networks, etc. and other disclosures that might be unverifiable are analyzed for their influence on P2P lending. According to Pope and Sydnor (2008), blacks are less likely to get funded and are demanded for higher interest rates when they manage to receive a loan. They also found evidence that the online P2P market shows favor towards women. Freedman and Jin (2008) found that loans with friend endorsements and friend bids have fewer missed payments and yield significantly higher rates of return than other loans. They continued their research on online friendship in Freedman and Jin (2014) and concluded that the ex-post performance (default rate) of those loans with social ties is not better. Brandes et al. (2011) analyzed how social information affects the interest rate of loans and on the credit default risk. They concluded that the information such as group membership and endorsements from other users also plays a vital role in lenders' judgment, besides the credit grade. In a similar study, Lin et al. (2011) pointed out that claiming online friendship with other users can act as signals of credit quality. Friendships increase the probability of being funded, reduce the interest rates on funded loans, and are related to lower ex-post default rates. On the other hand, Michels (2012) showed that voluntary disclosure of personal information such as intended use of the fund and explanations for poor credit ratings can help decrease the interest rate and increase the number of bidders (lenders) for the loan. However, soft information may not be fully trusted. Herzenstein et al. (2011) analyzed the influence of identity claims in narratives by borrowers. They discovered that while establishing identities about being trustworthy or successful in the narrative can help attract more bidding, such loans actually turn out to perform worse. Hence the borrowers' narratives can be misleading in some cases. Finally, Meyer (2013) studied the effect of transition from an auction pricing mechanism to a posted-price pricing mechanism on the platform Prosper.com and concludes that under the former mechanism lenders get higher returns for high-risk loans. He attributed this to the price flexibility and use of soft information, which enable lenders to screen out high-risk borrowers. The literature above focuses on the US market and all of them retrieve the data from Prosper. On the other hand, researchers have tried to extend the analysis into other platforms and different cultural backgrounds to see if the empirical results mentioned above still apply.


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Mach el al. (2014) obtained data from Lending Club1 and found that small businesses are twice as likely to be funded compared to loans for other purposes; however, the side effect is that they have to pay two times higher interest rates for P2P lending relative to loans from traditional sources such as banks. A few papers have tried to study the P2P platforms in other countries. Barasinska and Schäfer (2010) used the data from German P2P platform Smava2 and measured funding success by three different indicators. But they failed to find evidence that gender affects borrowing success. Pötzsch and Böhme (2010) also retrieved data from Smava and found that borrowers' hard information are given more weight by lenders and voluntarily disclosed personal information has little influence in affecting lenders' decisions. It seems that soft information plays only a minor role in German P2P market, in contrary to the situation of the US. Lee and Lee (2012) looked at the data from Popfunding3, one of the largest P2P platforms in Korea, and confirmed that there is herding behavior: bidders will have more incentive to participate in auctions with a higher participation rate. Very few studies cover more than one platform, or make cross country comparisons, to a large extent due to the fact that data from different platforms are not comparable (especially before Lending Club made public its database). Gonzalez and McAleer (2011) compared the loan characteristics between the US platform Prosper.com and UK platform Zopa. They found that the two platforms have significant differences in loan amount, maturity, interest rate, among other common characteristics. But the lenders’ behavior is similar in some way, e.g., higher loan amounts and loan purposes that might imply higher risk would lead to slower funding speed on both platforms. Due to data availability, there is little research on the P2P lending market in China, even though the establishment of the first Chinese P2P lending platform, PPDai4, can be dated back to June, 2007. Guo (2011) manually collected transaction records from PPDai and studied the financing cost and financing availability. The conclusions include that financing cost is negatively related to credit scores and loan size, and availability of financing is negatively related to the time remaining for the listing and the amount of borrowing. This paper is going to extend the previous literature further into the P2P lending market in China. Not only is the cultural background quite different, but China’s financial market is not as mature as that in Western countries as well. Therefore it would be interesting to see how peer-to-peer lending has developed in China, and look for the uniqueness and commonalities compared with other countries. Peer-to-peer lending has seen explosive growth in China recently and the market is 1 www.lendingclub.com 2 www.smava.de 3 www.popfunding.com 4 www.ppdai.com


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quite different from what is described in Guo (2011), so an update study is necessary. Furthermore, the data would be retrieved by hand collection from two platforms that are never studied before: Renrendai1 and Kaikaidai2. An interesting phenomenon in P2P lending market in China is that the interest rates posted with loans can deviate a lot from the actual financing costs of borrowers. This research is going to focus on the phenomenon. The methodology takes advantage of the fact that the data and information collected from the above two platforms allow me to calculate the borrowing costs. Following the majority of previous researches, this paper focuses on the borrowing side of peer-to-peer lending and less emphasis is given to the lending side. The words financing cost and borrowing cost are used interchangeably and they both refer to the price that borrowers pay for the loans.

1 www.renrendai.com 2 www.kaikaidai.com


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3 Background: P2P lending in China

3.1. Market overview P2P lending has witnessed rapid growth in China recently. According to the data from Wangdaizhijia 1 , an independent observer that regularly publishes platform-level statistics of peer-to-peer lending in China, the total number of P2P platforms has risen to 1184 in June, 2014. The outstanding loans have increased at a monthly rate of 7.71% to 4.77 trillion RMB (about 574 million euros) in the first half of 2014. Furthermore, statistics reveal that there have been about half a million lenders and 200 thousand borrowers on P2P platforms in China up to June, 2014.

Graph 1: Average P2P interest rate in first half of 2014

The above chart shows the trend of average interest rate of P2P lending loans in the first half of 2014. After its peak at an annualized rate of 22.23% in February, the interest rate steadily goes down, which is the evidence that this market has caught the attention of investors and an increasing amount of money has poured into peer-to-peer lending. In this ever more competitive market, P2P platforms have adopted different operation modes to pursue their respective strategies. Those modes are involved with varied risks for platforms and different cost structures. A review of those modes is necessary for the understanding of peer-to-peer lending in China.

3.2. Operation modes of P2P platforms in China 3.2.1. Basic P2P modes in Western countries P2P platforms mainly serve as the information intermediaries between lenders and 1 www.wangdaizhijia.com

19,45%

22,23%

21,01% 20,20%

19,60%

18,54%

16,00%

17,00%

18,00%

19,00%

20,00%

21,00%

22,00%

23,00%

Jan Feb Mar Apr May Jun


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borrowers, and a simple conceptual model is: BorrowersP2P platformLenders However, there can be a few complications to this simple model. For example, based on whether the platform is linked to its borrowers by online or offline links and whether the platform provides any form of safeguard for lenders, the successful P2P modes in Western countries can be distinguished by the following matrix. Some platforms provide safeguard for lenders’ money in various forms, so that the lenders will be paid back even if borrowers are behind on their loan repayments. On the other hand, P2P platforms usually keep in contact with the lenders by online methods, but they may develop new borrowers by either online (for example, they may attract borrowers by advertisements) or offline approaches (the common way is to outsource this to microfinance institutions who are responsible for contacting borrowers).

