INTEREST RATE RISK & AUTO LOAN PORTFOLIOS

Transcription

1 BRICK & ASSOCIATES, INC. JANUARY 2014 INTEREST RATE RISK & AUTO LOAN PORTFOLIOS by JOHN R. BRICK, PHD, CFA Copyright 2014, Brick & Associates, Inc., All rights reserved.

2 INTEREST RATE RISK & AUTO LOAN PORTFOLIOS T I. PURPOSE he purpose of this paper is to analyze new and used auto loan portfolios to determine their behavior and related risk attributes that should be considered when examining the interest rate risk (IRR) of a financial institution. Among the characteristics examined are the analytical differences between new and used auto loan portfolios in terms of prepayment speeds, stability of principal cash flows, average lives, and the extent of prepayment sensitivity to interest rates, all of which should be considered in the modeling process. From the time the Federal Reserve initiated its zero interest rate policy (ZIRP) in late 2008 and its subsequent quantitative easing (QE) programs, interest rates declined to record low levels. On both the managerial and regulatory fronts this market manipulation has raised serious and legitimate concerns over the potential for ultimately increasing both credit and interest rate risk. The specific problem this created for financial institutions was significant margin pressure as the spread between short-term rates and longer-term rates narrowed considerably, i.e., the yield curve flattened. (The yield curve steepened somewhat in mid after the Fed announced that it might soon begin to exit or taper its QE strategy.) In response, some institutions lowered credit standards or lengthened maturities on the asset side, or both. This has spawned regulatory warnings about increased levels of credit risk and IRR. Not surprisingly, credit-related stress testing and more effective IRR policies are now aggressively pursued by regulators. With respect to modeling IRR, the emphasis is virtually always focused on fixed-rate first mortgage loans. This is understandable given their long amortization terms and price volatility. 1 However, largely absent from these discussions is the role of auto loans in the IRR management process. This is due to the widespread perception that these loans have a low degree of IRR because of their short terms. As will be shown, this perception is correct but there may be more to consider, that is, the extent to which these loans offset or mitigate the IRR resulting from other loans. In other words, and as shown in the sections that follow, this risk mitigation may have a much stronger and desirable effect than is generally perceived. This is why it is important to thoroughly understand the characteristics of auto loan portfolios. II. CHARACTERISTICS OF AUTO LOAN PORTFOLIOS Industry-wide auto loan data for IRR assessments is not readily available. To overcome this problem and obtain insight, analysts often refer to publicly available data from asset-backed securities (ABS) made up of auto loans. For example, consider the results reported in a comprehensive ABS analysis by Citigroup Global Markets: Auto prepayment speeds are significantly more stable than prepayments on other assetbacked classes. This is because auto loans are not sensitive to refinancing if interest rates decline. Unlike the home mortgage market, auto owners cannot refinance their used vehicle at a better interest rate if rates decline. Autos are depreciating assets. Therefore, the cost of refinancing a used auto is greater than the cost to finance a new vehicle. Because auto loans are short obligations (generally 36 to 72 months), the effect of variability of speeds on cash flow is minimal. 2 1 For a discussion of the role of these loans in the collapse of the S&L industry in the 1980s, see Asset-Liability Management and the Role of Judgment, January 2012, by John R. Brick, available at 2 Citigroup Global Markets, Inc., Guide to Auto ASB, 3 rd ed, July 2009, p 58. 2

3 The Citigroup study did find some degree of interest rate sensitivity. Auto loans that were seasoned by 2.5 to 3 years and with an above-market weighted average coupon (WAC) tended to induce some borrowers to trade in their vehicle sooner for a new one. The study went on to say that the prepayment speed convention for valuation purposes on prime auto loans is to price such pools generally at 1.5 ABS. This means that the number of loans prepaying monthly is assumed to be around 1.5% of the number of loans in the pool. The study also cites a range of 1.3 to 1.7 ABS depending on the issuer. This range corresponds roughly to an annualized prepayment speed of 15 to 20%. Although studies such as this are certainly useful in the absence of more definitive information, the results should be used with care because ABS portfolios have different characteristics from those of financial institutions and they can affect the IRR assessment. Such pools are funded with relatively homogeneous loans with similar characteristics such as new car loans with, for example, six-year terms. In contrast, institutional portfolios are comprised of new and used car loans that are likely to have different characteristics. Furthermore, when an ABS portfolio is seasoned, this means that after two years a pool made up primarily of six-year loans would have a remaining amortization term of four years. However, institutional auto loan portfolios are fully seasoned. In contrast to ABS pools that contain loans with relatively homogeneous remaining terms, an institution s portfolio contains loans with remaining terms ranging from old loans with one month to brand new loans with up to 72 months or however many months they may extend. This is critical in an IRR assessment because the typical average life of an institution s auto loan portfolio is consistently quite short, much shorter than new or recently offered ABS pools. In addition to typically having a shorter average life than an ABS pool, a fully seasoned portfolio has a large number of loans with small balances and/or a small number of payments remaining. Unlike the large balances associated with mortgage loans and except under unusual circumstances, borrowers are unlikely to benefit in a material way by refinancing into a lower rate loan. This is in addition to the other refinancing barriers mentioned in the Citigroup study. Another important difference is in the weighted average coupons of ABS pools versus that of an institution s portfolio. ABS pools are primarily comprised of so-called standard loans that are made by the subsidiaries of auto companies along with several other lenders such as Capital One. This term refers to loans that are not incentivized with below-market rates so they have a higher contractual rate. Many vehicles are not subject to these incentives and in certain parts of the country, mainly major metropolitan areas, most of the cars sold by dealers are financed by standard loans. The rates on these loans are considerably higher than those offered by many institutions, especially credit unions. Thus, credit union portfolios should be even less susceptible to refinancing than ABS pools during a period of falling rates. Since Citigroup found no such refinancing sensitivity in ABS pools, a reasonable expectation is that there is little or no such sensitivity in credit union portfolios. If this is confirmed by the analysis later in this paper, the lack of prepayment sensitivity to interest rates would be another important aspect of IRR modeling. In addition to having higher interest rates, the standard dealer loans in auto loan pools usually have LTV ratios of %. These loans are less likely to be upside down over time in a meaningful way. In contrast, credit unions virtually always write loans in excess of 100% LTV ratios, often by a considerable margin by wrapping in the old loan and paying for taxes and expensive add-ons. This is yet another barrier to a credit union s loan portfolio refinancing in a low- or falling-rate environment. With respect to interest rate sensitivity, it is well recognized that in a rising rate environment any refinancing incentive will decline. Those same rising rates may also impede auto loan sales and thus trade-ins and the resulting prepayments on the old loans. However, it could also be argued that if rising rates are a result of a stronger economy as is usually the case, car sales may be stronger because of rising incomes and as more unemployed and under-employed people get back to work. In this way, prepayment experience may be unlike that of the mortgage loan market where prepayments due to refinancing collapse in a rising rate 3

