Mortgage Complexity and House Price Dynamics

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1 Mortgage Complexity and House Price Dynamics Gene Amromin Federal Reserve Bank of Chicago Jennifer Huang University of Texas at Austin Clemens Sialm University of Texas at Austin and NBER and Edward Zhong Federal Reserve Bank of Chicago May 2010 We thank Sheridan Titman and seminar participants at the the University of Texas at Austin for helpful comments and discussions. s:

2 Mortgage Complexity and House Price Dynamics Abstract Complex mortgages became a popular borrowing instrument during the bullish housing market of the early 2000s but vanished rapidly during the subsequent downturn. These non-traditional loans (interest only, negative amortization, and teaser mortgages) enable households to postpone loan repayment compared to traditional mortgages and hence relax borrowing constraints. We investigate the spatial and temporal evolution of complex contracts and focus on their relation with the level and volatility of house prices. We find that complex mortgages are geographically concentrated in areas of high house price levels and past appreciation. We document that complex mortgages were chosen by relatively high credit quality households seeking to purchase more expensive houses relative to their incomes. Borrowers of complex mortgages experience substantially higher ex post default rates than borrowers of traditional mortgages with similar characteristics. We further find that cities with higher shares of complex mortgages experience a subsequent increase in the cyclicality and volatility of house prices. 2

3 The availability of these alternative mortgage products proved to be quite important, and, as many have recognized, is likely a key explanation of the housing bubble. Ben S. Bernanke 1 Introduction Over the last decade, the home mortgage market has experienced a significant increase in product complexity, followed by a rapid reversion back to simple products. In this paper, we study the product choice of individual households and ask whether complex mortgage products fueled the housing bubble and contributed to the large volume of subsequent defaults and the substantial price declines. The menu of mortgage contract choices for individual households in the United States was dominated for decades by fully-amortizing long-term fixed-rate mortgages (FRM) and, to a lesser extent, by adjustable-rate mortgages (ARM) that locked in the initial interest rate for the first 5 to 7 years of the contract. However, the mortgage market has experienced a significant increase in product complexity in the early 2000s. The products that gained prominence during the period of rapid house price appreciation featured zero or negative amortization, short interest rate reset periods, and teaser introductory interest rates. We term these complex mortgages (CM). Unlike some other innovations that received much attention, such as the extension of credit to borrowers with subprime credit, the defining feature of complex mortgages is the deferral of principal repayment. Some of these products have been developed in the 1980s as risk management tools for households with high but variable income. They remained niche products until their reincarnation as affordability products in the early 2000s. Figure 1 shows the proportion of fixed-rate, adjustable-rate, and complex mortgage products originated over the period between 1995 and 2009, as reported by LPS Applied Analytics (our primary data source described in detail below). The share of complex products in the U.S. remains below 2% until the second half of 2003 before increasing 1

4 to make up about 30% of mortgage originations just two years later. The complex products faded almost as quickly, declining to less than 2% of originations in The availability of complex mortgage products can either improve or reduce the welfare of individual households. On the one hand, these products allow households to select loans that better fit their preferences and specific circumstances. In particular, many of the newly popular mortgage contracts reduce mortgage payments during the first few years of the mortgages and thereby relax household liquidity and borrowing constraints. Such constraints could be particularly binding in areas with high house price appreciation. On the other hand, complex mortgage products might reduce welfare if they overwhelm unsophisticated households, resulting in suboptimal financing decisions, as discussed in the theoretical work by Carlin and Manso (2008). For instance, the focus on initial loan affordability might motivate households to borrow too extensively and to underestimate refinancing risk, which is exacerbated by historically short reset periods and recasting of negative amortization loans. A key part of the emergence of complex mortgage products is the role played by financial intermediaries, in particular banks and mortgage brokers. Loan securitization may have led some intermediaries to push complex products to households that did not fully understand their mortgage obligations, consistent with evidence in Keys, Mukherjee, Seru, and Vig (2010) and Jiang, Nelson, and Vytlacil (2010). Yet, a significant amount of loans and MBS products were still retained on banks balance sheets. Our evidence suggests that lenders offered the most complex products (e.g., option ARMs or negative amortization loans) to relatively sophisticated households with high FICO credit scores and with high incomes, rather than to subprime borrowers. Still, the focus on borrower creditworthiness appears to have been made under the assumption of continued positive trends in income levels and house prices. The introduction of complex mortgages may also have contributed to adverse macroeconomic dynamics. In particular, by reducing financing constraints for many households, complex products increased demand for houses and likely exacerbated housing price pres- 2