Table 1: P2P modes

online offline safeguard Zopa Kiva

no safeguard Prosper & Lending Club 1. Zopa mode Established in March 2005 in the UK, Zopa is the first online peer-to-peer lending platform in the world. Since its launching it has helped to issue loans that amounted to more than 580 million pounds1. Today it has over 52 thousand active lenders who have made loans to about 80 thousand borrowers, and the average return to investors is about 5%. Zopa charges a closing fee from borrowers when their loans are approved and sets up a safeguard fund based on contributions from the closing fees. Whenever a borrower has missed the loan repayments for four months, Zopa would step in and pay the money as well as the interest back to lenders, but only if the safeguard fund can cover the payment. This cover mechanism to a large extent reduces the lending risk on the platform and the safeguard fund has kept a good record: it has covered all the bad loans and never run out since its creation. Zopa makes money by charging a one-time fee from both lenders and borrowers when a loan is approved. 2. Prosper & Lending Club mode Prosper is the first US peer-to-peer lending platform, and it was launched in February 2006. Today it has more than 2 million members and has helped funded over 1 billion dollars in loans. It is the second largest P2P platform in US in terms of loans issued, just behind Lending Club. Lending Club was initially set up as an application on Facebook and became a full-scale P2P lending company in August, 2007. As of June 1 Statistics related to Zopa, Prosper, Lending Club and Kiva are retrieved from their websites.


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30, 2014, loans worth more than 5 billion dollars have been issued through this platform. Prosper and Lending Club are pure information intermediaries: they make money by connecting borrowers with lenders and charging a service fee to both sides, but they would not pay for the defaulted loans in any case. All the business is done online. Most US citizens can borrow money from those two platforms by registering as a member. Thanks to a well-developed individual credit scoring system in the US, Prosper and Lending Club can have easy access to borrowers’ historical credit information. They would post a credit grade based on the credit information, in addition to some other necessary information about the borrowers, so that the lenders can judge about the quality of a loan and make investments. In recent years, the two platforms have securitized the loans and the lenders are actually buying notes, which can be easily traded in the market before maturity. There is only one loan behind every note so that it is easy to give a credit grade and judge the potential risk of every single note. 3. Kiva mode Kiva1 was founded in the US in October 2005. It is different from the platforms mentioned above in that it is a non-profit organization which provides micro-loans to developing countries all over the world. So far it has helped to issue over 590 million dollars in low-interest loans to entrepreneurs in 77 countries. Kiva operates under such a mechanism that the local microfinance institutions (MFIs) in developing countries maintain dynamic relationships with borrowers and meanwhile the P2P platform keeps dynamic relationships with lenders. Most of the MFIs are organizations established under the enlightenment of Muhammad Yunus2, and they focus on providing loans to poor local entrepreneurs. MFIs are responsible for contacting borrowers (they take advantage of their local knowledge and do this offline), and Kiva is responsible for raising fund from lenders. Lenders can view the information of entrepreneurs in developing countries online and decide which loans to fund. The loans are typically of zero interest rate to the lenders and Kiva also provide interest-free loans to those local MFIs, who in turn make loans to local entrepreneurs at their typical rates3. Local MFIs have to maintain a good record of repayment in order to keep receiving zero-interest loans from Kiva. Therefore, they have strong incentives to repay the loan for the clients in the case of default, and this essentially provides safeguard for the lenders’ money.

1 www.kiva.org 2 Muhammad Yunus is a Bangladeshi economist and civil society leader who pioneered the concepts of microcredit and microfinance. He founded the Grameen Bank which offers loans to entrepreneurs that are too poor to qualify for traditional bank loans. He was awarded the Nobel Peace Prize in 2006 for his efforts to create economic and social development from the poor. 3 For reference, Grameen Bank's interest rate is about 20%, according to Fernando (2006).


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McIntosh (2011) pointed out that the mode of Kiva is actually P2MFI (peer-to-microfinance institutions) rather than P2P. Despite some debates among scholars, in this study I still label Kiva as a P2P platform because we can consider both the platform and MFIs as intermediaries who serve to connect the lenders with borrowers. It is necessary for them to cooperate because Kiva has advantage at raising fund while MFIs understand the local economies. Their model adds one more link to the simple model, but it is still peer-to-peer in essence. BorrowersLocal microfinance institutionsP2P platformLenders 3.2.2. P2P modes in China Most P2P platforms in China were launched after 2012. As latecomers, they have taken lessons from the operation modes of P2P platforms in the West. Consequently most platforms are operating based on one or a combination of the modes mentioned above, and with some necessary adjustments to fit in Chinese market. According to the protection of lenders, the platforms in China could be divided into four categories as shown in the table below; besides, there are also differences in how they contact borrowers. Different modes may have varied fee schedules and therefore imply different cost structures for borrowers.

Table 2: P2P modes in China and representative platforms

Online Offline Guarantee Lufax Yooli Collateral Weidai Safeguard Renrendai &Kaikaidai Creditease No safeguard PPDai

1. Guarantee In this mode, the platform cooperates with third-party finance companies and provides full coverage for the lenders’ investment. Most P2P platforms in China are operating under this mode. The arrangement looks strange because the lenders are essentially making risk-free investment which can typically generate an annualized return between 6% and 12%. This phenomenon of rigid payment distorts the relationship between risk and return. But it is understandable in China as an unwritten rule: currently almost all the financial products facing retail investors have to pay the expected return printed on their statements, whether they actually make profits or not. The rigid payment in China’s retail financial market is beyond the scope of this research and is not detailed here. In order to compete with other retail financial products that pay essentially fixed and guaranteed returns, some P2P platforms adopt this mode, which can be regarded as an extreme case of Zopa mode.


