THEAMERICAN ECONOMIC REVIEW MARCH 2009 the average applicant. In particular, the company assigns each applicant a credit category, which we partition into"high, ""medium, " and"low"risk. The applicant pool is 27 percent low risk and 29 percent high risk, while the corresponding percentages for the pool of buyers are 35 and 17. The sales terms, summarized in the third panel of Table l, reflect the presumably limited options of this population. A typical car, and most are around three to five years old, costs around $6,000 to bring to the lot. The average sale price is just under $11,000. The average down payment is a bit less than $1,000, so after taxes and fees, the average loan size is similar to the sales priee. ee Despite the large loans and small down payments, it appears that many buyers would prefer to put down even less money. Forty-three percent make exactly the minimum down payment, which varies with the buyer's credit category but is typically between $400 and S1, 000. Some buyers do make down payments that are substantially above the required minimum, but the number is small. Less than 10 percent of buyers make down payments that exceed the required down pay ment by $1,000. In a financed purchase, the monthly payment depends on the loan size, the loan term, and the nterest rate. Much of the relevant variation in our data is due to the former rather than the lat ter. Over 85 percent of the loans have an annual interest rate over 20 percent, and around half the loans appear to be at the state-mandated maximum annual interest rate. Most states in our data have a uniform 30 percent cap. These rates mean that finance charges are significant. For instance, a borrower who takes an $11,000 loan at a 30 percent APR and repays it over 42 months will make interest payments totalling $6,000 The main reason for the high finance charges is evident in the fourth panel of Table 1. Most loans end in default. Our data end before the last payments are due on some loans, but of the loans with uncensored payment periods, only 39 percent are repaid in full. o Moreover, loans that do default tend to default quickly. Figure lA plots a kernel density of the fraction of payments made by borrowers who defaulted. Nearly half the defaults occur before a quarter of the pay- ments have been made, that is, within ten months. This leads to a highly bimodal distribution of per-sale profits. To capture this, we calculated the present value of payments received for each uncensored loan in our data, including both the down payment and the amount recovered in the event of default, using an annual interest rate of 10 percent to value the payment stream. We then divide this by the firms reported costs of purchasing and reconditioning the car to obtain a rate of return on capital for each transaction Figure 1B plots the distribution of returns, showing the clear bimodal pattern. It is also interesting to isolate the value of each stream of loan repayments and compare it to the size of each loan. when we do this for each uncensored loan in our data(and use annual discount rates of0 to 10 percent), we find an average repayment-to-loan ratio of 0.79-0.88. Moreover,a substantial majority of loans in the data, 54-57 percent, have a repayment-to-loan ratio below one. This calculation helps to explain why buyers who are going to finance heavily in any event 6 Car prices are subject to some degree of negotiation, which we discuss in Section Il. The price we report here is the negoLatid gandencte n he car parie. the dewnspamieetwh the lisan gehm n ghbenths. and R=1 + r the monthlv interes Co s The company offers lower rates to some buyers who have either particularly good credit records or make down pay- the monthly payment is given by m=(P-D)(R-1(I-R) ments above the minimum. Although we do not have direct data on the offers of competing lenders, it seems unlikely hat this population has access to better rates. Fair lsaac's Web page indicates that borrowers with FICO scores in th 500-600 range(that is, better than the majority of the applicants in our sample) should expect to pay close to 20 percent annual interest for standard used car loans in most states, and in some states will not qualify at all for"standard"loans. oJe.W states have lower caps that depend on characteristics of the car. A t kins(2008) provides more details on defaults and recoveries
54 THE AMERICAN ECONOMIC REVIEW March 2009 the average applicant. In particular, the company assigns each applicant a credit category, which we partition into “high,” “medium,” and “low” risk. The applicant pool is 27 percent low risk and 29 percent high risk, while the corresponding percentages for the pool of buyers are 35 and 17. The sales terms, summarized in the third panel of Table 1, reflect the presumably limited options of this population. A typical car, and most are around three to five years old, costs around $6,000 to bring to the lot. The average sale price is just under $11,000.6 The average down payment is a bit less than $1,000, so after taxes and fees, the average loan size is similar to the sales price. Despite the large loans and small down payments, it appears that many buyers would prefer to put down even less money. Forty-three percent make exactly the minimum down payment, which varies with the buyer’s credit category but is typically between $400 and $1,000. Some buyers do make down payments that are substantially above the required minimum, but the number is small. Less than 10 percent of buyers make down payments that exceed the required down payment by $1,000. In a financed purchase, the monthly payment depends on the loan size, the loan term, and the interest rate.7 Much of the relevant variation in our data is due to the former rather than the latter. Over 85 percent of the loans have an annual interest rate over 20 percent, and around half the loans appear to be at the state-mandated maximum annual interest rate.8 Most states in our data have a uniform 30 percent cap.9 These rates mean that finance charges are significant. For instance, a borrower who takes an $11,000 loan at a 30 percent APR and repays it over 42 months will make interest payments totalling $6,000. The main reason for the high finance charges is evident in the fourth panel of Table 1. Most loans end in default. Our data end before the last payments are due on some loans, but of the loans with uncensored payment periods, only 39 percent are repaid in full.10 Moreover, loans that do default tend to default quickly. Figure 1A plots a kernel density of the fraction of payments made by borrowers who defaulted. Nearly half the defaults occur before a quarter of the payments have been made, that is, within ten months. This leads to a highly bimodal distribution of per-sale profits. To capture this, we calculated the present value of payments received for each uncensored loan in our data, including both the down payment and the amount recovered in the event of default, using an annual interest rate of 10 percent to value the payment stream. We then divide this by the firm’s reported costs of purchasing and reconditioning the car to obtain a rate of return on capital for each transaction. Figure 1B plots the distribution of returns, showing the clear bimodal pattern. It is also interesting to isolate the value of each stream of loan repayments and compare it to the size of each loan. When we do this for each uncensored loan in our data (and use annual discount rates of 0 to 10 percent), we find an average repayment-to-loan ratio of 0.79–0.88. Moreover, a substantial majority of loans in the data, 54–57 percent, have a repayment-to-loan ratio below one. This calculation helps to explain why buyers who are going to finance heavily in any event 6 Car prices are subject to some degree of negotiation, which we discuss in Section II. The price we report here is the negotiated transaction price rather than the “list” price, which is slightly higher. 7 Letting p denote the car price, D the down payment, T the loan term in months, and R 5 1 1 r the monthly interest rate, the monthly payment is given by m 5 1p 2 D2 1R 2 12/11 2 R2T 2. 8 The company offers lower rates to some buyers who have either particularly good credit records or make down payments above the minimum. Although we do not have direct data on the offers of competing lenders, it seems unlikely that this population has access to better rates. Fair Isaac’s Web page indicates that borrowers with FICO scores in the 500–600 range (that is, better than the majority of the applicants in our sample) should expect to pay close to 20 percent annual interest for standard used car loans in most states, and in some states will not qualify at all for “standard” loans. 9 A few states have lower caps that depend on characteristics of the car. 10 Jenkins (2008) provides more details on defaults and recoveries
VOL 99 NO. I ADAMS ETAL.: SUBPRIME LENDING E 810 0 0.000.100.200.300.400.500.600.700800.901.00 Fraction of loan paid FIGURE IA KERNEL DENSITY OF FRACTION OF LOAN PAID CONDITIONAL ON DEFAULT Note: Figure based on data from uncensored loans that ended in default. 0.06 0.04 0.03 0.0 0.00具 1.0-0.5 1.5 (Revenue-cost)cost FIGURE 1B. RATE OF RETURN HISTOGRAM Notes: Figure based on data from uncensored loans. Revenue is calculated as down payment t present value of loan payments present value of recovery, assuming an internal firm discount rate of 10 percent. might maximize their loan size. In the majority of cases, the present value of payments on an extra dollar borrowed is significantly less than a dollar paid up front The point applies most clearly for small changes in loan size. As we show below, smaller loans decrease th ability of default, which generates a nonconvexity in loan demand. This effect is not reflected in our calculation, which takes the default process as fixed. It is also worth noting that the incentive to borrow on the margin increases with
VOL. 99 NO. 1 Adams Et al.: Subprime Lending 55 might maximize their loan size. In the majority of cases, the present value of payments on an extra dollar borrowed is significantly less than a dollar paid up front.11 11 The point applies most clearly for small changes in loan size. As we show below, smaller loans decrease the probability of default, which generates a nonconvexity in loan demand. This effect is not reflected in our calculation, which takes the default process as fixed. It is also worth noting that the incentive to borrow on the margin increases with 0.0 0.5 1.0 1.5 2.0 2.5 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Low risk Medium risk High risk Fraction of loan paid Density Figure 1A. Kernel Density of Fraction of Loan Paid Conditional on Default Note: Figure based on data from uncensored loans that ended in default. 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 21.0 20.5 0.0 0.5 1.0 1.5 2.0 2.5 Paid loans Defaulted loans (Revenue – cost ) / cost Frequency Figure 1B. Rate of Return Histogram Notes: Figure based on data from uncensored loans. Revenue is calculated as down payment 1 present value of loan payments 1 present value of recovery, assuming an internal firm discount rate of 10 percent
THEAMERICAN ECONOMIC REVIEW MARCH 2009 I. Evidence of Liquidity Constraints: Purchasing Behavior A consumer is liquidity constrained if he cannot finance present purchases using resources that will accrue to him in the future. Subprime borrowers are obvious candidates to find them selves in this position. While we cannot directly observe individual household balance sheets and credit options, our data do permit us to investigate the behavioral implications of liquidity constraints. We consider two such implications in this section The first concerns purchasing sensitivity with respect to current and predictable future cash flow. For an individual who can borrow freely against future resources, the response should be equal. In contrast, a high purchase response to a predictable temporary spike in cash flow, such as a tax rebate, suggests an inability to shift resources over time. The first piece of evidence we present is a striking seasonal increase in applications and sales at precisely tax rebate time Moreover, we show that there is a remarkably clear correlation between the seasonal effects we observe and the amount of the earned income tax credit, which is likely to be a significant por- tion of the tax rebate for many households in our data. The second empirical implication is the mirror image of the first. An individual who is not liquidity constrained should evaluate the cost of a given payment schedule based on its present value. In contrast, a liquidity constrained individual values the opportunity to defer payments to the future, and therefore views current payments as more costly than the present value of future payments. This is consistent with the second piece of evidence we present: individual purchase elasticity with respect to current payment(down payment)is an order of magnitude higher than with respect to future payments These findings are what one might expect from a population that is living check-to-check But are there alternative explanations? Explaining the seasonality finding without reference a cash-on-hand story is difficult. It seems unlikely that this population has a particular need for cars in the month of February. One also might ask whether the car purchase is a form of savings, rather than consumption. But given the price margins and very low down payments, the immedi ate post-purchase equity is negligible. 2 Moreover, given the high default rate, most consumers would have to be highly overoptimistic about their repayment ability to view the transaction as a form of savings One might ask in addition if our estimated demand sensitivities could be explained by con sumer impatience. We calculate that to rationalize the relative importance of the down payment with empirically correct expectations of default, consumers would have to equate a $100 cost today with a $1, 515 cost in one year. This number would be still higher if consumers were over- ptimistic about repayment. Moreover, even if consumers were this myopic, discounting alone cannot explain the seasonal pattern. This suggests that consumer purchasing behavior may be best explained by check-to-check existence A. The Effect of Tax Rebate Season We start by examining seasonal patterns in demand. Figure 2A displays the average number of applications and sales, by calendar week, over the 2002-2005 period. Both are markedly higher from late January to early March. Applications are 23 percent higher in February than in the other months, and the close rate(sales to applications ratio) is 40 percent compared to 33 percent buyers'subjective discount rates. Some researchers(e. g, David Laibson, Andrea Repetto, and Jeremy Tobacman 2003) have argued that borrowing behavior reflects a much higher degree of impatience than we assume here out of tax rebates focu
56 THE AMERICAN ECONOMIC REVIEW March 2009 II. Evidence of Liquidity Constraints: Purchasing Behavior A consumer is liquidity constrained if he cannot finance present purchases using resources that will accrue to him in the future. Subprime borrowers are obvious candidates to find themselves in this position. While we cannot directly observe individual household balance sheets and credit options, our data do permit us to investigate the behavioral implications of liquidity constraints. We consider two such implications in this section. The first concerns purchasing sensitivity with respect to current and predictable future cash flow. For an individual who can borrow freely against future resources, the response should be equal. In contrast, a high purchase response to a predictable temporary spike in cash flow, such as a tax rebate, suggests an inability to shift resources over time. The first piece of evidence we present is a striking seasonal increase in applications and sales at precisely tax rebate time. Moreover, we