Credit Risk: Contract Characteristics for Success

Transcription

1 Credit Risk: Contract Characteristics for Success

2 About The Equipment Leasing and Finance Foundation The Equipment Leasing and Finance Foundation is a 501c3 non-profit organization that provides vision for the equipment leasing and finance industry through future-focused information and research. Primarily funded through donations, the Foundation is the only organization dedicated to future-oriented, in-depth, independent research for the leasing industry. Foundation products include the Industry Future Council Report, the State of the Industry Report, and the Journal of Equipment Lease Financing and numerous research reports. Established in 1989, Founders include leasing industry member Paul S. Gass, the Equipment Leasing Association of America, and others. Visit the Foundation online at The Foundation is the source of future-focused research and information. The Foundation is donor-supported, a 501(c)3 organization, Donors receive copies of Foundation studies and reports, free and early released. Nondonor will purchase all Foundation studies and reports. Contact Information: Lisa A. Levine, CAE Executive Director 4301 N. Fairfax Drive, Suite 550 Arlington, VA llevine@elamail.com

3 For partnering to provide future focused research and reports, the Equipment Leasing & Finance Foundation wishes to express appreciation to PayNet. PayNet provided the statistical data used to execute in this study. Many thanks to

4 Credit Risk: Contract Characteristics for Success James P. Murtagh, PhD Clinical Assistant Professor Lally School of Management & Technology Rensselaer Polytechnic Institute Troy, NY January 2005 Corresponding author: James P. Murtagh, Lally School of Management & Technology, Rensselaer Polytechnic Institute, Troy, New York 12180; phone: , fax

5 Credit Risk: Contract Characteristics for Success Executive Summary The purpose of this research study is to investigate differences in the characteristics of successful (completed) lease transactions and failed (defaulted) transactions based on the PayNet data. Identifying measurable and persistent differences between successful and failed leasing contracts broadens the understanding of critical factors used in making leasing decisions. Data was provided through a cooperative agreement between the Equipment Leasing and Finance Foundation (ELFF) and PayNet. The study was supported in part by funding from ELFF. The findings indicate that contract characteristics, industry factors, and economic factors each influence default rates. The results support the use of higher risk premiums in specific industries and asset types. Insights from this study may be used to develop more effective risk management models. Sample included twelve quarterly data files covering the period April 2001 through January Average number of contracts studied per quarter = 85,487 Average number of failed contracts per quarter = 9,513 (11.1%) Industry classification o More than half of the contracts are in four broad industry classifications: services (SIC ), manufacturing (SIC ), transportation/public utilities (SIC ), and construction (SIC ). o The greatest portion of failed leases occurred in transportation/public utilities, services and manufacturing. o Finance/insurance/real estate had the smallest incidence of failed leases in the sample. o Results provide evidence of industry-level risk factors across the twelve quarters studied. o Greater industry risk may lead to higher risk premiums in specific industries and place greater emphasis on effective risk models in these industries. Contract term o The majority of contracts studied had original terms between 25 and 60 months. o Default rates increase with contract term, with nearly 17% of the longest contracts failing. Credit Risk: Contract Characteristics for Success -3- Equipment Leasing & Finance Foundation

6 Contract types o The data files include results for a variety of contract types. However, true leases represent nearly 70% of the overall sample. o For the true leases, the average default rate was 12%. o Revolvers had the lowest default rate at 6.5%. Asset type o Twenty-two distinct contract asset codes are represented in the study ranging from agricultural equipment to waste/refuse handling. o Over seventy percent of the contracts are found in four asset categories: copiers (36%), trucks (12%), computer equipment (12%) and construction/mining (11%). o Default rates vary widely among the different asset codes. This high variation supports the use of higher risk premiums for riskier asset classes. Contract guarantee o The majority of the contracts studied did not include a definitive guarantor code. o Within this sample, guarantor code provided very little additional insight into the development of effective risk management models. Characteristics of failed contracts o On average, failed contracts had higher original receivable s and larger reported loss s. o Failed contracts had lower average balances outstanding, possibly indicating an older subset of contracts. o Failed contracts had a greater number of times in all aging categories. The average number of days is significantly greater and the average dollar s are greater in all aging categories. Credit Risk: Contract Characteristics for Success -4- Equipment Leasing & Finance Foundation

7 Credit Risk: Contract Characteristics for Success ABSTRACT Equipment leasing companies need reliable information in order to assess the default risk on lease contracts. Lenders have historically built independent systems and expertise to support their credit-granting decisions. This approach created an incentive to specialize in order to leverage the value of these systems. The PayNet database was established in 2001 to provide an industry-wide source of information on a wide variety of lease contracts in a consistent, understandable format. Through the support of the Equipment Leasing and Finance Foundation (ELFF) and PayNet, this paper investigates specific characteristics of successful and failed equipment leases for twelve recent quarters. The findings indicate that contract characteristics, industry factors, and economic factors each influence lease default rates. The results support the existence of risk premiums for particular industries and asset types. This type of analysis could also be used to create more effective risk management models as proposed under the Basel II accords. Background In his article on The Future of Commercial Credit Data, Thomas Ware argues that the best predictor of default risk in leasing contracts is appropriate, comparable historical data. While this type of information is readily available in the consumer lending industry, commercial lenders have traditionally developed their own systems, based on specialization and expertise in their particular industry, equipment, or borrower niches. The PayNet database was established in the summer of 2001 to provide the industry-wide historical data in the equipment leasing market. The availability and successful use of industry-wide historical information can provide tangible Credit Risk: Contract Characteristics for Success -5- Equipment Leasing & Finance Foundation

8 benefits for lenders. Clearly, better information should lead to better credit decisions and lower default rates. A better understanding of credit risk can also provide rational support for credit risk premiums based on company, asset, or contract characteristics. An industry-wide database also provides the basis for lower costs through process automation and lower risks through diversification opportunities. These effects should improve lender cash flows and profitability. The availability of a broad industry database also provides researchers with a unique and valuable insight into the nature of successful and failed equipment lease transactions. The purpose of this research study is to investigate differences in the characteristics of successful (current) lease transactions and failed (defaulted) transactions based on a sample of the PayNet data. Identifying measurable and persistent differences in these characteristics expands the industry s understanding of critical factors used in making lease financing decisions and monitoring existing contracts. The study also investigates differences in characteristics between various types of lease customers and contracts in order to more clearly identify the most important factors in granting lease financing. This comparative information is useful to firms improving internal credit risk models, as required under the Basel II Accords. The eventual impact of the Basel II Accords (2006) on the leasing industry remains unclear. One apparent certainty is that Basel II will change the way a significant portion of the leasing industry measures risk and assesses risk exposures. In a 2003 white paper, The Basel II Accord: What does it mean for the North American Leasing Market?, the Equipment Leasing and Finance Foundation (ELFF) identified direct and indirect impacts of the Accord. The white paper is a call to action for leasing company managers. The Accord will affect leasing companies in North America either directly through participation, or indirectly, as the Credit Risk: Contract Characteristics for Success -6- Equipment Leasing & Finance Foundation