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The guarantee companies can play different roles under this mode. For instance, Lufax1 and its guarantee company (both belong to Ping’An Group) cooperates in risk control, but Lufax is in charge of both maintaining relationship with borrowers and raising funds. Besides the interests, borrowers also need to pay the guarantee fee to the third party and the origination fee to Lufax. Lenders do not have to pay any fees at the moment. On the other hand, the mode of Yooli 2 resembles Kira mode: it cooperates with a number of small loan companies all over China. The small loan companies (usually they only operate within one region) contact borrowers and provide guarantee for the loans, and Yooli is responsible for raising funds from lenders online and controlling risks with statistical models. Lenders are free of charge, and the fees to borrowers are unclear because they are actually signing contracts with the local small loan companies. But it is imaginable that the borrowing costs would be a lot higher than the interest rates, because only in this case the small loan companies would find it profitable to cooperate with Yooli and are willing to provide guarantee (just as the local MFIs depend on Kiva for cheap funding). Although this mode looks safe at first glance, it actually transfers the risks to third-party finance companies and the platforms. Lenders care nothing about the risks at all and they only need to see that they can get paid back in time. In case of large-scale defaults, neither the finance companies nor the P2P platforms can survive as they have to pay back the investment of lenders, but their registered capital (usually around one million RMB) is only a tiny proportion of their outstanding loans. Furthermore, most platforms lack the expertise to screen out bad borrowers. Hence there are systematic risks involved in this mode, and most platforms are trying to gradually stop providing guarantee to lenders. On the other hand, the guarantee mode increases borrowing costs in the peer-to-peer lending market since it distorts the risk-free rate to an extremely high level. It is acceptable if some borrowers pay for this guarantee service so that they could enhance their credit conditions and increase the chance of getting a loan, but it is not necessary to ask all the borrowers to pay for guarantee. 2. Collateral Under this mode, borrowers collateralize their cars to get loans from P2P platforms. While banks in China also prefer collateralized loans as a low-risk asset, they only accept houses because cars depreciate too quickly. Therefore such P2P platforms are actually in direct competition with small loan companies rather than banks. Offline teams are crucial for those platforms. For example, Weidai3 has set up its network in most major cities in China and their staff would check the status of collaterals on site and keep monitoring them until the loans are cleared. Its fee schedule to borrowers is not clear, and lenders are charged 6% of their investment gains as a service fee. The

1 www.lufax.com 2 www.yooli.com 3 www.weidai.com.cn


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collateral mode can effectively reduce the risks for lenders, and it has a clear targeted market: borrowers with cars in major cities. The drawback is that their operation costs are high because they have to maintain the widespread offline networks. 3. Safeguard A number of platforms have followed the mode of Zopa and established their own safeguard funds. The platforms make it clear that they would take responsibility for defaulted loans only to the extent that the safeguard funds can cover; in other words, they only offer a voluntary and limited cushion for the potential risks. This mode promises to take risks together with investors, but with only limited responsibility. Therefore, it not only provides sense of safety to lenders, but also avoids the potential systematic risks for the platforms. Renrendai is one of the leading P2P platforms currently in China and its mode is very similar to Zopa’s. Renrendai serves borrowers and investors from all over China. Borrowers have to pay a monthly fee and an origination fee to Renrendai, while lenders are free of charge. In comparison, Kaikaidai is a regional platform that only serves borrowers from Shandong Province. Its fee schedule is similar to that of Renrendai, except that lenders are charged between 5% and 10% of the interests as the service fee. The other representative platform of this mode is Creditease1, which almost completely depends on its own offline teams to contact borrowers and obtain new clients. Lenders are charged 10% of their investment gains as the service fee and borrowers are charged a monthly fee before they pay back the loans. 4. No safeguard As the first P2P platform in China, PPDai completely adopts the mode of Prosper except that it has to do most parts of credit grading by itself (in contrary to the US, the national credit system is still not ready yet in China, and the information contained in it is not complete). In its early years PPDai was a pure online information intermediary like Prosper. It is the only P2P platform in China that does not provide any forms of monetary protection to lenders. But it has been challenged by the huge number of platforms that provide guarantee in recent years and lost some clients. Now PPDai is also planning to transform into the safeguard mode. Lenders do not have to pay any fees and borrowers are charged an origination fee upon approval of loans on PPDai. 3.2.3. More suitable modes in China From the discussion above it can be concluded that collateral mode and safeguard

1 www.yirendai.com. It was already a well-established loan company before P2P entered China. To some extent, it simply takes advantage of the P2P platform to attract lenders but keeps its traditional way of contacting borrowers offline.


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mode are more suitable for the current situation in China. Both modes provide enough safeguard for lenders, and the platforms do not have to take unnecessary extra risks. Now that there is not a well-established national credit scoring system in China, although the platforms can give a credit grade to every borrower based on their own credit scoreboard, the implicated risk is hard for retail investors to understand. As a result it is not fair to let them take the risks alone. Both providing limited cover for the potential risks or requiring collaterals are effective ways to reduce lenders’ risks in peer-to-peer lending. While safeguard mode has broader market, the collateral mode is easier to carry out by specializing in the segmented market. On the other hand, promising full guarantee is not a wise way. Not only do platforms have to take tremendous risks, but it is also not good for retail investors to learn about the correct relationship between risk and return. It should be noted that among the representative platforms1 that I list above, half of them do not charge any fees to their investors (except the transferring fee which is rather trivial, about 5 RMB each time). Actually this is common with P2P platforms in China: as intermediaries, many of them only charge service fees from one side. This turns out to be an important strategy to attract investors. On the contrary, the western P2P platforms such as Zopa, Prosper and Lending Club all charges fees to both sides. This difference implies that assuming everything else equal, the gap between borrowing costs and interest rates would still be larger in China, because it has to cover the profits and costs of platforms. The platforms that I am going to analyze, Renrendai and Kaikaidai, belong to Zopa mode (safeguard+online) according to my categorization. I choose to analyze the data from platforms with Zopa mode in this study because: 1. for the platforms under guarantee mode, it is their own credibility rather than the credit of borrowers that matters to the lenders. Consequently all the loans with the same maturities pay the same interest rates, as can be observed on their websites. It is meaningless to study the data with little variations. 2. Collateral mode focuses on the segmented market and less information can be gained from studying them, compared to studying Zopa mode platforms. 3. P2P lending is still at development stage in China, and the platforms are not as transparent as their Western counterparts in many details. Therefore it is hard to collect useful information from P2P platforms, and as mentioned above, some platforms even do not publish their fee schedules to borrowers. Comparatively, platforms under Zopa mode disclose more information, especially about their credit grading systems and fee schedules. 4. Their modes are more similar to P2P platforms in western countries (Zopa from UK in this case), and therefore the comparison of results would be easier because fewer factors need to be controlled for.

1 All the platforms listed, except Kaikaidai, are quite large in scale and operate in the whole nation. They are among the best platforms in each category.


