THE EFFECT OF PREPAYMENT PENALTIES ON THE PRICING OF SUBPRIME MORTGAGES

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1 THE EFFECT OF PREPAYMENT PENALTIES ON THE PRICING OF SUBPRIME MORTGAGES Gregory Ellehausen, Fnancal Servces Research Program George Washngton Unversty Mchael E. Staten, Fnancal Servces Research Program George Washngton Unversty and Jevgenjs Stenbuks Department of Economcs George Washngton Unversty Aprl 2007

2 THE EFFECT OF PREPAYMENT PENALTIES ON THE PRICING OF SUBPRIME MORTGAGES ABSTRACT Ths paper nvestgates the effect of prepayment penaltes and state laws governng such penaltes on the prcng of subprme mortgages. The paper s the frst to consder that mortgage prce and prepayment penalty may be chosen jontly, makng sngle-equaton estmates of the effect of prepayment penalty on prce based. Usng a model that accounts for endogenety of prce, loan to value, and prepayment penalty, we fnd that prepayment penaltes are assocated wth lower prces for subprme mortgages and that state laws restrctng prepayment penaltes are assocated wth hgher prces. These fndngs are consstent wth prcng of mortgage credt accordng to rsk, a characterstc of the subprme market that allows lenders to make credt avalable to borrowers who would have dffculty obtanng such credt n the prme market. The fndngs are mportant because perceptons that prepayment penaltes are not reflected n mortgage prces have led to regulaton that rsks reversng the gans n mortgage credt avalablty that were acheved over the last decade. INTRODUCTION The development of a subprme mortgage market has been an mportant nnovaton n U.S. mortgage markets over the last decade. The subprme market has expanded avalablty of mortgage credt to many borrowers who for one reason or another dd not qualfy for mortgage credt n the prme market. Ths expanson of mortgage lendng to hgher rsk borrowers has come about because lenders, aded by technologcal advances that have facltated the mantenance and analyss of comprehensve nformaton on borrowers credt hstores, have been able to prce loans accordng to the rsk of the loan. Thus, hgher rsk borrowers pay hgher prces for mortgage credt n the subprme market, rather than face lmtatons on loan sze or turndowns n the prme market. Prcng n the subprme market depends on a varety of factors, ncludng a number that affects rsk of payment. The factors nclude the sze of the loan (smaller loans requre a hgher nterest rate to recover relatvely hgh fxed costs); the type of nterest rate (e.g., fxed vs. adjustable, whch affects the lender s nterest rate rsk); the rato of loan amount to home value and the type of home (whch may nfluence the borrower s ncentve to pay or mantan the property); the extent of documentaton of ncome or assets; ncome, debt payments relatve to ncome and purpose of the loan (both of whch reflect capacty to servce the debt); and credt bureau score and other measures of payment performance. The presence of a prepayment penalty s also promnent among the rsk factors that affect the prce of a subprme loan. A prepayment penalty s a fee that borrowers pay f they repay a mortgage wthn a specfed perod after orgnaton, usually wthn the frst two or three years. Borrowers may prepay for several reasons ncludng purchasng another home, takng advantage of a 1

3 declne n nterest rates, or refnancng the orgnal loan n order to obtan addtonal cash, or restructure exstng debts. Subprme borrowers may have an addtonal reason for prepayment: f ther fnancal crcumstances mprove they may qualfy for a lower nterest rate. Subprme borrowers prepay at a sgnfcantly hgher rate than prme borrowers (Phllps-Patrck, Hrschhorn, Jones, and LaRocca 2000). From the lender s standpont, prepayment reduces the proftablty of orgnatng loans and the predctablty of returns to nvestng n loans. Prepayment penaltes offset some of that rsk by encouragng borrowers to select loans based on ther prvate nformaton about the expected holdng perod and by compensatng lenders n the event of prepayment. As a result, subprme mortgages wth a prepayment penalty sell for hgher prces n the secondary market than do mortgages wthout a penalty. For these reasons, prce sheets for subprme loans typcally contan adjustments that ncrease the prce pad on loans wthout a prepayment penalty or wth relatvely short prepayment penalty perods. Whether prces for subprme mortgages actually nclude such adjustments s subject to controversy. Advocacy groups generally vew prepayment penaltes as nherently abusve and queston whether borrowers receve a lower loan prce n exchange for acceptng a prepayment penalty (e.g., Goldsten and Son 2003). One advocacy group has produced an emprcal analyss that concludes that prepayment penaltes are not assocated wth lower nterest rates n securtzed subprme loans (Ernst 2005). Usng dfferent data from several lenders, however, DeMong and Burroughs (2005) found that, other factors beng equal, loans wth prepayment penaltes have lower nterest rates than loans wthout prepayment penaltes. Reconclng these studes s dffcult. The dfferences n the studes estmated effects of prepayment penaltes do not appear to be solely a consequence of analyzng dfferent databases. The studes examned dfferent subprme mortgage products usng dfferent sets of explanatory varables. Both studes use only a small number of factors that lenders consder n prcng loans. Nether study accounted for effects of laws n many states that regulate prepayment penaltes n varous ways. And the estmated effect of prepayment penaltes may be based because of the falure to address possble endogenety n choce of prce and prepayment penalty. Avalable evdence smply does not resolve the queston of whether subprme mortgage prces reflect the presence of prepayment penaltes. Ths paper mproves on prevous nvestgatons n several ways: The mprovements nclude (1) consderaton of addtonal explanatory varables; (2) dsaggregaton n mortgage products to more closely reflect product defntons found n the market; (3) accountng for state regulaton of prepayment penaltes; and (4) consderaton of endogenety n nterest rate, loan to value, and prepayment penalty choces. METHODOLOGY The data for ths study are from the Fnancal Servce Research Program s (FSRP) subprme mortgage database. The database contans loan-level data on all orgnatons of the subprme subsdares of eght large fnancal nsttutons between the thrd quarter 1995 and the fourth quarter of The Federal Reserve estmated that the FSRP s subprme mortgage database covered nearly a quarter of the orgnatons of hgher prced 2