9 consequences of Basel II are felt globally. 1 The primary focus of the Accord is the global banking industry. By extension, lease subsidiaries of banks adopting the Accords will be directly affected. Also, the European Union has decided to adopt Basel II for all credit institutions operating in the EU. Clearly, lease companies operating in Europe also will be directly affected by the Accords. In their study of the residential subprime lending market, Cowan and Cowan find that default correlation is important in understanding risk management in these risky portfolios. Default correlation incorporates the fact that systemic events cause defaults to cluster. Coincident defaults among borrowers may be triggered by common underlying macroeconomic and/or industry factors. For example, it is reasonable to expect that increases in fuel costs coupled with an economic slowdown would squeeze cash flows in transportation industries, a result seen in the following study. Understanding default risk factors can assist with risk management through diversification. In order to build a diversified portfolio, however, lenders must learn and understand risk factors in unrelated industries in an effort to reduce the correlations between default factors. Data 2 & Methodology Through an agreement between ELFF and PayNet, this study provides descriptive statistics on a portion of the PayNet database. The study investigates the differences in the contract details and financial conditions of failed and successful (not failed) equipment leases. 1 The Basel II Accord: What does it mean for the North American Leasing Market?, Equipment Leasing and Finance Foundation, 2003, p Established in the summer of 2001, the mission of PayNet is to provide comparable payment history information for leasing contracts. The ELFF and PayNet have established a strategic partnership to provide access to the details of millions of transactions to be used for industry research studies. The PayNet database provides information on a wide variety of lease contracts in a consistent, understandable format. This database contains over fifty standardized fields including creditor business and financial background, a detailed credit check, current and historical debt levels, creditor payment habits, legal judgments and transaction-level loss information. The data is segmented by geographical region, industry, and equipment groupings to facilitate investigation of specific aspects of the industry. Credit Risk: Contract Characteristics for Success -7- Equipment Leasing & Finance Foundation

10 The contract characteristics studied include: the original contract receivable, contract payment, contract payment count, loss, balance, number of times past due at various aging categories, s at these aging categories and the average number of days. The analysis controls for industry classification, contract term, contract type, asset type and type of contract guarantee. This detailed investigation provides useful information to help lenders make better lending decisions and build better credit risk models. Data Description The subset of the PayNet database analyzed consists of forty-eight fields describing between 62,000 and 107,000 separate contracts. Data snapshots for twelve quarters beginning in April 2001 and ending in January 2004 were analyzed. The numbers of successful and failed contracts in each quarterly dataset are shown in Table 1. In simplest terms, a successful or good contract is one that is not in default in the quarter studied. Failed contracts are those with a Yes indication in the contract bad status field in the quarter studied. Data fields include leasing company information and contract-level information, supporting analysis at both levels. Given the size of the each quarterly data file, the SAS System for Windows, Release 8 was used for all data analysis. The data files studied represent a random subset of the complete PayNet database for any given quarter and were provided by the company in raw form without refinement. Descriptive Statistics (Table 1 through Table 6b) Table 1 shows the number of good and failed contracts in the quarterly data files studied. The total number of contracts included in the data files nearly doubles from 62,016 in April 2001 to 107,645 in January The number of failed contracts included in the data files nearly triples in that same time period, rising from 5,162 in April 2001 to 13,145 in January The Credit Risk: Contract Characteristics for Success -8- Equipment Leasing & Finance Foundation

11 percentage of failed contracts in a given quarter increases steadily from 8.3% in April 2001 to 12.2% in April 2003 and remains at that level for the remaining periods studied. Figure 1: Lease contracts by Industry Classification, January 2004 Agriculture, Forestry, Fishing 5% Public Administration 5% Mining 1% Construction 12% Services 27% Manufacturing 17% Finance, Insurance, Real Estate 8% Retail Trade 5% Wholesale Trade 7% Transportation, Public Utilities 13% The PayNet data categorizes each lender by a four digit SIC code. In order to more efficiently categorize the data, these four digit SICs were grouped into the ten commonly used industry classifications (Figure 1). Table 2a shows the number of good and failed contracts by industry classification and quarter. The data shows an increase in the percentage of failed leases over time in each of the SIC groups studied. The greatest proportion of failed leases for January 2004 are found in Transportation/Public Utilities group (17.6%), Services group (13.4%), and Manufacturing (12.9%). The failure percentage for the Transportation was more than double that in the Financial or Mining groups. This significantly greater failure rate in a certain industry Credit Risk: Contract Characteristics for Success -9- Equipment Leasing & Finance Foundation

12 supports the idea that industry-level risk factors are relevant to internally developed risk management models. Greater industry risk may lead to higher risk premiums for lenders/borrowers in these industries and place greater emphasis on effective risk models for lenders in this industry. Another important characteristic of the lending contract is the original contract term. Longer contract terms reduce lender liquidity and provide a longer time horizon during which the contract may fail. Table 3 shows the number and percentage of good and failed contracts categorized by original contract term and quarter. The majority of the contracts studied had an original contract term between 25 and 60 months (Figure 2). The percentage of defaults increases with respect to the original contract term. Nearly twice as many 61+ month contracts (16.8%) failed as 0-12 month contracts (8.8%). Credit Risk: Contract Characteristics for Success -10- Equipment Leasing & Finance Foundation

13 Figure 2: Percentage of contracts by original contract term, January months 8% 0-12 months 3% months 6% months 31% months 30% months 22% The PayNet database also categorizes contracts into a number of contract types. The majority of contracts are True Leases (68.4%). Table 4 shows the number and percentage of good and failed contracts by contract type and quarter. Default rates increased in all categories over time, with the largest increase on a percentage basis occurring in Conditional Sales and Loans. In the most recent period, the highest default rates are in Lease Purchase (14.4%) and Loans (13.9%), while the lowest default rate was in the Revolver Accounts (6.5%). An important characteristic of the lease contract is the underlying asset of the contract. The PayNet data studied lists twenty-two separate contract asset codes ranging from Agriculture to Waste & Refuse Handling. The number and percentage of good and failed contracts by contract asset code and quarter are shown in Table 5. Over seventy percent of the contracts are found in only four categories: Copiers (36.5%), Trucks (12.3%), Computer equipment (11.8%), Credit Risk: Contract Characteristics for Success -11- Equipment Leasing & Finance Foundation