5 sures. The slowdown in income growth and house price growth posed particular challenges for households with complex mortgages that relied on refinancing to avoid an increase in payments following the automatic reset of mortgage terms. However, refinancing was made difficult by loan-to-value ratios that were rising both because of declines in house values and negative amortization. This realization may have contributed to an increase in mortgage defaults and foreclosures and further deterioration in housing markets, as suggested by the leverage effect of Stein (1995) and Lamont and Stein (1999). The spiral was sustained when rising defaults virtually eliminated capital market funding for complex products in 2007, which led to a significant decline in their market share and a further reduction in housing demand. To address these questions, we make extensive use of the LPS Analytics data which contain loan level information for a representative sample of mortgages in the United States. We focus on mortgages originated between 1999 and 2008, which is the period most germane to analysis of complex loans. The LPS data are collected from a number of large mortgage servicers. Importantly, the data are not limited to subprime mortgages, nor to mortgages securitized in secondary markets. The LPS dataset contains extensive information on borrower and mortgage characteristics at the time of loan origination, including detailed contract description, loan amount, loan-to-value (LTV) ratio, borrower credit score, etc. For instance, the data on ARM loans records the initial interest rate, the time and frequency of rate resets, the rate margin, cap and floor interest levels, the presence of the negative amortization option, its limits, and the minimum required payment amount. The dataset also tracks mortgage performance at the monthly frequency and captures contractual changes in mortgage terms, such as interest rate resets and recasts of loan amortization schedule. This information allows us to isolate the effects of mortgage complexity from that of other contract terms. We make use of the geographic breadth of the data to study links between prevalence of complex mortgages and house price changes at the level of Metropolitan Statistical Areas (MSAs). Our paper contributes to the recent literature that analyzes the relationship between in- 3

6 novations in the mortgage market and the credit crisis of Mian and Sufi (2009) show that the sharp increase in mortgage defaults in 2007 is significantly amplified in geographic areas with a high density of subprime loans. They show that prior to the crisis these subprime areas experience an unprecedented growth in mortgage credit. Keys, Mukherjee, Seru, and Vig (2010) focus on the role of the securitization process of mortgages. They find evidence that securitization lowered the screening incentives of loan originators for their subprime borrowers. Similarly, Purnanandam (2010) finds that banks with greater reliance on the originate-to-distribute lending model originated mortgages of excessively poor quality. He further finds that the lack of screening incentives due to securitization coupled with leverage induced risk-taking behavior contributed to the subprime mortgage crisis. Corbae and Quintin (2010) present a model where heterogeneous households select from a set of mortgage contracts and choose to default on their payments. Using the model, they find that the presence of complex mortgages substantially amplifies foreclosure rates in the presence of a large exogenous shock to house prices. Our paper makes an empirical contribution to this recent literature by highlighting the important role played by complex mortgage products, in addition to those of the rise of securitization and the extension of credit to subprime borrowers. The remainder of this paper is structured as follows. Section 2 describes our data sources and reports summary statistics. In Section 3 we study the mortgage choice of households and describe the main features of the mortgage contracts. We document that complex mortgages are taken out by relatively sophisticated investors with high incomes and strong credit scores. In Section 4 we study the delinquency of different contract types. We find that borrowers with complex mortgages are more likely to default on their loans even while their mortgage payments are significantly lower than they would be with more traditional mortgage products. Section 5 evaluates the relationship between house prices and mortgage complexity. We find that cities where complex mortgages were more prevalent exhibit house prices that are much more volatile and more responsive to fluctuations in local income trends. This relationship is 4

7 robust even after we control for the prevalence of ARMs and subprime loans. 2 Data Sources and Summary Statistics Our study relies on several complementary sources of data that cover various aspects of the housing market during the period between 1998 and In particular, the micro level analysis of mortgage contract choice and performance relies heavily on the proprietary mortgagelevel database offered by Lender Processing Services (LPS) Applied Analytics (formerly known as McDash Analytics). LPS collects data from some of the nation s largest mortgage servicers that report contract and borrower details at the time of loan origination, as well as monthly information on mortgage performance. The LPS data coverage has grown steadily over time, with 9 out of 10 largest servicers reporting to the database by Our database cover about 52 million mortgages with a total loan value of about $10 trillion between For the purposes of our study, the availability of granular informationon mortgagecontract terms is of particular importance. For each of the loans, LPS provides information on loan interest rate, amortization schedule, and securitization status. For adjustable-rate mortgages (ARMs), we know the rate at origination, the frequency of resets, the reference rate and the associated contractual spread. For loans that do not amortize steadily over their term, we know the horizon of the interest-only period, whether negative amortization is allowed and if so, to what extent and over what period of time. This information allows us to precisely categorize loan contracts. The LPS data also contains key information on borrower and property characteristics at time of origination. These include the appraised property value, the loan-to-value ratio (LTV), property type (single family or condominium), whether the property was to be occupied by the borrower, and the borrower s creditworthiness as measured by their FICO (Fair Isaac Corporation) credit score. 1 1 As Bajari, Chu, and Park (2008) emphasize, an important feature of the FICO score is that it measures 5

