RMBS Loss Model 2007-08

8/9/2019 RMBS Loss Model 2007-08

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August 14, 2007

www.fitchratings.com

Residential MortgageCriteria Report

ResiLogic: U.S. Residential

Mortgage Loss Model

AnalystsBill Hunt+1 212 908-0857

[email protected]

Glenn Costello+1 212 [email protected]

Suzanne Mistretta+1 212 [email protected]

Related Research

Option ARM Risks and Criteria, datedOct. 4, 2006

U.S. RMBS Criteria for SubprimeInterest-Only ARMs, dated Oct. 4, 2006

U.S. RMBS Rating Criteria for Prime andAlt-A Interest-Only Mortgages, datedOct. 4, 2006

40-, 45-, and 50-Year Mortgages: OptionARMs, Hybrid ARMs, and FRMs, datedOct.4, 2006

Rating U.S. Residential MortgageServicers, dated Nov. 29, 2006

U.S. RMBS Cash Flow ModelingCriteria: Updated, dated Feb. 6, 2007

SummaryFitch Ratings introduces ResiLogic, its new rating model for analyzingcredit risk in U.S. residential mortgage-backed security (RMBS)transactions. Fitch will begin using ResiLogic as the basis for RMBSrating analysis on Nov. 6, 2006. With the introduction of ResiLogic,the Fitch RMBS model software will be available for licensing bymarket participants for the first time.

The ResiLogic model is based on performance history of over 1.6 millionresidential first lien and closed-end second (CES) mortgage loansoriginated between 1992 and 2000 and encompasses the three major

credit sectors: prime, Alternative-A (Alt-A), and subprime mortgages.Base frequency of foreclosure (FOF) and loss severity (LS) are computedat the loan level, based on each loans risks attributes, to derive anexpected base case loss amount.

In addition to base case loss expectations, the model generates losscoverage requirements for each rating category, reflecting FOF and LSsensitivity to economic stress. AAA ratings are based on the responseto severe, low probability stress simulation. The stressed loss levels arecomputed by simulating changes in economic conditions at both thenational and state level for each loan. Thus, the loss coveragecomputed for a pool of mortgages at each rating category reflects bothgeographic composition and concentration.

Through logistic regression analyses of the data samples loan attributesand performance from origination through 2005, Fitch identified 13credit dimensions (e.g. risk attribute categories such as occupancy, loanpurpose, and documentation type, among others) sufficiently significantto be incorporated in the models FOF calculation. Fitch found that thethree dimensions that most strongly influence FOF were Fair Isaac Corp.(FICO) score, credit sector, and combined loan-to-value ratio (CLTV).The regression analysis produced an FOF odds penalty or credit for theindividual loan attributes within each credit dimension. Application ofthese credits and penalties to a pool of loans produces a base case FOFfor each loan.

Fitchs expected base case default performance for a mortgage varies

by state. Fitch has selected University Financial Associates, LLC(UFA), a mortgage portfolio analysis software provider located in AnnArbor, MI, to provide default risk multipliers for each state. Based onits analyses, UFA formulates its loan multipliers by state, whichrepresent the expected level of defaults over the life of a loan relativeto the national average on a constant quality basis. Fitch applies thesemultipliers to each loans base case FOF to adjust for regional risk.

ResiLogic Criteria Update

Fitch has published two criteria reportsthat detail changes made to Fitchsresidential mortgage rating criteria andthe ResiLogic mortgage model:

U.S. RMBS: Criteria Update toResiLogic Model, dated Aug. 14,2007

U.S. RMBS: Updated Criteria forLoan Documentation in ResiLogic,dated Aug. 14, 2007

Both reports should be read inconjunction with this report, which wasoriginally published on Oct. 4, 2006.
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ResiLogic: U.S. Residential Mortgage Loss Model

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The ResiLogic model derives expected LS through astatistical analysis of historical LS performance. Themodel uses a slightly different set of 12 creditdimensions to compute a base LS expectation, similarto those used for the FOF calculation. The attributes

within each credit dimension are assigned a LS penaltyor credit derived from the statistical analysis, whichdetermine Fitchs base case LS assumption for eachloan. Fitch extracted loss data on loans that went intoforeclosure and incurred a realized loss and found thatof the 12 credit dimensions, original loan balance,CLTV, and mortgage coupon were closely correlatedto the incidence and amount of realized losses. Fitchalso found that servicer quality, as evidenced by theFitch servicer rating, directly affects LS.