14 and Construction/Mining (10.6%). Default rates also vary widely in the most recent period. Marine Craft, Medium/light duty trucks and Waste & Refuse handling show defaults rates less than 7%, while other asset codes are much higher in this sample. This considerable variation in default rates supports the use of higher risk premiums for certain asset classes and emphasizes the need for asset-specific factors in risk management models. Furthermore, an analysis of the default rates over time (Table 5b) shows that default correlations are not consistent across asset types. Recognizing the variation in default correlations provide managers with an opportunity to reduce the overall portfolio risk through diversification. The PayNet data also provides an indication of the manner by which contract cash flows are guaranteed. The contract guarantor code results are shown in Table 6. Under the PayNet system, guarantor codes include Corporate, Personal, Both, Yes - undefined, No, and Unknown. More than three-quarters of the contacts studied have a guarantor code of No or Unknown. For these guarantor codes, contract default rates remain fairly consistent across the twelve quarters studied, providing very little additional insight into the development of effective risk management models. Differences between the means analysis, January 2004 (Tables 7-12) In order to more clearly understand the characteristics of successful and failed leases in the sample, descriptive statistics were calculated for subsets of the January 2004 data file. The factors studied include the original contract receivable, contract payment, payment count, loss and balance. The number of times the account was reported and the balances were studied for accounts days, days and 90+ days. The average number of days was also studied. Using these statistics, differences between the means for successful and failed leases were calculated by Credit Risk: Contract Characteristics for Success -12- Equipment Leasing & Finance Foundation

15 subtracting the mean of the successful contracts from that of the failed contracts. A positive result (difference) indicates that the mean of the failed contracts is greater than that of the nonfailed contracts. Based on this calculation (failed - not failed), the difference between the means for the number of times, s, loss and average days should all be positive. The difference between the means for the original contract and payment are not expected to be significantly different from zero. Based on the assumption that lenders make effective initial assessments of credit risk, defaults early in the contract term are not expected. As a result, the difference in payment count is expected to be positive and the difference in balance is expected to be negative. The standard deviation and the Z-statistic were also calculated for these differences. The differences and levels of significance for the January 2004 data are summarized in Tables 7 through These results control for potentially influential factors such as SIC classification, contract term, contract type, asset type, and type of guarantee. The means and differences between means (failed-good) for the January 2004 data are presented in Table 7. For the overall sample, the only factor that was not statistically significant was the difference between the contract payment s. On average, failed contracts had higher original contract receivable s and larger reported loss s. Failed contracts also had lower average balance s, perhaps indicating an older subset of contracts. The failed contracts had a greater number of times in all aging categories, the average number of days is significantly higher and the mean dollar s in all aging categories are greater. Table 8 presents the differences between means for the same key contract characteristics. However, the results are further categorized by industry classification based on the reported SIC 3 The complete descriptive statistics from Tables 7-12 are available from the author upon request. Credit Risk: Contract Characteristics for Success -13- Equipment Leasing & Finance Foundation

16 code for the contracts. Differences in the results across industry classification may be indicative of industry influences in contract defaults. All industry classifications show a greater number of times for failed contracts in all aging categories. The difference in the average number of days is also positive, ranging from 13.5 days (Retail) to 75.3 days (Construction). The failed contracts also exhibited greater s and loss s in nearly all industry classifications. The difference in the original contract receivable was significant in three SIC groups; Agriculture, Public Administration, and Retail Trade. Failed contracts in these industries were characterized by larger original contract s than the non-failed contracts. This difference, though generally positive in the other industry groups, was not statistically significant. The results were mixed for the original contract payment. The Finance, Insurance, and Real Estate industry and Public Administration industry show statistically significant and larger payments for failed contracts. Manufacturing and Wholesale Trade have smaller payments for failed contracts. Finally, contract payment counts are greater and balance s are lower for failed contracts, perhaps indicating that the contracts were further along in the payment stream when they failed. The length of the contract term should be an important factor in assessing default risk. Longer term contracts offer a longer period for economic, industry, or company factors to negatively affect the firm s ability to fully repay the contract. Differences between the means for contract characteristics controlling for original contract term are shown in Table 9. The difference in the original contract receivable is significant and positive in the month term, indicating that the failed contracts in this term had larger original balances than the non-failed contracts. The opposite is true for the contracts over 60 months. In this instance, failed contracts appear to have been smaller. This result could indicate that riskier contracts were Credit Risk: Contract Characteristics for Success -14- Equipment Leasing & Finance Foundation

17 extended for longer terms. The difference in the contract payment was significant in four of the six term categories. In the short term contracts (0-12 month), the difference is negative indicating that failed contracts had lower payments. This is also true for the month contracts. The month and the month show a positive and significant difference. As expected, the differences for the number of times, s, and loss s are generally significant and positive. The average number of days for the failed contracts exceeds that of the non-failed contracts by days. The differences in the payment counts are positive and the significant differences in contract balances are negative. This result is consistent with the results found in Table 8. The PayNet data used in this study includes seven different contract types. While the True Leases account for nearly three-quarters of the sample, Conditional Sales, Lease Purchases and Loans represent a substantial portion of the sample as well. Other contract types include Rental Lease, Revolvers and Leveraged Leases. Table 10 shows the differences between means by contract type based in the January 2004 data. Again, the differences in the number of times, average days, and s are generally significant and positive. The exceptions are the Lease Purchases and the Revolver contracts which exhibit negative and significant differences. These mixed results persist in the other characteristics studied. For the original contract receivable and the contract payment, the differences are negative for Loans, Lease Purchases and Revolvers and positive for True Leases. The differences in the payment counts are similarly mixed. The differences in the balance s are all negative, with significant results for all contract types except Leveraged Leases and Revolvers. Table 11 shows the differences between the means organized by the reported contract asset code. The data studied includes twenty-one separate asset codes. This categorization Credit Risk: Contract Characteristics for Success -15- Equipment Leasing & Finance Foundation

18 provides the finest level of detail for identifying differences in the characteristics of failed and successful lease contracts. The differences are positive for the number of times for all aging categories and nearly all are significant. The difference in the average number of days past due is also positive. This difference is significant in thirteen of the twenty-one asset codes studied. The difference in the loss is also positive, as expected. The difference in the payment count is significant and positive in eleven of the asset codes, ranging from 1.45 days (Trucks) to days (Railroad). Although fewer show statistical significance, the differences in the s are generally positive for all aging categories. This result is consistent with expectations. The difference in the original contract receivable is significant in only five of twenty-one asset types. Copiers, Forklifts, and Marine Craft show positive differences for the original receivable. Manufacturing equipment and Medical equipment exhibit negative differences. The differences in the contract payment also show mixed results with only six of twenty-one asset types significant. The difference is negative for Computers, Medium/light duty trucks, Telecommunications equipment and the Unknown category. Forklifts and Retail equipment exhibit a positive difference, indicating that failed contracts for these asset types had a larger average payment. The differences in the balance s are negative in eighteen of the cases and significant in twelve of these. This result is consistent with previous findings, supporting the idea that the failed contracts are more mature than the non-failed. The PayNet database captures information about the type of contract guarantee through the guarantor code. More than three-quarters of the contracts have a guarantee code of None and twenty percent are either Yes-undefined or Unknown. Corporate, Personal and Both combined account for less than one percent of the sample. While Table 12 appears consistent with previous Credit Risk: Contract Characteristics for Success -16- Equipment Leasing & Finance Foundation