8 An important feature of the LPS database is that unlike some other data sources, it is not limited to a particular subset of the loan universe. The LPS data cover prime, subprime, and Alt-A loans, 2 and include loans that are privately securitized, those that are sold to the GSEs, and loans that banks hold on their balance sheets. Although this allows for a broadly representative set of mortgage contracts, the coverage is somewhat skewed in favor of securitized loans that are more likely to be serviced by large corporations reporting to LPS. The relative scarcity of portfolio loans is relevant to us since some of the contracts of interest, such as option ARMs, are commonly held in lenders portfolios. Still, the large overall size of the data ensures that we have ample coverage of all contract types. We complement borrower information in LPS with household income data collected under the Home Mortgage Disclosure Act (HMDA). Doing so allows us to compute some of the key measures of loan affordability, such as the ratio of house value to income (VTI). We further augment the loan-level data with information on trends in local home prices. Quarterly data on home prices is available by metropolitan statistical area (MSA) from the Federal Housing Finance Agency (FHFA)-an independent federal agency that is the successor to the Office of Federal Housing Enterprise Oversight (OFHEO) and other government entities. 3 We use the FHFA all transactions House Price Index (HPI) that is based on repeat sales information. These data can be used to construct borrower-specific variables on cumulative growth in a borrower s creditworthiness prior to taking out the mortgage. FICO scores range between 300 and 850 Typically, a FICO score above 800 is considered very good, while a score below 620 is considered poor. As reported on the Fair Isaac Corporation website ( borrowers with FICO scores above 760 are able to ake out 30-year fixed rate mortgages at interest rates that are 160 basis points lower, on average, than those available for borrowers with scores in the range. 2 Alt-A loans are a middle category of loans, more risky than prime and less risky than subprime. They are generally made to borrowers with good credit ratings, but the loans have characteristics that make them ineligible to be sold to the GSEs-for example, limited documentation of the income or assets of the borrower or higher loan-to-value ratios than those specified by GSE limits. 3 As part of the Housing and Economic Recovery Act of 2008 (HERA), the Federal Housing Finance Regulatory Reform Act of 2008 established a single regulator, the FHFA, for GSEs involved in the home mortgage market, namely, Fannie Mae, Freddie Mac, and the 12 Federal Home Loan Banks. The FHFA was formed by a merger of the Office of Federal Housing Enterprise Oversight, the Federal Housing Finance Board (FHFB), and the U.S. Department of Housing and Urban Development s government-sponsored enterprise mission team (see for additional details). 6

9 house prices realized prior to contract choice and on whether local housing prices had ever experienced sustained declines. At the more aggregate level, we utilize zip code level information from the 2000 U.S. Census to control for broad demographic characteristics, such as education and age distribution. We also make use of the annual per capita income data at the MSA level from the Bureau of Economic Analysis (BEA). Finally, we employ annual zip-level data from the Internal Revenue Service (IRS) on the prevalence of various tax schedules. Doing so allows us to relate mortgage contract choice to shares of population reporting income from self-employment and claiming mortgage interest deductions. The summary statistics on these variables are presented in Table 1 and we will discuss differences in these variables across mortgages types in more detail in Section 3.2. All of the variables discussed above are summarized in the Appendix Table Mortgage Choice This section describes in detail the differences in characteristics of the main mortgage contracts offered in the U.S. during the last decade and the determinants of the mortgage choice. 3.1 Example of Mortgage Payments We illustrate in this section the different payment patterns of some popular U.S. mortgage contracts over time. Figure 2 depicts the simulated annual mortgage payments and the remaining balances on typical Fixed-Rate Mortgages (FRM), Adjustable-Rate Mortgages (ARM), and Complex Mortgages (CM) over a 30-year time period. The initial loan balance is set equal to $100,000. The fixed rate mortgage is a level-payment fully-amortizing loan with a 30-year maturity. The fixed interest rate is 5%. The adjustable mortgage payment is set equal to a fully-amortizing payment according to the most recent simulated interest rates. The initial 7

10 adjustable rate is 4.5% and changes annually according to the current Treasury bill interest rate. The adjustable rate is set 1.5 percentage points higher than the simulated Treasury bill rate with a cap of 7% and a floor of 2%. Treasury bill rates are simulated to follow an AR(1) process with coefficients based on the time-series properties from Treasury bill rates are assumed to be non-negative. The complex mortgage is a negative amortization loan that only pays 50% of the interest payment during the first five years and then becomes a fully-amortizing 25-year adjustable-rate mortgage. The annual interest rates for the complex mortgage are assumed to be identical to the interest rates for the ARM loan. Panel A of Figure 2 shows the annual payments of one simulated realization for the three mortgage types over the 30-year period. Borrowers using a 30-year fixed rate mortgage make payments of $6,505 per year. The annual payments of an ARM mortgage fluctuate from year to year and range between $4,847 (in a year where the mortgage interest rate is 2.51%) and $7,651 (in a year where the mortgage interest rate is 7%). The mortgage payments for ARMs vary generally in a relatively narrow range because most ARM loans specify minimum and maximum interest rates. On the other hand, the annual payments of the complex mortgage fluctuate dramatically over time. The initial payment of $2,250 covers only half of the interest payment, which leads to an increase in the mortgage balance. After five years the complex mortgage becomes a fully-amortizing adjustable loan and the mortgage payment more than triples to $7,717. The mortgage payments on the complex mortgage range between $1,311 in year 3 to $9,374 in year 10. Panel B shows the time-series pattern of the remaining mortgage balance. Whereas the loan balance decreases gradually for ARMs and FRMs, the loan balance increases for the first five years for a negative amortization loan. 4 Treasury bill rates are assumed to follow an AR(1) process with the following coefficients: rf t = rf t 1 + ɛ t. The intercept has a standard error of , the slope coefficient has a standard error of , and the R-square is Future risk-free interest rates are simulated based on the coefficients in the AR(1) equation and based on a randomly generated ɛ that is normally distributed with a mean of zero and a standard deviation of If the simulated interest rate is negative, then it is replaced by zero. The steady-state interest rate implied by the coefficients above is %. 8