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4. Data and Methodology

4.1. Data sample Renrendai 1 and Kaikaidai 2 are chosen as two sample platforms, because they provide clear explanations about their credit grading systems and fee schedules, so that I can calculate the borrowing costs based on the interest rates. While Renrendai is a nationwide platform, Kaikaidai focuses on only one province. The sharp contrast is another reason that makes them interesting samples: by analyzing their data we can know about the respective roles of large and small platforms in the P2P lending market. While the sample might not be representative enough of the whole P2P lending market in China because it does not cover platforms of different modes, the Zopa mode under analysis is very promising in China and many platforms are transforming to this mode. The manually collected sample consists of 207 Kaikaidai and 579 Renrendai loan listings that were posted on their websites in July, 2014. No P2P platform in China has made their historical data available, so it is only possible to collect the relevant data one listing by one listing from their websites. Moreover, the platforms only post listings that changed status recently. Hence the loans I observed were either cleared, fully funded, or in default in July. Hardly any listings that were open for bidding were observed because almost every loan request is fully funded within one day. From the table below it can be seen that the two platforms provides similar sets of variables for every loan listing. The hard information includes borrower’s income level, credit grade and property possessions (but the platforms only show whether borrowers own houses and cars or not, and do not disclose further information about the value of properties). On the other hand, the soft information reveals borrowers’ gender, education level and marital status. Although users can add each other as friends on the platforms, this function is still not fully utilized and most borrowers do not have any friends on the platforms, in contrast to the case of Prosper. Most variables are defined on the same scales on both platforms, but it should be noted that Renrendai only shows loans that are in process or paid off, while Kaikaidai also posts the loans that are in default. In addition, Renrendai only reveals the range of monthly income for every borrower, for example, 10 to 20 thousand RMB. I take the median of the range as an approximation. Both platforms allow third parties to provide guarantee for borrowers3, and Kaikaidai also accepts collaterals1.

1 It was established in October, 2010 and can serve nearly 4,000 loans that amount to more than 200 million RMB per month now. 2 It was established in November, 2011 and can help generate about 200 loans with approximately 30 million RMB per month now. The statistics are from Wangdaizhijia. 3 Some borrowers may not be eligible for loans without guarantee, but asking for guarantee is up to personal decision and is not a must on Renrendai and Kaikaidai.


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Table 3: List of collected variables

Renrendai

Kaikaidai

borrower data

borrower data credit grade

credit grade

location

location monthly income(range,take median)

monthly income(exact)

education level

education level marital status

marital status

property possession(house,car)

property possession(house,car) borrowing history on the platform

monthly expenditure

outstanding loans from other sources

photo gender

gender

loan data

loan data amount

amount

interest rate

interest rate maturity

maturity

purpose

purpose guarantee

guarantee

collateral

status(default or settled)

Both platforms post the interest rate for investors on every loan listing. But the interest rates could be much lower than the actual financing costs of borrowers. One reason is mentioned above: some platforms only charge fees to borrowers, which implies that the borrowing cost should include all the costs and profits of the platforms. But a more important reason is that besides some fixed amount charges such as membership fees and bank transferring fees, the platforms also charge some proportional fees based on the loan amount. Following Zopa’s mode, Renrendai and Kaikaidai charge an origination fee that depends on the credit grade of borrower when the loan is fully funded, and the fees are put together to establish a safeguard fund. When a loan is due and the borrower fails to pay back the money in time, the amount would be withdrawn from the safeguard fund so that lenders can receive their proceeds at the promised time. In addition to the one-time origination fee, the platforms also require a monthly account management fee, which must be paid when the borrower repay part of the loans every month. By looking at the borrowing cost, more information may be revealed and I compute it as follows:

12*acc *12 /Cost Rate ori T= + + The fixed charges are neglected because they are quite small (about 110RMB on

1 Since the listings with collaterals are not prevalent (29 in 207 observations) on Kaikaidai, I still put it into the Zopa mode category.


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Renrendai and 180RMB on Kaikaidai) compared to the proportional charges. Both platforms posted annualized interest rate (Rate) and I add the annual rate of the monthly account management fee (acc) to it1. The origination fee is a one-time charge, and it is also converted into the annual rate. While the account management fee is the same for every loan, the origination fee is based on the credit grade of the borrower. Therefore, lower-credit borrowers face a higher borrowing cost to compensate for their higher risks. The equation also implies that loans with longer maturities (T months) actually pay lower borrowing costs per year, because the origination fee does not account for the maturity of loans. While the computation is not accurate because it ignores some small costs in the process of application for a loan, the three components considered can reflect the major borrowing cost. The proportional fee schedules are as below:

Table 4: Fee schedule by platform

Renrendai Kaikaidai

Origination fee based on credit grade AA 0.0% 0.5% A 1.0% 1.0% B 1.5% 1.5% C 2.0% 2.0% D 2.5% 2.5% E 3.0% 3.0%

HR 5.0% 4.0%

Account management fee per month 0.3% 0.6%

Table 5: Education level coding

Education level code Post-graduate 3

Bachelor 2 Technical school 1

High school or lower 0 Some necessary coding is done so that the data is easier for analysis. The education level is coded as the table above. Gonzalez and McAleer (2011) pointed out that urgency level implied by the loan purpose could also indicate the potential risk of a loan, and they coded the purposes into three categories: 1 stands for the most urgent need and hence the highest risk, and 3 represents the least urgent loans and the lowest

1 For convenience I do not use the compound rate here because the monthly fee is the same to everyone, and the difference between the simple rate and compound rate is linear and trivial (about 0.2% in terms of annual rate).


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risk. They argued that when people cannot pay for their immediate obligations or needs, they are more likely to be in financial troubles and face liquidity problems. All the purposes identifiable on Renrendai and Kaikaidai have been coded in their research; hence I adopt their categorization and code the purposes as shown in the table below.

Table 6: Loan purpose coding

Loan Purpose Code Debt 1 Medical expenses 1 School fees 1 Business use 2 Home 2 Car 2 Holiday 3 Electronics goods 3 Wedding expenses 3

4.2. Methodology The major interest of this research is to explore which factors determine the actual borrowing costs of peer-to-peer lending in China, and how the borrowing costs deviate from interest rates posted online. It would also be interesting to investigate how the mechanism differs between a national platform and a local platform. After the analysis of summary statistics which compares some basic variables of the two platforms, OLS regressions are employed to investigate the determinants of borrowing cost. The basic model is as follows:

1 2 3 4* * * *BorrowingCost Hard Soft Platform Controlsa β β β β ε= + + + + +

Hard represents the set of variables that reveals the financial conditions of borrowers such as the monthly income and possession of properties (house or car). Soft stands for the set of variables that provides personal information including gender and marital status. While it may reveal a broader picture by studying the data from two platforms together, separate analysis of each sample is also conducted to check the consistency of results. Since return is directly related to risk, it would also be interesting to check if the key factors that determine the borrowing costs are also related to the default rate. In other words, does the P2P platform pick the right variables to predict the potential risk of a


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loan? The basic model would be a logistic model:

1 2 3 4Pr(De 1) ( * * * * )fault f Hard Soft Controls Ratea β β β β ε= = + + + + +

Because only Kaikaidai reveals information on defaulted loans, the study would only use the 207 observations from Kaikaidai.


