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What role did piggyback lending play in the housing bubble

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What role did piggyback lending play in the housing bubble y i We ullerto omona state-level and Zip code-level data over the period 2001–2008, we find that the fraction of piggyback originations is related to higher foreclosure and default rates in subsequent years, and this relation is strongest for non-owner-occu...

What role did piggyback lending play in the housing bubble
y i We ullerto omona state-level and Zip code-level data over the period 2001–2008, we find that the fraction of piggyback originations is related to higher foreclosure and default rates in subsequent years, and this relation is strongest for non-owner-occupied properties. The pattern, how- others). One particular type of lending that grew rapidly during the recent housing boom is piggyback lending. Piggyback loans, more technically referred to as simultaneous close ers taking out a piggyback loan in 2006 (State of New York City’s Housing and Neighborhoods Report, 2007). Similarly, about 37.3% of California borrowers also used piggyback loans to finance home purchase in 2006 (Fishbien, 2006). The rise of piggyback lending during 2001–2006 may also have contributed to the rise in default and foreclosure rates. Some have argued that piggyback lending enables households to take on too much debt via the purchase of 1051-1377/$ - see front matter � 2011 Published by Elsevier Inc. ⇑ Corresponding author. E-mail addresses: mlacour-little@fullerton.edu (M. LaCour-Little), charles_calhoun@cox.net (C.A. Calhoun), weiyu@csupomona.edu (W. Yu). Journal of Housing Economics 20 (2011) 81–100 Contents lists available at ScienceDirect Journal of Housi w.el doi:10.1016/j.jhe.2010.11.002 The current financial crisis had its origins in 2006 as house prices began to fall and the mortgage market expe- rienced a sharp increase in subprime mortgage defaults and foreclosures. Numerous papers have studied the fac- tors that contributed to the unprecedented increase in de- fault and foreclosure rates. Irrational expectations regarding future house price growth, a proliferation of non-agency mortgage securitization, lax underwriting, and changing economic conditions are among the cited factors (Bajari et al., 2008; Doms et al., 2007; Keys et al., 2010; Mian and Sufi, 2009; Coleman et al., 2008, among chase. These are generally used by homebuyers to finance more than 80% of the house value without paying private mortgage insurance, at least if the first lien is GSE-financed. Piggyback lending played an important role in home sales, especially from 2004 to 2006, and was involved in about 22% of the one-to-four family owner- occupied home purchases in 2006 (Avery et al., 2007b). It is particularly popular in high-cost housing areas. For example, between 2004 and 2006, the number of piggy- back loans issued in New York city more than tripled, resulting in more than 30% of the home purchase borrow- JEL Classification: G01 G21 R31 Keywords: Mortgage Foreclosure Housing Piggyback lending 1. Introduction ever, appears to be limited to the use of subprime piggybacks, rather than a more general phenomenon. Using a topology-based housing supply elasticity measure as an instrument for house price growth, we further confirm that the strong association of subprime piggy- back origination with worse loan performance was not driven by the endogeneity of house price appreciation. � 2011 Published by Elsevier Inc. seconds, are junior lien mortgage loans taken out concur- rently with the first mortgage to finance the home pur- Article history: Received 29 April 2010 Available online 20 February 2011 We examine the use of simultaneous close junior lien lending (‘‘piggybacks’’) over the course of the recent housing bubble and subsequent mortgage market collapse. Using both What role did piggyback lending pla and mortgage collapse? Michael LaCour-Little a,⇑, Charles A. Calhoun b, aCollege of Business and Economics, California State University at Fullerton, F bCalhoun Consulting LLC, Annandale, VA 22003, USA cCollege of Business Administration, California State Polytechnic University, P a r t i c l e i n f o a b s t r a c t journal homepage: ww n the housing bubble i Yu c n, CA 92834-6848, USA , CA 91768, USA ng Economics sevier .com/locate / jhec inflated assets (WSJ, 2009), and therefore helped to further inflate the housing bubble. Once the bubble burst, it made highly leveraged households at greater risk of negative non-owner-occupied piggybacks and high-LTV piggybacks that we have been able to identify that directly addresses the determinants of home equity borrowing is Salandro as in most data on home equity lending, their data does 82 M. LaCour-Little et al. / Journal of Housing Economics 20 (2011) 81–100 have stronger impact on mortgage performance. Of partic- ular interest, we distinguish among three forms of piggy- back lending: (1) prime first lien and prime second lien, (2) prime first lien and subprime second lien, and (3) sub- prime first lien and subprime second lien, and further ex- plore the difference in these three piggyback lending patterns in explaining state-level and Zip code-level mort- gage performance. The plan of the paper is as follows. In the next section, we review the limited research related to this topic. In the third section, we describe our data and empirical meth- odology, including our method for identifying piggyback loans from HMDA data. In the fourth section we present re- sults of our empirical results. The final section concludes. 