International School of Economics at Tbilisi State University. Does a collateralized loan have a higher probability to default?

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1 International School of Economics at Tbilisi State University Does a collateralized loan have a higher probability to default? Irakli Ninua Supervisor: Frédéric Laurin June 2008

2 Introduction My research is part of a broader research project about ProCreditBank s impact on local communities in Georgia. That project investigates how banking services and credit affect business development. The purpose of the study is, using a ProCreditBank database containing their lending clients firm characteristics, to assess the bank s impact on firm development in terms of sales, employment and the degree of penetration in national and international markets and to estimate the impact on the local communities where ProCreditBank has opened new branches. A significant number of previous studies relate collateral issues and the probability of default. These studies generally concern developed countries. Georgia is a fast growing transition country, however, so the results might differ. The credit market in Georgia still has some imperfections and peculiarities. Many firms are still financing their project from alternative sources, such as private money lending, which is associated with very high interest rates. The existence of firms that never before applied for credit from any credit institution signals that alternative sources for obtaining loans in Georgia are still significant. These alternatives may even be preferable to institutional lending, if the probability of default is higher for collateralized loans. There are several reasons why firms may prefer alternative way rather than obtain loan from bank. One of the reasons is the lack of collateral. When firms apply for loans, some are required to pledge collateral, while others are not. Even within collateralized loans, the types and amounts of collateral differ. What characteristics determine whether the company will be asked to provide collateral in order to obtain credit? It happens often in Georgia that firms lacking collateral are unable to obtain a loan from financial intermediaries, such as banks and microfinance companies. What such firms can alternatively do is to get a loan from different sources - which could include private money lending, characterized by extremely high interest rates. Repaying a loan with such a high interest rate is hard for the company; thus such loans presumably carry a high probability of default. 2

3 The objective of my master s thesis is to investigate the relationship between collateralized loans and the probability of default. Does a collateralized loan have a higher probability to default? This question will be answered based on an analysis of data of loan profiles of ProCreditBank Georgia. In my research I investigate two empirical questions. The first objective is to examine which characteristics determine whether a firm will be asked to guarantee collateral in order to get a loan. The second question is does a collateralized loan have a higher the probability of default. To estimate what characteristics determine the probability of the loan to be collateralized I am using a logit model, the dependent variable is dummy for collateral, it takes value 1 if collateral is present or 0 in case when loan is noncollateralized. To investigate the relationship between probability of default and collateralized loans I am using cross section analyses. The dependent variable is the model is loan loss ratio, because loan loss ratio enables the bank to counter the default risk in a portfolio by using past as well as future data, and assigning probabilities for likely future losses. The data is provided by ProCreditBank of Georgia for the time period ProCreditBank is a German holding oriented on development and mainly specialized in micro credits. ProCreditBank Georgia data consist of 35,568 client loan profiles, but the data does not includes firms that were turned down for loans. Database includes a wide range of firm characteristics, owner/manager characteristics, firm creditworthiness and credit history, client s loan characteristics at ProCreditBank and identification of credit rationing. The existing empirical evidence related to collateral and probability of default relationship is mixed, with some studies showing that borrowers with collateralized loans are more risky, and other studies showing they are, in fact, less risky. Let s take a look at some explanations behind each of those two possibilities: A bank will ask for collateral if it believes the project is risky, the probability of default is high, and/or the prospective borrower is high risk. On the other hand, a borrower with a risky project would always prefer to have a non-collateralized loan (holding the interest rate constant), while a borrower with a safe project would be more willing to pledge collateral. Different empirical evidence exists to support each of these two hypotheses. Elsas and Krahnen (2000) find that the incidence of collateral is statistically independent of the borrower s default risk. Jamenez and Saurina (2003) argue that collateralized loans are always riskier and that collateral increases the probability of default of a loan. 3

4 The bank is more interested in collateral as risk increases, while the borrower is less interested in providing collateral as risk increases, ceteris paribus. Which effect will dominate, given that collateral itself can affect the interest rate and the risk of failure to repay? The ProCreditBank data will be sufficient to investigate whether collateralized loans have higher probabilities of default or whether the default risk is statistically independent from collateral requirements. I find a positive relationship between collateralized loans and loan loss ratio, which it self measures ex post risk of the loan. I am able to conclude that collateralized loans are more risky and have higher probability to default. My results are consistent with findings of Jimenec and Saurina (2003). Empirical evidence shows that the existence of collateral increases the risk of the loan, although the bank is protected if default occurs. The paper is organized as follows. Section 2 reviews the role of collateral in the theoretical and empirical literature on the probability of default. Section 3 explains the methodology of the two empirical questions: what characteristics affect the loan to be collateralized and how is collateral related with risk of the loan. Section 4 contains a description of the data set and a number of descriptive statistics. Section 5 describes an analysis of the empirical results of two models. Section 6 will contain the main conclusions of the study. 4