4 environment. Another difference is the fact that unlike the housing market, autos consistently depreciate over time and must be replaced regardless of the level of interest rates. Another potential impact of interest rates on auto loan prepayments relates to the irrational behavior of individuals. That is, it would be perfectly rational for someone getting 1% (or less) on a savings account to take those savings and pay off a car loan with a rate of 4 or 5%. Although it is likely that this has happened, its effect is likely to be minimal and not measurable. Extensive research over the past 20 years shows that models based on rational economic behavior are badly flawed because most people are not rational to say nothing of many people not having much in the way of savings to pay off the loan. As pointed out earlier, auto loans are widely and properly perceived as having very little interest rate risk due to their typically short average lives. However, other than work on auto-related, asset-backed securities by investment bankers, little research has been conducted on actual institutional portfolios. While common sense suggests that institutional auto loan portfolios have a low degree of IRR, based on a comparison with the characteristics of ABS pools the actual risk may be even lower than is widely perceived. If so, these assets may even be risk-reducing rather than risk-neutral. Now the question is what does an analysis of auto loan portfolio data tell us? In the following sections we will analyze extensive data to determine the analytical differences between new and used auto loan portfolios, principal cash flow stability, average lives, prepayment speeds, and the sensitivity of auto loan prepayments to interest rates. III. ANALYZING THE DATA Since industry-wide data is unavailable for institutional portfolios, the data used in this study is from a single institution, albeit one with large portfolios of both new and used auto loans. Monthly loan data for these portfolios was obtained over the period from January 2004 through June 2013 for a total of 114 data points. The new car portfolio was made up of 5,773 loans totaling $89 million as of June The used car portfolio had 26,530 loans totaling $260 million. Note that roughly one-half of this period encompasses the Fed s zero interest rate policy (ZIRP) that began in late The data for new car loans is shown in Exhibit 1 and the used auto loan data is in Exhibit 2. A. Prepayment Speeds & Average Life Since core systems do not separate monthly principal payments between contractual payments and prepayments, the breakdown must be estimated. This involved several steps the first of which was to determine the total principal payoffs on new auto loans on a monthly basis. The procedure is shown below for the month of June 2013 in Exhibit 1. Ending Balance ($ in 000s) $89,056 Less: Beginning Balance $86,083 Equals: Monthly Change $2,973 Less: New Loans Made $6,800 Equals: Principal Payoff s ($3,827) In order to find the breakdown between contractual payments and prepayments, the next step was to determine the average original term of the new auto loan portfolio. An extensive review of recent auto loans 4

5 found that this was 62 months. 3 Bear in mind that institutional auto loan portfolios are fully seasoned. This means that the portfolio is made up of loans with remaining terms ranging from one month to, in this case, an average of 62 months. Assuming zero prepayments and an actual portfolio interest rate of 3.58% on the new auto loan portfolio, the monthly required contractual portion of the $3,827 ($000s) total principal payoffs in June was determined to be $2,669 as shown in column 7. This represents 3.1% of the beginning loan balance of $86,083. Thus, the estimated prepayment portion of the June 2013 principal payoffs was $3,827 - $2,669 = $1,158 as shown in column 8. This was 1.35% of the beginning loan balance of $86,083, or about 16.1% annualized for that month. The average annualized prepayment speed over the entire 9.5-year period was 12.5% for the new auto loan portfolio. From a cash flow standpoint note in column 6a of Exhibit 1 that the Total Principal Payoff as a % of the Beginning Loan Balance was 4.4% for the month of June, or 53.3% annualized as shown in column 6b. Over the entire period this annualized payoff speed averaged a remarkably fast 49.7%. The same procedure was employed for the used auto loan portfolio shown in Exhibit 2. For this analysis the average original term of used auto loans was determined to be 56 months. Assuming a fully seasoned portfolio and a portfolio yield of 3.80%, the monthly required contractual payoffs were estimated to be 3.5% of the beginning loan balance. This resulted in a monthly prepayment speed of 1.65% for June with an annualized speed of about 19.8% for that month. The average annualized prepayment speed over the entire period was 15.5%. Since used cars are older than new cars and must be replaced more frequently, it makes sense that the average prepayment speed is several percentage points higher than for new cars, that is, 15.5% versus 12.5%. Again, from a cash flow standpoint, note in column 6a of Exhibit 2 that the Total Principal Payoff was 5.1% for the month of June, or about 61.8% annualized. Over the entire period this annualized payoff speed averaged 57.5%. Clearly, auto loan portfolios are an important source of liquidity and repricing opportunities, both of which are highly desirable ALM characteristics in a rising rate environment. These results raise two important questions related to IRR assessments. First, what is the average life of fully seasoned, new and used auto loan portfolios and second, how sensitive are the average lives to the estimated prepayment speeds? The answers are shown in Table 1 below using a 15% constant prepayment rate (CPR) as a baseline. Table 1 Weighted Average Life of Fully Seasoned Auto Loan Portfolios* (in months) 0% CPR 5% CPR 15% CPR 25% CPR New Autos Used Autos *The weighted average life (WAL) is the weighted average time to receipt of the principal cash flows. Generally, this term also refers to the time it takes to get back one-half of the principal. These results are important for several reasons. Assuming fully seasoned, institutional auto loan portfolios in general have a prepayment speed averaging around 15%, the average life of such portfolios is only about 18 months for new car loans and 16.4 months for used car loans. From an IRR perspective, these are important statistics because they are so short, shorter perhaps, than most analysts realize. This means that rather than 3 This figure was based on recent lending activity. Since older data was not available going back almost a decade or so, this average original term was assumed to be constant over the entire period. It is unlikely that this average remained constant over time but the results discussed later suggest that this assumption would not materially alter the conclusions. 5