2. Literature review Compared to other research on mortgage markets, ju- nior lien lending is a relatively unexplored arena. The still narrower topic of piggyback lending has received even less rigorous research. Beginning with the broader research on junior lien debt, Canner et al. (1988) describe the early stages and growth of the home equity lending segment, following passage of the 1986 tax law changes which are generally acknowledged to have spurred this segment of consumer lending.1 Canner and Luckett (1994) and Canner et al. (1998) update those findings, including Survey of Consumer Finance (SCF) data showing home equity balances outstanding reached $110 billion by 1994. Weicher (1997) reviews the growth of the home equity lending industry during the 1990s, describing it as business based on recapitalizing borrowers with sub- stantial housing equity, but impaired credit. The only paper 1 Prior to 1986 most interest on consumer debt was tax-deductible; after 1986 tax law changes, only debt secured by residential mortgage debt remained generally deductible for those who itemize deductions. equity and more vulnerable to default. The use of piggy- back loans has been shown to be important in explaining the magnitude of negative equity (LaCour-Little et al., 2009). Piggyback loans also make the loan modification process more complicated because first-lien and junior- lien loans are packaged and sold to different portfolio securitizations (Rosengren, 2008). Moreover, junior-lien lenders, if different from first-lien lenders, usually have lit- tle incentive in modifying the loan to avoid foreclosure if there is no equity protecting them (The Washington Post, 2008). In this paper, we study the relationship between the mortgage performance (delinquency, foreclosure, and de- fault rates) and homeowner piggyback borrowing patterns at both the state level and the Zip code level. We ask whether states and Zip codes with higher proportions of piggyback loans issued during 2001–2006 are associated with worse mortgage performance in later periods, espe- cially during 2007 and 2008. We also examine whether not contain information about the underlying first mort- gage loans, since first and junior debt is often held by dif- ferent lenders, a pattern that applies to piggyback lending as well. LaCour-Little et al. (2009) report that roughly 80% of Southern California borrowers facing foreclosure during 2006–2008 had at least one junior lien outstanding, though information on the loans themselves is limited. Calhoun (2006b) develops a method for identifying pig- gyback loans from HMDA data. Calhoun (2006a) argues that simultaneous-close or ‘‘piggyback’’ transactions sys- tematically raise risk throughout the mortgage finance sys- tem, yet presented no loan performance data. Bernstein (2008), as well as others mentioned in the introduction, document the increase in the use of piggyback lending over the period we study. Using American Housing Survey data, Bernstein (2008) reports that multiple-mortgage financing packages as a percent of newly originated mortgages in- creased from 14.8% in survey year 2001 to 21.5% in survey year 2007, corroborating the growth in this category docu- mented by others. Clearly considerable additional research is necessary to more completely understand this new market phenome- non, its causes, and its effects. Our effort here addresses this gap in the literature. 3. Data and empirical methodology 3.1. Data To calculate the proportion of piggyback loans to total home purchase loan originations for each state and Zip code, we use Home Mortgage Disclosure Act (HMDA) data from 2001 to 2008. We first identify piggyback loans2 using each of the methods proposed by Avery et al. (2007a) and Calhoun (2006b). To calculate the proportion of piggyback originations at the state level for a given year, we aggregate the number of piggybacks by state and year and divide it by the total number of first-lien home purchase loan origina- tions. For Zip code-level piggyback originations, we calculate the piggyback originations at the census tract level first and 2 Details of the identification method are addressed later. and Harrison (1997), who used 1989 and 1992 SCF data, well before the dramatic increase in home equity lending occurred. In more recent work, LaCour-Little (2004) argues that a borrower’s post-origination home equity borrowing di- lutes their equity, increasing the risk of default on the se- nior debt. Agarwal et al. (2006a) shows that patterns of home equity line use are also related to borrower credit quality, as measured