5 2. Review of literature Jimenez and Saurian (2003) argue that collateralized loans have a higher probability of default and that close bank-borrower relationships increase the willingness to take risk. The authors analyze whether the high or low risk borrowers are asked to pledge collateral more often. The objective of their paper is to analyze the determinants of the probability of default. Jimenez and Saurian (2003) use data on more than three million loans of Spanish financial intermediaries for collected by the Bank of Spain s credit register. They try to discern whether collateral is pledged by low risk borrowers. (It is argued in part of the theoretical literature that collateral is used as a device to sort borrowers by quality in the presence of information asymmetry.) They use a logit model to estimate probability of default using pooled cross sections (1987, 1990, 1993, 1997 and 2000), focusing on company loans above a threshold of 24,000 euro. They use doubtful loan as the dependent variable, which was considered to be a function of degree of guarantee, type of financial institution, purpose of loan, maturity, collateral type as 100 percent guarantee, partial guarantees (more than 50 percent) or other guarantees, amount lent, business sector, region and type of financial institution. To control for macroeconomic elements common to all borrowers and all loans, but which can differ across time, yearly dummy variables are included. The estimation of parameters is highly efficient as they use a very large database. Jimenez and Saurian (2003) also add to the model a variable on the number of borrower s banking relations with 100 percent guarantees, and interaction terms of type of financial intermediary and 100 percent guarantees. Alternative specifications do not have a significant impact on the results. Their paper concludes that collateralized loans have a higher probability of default collateral increases the ex post probability of default of a loan. Collateralized loans are riskier. The decline in the relative risk of non-collateralized loans is larger where the closeness of the bank to borrower is low. The results show that if the asymmetry between the bank and the borrower is high a loan contract with collateral might help to sort out borrowers by credit quality. Rajan and Winton (1995) find that lenders tend to collateralize loans with high risk borrowers. There is again a positive association between risk and collateral implied and the prediction of a positive correlation between project risk and collateral corresponds to the conventional wisdom in banking, which views collateral as a means to lower the risk exposure of a bank. Rajan and Winton (1995) claim, that a fully collateralized lender is 5

6 immunized from borrower performance and has no incentive to monitor. An unsecured lender suffers from poor borrower performance but has little incentive to take actions that could lead to a run on the firm; with little incentive to take actions such a lender may have little incentive to monitor, too. Another interesting issue discussed in the paper is that firms may not offer collateral at the time of set-up simply because offering assets as security has some cost or reduces the firm s operational flexibility. Rajan and Winton (1995) results suggest that the need to give lenders incentives to monitor and the ability to control borrowers may partly explain important features of loan contracts. Hempel, Coleman, and Simonson (1986) conclude that large prime borrowers are more likely to get non-collateralized loans because large prime borrowers tend to have stronger equity support in their capital structures, more stable cash flows, and more certain investment opportunities Manove and Padilla (2001) argue that collateral might decrease screening efforts by banks - which means that in the case of a collateralized loan, a bank is more protected in the event of client default and less accurate with screening potential borrowers (then it is in the case of dealing with non-collateralized loans). The authors claim that screening can reduce the number of project failures and therefore diminish their private and social costs. They claim that restrictions on collateral requirements and the protection of debtors in bankruptcy may increase efficiency of the credit market, by inducing better screening. Morsman (1986) concludes that banks are normally secured by a perfected security interest in accounts receivable, inventory, and equipment. However, Exceptions can occur with wellcapitalized companies with no other types of debt and a history of seasonal payout. That means that observably risky borrowers are required to pledge collateral, while observably safe borrowers are likely to obtain non-collateralized loans. Elsas and Krahnen (2000) find no statistically significant difference in the degree of collateralization between prime borrowers and low-quality borrowers, who have the highest default expectation. The objective of these authors is to investigate the connection between collateral, default risk and relationship lending based on data of corporate debtors of five major German banks, for The authors use a logit model, where the presence or absence of collateral is the dependent variable. The explanatory variables are bank type, 6

