We now move on to the first regression that estimates the relationship between the three main categories of explanatory variables and the probability that the loan will result in default, whilst controlling for loan attributes as well as industry and macroeconomic factors. The Probit models are estimated based on equation 6-1 below. In other words, we model the likelihood that ‘Default=1’. All explanatory variables (are defined in Table 3-1 in Chapter 3). Furthermore, as shown in the bivariate correlation matrix (in Table 3-2 in Chapter 3), in terms of explanatory variables, where significant, none of the correlations are above 0.5 demonstrating that in relation to the econometrics models, multicollinearity is unlikely to be a problem.
Pr (Defaultl|1) = α +β1 Owneri + β2 Firmi + β3 Information Attributes + β4 Loan Attributes
+ +β5Industryi + β6Macroi + µ (6-1)
The results are shown in Table 6-3. As stated previously, in all the estimations, a general- to - specific procedure to establish the model of interest was adopted (Darlington, 1990). Column 1
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of Table 6-3 shows the probit estimates for the general specification; which includes all the regressors, identified from literature, which could possibly be important determinants of default in the P2P lending context. The procedure begins with a full model and removes variables with a p-value above 0.10 resulting in a parsimonious model as shown in column 2. The results of the Likelihood Ration (LR) test confirm that the parsimonious model is a better estimation; the null hypothesis that the excluded regressors collectively have no role in predicting our dependent variable is decisively accepted (LRX2= - 511, df = 102, p <0.10). The third and fourth columns subsequently report the determinants of credit approval separate for existing firms and new business start-ups - with the main aim of testing the proposition firmly established in literature that new business start-up are more likely to default relative to already established firms.
...Table 6-3 goes around here...
In general, our results show that default is found to be related to risk as predicted by conventional theory in both formal credit rating and also information and track record (see for example Berger et al, 2005; Agarwal et al, 2007; DeYoung et al, 2007; Berger et al, 2009). More specifically, consistent with what we have already seen in the univariate analysis, the coefficients of the Credit_grade variable are positive and statistically significant. This indicates that lenders on Prosper, similar to traditional lenders like banks, do indeed lend with a conservative mind set - small business borrowers in high risk credit grade categories are more likely to default (DeYoung et al, 2007; Berger et al, 2009). We notice in fact from column (2) that the coefficients of the credit grade variable gets progressively stronger as we move from low risk borrowers (A) to high risk borrowers (HR) in predicting default risk (H1c). This result
underpins the importance of credit scoring as a method of predicting default, supporting results put forward by conventional theory (Berger et al, 2009; Berger and Frame, 2007). In terms of the remainder of owner variables - we find that Home_ownership, Delinquencies, and Judgements are not predictors of default in this context. This result is persisting even in the absence of the Credit_grade variable (given that we have shown Credit_grade can incorporate some of the information already captured by these other variables). Hence H1c and H7c are not supported.
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highlights characteristics of the P2P lending market – when lenders default, the borrower’s home cannot be taken to compensate the lenders. Loans are unsecured.
Importantly, in terms of our firm variables, we find that new small business start-ups are no more likely to result in default when compared to established firms our variable of interest, Existing_Firm is not statistically significant. This result is counter to what is typically found in banking literature where new firms are perceived to be the riskier class on the bases of severe information asymmetries (Stiglitz and Weiss, 1981, Cassar, 2004). Hence we do not find support for H4c. We also observe that default activity does not favour any industry or sector – all
industry dummies were not statistically significant. Therefore, from these results we assert that firm level information is not an important determinant of default in the P2P lending context. In terms of the information variables - reputation built over time through re-payment of loans attained from prosper decreases the likelihood of default (p<0.001). This result seems to offer support to Diamond (1989); affirming that information accumulated specific from Prosper is useful for lenders in determining default risk; especially given the fact that there are no opportunities to collect information through physical interactions (H3c). Hence, our findings
implies that a presence of a track record (developed on Prosper) matters and is useful in predicting default in the P2P lending context. Interestingly, we also find that reducing information gaps improves default activity (Stiglitz and Weiss, 1981; Bester, 1985). All else equal, we find that small business borrowers that include a picture in their loan request are less likely to default. The variable Include_picture is negative and significant at the 0.01 level; suggesting perhaps that inclusion of a picture somehow either humanizes the process or gives some information about the business or product which lenders seem to use to separate credit risk (H8c). Seemingly, the stories that small borrowers tell may not necessarily influence default; the
variable Elaborate is statistically insignificant (H6c).