4 home purchase and refnance mortgages on owner-occuped homes n 2004, whch reported rsk premums under the Home Mortgage Dsclosure Act (Avery, Canner, and Cook 2005). 1 Estmates of coverage for earler years are not avalable, because the Home Mortgage Dsclosure Act (HMDA) dd not requre reportng of rsk premums for hgher prced mortgages pror to Nevertheless, t seems reasonable to beleve that the FSRP s subprme mortgage database captures a consderable share of all subprme mortgage lendng. 2 The lenders contrbutng to the subprme database orgnate loans through brokers, orgnate loans drectly, and purchase loans from other lenders. Nearly a quarter of the loans orgnated n 2004 were purchased from other lenders, and 58 percent of all loans were orgnated through brokers. These percentages are typcal of the lenders loan acqustons durng the tme perod of the database. Nearly all of the loans, 94 percent n 2004, n the database are closed-end. Forty percent of these closed-end loans were frst lens. Table 1 descrbes selected characterstcs of closed-end frst mortgages, the type of loan analyzed n ths study. The average loan sze of closed-end frst mortgages n 2004 was $130,000. Fxed-rate mortgages were on average smaller than varable-rate and hybrd mortgages. Overall, 23 percent of closedend frst mortgages were used for home purchases, but loan purpose vared substantally by type of nterest rate. Varable-rate and hybrd loans were more than twce as lkely to be used for home purchases as fxed-rate loans. Average annual percentage rates were percent for fxed-rate mortgages, 8.43 percent for varable-rate mortgages, and 9.78 percent for hybrd mortgages. Borrowers obtanng fxed-rate loans had lower ncomes and hgher FICO scores than borrowers obtanng varable-rate or hybrd loans. Loan szes, property values, and borrower ncome were lower n earler year; and loan purpose dstrbutons, annual percentage rate, and FICO scores vared durng the entre perod. Nevertheless, the 2004 statstcs llustrate the dfferences n loan products and borrower characterstcs that prevaled durng ths perod. Model We specfy loan prce as a functon of loan terms, dstrbuton channel, and borrower rsk characterstcs. Prce s measured by the rsk premum, whch s defned as the annual percentage rate of nterest less the rate for a Treasury securty of comparable maturty. The annual percentage rate s the total mortgage prce because t ncludes both the contract nterest rate and any ntal ponts or fees. The rsk premum s used nstead of the annual percentage rate to remove the effects of movements n the market nterest rates. 1 Fnancal Servces Research Program was formerly named Credt Research Center. The center changed ts name when t moved to George Washngton Unversty n August For an earler verson of the FSRB s subprme mortgage database, Wallace, Ellehausen, and Staten (2004) estmated that the number of loans n 2002 was nearly a quarter of the number of HMDA-reportable loans orgnated by lenders on the U.S. Department of Housng and Urban Development s lst of subprme lenders. For further dscusson of coverage of subprme databases, see Wallace, Ellehausen, and Staten (2004). 3

5 Lenders typcally have dfferent prcng schedules for dfferent mortgage products. We, therefore, estmate separate models for (1) fxed-rate frst mortgages, (2) varable-rate frst mortgages, and (3) hybrd frst mortgages that have a 30-year term to maturty. These products accounted for nearly all frst mortgage loans orgnated by the eght subprme subsdares n the database. 3 We excluded loans wth loan amounts greater than 90 percent of home value because such hgh loan-to-value loans are not generally avalable to most subprme borrowers. Loan terms nclude loan amount, home value, the rato of loan to value, and whether the loan s a reduced-documentaton loan. Dstrbuton channel s ndcated by a dummy varable that equals one when the loan was orgnated by a mortgage broker and zero otherwse. Borrower rsk characterstcs nclude whether the home s owner-occuped, borrower ncome, and FICO rsk score. The loan term that s of partcular nterest for ths paper s the presence of a prepayment penalty. Loans havng a prepayment penalty are dentfed by a dummy varable, whch equals one f the loan has a prepayment penalty and zero otherwse. If lenders charge hgher prces on loans wthout prepayment penaltes, then the presence of a prepayment penalty should be nversely related to the rsk premum. Because loan prce and presence of a prepayment penalty may be determned smultaneously, we frst estmated a probt model predctng the presence of a prepayment penalty. The predcted probablty that the loan has a prepayment penalty s used n place of the dummy varable n the smultaneous equaton model. Many states restrct prepayment penaltes. Restrctons may lmt the tme perod allowed for prepayment penaltes, lmt the sze of the prepayment penalty, or prohbt prepayment penaltes. Generally, restrctons on prepayment penaltes would be expected to be postvely related to rsk premums snce such regulaton would ncrease prepayment rsk. Federal preempton allows certan lenders to offer loans wth prepayment penalty terms that state laws prohbt other types of lenders from offerng. Ths regulatory structure may nfluence competton and the range of loan offerngs n regulated states and weaken the observed effect of state law on mortgage prces. We specfy state regulaton of prepayment penaltes as a dummy varable that equals one f state law restrcts or prohbts prepayment penaltes. 4 Descrptve statstcs for the varables are reported n table 2. Estmaton Prevous papers examnng the effect of prepayment penaltes on mortgage prces (DeMong and Burroughs 2005; Ernst 2005) estmate a regresson model predctng prce as a functon of the presence of a prepayment penalty, the rato of loan to value, and other 3 These companes also made small numbers of open-ended frst mortgages and frst mortgages wth term to maturty of less than 30 years. Second mortgages are not ncluded n ths analyss because lack of nformaton on the amount outstandng on the frst mortgage precluded calculaton of loan to value. 4 See Ho and Pennngton-Cross (2005) for a summary of state restrctons on prepayment penaltes. 4