7 company industry, annual sales, credit volume, and the ratio of borrower fixed assets to borrower total assets. The asset ratio measures the clients potential ability to pledge collateral, while also indicating the relative importance of bank financing and reflecting the banks internal rating of borrower risk. The paper concludes that there is no significant correlation between borrower quality and the degree of collateralization. Elsas and Krahnen (2000) empirical study shows that the incidence of collateral, which means the choice between an unsecured and secured loan, is independent of the borrower s default risk. There is no statistically significant difference between prime borrowers and low-quality borrowers, who have highest default expectations. The objective of Berger and Udell (1990) is to investigate the relationship between collateral and credit risk. These author claim, that when borrowers have private information about risk then the lowest-risk borrowers are more likely to pledge collateral; when risk is observable, however the highest-risk borrowers tend to pledge collateral. Collateralized loans can be safer or riskier than non-collateralized ones. The empirical findings of Berger and Udell suggest that collateral is most often associated with riskier borrowers, riskier loans, and riskier banks. The authors develop three main hypotheses, and using empirical analysis investigate which of them is supported in most cases. The First hypothesis is that safer borrowers more often pledge collateral and that secured loans are less risky than unsecured loans. The second hypothesis is that riskier borrowers more often pledge collateral, but choice against collateral more than fully offsets the difference in borrower risk, so that secured loans are safer than loans to borrowers who borrow only on an unsecured basis. The third hypothesis claims that Riskier borrowers more often pledge collateral, and recourse against collateral less than fully offsets the difference in borrower risk, so that secured loans are riskier than loans to borrowers who borrow only on an unsecured basis. To test the model developed in the paper, the authors use the Federal Reserve s Survey of Terms of Bank Lending, which includes contract information on over 1,000,000 commercial loans made from 1977 to 1988 by a stratified sample of 460 commercial banks. The data constantly suggest that collateral is associated with higher credit risk and suggest that borrowers who pledge collateral are riskier on average than borrowers who do not. Banks with a higher proportion of secured lending tend to have more borrowers with nonperforming loans. The data also suggest that secured loans are riskier than unsecured loans; this means that value of recourse against collateral does not fully offset the higher risk of secured borrowers. Banks which tend to specialize in secured lending are riskier, as evidenced by the aforementioned relationship between charge- 7

8 off rates and the secured loan proportions. Berger and Udell (1990) investigated the relationship between collateral and three types of risk: risk of the borrower, the risk of the loan, and the risk of the bank. For all three types, they find a positive relationship between collateral and risk: riskier than average firms tend to borrow on a secured basis, the average secured loan tends to be riskier than the average unsecured loan, and banks which make a higher fraction of unsecured loans tend to have riskier portfolios. Although the paper by Berger and Udell (1990) is very relevant to my research, their model is not appropriate in my case, although I want to test similar questions, the database authors use is different and I don t have in my data same variables. They investigate the relationship between risk and collateral using cross-section regressions. Along with this question I am interested in what characteristics determine if a loan is collateralized. Orgler (1970) provides empirical studies based on data on individual loans from bank examination files. He distinguishes good from bad loans on the basis of whether the loans were ultimately criticized by the bank examiners. He regresses a good-bad dummy variable on a secured-unsecured dummy variable; as control variables he uses dummy variable for current loan, audited companies, profit making companies and criticized loans. The ratio of working capital to current assets was the only independent variable in the regression not restricted to the values (0;1). Orgler s (1970) Empirical evidence finds secured loans to be riskier than unsecured ones. 8

9 3. Methodology In this section I explain two empirical methodologies. The fist methodology explains determinants of the probability of loan to be collateralized. The second methodology explains relationship between collateral and probability of default. 3.1 Model 1: the probability of loan to be collateralized To estimate the determinants of the probability that the client is granted a non collateralized loan, I am going to use a logit model, with the probability of existence of collateral as a dependent variable. The logit modeling explains one or more dependent categorical variables when the independent variables are continuous. In my model, the dependent variable equals 1 if loan is collateralized or 0 for unsecured loan. The probability that a firm s loan involves collateral depends of certain explanatory variables. As determinants of the probability of having to secure the loan, I am including in the model the requested amount of loan (RAMOUNT). This is the amount initially requested by client and it is not always equal to approved amount of the loan. I expect that its coefficient will be positive, as larger loans are more likely to be collateralized. Greater loan size should positively affect the probability for the loan to be collateralized, as the bank in the case of clients default loses more. So the bank is more likely to ask for guarantee in case with higher amount of loan. The next variable I am including in the regression is requested length of the loan (RLENGHT). I expect that the coefficient of the variable will be positive, as risk increases with time. I expect that loans with longer length are more likely to be collateralized. I also include in the model the ratio of approved amount of loan to required amount of loan (RATIOAR). A higher ratio of approved to required loan should have a negative effect on the probability of loan to be collateralized. The explanation is that clients who get only part of initially required amount are probably assumed to have a risky project, suggesting an increased likelihood to have a secured loan. 9

