Methodology. The Drivers of U.S. Auto Loan Credit Enhancement

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1 Methodology The Drivers of U.S. Auto Loan Credit Enhancement february 2005

2 CONTACT INFORMATION Claire J. Mezzanotte Managing Director Head of U.S. ABS/RMBS Tel Christopher O Connell Senior Vice President U.S. Structured Finance ABS Tel coconnell@dbrs.com David D. Hartung Senior Vice President U.S. Structured Finance ABS Tel dhartung@dbrs.com Richard Bianchi Vice President U.S. Structured Finance Tel rbianchi@dbrs.com MODELING AND RESEARCH Jan Buckler Senior Vice President U.S. Structured Finance Tel jbuckler@dbrs.com OPERATIONAL RISK Kathleen Tillwitz Senior Vice President U.S. Structured Finance ABS/RMBS Tel ktillwitz@dbrs.com DBRS is a full-service credit rating agency established in Privately owned and operated without affiliation to any financial institution, DBRS is respected for its independent, third-party evaluations of corporate and government issues, spanning North America, Europe and Asia. DBRS s extensive coverage of securitizations and structured finance transactions solidifies our standing as a leading provider of comprehensive, in-depth credit analysis. All DBRS ratings and research are available in hard-copy format and electronically on Bloomberg and at DBRS.com, our lead delivery tool for organized, Web-based, up-to-the-minute information. We remain committed to continuously refining our expertise in the analysis of credit quality and are dedicated to maintaining objective and credible opinions within the global financial marketplace.

3 The Drivers of U.S. Auto Loan Credit Enhancement TABLE OF CONTENTS Introduction 4 Loan and Borrower Characteristics 4 Loan-to-Value 4 Seasoning 4 Vehicle Age 4 Loan Term 4 Credit Score 5 Macroeconomic Environment 5 The Servicer 5 Loss History 5 Recovery Values 5 Loss Multiples 6 Prepayments 6 Comments on DBRS s Approach to Credit Enhancement 9 Absolute Prepayment Speed 9 Conclusion 11 3

4 Introduction Variables that determine base case loss estimates are derived from, and/or influenced by, loan and borrower characteristics, macroeconomic forces, the loan servicer, and the issuer s loss history. Dominion Bond Rating Service ( DBRS ) estimates an expected loss rate after analyzing all of these factors. The main drivers that underlie credit enhancement are stressed loss variables and stressed Absolute Prepayment Speeds ( ABS ). This article discusses issues associated with and influencing loss variables and ABS speeds. Loan and Borrower Characteristics LOAN-TO-VALUE The loan-to-value ratio (LTV) represents the relationship between loan balance and collateral value. The greater the loan balance relative to the auto s value, the higher the LTV. Since LTV ties together loan balance and collateral value, it is one of the loan characteristics most directly correlated to default rates and loss severity after default. The higher the LTV, the greater the risk of default. Similarly, the higher the LTV, the greater the loss severity after default. Higher LTVs are correlated with both default risk and loss severity, particularly when higher LTV loans are longer term or when higher LTV loans are made to lower credit-tiered borrowers. There are different sources that can be used to determine a vehicle s value and its LTV. One of the effects of these variations is that LTVs for used autos may be less meaningful than those on new autos since it is more difficult to value a used auto. Therefore, a 120% LTV at one issuer could vary greatly from a 120% LTV at another issuer and performance between these two issuers portfolios could also differ dramatically. SEASONING Seasoning represents the number of months since a loan s origination. Like LTV, seasoning is a factor in determining loan losses. Loans with maturities of less than 60 months typically have losses that tend to occur between six and 24 months following contract origination. Therefore, highly seasoned loans are typically less risky than newly originated loans. If there is significant seasoning in a pool of loans, DBRS might provide benefit to a transaction for such seasoning. VEHICLE AGE Typically, borrowers with less financial capacity will purchase cheaper cars, which tend to be used vehicles. Therefore, borrowers with loans on used vehicles, since they are a surrogate for borrowers with limited financial capacity, tend to default at a higher rate than borrowers with loans on new vehicles. LOAN TERM DBRS considers loans in excess of 60 months to be longer term loans. Longer term loans to better quality borrowers will default at a lower rate than shorter term loans as a result of the smaller monthly payments. In contrast, default frequency will increase when longer term loans are made to lower quality borrowers. In all cases, loss severity will increase on longer term loans since it takes longer for loan principal to amortize down to the current value of the vehicle. 4