6 varables such as ncome and FICO rsk score. A potental confoundng factor s that the prce may be chosen smultaneously wth other loan terms such as loan amount (and therefore loan to value) and the presence of a prepayment penalty. Lenders typcally offer a number of dfferent equty and prepayment optons, wth each opton entalng a dfferent nterest rate. The borrower chooses from among these optons. Consequently, nterest rate, loan to value, and the prepayment penalty opton are all endogenous, a condton that causes sngle-equaton coeffcents to be based and nconsstent. A based parameter estmate wll tend to ether overestmate or underestmate the true parameter. An nconsstent estmate wll not provde a smaller error as the number of observatons ncreases. Ernst (2005) does consder loan to value as endogenous but treats prepayment penalty as exogenous. DeMong and Burroughs (2005) treat both terms as exogenous. Falure to account for endogenety n loan decsons can have serous consequences. In ther assessment of models of mortgage rejecton and default decsons, Yezer, Phllps, and Trost (1994) found that sngle-equaton models dd not provde relable evdence on the structural parameters descrbng the behavor of borrowers or lenders. Smultanety s one of several problems n modelng loan choces that may cause sngle-equaton estmates of parameters to be based and senstve to dfferences n model specfcatons. Although we are nterested n dfferent choces than Yezer, Phllps, and Trost, smultanety clearly s a consderaton. An analyss of mortgage loan performance by Rose (2007) also supports consderaton of smultanety n mortgage decsons. Rose examned the effects of long prepayment penalty perods, balloon payments, and reduced documentaton on foreclosures. He found that long foreclosure perods dd not have a unform effect on the probablty across dfferent loan products, whch were defned by loan purpose and type of nterest rate. Long prepayment penalty perods had no sgnfcant effects on foreclosures for purchase-fxed and adjustable-rate mortgages, a sgnfcant postve effect for refnance adjustable-rate mortgages, and a sgnfcant negatve effect for fxed-rate purchase mortgages. Rose hypotheszed that the dfferent fndngs mght be explaned by borrowers choosng a long prepayment penalty perod to sgnal that they may be better credt rsks, whch he argued would lkely be more necessary for refnancngs and more credble for fxed-rate mortgages. Thus, choce of prepayment penalty would be endogenous n the loan decson. To address the endogenety ssue, we develop the followng smultaneous equatons model: y d ltv = ltv ' α + d ' γ + X = y ' λ + X = y ' λ + X ' β + Z ' β + Z 2 1 ' β + Z ltv, d, 0 ' φ + v ' φ + ξ. 2 1 y, ' φ + u 0 (1) Ths system of smultaneous equatons (2) comprses three endogenous varables the nterest rate, y ; loan to value, ltv ; and the presence of a prepayment penalty, d. Vector 5

7 d s the dummy varable ndcatng the presence of a prepayment penalty. As mentoned, borrowers typcally choose from a menu of nterest rate and loan-to-value optons, and choce of a prepayment penalty trggers an adjustment to the nterest rate. Thus, ltv and d are endogenous varables n the nterest rate equaton. We have no reason to beleve that loan to-value and prepayment penalty are smultaneously determned. Therefore, d does not appear n the loan-to-value equaton, and ltv does not appear n the prepayment penalty equaton. Matrx X comprses exogenous explanatory varables: loan characterstcs (owner occuped, loan purpose, documentaton requrements); borrower characterstcs (ncome and FICO score); and dstrbuton channel (broker orgnaton). The last matrx n each equaton Z y,, Z ltv,, or Z d, comprses the nstruments excluded from ether of equatons to dentfy our system of equatons. Ths model s, of course, a smplfcaton. Other terms such as type of nterest rate, the term to maturty, and dstrbuton channel may be endogenous as well. Nevertheless, by consderaton of smultanety n the choce of nterest rate and prepayment penalty, we are able to address the ssue of possble of bas n estmates of the effect of prepayment penaltes on loan prces. For the frst equaton explanng the rsk premum, we use the prme rate as an nstrument. Ths varable s prmarly used to prce busness loans and reflects an opportunty cost of producton of the mortgage loans. The prme rate s not wdely used as an ndex rate for varable-rate or hybrd closed-end subprme mortgages. 5 The prme rate s an admnstered rate that changes relatvely nfrequently and s nfluenced by many consderatons other than the cost of funds (see Nabar, Park, and Saunders 1993). As such, the prme rate s not very responsve to changes n market rates and s largely uncorrelated wth borrowers decsons to choose a hgher a loan wth or wthout a prepayment penalty. For the second equaton explanng loan to value, we use the age of the borrower and the average property value n borrower s ZIP code area as nstruments. Use of these varables as nstruments s motvated by observatons that older households tend to have hgher wealth than younger households, whch may make them less lkely to seek a large loan amount relatve to home value, and that wealther borrowers tend to choose hgher value propertes than less wealthy borrowers (Bucks, Kennckell, and Moore 2006). These values would not be expected to be correlated wth borrower choces for rsk premum or prepayment penalty. For the last equaton explanng choce of prepayment penalty, we use the share of homeowners that recently moved n the borrower s metropoltan area and a dummy varable ndcatng whether the borrower s state passed a law restrctng prepayment penaltes. A hgh share of homeowners that recently moved s an ndcaton of hgh turnover n the local real estate market, whch may lessen demand for mortgages wth prepayment penaltes. Ths ndcator would be uncorrelated wth the loan s nterest rate or loan-to- 5 By far, most varable-rate and hybrd mortgages n the subprme mortgage database use LIBOR or a constant maturtes Treasury rate as an ndex. The prme rate s wdely used n prcng open-end mortgages, but open-end mortgages are only a very small percentage of these lenders orgnatons. 6