19 tables, the lack of variation in guarantor codes across this sample makes it more difficult to draw conclusions from the results. Conclusions The PayNet database provides an extensive resource for analyzing lease contract characteristics, industry factors, and economic factors that influence lease default rates. Consistent differences in industry and asset class default rates can be seen in the sample studied. Based on industry classification, the results provide evidence of industry-level risk factors across each of the twelve quarters studied. Greater industry risk may lead to higher risk premiums in specific industries and place greater emphasis on effective risk models in these industries. Similar results are found when contract term, contract type and asset type are studied. The results support the existence of risk premiums for particular industries and asset types. When comparing means between failed and successful contracts, payment counts tend to be higher and balance s lower for failed contracts. This result suggests that some form of ongoing credit monitoring could be useful to identify changes in default risk during the life of the contract. This analysis could also be used to create more effective risk management models as proposed under the Basel II accords. Future research could combine economic/industry factors such as interest rates and interest rate spreads, economic growth, and industry activity measures with PayNet data to develop more comprehensive default models. At the firm-level, further research should empirically investigate the opportunities for diversification in contract characteristics and subsequent reductions in borrowing costs. Credit Risk: Contract Characteristics for Success -17- Equipment Leasing & Finance Foundation

20 Acknowledgements The author acknowledges the financial support of the Equipment Leasing and Finance Foundation and data support from PayNet. Corey Trager of PayNet was especially helpful in providing valuable insights into the available PayNet data fields and creating the quarterly data files. ELFF referees provided helpful editorial recommendations. Any remaining errors are the responsibility of the author. References The Basel II Accord: What does it mean for the North American Leasing Market?, Equipment Leasing and Finance Foundation, Cowan, Adrian and Charles Cowan, Default correlation: an empirical investigation of a subprime lender, Journal of Banking & Finance, 28 (2004) Jackson, Suzanne, Better credit data, better credit decisions, ELT, June/July 2001, Kleinman, Robert, The characteristics of venture lease financing, Journal of Equipment Lease Financing, Spring 2001, Vol. 19, No. 1, Neagle, Bob, Time to get busy information-based risk management and tools for Basel II, ELT, June/July Song, Michael and Ben Sopranzetti, Leasing and the Small Firm, Research Report for Equipment Leasing and Financing Foundation, Ware, Thomas, The future of commercial credit data, ELT, June/July 2002, Credit Risk: Contract Characteristics for Success -18- Equipment Leasing & Finance Foundation

21 Table 1 Number of good and failed contracts by quarter Snapshot date Good contracts Failed Contracts Total % Failed April, ,854 5,162 62, % July, ,917 6,077 68, % October, ,530 6,803 72, % January, ,674 7,713 76, % April, ,523 8,574 79, % July, ,796 9,374 83, % October, ,510 9,876 86, % January, ,266 10,859 91, % April, ,649 11,532 94, % July, ,280 12, , % October, ,185 12, , % January, ,500 13, , % Credit Risk: Contract Characteristics for Success -19- Equipment Leasing & Finance Foundation

22 Table 2a Number of good and failed contracts by industry classification and quarter SIC Status Agriculture, Forestry, Fishing Good 3,890 3,922 4,007 4,073 4,134 4,213 4,291 4,355 4,409 4,479 4,660 4,698 Failed Mining Good Failed Construction Good 6,891 8,347 8,641 9,065 9,281 9,566 9,916 10,206 10,339 10,667 10,971 11,221 Failed ,028 1,098 1,167 1,262 1,321 1,355 1,383 Manufacturing Good 9,920 11,299 11,703 12,051 12,383 12,930 13,503 13,968 14,179 14,874 15,161 15,661 Failed 990 1,208 1,297 1,440 1,577 1,691 1,887 1,972 2,053 2,207 2,279 2,328 Transportation, Public Utilities Good 8,313 8,981 9,283 9,511 9,451 10,148 10,430 10,624 10,802 11,052 11,416 11,877 Failed ,065 1,230 1,510 1,763 1,883 2,204 2,353 2,459 2,486 2,535 Wholesale Trade Good 4,943 5,248 5,391 5,500 5,589 5,736 5,816 6,108 6,292 6,685 7,038 7,305 Failed Retail Trade Good 2,413 2,615 2,676 3,816 4,135 4,185 4,250 4,472 4,519 4,687 4,799 4,863 Failed Finance, Insurance, Real Estate Good 4,458 4,799 5,066 5,276 5,438 5,822 6,243 6,602 6,832 7,360 7,775 8,103 Failed Services Good 13,929 15,361 16,209 16,631 17,182 18,123 18,886 20,460 21,568 23,389 24,162 25,076 Failed 1,424 1,682 1,957 2,325 2,533 2,696 2,776 3,086 3,309 3,594 3,706 3,895 Public Administration Good 1,587 1,745 1,921 2,092 2,244 2,350 2,401 2,656 2,870 4,226 4,337 4,812 Failed Table 2b Percentage of failed contracts by industry classification and quarter Agriculture, Forestry, Fishing 7.4% 7.7% 8.0% 8.2% 8.4% 8.3% 8.4% 8.6% 8.9% 8.9% 9.2% 9.2% Mining 5.2% 4.8% 5.2% 5.3% 5.5% 5.9% 7.2% 7.3% 8.2% 8.8% 8.2% 8.3% Construction 8.5% 8.2% 8.6% 9.0% 9.5% 9.7% 10.0% 10.3% 10.9% 11.0% 11.0% 11.0% Manufacturing 9.1% 9.7% 10.0% 10.7% 11.3% 11.6% 12.3% 12.4% 12.6% 12.9% 13.1% 12.9% Transportation, Public Utilities 8.2% 9.3% 10.3% 11.5% 13.8% 14.8% 15.3% 17.2% 17.9% 18.2% 17.9% 17.6% Wholesale Trade 6.0% 6.3% 7.0% 7.7% 8.2% 8.7% 8.4% 8.5% 8.6% 8.4% 8.9% 8.9% Retail Trade 8.8% 8.9% 9.7% 7.4% 7.1% 8.8% 9.1% 9.6% 9.8% 9.8% 9.9% 10.0% Finance, Insurance, Real Estate 7.4% 7.9% 8.1% 8.2% 8.5% 8.8% 7.9% 7.5% 7.7% 7.6% 7.8% 7.9% Services 9.3% 9.9% 10.8% 12.3% 12.8% 12.9% 12.8% 13.1% 13.3% 13.3% 13.3% 13.4% Public Administration 7.0% 7.3% 7.2% 8.2% 9.6% 10.0% 10.6% 12.3% 12.2% 10.2% 10.0% 9.3% Credit Risk: Contract Characteristics for Success -20- Equipment Leasing & Finance Foundation