6 just having a low or neutral degree of IRR, these portfolios are so short that they may be risk-reducing. That is, like a very short investment portfolio, they may help to mitigate the risk of other assets with much longer average lives such as fixed-rate mortgage loans. When analyzing the IRR attributes of mortgage loans it is well recognized that the prepayment assumption plays a critical role. In fact, this assumption dominates the principal cash flow estimates for the purpose of valuation. This means that mortgage loan prepayments can overwhelm the contractual payments and by a wide margin, often exceeding ten to one on long-term mortgage loans. This is due to the long amortization term and the resulting monthly payment that has a very small contractual principal component. In contrast, auto loan portfolios are so short that the contractual principal payments are very large relative to the prepayments so they overwhelm the prepayments by about three or four to one. This relationship is evident when comparing the data in columns 7 and 8 in Exhibits 1 and 2. This has important IRR implications. As shown in Table 1 above, note that when the prepayment estimate is incorrect by as much as +/-10 percentage points from the 15% baseline, the WAL changes in both directions by only about two months! Thus, unlike mortgage loans and regardless of the cause, incorrect prepayment estimates have a minimal distorting effect on cash flows and the IRR assessment. 4 The lack of meaningful extension or contraction risk is a distinct benefit because the resulting principal cash flows are stable rather than increasing or decreasing significantly at inopportune times as is the case with mortgage loan portfolios. But this does raise a question to what extent are auto loans and their IRR characteristics sensitive to interest rates? B. Interest Rates & IRR Characteristics When analyzing the relationship between movements in interest rates and the IRR attributes of auto loan portfolios, the ultra-low rate environment since late 2008 greatly complicates the analytical procedure. From the inception of the study in 2004 until late 2008, interest rates were subject to the normal variation that characterizes the bond market. However, around the end of 2008 and during the Great Recession the Federal Reserve began to manipulate the bond market in an unprecedented manner by means of reducing the Fed Funds target to 0 to.25% and implementing its Quantitative Easing (QE) policy of buying Treasury Bonds and mortgage-backed securities, a program designed to lower intermediate- and long-term interest rates and flatten the yield curve. This program continued through the end point of the study, June Regarding short-term interest rates, the Fed has been using forward guidance to convince the market that these rates will remain low for an extended period. Volatility in the ultra-short sector was dramatically reduced from that of the earlier period. 1. Analytical Procedure To at least partially overcome the potentially distorting effects of this environment, several steps were taken. First, the 2-year Treasury rate was selected as the rate index since this series still had some degree of volatility unlike the overnight rate that was virtually fixed. Furthermore, the maturity roughly corresponds with the average life of auto portfolios as discussed earlier. The data was first analyzed by determining the relationship between contemporaneous changes in interest rates and changes in the monthly prepayments over the entire period from 2004 to mid The entire period was then re-analyzed but the changes in prepayments were lagged three months behind the changes in interest rates in order to detect any delayed responses. Then the period was broken down into two subperiods to reflect the vastly different rate environments, that is, the free market and the Fed s QE market. 4 Recall that an assumption was made earlier that the estimated original term was constant. This is clearly not the case since these terms have increased over the past decade. However, the fact that these portfolios are so short even with the more recent, longer average original term suggests that this assumption would not alter the conclusions in a meaningful way. 6

7 The analytical procedure used was a correlation analysis. The correlation coefficient ranges from +1.0 which reflects a perfect positive relationship to -1.0 reflecting a perfect negative relationship. If falling or low interest rates are associated with an increase in prepayments as is the case in the mortgage loan market, a high and statistically significant degree of negative correlation would be indicated. Coefficients around zero would indicate no relationship. Using this method, the monthly changes in 2-year Treasury rates were correlated with the monthly changes in the prepayment speeds as a percent of the beginning loan balance Graphical Overview & the Results It is insightful to obtain a graphical representation of the data and the analytical results. In Figure 1 below for new auto loans, the volatility of the 2-year Treasury rates in the pre-qe environment is evident and in sharp contrast to that of the more stable QE environment from January 2009 to mid With the exception of a few outliers, the monthly prepayment speeds generally range from a low of around.5% to about 1.5% over the entire period with an average of 1.04%. 6 Figure 1 New Auto Loan Prepayments & Interest Rates 6% 5% r = -.07 (r =.08 Lagged 3 mo) 6% 5% r = -.19 r =.29 4% 4% 3% 2-Yr Treasury Rates Monthly % Prepayments 3% 2% 2% Avg = 1.04% 1% 1% 0% % Copyright (c) Brick & Associates, Inc., All rights reserved. YEAR Shaded area represents recession. If there is a linkage between low or falling interest rates and higher prepayments resulting from more tradeins or refinancing, a high and statistically significant negative correlation coefficient would result. Note that the correlation coefficient (r) over the entire period was only -.07 which is not statistically different from zero. 7 When changes in prepayments are lagged three months behind changes in interest rates, the coefficient was.08. Again, this is not statistically significant. 5 From a statistical standpoint, when the actual data from two non-stationary times series such as these are correlated, a high but spurious (false) correlation coefficient often results. Taking the first difference (monthly change) usually induces the necessary correlation requirement, a stationary process. 6 The prepayment outliers in 2011 were a result of extensive and competitive loan rate promotions in the institution s primary service area. To the extent that these were related to below-market interest rates, the promotions may have induced trade-ins and thus prepayments. However, such prepayments should be considered self-induced rather than brought about primarily by market conditions. 7 Statistical significance at the 95% level is given by +/- 1.96/(n-k).5 where n = the number of observations and k = the lag term. The level of significance for the entire period of 114 observations is +/