8 value rato. State laws restrctng prepayment penaltes drectly affect the supply of loans wth prepayment penaltes. State laws would be uncorrelated wth choce of loan to value. Smultaneous equatons systems can be estmated usng a full nformaton systems method such as full nformaton lkelhood or generalzed method of moments or a lmted, equaton-by-equaton method such as two-stage least squares. System procedures are asymptotcally more effcent than equaton-by-equaton procedures f all equatons n a system are specfed correctly. However, any msspecfcaton n a system of equatons wll be transmtted to the entre system of equatons, and systems method estmates of parameters wll be generally nconsstent (Wooldrdge 2002, pp ). Equaton-by-equaton methods lmt a msspecfcaton problem to the equaton n whch t appears, makng equaton-by-equaton methods more robust than systems methods. Because our dataset does not contan all of the nformaton used n prcng loans, and other loan characterstcs are also potentally endogenous, we opt for the more robust, equaton-by-equaton approach for estmaton. To dentfy frst two equatons n (1), we frst ft probt model for the thrd equaton usng exogenous varables and nstruments on the rght-hand sde to obtan predctor of d : dˆ ~ ~ φ ( Zθ0 ) Z'ε ( θ0 ) = ~ ~ Φ( Zθ )[ 1 Φ( Zθ )] where Z [ Z : X ] 0 ~ =, ( ~ = Φ Z ) θ0 probt log-lkelyhood functon. 0, (2) ε d, and 0 θ s a unque soluton to maxmzaton of Then, we estmate the frst two equatons n (1) by two-stage least squares (2SLS): { ˆ ~ 1 ~ 1 ~ ~ 1 ˆ, λ } = [ X ' Z ( Z ' Z ) Z ' X ] X ' X ( Z ' Z ) Z ' Y α, Y { y, ltv } ~ = (3) =, X [ X : dˆ ] To dentfy the last equaton n (1), we mplement Amemya (1978) Generalzed Least Squares (AGLS) estmator for probt wth endogenous regressors. 6 FINDINGS Sngle-equaton estmates and two-stage least squares estmates of our equatons are presented n Table 3. F-ratos ndcate that each of the models estmated by ordnary least squares (OLS) for rsk premum and loan to value are statstcally sgnfcant (panels A and B, respectvely). Ch-square statstcs ndcate that the probt models for prepayment penalty are statstcally sgnfcant (Table 3, panel C). Statstcal tests support the concern about endogenety of loan to value and presence of a prepayment penalty. In each equaton, a Hausman test rejects the hypothess that the coeffcents of the sngle equaton and nstrumental varable models are equal (Table 4). Ths result suggests that the sngle-equaton model s nconsstent (Hausman 1978) and supports use of 2SLS. 6 See also Newey (1987) for dscusson. 7

9 Rsk Premums The estmated equatons for rsk premum generally explan a large percentage of the varaton n rsk premums. In the sngle-equaton OLS models, the effect of loan to value on rsk premums s qute small and postve for fxed-rate and hybrd loans but small and negatve for varable-rate loans. In the 2SLS models, the effects of loan to value on rsk premums are unformly postve, consstent wth expectatons, and larger n absolute value. Thus, OLS estmates of loan-to-value coeffcents appear to be based toward zero. The prepayment dummy varable n the sngle-equaton models and the predcted probablty of a prepayment penalty n the 2SLS models are statstcally sgnfcant and negatvely related to rsk premums. Sngle-equaton and 2SLS results for prepayment penaltes are not drectly comparable because the prepayment varables are dfferent. Multplyng the 2SLS parameter estmates by the dfference n the mean-predcted probabltes for loans wth and wthout prepayment penaltes suggests that presence of a prepayment penalty reduces rsk premums by 38 bass ponts for fxed-rate loans, 13 bass ponts for varable-rate loans, and 19 bass ponts for hybrd loans (numbers not n table). These estmated reductons are smaller than the sngle-equaton estmates for fxed-rate and varable-rate mortgages and larger than the sngle-equaton estmate for hybrd mortgages. Our estmated reductons for prepayment penaltes are wthn the sze range of nterest rate adjustments for prepayment penaltes commonly found n lenders loan prcng sheets. Rsk prce adjustments for factors such as loan purpose, owner occupancy, type of property, loan amount, and loan term are often of comparable magntudes. In contrast, rsk prce adjustments for relatvely low FICO scores or hgh loan-to-value percentages often exceed 100 bass ponts. Parameter estmates for the exogenous varables are statstcally sgnfcant n sngleequaton and 2SLS models. Borrower ncome and FICO rsk score are both negatvely related to rsk premums n all models, consstent wth expectatons. Hgher ncome s generally assocated wth hgher dsposable ncome after provdng for necesstes. Hgher FICO rsk score ndcates a lower probablty of serous delnquency, bankruptcy, or other derogatory event. Sgns of the other exogenous varables sometmes had dfferent sgns across products. The changes n sgns across products may reflect correlatons wth explanatory varables that are not avalable n the dataset or possble endogenety. Loan to Value The effect of rsk premum on loan to value s postve n two of the three sngle-equaton models (.e., hgher rsk premums are observed on loans wth less borrower equty) but negatve n all three 2SLS models. FICO rsk score s postvely related to loan to value for the sngle-equaton models estmated by OLS but negatvely related to loan to value for two of the three 2SLS models. Income s generally postvely related to loan to value n both OLS and 2SLS models. Loans for owner-occuped homes have lower loan to 8

