Legal Restrictions in Personal Loan Markets

Transcription

1 Legal Restrctons n Personal Loan Markets Brent Ambrose Professor of Fnance and Kentucky Real Estate Professor Unversty of Kentucky Lexngton, KY (859) ambrose@uky.edu and Anthony B. Sanders John W. Galbreath Char and Professor of Fnance The Oho State Unversty 2100 Nel Avenue Columbus, OH (614) sanders.12@osu.edu Aprl 17, 2003 We thank Paul Malatesta, Kerry Vandell, and Abdullah Yavas for ther helpful comments and suggestons. An earler verson of ths paper was presented at the Georgetown Unversty Credt Research Center Subprme Lendng Symposum.

2 Legal Restrctons n Personal Loan Markets Abstract Ths study examnes the prcng of personal loans n the form of second mortgages to determne whether state-specfc default laws have an mpact on the avalablty and cost of that debt. We examne whether lenders ratonally prce loans to hgher rsk borrowers and whether borrowers n states that lmt lender ablty to seek default remedes pay hgher credt costs. Our results ndcate that lenders ratonally prce loans to hgher rsk borrowers for the most part; however, when we focus on smaller and smaller FICO scores buckets, the results ndcate that the mean actual loan rates are hgher than those predcted by our model. The results also ndcate that state-specfc default laws do have an mpact on the prce of credt. The results also show that there s a greater degree of error n the prcng of second mortgage loans to low FICO borrowers than to hgh FICO borrowers.

3 Debt, Credt Rsk and Regulatons I. Introducton Debt usage contans mportant sgnals regardng borrower qualty and thus reveals nformaton. Whle the use of debt s wdely recognzed n the nformaton asymmetry lterature, unfortunately, few studes have ted the sgnalng aspect of debt usage to broader market condtons where legal restrctons and regulatons also nteract to determne optmal debt usage. Gven the debate currently surroundng the ssue of predatory lendng practces, t s mportant for publc polcy analysts to understand the equlbrum tradeoff between debt amount and cost and the mpact that the regulatory envronment has on ths tradeoff. Several observatons exst on the use of hgh debt levels. Frst, n the resdental mortgage market t s well understood that second mortgage loans such as hgh loan-tovalue (LTV) loans carry sgnfcant default rsk. Tradtonal opton prcng models, where default s endogenous and determned only by nteracton of house value and nterest rates, fnd that the default opton value s sgnfcant when the LTV s greater than 100%. 1 As a result, hgh-ltv loans are usually junor debt wth lower prorty of clam on the asset, wth the majorty of hgh-ltv loans orgnated for the purpose of debt consoldaton. Furthermore, hgh debt levels are also correlated wth the probablty of bankruptcy. Thus, hgh-ltv loans are often lke unsecured debt or credt cards, and as a result, the equlbrum tradeoff between borrower credt sgnals, debt amount and cost, and regulatory envronment should be most apparent n ths market. Second, prepayment rates also vary wdely across heterogeneous borrower groups. Whle tradtonal opton 1 See Kau and Keenan (1995) and Querca and Stegman (1992) for an overvew of the opton-prcng model as appled to mortgages and mortgage default.

4 prcng models only capture the fnancal ncentves to prepay, t s well understood that non-fnancal factors also determne observed prepayment rates. Thus, gven that a varety of mortgages are orgnated n the U.S. that have dfferent characterstcs n terms of prorty (frst and home equty loans), loan-to-value rato (LTV) and credt qualty of the borrower (A-rated and B/C-rated borrowers), we would expect that the dfferent mortgages would have dfferent default rates as well as dfferent prepayment rates. The goal of ths study s to examne the prcng of a mortgage product that has receved lttle academc attenton but has become poltcally senstve: the hgh-ltv mortgage. The hgh-ltv product s often lnked to the controversy surroundng predatory lendng. Thus, ths study s the frst to document rsk-based prcng n ths market segment. A secondary goal s to examne the mpact of state-specfc default laws on the avalablty and cost of that debt. We begn wth a revew of the theoretcal models of borrower choce of credt and credt avalablty. From ths revew, we develop predctons concernng borrower and lender choce of mortgage terms under dfferng assumptons regardng state default regulatons. Usng these predctons as a gude for the emprcal analyss, the study has two man objectves. The frst s to determne whether lenders ratonally prce loans to hgher rsk borrowers. The second s to determne the mpact of borrower protecton laws on the prce of credt and whether borrowers n states that lmt lender ablty to seek default remedes bear hgher credt costs. To prevew our results, we fnd that lenders do appear to ratonally prce loans to hgher rsk borrowers at the aggregate level. However, our fndngs ndcate that actual loan rates are hgher than those predcted by our model as we focus on smaller and 2