8 When the data is partitioned as explained above, the correlation coefficient for the pre-qe period was -.19 which is not statistically significant at the 95% level (+/-.25). However, the coefficient and negative sign indicates that there may have been weak prepayment sensitivity during this period. A visual inspection of Figure 1 indicates that as interest rates increased significantly, prepayments tended to decline somewhat during this period although not in a statistically meaningful or material way. As rates declined toward the end of that period, i.e., just prior to and during the recession, the behavior of prepayments was mixed. In the partitioned QE period beginning in 2009 the results are the opposite of the earlier period with a coefficient of This was statistically significant (+/-.27). The problem here is that the sign of the coefficient is positive rather than negative. That is, as interest rates declined prepayment speeds tended to decline and when rates increased, prepayments appeared to increase as well with the exception of the few outliers mentioned earlier. Before explaining these results in more detail, it is insightful to examine the results for used car portfolios. With the exception of the one statistically significant coefficient for new auto portfolios, the analytical results for the used car loan portfolio are similar to those of new car loans as shown in Figure 2 below. Over the entire period the correlation coefficient (r) was.08. When prepayments are lagged three months to detect a delayed response, the correlation coefficient was.10. Neither measure is statistically significant. Figure 2 Used Auto Loan Prepayments & Interest Rates 6% 5% r =.08 (r =.10 Lagged 3 mo) 6% 5% r =.10 r =.03 4% 4% 3% 2-Yr Treasury Rates Monthly % Prepayments 3% 2% 2% Avg = 1.30% 1% 1% 0% % Copyright (c) Brick & Associates, Inc., All rights reserv ed. YEAR Shaded area represents recession. When the data was partitioned, the correlation coefficient for the pre-qe period was only.10 and.03 during the quantitative easing period beginning in Again, neither coefficient was statistically different from zero. Note that the average prepayment as a percent of the beginning monthly balance was only 1.30% with a range of about.5% to 2%, excluding several outliers. Like new auto loans this indicates that cash flows are dominated by the much more stable contractual payments. 8

9 IV. INTERPRETING THE RESULTS The results are summarized in Table 2 below. With the exception of the more recent partitioned section for new auto loans, all of the coefficients are weak and statistically insignificant. The Pre-QE negative coefficient of -.19 seems to indicate that there may have been some relationship between rising interest rates and declining prepayments similar to that of the mortgage loan market. However, the coefficient is not statistically significant and numerically weak at best. Furthermore, the results are inconsistent with those of the QE period that has a statistically significant positive coefficient of This means that interest rates and prepayments were positively related. That is, falling interest rates were accompanied by falling prepayments during the period from 2009 through mid Thus, the question arises what could account for these conflicting results relative to the Pre-QE period? Table 2 Summary of Correlation Coefficients Entire Period Partitioned No Lag Lagged 3 Mos. Pre-QE QE Period New Autos * Used Autos *Significant at 95% level (+/-.27). One answer to this question may lie in the Great Recession which was officially dated December 2007 to June During and subsequent to this period, unemployment and under-employment increased, real income fell, and car sales and thus trade-ins collapsed despite falling interest rates. This disrupted the normal replacement cycle. But the aftermath of this recession continued for several years after the official end of the recession. Prepayments fell correspondingly throughout the recession and for several years thereafter as people kept their cars for longer periods, extending the average age of cars on the road to a recent record of 11 years in Thus, it is likely that the positive correlation (.29) was an anomaly and primarily a result of the Great Recession, its extended aftermath, and a delayed replacement cycle. If so, it is also likely that as the economy recovers, a return to a more normal replacement cycle will once again alter the weak relationship between interest rates and prepayments. Perhaps the most interesting finding of this study is the impact of interest rates on prepayments. This is important because if prepayments are influenced by changing interest rates as is the case with mortgage loans, a negative convexity function would be necessary when modeling IRR. 8 However, the results are inconsistent over time and the effect ranges from zero to immaterial in a IRR assessment so such a function is unnecessary. It appears that this would be the case even if all of the coefficients were statistically significant and they had the proper (negative) sign. When a correlation coefficient is statistically significant this means that there is a relationship but not necessarily a material one. This becomes evident when examining the relationship between prepayments and the total principal payments. Recall from Table 1 that the weighted average life (WAL) of fully seasoned auto loan pools is only about 18 months for new auto loans and 16.4 months for used car loans assuming a 15% annual prepayment speed. The sensitivity analysis showed that an extreme error range of +/-10 percentage points in the prepayment speed changes the WAL by only about two months. The reason is that since auto loans are so short with original terms typically of three to seven years, a large proportion of the contractual monthly payment is principal starting in the first month. When the portfolio is fully seasoned, this proportion increases even more. This interpretation is further supported by the data itself. At the bottom of Exhibit 1 in column 6a, note that the average monthly Total Principal Payoffs as a % of the Beginning Loan Balance was 4.1% over the entire period. In column 9a, however, the average of the monthly Prepayments as a % of the Beginning Loan 8 Such a function would cause the average life of an auto loan portfolio to extend as rates increase and contract as rates decrease as is the case with mortgage loan portfolios. 9