10 I am including a dummy variable taking the value 1 when the client is repeat customer (TYPE CLIENT) or 0 otherwise, as I expect that presence of collateral can depend on whether client is repeated or one time client. I expect that variable TYPE CLIENT should have positive sign, as the repeat borrowers tend to have a regular loan schedule and regular loans are mostly collateralized, while express and automatic loans are non-collateralized business loans. This happens because express and automatic loans have higher interest payments, so for repeat borrowers it is more desirable to apply for regular loans with low interest payment. I control for size of the firm by using in model the number of employees (EMPLOYMENT). I expect that the number of people working in the firm should have a positive effect on probability of loan to be collateralized. Bigger firms are asking for the loans with higher amount, that s why I expect positive relation between number of employers in form and the probability of the loan to be collateralized. Dummies for cities are important as across regions risk of default can be different which can cause different guarantee from clients. Coefficient of variable will allow us to see in which town borrowers have, in average, the highest probability to obtain secured and/or unsecured loans. I am including in the regression industry dummies. Client firms of ProCreditBank are split on 11 industries, according the standard European classification of industries (NACE). It allows investigating firm from which industries are more likely to have secured and/or unsecured loans. Table 1: List of Variables for the estimation of probability of loan to be collateralized Dependent variable Dummy variable =1 if collateral is plugged, =0 if loan is non-collateralized. (COLLATERAL) Solvency variables Requested amount of loan (RAMOUNT) Requested length of loan (RLENGTH) Ratio of approved to request amount of loan (RATIORA) Dummy for type of client =1 if repeat borrower, =0 if one time client (CLIENTTYPE) Dummy for sex =1 if male, =0 if female (SEX) Age of owner (AGE) Income of the owner at the time of the loan (INCOME) Yeas of existence of the firm (YEAREXISTANCE) Dummy variables for city in which the branch is located Dummy variables for industrial sector of the firm 10

11 3.2 Model 2: relationship between collateral and loan loss ratio Using a cross section analyses, I am going to investigate the. To study characteristic of loans which are risky, and have higher probability of default, I use loan loss ratio as my dependent variable. Loan Loss Ratio is a very important variable of my model as it attempts to pragmatically enable the bank to counter the default risk in a portfolio by using past as well as future data, and assigning probabilities for likely future losses. Reserve ratio indicates what percentage of the loans outstanding is expected to be unrecoverable. The information about the risk of the loan is assessed thought the variable loan loss Ratio (LLR). The loans with high LLR are identified as risky loans and the ones with low LLP as less risky loans. So the link between probability of default and loan loss ratio is clear: loans with higher loan loss ratio have higher probability to not be repaid. Loan Loss Ratio or LLR is a percentage that reflects accumulated provision expenses and gives an indication of the management's expectation of future loan losses. It is a rough indicator of the overall quality of the portfolio, and it represents the loan loss reserve amounts maintained by a Bank to offset the default risk in its total loan portfolio 1 In the ProCreditBank Database LLR is indicated for each loan; I am using LLR for my model as a dependent (continuous) variable. As determinants of risk, I am including a dummy variable, which equals 1 if collateral is pledged, and equals 0 if the loan is non-collateralized (COLLATERAL). This allows me to investigate what effect negative or positive has collateral on loan loss ratio. I expect that collateral should have a positive relation with the probability of defaults. Following the line of investigations of Jimenez and Saurian (2003), I also expect to find a positive relation between collateralized loans and probability of default, so we should see a positive relation between collateral and LLR. Bank-borrower relationships increases the willingness to take risk, if the bank underestimates the risk of its repeat borrower, based on her ex ante reputation or credit history. If this result of Jimenez and Saurian (2003) holds then I should see positive relationship between repeat borrower and loan loss ratio. 1 Sa-Dhan Microfinance Manager Series, Technical Note #4. How to use the Loan Loss Ratio in Microfinance. 11

12 I am including in model ratio of approved to requested amount of loan (RATIOAR). I use this variable as a proxy for ex ante risk. If the bank gives to the client less than initially requested amount, this happens because the risk present and it is observable for the bank. When clients approved amount coincides with initially required amount of loan this means that project was assumed to be safe. I expect that high ratios should negatively affect LLR, as the ratio is high for clients who received required, or close to required amount. That means that budget of project the loan was required for was convincing and safe for the loan officer. I control for the size of the firm by using the number of employees (EMPLOYMENT). I expect that the number of people working in the firm should have a positive effect on LLR. I think that the bank is putting much more effort in screening the self-employed clients and entrepreneurs. The bank is more careful with giving a loan to one person, rather than to company with two or more employers. If the screening is really done carefully, then the LLR should be higher for firms with more employees. Bank is much more careful with giving loans with high amount, as in the case of the clients default bank loses more. I am including in model the requested amount (RAMOUNT) to control for the size of loan. I expect that loans with higher amount should have higher loan loss ratio, as a grater loan is risky for both lender and borrow. Although it can be opposite if the bank is more careful with screening the projects which require greater loans, than the coefficient of RAMOUNT will be negative. I think it is relevant to include in the model the requested length of the loan (RLENGHT). I expect that loan with higher length should be more risky, although it can be that bank is more careful with approving loans with longer length, so the sing of coefficient is ambiguous. I am including a dummy variable taking the value 1 when the client is repeat customer (TYPE CLIENT) or 0 otherwise. The reason for putting this variable in model is that Jimenec and Saurina (2003) explore that probability of default is positively related with bank and borrowers relationship. I expect that variable TYPE CLIENT should have positive sign, as the repeat borrowers are people who mostly apply for regular loans, which often require collateral but have lower interest rate. Repeat borrowers should have greater loans what can have positive affect on loan loss ratio. TYPE CLIENT can be positively related to loan loss ratio also in case if bank is less accurate with repeat borrowers projects screening. 12