5 smaller FICO score buckets. We also fnd that borrowers n states that lmt lender ablty to seek default remedes pay hgher credt costs. Fnally, we fnd a greater degree of error n loan prcng to low qualty borrowers. II. Lterature Revew and Hypotheses Development The lnkage between borrower credt qualty, default propensty, and loan amount s controversal. For example, the mortgage market models presented by Brueckner (1994, 2000) and Harrson, et al (2002) provde contrastng predctons for the mpact of borrower credt qualty on choce of loan amount. Brueckner (1994, 2000) develops a smple two-perod model of borrower default that examnes the mpact of borrower rsk on choce of loan amount. In Brueckner s model, default s trggered by declnes n the underlyng collateral asset value and hs analyss mples that low rsk borrowers selfselect smaller loans whle hgh-rsk borrowers select larger loans. Ths result follows the observaton that default costs appear to be mportant n understandng the emprcal ncdence of default. Brueckner s model orgnates from the nformaton asymmetry arguments frst appled to the nsurance market by Rothschld and Stgltz (1976). Rothschld and Stgltz s (1976) analyss of the nsurance market demonstrated that when nsurers cannot dscern rsky applcants from non-rsky applcants, the safe applcants sgnal ther rsk profle by applyng for less nsurance than the rsky applcants. Smlarly, Brueckner s model ndcates that, n the presence of non-trval default costs, only hgh-rsk borrowers are wllng to pay the premum for a hgh LTV rato. 2 2 Brueckner s model s consstent wth models corporate borrowng. For example, Bolton and Scharfsten (1996) develop a model of debt ssuance that predcts that low-rsk frms should borrower from a greater number of credtors whle hgh-rsk frms wll only borrower from a few credtors. Ther model also mples that low rsk borrowers wll have larger second loans relatve to hgh-rsk borrowers. 3

6 In contrast, Harrson, et al (2002), who modfy Brueckner s model to examne the mpact of borrower ncome on default, motvate default based on uncertanty regardng the borrower s future ncome (holdng the asset value fxed). Thus, default occurs f the borrower s future ncome s nsuffcent to repay the debt n the presence of a declne n asset value. Wth default condtonal on ncome, ther model shows that when default costs are hgh, rsky borrowers choose low LTV ratos to mnmze default costs. However, ther model provdes addtonal nsghts by ndcatng that when default costs are low, rsky borrowers may actually choose hgher LTV ratos. It s also well known that state-level regulatons regardng borrower rghts and responsbltes can have consderable mpact on expected default losses (or recovery rates) and as result would mpact borrower credt costs n equlbrum. For example, early studes by Barth, Cordes, and Yezer (1986), Meador (1982), Alston (1984), and Claurete and Herzog (1990) demonstrated that state laws that ncrease the lender s costs assocated wth borrower default result n addtonal costs of credt for all borrowers. More recently, Ambrose, Buttmer, and Capone (1997) document that a sgnfcant delay can exst between borrower default (mssed payment) and lender foreclosure. Ambrose and Buttmer (2000) then show that ths delay ntroduces a number of potental optons to the borrower wth respect to curng default pror to the lender foreclosng on the property. Usng these concepts, Ambrose and Pennngton-Cross (2000) dscuss how state laws that defne the foreclosure process and establsh credtor rghts can mpact the supply of mortgage credt. 3 For example, state default laws can mpact the credt supply by defnng how foreclosure s accomplshed and whether credtors may pursue other borrower assets n the event that the collateral sale does not dscharge the debt. 3 Pence (2002) confrms ths fndng usng HMDA loan level data. 4

7 Furthermore, state bankruptcy laws and regulatons allow borrowers to protect a porton of ther housng equty and non-housng property va homestead and personal property exemptons. 4 To summarze, the Brueckner (1994, 2000) and Harrson et al (2002) models, along wth the nsghts derved from the lterature on regulatory mpact on mortgage rates, provde several testable hypotheses concernng the lnkages between default, default cost, and borrower characterstcs. Frst, we hypothesze that borrowers n states wth low default costs wll have hgher secured second loan amounts relatve to borrowers n states wth hgh default costs. Second, secured junor loan amounts should be drectly correlated wth borrower credt qualty snce the lender looks to both the underlyng collateral as well as future ncome for loan repayment. That s, hgher qualty borrowers wll have hgher loan amounts relatve to lower qualty borrowers. Ths s consstent wth the predctons of Harrson et al (2002) and drectly counters to the predctons of Brueckner (1994, 2000). In addton, to the extent that lenders are able to dfferentate borrower qualty based on credt scores, we expect that loan costs should be negatvely related to borrower credt scores. III. Data In order to test the predctons from our model, we employ a dataset of 132,184 second mortgage loans orgnated for securtzaton between 1995 and Ths dataset s unlke most other mortgage datasets n that these mortgages represent second loans that 4 As dscussed by Berkowtz and Hynes (1999) and Ln and Whte (2001), Federal bankruptcy law provdes a homestead exempton of $7,500 but each state s allowed to set ts own exempton level. As a result, ndvdual state homestead exempton levels vary wdely wth some beng unlmted and others beng very restrcted. Ln and Whte (2001) note that personal property exemptons have smaller varaton across states. 5

8 are secured by the underlyng property. However, n many cases, when the orgnal mortgage loan balance s combned wth the second loan amount, the total mortgage debt exceeds the value of the collateral asset. As a result, these loans are often referred to as 125% LTV loans. The 125 desgnaton denotes the fact that the maxmum LTV rato s normally 125 percent of the property collateral value. In order to make the dataset as clean as possble, we nclude only subordnate loans wth sngle-famly resdental collateral. The dataset contans nformaton regardng the borrower s reason for desrng the mortgage, allowng a test of whether loans orgnated for the purpose of debt consoldaton dffer from loans orgnated for other purposes (home mprovement, refnancng, etc.). Table 1 shows the dstrbuton of the loans by orgnaton year. We note that the majorty of the mortgages (50%) were orgnated n The mortgages were orgnated across the US, but have sgnfcant concentraton n Calforna (21.5%) wth the next hghest concentraton n Florda (7.8%). 5 Consstent wth Texas bankng laws regardng second mortgages, there were only 206 loans orgnated n Texas. Furthermore, consstent wth the fndngs of Ambrose, LaCour-Lttle, and Sanders (2002), we fnd that the orgnaton spread for hgh credt qualty borrowers s sgnfcantly lower than the orgnaton nterest rate spread for low credt qualty borrowers (Table 2). 6 Table 2 shows that borrower s wth FICO scores n the bottom quartle of the credt score dstrbuton have spreads 288 bass ponts hgher than borrowers n the top quartle of the credt score dstrbuton. 5 A table detalng the geographc dstrbuton of mortgages orgnatons s avalable from the author upon request. 6 The orgnaton nterest rate spread s defned as the mortgage effectve yeld at orgnaton less the 10- year Treasury rate at date of orgnaton. 6