10 value n the 2SLS models and two of the three OLS models, and home purchase loans generally have greater loan to value than cash out refnance loans. The estmates for rsk premum and FICO score may reflect a selecton ssue not adequately addressed by our 2SLS model. Except for the lowest age and property value groups (the omtted groups), greater age and hgher value of the property reduce loan-tovalue, consstent wth our expectaton. However, t may be the case that some borrowers wth hgher ncomes and wealth use mortgage debt to allocate a greater share of ther wealth toward fnancal assets or to reduce the share of nonmortgage debt. Indeed, many of the hgh loan-to-value mortgages that we observe are owed by borrowers wth relatvely hgh ncomes and FICO scores. Calomrs and Mason (1999) made a smlar observaton that hgh loan-to-value borrowers tend to be low credt rsks, unlke other segments of the subprme market. The presence of such borrowers may confound results despte the excluson of the hghest loan-to-value loans. Prepayment Penaltes Rsk premum s nversely related to presence of a prepayment penalty for fxed-rate and varable rate mortgages and drectly related to prepayment penalty for hybrd mortgages n both nstrumental varable and sngle-equaton probt models. Income s negatvely related to prepayment penalty, except for the nstrumental varable estmate for fxed-rate loans whch s not sgnfcantly dfferent from zero. Estmated coeffcents for FICO score are postve for fxed-rate and hybrd loans but negatve for varable-rate loans n the nstrumental varable model. Fxed-rate and hybrd home purchase loans are sgnfcantly more lkely to have a prepayment penalty than fxed-rate or hybrd cash-out refnance loans (the omtted loan purpose dummy varable). Estmated coeffcents for other refnancngs on fxed-rate and other refnance loans are not sgnfcantly dfferent from zero but sgnfcant and negatve on varable-rate loans. That estmated coeffcents for these explanatory varables dffer across types of nterest rate should not be partcularly surprsng snce dfferng crcumstances may nfluence both choce of prepayment penalty and type of nterest rate. A worthwhle area for further research s how borrower crcumstances affect choces of prepayment penalty and choce of nterest rate. Loans orgnated through brokers are more lkely to have prepayment penaltes than loans orgnated drectly by the lender. As evdence suggests that loans orgnated through brokers prepay faster than loans orgnated drectly through lenders (LaCour-Lttle and Chun 1999), lenders may gve brokers ncentves to orgnate loans wth prepayment penaltes. 7 Ths result may be nfluenced by selecton bas, however. Choce of dstrbuton channel may tself be endogenous wth choce of prepayment penalty. 7 LaCour-Lttle and Chun hypotheszed that lenders encounter an agency problem when thrd partes, such as brokers or correspondents, orgnate mortgages because thrd-party orgnators receve revenue from orgnatons, not from the stream of mortgage payments. Snce completng transactons wth prevous customers s often easer than fndng new customers, thrd-party orgnators have an ncentve to contact prevous customers about refnancng exstng loans. Thrd-party orgnators would also have lttle ncentve to dscourage refnancng f contacted by prevous customers. 9

11 Fnally and not surprsngly, state regulaton of prepayment penaltes nfluences the lkelhood of a prepayment penalty for loans n the sample. 8 Loans n states wth restrctons on prepayment penaltes are sgnfcantly less lkely to nclude prepayment penaltes than loans n states wth no restrctons. Ths estmated relatonshp nfluences the predcted probablty of a prepayment penalty, whch s used n place of the prepayment penalty dummy for the 2SLS rsk-premum model. RELATIONSHIP TO OTHER STUDIES In order to assess whether our fndngs are unque to the companes contrbutng data to the FSRP s subprme mortgage database, we used the database to attempt to replcate the DeMong and Burroughs (2005) and Ernst (2005) studes that nvestgated the relatonshp between prepayment penaltes and mortgage prces. We found that specfcatons smlar to those n prevous studes produced smlar results n the FSRP s subprme mortgage database. DeMong and Burroughs (2005) DeMong and Burroughs (2005) estmated a sngle-equaton model usng OLS. The data conssted of 961,344 mortgages from several large natonal subprme lenders durng The dependent varable was the annual percentage rate. They used fve explanatory varables: (1) the FICO rsk score, (2) the borrower s ncome, (3) the loanto-value rato, (4) a dummy varable ndcatng that the loan had reduced documentaton requrements, and (5) a dummy varable ndcatng whether or not the loan had a prepayment penalty. FICO score, ncome, and the loan-to-value rato were ncluded as contnuous varables. They estmated ther model for frst mortgage loans wth a 30-year term to maturty. We estmated the same model usng data from We obtaned smlar results. FICO rsk score and ncome were negatvely related to the annual percentage rate. Reduced documentaton requrements were postvely related to the annual percentage rate (Table 5). The presence of a prepayment was negatvely related to the annual percentage rate. All estmated coeffcents were statstcally sgnfcant. Ernst (2005) Ernst (2005) estmated a model explanng the nterest rate as a functon of loan to value, FICO rsk score, the borrower s debt-to-ncome rato, whether or not ncome was fully documented, dummy varables ndcatng property type, whether or not the loan conforms to Fanne Mae /Fredde Mac lendng lmt; the proporton of the populaton n the ZIP code area that s mnorty (non-whtes); dummy varables for month of orgnaton; and a dummy varable ndcatng whether or not the loan had a prepayment penalty. Data were from the Loan Performance System s subprme asset-backed securtes database. The 8 Recall some loans n the sample may be exempt from state level regulatons that restrct prepayment penaltes. 10