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5. Analysis

5.1. Summary statistics The following two tables provide the summary statistics for variables that describe the basic characteristics of a loan on Renrendai and Kaikaidai respectively. The displayed statistics represent weighted averages (take loan amount as the weight) for interest rate and borrowing cost. As is mentioned above, Renrendai is far larger in scale compared to the local platform Kaikaidai. Therefore it is not surprising that all the basic characteristics are significantly different on the two platforms. The amount requested varies a lot on both platforms from a few thousand RMB to nearly half a million RMB. But the upper limit of loan amount is lower on Renrendai, which shows that they take a very cautious attitude towards controlling risks. Kaikaidai is featured with loans of short maturities, though some loans can last for two years. On the other hand, Renrendai primarily matches long-term financial demand with supply. Their average loan maturity is about two year, but there are also short loans for only three months. Lenders on Kaikaidai gain higher interest rates compared to their counterparts on Renrendai, and this is mainly because they take higher risks and Kaikaidai has to lure investors with higher returns in order to compete with large platforms, as a smaller and less famous platform. However, it is surprising to see that the highest annualized borrowing cost on Renrendai could be 47%, and the maximum borrowing cost reaches 75% on Kaikaidai (for reference, consider that the basis loan interest rate from banks with a maturity between one to three years is only 6.15%). The borrowing cost could be a lot higher than the interest rate returned to lenders. Even on average, we could see that the difference between borrowing cost and interest rate is around 5% on Renrendai, and almost 15% on Kaikaidai. This is mainly due to two facts. First, the origination fee is only charged once based on the credit grade, but it is not related to the maturity of the loan. For short-term loans with a low credit grade, this turns into a very high cost. The average maturity on Kaikaidai is one-third of that on Renrendai, so it explains why the annualized origination fee and consequently the gap between borrowing cost and interest rate are much higher on Kaikaidai. Second, due to the incompleteness of the nationwide credit system, the platforms can only learn about the credit history of a borrower mostly by conducting due diligence by themselves. The high costs have to be undertaken by the borrowers in the end. After all, it should be pointed out that for small loans, the availability of financing is much more important than the costs. Even though the annualized costs are high, the platforms provide the most-needed short-term liquidity to borrowers. The summary statistics imply that there might be an arbitrage opportunity of about 2% by borrowing from Renrendai and lending the money out on Kaikaidai. But note that


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the fixed costs such as registration fees and transferring fees are not taken into account here. In order to arbitrage, the ‘wise’ guy has to pay those fees twice on both platforms. It would not be profitable at all at a scale of 50,000 RMB.

Table 7: Renrendai Summary Statistics

Mean Median Std. Dev. Maximum minimum Loan amount (thousand RMB) 53.67* 46.7 38 300 3 Loan maturity (month) 24.72* 24 11.23 36 3 Interest rate (annualized,%) 12.64* 13 0.81 24 10 Borrowing cost (annualized,%) 17.25* 17.13 2.15 47.6 15.46 Income (thousand RMB) 14.6* 7.5 13.35 50 1.5

*Statistically different from Kaikaidai at 0.001 level

Table 8: Kaikaidai Summary Statistics

Mean Median Std. Dev. Maximum minimum Loan amount (thousand RMB) 74.2 35 97.7 500 1.5 Loan maturity (month) 7.97 6 5.04 24 1 Interest rate (annualized,%) 19.59 19 1.64 26 15 Borrowing cost (annualized,%) 34.18 31.2 9.17 75.2 26.53 Income (thousand RMB) 49.69 14.3 110.05 1000 1.2

It has been pointed out that the two platforms adopt different criteria in deciding the credit grade of a borrower. As is shown in the histograms below, the percentages of credit grades differ a lot. On Kaikaidai, nearly 67% of borrowers are categorized to be of high risk, and the proportions of other credit grades show a decreasing trend: there tend to be fewer borrowers with higher credit grades. This could either imply that they adopt a very strict grading standard or that due to limited sources they cannot attract high-quality borrowers. On Renrendai, the graph shows another picture: more than 70 percent of the borrowers have A-grade, and another 20 percent are labelled as high-risk borrowers. Although it can be argued that Renrendai is able to attract better-quality borrowers because of it large scale, suspicion about their credit grading system remains: it might be due to a poorly-designed system that so many borrowers can be categorized into A-grade, and the distribution of credit grades is extremely polarized. On the whole, there is enough evidence to believe that the two platforms’ credit grading systems are not comparable.


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Graph 2: Distribution of Credit grade, by platform

Since the borrowers on Renrendai are drawn from all over China but only from one province on Kaikaidai, there may also be some differences in personal information such as gender ratio and education level. The Wilcoxon rank-sum test reveals that the gender ratio and distribution of education level differ significantly on the two platforms at 0.01 level. On Kaikaidai, about 16% of the borrowers are female; the proportion is approximately 24% on Renrendai, on the other hand. While nearly two-thirds of the borrowers on Kaikaidai have never received tertiary education, this is the case for only about 27% of the borrowers on Renrendai.

Graph 3: Distribution of Gender, by platform

66.67

13.53

5.314 3.382 5.3142.899 2.899

23.32

2.936.5181 .1727 .3454

72.71

050

100

HR E D C B A AA HR E D C B A AA

Kaikaidai Renrendai

Per

cent

Distribution of Credit grade, by platform

84.06

15.94

76.51

23.49

050

100

male female male female

Kaikaidai Renrendai

Per

cent

Distribution of Gender, by platform


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Graph 4: Distribution of Education level, by platform

Borrowers on the platforms differ in purposes as well. Over 60% of borrowers resort to Kaikaidai for business concerns (there are very few cases of borrowing for home or car in this category from my observation when collecting the data). While on Renrendai, only about 39% of borrowers are for business concerns, and another 38% of borrowers are borrowing for less urgent needs such as entertainment. However, it is weird that the platforms only provide personal information when the borrower’s purpose is to run business. The owner’s information is valuable if it is one-man company or family business, but for firms that hire a lot of employers, it is the firm’s credit that matters more in this case.

Graph 5: Distribution of Purpose, by platform

The summary statistics reveal the following facts about the two platforms under study. Above all, the borrowing costs from P2P platforms are much higher than from

65.7

17.87 15.94

.4831

27.12

49.57

21.76

1.554

050

100

1 2 30 0 1 2 3

Kaikaidai Renrendai

Per

cent

Distribution of Education level, by platform

30.92

62.32

6.763

21.76

39.72 38.51

050

100

1 2 3 1 2 3

Kaikaidai Renrendai

Per

cent

Distribution of Purpose, by platform


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traditional sources such as banks. This is because the borrowers are usually those who have been turned down by banks and are categorized as high risk in banking system. They represent the major market of peer-to-peer lending and have to pay high costs both as risk premiums and as compensation for P2P platforms’ high operation costs. Most loans have short maturities due to the high costs, and P2P lending mainly provides access to short-term liquidity in China currently. Next, the credit scoring system is still not completed yet in China and the P2P platforms have to come up with their own systems as a complement. The resulting credit grades are not comparable and might not even be trustworthy. However, the key for success of P2P platforms is to filtering out the truly bad borrowers from those regarded as high-risk borrowers by banks. It would be hard for platforms to improve their risk control with poor-quality inputs. Therefore, a well-established national credit scoring system is badly needed. When such a system is available, the borrowing costs of P2P lending would decrease because platforms do not have to spend so much on due diligence. Finally, the large difference between the borrowing costs and posted interest rates (to lenders) is mainly due to the fact that the platforms offer to take risks together with borrowers and pay for the defaulted loans with their safeguard funds1. The P2P platforms are not simply information intermediaries in China, they are also credit intermediaries. To some extent, they are like banks to high-risk borrowers.