10 Balance is only 1.04%. (This is also shown in Figure 1.) In other words, prepayments made up only about one-quarter of the total principal cash flows over the entire period. This is why the extreme 10 percentage point error range for prepayments in Table 1 had such a small effect regardless of any interest rate effects. The used auto loan portfolio has a similar interpretation. The monthly Total Principal Payoffs averaged 4.8% over the entire period with prepayments making up 1.3 percentage points, or a little over one-quarter of the total. Again, reasonable prepayment estimation errors regardless of the cause are unlikely to have a material impact on an IRR assessment due to the very short-term nature of these portfolios. Furthermore, it is likely that this interpretation extends to other short-term consumer loans as well. V. CONCLUSION Despite the lack of evidence, interest rates may have some effects on auto loan portfolio IRR characteristics. However, unlike the mortgage loan market, these effects appear to be immaterial or not measurable from a modeling standpoint. Even to the extent that such effects do exist, they are offset or obscured by numerous other factors that are unique to the auto loan market and not relevant in the mortgage loan market. The most important of these factors are the very short average lives, low prepayments relative to contractual payments, impact of economic conditions, the fact that autos are depreciating assets, and a replacement cycle that extends and contracts independent of interest rates. The lack of consistency in the results over time may be a product of the vastly different rate environments, a fundamentally unstable relationship in the underlying data over time, the effects of the other factors that distinguish auto loans from mortgage loans, or some combination of these. Auto loan portfolios are widely and properly perceived as having a low degree of interest rate risk. The high proportion of principal cash flows from contractual payments results in stable cash flows regardless of interest rates and the actual or estimated prepayment speed. This is a very desirable ALM attribute since those cash flows do not increase or decrease at inopportune times as is routine in the mortgage loan market. With an estimated weighted average life of only about 16 to 18 months under typical underwriting criteria, very little prepayment risk, and stable cash flows, these fully seasoned portfolios may not only be low-risk, they may be risk-reducing and thus help offset the risk posed by other assets. The results are consistent with the findings of the Citigroup study on auto-related asset-backed securities cited earlier. Finally, the stable and unusually rapid cash flows may also be a largely unrecognized source of liquidity, much like the maturity runoff from a short-term investment portfolio. ABOUT THE AUTHOR John R. Brick is President of Brick & Associates, Inc., the developer of the CU/ALM-ware System, an assetliability management model, and CU/BUDGET-ware, a budgeting and a multi-year strategic planning model. The firm specializes in ALM Consulting and Strategic Planning for depository financial institutions. Dr. Brick has conducted educational and training sessions for leagues and trade associations as well as state and federal examiners. He was also Professor of Finance on the faculty of Michigan State University where he taught Financial Markets, Management of Financial Institutions, Bank Management, and Investments. Dr. Brick has authored/edited three textbooks dealing with financial institutions and the financial markets, and has published over twenty articles dealing with asset-liability management, interest rates, investments, and the management of financial institutions. He received his undergraduate, MBA and PhD degrees from the University of Wisconsin-Madison and holds the Chartered Financial Analyst (CFA) designation. He has served as Chairman of the Michigan State University Federal Credit Union Board of Directors and Co-Chair of its asset-liability management committee (ALCO). This institution has assets of $2.5 billion and over 180,000 members. He may be reached at (800) or via at [email protected]. 10

11 Exhibit 1 New Auto Loans ($ in 000s) Average Annualized Prepayments: 12.5% 7. Required Contractual 11. Monthly Change in 1. Ending 2. Less: 3. Equals: 5. Equals: 6. Total Principal Payoffs as Payoffs 9. Prepayments as Year a. Prepayments b. 2-Year Loan Begin Loan Monthly 4. Less: Total Principal a % of Beg. Loan Bal. Monthly 8. Estimated % of Beg. Loan Bal. Treasury as % of Beg. Treasury Balance Balance Change New loans Payoffs a. Monthly b. 3.1% Prepayments a. Monthly b. Annualized Rate Loan Bal. Rate 6/30/ % 53.3% % 16.1% 0.32% 0.21% 0.08% 5/31/ % 50.8% % 13.6% 0.24% -0.58% 0.01% 4/30/ % 57.8% % 20.6% 0.23% 0.36% -0.01% 3/31/ % 53.5% % 16.3% 0.24% 0.39% -0.01% 2/28/ % 48.9% % 11.7% 0.25% -0.29% 0.00% 1/31/ % 52.4% % 15.2% 0.25% 0.09% 0.00% 12/31/ % 51.3% % 14.1% 0.25% -0.01% -0.01% 11/30/ % 51.4% % 14.2% 0.26% -0.29% -0.01% 10/31/ % 54.9% % 17.7% 0.27% 0.51% 0.02% 9/30/ % 48.8% % 11.6% 0.25% -0.56% -0.01% 8/31/ % 55.4% % 18.2% 0.26% 0.40% 0.02% 7/31/ % 50.6% % 13.4% 0.24% -0.23% -0.04% 6/30/ % 53.4% % 16.2% 0.28% -0.31% 0.01% 5/31/ % 57.1% % 19.9% 0.27% 0.30% -0.02% 4/30/ % 53.5% % 16.3% 0.29% -0.29% -0.05% 3/31/ % 56.9% % 19.7% 0.34% 0.37% 0.07% 2/29/ % 52.5% % 15.3% 0.27% -0.25% 0.04% 1/31/ % 55.5% % 18.3% 0.23% 0.56% -0.02% 12/31/ % 48.8% % 11.6% 0.25% -0.76% 0.01% 11/30/ % 57.9% % 20.7% 0.24% -1.57% -0.03% 10/31/ % 76.8% % 39.6% 0.27% 1.91% 0.07% 9/30/ % 53.9% % 16.7% 0.20% -0.72% -0.02% 8/31/ % 62.6% % 25.4% 0.22% 0.41% -0.17% 7/31/ % 57.6% % 20.4% 0.39% 0.14% -0.01% 6/30/ % 55.9% % 18.7% 0.40% -0.56% -0.14% 5/31/ % 62.7% % 25.5% 0.54% 0.27% -0.17% 4/30/ % 59.4% % 22.2% 0.71% 0.05% 0.03% 3/31/ % 58.8% % 21.6% 0.68% 0.32% -0.08% 2/28/ % 55.0% % 17.8% 0.76% 0.43% 0.16% 1/31/ % 49.8% % 12.6% 0.60% 0.08% 0.00% 12/31/ % 48.8% % 11.6% 0.60% 0.24% 0.16% 11/30/ % 46.0% % 8.8% 0.44% -0.12% 0.07% 10/31/ % 47.4% % 10.2% 0.37% -0.11% -0.10% 9/30/ % 48.6% % 11.4% 0.47% -0.13% -0.04% 8/31/ % 50.2% % 13.0% 0.51% 0.16% -0.09% 7/31/ % 48.3% % 11.1% 0.60% 0.27% -0.11% 6/30/ % 45.0% % 7.8% 0.71% -0.07% -0.10% 5/31/ % 45.8% % 8.6% 0.81% -0.50% -0.22% 4/30/ % 51.8% % 14.6% 1.03% 0.12% 0.09% 3/31/ % 50.3% % 13.1% 0.94% 0.66% 0.10% 2/28/ % 42.4% % 5.2% 0.84% -0.17% -0.07% 1/31/ % 44.5% % 7.3% 0.91% 0.05% 0.06% 12/31/ % 43.9% % 6.7% 0.85% 0.24% 0.06% 11/30/ % 41.0% % 3.8% 0.79% -0.11% -0.14% 10/31/ % 42.4% % 5.2% 0.93% 0.19% -0.01% 9/30/ % 40.1% % 2.9% 0.94% -0.43% -0.15% 8/31/ % 45.2% % 8.0% 1.09% 0.04% 0.10% 7/31/ % 44.7% % 7.5% 0.99% -0.38% -0.17% 6/30/ % 49.2% % 12.0% 1.16% 0.43% 0.25% 5/31/ % 44.1% % 6.9% 0.91% -0.14% 0.00% 4/30/ % 45.8% % 8.6% 0.91% -0.19% -0.01% 3/31/ % 48.1% % 10.9% 0.92% 0.20% -0.04% 2/28/ % 45.6% % 8.4% 0.96% -0.21% 0.17% 1/31/ % 48.2% % 11.0% 0.79% -0.12% -0.03% 12/31/ % 49.7% % 12.5% 0.82% 0.43% -0.38% 11/30/ % 44.5% % 7.3% 1.20% -0.38% -0.38% 10/31/ % 49.0% % 11.8% 1.58% 0.01% -0.47% 9/30/ % 48.9% % 11.7% 2.05% -0.38% -0.36% 8/31/ % 53.5% % 16.3% 2.41% 0.13% -0.15% 7/31/ % 52.0% % 14.8% 2.56% 0.11% -0.19% 6/30/ % 50.6% % 13.4% 2.75% -0.46% 0.32% 5/31/ % 56.2% % 19.0% 2.43% 0.08% 0.40% 4/30/ % 55.3% % 18.1% 2.03% 0.16% 0.43% 3/31/ % 53.4% % 16.2% 1.60% 0.12% -0.36% 2/29/ % 52.0% % 14.8% 1.96% 0.08% -0.51% 1/31/ % 51.0% % 13.8% 2.47% 0.43% -0.64% 12/31/ % 45.8% % 8.6% 3.11% -0.21% -0.22% 11/30/ % 48.4% % 11.2% 3.33% 0.06% -0.64% 10/31/ % 47.7% % 10.5% 3.97% -0.08% -0.03% 9/30/ % 48.7% % 11.5% 4.00% -0.11% -0.31% 8/31/ % 50.0% % 12.8% 4.31% 0.04% -0.49% 7/31/ % 49.5% % 12.3% 4.80% 0.15% -0.17% 6/30/ % 47.7% % 10.5% 4.97% -0.03% 0.21% 5/31/ % 48.0% % 10.8% 4.76% -0.44% 0.10% 4/30/ % 53.3% % 16.1% 4.66% -0.09% 0.08% 3/31/ % 54.5% % 17.3% 4.58% 0.46% -0.26% 2/28/ % 48.9% % 11.7% 4.84% 0.06% -0.03% 1/31/ % 48.3% % 11.1% 4.87% 0.51% 0.20%