12 model was estmated for 30-year frst mortgages orgnated n 2000, 2001, and The model was estmated for home purchase and refnance loans. Ernst used an nstrumental value n place of the actual loan to value. Dfferences n databases precluded exact replcaton of Ernst s model. The FSRP subprme mortgage database does not contan nformaton on the type of property or the borrower s debt-to-ncome rato. Also, for the perod, jumbo loans were not ncluded and documentaton was not reported. Type of property, documentaton, and jumbo loan status could not be ncluded. We substtuted a proxy for the debt to ncome rato, whch was calculated as a rato of the average debt per person n the borrower s zp code area and borrower s reported ncome. 9 We also used actual loan to value rather than an nstrumental value n place of loan to value. Ernst dd not provde nformaton on how the nstrumental varable was obtaned. Despte the few dfferences between our model and Ernst s model, we dd replcate Ernst s key fndng that the presence of prepayment penaltes was assocated no dfference n nterest rates or hgher nterest rates. Table 6 shows results for orgnatons n Results for other years were smlar. However, the FSRP s subprme mortgage database contans addtonal rsk-related varables, whch are not avalable n the Loan Performance System database. These varables nclude borrower ncome, whether owner-occuped or not, and whether the loan was orgnated by a broker. Wth these varables added to Ernst s model, the estmated effect of a prepayment penalty s negatve. Table 7 shows results for 2002 orgnatons. Agan, results for other years were smlar. CONCLUSIONS Ths paper nvestgates the effect of prepayment penaltes and the state laws that govern such penaltes on the prcng of subprme mortgages. The paper s the frst to consder that mortgage prce and prepayment penalty may be chosen jontly, makng sngleequaton estmates of the effect of prepayment penalty on prce based. Usng a model that accounts for endogenety of prce, loan to value, and prepayment penalty, we fnd that prepayment penaltes are assocated wth sgnfcantly lower prces for subprme mortgages. Ths fndng s consstent wth prcng of mortgage credt accordng to rsk, a characterstc of the subprme market that allows lenders to make credt avalable to borrowers who would have dffculty obtanng such credt n the prme market. The fndng s mportant because perceptons that prepayment penaltes are not reflected n mortgage prces have led to regulaton that could reduce the gans n mortgage credt avalablty that were acheved over the last decade. Our estmates from 2SLS models whch address endogenety of prce, loan to value, and presence of a prepayment penalty suggest that prepayment penalty reduces rsk 9 Average debt per person was obtaned from TransUnon s TrenData database. TrenData provdes aggregate statstcs on credt use and payment behavor for dfferent geographc areas. These statstcs derved from a large sample of borrowers credt fles. See 11

13 premums by 38 bass ponts for fxed-rate loans, 13 bass ponts for varable-rate loans, and 18 bass ponts for hybrd loans. Our estmated reductons for prepayment penaltes are wthn the sze range of nterest rate adjustments for prepayment penaltes commonly found n lenders loan prcng sheets and comparable n magntude to common rsk prcng adjustments for terms such as loan purpose, owner occupancy, type of property, loan amount, and term to maturty. Our estmated reductons n prce for prepayment penaltes are smaller than the sngle-equaton estmates for fxed-rate and varable-rate mortgages and larger than the sngle-equaton estmate for hybrd mortgages. It s doubtful that our results are unque to our database of subprme mortgages. We replcate the models of two prevous studes of the prcng of prepayment penaltes usng our database and fnd smlar results n our study. Consderaton of addtonal varables n one model reverses the prevous author s fndng on the effect of prepayment penaltes on prce. Clearly, results are senstve to model specfcaton and cautous nterpretaton of fndngs s warranted. Mortgage choces are complex decsons nvolvng smultaneous consderaton of numerous loan terms. Choces may be nfluenced by borrower nteractons wth loan offcers, mortgage brokers, or real estate agents. Data on mortgage transactons often do not nclude nformaton on varables that play an mportant role n decsons. Such decsons requre careful modelng to avod bases due to smultanety and selecton. We share Yezer, Phllps, and Trost s (1994) skeptcsm of the ablty of smple sngleequaton models to provde relable estmates of many of the structural parameters of complex mortgage choces that are of nterest for publc polcy and economc modelng. 12