9 In order to estmate the mpact of state-specfc default laws, we follow the analyss of Ambrose and Pennngton-Cross (2000) who categorze the states based on whether credtors must use judcal or non-judcal foreclosure and whether the states have ant-defcency judgment statutes. 7 From the lender s perspectve, ths classfcaton system defnes a hgh default cost state as one that requres judcal foreclosure proceedngs but does not allow defcency judgments. Smlarly, a low default cost state s one that does not requre judcal foreclosure and allows lenders to obtan defcency judgments aganst borrower assets. Gven that defcency judgments ncrease the rsk to the borrower, the theory proposed by Harrson et al (2002) suggests that borrowers n states that allow defcency judgments should self select lower debt amounts than borrowers n states that lmt defcency judgments, all else beng equal. As a prelmnary test of ths hypothess, we report n Table 3 the mean total debt loan-to-value rato and senor debt loan-to-value ratos based on whether or not the borrower lves n a state that allows defcency judgments. We fnd that borrowers n states that have do not allow defcency judgments carry sgnfcantly hgher senor debt amounts but sgnfcantly lower total debt amounts than borrowers n states that allow defcency judgments. Snce judcal foreclosure has the percepton of provdng greater borrower protecton than non-judcal foreclosure proceedngs, total debt amounts and junor loan amounts n states that requre judcal foreclosure should be hgher than n states that allow non-judcal foreclosure. Thus, Table 3 also reports the mean total loan-to-value 7 Judcal foreclosure proceedng are more costly and tme-consumng than non-judcal proceedngs snce credtors are requred to obtan a court order to foreclosure on the property to satsfy the debt. Antdefcency judgment statutes prohbt credtors from attachng other assets or garnshng future wages to satsfy losses that occur due to default. 7

10 rato and senor loan-to-value rato classfed by state law regardng foreclosure. Contrary to expectatons, we fnd that the mean senor loan-to-value rato s sgnfcantly lower n states that requre judcal foreclosure. 8 However, total debt loan-to-value ratos are hgher n states that requre judcal foreclosure. Snce default costs are n general a zero sum game (borrower protectons lmt lender default recovery and pro lender regulatons ncrease potental borrower losses), one possble explanaton for ths result s that lenders may raton credt n states where legal regulatons lmt lender abltes to quckly recover assets n case of default. Snce most borrowers n default do not have other assets to attach, lenders may vew defcency judgments as less mportant than the ablty to utlze non-judcal foreclosure proceedngs. When factorng borrower credt and nformaton sgnalng, Harrson et al (2002) suggest that holdng default costs constant, hgh qualty borrowers n hgh default cost states self-select hgher loan amounts whle low qualty borrowers self select lower loan amounts to mnmze the potental cost of default. Therefore, we test whether hgher rsk borrowers select larger loans and whether hgher rsk borrowers n hgh default cost states select lower loan amounts, holdng all else constant. Table 4 shows the dfferences n mean loan-to-value ratos based on whether the borrower s FICO score s greater than or less than the average FICO score n the sample. Consstent wth our theory, hgher qualty borrowers do have sgnfcantly hgher senor loan amounts. However, lower qualty borrowers have hgher loan-to-values based on total debt. Ths fndng s nconsstent wth the debt-sgnalng hypothess proposed by Bolton and Scharfsten (1996). Holdng all else constant, Bolton and Scharfsten s (1996) theory s that lower 8 Ths s consstent wth the fndngs of Pence (2002). 8

11 rsk borrowers wll have larger second loans as they are n a poston to take on more debt. IV. Emprcal Modelng In the prevous secton, we establshed that hgh qualty borrowers have larger senor loan amounts than low qualty borrowers, but low qualty borrowers have greater loan-to-value ratos based on total debt. In ths secton, we test whether lenders prce loans based on borrower rsk and default costs. Merton (1974) predcts that borrower yeld spreads are a postve functon of total debt. That s, hgher loan amounts expose lenders to greater rsk who then compensate by chargng a hgher rsk premum. In addton, our analyss suggests that after controllng for default costs, the loan spread wll be nversely related to borrower credt qualty. Buldng on the work of Merton (1974), numerous artcles have proposed models of corporate bond yeld spreads. 9 In a recent example, Baksh, Madan and Zhang (2000) test for the presence of frm-specfc dstress factors n corporate bond spreads. Ther results confrm that frm-specfc rsk factors as well as market nterest rates determne corporate bond yelds. Thus, followng Baksh et al (2000) and Ercsson and Renault (2000), we propose the followng generalzed model of the mortgage spread: Spread = α log( loanamt ) + α J + α + α j= 13 σ rtreas α YrDUM j + α yeldcurve + α credtspread + α Term + α FICO + α k= 17 α QtrDUM k ( r r ) mkt treas + α D + α debtconsol + α cashout + α mprove ε 4 9 (1.) 9 For example, see Duffee (1998, 1999), Baksh, Madan and Zhang (2000), Ercsson and Renault (2000), Colln-Dufresne, Goldsten, and Martn (1999), and Duffe and Sngleton (1999). 9