5.2. Regression analysis The multivariate study consists of four parts. After analyzing the whole sample by pooling data from two platforms together, I analyze the sub-samples from each platform respectively. The reason for separate study is twofold: first, the two platforms are quite distinct in scale, and there might be some differences in how they operate; second, the credit grades on the two platforms are not comparable, but the literature has reached consensus that it plays vital role in determining one’s borrowing costs. Finally, logistic regressions are used to analyze whether the variables that determines ‘return’ (the borrowing cost) are the ones that can predict ‘risk’ (default probability) correctly.

1 Another reason is that some platforms only charge fees to borrowers, as mentioned in Section 3. Consequently the borrowing cost should cover the reasonable profits and costs for platforms.


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5.2.1. Anaylsis of the pooled sample

Table 9: Regression 1 results

(1) (2) (3) (4) (5) VARIABLES Cost Cost Cost Rate Rate Loan -0.0125* -0.0122* -0.0113* -0.00448*** -0.00435*** (0.00650) (0.00632) (0.00652) (0.000844) (0.000855) Term -0.224*** -0.237*** -0.236*** 0.0537*** 0.0516*** (0.0197) (0.0218) (0.0207) (0.00577) (0.00573) Plat -14.62*** -14.67*** -14.85*** -8.617*** -8.640*** (0.619) (0.619) (0.621) (0.174) (0.176) Guarantee -2.332*** -1.929*** -1.805*** -0.736*** -0.655*** (0.468) (0.535) (0.534) (0.0857) (0.0900) Income -0.00999*** -0.00862** -0.00980*** -0.00136** -0.00121** (0.00358) (0.00343) (0.00355) (0.000588) (0.000592) Female -0.867*** -0.784** -0.754** -0.180** -0.162* (0.316) (0.315) (0.316) (0.0828) (0.0838) Business -1.370*** 0.130 (0.509) (0.0973) Consump 1.408*** 0.116 (0.437) (0.109) Constant 39.96*** 40.74*** 39.74*** 20.30*** 20.36*** (0.797) (0.905) (0.791) (0.155) (0.144) Observations 786 786 786 786 786 R-squared 0.728 0.732 0.731 0.897 0.897

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Robust standard errors are employed because heteroskedasticity is detected by white test, and the same is done to regressions in the following subsections. The credit grades are not used in the regression because the grading systems are not comparable on the two platforms. As is suspected, platform difference plays the most important role here. Even after controlling for other factors, it is still about 14.7% more expensive to borrow money on Kaikaidai, and the investors on Kaikaidai receive on average 8.6% more as the risk premium. This gap is impressing, and careful analysis is needed to understand it. First, the monthly account management fee is 0.6% on Kaikaidai and 0.3% on Renrendai, which means there is a 3.6% difference in annual cost. This could be attributed to the difference in their operation costs: Renrendai enjoys economies of scale and has lower costs. Then we take away the 8.6% difference in interest rate for the time being; there


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is still a difference in the cost of about 2.5%. A possible explanation could be that the regression does not control for credit grades of borrowers. As discussed before, the origination fee depends on the credit grades. According to their respective credit grading systems, on average the credit quality of borrowers on Kaikaidai is poorer than that on Renrendai. Thus, Kaikaidai naturally charges more from borrowers. Finally, there remains the 8.6% difference in interest rate. Part of it is the credit risk premium because borrowers of Kaikaidai are of higher risks, and the other part is also due to the missing credit grades in the regression. But even if the credit grades are controlled for, considerable differences in borrowing costs still remain, and the question is why those who need money would borrow from Kaikaidai? One reason is that they might be regarded as low-quality borrowers or even rejected by Renrendai, but Kaikaidai accepts their applications. By specializing in one province, Kaikaidai could have better knowledge about their probability of defaults. Given the modest scale of average loan size, it is not profitable to arbitrage from the difference of costs and interest rates between the two platforms, as mentioned before. Longer-term loans are actually cheaper for borrowers because the origination fee does not depend on the maturity, as has been explained before. But those loans with longer maturities have to pay more in order to attract investors, as is shown is regression (4) and (5). The inverse of sign here indicates that the platforms have larger margins on short-term loans, whose borrowing costs are higher but pay less to lender. Loans with guarantees enjoy cheaper borrowing costs and also pay less to lenders because the risk is lower. Loan-to-income ratio does not have a significant impact on the borrowing costs or interest rates, opposite to the findings in Klafft (2008), but borrowers with higher income receive better conditions. On the other hand, females seem to be favored over males as their borrowing costs are lower. In contrast to the argument of Gonzalez and McAleer (2011), the results show that borrowing for business purposes are favored over borrowing for entertainment purposes: the latter faces a higher borrowing cost. It seems that the platforms do not consider the risks implied by borrowing purposes in the same way as scholars; they might simply consider the borrowers for business purposes more reliable than borrowers for personal consumption purposes. It is also interesting to see that purpose does not affect interest rate at all, and perhaps lenders do not take this information into account. Lastly, education level and marital status are found to have little impacts here.


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5.2.2. Anaylsis of Renrendai


(1) (2) (3) (4) VARIABLES Cost Cost Cost Rate Loan -0.0147*** -0.0140*** -0.0148*** -0.000468 (0.00251) (0.00257) (0.00252) (0.000638) Term -0.0740*** -0.0763*** -0.0722*** 0.0608*** (0.0137) (0.0137) (0.0136) (0.00489) Credit_A -4.521*** -4.439*** -4.673*** -0.659*** (0.277) (0.279) (0.294) (0.0961) Guarantee -0.178 -0.176 -0.184 -0.478*** (0.222) (0.223) (0.220) (0.0479) Payable 2.874*** 2.827*** 2.856*** -0.409*** (0.503) (0.516) (0.503) (0.111) Other_loan 0.762*** 0.790*** 0.780*** -0.118** (0.251) (0.249) (0.250) (0.0566) Gansu -1.981*** (0.659) Shanghai -2.038*** (0.461) Xinjiang -3.082*** (0.368) Single 0.515* (0.302) Constant 23.31*** 23.17*** 23.42*** 11.87*** (0.502) (0.509) (0.511) (0.219) Observations 579 579 579 579 R-squared 0.593 0.596 0.600 0.304


Similar to the results in Regression 1, longer-term loans cost less for borrowers and pay more to investors. Hence Renrendai reaps higher margins on shorter-term loans, mainly due to the arrangement of the one-time origination fee. Nevertheless, guarantee does not reduce the borrowing costs, nor does gender or education has any significant effect. Since very few borrowers in the sample have credit grades other than A or HR, a dummy variable would be more suitable to study the effect of credit level. The dummy A-credit equals 1 if the borrower has A-grade credit according to Renrendai.