14 REFERENCES Amemya, Takesh. (September 1978). The Estmaton of a Smultaneous Equaton Generalzed Probt Model. Econometrca (46): Avery. Robert B., Glenn B. Canner, and Robert E. Cook. (Summer 2005). New Informaton from HMDA and Some Implcatons for Far-Lendng Enforcement. Federal Reserve Bulletn (91): Bucks, Bran K., Arthur B. Kennckell, and Kevn B. Moore (March 2006). Recent Changes n U.S. Famly Fnances. Federal Reserve Bulletn (92): A1-A38. Calomrs, Charles W. and Joseph R. Mason. (1999). Hgh Loan-to-Value Mortgage Lendng: Problem or Cure? Washngton: AEI Press. DeMong, Rchard F. and James E. Burroughs. (2005). Prepayment Fees Lead to Lower Interest Rates. Workng Paper. Charlottesvlle, Vrgna, Unversty of Vrgna, McIntre School of Commerce. Ellehausen, Gregory, Mchael E. Staten, and George J. Wallace. (2005). Are Legslatve Solutons to Abusve Mortgage Lendng Practces Throwng Out the Baby wth the Bathwater? The Art of the Loan n the 21 st Century: Producng, Prcng, and Regulatng Credt. Proceedngs of the 41st Annual Conference on Bank Structure and Regulaton. Chcago: Federal Reserve Bank of Chcago. Ernst, Keth. (Jan. 2005) Borrowers Gan No Interest Rate Benefts from Prepayment Penaltes on Subprme Mortgages. Research Report. Durham, N.C.: Center for Responsble Lendng. Goldsten, Debbe and Stacy Strohauer Son. (Aprl 2, 2003). Why Prepayment Penaltes are Abusve n Subprme Loans. CRL Polcy Paper No. 4. Durham, North Carolna: Center for Responsble Lendng. Hausman, Jerry A. (November 1978). Specfcaton Tests n Econometrcs. Econometrca (46): Ho, Gang and Anthony Pennngton-Cross. (June 2005). The Impact of Local Predatory Lendng Laws. Workng Paper A. St. Lous, Mssour: Federal Reserve Bank of St. Lous. LaCour-Lttle, Mchael and Gregory H. Chun. (January 1999). Thrd Party Orgnators and Mortgage Prepayment Rsk: An Agency Problem? Journal of Real Estate Research (17):

15 Newey, Whtney K. (November 1987). Effcent Estmaton of Lmted Dependent Varable Models wth Endogenous Explanatory Varables. Journal of Econometrcs (36): Nabar, Prafulla G., Sang Yong Park, and Anthony Saunders. (January 1993). Prme Rate Changes: Is There an Advantage to Beng Frst? Journal of Busness (66): Phllps-Patrck, Fred, Erc Hrschhorn, Jonathan Jones, and John LaRocca. (June 2000). What about Subprme Mortgages? Mortgage Market Trends, vol. 4. U.S. Department of the Treasury, Offce of Thrft Supervson. Rose, Morgan. (March 2007). Predatory Lendng Practces and Subprme Foreclosures Dstngushng Impacts by Loan Category. Paper presented at Fnancng Communty Development, Federal Reserve System Communty Affars Research Conference, Washngton, D.C. Wallace, George, Gregory Ellehausen, and Mchael Staten. (Aprl 2004). Are Legslatve Solutons to Abusve Mortgage Lendng Practces Throwng Out the Baby Wth the Bath? Gudance from Emprcal Research. Workng Paper No. 68. Washngton, D.C: Georgetown Unversty, McDonough School of Busness, Credt Research Center. Woolrdge, Jeffrey M. (2002) Econometrc Analyss of Cross Secton and Panel Data. Cambrdge, Mass.: MIT Press. Yezer, Anthony M.J., Robert F. Phllps, and Robert P. Trost. (Nov. 1994) 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):

16 Table 1. Selected Characterstcs of Closed-End Frst Mortgages, 2004: By Type of Interest Rate All Type of Interest Rate Loans Fxed Varable Hybrd Characterstc Average loan amount (dollars) 130,000 94, , ,100 Loan purpose (percent) Home purchase Cash-out refnancng Other refnancng Average apprased value of property (dollars) 162, , , ,300 Average annual percentage rate Average loan to value (percent) Average borrower ncome (dollars) 54,000 44,100 64,300 60,200 Average FICO score

17 Table 2. Descrptve Statstcs of Regresson Varables Standard Varable Mean Devaton Rsk premum (percent) Loan to value Prepayment penalty (dummy varable) Monthly ncome (dollars) 4,252 3,481 FICO score Loan purpose (dummy varables) 1 Home purchase loan Refnance loan, no cash-out Owner occuped (dummy varable) Broker orgnaton (dummy varable) Documentaton (dummy varables) 1 Full documentaton Low documentaton Borrower age (dummy varables) 1 Age years Age years Age 60 or older Value of homes n ZIP code area (proporton) 1 $100, , $200, , $300, , $500,000 or more Homeowner moblty n ZIP code area (proporton) 1 Moved wthn last year Moved 1-4 years ago Moved 5-10 years ago Prepayment penaltes restrcted (dummy varable) Excluded categores: Loan purpose, cash-out refnancngs; documentaton, unknown; borrower age, less than 20; value of homes, less than $100,000; homeowner moblty, moved more than 10 years ago. 16

18 Table 3. Regresson Results A. Rsk Premum Equaton Two-Stage Least Squares Ordnary Least Squares Fxed Varable Fxed Varable Varable rate rate Hybrd rate rate Hybrd Loan to value 0.027** 0.051** 0.167** 0.008** ** 0.008** Prepayment penalty ** ** ** ** ** ** Monthly ncome ** ** ** ** ** ** FICO score ** ** ** ** ** ** Home purchase loan 0.184** ** 0.162** * 0.283** Refnance, no cash out ** ** ** ** ** 0.214** Owner occuped ** 0.467** ** ** 0.467** ** Broker orgnaton 0.903** ** 0.650** 0.156** ** 0.274** Full documentaton ** ** n.a ** ** n.a Low documentaton ** ** n.a ** ** n.a Prme rate 0.172** 0.602** 0.170** 0.184** 0.588** 0.131** Constant ** 7.059** 2.647** ** 5.900** ** Observatons 263, , , , , ,645 R-squared F-statstc 20,314** 45,188** 4,538** 17,981** 58,896** 12,259** 17