11 3.2 Summary Statistics by Mortgage Type Table 2 reports statistics for our broad mortgage categories - fully-amortizing fixed-rate (FRM), fully-amortizing adjustable rate (ARM) and complex (CM) mortgage types. As described in Section 3.1, a common feature of complex mortgages is the deferral of principal repayment. In contracts that only require the payment of interest - the interest-only, or IO mortgages - principal repayment begins after a pre-specified period, and is amortized over a shorter number of years than an otherwise similar FRM or ARM loan. Other complex mortgages allow the borrower to choose the amount of principal to repay or accrue in any given month. These so-called option ARM or negative amortization loans place limits on the minimum monthly payment, the maximum principal level, and the maximum number of years before full amortization is required to take place. Yet, subject to these limits, a borrower has considerable latitude in structuring the schedule of payments. 5 Our data contain in excess of 5.6 million complex mortgage contracts originated between 1998 and Such contracts, on average, are associated with higher loan amounts relative to the traditional ARM and FRM mortgages, and are used to finance more expensive houses. Counter to some of the commonly made assertions about complex mortgages, they are extended to borrowers with high income and high credit scores. Indeed, the mean income of a complex mortgage borrower is about 80% higher than that of a borrower with a traditional plain-vanilla fixed rate mortgage. Nevertheless, the average ratio of value to income (VTI) - a measure of affordability - is considerably higher in complex mortgage contracts. At a first glance, the finding of lower loan-to-value (LTV) ratios among complex mortgage borrowers appears to contradict the common view of such products being used to ratchet up leverage. However, the LPS data are collected at the loan and not property level, which severely limits one s ability to construct an accurate estimate of the total debt secured by the house. In 5 Piskorski and Tchistyi (2008) show that complex mortgages can be an optimal mortgage design in a continuous time setting with volatile and privately observable income, costly foreclosure, and a stochastic market interest rate. 9

12 particular, we are unable to account for second-lien mortgages loans (the so-called piggyback loans ) used to finance the house. Primarily for this reason, we do not emphasize the importance of LTV in our empirical analysis and instead focus on the value-to-income ratio. Several other loan characteristics are markedly different for complex mortgages. They are more prevalent among investors, i.e. borrowers who do not intend to reside in the property they are financing. We also find significant differences in the prepayment penalties across mortgage types. Whereas few FRMs have prepayment penalties, a significant fraction of ARMs and CMs face penalties if the loans are prepaid within the first 2-3 years. Since complex loans are particularly popular for expensive homes, complex loans are also more likely to be jumbo loans (i.e., loans where the loan amount exceeds the conforming loan limit). We also find substantial differences in securitization patterns. Whereas 81% of FRMs are securitized by government-sponsored enterprises (GSEs, such as Fannie Mae, Freddie Mac, Ginnie Mae), only 25% of CMs go through the GSEs. Private securitization partially offsets the lack of GSE involvement in ARMs and CMs. From a spatial standpoint, complex mortgages are more common in geographic areas that experienced high house price appreciation. The average 3-year cumulative price appreciation among complex borrowers amounted to a staggering 43%, as compared with 20% among traditional FRM borrowers. We also document that only 11% of complex mortgages were originated in areas that had experienced four quarters of declines in house prices over the preceding 10 years, as opposed to 20% of FRMs. Complex mortgage contracts come in a variety of flavors. In addition to the IO and option ARM mortgages described above, some complex contracts offer a discounted interest rate during the first few months of the loan. These mortgages are commonly known as teaser loans and the vast majority of them further allow negative amortization of the loan balance. For us, the existence of such contracts opens the possibility of testing whether complexity derives from a multitude of terms that change over loan s lifetime or from confusion about 10

13 persistence of low introductory payments. Table 3 breaks out the key summary characteristics among different complex mortgage types. Teaser loans, on average, appear to be used to finance more expensive homes and are associated with higher loan values. The somewhat higher incomes of teaser loan borrowers result in average LTV ratios on par with those with other complex products. It is worth noting that few of the teaser contracts are offered to subprime borrowers. As expected, teaser loans commonly carry prepayment penalties. Finally, even among complex products, teaser loans are taken out in areas with much higher house price appreciation. 3.3 Geographic Distribution of Mortgages Figure 3 shows the concentration of complex mortgages in different zip codes across the United States in 2002 and Consistent with Figure 1 we find that complex mortgages were fairly uncommon in 2002, although some zip codes in California and Colorado had shares of complex mortgages that exceeded 10%. The distribution of complex mortgages looks dramatically different in Many zip codes in California, Arizona, Colorado, and Florida had complex shares exceeding 50%. While this pattern looks suggestive, numerous areas with high house price appreciation had few complex mortgages even at the peak of the housing boom. For example, complex mortgages accounted for only about 5% of loans in the Albany, NY metropolitan area where house prices rose by more than 70% between 2001 and It is also worth noting that in some areas rapid price increases preceded the surge in CM contracts, whereas other areas had the opposite relationship. 6 6 Granger causality tests carried out at the MSA level present mixed evidence of the relationship between changes in house prices and CM shares. The results are also highly sensitive to the choice of evaluation period. This subject is discussed in greater detail in a concurrent paper by Barlevy and Fisher (2010). 11