13 Dummies for cities are important as across regions risk of default can be different which can cause different guarantee from clients. The sign of each dummy variable will make it possible to see in which city loans have higher loan loss ratio in average. I am including in the regression industry dummies. I divide the client firms of ProCreditBank in 11 industries, according the standard European classification of industries (NACE). It will allow me to investigate firm from which industries are more likely to have high and low loan loss ratio. Table 2: List of Variables for the estimation of Loan Loss Ratio determinants Dependent variable Loan Loss Ratio ( LLR) Solvency variables Dummy variable =1 if collateral is plugged, =0 if loan is non-collateralized. ( COLLATERAL) Requested amount of loan (AAMOUNT) Requested length of loan (RLENGTH) Ratio of approved to request amount of loan (RATIORA) Dummy for type of client =1 if repeat borrower, =0 if one time client (CLIENTTYPE) Number of employees of client at the time of the loan (EMPLOYMENT) Dummy variables for city in which the branch is located Dummy variables for industrial sector of the firm 13

14 4. Description of Data All data are provided by the ProCreditBank of Georgia, covering the time period ProCredit Bank Georgia is a development-oriented credit institution, which offers a wide range of credit products. ProCredit Bank focus on lending to very small, small and mediumsized enterprises, as it is convinced that businesses create the largest number of jobs and make a vital contribution to the economies in which they operate. Unlike other banks operation in Georgia, ProCredit does not promote consumer loans, instead it focus on responsible lending, by building long-term partnership with its customers. The micro loans are available to small and medium enterprises with and without collateral. Loans without collateral are called express loans. These loans were introduced in 2004 and are pretty popular, as collateral has always been problem for small and medium enterprises in Georgia. ProCreditBank Georgia data consist of 35,568 client loan profiles, those are firms that received loan, so the data does not includes firms that were turned down for loans. Database include a wide range of firm characteristics, owner/manager characteristics, firm creditworthiness and credit history, client s loan characteristics at ProCreditBank and identification of credit rationing. For my empirical study, I am using a sample consisting of 600 observations from the dataset: 400 are repeated borrowers and 200 are one time clients. The sample is chosen taking into consideration three clusters: city, loan type and amount of loan. In the sample, in each city, the percentage of the low loan clients is the same as in the overall database. For example in the database we observe in Tbilisi that 41% of total loans, so in the sample we should also have 41 % of loans in the same town. In the database, we have seven different type of loans: 1) Regular loan a collateralized business loan, with regular or seasonal payment schedule, 2) Credit line; 3) Overdraft; 4) Express loan uncollateralized business loan, with regular or seasonal payment schedule; 5) Automatic loan uncollateralized business loan, for customer with good credit history; 6) Start up World Vision Project and 7) Tourism loan. If we have in Tbilisi 33 % of regular loans, in the sample we have to have 33 % of regular loans in the capital city. The amount of loan is a dummy variable, taking value 1 if loan is more than 5000 USD or 0 otherwise. Within the sample the percentage of, lets say, regular loans more than 5000 USD in Batumi should be identical as it is in the database. 14

15 Table 3: Distribution of different types of loans across cities Town Type of Loan Regular Express loan loan schedule Batumi Gori Khashuri Kobuleti Kutaisi Marneuli Poti Rustavi Tbilisi Telavi Zugdidi Automatic The first question of my research is which characteristics determine whether a loan is collateralized. The percentage of collateralized loans in the sample is 23.4, but this percentage varies across the cities. In Batumi, 34 percent of loans are collateralized. This is the highest ratio of secured to unsecured loans in comparison with all to other towns. The second town in this ranking is Kutaisi, where 30 percent of loans are collateralized. Furthermore, 3.4 percent of loans have 12 percent interest, which is the lowest interest rate in the sample. 14 percent of loans were given at a 32 percent interest rate percent of total number loans were given for interest rate higher than 34 annual rates. Regular, express and automatic loans are about 80 percent of total loans. If we rank loans by purpose 37 percent of total are for working capital, 37.5 percent for fixed assets, 17.1 for consumer loans, and 3.6 percent for working capital and assets. Loans for all other purposes form 4.8 percent of total loans. 15