This report describes Fitchs analytical model fordetermining loss coverage requirements for ratingRMBS backed by first and second lien residentialmortgages.

Base FOF and LS Collateral RiskResiLogic makes a full credit assessment for aRMBS pool using 13 credit dimensions and a statedimension for determining FOF, together with aslightly different set of 12 credit dimensions for LS.The FOF and LS credit dimensions, as well as theindividual loan attributes that constitute each

dimension, are listed in the tables above and on page 3,respectively.

The tables show each credit dimension ranked in orderof its influence on FOF and LS. The specific attributes

within each dimension determine its relative risk. Forexample, the occupancy dimension consists of owner-occupied, owner-occupied second home, and non-owner-occupied (investor) loan attributes. Oneattribute within each dimension represents the baselinefrom which the relative risk of the other attributes ismeasured, when holding all other dimensions constant.In the example, owner occupied is the baseline.

The historical default and loss experience of each loanattribute relative to the baseline determines the relativeFOF odds and LS sensitivity for that attribute. Therelative risk of various loan attributes in each creditdimension are described in terms of higher or lowerodds of default for the FOF dimensions and assensitivity credits (lower severity) and penalties(higher severity) for the LS dimensions, as shown inthe table above and on page 3.

FOF odds and LS sensitivity for continuous dimensions,such as FICO score, CLTV, loan balance, and coupon,are themselves generated by continuous functions.Higher CLTVs increase the odds of default and LS and,

Frequency of Foreclosure Credit Dimensions

Order ofInfluence

Frequency of ForeclosureDimensions Loan Attributes

Baseline Attribute/Odds ofDefault Relative to Baseline

1 FICO Continuous Higher FICO scores = lower odds2 Credit Sector Prime Baseline

Alt A HigherSubprime Higher3 CLTV Continuous Higher CLTV = higher odds4 Property Type Single-family detached Baseline

Condo/Co-op LowerMultifamily HigherTownhouse/other HigherPUD LowerManufactured housing Higher

5 Product Type Fixed-rate loan BaselineAdjustable-rate loan HigherBalloon loan Higher

6 Documentation Type Full documentation BaselineLow documentation HigherNo documentation Higher

7 Loan Term Term = 360 months BaselineTerm < 360 months Lower

8 Prepayment Penalty No prepayment penalty BaselinePrepayment penalty Higher

9 Occupancy Owner-occupied primary BaselineOwner-occupied secondary HigherNon-owner-occupied Higher

10 Front-End DTIs Continuous Higher DTI = higher odds11 Loan Balance at Closing Continuous Higher balance = lower odds12 Loan Purpose Purchase/other Baseline

Refinance Higher13 Preseasoning Continuous More seasoning = lower odds

FICO Fair Isaac Corp. PUD Planned unit development. CLTV Combined loan-to-value ratio. DTIs Debt-to-income ratios.


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therefore, are assigned higher FOF and LS percentages.Similarly, higher FICOs reduce the odds of default (i.e.lower FOFs) and the severity of loss.

Predictive CapabilityEach of the credit sectors (prime, Alt-A, and subprime)was divided into in-sample and out-of-sample datasets (build and test data sets), such that roughly 10% ofthe 1.6 million observations were set aside during the

variable selection phase, as shown in the table below.Segregating the data set allows the model to test the out-of-sample (unseen) data. Fitch obtained the loan-leveldata from Loan Performance.

For the Alt-A and subprime credit sectors, the out-of-sample included the 2000 vintage, the most recentdata set, which, coincidentally, represented a poorquality vintage. The out-of-sample data set for theprime sector consisted of the 1999 vintage, whichcontained the most data of all the vintage years.