23 Table 3a Number of good and failed contracts by original contract term and quarter Contract term Status months Good 1,424 1,635 1,691 1,858 1,916 1,986 2,066 2,216 2,332 2,478 2,619 2, months Failed months Good 3,322 3,798 3,995 4,394 4,539 4,787 4,917 5,182 5,317 5,526 5,703 5, months Failed months Good 17,187 19,062 19,997 20,764 21,402 22,392 23,146 24,380 25,205 26,944 27,890 29, months Failed 1,452 1,682 1,855 2,044 2,249 2,431 2,547 2,814 2,985 3,239 3,332 3, months Good 12,035 13,309 13,868 14,649 15,180 15,931 16,514 17,148 17,693 19,754 20,468 21, months Failed 1,113 1,287 1,452 1,605 1,776 1,957 2,073 2,285 2,394 2,560 2,631 2, months Good 18,275 19,943 20,647 21,353 21,794 22,819 23,747 24,913 25,588 26,629 27,424 28, months Failed 1,711 2,067 2,347 2,771 3,089 3,382 3,534 3,860 4,142 4,328 4,486 4, months Good 4,611 5,170 5,332 5,656 5,692 5,881 6,120 6,427 6,514 6,949 7,081 7, months Failed ,059 1,199 1,266 1,370 1,422 1,463 Table 3b Percentage of failed contracts by original contract term and quarter Contract term months 6.6% 6.5% 7.7% 8.1% 8.5% 8.7% 8.9% 8.7% 9.1% 9.0% 8.7% 8.8% months 7.7% 7.8% 8.0% 7.9% 8.3% 8.4% 8.6% 8.6% 8.8% 9.4% 9.6% 9.6% months 7.8% 8.1% 8.5% 9.0% 9.5% 9.8% 9.9% 10.3% 10.6% 10.7% 10.7% 10.6% months 8.5% 8.8% 9.5% 9.9% 10.5% 10.9% 11.2% 11.8% 11.9% 11.5% 11.4% 11.5% months 8.6% 9.4% 10.2% 11.5% 12.4% 12.9% 13.0% 13.4% 13.9% 14.0% 14.1% 14.0% 61+ months 9.9% 10.5% 11.0% 11.7% 13.3% 14.2% 14.8% 15.7% 16.3% 16.5% 16.7% 16.8% Credit Risk: Contract Characteristics for Success -21- Equipment Leasing & Finance Foundation

24 Table 4a Number of good and failed contracts by contract type and quarter Contract type Status Conditional Sale Good 11,179 11,555 11,828 12,814 13,158 13,775 14,091 14,903 15,275 15,688 16,100 16,654 Failed ,138 1,303 1,559 1,594 1,790 1,896 2,023 2,093 2,160 Leveraged Lease Good Failed Loan Good 3,016 3,403 3,667 4,094 4,195 4,920 5,308 5,416 5,444 5,596 5,995 6,328 Failed ,002 1,022 Lease Purchase Good 4,282 4,338 4,393 4,469 4,520 4,557 4,603 4,708 4,768 4,842 4,885 4,936 Failed Revolver Good Failed Rental Lease Good 1,049 1,093 1,110 1,268 1,294 1,322 1,334 1,363 1,382 1,414 1,486 1,534 Failed True Lease Good 37,303 42,484 44,487 45,980 47,305 49,169 51,118 53,789 55,637 60,511 62,418 64,685 Failed 3,393 4,121 4,587 5,244 5,849 6,269 6,699 7,351 7,775 8,358 8,619 8,936 Table 4b Percentage of failed contracts by contract type and quarter Contract type Conditional Sale 6.3% 6.8% 7.7% 8.2% 9.0% 10.2% 10.2% 10.7% 11.0% 11.4% 11.5% 11.5% Leveraged Lease 71.4% 71.4% 71.4% 71.4% 71.4% 71.4% 42.9% 71.4% 71.4% 71.4% 71.4% 71.4% Loan 8.4% 9.0% 10.1% 10.3% 11.4% 11.8% 11.2% 12.5% 14.0% 14.4% 14.3% 13.9% Lease Purchase 13.0% 13.4% 13.7% 14.2% 14.3% 14.3% 14.5% 14.6% 14.5% 14.6% 14.5% 14.4% Revolver 4.2% 2.3% 4.4% 7.8% 7.5% 7.3% 8.8% 5.6% 9.7% 7.8% 7.2% 6.5% Rental Lease 7.9% 8.5% 9.2% 8.4% 8.5% 8.3% 8.4% 9.0% 9.3% 9.3% 9.4% 9.7% True Lease 8.3% 8.8% 9.3% 10.2% 11.0% 11.3% 11.6% 12.0% 12.3% 12.1% 12.1% 12.1% Credit Risk: Contract Characteristics for Success -22- Equipment Leasing & Finance Foundation

25 Table 5a Number of good and failed contracts by contract asset code and quarter Contract asset code Status Agricultual Good 2,704 2,958 3,022 3,075 3,122 3,159 3,206 3,266 3,291 3,330 3,362 3,378 Failed Aircraft Good Failed Marine craft Good Failed Buses & Motor coaches Good Failed Construction & Mining Good 6,932 7,934 8,187 8,665 8,821 9,012 9,294 9,526 9,600 9,790 9,934 10,129 Failed ,019 1,049 1,121 1,148 1,174 1,242 Computer Good 5,463 6,078 6,314 6,512 6,770 7,250 7,657 8,167 8,609 9,945 10,582 11,213 Failed ,033 1,078 1,215 1,275 1,377 1,410 1,447 Copier & fax Good 19,900 21,459 22,815 23,691 24,579 25,802 26,785 28,542 29,817 32,065 33,035 34,419 Failed 1,772 2,114 2,431 2,849 3,148 3,352 3,586 4,019 4,279 4,529 4,690 4,866 Forklift Good ,028 1,167 1,343 1,400 1,413 1,426 1,489 Failed Logging & Forestry Good Failed Medium/Light duty truck Good 2,456 2,784 2,862 2,920 2,963 3,027 3,099 3,147 3,192 3,242 3,265 3,296 Failed Medical Good Failed Manufacturing Good 3,446 3,895 3,963 4,010 4,055 4,133 4,271 4,400 4,456 4,558 4,681 4,792 Failed Office Equipment Good 1,107 1,163 1,210 1,237 1,281 1,575 1,608 1,721 1,780 2,478 2,630 2,791 Failed Printing & Photographic Good Failed Railroad Good Failed Retail Good 1,047 1,274 1,318 2,428 2,662 2,713 2,790 2,940 3,037 3,146 3,282 3,413 Failed Telecommunications Good 1,278 1,631 1,705 1,711 1,730 1,753 1,785 1,916 1,951 1,995 2,075 2,117 Failed Truck Good 8,405 8,773 8,916 9,042 9,078 9,791 10,044 10,243 10,322 10,492 10,903 11,233 Failed ,040 1,190 1,405 1,498 1,659 1,796 1,913 1,978 2,022 Unknown Good 1,248 1,533 1,584 1,616 1,638 1,660 1,718 1,792 1,824 2,329 2,409 2,496 Failed Vending & Restaurant Good ,019 1,052 Failed Waste & Refuse Handling Good Failed Credit Risk: Contract Characteristics for Success -23- Equipment Leasing & Finance Foundation