5 CREDIT SCORE Borrowers with lower credit scores tend to default at higher rates. Borrowers with lower credit scores also tend to prepay slower due to limited credit alternatives. MACROECONOMIC ENVIRONMENT While collateral characteristics are important determinants of auto performance, the macroeconomic environment also exerts a strong influence. More borrowers become delinquent when the economy weakens. Since delinquencies clearly indicate charge-offs, these can be crucial in predicting an asset securitization s ultimate performance. Economic forces that impact consumers and used vehicle prices include the unemployment level and labor-market conditions, disposable income, consumer confidence, and the cost of owning and operating vehicles. THE SERVICER The servicer and its ability to service a loan portfolio is critical to estimating loss performance. While servicers who have experienced financial difficulty in the industry are a limited set, collateral deterioration has always been accompanied by servicer difficulties. Therefore, a servicer s financial health always factors into DBRS s analysis. LOSS HISTORY DBRS s credit enhancement analysis also includes loss history of the servicer s portfolio and the proposed pool. DBRS relies heavily on static pool data to project expected cumulative losses. Static pool data best reflects expected losses over a transaction s life because portfolio data can mask losses during periods of rapid portfolio growth. Generally, the more years of historical data, the more comfort DBRS can take in using such data to derive assumptions. Longer periods of historical data allow DBRS to effectively observe trends and often observe performance through a business cycle. If static pool performance data is not available, DBRS uses growth-adjusted portfolio data to determine expected loss history. The lag period in determining the adjustment factor depends on factors such as the portfolio growth rate and the expecting timing of losses. Once expected annual losses are estimated, DBRS multiplies them by the weighted average life of the pool to estimate expected cumulative losses. DBRS also adjusts the resulting expected loss levels to reflect current pools changes in credit, sales channel, and risk. Performance consistency is also valuable in determining loss estimates. To the extent that poor underwriting and/or poor servicing have caused performance volatility, DBRS will require a higher credit enhancement for the transaction. RECOVERY VALUES A successful servicing operation depends on repossession, disposition methods, and recovery values. In many servicing shops, teams are dedicated to these specific functions to ensure that autos are repossessed and sold as quickly as possible after a borrower defaults on their loan. A servicer may be constrained by legal limitations regarding the frequency and timing of contacting the borrower and the time required before repossession. For example, once the vehicle is repossessed, it will take time for the notice of intent to expire and the vehicle s sale to occur. During this time, depreciation continues to eat away at the vehicle s value. The quality of servicing staff is also crucial in managing these timelines and minimizing loss severity. Vehicle disposition can occur in a variety of locations. Manheim Auctions and ADESA auctions are wholesale auction companies that conduct auctions throughout the year. Often, the vehicles are sold at auction expeditiously, although at lower values. Most finance companies utilize wholesale auctions to dispose of repossessed collateral because it can result in higher recovery values over extended time frames. 5