The charts at right and on page 4 illustrate the

models predictive capability based solely on loanattribute credit risk.

The disparity between the predicted and actualperformance for the 2000 Alt-A and prime vintagesreflects the recessionary environment of that year,which is not accounted for in the test models.

Still, the model was able to capture the drift of credit

quality in all the credit sectors exhibited by the 2000

Model Data Set Summary

Sector SampleNo. ofLoans

PredictedFOF (%)

Act ualFOF (%)

Subprime In-sample 691,838 19.03 19.03Subprime Out-of-sample 71,586 22.59 25.18Alt-A In-sample 332,113 3.29 3.29Alt-A Out-of-sample 33,277 4.10 6.22Prime In-sample 464,758 1.31 1.31Prime Out-of-sample 50,818 0.90 0.82

FOF Frequency of foreclosure.

Loss Severity DimensionsOrder ofInfluence Loss Severity Dimension Loan Attribut es

Baseline Attribute/Loss ExperienceRelative t o B aseline

1 Closing Balance Continuous Higher balance = lower loss2 CLTV Continuous Higher CLTV = higher loss3 Loan Coupon Continuous Higher coupon = higher loss

4 Property Type Single-family BaselineCondo/Co-op LowerMultifamily HigherTownhouse HigherPUD LowerManufactured housing Higher

5 Occupancy Owner-occupied, primary BaselineOwner-occupied, secondary HigherNon-owner-occupied Higher

6 Loan Purpose Purchase BaselineRefinance Higher

7 Credit Sector Subprime BaselineAlt-A LowerPrime Lower

8 Product Type Fixed BaselineARM HigherBalloon Higher

9 Preseasoning Continuous More seasoning = lower loss10 FICO Continuous Higher FICO = lower loss

11 Loan Term Term = 360 months BaselineTerm < 360 months Lower12 Servicer Rating Continuous Higher rating = lower loss

CLTV Combined loan-to-value ratio. PUD Planned unit development. ARM Adjustable-rate mortgage. FICO Fair Isaac Corp.

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Predicted (In-Sample/Build Model)

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Frequency of Foreclosure for Prime Loans

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vintage. The deterioration in credit quality typicallyoccurs during a recessionary period, as lenders tend toloosen credit standards to maintain origination volume.However, this is less noticeable in the subprime sector,as evidenced by the proximity of the actual andpredicted results for the 2000 vintage.

Base Frequency of Foreclosure

Discussed below and listed in the table on page 2 arethe 13 credit dimensions ranked in order of influenceon default risk for first liens and CES.

Loan attributes that exhibited default rates higherthan those of the baseline are assigned a FOF oddspenalty to reflect the higher default probability of thatattribute. Conversely, loan attributes that exhibited

lower defaults relative to the baseline are applied aFOF credit to reflect their lower risk of default. Theaggregate of the baseline FOF and odds adjustments,together with the state FOF penalty or credit,produces a base FOF percentage for each loan. Thestate FOF dimension is discussed on page 10.

In Fitchs regression analysis, the vast majority of CESanalyzed were in the subprime sector. Subprime CESare assigned the same FOF odds and credits as subprimefirst liens for each dimension affected by credit sector.For those dimensions not affected by credit sector, CESare assigned the same penalties and credits as first liens.High credit quality CES pools are less common but can

also be analyzed by ResiLogic.

Credit Score and Credit Sector (Prime, Alt-A, and

Subprime): The highest ranking dimensions forpredicting mortgage defaults are FICO scores and creditsector (i.e. prime, Alt-A, and subprime). Credit scoreand sector closely interact with default risk, particularlyfor prime and Alt-A loans. The credit sector baseline is aprime mortgage.

The FOF by FICO chart on page 5 shows the defaultrates for each FICO value by credit sector for asample loan, whose attributes reflect the weighted

average of the data sample. Prime and Alt-A defaultrates are very low for FICOs above 700 and continueto steadily decline as scores increase. Alt-Aunderperformed the prime baseline; therefore, theAlt-A credit sector is assigned higher odds thatincrease the loans FOF percentage.