12 In equaton (1), Spread represents the second mortgage orgnaton spread, loanamt s the second (junor-secured) loan amount, and Term s the length (term) of the second loan. To control for varaton n captal markets at the tme of loan orgnaton, we nclude the spread between the current mortgage rate as proxed by the Fredde Mac 30- year fxed-rate mortgage rate (r mkt ) and the 10-year constant maturty treasury rate (r treas ). In addton, we also nclude a proxy for the current yeld curve (yeldcurve) measured as the 10-year constant maturty treasury rate less the 1-year constant maturty treasury rate as well as a measure of the volatlty n nterest rates (σ rtreas ). To control for varaton n the captal market rsk premum, we nclude the bond market credt rsk spread (credtspread) as proxed by the dfference n the BAA and AAA corporate bond rates. We nclude the borrower s credt score at orgnaton (FICO ) to control for dfferences n borrower specfc credt rsk. Snce the borrower s ntended use of funds may provde lenders wth a sgnal of credt qualty, we nclude three varables that denote the percentage of funds used for the most common purposes: debtconsol s the percent of the second loan used for debt consoldaton purposes, cashout s the percent of the second loan that s taken as cash at closng, and mprove s the percent of the second loan used for home mprovement purposes. To capture dfferences n state laws regardng default and foreclosure, we nclude a dummy varable denotng states that allow lenders to pursue defcency judgments (D) aganst borrowers n default and a dummy varable denotng states that requre judcal foreclosure proceedngs (J). Fnally, YrDUM s a seres of dummy varables denotng the year of orgnaton ( wth 1995 beng the reference year), and QtrDUM s a seres of three dummy varables denotng the orgnaton quarter (the frst quarter s the reference). 10

13 One of the prmary problems wth analyzng the mpact of state level default costs on the avalablty of credt s the endogenous relatonshp between the mortgage loan terms, the loan amount, the collateral qualty, and the borrower s credt qualty. Ths endogenous relatonshp s wdely recognzed n the lterature that examnes borrower choce concernng loan amount and housng consumpton. 10 However, our analyss s more complcated n that we examne the borrower s choce of junor loan debt and the mpact of default costs on the avalablty and cost of that debt. In ths context, the amount of housng consumpton s already determned. Thus, the endogenous terms are related to the amount of the second loan, ts costs (nterest rate spread), and loan maturty, assumng that the borrower s house (collateral) value, credt qualty and ncome are exogenous to the decson. Therefore, to control for ths endogenous relatonshp we also estmate the followng model of the borrower s second loan amount: log ( loanamt ) = β + β Spread + β Term + β frstmtgbal 0 + β cashout + β mprove + + β house + β FICO + β J + β D + β debtconsol j= 11 β YrDUM j k= 15 β QtrDUM k + ε (2.) In equaton (2), we note that the prmary determnates of second loan amount are the borrower s current debt level and collateral value. Thus, we nclude the value of the house at second loan orgnaton (house ) and the frst (senor) mortgage amount (frstmtgamt ). As further control varables that mpact the amount of funds requested by the borrower, we also nclude the varables denotng the ntended use of funds 10 See Lng and McGll (1998) for an example of a smultaneous equaton model where mortgage demand s a functon of borrower ncome, nonhousng wealth, desred housng consumpton, and demographc characterstcs, and housng consumpton s a functon of the level of mortgage debt as well as economc and demographc factors. Also, Ambrose, LaCour-Lttle, and Sanders (2002) employ a smultaneous equatons system to recognze the well-known endogenous relatonshp between LTV and house value. 11

14 (debtconsol, cashout, and mprove), the borrower s credt qualty (FICO), state default law dummy varables (J, D), and tme trend dummy varables (YrDUM and QtrDUM). Fnally, we recognze that borrowers may trade off mortgage cost (Spread) and loan amount (loanamt) wth loan maturty (term). Durng perods of ncreased mortgage cost, borrowers can mantan housng affordablty by selectng longer terms. In addton, borrowers can take out larger loan amounts and mantan current housng payments by alterng the mortgage term. Thus, we estmate the followng equaton of mortgage term: Term = γ 0 + γ 1Spread + γ 2 log( loanamt ) + γ D j= 6 γ YrDUM j + 12 k= 10 + γ FICO + γ J γ QtrDUM k 3 + ε 4 (3.) In equaton (3), we recognze that the mortgage term s a functon of the current mortgage cost and amount, as well as the borrower s qualty. Furthermore, we nclude the dummy varables controllng for state level default and foreclosure laws n order to test for the mpact that these regulatons have on borrower choce of term. We estmate the system for the mortgage spread (1), loan amount (2), and loan maturty (3) va three-stage least squares regresson (3SLS). The orgnaton Spread s calculated as the effectve yeld assumng a 10-year holdng perod less the 10-year constant maturty treasury rate. In calculatng the effectve yeld, we nclude the mpact of closng costs and ponts. Approxmately 10% of the sample had mssng or ncorrectly coded closng cost amounts. Thus, we mputed the closng costs on loans wth mssng data usng the mean closng cost amount for the top 75 percent of the sample. The dataset does not contan actual nformaton about the ponts charged to borrowers; however, dscussons wth lender representatves ndcate 12