14 3.4 Affordability of Different Mortgage Contracts Complex mortgage products have relatively low payments during their first years and thereby enable households to purchase more expensive homes. Figure 4 depicts the ratio between the monthly payments of ARMs and CMs relative to fully-amortizing FRMs originated in the same months which have similar borrower characteristics (i.e., loans originated in the same states with similar FICO scores and loan-to-value ratios). We observe that 77.5% of ARMs and 89.1% of CMs have payments that are less than the payments of comparable FRMs after one year. Furthermore, 12.6% of ARMs and 50.0% of CMs have payments that are more than 20% lower than comparable FRMs after one year. The bottom figure shows that the payments on the vast majority of CMs remain lower than the payments on FRMs even five years after the origination. Thus, a relatively small fraction of complex mortgages have substantial resets of mortgage payments during the first five years. 7 These results indicate that households enjoy relatively low payments on their complex mortgages for extended time periods. The low payments of ARMs and CMs compared to FRMs can be explained by several factors. First, fixed rate mortgages tend to charge higher interest rates because the term premium is usually positive and because of a refinancing option premium. 8 Second, shortterm interest rates have decreased over our sample period, which reduces the payments on ARMs and CMs, which are generally tied to such rates. Third, we can only observe the payments of mortgages that survived and were not previously refinanced. Households that obtain mortgages with lower interest rates and lower total payments are less likely to refinance a loan, resulting in a tendency of the actual payments on surviving ARMs and CMs to decrease over time relative to the FRMs. Figure 5 depicts the distribution of the remaining mortgage balance one and five years after 7 Unfortunately we do not yet have sufficiently long time series available to study the resets in more detail since most of the complex mortgages in our sample were originated between 2004 and Fixed rate mortgages can be refinanced in the case where interest rates increase, which is a very valuable option that is priced in the initial interest rate. 12

15 mortgage origination relative to the original balance. Whereas borrowers using FRMs and ARMs gradually pay down their mortgages, the vast majority of complex mortgage borrowers maintain a constant balance (primarily because the majority of complex loans are interest only loans). For example, after five years around two-thirds of complex mortgages are within 5% of their initial loan balance and around 11% of borrowers increased their loan balance by more than 5%. Thus, borrowers of complex mortgages tend to keep substantially higher debt levels than borrowers of more traditional mortgage products which makes such borrowers more susceptible to economic shocks. Thus, the leverage steadily increases over time relative to more traditional amortizing mortgage products. This dynamic deterioration in leverage ratios becomes particularly dramatic in the event of slower house price appreciation. An alternative way to study the affordability of different mortgage contracts is to analyze the distribution of the value-to-income ratio (VTI) of mortgage products at the time of origination. The VTI is defined as the assessed value of the house relative to the indicated income level of the household. Panel A of Figure 6 indicates that borrowers using CMs tend to have substantially higher value-to-income ratios than both borrowers using FRMs and ARMs. Median households using FRMs, ARMs, and CMs have value-to-income ratios of 2.8, 3.1, and 3.7, respectively. Thus, the lower initial payments on complex mortgages enable households to purchase expensive homes relative to their income levels. Panel B of Figure 6 summarizes the cumulative distribution function of the FICO credit score for borrowers with different mortgage contracts. Whereas many borrowers using ARMs tend to be sub-prime borrowers with poor credit scores, the credit scores of borrowers using CMs are not significantly different from the borrowers using FRMs. These results emphasize that the clientele for complex mortgages differs significantly from the clientele of subprime loans. 13

16 3.5 Determinants of Mortgage Choice In this section we analyze the determinants of mortgage choice more systematically. Table 4 reports the marginal effects of probit regressions explaining the propensity of households to select either an adjustable-rate mortgage (first three columns) or a complex mortgage (last three columns). All regressions include time-fixed effects and the standard errors are clustered by CBSA (i.e., Core Based Statistical Areas). Our main results are not affected if we include CBSA fixed effects. Since some of the control variables are not available for our complete sample, the specifications include fewer observations than the overall sample summarized in Table 2. We find that households with higher income levels are significantly more likely to obtain a complex mortgage relative to the more traditional FRMs and ARMs. Consistent with Figure 6, households with higher value-to-income ratios and with higher credit scores are more likely to use a complex loan, which enhances their affordability. In addition, we find that ARMs and CMs are more prevalent in areas with a better educated population. Overall, we find that complex mortgages are not taken out by relatively poor and naive households with marginal credit histories. Complex mortgages are used by sophisticated households that have preferences for relatively expensive homes relative to their income levels. We also find that the type of property has an impact on mortgage contract choice. Mortgages used to finance condominiums are more likely to be ARMs or CMs. Such back-loaded borrowing instruments may carry greater appeal for condominium owners that tend to have shorter expected tenure. Complex mortgages are particularly beneficial for households that expect high income growth and high house price appreciation. For these households it makes sense to purchase expensive homes relative to their incomes under the permanent income hypothesis (Gerardi, Rosen, and Willen (2010) and Cocco (2010)). Unfortunately, we cannot observe household expectations. However, as many households might have adaptive expectations, we use past 14

17 income and house price growth as proxies for expected income and house price growth rates. We find that areas where house prices grew more during the previous three years and areas where house prices were less likely to decline over the prior ten years tend to have higher shares of complex mortgages. Borrowers and lenders in areas which experienced a recent decline in house prices might have been more cautious in choosing instruments that exhibit low or even negative amortization. On the other hand, borrowers and lenders in geographic areas where appreciation was substantial might have been more willing to accept low amortization loans if they expected the appreciation to continue in the future. However, we do not find higher CM shares in areas of high income growth. Rather, such areas were more likely to use adjustablerate mortgages. Some specifications include an indicator variable for whether the loan amount is above the confirming loan limit. Securitization of loans by the GSEs is substantially more likely for loans below the conforming loan limit and for traditional loans (i.e., FRMs and ARMs), as shown in Table 2. Thus, complex mortgages are more prevalent for households that have preferences for expensive homes, which do not qualify for the implicit government subsidies. 4 Mortgage Delinquencies In this section we study the delinquency of different types of mortgages. A mortgage is delinquent if the borrower is at least 60 days late in making the mortgage payments. We study delinquencies at the one, three, and five year horizon. In this section we restrict our analysis to mortgages that were originated between 1998 and For the three and five year delinquency measures, we count a mortgage as delinquent if either it was delinquent within three or five years or if it was delinquent by the end of the sample if the mortgage was originated less than three or five years ago. 15

18 4.1 Reasons for Mortgage Delinquencies Delinquencies might differ across mortgage types for various reasons. First, ARMs and CMs are generally adjusted according to short-term interest rates and might have higher delinquency rates because their mortgage payments increase in rising interest rate environment. Over our sample period the interest rates have not risen substantially, suggesting that this channel is likely not of significant importance. Second, CMs generally exhibit an increasing payment trend over the life of the loan since the initial payments are not fully amortizing as described previously. Mortgage delinquencies might become more likely after the various resets when the payments suddenly increase. On the other hand, CMs might exhibit lower delinquency rates during the initial period when mortgage payments are relatively low. Some complex mortgage contracts (e.g., Option ARMs) give borrowers the flexibility to adjust their mortgage payments as their income levels fluctuate. Giving borrowers more flexibility might reduce the probability of defaults. As we observe in Figure 4, most complex mortgages have lower mortgage payments than corresponding FRMs or ARMs over the first five years since origination. Third, CMs pay down their mortgage balance at a slower rate than FRMs and ARMs as summarized in Figure 5. Therefore, borrowers of complex loans have a bigger incentive to default on their loans in case of cash flow difficulties or for strategic reasons. Whereas a borrower with a complex mortgage might just walk away from their mortgage contract if they experience financial difficulties, a borrower with a FRM or an ARM might be more likely to sell their home since the embedded equity is higher for fully amortizing mortgage contracts. Fourth, borrowers that are attracted to ARMs and CMs might differ in their preferences. Borrowers that are willing to bear interest-rate risk might be more risk-tolerant as derived by Campbell and Cocco (2003). In addition, borrowers using CMs will tend to have higher leverage after the origination than borrowers using FRMs or CMs. Finally, borrowers using traditional mortgage products might be more influenced by ethical norms that motivate them 16

19 to pay back their debt even if it would be more economical to default on a mortgage contract, as discussed by Guiso, Sapienza, and Zingales (2009). Thus, delinquency rates could also be higher for ARMs and for CMs compared to FRMs due to differences in their borrower characteristics. 4.2 Determinants of Mortgage Delinquency Panel A of Table 5 reports the proportion of mortgages that are delinquent after one, three, and five years by mortgage type. We observe that at short horizons ARMs have higher delinquency rates than CMs and FRMs. However, the delinquency rate of CMs substantially exceeds the delinquency rates of FRMs and ARMs at longer horizons. For example, 18.18% of CMs, 13.62% of ARMs, and 8.52% of FRMs are delinquent at a 5-year horizon. Thus, at longer horizons the incentive to default on CMs increases. Panel B of Table 5 summarizes the proportion of mortgages that are prepaid. Mortgages are prepaid if the borrowers pay-off their loan before maturity either by refinancing the loan or by paying off the mortgage using the proceeds from selling the house or through other means. We find that ARMs were more likely to be prepaid than FRMs and CMs. Table 6 summarizes the marginal effects of probit regressions for mortgage delinquency at a one, three, and five year horizon. The regressions include time fixed effects and the standard errors are clustered by CBSAs. We find that borrowers of CMs and ARMs are significantly more likely to be delinquent at all horizons then borrowers of FRMs. For example, at a five year horizon, a borrower using an ARM has a 3.11% and a borrower using a CM has a 8.74% higher likelihood of being delinquent than a borrower using a FRM. The propensity to be delinquent decreases with the income level at origination. Furthermore, borrowers with lower credit scores, subprime borrowers, and borrowers in areas with lower eduction levels are significantly more likely to be delinquent. The last three columns in Table 6 introduce additional factors that might explain the 17