16 Table 4: Distribution of purpose of the loan for different types of loan Regular loan schedule Type of Loan Express loan Purpose of the Loan Consumer Consumer - Car loan Consumer - Education Consumer - Medical ex Consumer - Others Fixed assets Home Impr.-Renovation Office renovation Other Purchase of House or Flat Renovation of House or Flat Working capital Working capital and fixed assets Automatic 80 percent of loans are less than 5000 USD. For those loans 61.9 percent are regular loans, 30.8 percent are express loans, and 6.1 percent are automatic loans. For large loans where the amount exceeds 5000 USD are express loans, 23.3 percent are automatic loans, and only 3.2 percent are regular loans. 66 percent of loans with high amount (more then 5000 USD) are approved for repeat borrowers. I look at the ratio of the requested amount of loan to approved amount of loan. For 72 percent of all loans this ratio is one, meaning that the client got exactly the amount she was seeking. For 3.4 percent of loans this ratio is more than one, what means that the client received more than she initially requested. Now we see 24.6 percent of borrowers received a smaller amount than requested. One explanation is that the bank found the project risky, or the amount needed to be overestimated. It makes sense to look at ratio of length of loan approved to the length of loan required. This ratio shows us the how often decision of bank does not coincide with borrower s requested length of the loan. The ratio is one for 73.8 percent of loans: in 412 cases requested and approved length of loan coincides. For 13.3 percent of loans the approved length was less than initially requested by client. 99 percent of clients serve the local and national markets; about 6 percent of clients have foreign suppliers percent of clients don t have debt or loan at any credit institution at the time when they apply for loan at ProCreditBank. In the introduction I mentioned that other 16

17 sources of financing are still strong in Georgia; let s look at what other options the firm was considering for financing before choosing ProCreditBank: 7.6 percent of clients would try applying for loan at other financial institutions, 2.3 percent would borrow from family, friends or other individuals, 33.8 percent would try to finance the project from individual savings. Half of clients didn t answer this question, perhaps they fear that by giving this information they will decrease their chance of getting a loan percent of clients respond that they had no need to apply for loan before. 33 percent of clients ask for loan with length less than one year percent of clients are female percent of clients are firms of individual ownership percent jointly shared firms, 57 percent of client are self-employed. Only 3 clients confess bankruptcy before the loan. Only 11.3 percent of all clients have been granted at least one loan at any other credit institution before being approved for a loan at ProCreditBank. No more than 4 clients declare that their firm has been denied at least one loan at any other credit institution before being approved for a loan at ProCreditBank. Out of all clients 64.5 percent are repeat customers, those who have received a loan more then once. About 67 percent of collateralized loans are given to repeat customers. 17

18 5. Empirical evidence I will present results for two empirical models. Model 1 tests which characteristics determine the probability of loan to be collateralized. Model 2 tests the relationship between collateral and loan loss ratio. 5.1 Model 1: the probability of loan to be collateralized The results of model 1 are presented in Table 5. In analyzing the regression results, note that the requested length of the loan (RLENGTH) is a significant variable and has a positive sign. As expected, a longer requested length of loan increases the probability that the loan will be collateralized. The requested amount of loan is also significant and positively related to the probability that collateral is present. As expected - for higher amount of loan it is more likely that collateral will be required. TYPECLIENT is dummy variable for type of client =1 if repeat borrower, =0 if one time client. The variable turns out to be insignificant. But, as we see the fact that whether client is repeat borrower or not, it doesn t affect the probability of loan to be collateralized. This means that other characteristics dominate in determining whether a loan will be collateralized or not. When controlling for other characteristics, TYPECLIENT is statistically independent from probability of loan to be collateralized. A client with a higher ratio of approved to initially requested amount of loan (RATIOA) is insignificant in the regression. So, I can say that the ratio of approved to requested amount has no affect on the probability that loan will be collateralized. Controlling for size of the firm, the number of employees (EMPLOYMENT) is significant and has a positive sign. As expected, the number of people working in the firm should have a positive effect on probability of loan to be collateralized. Bigger firms are asking for the loans with higher amount, that s why I find a positive relation between number of employers in form and the probability of the loan to be collateralized. 18

19 The variables AGE, SEX, INCOME and YEARSEXISTANCE are insignificant. I can say that the probability of loan to be collateralized for ProCreditBank clients is not affected by age of company owner, the fact whether borrower is male or female, of existence of the firm, or the income of borrower. This shows absence of discrimination by sex and age. ProCreditBank also equally treats new and relatively old enterprises when it requires (or not requires) collateral. As we see the income of the owner at the time of the loan has no affect on probability that collateral will be required. The dummy variables which stand for the cities show that the probability of the loan to be collateralized is statistically independent from the location of client since all dummies are insignificant, except Tbilisi, which is significant at 1 percent level and has negative sign. In the capital-city, it is easier, apparently, to get a loan without having collateral. Perhaps a client from the capital is considered to have at least some informal collateral. But, there can be also more sophisticated explanation, such as that in Tbilisi there are more developed enterprises, more skilled workers, average revenue is higher. Because of this, we have in Tbilisi a concentration of low risk borrowers. By now, it is reasonable to conclude that clients in Tbilisi are more likely to have non-collateralized loan. Analyzing the regression results for industry dummies, we see that only variable which is significant is industry of Hospital activities, practice activities and education. Client firms of ProCreditBank who operate in this industry are more likely to have secured and loans. 19