26 Table 5b Percentage of failed contracts by contract asset code and quarter Contract asset code Agricultural 7.4% 7.3% 7.5% 7.8% 8.1% 8.5% 8.7% 8.9% 9.0% 9.1% 9.1% 9.2% Aircraft 7.5% 6.2% 8.1% 7.6% 9.3% 9.0% 7.8% 7.7% 7.7% 7.6% 7.6% 7.5% Marine craft 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 5.3% Buses & Motor coaches 9.3% 8.6% 7.8% 9.5% 13.7% 14.8% 14.9% 24.0% 26.0% 25.7% 28.2% 28.1% Construction & Mining 9.1% 8.7% 9.1% 9.1% 9.5% 9.5% 9.9% 9.9% 10.5% 10.5% 10.6% 10.9% Computer 8.6% 9.0% 9.5% 10.8% 12.3% 12.5% 12.3% 13.0% 12.9% 12.2% 11.8% 11.4% Copier & fax 8.2% 9.0% 9.6% 10.7% 11.4% 11.5% 11.8% 12.3% 12.5% 12.4% 12.4% 12.4% Forklift 7.4% 6.5% 6.6% 7.4% 8.2% 8.4% 8.5% 7.6% 8.1% 11.9% 11.8% 11.4% Logging & Forestry 4.6% 5.2% 5.0% 5.3% 6.3% 6.7% 7.9% 7.7% 7.7% 8.0% 7.9% 7.7% Medium/Light duty truck 2.8% 3.1% 3.2% 3.8% 4.3% 4.6% 4.7% 5.2% 5.5% 5.7% 5.8% 5.8% Medical 12.0% 11.5% 12.4% 12.3% 12.6% 12.2% 11.6% 11.6% 12.7% 12.3% 13.1% 13.1% Manufacturing 12.4% 12.8% 13.1% 13.9% 14.7% 15.0% 15.5% 15.8% 15.8% 15.6% 15.5% 15.2% Office Equipment 7.4% 8.1% 8.7% 9.6% 10.0% 9.0% 9.8% 10.6% 10.6% 10.6% 10.3% 10.6% Printing & Photographic 14.0% 14.3% 14.8% 16.9% 17.6% 17.5% 17.4% 16.2% 16.8% 17.0% 17.1% 16.4% Railroad 13.0% 10.0% 10.0% 9.4% 9.1% 9.1% 2.9% 8.1% 10.8% 25.6% 25.6% 25.6% Retail 9.7% 8.6% 9.0% 5.5% 5.2% 6.7% 5.8% 6.1% 6.6% 7.1% 7.1% 7.1% Telecommunications 8.9% 10.5% 11.2% 12.8% 13.4% 14.5% 14.6% 14.3% 14.5% 14.3% 14.4% 14.4% Truck 7.2% 8.2% 9.4% 10.3% 11.6% 12.5% 13.0% 13.9% 14.8% 15.4% 15.4% 15.3% Unknown 13.3% 13.0% 13.3% 13.7% 14.9% 15.6% 16.0% 16.1% 16.3% 15.0% 15.1% 15.0% Vending & Restaurant 4.8% 6.3% 7.0% 7.6% 8.7% 17.1% 10.7% 10.7% 11.0% 11.9% 12.4% 12.7% Waste & Refuse Handling 3.0% 3.3% 3.2% 3.7% 3.9% 3.8% 3.4% 3.5% 4.0% 3.9% 4.1% 3.8% Credit Risk: Contract Characteristics for Success -24- Equipment Leasing & Finance Foundation

27 Table 6a Number of good and failed contracts by guarantor code and quarter Guarantor code Status Both Good Failed Corporate Good Failed None Good 43,056 47,438 49,612 52,193 53,847 56,463 58,639 62,046 64,120 68,977 71,366 74,050 Failed 3,857 4,529 5,057 5,741 6,400 6,955 7,328 8,071 8,588 9,202 9,504 9,846 Personal Good Failed Unknown Good 2,539 2,589 2,603 2,642 2,627 2,618 2,595 2,723 2,760 2,786 2,833 2,876 Failed Yes Good 11,148 12,773 13,191 13,675 13,879 14,542 15,093 15,308 15,578 16,328 16,794 17,379 Failed 1,152 1,365 1,515 1,697 1,834 2,020 2,093 2,285 2,415 2,538 2,627 2,697 Table 6b Percentage of failed contracts by guarantor code and quarter Guarantor code Both 33.3% 25.0% 25.0% 16.7% 16.7% 14.3% 12.5% 12.5% 12.5% 12.5% 12.5% 12.5% Corporate 0.0% 0.0% 0.0% 0.0% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% 25.0% None 8.2% 8.7% 9.3% 9.9% 10.6% 11.0% 11.1% 11.5% 11.8% 11.8% 11.8% 11.7% Personal 4.6% 6.9% 7.3% 8.5% 9.3% 11.2% 11.2% 11.7% 12.1% 14.8% 14.6% 14.4% Unknown 5.5% 6.3% 7.8% 9.0% 10.9% 12.5% 14.2% 14.9% 15.4% 16.3% 16.6% 16.5% Yes 9.4% 9.7% 10.3% 11.0% 11.7% 12.2% 12.2% 13.0% 13.4% 13.5% 13.5% 13.4% Credit Risk: Contract Characteristics for Success -25- Equipment Leasing & Finance Foundation

28 Table 7 Means and difference between means (failed-good) for all contracts, January 2004 data January, 2004 data Mean Difference variable Good Failed (Failed-Good) Contract receivable 114, , , ** Contract payment 4, , (106.83) Active scheduled payments count ** Loss , , ** Balance 21, , (7,073.38) * Times days ** Times days ** Times over 90 days ** days ** days ** 90+ days ** Average number of days ** ** significant at 1% level, * significant at 5% level Credit Risk: Contract Characteristics for Success -26- Equipment Leasing & Finance Foundation