12 Exhibit 1 New Auto Loans ($ in 000s) Average Annualized Prepayments: 12.5% 7. Required Contractual 11. Monthly Change in 1. Ending 2. Less: 3. Equals: 5. Equals: 6. Total Principal Payoffs as Payoffs 9. Prepayments as Year a. Prepayments b. 2-Year Loan Begin Loan Monthly 4. Less: Total Principal a % of Beg. Loan Bal. Monthly 8. Estimated % of Beg. Loan Bal. Treasury as % of Beg. Treasury Balance Balance Change New loans Payoffs a. Monthly b. 3.1% Prepayments a. Monthly b. Annualized Rate Loan Bal. Rate 12/31/ % 42.2% % 5.0% 4.67% -0.50% -0.07% 11/30/ % 48.2% % 11.0% 4.74% 0.37% -0.05% 10/31/ % 43.8% % 6.6% 4.79% 0.00% 0.02% 9/30/ % 43.8% % 6.6% 4.77% -0.13% -0.13% 8/31/ % 45.4% % 8.2% 4.90% -0.08% -0.21% 7/31/ % 46.4% % 9.2% 5.11% 0.24% 0.00% 6/30/ % 43.5% % 6.3% 5.11% -0.45% 0.15% 5/31/ % 48.9% % 11.7% 4.96% 0.26% 0.08% 4/30/ % 45.8% % 8.6% 4.88% -0.62% 0.15% 3/31/ % 53.2% % 16.0% 4.73% 1.25% 0.07% 2/28/ % 38.2% % 1.0% 4.66% -0.75% 0.27% 1/31/ % 47.2% % 10.0% 4.39% 0.37% 0.00% 12/31/ % 42.8% % 5.6% 4.39% 0.35% -0.02% 11/30/ % 38.6% % 1.4% 4.41% -0.54% 0.16% 10/31/ % 45.1% % 7.9% 4.25% -0.02% 0.31% 9/30/ % 45.3% % 8.1% 3.94% -0.53% -0.09% 8/31/ % 51.7% % 14.5% 4.03% 0.30% 0.17% 7/31/ % 48.1% % 10.9% 3.86% -0.13% 0.23% 6/30/ % 49.6% % 12.4% 3.63% 0.42% 0.00% 5/31/ % 44.5% % 7.3% 3.63% -0.19% 0.00% 4/30/ % 46.8% % 9.6% 3.63% -0.25% -0.08% 3/31/ % 49.8% % 12.6% 3.71% 0.49% 0.34% 2/28/ % 44.0% % 6.8% 3.37% 0.14% 0.17% 1/31/ % 42.2% % 5.0% 3.20% -0.01% 0.21% 12/31/ % 42.3% % 5.1% 2.99% 0.03% 0.15% 11/30/ % 42.0% % 4.8% 2.84% -0.55% 0.28% 10/31/ % 48.5% % 11.3% 2.56% -0.48% 0.06% 9/30/ % 54.2% % 17.0% 2.50% 0.35% -0.01% 8/31/ % 50.1% % 12.9% 2.51% -0.32% -0.10% 7/31/ % 53.9% % 16.7% 2.61% 0.09% -0.13% 6/30/ % 52.8% % 15.6% 2.74% 0.47% 0.24% 5/31/ % 47.2% % 10.0% 2.50% -0.37% 0.46% 4/30/ % 51.7% % 14.5% 2.04% -0.51% 0.49% 3/31/ % 57.8% % 20.6% 1.55% 0.88% -0.16% 2/29/ % 47.3% % 10.1% 1.71% -0.19% -0.01% 1/31/ % 49.6% % 12.4% 1.72% 0.25% -0.16% 12/31/ % 46.6% % 9.4% 1.88% -0.25% 1.88% % 49.7% % 12.5%