20 propensity to default strategically or due to cash flow difficulties. We find that households in areas with depressed income growth since the origination of the loan are more likely to default. In addition, households with higher loan-to-value ratios are significantly more likely to default for strategic reasons. It is remarkable that the coefficients on CMs remain highly statistically significant even after controlling for the loan-to-value ratio and the income growth rate. These results are consistent with the structural model of Corbae and Quintin (2010), who find that the presence of nontraditional mortgages amplified the foreclosure crisis between the first quarter of 2007 and the first quarter of House Price Dynamics In this section we analyze the relationship between mortgage complexity and various measures of local house price risk. Although we measure mortgage complexity at the beginning of the sample period and analyze the subsequent risk levels we are careful in interpreting the causality of the results. For example, geographic areas where mortgages might have been more prevalent might also have been areas where houses were the most over-valued initially or where house price risks were the most pronounced. 5.1 Measures of House Price Risk We use three different measures to define the risk level of local house prices. The first measure is simply the standard deviation in the growth rate of quarterly housing prices according to the Office of Federal Housing Enterprise Oversight (OFHEO). OFHEO computes a quarterly price index p for several hundred major metropolitan statistical areas (MSA) for single-family detached properties using data on conventional conforming mortgage transactions obtained from the Federal Home Loan Mortgage Corporation (Freddie Mac) and the Federal National Mortgage Association (Fannie Mae): 18

21 σ msa,t = Var(r msa,t ), (1) where the growth rate of house prices is defined as r msa,t =(p msa,t p msa,t 1 )/p msa,t 1.We use a sample period of 20 quarters to define the standard deviation. The second risk measure captures the systematic house price risk and is defined similarly to the CAPM beta for stock returns: β msa,t = Cov(r msa,t,r t ), (2) Var(r t ) where r t is the growth rate of the national house price index according to OFHEO. The covariances of the growth rates are computed over a five year time period using quarterly data. This risk measure captures the sensitivity of local house price shocks to national house price shocks. National house price changes can be caused by macro-economic factors, such as changing national growth prospects or changing monetary and fiscal policies. Geographic areas where house prices are unrelated to national house price shocks will have betas close to zero and areas which experience larger shocks than the national market will have betas above one. The third risk measure captures the sensitivity of local house price shocks to local income shocks: η msa,t = Cov(r msa,t,g msa,t ), (3) Var(g msa,t ) where g msa,t is the growth rate at time t of the mean income level in a specific MSA. The covariances of the income and house price growth rates are computed over a five year time period using annual data. This risk measure will be higher for geographic areas where income shocks have a larger impact on the housing prices. Stein (1995) and Lamont and Stein (1999) argue that house prices tend to have a higher sensitivity to income shocks in geographic areas 19

22 where the average leverage tends to be higher. 5.2 Risk Levels by Complexity Groups To obtain an impression of the relation between risk levels and mortgage complexity, we aggregate the individual loan-level data into 366 Metropolitan Statistical Areas (MSAs) and subsequently sort all MSAs into groups according to the proportion of complex mortgage loans in Figure 7 summarizes the average quarterly house price appreciation according to OFHEO for the lowest, the middle, and the highest quintile of MSAs according to the proportion of complex mortgages in We observe that MSAs in quintile 5 experience higher house appreciation before 2006 and higher depreciation after Table 7 shows in the first column the distribution of complex shares in As shown in Figure 3, we find significant variation in the prevalence of complex mortgages across geographic areas. Whereas the lowest 10% of MSAs have an average complex share of 2.12%, the highest 10% of MSAs have an average complex share of 39.33%. The remaining columns report the three risk measures for the two subperiods and Complex mortgages were not very prevalent in the first subperiod, but became an important contract during the second time period. We find that MSAs with a high prevalence of complex mortgages in 2004 had slightly higher standard deviations and house price betas during , but did not have higher income sensitivities. For example, the Spearman rank correlation coefficient equals 0.65 for the standard deviation, 0.75 for house price betas, and for the income sensitivities. The standard deviations and the betas in decile 10 are about double the corresponding values in decile 1. The relationship between our three measures of house price risk and mortgage complexity strengthens significantly during the second subperiod. For example, the standard deviation of house price risk equals 3.15% for MSAs in the lowest decile and increases to 17.90% for the 20