29 Table 8 Differences between means (failed-good) by SIC code, January 2004 data SIC n Contract receivable Contract payment Payment count Loss Agriculture, Forestry, Fishing 5,173 29, ** (176.27) 5.32 ** 12, ** Construction 964 1, (399.34) 4.91 ** 5, ** Finance, Insurance, Real Estate 12,604 31, , * 4.01 ** 1, Manufacturing 17,989 (769.01) (3,624.84) ** 4.59 ** 4, ** Mining 14,412 (18,057.29) (602.03) Public Adminstration 8,019 75, ** 1, * 9.17 ** ** Retail Trade 5,403 62, * 10, ** 1, ** Services 8,802 4, ** 1, ** Transportation, Public Utilities 28,971 43, (1,021.30) 4.05 ** 2, ** Wholesale Trade 5,308 (4,514.91) (687.97) * 6.22 ** 3, ** Times days Times days Times 90+ days SIC n Balance Agriculture, Forestry, Fishing 5,173 (5,204.04) * 5.86 ** 3.99 ** 5.66 ** Construction 964 (4,623.40) 7.09 ** 3.85 ** 4.19 ** Finance, Insurance, Real Estate 12,604 2, ** 2.40 ** 3.16 ** Manufacturing 17,989 (15,386.49) ** 5.98 ** 3.72 ** 5.25 ** Mining 14,412 (23,856.62) 3.75 ** 2.40 ** 4.56 ** Public Adminstration 8,019 (11,972.82) ** 6.08 ** 4.98 ** 6.35 ** Retail Trade 5,403 (14,070.52) ** 4.91 ** 2.79 ** 3.26 ** Services 8,802 (11,356.45) ** 4.87 ** 3.16 ** 3.54 ** Transportation, Public Utilities 28,971 5, ** 5.82 ** 5.33 ** Wholesale Trade 5,308 (19,924.55) ** 6.40 ** 3.70 ** 4.47 ** SIC n day day 90+ day Avg days Agriculture, Forestry, Fishing 5, * * ** Construction ** ** ** ** Finance, Insurance, Real Estate 12, * * ** Manufacturing 17, ** ** 1, ** Mining 14,412 (13.43) Public Adminstration 8, * ** * ** Retail Trade 5, ** * * ** Services 8, * ** * ** Transportation, Public Utilities 28, ** ** ** ** Wholesale Trade 5, * ** ** ** significant at 1% level, * significant at 5% level Credit Risk: Contract Characteristics for Success -27- Equipment Leasing & Finance Foundation

30 Table 9 Differences between means (failed-good) by contract term, January 2004 data Contract term n Contract receivable Contract payment Payment count Loss 0-12 months 2,989 59, (29,185.19) ** 3.18 * 12, * months 6,532 71, , ** 2.96 ** 1, * months 32,594 57, * ** 2.78 ** 1, ** months 23,891 10, ** 3, ** months 32,911 (9,814.04) (500.12) * 4.60 ** 2, ** 61+ months 8,728 (50,952.84) * (953.83) 7.76 ** 8, ** Times days Times days Times 90+ days Contract term n Balance 0-12 months 2,989 14, ** 2.46 ** 5.00 ** months 6,532 12, ** 2.78 ** 4.04 ** months 32,594 8, ** 3.29 ** 4.06 ** months 23,891 (10,627.74) ** 5.72 ** 3.50 ** 4.11 ** months 32,911 (12,938.98) ** 7.80 ** 4.41 ** 4.28 ** 61+ months 8,728 (56,658.40) ** 9.07 ** 5.38 ** 6.75 ** Contract term n day day 90+ day Avg days 0-12 months 2, months 6,532 (1.85) * months 32, * ** ** ** months 23, ** ** 1, ** months 32, ** ** * ** 61+ months 8, ** ** 1, ** ** ** significant at 1% level, * significant at 5% level Credit Risk: Contract Characteristics for Success -28- Equipment Leasing & Finance Foundation

31 Table 10 Differences between means (failed-good) by contract type, January 2004 data Contract type n Contract receivable Contract payment Payment count Loss Conditional Sale 18,814 53, ** ** 4, ** Leverage Lease 7 (625,726.67) (55,540.22) N/A Loan 7,350 (122,253.35) ** (9,868.73) ** 3.82 ** 4, ** Lease Purchase 5,769 (173,356.04) ** (5,624.29) ** (65.14) ** (4,949.44) ** Revolver 382 (670,136.69) ** (118,549.57) ** (10.06) ** (2,835.56) Rental Lease 1,698 (29,112.74) (2,602.05) ** 8.24 ** * True Lease 73,621 23, ** 1, ** 5.06 ** 2, ** Times days Times days Times 90+ days Contract type n Balance Conditional Sale 18,814 (12,645.76) ** 9.96 ** 6.46 ** 5.40 ** Leverage Lease 7 (5,270,487.80) 6.00 * * Loan 7,350 (27,253.21) ** 4.53 ** 2.12 ** 2.19 ** Lease Purchase 5,769 (18,652.29) ** (18.24) ** (8.79) ** (6.92) ** Revolver 382 (179,097.70) (2.83) ** (1.71) ** (6.32) ** Rental Lease 1,698 (12,657.44) ** 8.48 ** 4.96 ** 5.09 ** True Lease 73,621 (8,520.20) ** 4.94 ** 3.08 ** 4.24 ** Contract type n day day 90+ day Avg days Conditional Sale 18, ** ** 2, * ** Leverage Lease 7 N/A N/A N/A * Loan 7, ** ** 1, ** ** Lease Purchase 5,769 (165.99) ** (261.36) ** (1,627.09) ** (149.28) ** Revolver 382 N/A N/A N/A (0.07) Rental Lease 1, * * 1, ** True Lease 73, ** ** ** ** ** significant at 1% level, * significant at 5% level Credit Risk: Contract Characteristics for Success -29- Equipment Leasing & Finance Foundation