15 that the lender unformly charged 8 ponts on all loans orgnated. Thus, n estmatng the effectve yeld we also assume that 8 ponts were charged at orgnaton. Gven the large number of observatons avalable, we segmented the sample nto an estmaton subsample and a holdout subsample. The estmaton subsample was created by randomly drawng 75 percent of the full sample wth the remanng 25 percent held as the holdout sample. The mortgage spread system was estmated usng the estmaton subsample wth the holdout subsample used for testng model ft and accuracy. Table 6 presents the 3SLS parameter estmates for the mortgage spread system. As expected, the estmated coeffcents for loan spread, term, and loan amount ndcate a negatve relatonshp (sgnfcant at the 1 percent level) between loan amount and cost (loan amounts declne as the cost ncreases) and a postve relatonshp (sgnfcant at the 1 percent level) between cost and term and loan amount and term. The parameter estmates also show that borrower credt qualty (FICO score) s negatvely related to credt cost (sgnfcant at the 1 percent level) and loan amount (sgnfcant at the 10 percent level). That s, hgher qualty borrowers (hgher FICO scores) have lower second loan orgnaton spreads all else beng equal. In addton, borrower credt qualty s postvely related to the mortgage term (sgnfcant at the 10 percent level) wth hgher qualty borrowers selectng longer-term loans. Ths s counter to the debt-sgnalng hypothess dscussed by Flannery (1986) that hgher qualty borrowers are less susceptble to fnancal shocks and thus borrower over shorter terms. However, our result s consstent wth the Damond s (1991) theory that low qualty borrowers are unable to ssue longer-term debt snce lenders are unwllng to lend longer term. Furthermore, after controllng for other factors, the model parameter estmates 13

16 ndcate that hgher qualty borrowers actually have lower second loan amounts. Ths s counter to the smple comparson of means reported earler. However, ths result s consstent wth Brueckner s (2000) theory that, n equlbrum, hgher qualty borrowers do not request larger loan amounts. The model coeffcents provde strong support for a postve relatonshp between borrowers n states that requre judcal foreclosure proceedngs and the second loan terms (sgnfcant at the 1 percent level). The parameter estmates ndcate that borrowers n states that requre judcal foreclosure have hgher second loan amounts, pay more for the loan (orgnaton spread s larger), and borrow over a shorter term. However, we fnd the opposte effect for states that lmt borrower defcency judgments (sgnfcant at the 1 percent level). The negatve coeffcents for defcency judgments n the spread and loan amount equatons ndcate that borrowers n states that prevent lenders from seekng defcency judgments have lower spreads and loan amounts. Ths s consstent wth the theory that lenders tradeoff loan costs wth loan amounts. The results are also consstent wth the theory that lenders restrct credt n states wth regulatons that lmt ther ablty to recover losses (ant-defcency judgment statutes) whereas lenders do not restrct credt n states that smply ncrease the costs assocated wth default (requre judcal foreclosure) but do not lmt the lender s ablty to recover losses. The coeffcents regardng the use of funds do not reveal a sgnfcant relatonshp between loan amount or cost and the percentage of funds used to consoldate other debts. However, we do fnd that that the cost of second loan debt s sgnfcantly lower (at the 1 percent level) as the percentage of the loan amount used for home mprovements or cash 14

17 out ncreases. At the same tme, borrowers seekng loans for home mprovements or to cash out also have lower amounts. Examnng the other macro economc and borrower specfc factors, the parameter estmates are sgnfcant at the 1 percent level and have the predcted sgns. We see that borrowers wth hgher house values have hgher second loan amounts whle borrowers wth larger frst mortgages have lower second mortgages. We also fnd that the cost of second loans s postvely related to the mortgage market nterest rate spread and the overall market credt rsk premum (corporate bond credt rsk spread). Ths s consstent wth a number of prevous studes who fnd that the mortgage market s ntegrated wth the larger captal markets. 11 V. Model Predctons We assess the estmated systems predcted accuracy usng the holdout sample as an out-of-sample test. Predcted spread, loan amount, and term were estmated va Newton s method for each observaton n the holdout sample usng the parameter coeffcents reported n Table 6. The predcted values are then subtracted from the actual values to obtan an error estmate. Postve resduals ndcate that the model underfts whle negatve resduals ndcate that the model overfts. Table 7 reports the mean and medan predcton error across the holdout sample. Snce ths s an out-of-sample test, the mean predcton errors are not zero, however, the results ndcate that the system has hgh predctve accuracy. Panel A reports the mean and medan predcton errors (resduals) for the full sample. We fnd that the mean spread predcton error s 0.1 bass ponts, the mean log of the loan amount predcton error s , and the mean term 11 For example, see Gonzalez-Rvera (2001) and Kolar, Fraser, and Anar (1998) for example. 15

18 predcton error s.34 months suggestng that the model tends to overft the spread and underft the loan amount and term. In dscussng the followng results, we concentrate on the medan values as these are less lkely to be mpacted by extreme outlers. In order to detect any systematc pattern n prcng loans based on borrower credt qualty, we dvde the sample nto hgh and low credt qualty subgroups. In ths table, we smply classfy borrowers as hgh qualty f ther FICO score at orgnaton was above the sample mean (FICO scores greater than 683) and low qualty refers to borrowers wth FICO scores less than the sample mean (FICO scores less than 684). Interestngly, we fnd that the spread predcton error s smaller for the low FICO score sample. For the hgh FICO subsample, the medan predcted spread s 25 bass ponts hgher than the actual spread whle the mean predcted spread for the low FICO subsample s 0.69 bass ponts lower than the actual. Thus, t appears that our model more accurately predcts the actual loan spread for lower qualty borrowers. In Panel B we explore the mpact that the borrower s self-reported reason for orgnatng the second loan may have on loan prcng. For hgh qualty borrowers (FICO scores above 683), analyss of the resduals ndcates that the predcton error s hghest for borrowers ndcatng that they are usng at least 90% of the loan amount for debt consoldaton (121 bass ponts above actual) and lowest for borrowers utlzng the funds for home mprovements (40 bass ponts above actual). For low qualty borrowers, the predcton error s hghest for borrowers ndcatng that they are utlzng the funds to cash out equty (104 bass ponts above actual) whle the lowest error s agan for borrowers utlzng the funds for home mprovements (73 bass ponts). Further reflecton upon these predcton errors suggests a couple of nterestng observatons. 16