show that there is a remarkably clear correlation between the seasonal effects we observe and the amount of the earned income tax credit, which is likely to be a significant portion of the tax rebate for many households in our data. The second empirical implication is the mirror image of the first. An individual who is not liquidity constrained should evaluate the cost of a given payment schedule based on its present value. In contrast, a liquidity constrained individual values the opportunity to defer payments to the future, and therefore views current payments as more costly than the present value of future payments. This is consistent with the second piece of evidence we present: individual purchase elasticity with respect to current payment (down payment) is an order of magnitude higher than with respect to future payments. These findings are what one might expect from a population that is living check-to-check. But are there alternative explanations? Explaining the seasonality finding without reference to a cash-on-hand story is difficult. It seems unlikely that this population has a particular need for cars in the month of February. One also might ask whether the car purchase is a form of savings, rather than consumption. But given the price margins and very low down payments, the immediate post-purchase equity is negligible.12 Moreover, given the high default rate, most consumers would have to be highly overoptimistic about their repayment ability to view the transaction as a form of savings. One might ask in addition if our estimated demand sensitivities could be explained by consumer impatience. We calculate that to rationalize the relative importance of the down payment with empirically correct expectations of default, consumers would have to equate a $100 cost today with a $1,515 cost in one year. This number would be still higher if consumers were overoptimistic about repayment. Moreover, even if consumers were this myopic, discounting alone cannot explain the seasonal pattern. This suggests that consumer purchasing behavior may be best explained by check-to-check existence. A. The Effect of Tax Rebate Season We start by examining seasonal patterns in demand. Figure 2A displays the average number of applications and sales, by calendar week, over the 2002–2005 period. Both are markedly higher from late January to early March. Applications are 23 percent higher in February than in the other months, and the close rate (sales to applications ratio) is 40 percent compared to 33 percent buyers’ subjective discount rates. Some researchers (e.g., David Laibson, Andrea Repetto, and Jeremy Tobacman 2003) have argued that borrowing behavior reflects a much higher degree of impatience than we assume here. 12 This would not be the case for a nonfinanced car purchase, which is a reason that studies of the marginal propensity to consume out of tax rebates focus on expenditure on nondurables
VOL 99 NO. I ADAMS ETAL.: SUBPRIME LENDING Applications 38品NE Sales Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Calendar week FIGURE 2A. SEASONALITY IN APPLICATIONS AND SALES Notes: Figure based on data from all applications. Both number of applications and sales are normalized by the aver- over the rest of the year. These seasonal patterns cannot be attributed to sales or other changes in the firms offers. In fact, required down payments are almost $150 higher in February, averaging across applicants in our data, than in the other months of the year. Indeed we initially thought these patterns indicated a data problem, until the company pointed out that prospective buyers receive their tax rebates at precisely this time of year. But can tax rebates be large enough to explain such a dramatic spike in demand? All loan pplicants must hold a job to be eligible for a loan, and most are relatively low earners, making them eligible for the earned income tax credit(EltC). The associated rebate, which varies with income and the number of dependents, can be as high as $4, 500. To assess whether purchasing patterns might reflect EITC rebates, we classified applicants into 12 groups depending on their monthly household income and their number of dependents. For each group, we calculated the earned income tax credit for the average household in the group, and also the percent increase in applications, close rate, and sales in February relative to the other months. Figure 2B plots the relationship between the calculated ElTC rebate and the seasonal spike in demand for each group. There is a sharp correlation. For households with monthly incomes below $1, 500 and at least two dependents, for whom the EITC rebate could be around $4, 000, the number of applications doubles in February and the number of purchases more than triples. In contrast, for households with monthly incomes above $3, 500 and no dependents, for whom the EITC rebate is likely zero, the number of applications and purchases exhibits virtually no increase in tax rebate season Because minimum down payment requirements are raised during tax season, it is interesting to isolate the seasonal effect in demand, holding all else constant. Our demand estimates in the next section. which control for the relevant offer terms as well as individual characteristics such as credit score and household income, indicate that the demand of applicants who arrive on the 13The details of the EITC schedule did not change much over our observation period (2001-2005). The particular numbers we report are based on the 2003 schedule