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Just as previous researches have pointed out, credit grade has a significant effect on the interest rate. The financing costs for A-grade borrowers are significantly lower, and accordingly, the posted loans offer lower returns. Borrowers with outstanding loans on their houses or cars face higher financing costs, as is expected. But it is surprising that those loans actually pay less to the lenders. The logic could be that since the platform actually takes the risks on behalf of lenders with its safeguard fund, the platform demands higher returns for extra risks. On the other hand, the fact that borrowers possess houses or cars do not significantly affect their borrowing costs or interest rates, perhaps because there are huge variations in the values of properties. Owning a cheap house or car does not mean that the borrower is more likely to pay back the loan. The variable Payable is constructed by dividing the loan-to-income ratio by the maturity of the loan. It provides a rough measure about the solvency of the borrower: the loan-to-income ratio gives the shortest time during which the borrower could pay off the loan in theory (because the borrower cannot have any other expenditures, assuming he has no savings); when the ratio is compared to the maturity of the loan, we can see whether the loan is a big challenge to the solvency of the borrower. If the variable is close to 1 or even larger, then it is almost impossible for the borrower to pay back the loan in time unless he raises new debt. Therefore, the smaller the variable is, the more likely that the borrower can pay back the loan, ceteris paribus. The regression results show that borrowers that face more serious solvency problems (higher value of the variable Payable) are charged with higher borrowing costs to account for their potential default risks. But again those loans pay less to the lenders because the platform acts as the cushion against default and it demands more risk premium. Finally, when I add the province dummies to the regression, the result shows that three regions enjoy lower borrowing costs. The reason is unclear and could simply be due to the fact that there are only 13 loans from those three areas (the sample consists of 579 loans to 28 different provinces) and they happen to be safer loans on coincidence. Furthermore, it seems that borrowers that remain single are charged with higher borrowing costs, compared to those who are married.


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5.2.3. Anaylsis of Kaikaidai


(1) (2) (3) (4) VARIABLES Cost Cost Rate Rate Loan 0.000258 -0.00548 -0.00486*** -0.00354*** (0.00849) (0.00761) (0.000941) (0.00108) Term -1.157*** -1.108*** 0.0283 0.0813*** (0.160) (0.149) (0.0282) (0.0300) Credit_HR 6.052*** 6.493*** 0.687** 0.985*** (1.192) (1.238) (0.271) (0.256) Guarantee -0.537 -1.313*** (1.239) (0.264) Col -2.171** -1.952* -0.0845 -0.0184 (1.094) (1.044) (0.290) (0.307) Female -2.069** -1.588* -0.622** -0.322 (0.890) (0.910) (0.256) (0.253) Income -0.0114* -0.000752 (0.00626) (0.000589) Business -2.222* 0.366 (1.149) (0.270) Photo -1.349*** (0.348) Constant 42.65*** 43.01*** 19.81*** 19.65*** (1.550) (1.614) (0.300) (0.315) Observations 207 207 207 207 R-squared 0.463 0.462 0.178 0.266


Similar to the above findings, Kaikaidai also makes larger margins on short term loans: the maturities have negative impact on the borrowing costs but are positively related to interest rates. On the other hand, when we compare the results of the three sets of regressions, only vague evidence for the findings of Puro et al. (2010) is confirmed. Loan amount does seem to have negative impacts on ‘loan prices’ (borrowing cost and interest rate), but its influence seems limited. After all, the amount requested for every loan listing is modest on P2P platforms, and little information could be inferred from it when other factors are controlled for. Since over half of the borrowers are HR-grade, and other credit grades have only a few observations in the sample, I use the dummy HR_credit to depict the dichotomy


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between HR-graders and borrowers with higher credit grades. The results show that borrowers with HR-grade pay significantly much more financing costs, and their loans are posted with higher interest rates. Similar to the results of Renrendai, guarantee does not significantly affect the borrowing costs, though the posted interest rates are lower. This is comprehensible in that the loans with guarantees usually have some intrinsic problems, the guarantee cannot reduce the borrowing costs because the third party who provides guarantee would charge a fee as well. But the posted interest rates are lower since the risks are transferred to the guarantee companies. On the other hand, borrowers can reduce their financing costs by collateralizing their houses or cars, but those loans pay the same level of returns to investors. Female borrowers seem to have some advantages as they pay less for the financing costs and interests. But education and marital status are irrelevant. Interestingly, the R-square of regressions for borrowing costs are much higher than that for interest rates, as shown in regression set 2 and 3. But it is the other way in regression set 1: R-square of regressions on interest rates is larger. This might imply that lenders consider more about which platforms to invest on (regression set 1) than the potential default risk of specific borrowers (regression set 2 and 3), and they rely on the platforms to control risk. However, contrary to the literature, personal income, loan-to-income ratio and monthly expenditure are not found to have significant impacts on the borrowing costs. One explanation could be that more than half of the loans are for business purposes, and while Kaikaidai does not reveal the information about those businesses, the actual financing costs are based on the credit level of businesses rather than borrowers. In addition, the results show that business-purpose loans enjoy lower borrowing costs, but purpose does not affect interest rate. While posting photos together with loans do not reduce the borrowing costs, lenders are willing to accept lower returns on those loans possibly because they think the borrowers are more trustworthy.


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5.2.4. Anaylsis of Kaikaidai’s default rate


(1) (2) (3) VARIABLES Default Default Default

Loan -0.0164*** -0.0184*** -0.0176***

(0.00557) (0.00655) (0.00654) Term -0.116* -0.160* -0.189**

(0.0691) (0.0817) (0.0899) Cost 0.119** 0.165** 0.153**

(0.0541) (0.0675) (0.0692) Guarantee -1.168** -1.111* -0.945

(0.592) (0.669) (0.689) Col -2.701** -3.399*** -3.398***

(1.151) (1.223) (1.224) Credit_HR 1.968*** 1.833*** 1.558**

(0.545) (0.607) (0.630) Female -1.746*** -2.448*** -2.633***

(0.609) (0.693) (0.738) Income -0.0168** -0.0242*** -0.0243***

(0.00685) (0.00796) (0.00812) Business 2.159***

(0.568) Urgent -2.808***

(0.629) Constant -2.714 -4.812* -1.817

(2.059) (2.632) (2.702) Observations 207 207 207

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Logistic regressions are performed here as the dependent variable, Default, is a dummy that equals 1 if the loan is in default. Logistic model is favored here because its pseudo R-square is higher than probit model. The result shows that the borrowers on Kaikaidai generally pay a ‘fair’ price for the loans: borrowers that pay higher financing costs indeed are more likely to default on the loans. Collateral can significantly reduce default probability, but the evidence for guarantee is vague. High-risk and poorer borrowers are found to be more likely to default. In addition, the results also kind of prove that it is reasonable to ‘favor’ female borrowers, because males tend to have higher default rates. Larger loans and loans with longer maturities are less likely to default, perhaps because the platform is more careful on those loans. Education level or marital status does not affect the default probability significantly.