19 Frst, consstent wth the theory that nvestment n the underlyng collateral (home mprovement) ncreases the securty on the underlyng mortgage, the lender has more accurate prcng. For hgh qualty borrowers, the average predcted spread for borrowers usng funds for home mprovements s lower than for other borrowers (12.08 percent versus percent, respectvely). However, for low qualty borrowers, the average predcted spread for borrowers usng funds for home mprovements s almost the same as for other borrowers (14.52 percent versus percent, respectvely). Second, for hgher qualty borrowers who ndcate that they are utlzng the funds for debt consoldaton, the model devaton s greater suggestng less accurate prcng (12.96 percent versus percent, respectvely). Ths result may be attrbutable to the fact that the FICO score may not accurately reflect borrower rsk for borrowers needng funds for debt consoldaton. As a result, the prcng for these loans reflects unobserved underwrtng crtera that are not observed n the FICO score. Thrd, for lower qualty borrowers, the greatest devaton n prcng occurs for borrowers seekng to cash out equty. Ths fndng may be attrbutable to lenders uncertanty about the rsk of these loans. In Panel C, we examne the predcton errors for hgh qualty and low qualty borrowers based on ther state default regulatons. We classfy hgh default cost states (from the lender s perspectve) as states that requre judcal foreclosure proceedngs (J=1) but do not allow defcency judgments (D=1). The average predcted loan spread for hgh qualty borrowers n these states s percent whle the average predcted spread for low qualty borrowers s percent. Low default cost states are classfed as those that do not requre judcal foreclosure (J=0) but allow defcency judgments 17

20 (D=0). In these states, the average predcted spread for hgh qualty borrower s percent whle the average predcted spread for low qualty borrowers s percent. Interestngly, we fnd that the spread predcton error s unformly negatve (model over predcts the spread) across all state default regulaton categores for the hgh qualty borrower subsample. However, the model appears to unformly under predct loan costs for the low FICO subsample (errors are postve). By controllng for borrower rsk characterstcs, nterrelated loan terms, market condtons, and state-level default laws, we are able assess the degree of under- or overprcng of junor secured mortgages. To carry out ths test, we create a seres of hypothetcal borrowers dfferentated by rsk and locaton. Frst, we segment the holdout sample nto very hgh and very low qualty borrowers where very hgh qualty s defned as any borrower wth a FICO score above the 75 th percentle of the whole sample (FICO>706) and very low qualty s defned as any borrower wth a FICO score below the 25 th percentle of the whole sample (FICO < 658). Next, we calculate the ndependent varable means for these hgh and low qualty subsamples further segmented by whether ther state requres judcal foreclosure (J=1) or does not allow defcency judgments (D=1). Usng the relevant mean values of these hypothetcal borrowers, we then estmate predcted loan spreads, terms, and amounts. Comparng these predcted values to the actual means for each borrower segment allows us to quantfy the degree of lender under or over prcng. Table 8 shows the comparson for borrowers lvng n hgh default cost and low default cost states. Consstent wth the predcton errors reported above, we see that predcted as well as actual spreads are lower n low default cost states. For example, the 18

21 actual average spread for very hgh qualty borrowers n low default cost states was percent, whle the actual spread for very hgh qualty borrowers n hgh default cost states was percent. Lookng at the average predcted loan spreads for very hgh qualty borrowers, we see that the model under predcts the spread n low default cost states and over predcts the spread n hgh default cost states. However, t s nterestng to note that low qualty borrowers are consstently over-charged relatve to the model predctons. For example, the nterest rate charged on a loan to a low qualty borrower lvng n a hgh cost state was, on average, 64 bass ponts hgher than the predcted value. On the other hand, hgh qualty borrowers lvng n states wth hgh default costs were consstently under charged by 18 bass ponts, on average. VI. Summary and Conclusons The hgh LTV mortgage examned n ths paper s an nterestng twst on the home equty loan contract n that t has a hgher nterest rate and aggregate LTV than tradtonal home equty loans. As the market contnues to grow for the varous permutatons of home equty loans, the mpact of credt on mortgage rates becomes qute mportant (partcularly when compared to conformng frst mortgages purchased by the government sponsored agences where credt rsk s of lttle concern). In ths paper, we examne the prcng of hgh-ltv debt and determne whether state-specfc default laws have an mpact on the avalablty and cost of that debt. Frst, we fnd that lenders ratonally prce loans to hgher rsk borrowers for the most part; however, when we focus on smaller and smaller FICO scores buckets, the results ndcate that the mean actual loan rates are hgher than those predcted by our model. Second, we 19