Looking at the control variables, a key finding of our result is that the cost of credit is associated with default. All else equal, we find that those high interest rates translate to a higher likelihood of default; the variable Final_interest_rate is positive and significant at the 0.01 level. This result seems to support the moral hazard argument developed by the model of Stiglitz and Weiss, (1981) where small business borrows (in the absence of collateral) may be encouraged to take additional risk. After all, if their projects succeed, they will keep all the gains - and yet when
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they fail, it is the P2P lenders that will lose their capital (Stiglitz and Weiss, 1981). This finding presents an interesting dilemma for lenders. For example, if holding all else constant, and lenders opt to lower interest rates that they will accept from prospective borrowers on a loan, and this reduces the default probability, at what point do lenders make a higher expected return even with lower income stream? This argument seems to suggest that there may be a potential or real trade-off between interest rates and default which lenders might have to take into considerations; which presents an interesting dilemma for lenders. However, it is also plausible that some lenders extend credit to firms in this context for fun - as some form of ‘gambling’. Hence it would not be necessary to consider a potential trade-off between interest rates and default. In this case, lenders would simply accept loan requests from borrowers offering high interest rates with the view that knowing that if the borrowers pay back the loan, they win big. However, if some of the loans in the portfolio result in default, the loss is not too big. Furthermore, it is also reasonable to consider that some of the lenders choose to extend credit in this context driven by philanthropic reasons. This is supported by evidence we found earlier that lenders in this market extend credit focusing on people – hence their idiosyncrasies may be at play. Therefore, lenders driven by philanthropy might not necessarily have wealth maximisation as his main goal for funding these firms (see for example Argawa et al, 2011).
Finally, our results show that larger loans are more likely to result in default, as evidenced in the positive sign and high significant coefficient of the variable Requested_amount. This highlights the acute moral hazard issues entrenched in P2P lending. Typical in traditional lending; large credits usually induce higher borrower motivation because the borrower stands to lose a lot in the event of default given that borrowers offer collateral (Hanley and Crook, 2005). But in this case, since these loans have no security – it seems moral hazard risk is heightened. We can also interpret this result in the context of borrower motivation: smaller credits are taken very seriously in P2P lending. All things equal, the cost of default - which in this case default may result in a judgement and the threat of losing access to an external source of credit - may be damaging to a small business borrower (especially those in the early stages) who might otherwise have limited avenues of external (unsecured) small business finance.
Next, in order to confirm the result as shown by the indicator variable, Existing_firm, in column 3 and column 4 of Table 6-3 we extend the analysis to determine factors associated with the
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likelihood of default separately for existing firms and new business start-ups as defined by equations (6-2) and (6-3).
Pr (default: Existing_firms|1) = α+ β1Owneri + β2Information + β3LoanAttributesti + β4Industryi
+ +β5Macroi + µ (6 -2)
Pr (default: New_business_start-ups|1) = α +β1Owneri + β2Information + β3LoanAttributesti + +β4Industryi + β5Macroi + µ (6 -3)
We see from Table 6-3 that the two key variables that help disentangle information borrower risk namely, the Credit_grade variable and the Include_picture variables differ between new business start-ups and established firms. All other factors driving the likelihood of default for new firms and established firms are generally similar. More specifically, in both cases the determinants of default include the following variables: Repeat_loan, Requested_amount, Offer interest_ rate. Interestingly, we find that although Credit_grade remains significant for established firms; the variable is insignificant in the new business start-ups estimation. This result seem to suggests that, in the absence of a track record, lenders may be unable to disentangle credit risk associated with small business borrowers starting new firms. Interestingly, we also see that, inclusion of a picture becomes insignificant for the established firm, and remains negative and significant for new business start-ups. One explanation for the modest role for Include_picture in determining default for existing firms could be that perhaps better borrowers have learnt that lenders in the market simply values pictures in general and they tend to include pictures. Finally, looking at loan characteristics, for both new business start-ups and established firms, we find that loans with higher interest rates are indeed more difficult to repay - lending support to the fact that higher interest rates are an indicator of higher risk. Similarly, borrowers with larger loans are more likely to drive default.
6.4 Average marginal effects
To assess the impact of the observed factors, that drive default risk, we compute marginal effects at the mean as shown in Table 6-4. For an otherwise average small business owner with a C credit rating, all else equal, the predicted probability of default is 8 percentage points greater
154 when compared to the prime credit grade AA.
...Table 6-4 goes around here...
When this business owner increases the loan amount by $1,000 (from $8,108 to $9,108) we observe an increase in default risk of 1.3 percentage points; meaning a 5 percent increase in default probability. Increasing the requested loan amount by a factor of 10 however, to $18,108, the default probability increases by 13 percentage points; meaning a 52 percent increase in default probability- which may render the loan unplayable.
Likewise, we see from Table 6-4 that increasing the interest rate by 1 percentage point (for example from 18 percent to 19 percent), meaning a 5 percent increase in default probability. Similarly increasing the interest rate by a factor of 10 from 18.1percent to 28.1percent, results in a 52 percent increase in default probability.
We also observe find that all else equal, the predicted probability of default is decreased by 7.7 percentage points if the small business owner includes a picture. In relation to the average probability of default, 25 percent, a difference of 7.7 percentage points means a 31 percent decrease in the likelihood that the loan will result in non-payment. Interestingly, the impact of pictures is especially substantial for new firms, such that including a picture results in a decrease in default probability of 15.7 percentage points. Compared to an average probability of default, 25 percent, this represents an approximately 62 percent in reduction in the likelihood of default.