19 Table 3. Regresson Results (contnued) B. Loan-to-Value Equaton Two-Stage Least squares Ordnary Least Squares Fxed Varable Fxed Varable rate rate Hybrd rate rate Hybrd Rsk premum ** ** ** 0.240** ** 0.418** Monthly ncome 0.250** 0.142** 0.075** 0.263** 0.143** 0.163** FICO score ** 0.023** ** 0.009** 0.022** 0.024** Home purchase loan 4.106** 0.522** 4.736** 3.911** 0.497** 3.064** Refnance, no cash out ** ** 3.095** ** ** 1.891** Owner occuped ** ** * ** ** 3.943** Broker orgnaton ** ** ** * ** Full documentaton ** ** n.a ** ** n.a Low documentaton ** ** n.a ** n.a Age years 0.318** ** 0.590** ** Age years ** ** ** ** ** ** Age 60 or older ** ** ** ** % of housng unts ** ** ** ** ** ** $100, , % of housng unts ** ** 5.601** ** ** ** $200, , % of housng unts 8.192** 9.095** ** ** 9.048** ** $300,00-499, % of housng unts ** $500,000 or more Constant ** ** ** ** ** ** Observatons 263, , , , , ,643 R-squared F-rato 1,355** 2,007** 1,245** 1,387** 2,243** 1,680** 18

20 Table 3. Regresson Results (contnued) C. Prepayment Penalty Equaton Instrumental Varable Probt Probt Fxed Varable Fxed Varable rate rate Hybrd rate rate Hybrd Rsk premum * ** 0.382** ** ** 0.008** Monthly ncome 0.001* ** ** ** ** FICO score 0.001** ** 0.003** 0.000** ** ** Home purchase loan 0.094** ** 0.123** ** Refnance, no cash out ** 0.037** ** ** 0.141** Owner occuped 0.039** * 0.256** ** ** Broker orgnaton 0.443** 0.115** 0.144** 0.438** 0.072** 0.272** Full documentaton 0.235** ** n.a ** ** n.a Low documentaton 0.279** ** n.a ** ** n.a % moved wthn last year 0.008** 0.027** 0.040** 0.005** 0.026** 0.015** % moved 1-4 years ago ** ** 0.058** ** ** 0.070** % moved 5-10 years ago 0.023** 0.021** 0.019** 0.021** 0.020** 0.023** Prepayment penalty ** ** ** ** ** ** restrcted Constant ** 0.551** ** 0.506** 1.302** ** Observatons 263, , , , , ,642 Ch-squared 12,946** 5,253** 26,186** 17,797** 8,531** 3,0106** Notes: t-ratos or ch-squared statstcs are below coeffcents. ** Sgnfcant at 1 percent level * Sgnfcant at 5 percent level n.a. Not avalable 19

21 Table 4. Hausman Test Loan Type Ch-Squared A. Mortgage prce equaton Fxed rate ** Varable rate 10,300** Hybrd 19,300** B. Loan-to-Value Equaton Fxed rate ** Varable rate ** Hybrd ** C. Prepayment Penalty Equaton Fxed rate ** Varable rate 1,886.55** Hybrd 1,474.73** ** Sgnfcant at 1 percent level. 20

22 5. Replcaton of DeMong and Burrows (2005) Varable Coeffcent t-statstc A. Fxed-Rate Loans FICO score ** Income 0.000** Loan to value 2.274** Low documentaton ** Full documentaton ** Prepayment penalty ** Constant ** R-squared 0.44 F-rato 12,186** B. Varable-Rate Loans FICO score ** Income ** Loan to value 0.369** Low documentaton 0.249** 4.13 Full documentaton -.212** 3.51 Prepayment penalty ** Constant ** R-squared 0.29 F-rato 6,794** C. Hybrd Loans FICO score ** Income 0.000** 0.00 Loan to value 1.883** 0.00 Low documentaton n.a. n.a. Full documentaton n.a. n.a. Prepayment penalty ** Constant ** R-squared 0.27 F-rato 11,686** Notes: Values reported as are less than n absolute value. ** Sgnfcant at 1 percent level. n.a. Not avalable. 21

23 Table 6. Replcaton of Ernst (2005), 2002 Orgnatons Varable Coeffcent t-statstc Prepayment penalty 1.197** Loan to value 0.005** Debt to ncome 0.000** 3.94 Mnorty share 0.181** 9.49 FICO score ** Constant ** R-squared 0.26 F-rato 5,306 ** Notes: Coeffcents of monthly dummy varables are not shown. Values reported as are less than n absolute value. ** Sgnfcant at 1 percent level. 22

24 Table 7. Augmented Ernst Model, 2002 Orgnatons Varable Coeffcent t-statstc Prepayment penalty ** Loan to value 0.014** Debt to ncome 1.714** Mnorty share 0.654** Owner occuped ** Broker orgnated 0.180** 9.62 Borrower s ncome 0.00** 3.68 FICO score ** Constant ** R-squared 0.30 F-rato 8,602** Notes: Coeffcents of monthly dummy varables are not shown. ** Sgnfcant at 1 percent level. 23

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