20 Table 5: Estimation Results for probability of loan to be collateralized Dependent variable: COLLATERAL RAMOUNT RLENGTH RATIORA CLIENTTYPE EMPLOYMENT SEX AGE INCOME YEARSEXISTANCE Batumi Gori Khashuri Kobuleti Kutaisi Poti Tbilisi Zugdidi Manufacture of footwear Apparel Manufacture of food products Construction material Supermarkets, shops and Aphotecs Hotels and restaurants Taxi operation, Cargo handling Real estate activities Hospital activities, practice activities, Education *** (4.83) (3.65)*** (-1.11) (1.16) (3.22)*** (-0.63) (-1.29) (-0.27) (0.88) (-0.33) (-0.49) (-0.02) (-0.63) (-0.42) (0.06) (-2.92)*** (1.56) (0.37) (0.42) (-0.4) (0.62) (0.92) (0.49) (0.22) (-0.66) (2.12)** R Nb of obs 292 Note: estimation using logit model. In parentheses below coefficient: t-statistics. * = significant at 10%; **=significant at 5%; ***= significant at 1%. 20

21 5.2 Model 2: relationship between collateral and loan loss ratio I will now present the results for the model 2 which explains relationship between loan loss ratio (as proxy for default risk) and collateralized loans using cross section analyses. In analyzing the regression results, we see that collateral (COLLATERAL) is significant, and has a positive sign. That shows us that the presence of collateral affects positively loan loss ratio. Based on this, we can say that collateralized loans have higher probability of default compare to loans without guarantee. Ratio of approved amount to required amount of loan (RATIOAR) is significant and has a negative sign. As I expected, the client who received initially required amount are better in repaying the loan, have lower LLR and lower probability of default. Firms with more employs tend to have higher LLR. This variable is significant and has a positive sign. The explanation here is the screening effort. Banks tend to trust bigger companies more than single individuals. By putting less effort in screening the loan applications of big firms, banks are underestimating the risk. So, ex post, we observe the higher LLR for firms with larger number of employers. The alternative interpretation is that larger firm request usually greater loans, and loans with higher amount are more risky and should have higher loan loss ratio. The requested amount of loan is insignificant variable in this model. The same can be said about requested length of loan: this variable is statistically not significant. Most probably, it will depend on not requested but on approved length and/or amount. The dummy variable TYPECLIENT has positive sign and it is significant at 1 percent level. This result coincides with finding of Jimenez and Saurina (2003) that relationship of bank and borrower increases the risk of default. I find a positive relation between repeat borrowers and loan loss ratio. That means that indeed, close relationship between client and bank increases the willingness to take risk. The result supports the idea that the bank is more careful with one time clients, and in the case with repeat borrowers the risk of the loan can be underestimated 21

22 because of good reputation of the client in the past. Then it is sensible that loans of repeat borrowers have higher loan loss ratio. Analyzing the dummy variables for cities in regression, we see that Batumi, Gori and Tbilisi are significant at 10 percent level and have a negative sign. We can say that borrowers in these towns have more safe loans, more stable repayments, lower risk and lower probability to default. In case of Gori, the factor which causes the lower the risk and better loan repayments can be closeness to the capital Tbilisi. The intuition is that Batumi and Tbilisi are big cities with more developed enterprises, which have higher average revenues, more skilled workers. This explains the better concentration of low risk borrowers. Looking on the sign of dummy variables for industries, we see that manufacture of food products has a positive sign and it is significant at 10 percent level. Firms operating in Manufacture of food products have higher LLR and are assumed to be more unstable repairers. It seems that for ProCredit customers who operate in food production are assumed to be more risky compare to all other industries, as LLR is higher for firms working in food production sector. 22