VOL. 99 NO. 1 Adams Et al.: Subprime Lending 57 over the rest of the year. These seasonal patterns cannot be attributed to sales or other changes in the firm’s offers. In fact, required down payments are almost $150 higher in February, averaging across applicants in our data, than in the other months of the year. Indeed we initially thought these patterns indicated a data problem, until the company pointed out that prospective buyers receive their tax rebates at precisely this time of year. But can tax rebates be large enough to explain such a dramatic spike in demand? All loan applicants must hold a job to be eligible for a loan, and most are relatively low earners, making them eligible for the earned income tax credit (EITC). The associated rebate, which varies with income and the number of dependents, can be as high as $4,500. To assess whether purchasing patterns might reflect EITC rebates, we classified applicants into 12 groups depending on their monthly household income and their number of dependents. For each group, we calculated the earned income tax credit for the average household in the group,13 and also the percent increase in applications, close rate, and sales in February relative to the other months. Figure 2B plots the relationship between the calculated EITC rebate and the seasonal spike in demand for each group. There is a sharp correlation. For households with monthly incomes below $1,500 and at least two dependents, for whom the EITC rebate could be around $4,000, the number of applications doubles in February and the number of purchases more than triples. In contrast, for households with monthly incomes above $3,500 and no dependents, for whom the EITC rebate is likely zero, the number of applications and purchases exhibits virtually no increase in tax rebate season. Because minimum down payment requirements are raised during tax season, it is interesting to isolate the seasonal effect in demand, holding all else constant. Our demand estimates in the next section, which control for the relevant offer terms as well as individual characteristics such as credit score and household income, indicate that the demand of applicants who arrive on the 13 The details of the EITC schedule did not change much over our observation period (2001–2005). The particular numbers we report are based on the 2003 schedule. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Sales Applications Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Calendar week Normalized count per week Figure 2A. Seasonality in Applications and Sales Notes: Figure based on data from all applications. Both number of applications and sales are normalized by the average number of applications per week
THEAMERICAN ECONOMIC REVIEW MARCH 2009 Applications s中 Earned Income Tax Credit(EITc) Sales 1L s1000 s1,000 s2000 Close rate FIGURE 2B. TAX CREDIT EFFECTS ON APPLICATIONS AND SALES Notes: Figures based com all a ions. Each point represents a group of applicants with a nber of dependents are: 0= no dependents, 1 =1 dependent, and 2 or more dependen for incom (in dollars per month) are: VL less than 1,500, L 1, 500-2.000 M=2.000-3.000. and H more than 3.000
58 THE AMERICAN ECONOMIC REVIEW March 2009 0 20 40 60 80 100 120 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 2VL 2M 1L 2L 1VL 1M 0M 0H 1H 0VL 0L 2H 0 50 100 150 200 250 300 2VL 2M 1L 2L 1VL 1M 0M 0H 1H 0VL 0L 2H 0 20 40 60 80 2VL 2M 1L 2L 1VL 2H 1M 0H 1H 0VL 0M 0L Applications Percent increase in applications in February Earned Income Tax Credit (EITC) EITC Percent increase in sales in February Percent increase in close rate in February 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 Sales Close Rate EITC Figure 2B. Tax Credit Effects on Applications and Sales Notes: Figures based on data from all applications. Each point represents a group of applicants with a given income level and number of dependents. Labels for number of dependents are: 0 5 no dependents, 1 5 1 dependent, and 2 5 2 or more dependents. Labels for income level (in dollars per month) are: VL 5 less than 1,500, L 5 1,500–2,000, M 5 2,000–3,000, and H 5 more than 3,000. 0 20 40 60 80 100 120 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 2VL 2M 1L 2L 1VL 1M 0M 0H 1H 0VL 0L 2H 0 50 100 150 200 250 300 2VL 2M 1L 2L 1VL 1M 0M 0H 1H 0VL 0L 2H 0 20 40 60 80 2VL 2M 1L 2L 1VL 2H 1M 0H 1H 0VL 0M 0L Applications Percent increase in applications in February Earned Income Tax Credit (EITC) EITC Percent increase in sales in February Percent increase in close rate in February 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 Sales Close Rate EITC 0 20 40 60 80 100 120 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 2VL 2M 1L 2L 1VL 1M 0M 0H 1H 0VL 0L 2H 0 50 100 150 200 250 300 2VL 2M 1L 2L 1VL 1M 0M 0H 1H 0VL 0L 2H 0 20 40 60 80 2VL 2M 1L 2L 1VL 2H 1M 0H 1H 0VL 0M 0L Applications Percent increase in applications in February Earned Income Tax Credit (EITC) EITC Percent increase in sales in February Percent increase in close rate in February 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 2$1,000 $0 $1,000 $2,000 $3,000 $4,000 $5,000 Sales Close Rate EITC