6 In general, gross losses and net losses are analyzed separately. Gross losses reflect the contract s outstanding balance at the time of default. Net losses should incorporate the loan amount plus all expenses incurred in repossessing, refurbishing, and liquidating the vehicle minus the proceeds from disposition. Analyzing gross losses and net losses independently permits DBRS to examine both the frequency and severity of default. If an asset-backed securitization document provides for recoveries to flow through the cash flow waterfall, DBRS will consider a pool s net loss history as opposed to gross loss history when determining credit enhancement levels. To arrive at net loss levels, historic recovery rates are multiplied by the maximum recovery credits (see the table below). DBRS limits the amount of credit to recoveries at each rating level. AAA AA A BBB BB B Maximum Recovery Credit 50% 65% 75% 80% 85% 90% LOSS MULTIPLES Expected losses serve as the basis for DBRS s base case loss assumptions. Enhancement levels by rating category are created by multiplying the base case loss levels by the multiples shown in the following table. After the multiples are applied to DBRS s base case losses, the resulting adjusted loss levels are used for stress-testing purposes. Rating Level AAA AA A BBB BB B Loss Range Multiple 4 6 times 3 4 times 2 3 times times times times There may be adjustments to the application of the multiples. Consider a scenario in which the base case assumption of the default rate for a sub-prime pool is 20%. If the issuer was hoping to obtain a AAA rating, a five-time multiple on this default rate would imply that all the loans in the pool default. In reality, it is hard to imagine a case in which 100% of the loans default, even in a worst-case scenario. PREPAYMENTS Aside from defaults, scheduled payments and prepayments make up a majority of the source of cash flows in an asset securitization. Prepayment speeds can dramatically affect the average life of a retail auto transaction and the enhancement determined at each rating category. Historically, prepayments are not driven by interest rates as much as they are impacted by all of the issues previously outlined, as well as the borrower s credit characteristics. In addition, prepayment speeds increase with a loan s age and the borrower s perceived or actual equity in a vehicle. One measure of a pool s prepayment rate is the ABS speed. ABS is the percentage of a deal s original loans that prepay in a given month. Defaults are embedded in the ABS curves. ABS speeds associated with prime and sub-prime issuers differ because higher default rates on sub-prime deals are more front-loaded than on prime transactions. In addition, prime collateral tends to be more seasoned than sub-prime. Since loan age is a factor in the ABS calculation, prime pools typically have slightly lower ABS speeds (which include voluntary prepays and defaults) than sub-prime pools, all other factors being constant. 6 ABS speed is a unique calculation. This speed is measured differently than in other asset classes. Other asset classes use conditional prepayment rate (CPR) measurements. A constant ABS speed implies an increasing CPR speed over time.

7 DBRS s assumptions for ABS speeds are reflective of the actual speeds associated with an issuer s securitization history. To arrive at a base case ABS speed for modeling purposes, DBRS focuses on historical pool performance. DBRS lines up individual pools, adjusted for loan seasoning, to see how ABS speeds mature as a pool ages. Once a sequence of pools is chronologically fitted, seasonal trends are clearly evident. The same types of adjustments are made for issuers that use prefunding periods. Individual issuers may have different ABS histories due to varying economic conditions. The graph below (Chart 1) indicates a typical ABS data set for a hypothetical issuer of prime collateral loans. DBRS Rating Approach for Absolute Prepayment Speeds (ABS) Prime Issuer 2.10% 1.90% 1.70% ABS Speed 1.50% 1.30% 1.10% 0.90% 0.70% Months Pool One Pool Two Pool Three Pool Four Pool Five Pool Six Pool Seven Pool Eight Source: DBRS DBRS calculates an average of all the ABS data points to determine ABS speeds for the data group under consideration. Since this is an average, the points are then fitted to arrive at a smooth base-line ABS speed (see the Chart 2 below). Chart 2: DBRS Rating Approach for Absolute Prepayment Speeds (ABS) Prime Issuer 2.10% 1.90% 1.70% ABS Speed 1.50% 1.30% 1.10% 0.90% 0.70% Source: DBRS Months Pool One Pool Two Pool Three Pool Four Pool Five Pool Six Pool Seven Pool Eight Average Fittted 7