32 Table 11 Differences between means (failed-good) by asset code, January 2004 Contract asset code n Contract receivable Contract payment Payment count Loss Agricultual 3,720 38, (325.78) 7.29 ** 12, ** Aircraft 213 6,639, , , Buses & Motor coaches 19 2,137, , Computer 526 (50,509.07) (2,296.53) ** , Construction & Mining 11,371 (12,830.69) (294.02) 5.87 ** 6, ** Copier & fax 12,660 61, ** 1, ** 1, ** Forklift 39,285 4, ** ** 4.90 ** ** Logging & Forestry 1,680 (79,060.58) (990.26) , ** Manufacturing 207 (74,920.91) ** (6,404.06) , Marine craft 3, , ** 11, ** 4, ** Medical 1,048 (154,266.94) ** (3,984.93) (1.16) 14, ** Medium/Light duty truck 5,653 (18,024.57) (4,980.87) ** 4.91 ** 10, ** Office Equipment 3,122 47, , ** Printing & Photographic 487 (75,687.38) , Railroad , (9,375.10) ** Retail 3,672 49, , ** ** 2, Telecommunications 2,472 19, (5,305.60) ** , ** Truck 13,255 (2,603.49) (1,495.75) 1.45 ** 2, ** Unknown 2, , (18,035.16) ** 7.05 ** 4, Vending & Restaurant 1,205 1, (4,759.63) (1.52) 1, Waste & Refuse Handling , , (2.46) 47, Times days Times days Times 90+ days Contract asset code n Balance Agricultual 3,720 (11,868.06) ** 6.59 ** 4.33 ** 5.73 ** Aircraft 213 3,310, ** ** Buses & Motor coaches 19 1,374, ** 6.94 ** 2.00 Computer 526 (60,639.09) ** 7.98 ** 4.90 ** 5.91 ** Construction & Mining 11,371 (13,641.00) 6.96 ** 3.67 ** 4.59 ** Copier & fax 12,660 (5,277.70) 5.04 ** 3.88 ** 4.72 ** Forklift 39,285 (4,090.79) ** 4.72 ** 2.74 ** 3.10 ** Logging & Forestry 1,680 (49,516.68) ** 4.09 ** 2.53 ** 4.73 ** Manufacturing 207 (23,634.39) 8.35 ** 4.37 ** ** Marine craft 3,500 (10,223.92) ** 8.74 ** 4.54 ** Medical 1,048 (45,993.45) ** 5.51 ** 3.62 ** 3.22 ** Medium/Light duty truck 5,653 (32,971.28) ** 9.00 ** 5.48 ** 7.38 ** Office Equipment 3,122 (6,752.17) 6.63 ** 4.17 ** 4.09 ** Printing & Photographic 487 (52,140.50) ** 8.57 ** 5.44 ** 5.56 ** Railroad , ** 4.10 ** 3.10 ** Retail 3,672 (12,968.52) ** 8.17 ** 4.22 ** 4.34 ** Telecommunications 2,472 (1,132.99) 6.27 ** 4.18 ** 4.49 ** Truck 13,255 (17,274.55) ** 9.41 ** 5.77 ** 4.90 ** Unknown 2,938 (21,714.63) ** 7.44 ** 5.04 ** 6.67 ** Vending & Restaurant 1,205 (25,382.56) ** 4.86 ** 3.18 ** 2.44 ** Waste & Refuse Handling 573 (10,480.97) ** 6.77 ** 8.41 ** Credit Risk: Contract Characteristics for Success -30- Equipment Leasing & Finance Foundation

33 Contract asset code n day days 90+ day Avg days Agricultual 3, ** Aircraft Buses & Motor coaches ** Computer ** 1, Construction & Mining 11, ** ** ** ** Copier & fax 12, ** ** ** Forklift 39, ** ** ** ** Logging & Forestry 1, Manufacturing Marine craft 3, ** Medical 1,048 (4.08) Medium/Light duty truck 5, ** ** 1, ** ** Office Equipment 3, , ** Printing & Photographic , Railroad 39 (0.57) Retail 3, ** Telecommunications 2, , ** Truck 13, ** ** 1, ** ** Unknown 2, , ** Vending & Restaurant 1, ** Waste & Refuse Handling , ** significant at 1% level, * significant at 5% level Credit Risk: Contract Characteristics for Success -31- Equipment Leasing & Finance Foundation

34 Table 12 Differences between means (failed-good) by guarantor code, January 2004 data Guarantor code n Contract receivable Contract payment Payment count Loss Both 8 52, ** (20.43) ** N/A Corporate , , (19.89) ** 284, ** None 83,896 30, ** ** 2, ** Personal 209 (16,878.04) ** (284.32) (2.82) 6, * Unknown 3,444 (15,109.65) * (1,358.74) ** (0.13) 3, ** Yes - undefined 20,076 (3,101.22) (1,227.47) 6.09 ** 5, ** Guarantor code n Balance Times days Times days Times 90+ days Both 8 (17,339.14) * 0.43 N/A N/A Corporate 12 (51,207.67) 2.44 ** N/A N/A None 83,896 (3,118.86) 5.97 ** 3.66 ** 4.24 ** Personal 209 (11,853.60) ** 7.09 ** 5.93 ** 8.70 ** Unknown 3,444 (18,416.38) ** 5.71 ** 2.75 ** 2.82 ** Yes - undefined 20,076 (22,201.14) ** 8.30 ** 5.08 ** 5.58 ** Guarantor code n day day 90+ day Avg days Both 8 N/A N/A N/A N/A Corporate 12 N/A N/A N/A N/A None 83, ** ** ** ** Personal * ** Unknown 3, ** * * ** Yes - undefined 20, ** ** 1, ** ** ** significant at 1% level, * significant at 5% level Credit Risk: Contract Characteristics for Success -32- Equipment Leasing & Finance Foundation

35 Products you trust! Important Research! Credible, Forward Focused Studies! What does the Foundation do for your industry? Industry Future Council Report Each year, we gather dozens of the sharpest executives from around the industry, ask them pointed questions about leasing s challenges and strengths, and let them engage in a freewheeling discussion about the near term future of leasing. The resulting Report offers a look at the influencing forces, variables and market-changing trends that signal the pace and direction of the business for the next couple of years. This is an invaluable strategic and tactical tool. State of the Industry Report The report that puts meat on the statistical bones! Based on ELA and government data, and using interviews with diverse industry executives, this is the comprehensive annual report on the health and future of the industry. The in-depth analysis contains market segment trends and benchmarking statistics to help you size up your place in the market. Journal of Equipment Lease Financing The only scholarly journal dedicated to equipment leasing! Published quarterly, the Journal spotlights research, case studies, trends and practical information through in-depth articles. Subscription info at Stay Connected: Foundation Forecasts New this year! Forecasts is the Foundation s bi-monthly e-newsletter. Forecasts brings you timely information on Foundation research, new products and fundraising highlights. Subscribe at the Foundation website today! University and Academic Relations The Foundation, always looking forward, works closely with academics to conduct research in the equipment lease financing industry. Foundation initiatives include: Funding research grants; Funding authorship honorariums; Academic professors serve on the Foundation Board of Trustees; Comprehensive statistical database of leasing transactions for research Case study development and teaching modules Industry Studies Through the Foundation, industry leaders have access to the research tools that help them make wise decisions for their corporations and employees. Foundation commissioned research includes: New Capital Adequancy: Is Your Company Prepared for Basel II Implementation? New Indicators of Success Study Basel II Accords: Impact on the North American Leasing Market Securitization Marketplace Perfect Storms: Why Major Lessors Exited the Marketplace Case Studies: 7 Case Studies useful in corporate training and many others. Website Resources The Equipment Leasing and Finance Foundation website contains myriad research, statistical material, and industry related information for your use and exploration. All material mentioned above, and so much more, is available through the website, The Foundation is the nonprofit organization that provides vision for the equipment leasing and finance industry through future-focused information and research. The Foundation is supported through corporate and individual donations. Equipment Leasing and Finance Foundation 4301 N. Fairfax Drive, Suite 550, Arlington, VA Phone: Fax: