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The great surge in mortgage defaults 2006–2009

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The great surge in mortgage defaults 2006–2009 s 2 g a niver ersity, explo 2007 s in ation c con om th of fin to m f the gulat ng fro that attempt to decompose the causes of the great surge in defaults from 2006 to 2009 using a large data set with loan level information. One outgrowth of this research...

The great surge in mortgage defaults 2006–2009
s 2 g a niver ersity, explo 2007 s in ation c con om th of fin to m f the gulat ng fro that attempt to decompose the causes of the great surge in defaults from 2006 to 2009 using a large data set with loan level information. One outgrowth of this research is a set of metrics that can be used to track the quality of mortgage writing characteristics that were known to investors in the pools. With these data we can decompose defaults into three parts: those due to worsening of economic condi- tions, those due to observable changes in underwriting standards, and a set of time varying fixed effects that we attribute to moral hazard. We identify moral hazard by looking for discontinuities or ‘‘notches’’ in default behavior. The underlying economics 1051-1377/$ - see front matter � 2011 Published by Elsevier Inc. ⇑ Corresponding author. E-mail address: rvo@gwu.edu (R.V. Order). Journal of Housing Economics 20 (2011) 141–151 Contents lists available at ScienceDirect Journal of Housi w.el doi:10.1016/j.jhe.2011.04.005 and housing gyrations of the last few years. For example, in October 2005, near the peak of the housing ‘‘bubble’’ Federal Reserve Chairman Ben Bernanke was still arguing that housing prices ‘‘largely reflect strong economic funda- mentals’’ and were no cause for concern. Given the fire- storm that erupted shortly thereafter and that spread from the mortgage and housing markets to the national and world economy, it is undoubtedly meiosis to say that better methods and metrics for monitoring mortgage mar- kets are urgently needed. Toward this end, this research presents initial estimates which they can only measure indirectly, using fixed effects for observation year. Their results suggest that there was a trend toward lower underwriting standards characterized by two major periods of deterioration, one in the middle and late 1990s and one after 2002. After 2002 the favorable economic conditions that had masked underwriting deteri- oration changed, and defaults increased sharply. The data in this research contains loan level information on mortgages in non-agency (not Fannie Mae, Freddie Mac, or Ginnie Mae) mortgage-backed securities that were orig- inated from 2000 to 2008. This data set includes the under- 1. Introduction and overview Perhaps the most important task since the great depression has been only of individual banks, but also o i.e., systemic risk. Unfortunately, re to anticipate the systemic risks arisi ancial regulators onitor risks not banking system, ors were unable m the mortgage portfolios and separate the sources of defaults into indices for underwriting, moral hazard and economic conditions. Our analysis is an extension of the earlier work in Anderson et al. (2011), ACV henceforth. ACV use aggre- gated data (foreclosure rates by state) that extend back to the 1990s to decompose defaults into those caused by economic conditions and those caused by underwriting, conditions. � 2011 Published by Elsevier Inc. The great surge in mortgage default of economic conditions, underwritin Dennis R. Capozza a, Robert Van Order b,⇑ aDale Dykema Professor of Business Administration, Ross School of Business, U bOliver T. Carr Professor of Finance and Real Estate, George Washington Univ a r t i c l e i n f o Article history: Available online 12 May 2011 Keywords: Credit risk Credit crunch Moral hazard a b s t r a c t In this research we nated from 2000 to economic condition observe the inform as well as economi hazard. Estimates fr journal homepage: ww 006–2009: The comparative roles nd moral hazard sity of Michigan, USA USA it the power of a large and rich sample of individual loans origi- to study the relative roles of underwriting, moral hazard and local the Great Surge in mortgage defaults. With these data we can available to investors and control for observable underwriting ditions. We can also use the data to infer the share due to moral ese data suggest that much of the variation was due to economic ng Economics sevier .com/locate / jhec through 2010. There is a rising trend with occasional level- uarter 142 D.R. Capozza, R.V. Order / Journal of Housing Economics 20 (2011) 141–151 suggest that default should be a continuous function of underlying variables like loan to value ratio (LTV) and credit score, as well other, harder-to-observe variables. However, pricing and screening tend to be done over discrete inter- vals in pricing and underwriting matrices. The conditions for moral hazard occur when loan sellers and securitizers have access to better information than that available to investors. The question is: what sorts of mortgages will be delivered by traders with asymmetric information? An 80% LTV and a 620 credit (FICO) score appear to be critical minimum standards of quality because an LTV above 80 requires insurance, and at least a 620 FICO is gen- erally required for agency purchase. Our hypothesis is that loan sellers who possess superior information relative to investors will tend to deliver loans that just meet these minimum standards. Because of these cutoff points in the standards for loan quality, we expect to see notches in the default function at or around these points. We do indeed find notches, particularly at 80% LTV. However, we find that they are not especially important in explaining the surge in defaults, which appears to be due primarily to deteriorating economic conditions, partic- ularly house price declines, which had previously been Fig. 1. All foreclosures started: US 1979–2010 quarterly data. Four q Delinquency Survey. very favorable. Because economic conditions were so favorable, they masked, or perhaps even contributed to, a trend of deteriorating underwriting conditions. In the next section we document the Great Surge, its implications for both prime and subprime loans, and the concurrent economic conditions, especially house prices. The third section develops and estimates models of default using the loan level data set. The model is then applied to extract two types of estimates of the importance of eco- nomic conditions, underwriting and moral hazard in the great surge. 2. Background and summary data 2.1. The long run trend in foreclosures Fig. 1 graphs the time series of foreclosures started as a percent of the outstanding number of loans from 1979 ing off. Between 1979 and 2002 foreclosure rates quadru- pled from .48% to 1.96%. In the impressive Great Surge from 2006 to 2009, foreclosure rates almost quadrupled again from 1.60% to 5.68% in just three years. Our purpose with this research is to analyze the available data to enable a deeper understanding of both the trend and the surge. The deterioration in mortgage performance has varied by loan type. Fig. 2 presents data for 1998–2010 on foreclo- sures started by major product type. The vertical axis is the annualized percent of loans that enter the foreclosure pro- cess over each four quarters.1 Note in particular the history of subprime. Foreclosures fell after the 2001 recession but then increased sharply after 2005. A similar pattern, but on a smaller scale and with about a one year lag, occurred in the prime mortgage data, suggesting that there is a com- mon factor affecting both prime and subprime and that the surge in foreclosures is not just a subprime issue. The lag be- tween prime and subprime makes subprime the canary in the coal mine. Subprime borrowers respond more quickly to financial stress than prime borrowers.2 averages annualized. Source: Mortgage Bankers Association National 3. The trends in house prices The data in the previous section suggest that there may be an important role that economic conditions play in the pattern of defaults. Modern contingent-claims based theo- ries of mortgage valuation treat the borrower’s position as long a put on the collateral.3 One implication of this ap- proach is that the put option should be sensitive to the value of the collateral. When collateral prices are rising, a finan- cially stressed borrower with equity will rationally choose to sell the collateral rather than default. Correspondingly, when collateral prices are falling and equity becomes nega- tive, the stressed borrower is more likely to choose default. 1 The MBA data do not track how many actually went through foreclosure to REO, real estate owned by lenders. 2 Subprime responds earlier; but, the eventual total response is smaller in percentage terms than for prime loans, albeit much larger in absolute terms. 3 The early example is Findlay and Capozza (1977). D.R. Capozza, R.V. Order / Journal of Housing Economics 20 (2011) 141–151 143 Therefore, it is important to understand both collateral prices and financial stress as they relate to foreclosures. Fig. 3 plots real and nominal house prices since 1975. There is a cyclical pattern to real house prices from 1975 to 1997 within a narrow range. However, during the boom years from 1997 to 2007, real house prices rose about 40% above what had been the long run level until that time. Such steep increases should be expected to greatly reduce the need for stressed borrowers to default. Since many lenders develop underwriting models by evaluating recent loan performance data, any underwriting models created using data from this boom period would underestimate the risks to lenders in more average times. Fig. 3. Real and nominal house price indices, Fig. 2. Rate of foreclosures started by loan type, 1998–2010 (%). Sourc Fig. 4 provides another perspective on the recent period by plotting the real appreciation rate of house prices. The long run evidence (e.g., Eichholz, 1997) is that real house prices appreciate at rates close to zero over decades and centuries. Thus the 4–6% annual appreciation rates of the last decade are extraordinary. Fig. 5 illustrates the variation in house prices for se- lected metro areas. It shows the extreme ups and down of some the metro areas like San Diego and Miami that had price ‘‘bubbles.’’ These areas currently have high fore- closure rates following price declines. Other metro areas had smaller increases (Boston and Detroit) with slightly displaced peaks and troughs. Detroit did not experience 1975–2008 (2008 = 1.0). Source: FHFA. e: Mortgage Bankers Association National Delinquency Survey. the ‘‘bubble’’ level of price increases, but nevertheless has been experiencing elevated default rates. High and persis- These non-Agency data extend only to 2000 but contain a large amount of data at the loan level on borrower and loan characteristics. 144 D.R. Capozza, R.V. Order / Journal of Housing Economics 20 (2011) 141–151 5. Default models ACV use the data set from the Mortgage Bankers Asso- ciation which is longer, but aggregated by state. Here we estimate using the loan level data from non-Agency pools. 4 The UFA indices are available to academic researchers at www.ufanet.com. 4. A summary statistic for economic conditions The Fig.s above highlight the possible effects of eco- nomic conditions on foreclosures. In the analysis that fol- lows we split foreclosure rates into components arising from underwriting policy and changes in economic condi- tions. As a summary measure of economic conditions we use the quarterly ‘‘ForeScore’’ Default Risk indices by state compiled by University Financial Associates (UFA), which track the effect of local and national economic conditions on the probability of a constant quality loan ever default- ing.4 The UFA indices are derived from a model of default, using both local conditions and loan characteristics to ex- plain default. The indices holds the loan characteristics con- stant and project the impact of economic conditions on default. House price changes are the most important driver of the indices with other economic, demographic, political and topographic variables explaining the balance. The indi- ces enable parsimonious estimation of the equations that follow. A more detailed explanation is contained in ACV (2011). Fig. 6 illustrates one use of the index to track nation- wide default risks over time. In this case the constant qual- ity loan is moved through time and space to create a na- tional index for each vintage by averaging across locations each year. The Index has varied between 60 and 290, i.e., the variation in economic conditions has been suf- ficient to cause a quadrupling of default rates from trough to peak during the last decade on a constant quality loan. There have been two trends in the Default Risk Index: improvement from 1990 until around 2002 and then a sharp deterioration. It should be noted that the Index is a forward looking life-of-loan prediction for loans of the indi- cated vintage. The projections in the figure use actual data to the extent they are available, and then (for recent indi- ces) use forecasts, e.g., of house prices, over the life of each loan vintage. When the index begins to increase from 2003 on, it is not necessarily because the model ‘‘expects’’ the indicated vintage to default at high rates immediately. Any increase during the life of the loan will affect the life-of-loan index value for that vintage. The figure sug- gests that indeed economic conditions could be a major factor in explaining recent history. tent levels of unemployment in Detroit create high levels of financial stress for borrowers that interact with the declining collateral prices. ACV exploit this time and spa- tial variation in foreclosures in their analysis of the rate of foreclosures in the MBA serviced portfolio data. The default model is as follows: let the conditional probability of default for a loan to borrower i, originated at time v in region r, observed at time t be: dvitr ¼ aðt � vÞebXðr;tÞþcY iðr;vÞþdGðrÞ ð1Þ where X(r,t) is a vector of time varying covariates that de- scribe the economy in region r at time t; Yi(r,v) is a vector of characteristics of loans in region r at time of origination, v; G(r) is a vector of variables that are not time varying and describe region r; a(t-v) is the baseline hazard for loan age t-v;b, c and are vectors of coefficients. 