22 examne the mpact of borrower protecton laws on the prce of credt and f borrowers n states that lmt the lender s ablty to seek default remedes pay hgher credt costs; we fnd that states that do not requre judcal foreclosure and allow defcency judgments on hgh LTV loans have lower lendng rates (by about 33 bass ponts) than loans n states that requre judcal foreclosure and do not allow defcency judgments. Thrd, we fnd that there s a greater degree of error n the prcng of hgh LTV loans to low FICO borrowers than to hgh FICO borrowers. Stated n a dfferent way, t s more dffcult to explan the rate charged to lower credt rsk borrowers n that the rates charged are hgher than those predcted by our ratonal model of loan prcng. 20

23 REFERENCES Alston, L. Farm Foreclosure Moratorum Legslaton: A Lesson from the Past. Amercan Economc Revew 74 (1984) Ambrose, B.W., and R.J. Buttmer, Jr. Embedded Optons n the Mortgage Contract. The Journal of Real Estate Fnance and Economcs 21:2 (2000), Ambrose, B.W., R.J. Buttmer, Jr., and C.A. Capone, Jr. Prcng Mortgage Default and Foreclosure Delay. Journal of Money, Credt, and Bankng 29:3 (1997), Ambrose, B.W., and A. Pennngton-Cross. Local Economc Rsk Factors and the Prmary and Secondary Mortgage Markets. Regonal Scence and Urban Economcs 30:6 (2000), Ambrose, B.W., M. Lacour-Lttle, and A. Sanders. Credt Spreads and Captal Structure: Evdence from the Mortgage Market, Oho State Unversty Workng Paper (2002). Barth, J.R., J. Cordes, and A. Yezer. Benefts and Costs of Legal Restrctons on Personal Loan Markets. Journal of Law and Economcs 29 (1986), Berkowtz, J. and R. Hynes. Bankruptcy Exemptons and the Market for Mortgage Loans. Journal of Law and Economcs 42 (1999), Bolton, P. and D. Scharfsten. Optmal debt structure and the number of credtors. Journal of Poltcal Economy 104:1 (1996), Brueckner, J.K. Mortgage Default wth Asymmetrc Informaton. Journal of Real Estate Fnance and Economcs 20 (2000), Brueckner, J.K. Unobservable Default Propenstes, Optmal Leverage, and Emprcal Default Models: Comments on Bas n Estmates of Dscrmnaton and Default n Mortgage Lendng: The Effects of Smultanety and Self-Selecton. Journal of Real Estate Fnance and Economcs 9:3 (1994), Claurete, T.M. and T. Herzog. The Effect of State Foreclosure Laws on Loan Losses: Evdence from the Mortgage Insurance Industry. Journal of Money, Credt and Bankng 22:2 (1990), Damond, D. Debt Maturty Structure and Lqudty Rsk. Quarterly Journal of Economcs (1991), Flannery, M. Asymmetrc Informaton and Rsky Debt Maturty Choce. Journal of Fnance 41 (1986),

24 Gonzalez-Rvera, G. Lnkages Between Secondary and Prmary Markets for Mortgages. Journal of Fxed Income (2001), Harrson, D.M., T.G. Noordewer, and A. Yavas. Do Rsker Borrowers Borrow More?. Pennsylvana State Unversty Workng Paper (2002). Kau, J.B. and D.C. Keenan. An Overvew of the Opton-Theoretc Prcng of Mortgages. Journal of Housng Research 6:2 (1995), Kolar, J.W., D.R. Fraser, and A. Anar. The Effects of Securtzaton on Mortgage market Yelds: A Contegraton Analyss. Real Estate Economcs 26:4 (1998), Ln, E.Y., and M.J. Whte. Bankruptcy and the Market for Mortgage and Home Improvement Loans. Journal of Urban Economcs 50 (2001), Lng, D.C., and G.A. McGll. Evdence on the Demand for Mortgage Debt by Owner- Occupants. Journal of Urban Economcs 44 (1998), Meador, M. The Effect of Mortgage Laws on Home Mortgage Rates. Journal of Economcs and Busness 34 (1982), Merton, R.C. Theory of Ratonal Opton Prcng. Bell Journal of Economcs 4 (1974), Pence, K.M. Foreclosng on Opportunty: State Laws and Mortgage Credt. Board of Governors of the Federal Reserve System workng paper (2002). Querca, R., and M.A. Stegman. Resdental Mortgage Default: A Revew of the Lterature. Journal of Housng 3 (1992), Rothschld, M. and J. Stgltz. Equlbrum n Compettve Insurance Markets: An Essay on the Economcs of Imperfect Informaton. Quarterly Journal of Economcs 90 (1976),

25 23

26 Table 1: Dstrbuton by Orgnaton Year Year Frequency Percent , , , Total 132, Table 2: Mean Loan Orgnaton Spread by Borrower FICO Score Borrower Fco Range mean std dev [0, 658) [658, 682) [682, 706) [706+) Table 3: Mean Loan Amounts Classfed by State Foreclosure Laws. (Standard Devatons n parentheses) Defcency Judgments Judcal Foreclosure Not Not Allowed Allowed t-stat. Requred requred t-stat. Senor Debt LTV *** *** (14.54) (13.60) (15.03) (13.88) Total Debt LTV *** *** (12.45) (13.01) (12.65) (12.70) N 79,831 52,353 93,167 39,017 Note: t-statstc test for equalty of means under assumpton of unequal varance. *** sgnfcant at the 1% level. 24