by their FICO score. Extending that analysis further, Agarwal et al. (2006b) examine the perfor- mance of home equity lines and loans, finding considerable difference in terms of default and prepayment risk. Mian and Sufi (forthcoming) examine home equity-based bor- rowing from 2002 to 2006 and find that it is associated with high default rates from 2006 to 2008. Unfortunately, then aggregate it to the Zip code using a database that matches census tract numbers with Zip codes from Missouri Census Data Center.3 Because a given census tract can corre- spond to more than one Zip codes, we create a weight vari- able based on the share of housing units in each census tract that lie within a given Zip codes. Using this weight variable, we can calculate the weighted average piggyback origina- tions at the Zip code level.4 For state-level loan performance measures, we use the state-level percentage of mortgage foreclosure inventory from 2001 to 2008, obtained from the Mortgage Banker’s Association (MBA), as our proxy for state foreclosure rates. Since the MBA data are only available at the state level, we use the Zip code proportion of noncurrent (delinquent5 and defaulted6) mortgages and the proportion of mortgages in default from 2001 to 2008, obtained from Equifax, as our proxy for Zip code delinquency and default rates. We supplement the data with additional economic vari- ables that may also affect loan performance. First, our state-level house price data come from Federal Housing Fi- nance Agency (FHFA, formerly Office of Federal Housing M. LaCour-Little et al. / Journal of Housing Economics 20 (2011) 81–100 83 Enterprise Oversight, or OFHEO) purchase-only House Price Indexes from 2001 to 2008. We use this data to calcu- late annual house price appreciation rates. At Zip code le- vel, we use the CoreLogic7 Zip code Single Family Detached House Price Index to calculate house price appre- ciation rates. The CoreLogic House Price Index covers about 7600 Zip Codes in the United States. Second, we use both state and MSA level per-capita income data from the Bureau of Economic Analysis. The state per-capita income is used in our state-level analysis, and the MSA per-capita income is used as a proxy for Zip code income. Third, we obtained state unemployment data from the Department of Labor Statis- tics. We use the MSA data as a proxy for the Zip code income and unemployment rate. Fourth, we supplement the Zip code-level data with Zip code credit risk data obtained from Equifax. Lastly, subprime loans are identified in two ways: For loans in HMDA from 2004 and later years we treat high- 3 We recognize that the HMDA data uses the 1990 census tract definitions before 2003 and the 2000 census tract definitions starting 2003. Therefore, we use a database that matches 1990 census tract numbers with Zip codes for data before 2003 and another database that matches 2000 census tract numbers with Zip codes for data on and after 2003. Both databases are available from Missouri Census Data Center. 4 For example, census tract 1 has 500 housing units and 20% of the housing units are within Zip code A. Census tract 2 has 1000 housing units and 15% of the housing units are within Zip code A. Census tract 1 has 100 piggyback originations and census tract 2 has 200 piggyback originations. For simplicity, Zip code A are solely composed of census tract 1 and 2. To calculate the piggyback origination for Zip code A, we first create a weight variable for census tract 1 and 2 based on the number of housing units of each census tract that lie within Zip code A. The weight for census tract 1 is 40% (500 � 20%/(500 � 20% + 1000 � 15%)) and the weight for census tract 2 is 60% (1000 � 15%/(500 � 20% + 1000 � 15%)). We then calculate the weighted average piggyback originations for Zip code A as 100 � 40% + 200 � 60% = 160. 5 A mortgage is coded as delinquent if it is more than 30 days and less than 90 days past due. 6 A mortgage is coded as in default if it is more than 90 days past due, in bankruptcy status or in severe derogatory status. 7 CoreLogic was spun off in an IPO from The First American Corporation on June 1, 2010. cost or spread-reportable loans as subprime loans. For years prior to 2004 we use the Department of Housing and Urban Development’s (HUD) list of subprime lenders to identify subprime loans based on the lender IDs in HMDA. We then aggregate the subprime loans by state and Zip code and calculate the proportion of subprime loan originations to total loan originations in a given year. Both measures of subprime loans are imperfect. Use of the HUD list is a somewhat crude proxy because all loans made by a subprime lender will be classified as subprime loans and all loans made by a prime lender will be classified as prime loans. Reliance on the spread-reportable threshold in HMDA may under- or over-estimate the prevalence of sub- prime loans depending on current interest rate conditions, and specifically the relationship between mortgage rates and comparable maturity Treasury rates. Detailed defini- tions of the key variables used in state-level and Zip code-level regressions and their sources are listed in Table 1. 