23 highest decile. Similarly, house price betas and income sensitivities range from 0.30 to 2.57 and from to 4.52 between the extreme deciles. The Spearman rank correlations are close to 1 and become highly statistically significant. Figure 8 depicts the three risk measures for the two sub-periods. These results indicate that geographic areas with a higher share of complex mortgages have significantly higher risk levels than areas with a lower share of complex mortgages. 5.3 Double-Sorts by Mortgage Complexity and Other Mortgage Characteristics The proportion of complex mortgages might be correlated with other local characteristics that have an impact on house prices. In Table 8 we first sort MSAs into equal-sized quartiles according to the proportion of ARMs (Panel A), the proportion of subprime loans (Panel B), and the average value-to-income ratio (Panel C). Subsequently, we sort each quartile into four equal-sized groups according to the proportion of complex mortgages. Due to the sequential sorting, this method captures the role of mortgage complexity that is broadly independent of these other mortgage characteristics. In each panel, we report the average levels of the three house price risk measures over the period between 2004 and 2008 for each of the 16 groups. We find that the strong relationship between mortgage complexity and house price risk remains even after controlling for other mortgage characteristics. Whereas the role of mortgage complexity appears to be roughly independent of the ARM share and the subprime share, we find that mortgage complexity is particularly important for areas that exhibit also high leverage levels. For example, the difference of the standard deviation between the two extreme complexity quartiles increases from 0.18 percentage points for the lowest value-to-income quartile to 6 percentage points for the highest quartile. Thus, geographic areas that have both a high share of mortgage complexity and high value-to-income ratios are particularly susceptible to house price shocks. 21

24 5.4 Determinants of House Price Risk Table 9 reports the multivariate relationship between the three house price risk levels from 2004 to 2008, the proportion of CMs and ARMs in 2004 and control variables that capture local characteristics of the mortgage market. We find that all three measures of house price risk are significantly related to the share of mortgage complexity even after controlling for other mortgage characteristics. On the other hand, the proportion of ARMs does not appear to be positively related to house price risk. Consistent with Mian and Sufi (2009) and Keys, Mukherjee, Seru, and Vig (2010), we find that areas with a high propensity of subprime loans are more exposed to house price shocks. Finally, house price risk appears also to be more pronounced in areas with a high proportion of investment properties as opposed to owneroccupied residences. 5.5 Determinants of House Price Changes Table 10 studies in more detail the relationship between local house price changes and national house price changes (Panel A) and local income growth rates (Panel B). The dependent variable in this panel regression is the annual growth rate of housing prices in specific MSAs. The independent variables are annual local income growth rates interacted with the share of complex mortgages and the share of ARMs in the prior year. Some specifications include CBSA fixed effects. The standard errors are clustered by CBSA in the specifications without CBSA fixed effects and by state in the specifications with CBSA fixed effects. Consistent with the results from Table 7 we find that the sensitivity to aggregate house price shocks and local income shocks is significantly strengthened for areas and time periods with a relative high proportion of complex loans. We also find that areas of high mortgage complexity generally experience lower house price growth. This effect is primarily driven by the fact that areas that had a high proportion of complex mortgages experienced the largest declines in house prices after 2006 as summarized in Figure 7. 22

25 References Bajari, P., C. S. Chu, and M. Park (2008). An empirical model of subprime mortgage default from 2000 to University of Minnesota and Federal Reserve Board. Barlevy, G. and J. Fisher (2010). Backloaded mortgages and house price speculation. Federal Reserve Bank of Chicago. Campbell, J. Y. and J. F. Cocco (2003). Household risk management adn optimal mortgage choice. Quarterly Journal of Economics 118, Cocco, J. F. (2010). Understanding the trade-offs of alternative mortgage products. London Business School. Corbae, D. and E. Quintin (2010). Mortgage innnovation and the foreclosure boom. University of Texas and University of Wisconsin. Gerardi, K. S., H. S. Rosen, and P. S. Willen (2010). The impact of deregulation and financial innovation on consumers: The case of the mortgage market. Journal of Finance 65, Guiso, L., P. Sapienza, and L. Zingales (2009). Moral and social constraints to strategic default on mortgages. European University Institute, Northwestern University, and University of Chicago. Jiang, W., A. A. Nelson, and E. Vytlacil (2010). Liar s loan? Effects of origination channel and information falsification on mortgage delinquency. Columbia University. Keys, B. J., T. Mukherjee, A. Seru, and V. Vig (2010). Did securitization lead to lax screeing? evidence from subprime loans. Quarterly Journal of Economics 125, Lamont, O. and J. C. Stein (1999). Leverage and house-price dynamics in U.S. cities. RAND Journal of Economics 30, Mian, A. and A. Sufi (2009). The consequences of mortgage credit expansion: Evidence from the U.S. mortgage default crisis. Quarterly Journal of Economics 124, Piskorski, T. and A. Tchistyi (2008). Optimal mortgage design. Columbia Business School and UC Berkeley. Purnanandam, A. (2010). Originate-to-distribute model and subprime mortgage crisis. University of Michigan. Stein, J. C. (1995). Prices and trading volume in the housing market: A model with downpayment effects. Quarterly Journal of Economics 110,

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