13 Exhibit 2 Used Auto Loans ($ in 000s) Average Annualized Prepayments: 15.5% 7. Required Contractual 11. Monthly Change in 1. Ending 2. Less: 3. Equals: 5. Equals: 6. Total Principal Payoffs as Payoffs 9. Prepayments as Year a. Prepayments b. 2-Year Loan Begin Loan Monthly 4. Less: Total Principal a % of Beg. Loan Bal. Monthly 8. Estimated % of Beg. Loan Bal. Treasury as % of Beg. Treasury Balance Balance Change New Loans Payoffs a. Monthly b. 3.5% Prepayments a. Monthly b. Annualized Rate Loan Bal. Rate 6/30/ % 61.8% % 19.8% 0.32% -0.1% 0.08% 5/31/ % 63.2% % 21.2% 0.24% -0.3% 0.01% 4/30/ % 67.4% % 25.4% 0.23% 0.0% -0.01% 3/31/ % 67.0% % 25.0% 0.24% 0.8% -0.01% 2/28/ % 57.6% % 15.6% 0.25% -0.1% 0.00% 1/31/ % 58.3% % 16.3% 0.25% 0.4% 0.00% 12/31/ % 53.3% % 11.3% 0.25% -0.5% -0.01% 11/30/ % 59.4% % 17.4% 0.26% -0.1% -0.01% 10/31/ % 60.4% % 18.4% 0.27% 0.7% 0.02% 9/30/ % 52.6% % 10.6% 0.25% -0.7% -0.01% 8/31/ % 61.0% % 19.0% 0.26% 0.3% 0.02% 7/31/ % 57.6% % 15.6% 0.24% -0.1% -0.04% 6/30/ % 59.3% % 17.3% 0.28% -0.1% 0.01% 5/31/ % 60.6% % 18.6% 0.27% 0.0% -0.02% 4/30/ % 60.6% % 18.6% 0.29% -0.2% -0.05% 3/31/ % 63.3% % 21.3% 0.34% 0.7% 0.07% 2/29/ % 55.4% % 13.4% 0.27% -0.2% 0.04% 1/31/ % 57.4% % 15.4% 0.23% 0.3% -0.02% 12/31/ % 53.7% % 11.7% 0.25% -0.7% 0.01% 11/30/ % 61.8% % 19.8% 0.24% -0.6% -0.03% 10/31/ % 68.4% % 26.4% 0.27% 0.7% 0.07% 9/30/ % 59.8% % 17.8% 0.20% -0.4% -0.02% 8/31/ % 64.3% % 22.3% 0.22% 0.1% -0.17% 7/31/ % 63.1% % 21.1% 0.39% 0.1% -0.01% 6/30/ % 61.5% % 19.5% 0.40% 0.1% -0.14% 5/31/ % 60.1% % 18.1% 0.54% -0.4% -0.17% 4/30/ % 64.5% % 22.5% 0.71% -0.5% 0.03% 3/31/ % 70.2% % 28.2% 0.68% 1.3% -0.08% 2/28/ % 55.1% % 13.1% 0.76% -0.2% 0.16% 1/31/ % 57.4% % 15.4% 0.60% 0.4% 0.00% 12/31/ % 52.6% % 10.6% 0.60% -0.3% 0.16% 11/30/ % 55.9% % 13.9% 0.44% 0.2% 0.07% 10/31/ % 53.5% % 11.5% 0.37% -0.1% -0.10% 9/30/ % 54.3% % 12.3% 0.47% -0.2% -0.04% 8/31/ % 56.2% % 14.2% 0.51% -0.1% -0.09% 7/31/ % 57.3% % 15.3% 0.60% 0.1% -0.11% 6/30/ % 55.6% % 13.6% 0.71% 0.5% -0.10% 5/31/ % 49.5% % 7.5% 0.81% -0.4% -0.22% 4/30/ % 54.1% % 12.1% 1.03% -0.3% 0.09% 3/31/ % 58.3% % 16.3% 0.94% 0.7% 0.10% 2/28/ % 49.4% % 7.4% 0.84% 0.1% -0.07% 1/31/ % 48.5% % 6.5% 0.91% 0.0% 0.06% 12/31/ % 49.0% % 7.0% 0.85% 0.0% 0.06% 11/30/ % 49.0% % 7.0% 0.79% 0.0% -0.14% 10/31/ % 48.4% % 6.4% 0.93% -0.1% -0.01% 9/30/ % 49.4% % 7.4% 0.94% 0.0% -0.15% 8/31/ % 49.9% % 7.9% 1.09% -0.1% 0.10% 7/31/ % 51.4% % 9.4% 0.99% 0.0% -0.17% 6/30/ % 51.1% % 9.1% 1.16% 0.2% 0.25% 5/31/ % 48.7% % 6.7% 0.91% -0.4% 0.00% 4/30/ % 54.1% % 12.1% 0.91% 0.0% -0.01% 3/31/ % 54.4% % 12.4% 0.92% 0.3% -0.04% 2/28/ % 50.6% % 8.6% 0.96% -0.1% 0.17% 1/31/ % 51.7% % 9.7% 0.79% 0.1% -0.03% 12/31/ % 50.0% % 8.0% 0.82% 0.5% -0.38% 11/30/ % 44.3% % 2.3% 1.20% -0.6% -0.38% 10/31/ % 51.8% % 9.8% 1.58% 0.0% -0.47% 9/30/ % 51.4% % 9.4% 2.05% -0.3% -0.36% 8/31/ % 54.5% % 12.5% 2.41% 0.2% -0.15% 7/31/ % 51.6% % 9.6% 2.56% -0.2% -0.19% 6/30/ % 53.9% % 11.9% 2.75% -0.6% 0.32% 5/31/ % 61.0% % 19.0% 2.43% 0.3% 0.40% 4/30/ % 57.8% % 15.8% 2.03% -0.2% 0.43% 3/31/ % 60.0% % 18.0% 1.60% 0.2% -0.36% 2/29/ % 57.6% % 15.6% 1.96% 0.1% -0.51% 1/31/ % 56.7% % 14.7% 2.47% 0.6% -0.64% 12/31/ % 49.5% % 7.5% 3.11% -0.1% -0.22% 11/30/ % 50.3% % 8.3% 3.33% -0.6% -0.64% 10/31/ % 57.4% % 15.4% 3.97% 0.4% -0.03% 9/30/ % 53.0% % 11.0% 4.00% -0.3% -0.31% 8/31/ % 56.8% % 14.8% 4.31% 0.3% -0.49% 7/31/ % 53.2% % 11.2% 4.80% -0.2% -0.17% 6/30/ % 55.3% % 13.3% 4.97% -0.3% 0.21% 5/31/ % 58.7% % 16.7% 4.76% -0.2% 0.10% 4/30/ % 61.1% % 19.1% 4.66% 0.1% 0.08% 3/31/ % 59.4% % 17.4% 4.58% 0.1% -0.26% 2/28/ % 58.2% % 16.2% 4.84% 0.1% -0.03% 1/31/ % 57.1% % 15.1% 4.87% 0.6% 0.20%