23 Table 6: Estimation Results for Loan Loss Ratio determinants. Dependent Variable: LLR COLLATERAL RAMOUNT RLENGHT RATIOAR TYPECLIENT EMPLOYMENT Batumi Gori Khashuri kutaisi Kobuleti Poti Rustavi Tbilisi Telavi Farming, forestry, agriculture Manufacture of footwear Apparel Manufacture of food products Construction material Supermarkets, shops and Aphotecs Hotels and restaurants Taxi operation, Cargo handling Real estate activities Hospital activities, practice activities, education (7.12) *** (-0.52) (-0.51) (-3.07) *** (2.94) *** (5.72) *** (-1.73) * (-1.87) * (-0.34) (-0.56) (-0.72) (-0.49) (-0.49) (-1.81) * (-0.66) (-1.16) (-1.61) (0.41) (1.84) * (-0.74) (0.35) (1.26) (0.42) (0.14) (1.17) R Nb of obs. 318 Note: estimation using cross section analysis In parentheses below coefficient: t- statistics. * = significant at 10%; **=significant at 5%; ***= significant at 1%. 23

24 Conclusions The objective of my research is to investigate the relationship between collateralized loans and probability of default, based on analyses of ProCreditBank s database sample on about 600 loan profiles. I find a positive relationship between collateralized loans and loan loss ratio. I am able to conclude that collateralized loans are more risky and have higher probability to default. My results are consistent with findings of Jimenec and Saurina (2003). Empirical evidence shows that the risky clients end up pledging collateral to get a loan from the financial intermediate. Another important finding of my paper is that repeat borrowers tend to be more risky clients than one time borrowers. This outcome coincides with result of Jimenec and Saurina (2003) where they ague that borrowers and banks relationship increases the likelihood of risk. It s proves the assumption that bank underestimates the risk of projects of the clients who had some relationship with this bank before. Based on analyses of Data from ProCreditBank, Georgian repeat borrowers have more risky loans compare one time clients. My findings are contrary with results of Elsas and Krahnen (2000), who showed that choice between unsecured and secured loan is statistically independent of the borrowers default risk. My results show strong positive relation between secured loans and probability of default, caused by higher loan loss ratio for collateralized loans. Bank is more likely to ask collateral from risky borrowers when the risk is possible to observe. The ProCreditBank is oriented on lending to small and medium enterprises. Before the loan bank is approved bank always tries to observe risk. In most of cases loan officers are unbiased; they objectively look at the risk. The ProCreditBank in fact asks collateral from most risky clients. It not discriminates borrowers by sex and age. ProCreditBank also equally treats new and relatively old enterprises when it requires (or not requires) collateral. As we see the income of the owner at the time of the loan has no affect on probability that collateral will be required. 24

25 The policy implications of my results are that institutional support of small and medium enterprises that are credit constrained because of lack of the collateral. Secured loans tend to be most risky ones. The default of the client is always undesirable for the bank, so by diminishing the collateral requirement in cases when it is possible, bank will significantly reduce the risk of the loans, as secured loans have higher loan loss ratio. Bank should give loans to client who present good project, although they lack collateral. Absent of collateral should not be for the bank signal for risky borrower, my empirical evidence strongly suggest opposite. It is important to assist small and medium enterprises who fail to get a loan from the financial intermediaries only because of lack of collateral. These firms often become clients of private money lenders and a significant share of their profits goes to interest payments, which are extremely high compare to rates charged by bank and micro-finance institutions. Lack of collateral should not cause failure of loan application, what often happens in Georgia. Broaden research should be done to understand problems of SME s related to loan accessibility and credit market imperfections. It is important to understand the reasons why alternative sources to finance projects are frequently preferable to institutional lending. It would be interesting to investigate why some entrepreneurs and firms are afraid to borrow from the bank and prefer to get a loan from private money lender. In my study, I use a sample of firms who get loan from the financial intermediate. It is essential to understand characteristics which determine firms to be credit constraint. Further investigation would requires to devise a survey for firs which never applied for a loan or been rejected. 25

26 Bibliography Berger A. N., Udell G. F., 1990, Collateral, Loan Quality and Bank Risk, Journal of Monetary Economics Vol.25, pp Elsas R., Krahnen J. P. 2000, Collateral, Default Risk and Relationship Lending: An Empirical Study on Financial Contracting. Working paper, Goethe-Universität Frankfurt. Hempel, G., Coleman, A., Simonson, D., 1986, Bank Management Text and Cases. Fourth edition. John Wiley and Sons, Inc.: New York. Jimenec G., Saurina J. 2004, Collateral, Type of Lender and Relationship Banking as Determinants of Credit Risk. Journal of Banking & Finance, Vol. 28, Issue 9, Pages Manove, M. and Padilla A. J., 2001, Collateral Versus Project Screening: a Model of Lazy Banks, RAND Journal of Economics, Vol. 32, Morsman, E., Jr,, 1986, Commercial loan structuring, Journal of Commercial Bank Lending 68-10, Orgler, Y., 1970, A credit scoring model for commercial loans, Journal of Money, Credit and Banking, Vol. 2, Winton, Rajan R. and Andrew, 1995, Covenants and Collateral as Incentives to Monitor, The Journal of Finance, Vol. 50, pp