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The problem is that while loans for business purposes have lower borrowing costs than loans for other purposes, they actually turn out to perform worse. This might imply that Kaikaidai lacks the expertise to verify the credibility of small businesses. In addition, the most urgent loans actually have lower probability of defaults, contrary to the argument of Gonzalez and McAleer (2011). But it demonstrates that peer-to-peer lending is a useful way to provide liquidity to borrowers who are in temporary difficulties.

5.3. Discussion To sum up, some conclusions can be made based on the results of the four sets of regressions as a whole. First, credit grades play a vital role. While the credit grading systems on the two platforms are not comparable, it is much cheaper to get a loan with a high credit grade on both platforms. Second, there are obvious differences of borrowing costs and interest rates between the two platforms. The distinctions in targeted markets, scales and credit grading systems are the underlying reasons. Third, longer-term loans have lower borrowing costs but pay higher interest rates. This is because the origination fees are not related to the maturity of loans, leading to higher borrowing costs; on the other hand, borrowers request a premium for longer maturities. The combination of those two effects makes short-term loans more profitable for P2P platforms. Fourth, the determination of interest rates is different from how borrowing costs are determined. Lenders seem to care more about which platforms to choose than the default risk of specific borrowers. Due to the existence of safeguard fund, lenders seem to rely on platforms to control risk. Furthermore, personal income and financial conditions are important, but only to the extent that the borrowing is for personal purposes. In cases of borrowing for business purposes, personal information is not so relevant any more. It is important for P2P platforms to have the expertise to screen out bad business borrowers, because peer-to-peer lending is an important source of financing for small businesses that cannot get loans from banks. But in the case of Kaikaidai, it seems the platform performs worse on business loans. The evidence on the effects of gender, marital status and location is vague and requires further research. On the other hand, in regression 2 and 3, we see that guarantee cannot reduce the borrowing costs, because the third party who offers to take the risks would necessarily ask for a fee. This is confirmed by regression table 4, in which guarantee does not predict default probability well. Nevertheless, in regression 1, guarantee is found to lower borrowing costs, and that could be because credit grade is not considered in the regression. Education level is irrelevant in all regressions, so no evidence for discrimination against low or high education levels is found.


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Compared with empirical evidences in the US market, soft information is not valued so much in China, probably because both platforms and lenders are still learning about this new mode. As argued by Barasinska and Schäfer (2010), the fact that platforms are relatively young and lenders do not have enough ex-post evidence on borrowers’ payment behavior can reduce the importance of soft information. Braga et al. (2009) found that market experience and especially loss experience significantly affect behavior of market participants. Therefore, it is expected that platforms and lenders will adjust their behavior if they learn from updated information that certain soft information may affect payment behavior. On the whole the results are more similar to the empirical evidence in Germany: hard information is given more weight and soft information’s influence is vague. Credit grade is found to be the most important factor. On the other hand, different measures of borrowers’ financial conditions are also highly emphasized. Those factors play the determinant role in the borrowing costs. But when it comes to business loans, personal financial information may not be relevant enough for analysis. Currently in China, only the best-quality borrowers can get loans from banks. As a complement source of finance, P2P platforms mainly serve those who are rejected by banks. The key to their survival is the ability to screen out truly bad borrowers from those who might just fail to meet the strict criteria of banks. As is pointed out by Khwaja et al. (2013), higher dependence on soft information is the key reason that peer-to-peer lending can complement traditional sources of finance and improve access to credit. Successful platforms in the future are expected to incorporate more soft information in their risk control process. However, the structure is also like a pyramid within the P2P lending market. While large platforms can attract more borrowers by lower costs and more lenders by lower risks, smaller platforms make a living on their local knowledge. They should be better at filtering out good local borrowers from the rejected list of large platforms (the regression results confirm that soft information is more important on Kaikaidai compared with Renrendai).


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6. Conclusion This study calculates the actual financing costs of borrowers on two Zopa mode P2P platforms in China: the nationwide platform Renrendai and regional platform Kaikaidai. It is found that in some cases, the borrowing costs are much higher than the interest rates of loans. Then I check the previous empirical results about the determinants of interest rate (borrowing cost in this case), and find that hard information (especially credit grade) plays much more important role than soft information (such as gender). In previous studies on P2P platforms in western countries, researchers do not specifically distinguish between the (posed) interest rates (to lenders) and the actual financing costs of borrowers, because the difference is not too large. But in this study I find it is important to distinguish between the two rates because they can differ a lot, which is a unique phenomenon of P2P lending in China. There are mainly two reasons: 1. many platforms only charges borrowers while lenders do not have to pay any fees. As a result the borrowing costs must cover the costs and profits for platforms. 2. The platforms take extra risks on behalf of lenders by offering safeguard mechanisms, and hence they pocket part of the risk premiums. Although I only analyze the platforms that provide safeguard fund (Zopa mode), it can be extrapolated that the difference between interest rates and borrowing costs would be even larger on platforms that provide guarantee, because the latter mode implies much higher risk to platforms. The determination of borrowing costs in China’s P2P lending market is more similar to Germany: the role of hard information is more emphasized and the influence of soft information is rather ambiguous. This is different from the case of US, where soft information is highly valued in peer-to-peer lending. Three problems with P2P lending in China are identified: 1. the nationwide credit scoring system is of great need to P2P platforms. Such a system would provide them with clear guidelines and better inputs to improve their risk control models. 2. P2P platforms mainly depends on hard information currently, but with the growth of experience and database, they should incorporate more soft information into their risk control models in order to lower default rates and provide better access to credit. 3. Many small businesses are turning to P2P lending as an alternative source of finance; platforms should differentiate between the case of individual loans and business loans, and develop special services to satisfy their needs. It is not proper to evaluate individuals and businesses with the same credit grading system. In this study, data is manually collected in a short period time from two platforms. Hence the results might not be representative enough, but useful insights can be gained. Furthermore, it should be noted that the credit grading systems of the two platforms may not be well designed. For example, on Renrendai more than 90% of the


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observations belong to A-grade or HR-grade and there are very few observations with grades in between. Such polarized distribution might exaggerate the influence of credit grades in my analysis. The P2P platforms are trying to connect their database to the national credit system currently. When the system is built well, platforms will have better information about borrowers’ credit history and may adjust their own credit grading systems to be more reasonable. On the other hand, larger dataset with panel data may reveal more information: e.g. how the borrowing costs and interest rates fluctuate with the rate in money market.


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