27 Table 4: Mean Loan Amounts Classfed by Borrower FICO Score. (Standard Devatons n parentheses) FICO Scores Hgh FICO Low FICO t-stat. Senor Debt LTV *** (14.53) (13.99) Total Debt LTV *** (13.10) (12.31) N 61,473 70,711 Note: t-statstc test for equalty of means under assumpton of unequal varance. Hgh FICO borrowers have FICO scores greater than the mean FICO score for the sample (684) and Low FICO borrowers have FICO scores less than or equal to the mean FICO score for the sample. *** sgnfcant at the 1% level. 25

28 Table 5: Descrptve Statstcs Varable Label N Mean Std Dev Orgnal_Interest_Rate Junor Mortgage Interest Rate Loanamt Junor Mortgage Loan Amount $31, $12, Yeld Junor Mortgage Effectve Interest Rate Spread Junor Mortgage Orgnaton Spread Frstmtgbal Frst Mortgage Loan Amount $94, $45, Value House Value (Apprased) $114, $49, Loan_To_Value Loan_To_Value (total debt) FICO Borrower FICO Score r mkt 30 - Fxed Conventonal Market Rate Yeldcurve 10 year Treasury - 1 year Treasury σ r treas Standard Devaton of 10-year Treasury Credtspread Baa - AAA Bond Spread J 1=requre judcal D 1=allows defcency

29 Table 6: Non-lnear Three-Stage Least Squares Estmaton of Mortgage Orgnaton Terms (t-statstcs reported n parentheses) Spread log(loanamt) Term Intercept *** *** *** (81.25) (26.28) -(23.81) Spread *** *** -(14.39) (13.64) log(loanamt) *** *** -(38.83) (121.03) Term *** *** (31.41) (111.41) FICO *** -4.30E-04 * * -(20.58) -(1.84) (1.60) debtconsol 4.40E (0.02) (0.44) homemprove *** *** -(15.23) -(2.96) cashout *** *** -(15.65) -(3.31) J *** *** *** (7.29) (4.29) -(4.16) D *** *** *** -(26.27) -(22.53) (22.34) (r mkt -r treas ) *** (7.37) σ treas *** -(3.95) yeldcurve *** (5.53) credtspread *** (13.73) frstmtgbal -5.61E-08 * -(1.62) Value 1.07E-07 *** (4.36) Yr *** *** *** -(9.96) -(5.09) (4.99) Yr *** *** *** -(16.29) -(11.37) (11.20) Yr *** *** *** -(23.35) -(20.94) (21.10) Yr *** *** *** -(14.00) -(9.56) (9.56) Qtr ** *** (0.35) (2.23) -(2.26) Qtr * (1.93) (0.14) -(0.18) Qtr *** *** *** -(5.49) -(3.01) (2.94) 27

30 *** sgnfcant at the 1% level. ** sgnfcant at the 5% level. * sgnfcant at the 10% level. 28

31 Table 7: Mean (Medan) Predcton Errors (Random Holdout Sample). Predctons obtaned from coeffcents estmated on 75% random sample. Hgh FICO Borrower sample have FICO scores greater than or equal to 684 and the low FICO Borrower sample have FICO scores less than or equal to 683. (Actual Predcted) Number of Observatons Spread Log(Amount) Term Panel A: Full Sample 33, (0.1203) (0.0269) ( ) Hgh Fco Borrower 15, (0.2497) (0.0328) ( ) Low FICO Borrower 17, (0.0069) (0.0229) ( ) Panel B: Analyss of Borrower Motvaton Hgh FICO Borrower Debt Consoldaton 1, (1.2117) -(0.0934) (2.7961) Home Improvement (0.3991) (0.2049) ( ) Cash Out (0.4818) (0.2805) (7.9445) Low FICO Borrower Debt Consoldaton 2, (0.9709) -(0.0522) (9.9774) Home Improvement (0.7301) (0.2568) ( ) Cash Out (1.0412) (0.2963) -(8.7189) Panel C: Analyss of State Default Laws Hgh FICO Borrower Judcal=0, Defcency = 0 5, (0.2807) (0.0397) ( ) Judcal=0, Defcency = 1 6, (0.2471) (0.0238) ( ) Judcal=1, Defcency = 0 4, (0.2283) (0.0264) ( ) Judcal=1, Defcency = (0.1959) (0.1575) ( ) Low FICO Borrower Judcal=0, Defcency = 0 5, (0.0715) (0.0275) ( ) Judcal=0, Defcency = 1 6, (0.1189) (0.0354) ( ) Judcal=1, Defcency = 0 4, (0.0462) -(0.0065) ( ) Judcal=1, Defcency = (0.4573) (0.1147) ( ) 29

32 Note: Estmaton sample created by drawng a 75% random sample of the full sample wth the remanng 25% used to test the model ft. Hgh FICO borrower sample ncludes all borrowers wth FICO scores greater than or equal to 684 and low FICO borrower sample ncludes all borrowers wth FICO scores less than or equal to 683. Fund utlzaton samples are all borrowers utlzng greater than 90% of funds for the purpose dentfed. 30

33 Table 8: Actual versus Predcted Loan Terms Mean Values Predcted Values Spread Log(Amount) Term Spread Log(Amount) Term Hgh FICO Borrower Low Cost (J=0, D=0) Hgh Cost (J=1, D=1) Low FICO Borrower Low Cost (J=0, D=0) Hgh Cost (J=1, D=1) Hgh FICO Borrower subsample ncludes all borrowers wth FICO scores n the 75 th percentle. Low FICO Borrower subsample ncludes all borrowers wth FICO scores n the 25 th percentle. Predcted values are estmated usng mean values of the ndependent varables n each respectve subsample. 31