14 Exhibit 2 Used Auto Loans ($ in 000s) Average Annualized Prepayments: 15.5% 7. Required Contractual 11. Monthly Change in 1. Ending 2. Less: 3. Equals: 5. Equals: 6. Total Principal Payoffs as Payoffs 9. Prepayments as Year a. Prepayments b. 2-Year Loan Begin Loan Monthly 4. Less: Total Principal a % of Beg. Loan Bal. Monthly 8. Estimated % of Beg. Loan Bal. Treasury as % of Beg. Treasury Balance Balance Change New Loans Payoffs a. Monthly b. 3.5% Prepayments a. Monthly b. Annualized Rate Loan Bal. Rate 12/31/ % 50.5% % 8.5% 4.67% -0.3% -0.07% 11/30/ % 53.9% % 11.9% 4.74% -0.3% -0.05% 10/31/ % 57.5% % 15.5% 4.79% 0.2% 0.02% 9/30/ % 54.8% % 12.8% 4.77% -0.2% -0.13% 8/31/ % 57.5% % 15.5% 4.90% -0.3% -0.21% 7/31/ % 61.3% % 19.3% 5.11% 0.3% 0.00% 6/30/ % 57.4% % 15.4% 5.11% 0.0% 0.15% 5/31/ % 57.8% % 15.8% 4.96% 0.1% 0.08% 4/30/ % 57.1% % 15.1% 4.88% -0.7% 0.15% 3/31/ % 65.6% % 23.6% 4.73% 0.7% 0.07% 2/28/ % 57.4% % 15.4% 4.66% 0.1% 0.27% 1/31/ % 56.6% % 14.6% 4.39% 0.3% 0.00% 12/31/ % 52.9% % 10.9% 4.39% 0.0% -0.02% 11/30/ % 52.9% % 10.9% 4.41% 0.0% 0.16% 10/31/ % 52.9% % 10.9% 4.25% -0.4% 0.31% 9/30/ % 58.0% % 16.0% 3.94% -1.1% -0.09% 8/31/ % 70.8% % 28.8% 4.03% 0.8% 0.17% 7/31/ % 61.2% % 19.2% 3.86% 0.0% 0.23% 6/30/ % 60.8% % 18.8% 3.63% 0.0% 0.00% 5/31/ % 60.6% % 18.6% 3.63% -0.4% 0.00% 4/30/ % 65.5% % 23.5% 3.63% -0.4% -0.08% 3/31/ % 70.2% % 28.2% 3.71% 1.0% 0.34% 2/28/ % 58.7% % 16.7% 3.37% -0.1% 0.17% 1/31/ % 59.5% % 17.5% 3.20% 0.0% 0.21% 12/31/ % 59.2% % 17.2% 2.99% 0.0% 0.15% 11/30/ % 59.1% % 17.1% 2.84% -0.5% 0.28% 10/31/ % 64.6% % 22.6% 2.56% 0.1% 0.06% 9/30/ % 63.5% % 21.5% 2.50% -0.3% -0.01% 8/31/ % 67.1% % 25.1% 2.51% 0.2% -0.10% 7/31/ % 65.0% % 23.0% 2.61% 0.1% -0.13% 6/30/ % 63.6% % 21.6% 2.74% 0.0% 0.24% 5/31/ % 63.4% % 21.4% 2.50% -0.1% 0.46% 4/30/ % 64.4% % 22.4% 2.04% -0.3% 0.49% 3/31/ % 68.4% % 26.4% 1.55% 0.9% -0.16% 2/29/ % 57.1% % 15.1% 1.71% -0.2% -0.01% 1/31/ % 59.1% % 17.1% 1.72% -0.3% -0.16% 12/31/ % 63.2% % 21.2% 1.88% 0.5% 1.88% % 57.5% % 15.5%