8 After calculating the average and fitted curves from the issuer s actual ABS data, DBRS applies rating stresses. The fitted ABS curve acts as a starting point for the stressed cases. DBRS flattens out the ABS speeds associated with each stressed scenario at some point to provide an additional stress to the excess spread associated with any securitization. To reiterate, the calculation for ABS includes defaults and voluntary prepayments. Therefore, loan defaults are netted out of the ABS values for each rating category to arrive at purely voluntary ABS prepayment speeds. Chart 3 below indicates how DBRS s ABS speed curves by category relate to the fitted curves and ABS speed data. Chart 3: DBRS Rating Approach for Absolute Prepayment Speeds (ABS) Prime Issuer 2.10% 1.90% 1.70% ABS Speed 1.50% 1.30% 1.10% 0.90% 0.70% Months Source: DBRS. Pool One Pool Two Pool Three Pool Four Pool Five Pool Six Pool Seven Pool Eight Fittted BB BBB A AA AAA 8

9 Comments on DBRS s Approach to Credit Enhancement Once all components pertaining to the analysis of defaults are taken into account as previously described, DBRS determines a base case loss level and multiplies that level by the applicable rating category. For example, if 2.90% is the base case net loss level, and a AAA multiple of 4.55 times is selected, the net loss rate at AAA is equal to 13.18%. This is the net loss level used for modeling cash flows. The DBRS net loss amount is incorporated into the cash flows. The sequence of losses is different for particular issuers. Loss curves would be the primary determinant in whether or not loss levels should be evenly applied, front-ended, or back-ended in the cash flow models that are run. In general, for prime collateral, the sequence of losses chosen is front-ended: 40% of the total DBRS net loss occurs over the first quarter of the deal, 30% of the total DBRS net loss occurs over the second quarter of the deal, 20% of the total DBRS Net Loss occurs over the third quarter of the deal, and the final quarter of the cash flows would be when the last 10% of the total DBRS net loss levels occur. Allocated Net Loss Amount Month % Month % Month % Month 37 48* 1.32% Total DBRS Net Loss 13.18% * Table assumes a 48-month cash flow modelling exercise. ABSOLUTE PREPAYMENT SPEED As indicated above, DBRS determines the ABS speeds after averaging the individual speeds and fitting a curve to the average to arrive at a base-line ABS speed. Since reported ABS speeds include defaults, DBRS requires the defaults to be netted out from ABS speeds for each rating category to arrive at purely voluntary ABS prepayment speeds. Without this deduction of defaults from the ABS, defaults would be double counted in the cash flow exercises. The above example demonstrates how the two variables, defaults and voluntary prepayments, work alongside one another in the cash flow modeling exercises. The imposition of a stressed front-loaded default scenario on the ABS curve has the effect of reducing the front end of the voluntary prepayments and compressing the voluntary prepayment speeds between the various rating categories. The following sequence of graphs will demonstrate this point. Since ABS speeds are impacted by the age of a loan pool, and defaults and voluntary prepayments are included in the calculation, it is technically incorrect to simply subtract defaults from the ABS speed calculation. The charts provided give a good depiction of the discussion above. The cash flow models do incorporate the concepts more precisely. 9

10 Chart 4: DBRS Rating Approach for Absolute Prepayment Speeds (ABS) Prime Issuer 2.10% 1.90% 1.70% ABS Speed 1.50% 1.30% 1.10% 0.90% 0.70% Source: DBRS Months BB BBB A AA AAA Chart 5: DBRS Stressed Default Levels Prime Issuer 0.70% 0.60% 0.50% ABS Speed 0.40% 0.30% 0.20% 0.10% 0.00% Source: DBRS Months BB BBB A AA AAA 10

11 Chart 6: Effect on ABS Speeds after Subtracting Stressed Default Levels Prime Issuer 2.10% 1.90% 1.70% ABS Speed 1.50% 1.30% 1.10% 0.90% 0.70% Source: DBRS Months BB BBB A AA AAA Conclusion DBRS considers many factors when determining credit enhancement for auto securitizations. The primary determents of credit enhancement are net losses and ABS speeds. Analysis of ABS speeds on such securitized pools, particularly in greater detail, has been lacking in the auto securitization market. The application of DBRS s approach to ABS speeds is a more accurate method of determining credit enhancement levels. 11

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