3.2. Identification of piggyback loans We use two methods to identify piggyback loans in the HMDA data. The first method is based on Avery et al. (2007a). Since information about lien status is only avail- able starting in 2004, the identification process is a little different before 2004 and after 2004. Before 2004, we sort home purchase loans each year by state, county, census tract number, lender ID, owner-occupancy status, bor- rower income, race, and sex. If we find duplicate loan re- cords according to this set of matching factors, then the one with the smaller loan amount is identified as a piggy- back loan. The basic assumption underlying this method is that if two home purchase loans involve a property in the same census tract and same owner-occupancy status, bor- rowers with identical income, race and sex, and was issued by the same lender, then most likely these two loans are used for the purchase of the same home. After 2004, with the addition of lien status in HMDA, we separate the home purchase loans into two samples. The first sample includes all junior-lien purchase loans and the second sample in- cludes all first-lien purchase loans. We then match the sec- ond sample to the first sample by census tract, lender ID, owner-occupancy status, borrower income, race and eth- nicity, and sex. If there is a match, then the matched junior-lien loan is identified as piggyback loans. One limi- tation of the Avery method is that it may underestimate the number of piggyback loans because it can not identify piggyback loans that are issued by lenders different from the first-lien lenders. The second method follows Calhoun (2006b). The Cal- houn approach is similar to that of Avery, but recognizes the following two potential problems: First, piggyback loans may be issued by a different lender from the first-lien lender. Second, the lien status is usually missing for loans that are reported as secondary market purchases in HMDA. Calhoun proposed a two-step matching procedure. The first step follows Avery, by finding duplicates according to a set of matching factors. In the second step, if the lien status is not missing, then the data are sorted by state, county, census tract, borrower income, lien status, and Source Mortgage Banker’s Association -lien purchase loan originations HMDA l foreclosure procedures and 3-year lag of piggyback HMDA Bureau of Economic nation rtgage er of m -lien l forec 84 M. LaCour-Little et al. / Journal of Housing Economics 20 (2011) 81–100 Table 1 Variable definitions. Variable Definition Panel A: State-level regression Foreclosure State foreclosure rate Piggyback Proportion of piggyback loan originations to total first Piggylag Two-year lag of piggyback for states with non-judicia for states with judicial foreclosure procedures Lnincome Log of state per-capita income Unemploy State unemployment rate Pctsubprimelag Two-year lag of the proportion of subprime loan origi Hpigrowth House price appreciation rate Hpigrowthlag One-year lag of house price appreciation rate Panel B: Zip code-level regression Noncurrent Proportion of noncurrent (delinquent and default) mo given Zip code Default Proportion of defaulted mortgage loans to total numb Piggyback Proportion of piggyback loan originations to total first Piggylag Two-year lag of piggyback for states with non-judicia for states with judicial foreclosure proceduresa Lnincome Log of MSA per-capita income Unemploy MSA unemployment rateb loan amount. Adjacent first and second liens with identical values of borrower income are identified as piggyback combinations and removed from the data. If the lien status is missing, then the remaining data are sorted by state, county, census tract, borrower income, and loan amount and adjacent loans with identical values of borrower in- come are matched. In the second step of this two-step matching, the matching factors do not include lender ID. The relaxation of the same lender assumption helps to identify additional piggyback combinations that are issued by different lenders. To further confirm a piggyback match, Calhoun then calculated the ratio of the lower loan amount to higher loan amount for the duplicates. The piggyback combinations are further confirmed if the ratio of the smal- ler loan amount to the larger loan amount falls into certain ranges that are consistent with the piggyback loan struc- tures (such as 80-20-0 structure, 80-10-10 structure, etc.). To examine whether non-owner-occupied and highly- leveraged piggybacks have a stronger effect on default and foreclosure rates, we also identify piggyback loans used for non-owner-occupied house purchases and piggy- back loans with combined loan-to-value (CLTV) ratios above 100%. Since information on the house value is not provided by HMDA data, we assume the first lien is approximately 80% of the house value. The combined Pctsubprimelag Two-year lag of the proportion of subprime loan origination Hpigrowth Zip code house price appreciation rate Hpigrowthlag One-year lag of ho
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