6. The ACV Model ACV estimate Eq. (1) from the MBA data but do not ob- serve individual loans, nor do they know origination year, so only dtr, the share of loans in region (state) r that go into foreclosure at time t, is observed. The ACV model aggre- gates across individuals and origination years. dtr ¼ ebXþdGðrÞ X v X i aðt � vÞecYiðr;vÞ=nrt ð2Þ where nrt are is the number of loans originated prior to time t in region r that are still alive at time t. This is what we estimate first. Taking logarithms of both sides of (2): logðdtrÞ ¼ bXðr; tÞ þ dGðrÞ þ log X v X i aðt � vÞecYiðr;vÞ=nrt ! ð3Þ which can be simplified to logðdtrÞ ¼ bXðr; tÞ þ dGðrÞ þ et ¼ mtr þ drfr þ et ð4Þ where fr is a fixed effect for region r and mtr is the Fore- Score index that applies to loans originated in state r at time t, and et is an error term. The error term is quite complicated. It is a weighted average of underwriting characteristics of the pool of loans across the different vintages. ACV decompose the error term in (4) into time fixed effects and everything else to get: logðdtrÞ ¼ mtr þ drfr þ dtft þ uðr; tÞ ð5Þ where ft is a set of fixed effects for time and u is again com- plicated. Use of the time effect, ft, as a proxy for credit stan- dards means they cannot distinguish changes in loan quality that are deliberate changes in the Y vector from other unobserved changes in loan characteristics. A short- coming of this aggregation across vintages is that it risks confusing changes in standards with changes in the his- toric distribution of loans by vintage and their survival rates.5 5 We also do not consider the possibility that the time fixed effects might be due to changes in borrower behavior, such as an increased willingness to default. and include state fixed effects. purch D.R. Capozza, R.V. Order / Journal of Housing Economics 20 (2011) 141–151 145 ACV estimate equations of the form. logðdtrÞ ¼ X�l t¼�1 atmðr; tÞ þ drfr þ dtft þ X�q t¼0 ctut þ et ð6Þ where bars over variables indicate a four-quarter moving average of the variable, and l and q are lag lengths. From (5) we should expect the sum of the coefficients of mðr; tÞ in (6) to be close to one. 7. Results for MBA data: the relative roles of economic conditions and underwriting 7.1. Simulations ACV use their estimated equations along with the fixed Because the mtr are the probability of ever defaulting they do not apply to the same time period as dtr; and be- cause there are lags in adjustment of dtr to changes in mtr, ACV estimate versions of (5) where both dtr and mtr are four quarter moving averages and the right hand side has lags. ACV allow u to be an autoregressive process, Fig. 4. US real house price appreciation, 1992–2010 seasonally adjusted, effects to decompose foreclosure rates into a part due to the economic multipliers and a part due to the year fixed effects. The year fixed effects conditional on the multipliers are the estimates of the underwriting component, i.e., of default rates after controlling for economic conditions. When normalized, the fitted values from the regression, i.e., difference between the unconditional year indicators (i.e., the actual yearly foreclosure rates) and the year indi- cators conditional on economic conditions is an estimate of the economic component. By construction the two add up to the actual level of foreclosures. Fig. 7 presents results using the ACV Model for all loans. The yellow line gives the part due to economic condi- tions (how foreclosures would have moved had underwrit- ing not changed), which promoted declining foreclosures until 2004. The pink line shows the contribution of under- writing (how foreclosures would have changed had eco- nomic conditions not changed), which was positive early in the period, negative later and sharply positive in 2006 to 2007. For example, in Fig. 7 the red curve for underwrit- ing in 2004 is 1.0 while actual defaults are 0.25 and the economic conditions index is �0.75. The interpretation is that while actual default rates rose 25% from 1990 to 2004, if economic conditions had not been so favorable, foreclosures started would have risen by 100% instead of 25%. Stated differently, underwriting quality eroded en- ough to double the level of foreclosures started by 2004; but only a 25% increase was realized because favorable economic conditions offset 3=4 of the potential increase. During this period house prices appreciated steadily in most of the country. Note that the underwriting effects refer to the year in which the loans are observed, not the year in which they were originated. The poor underwriting results in 2006 and 2007 are for loans that were originated earlier. The fig- ure suggests that the post 2005 increase in foreclosures can be apportioned about equally between the underwriting and economic conditions. The spectacular increase in foreclosures after 2005 is unprecedented in the data. Economic conditions and underwriting quality typically moved in opposite direc- tions in the 1990s. This negative correlation is consistent ase only index, quarterly data, four quarter price changes. Source: FHFA. with lenders becoming more conservative when economic conditions are weak. However, after 2002–2005, economic conditions and quality both deteriorated, breaking the ear- lier pattern and suggesting a possible structural break or regime shift in this market that is consistent with a moral hazard story. The data suggest that the post 2005 increase in foreclosures can be apportioned about equally between the underwriting and economic conditions explanations. 7.2. Loan level model In this section we present results for the loan level mod- el, which uses the non-Agency data to estimate a full ver- sion of Eq. (1). The data include prime, subprime and Alt- A loans.6 While that data set is rich
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