Still, the Alt-A sector exhibited the same sensitivityto increases in FICO score as the prime sector.Default probability decreased by 28% for every 20-point increase in a prime or Alt-A FICO score. Infact, prime default rates for scores above 750 wereunder 35 basis points (bps) and defaults were roughly

1% and less for Alt-A loans with similar scores. TheFICO score penalty is based on the inverse relationshipbetween FICO score and default rates.

The slope differential of the subprime loan FOF byFICO function clearly distinguishes the sector fromprime and Alt-A. The default rate decrease relative to a20-point increase in FICO score is 12%, less than halfthat for the prime and Alt-A loans. This lowersensitivity of subprime default rates to changes in

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FICO score is due to the high absolute levels ofdefaults. These high rates indicate the vulnerability ofthe subprime borrower to risk factors beyond the loanattributes, such as deteriorating financial circumstances,an inability to cash out equity due to slow home priceappreciation, and personal hardship, among others. Incontrast, the very low absolute levels of defaults in theprime and Alt-A sectors and the clear response to FICOscores indicate less vulnerability to extraneous factors.

CLTV: FOF responds smoothly to increases inCLTV for prime and Alt-A loans, as shown in the

chart below. The analysis showed that a 10% increasein CLTV correlated to a 33% rise in FOF. Decades-worth of data demonstrate a strong correlationbetween a borrowers propensity to default andCLTV. Historical, as well as recent, performanceconfirms that low homeowner equity reducesborrower incentive to avoid foreclosure.

Subprime loans with a 70% CLTV or higher exhibit

incrementally smaller increases in defaults. This isillustrated in the FOF by CLTV chart below.Subprime borrowers are less sensitive to lack of

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Prime ALTA Subprime

FOF by CLTV

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FOF Frequency of foreclosure. CLTV Combined loan-to-value ratio.Note: Assumes weighted average of the data samples characteristics.

CLTV (%)

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FOF Frequency of foreclosure. FICO Fair Isaac Corp.Note: Assumes weighted average of the data samples characteristics.

FICO Score


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homeowner equity than they are to changes infinancial circumstances and home price appreciation.The analysis showed that default probability rose by13% when increasing the CLTV from 65% to 70%

for subprime loans; however, the rise in defaultprobability was just 2% when increasing the CLTVfrom 75% to 80%. The CLTV FOF odds reflect thepositive relationship between CLTV and default riskand the increased probability of default when CLTVrises by 5%. However, subprime loans with CLTVsof 70% or higher are applied a lower FOF penalty toreflect the lower rise in default risk when CLTVs of70% or higher increase by 5%.

For first liens originated simultaneously with apiggyback loan, i.e. a second lien used for financing

the borrowers downpayment, as well as pools securedby CES, Fitch applies the FOF odds penalty to theCLTV of both the first and second lien mortgages forevery loan that has more than one lien. This ensures

that the default risk potential adequately reflects thereduced homeowner equity associated with thepresence of a second lien.

Fitchs previous methodology of applying the FOF toa higher CLTV only for piggybacks in excess of 35%of the pool is no longer applicable. The new modelincludes both liens in the FOF CLTV calculation for100% of the loans that have a second lien attached.

The table below shows the base case FOF projectionfor a range of FICO and CLTV combinations for

Base Case FOF FICO\CLTV Matrix(%)

CLTV

50 55 60 65 70 75 80 85 90 95 100

FICO Prime600 2.06 2.38 2.75 3.17 3.65 4.20 4.83 5.55 6.37 7.30 8.36620 1.48 1.71 1.97 2.28 2.63 3.03 3.49 4.02 4.62 5.31 6.10640 1.06 1.22 1.41 1.63 1.88 2.17 2.51 2.89 3.33 3.84 4.42660 0.75 0.87 1.01 1.17 1.35 1.56 1.80 2.08 2.40 2.76 3.19680 0.54 0.62 0.72 0.83 0.96 1.11 1.29 1.49 1.72 1.98 2.29700 0.38 0.44 0.51 0.59 0.69 0.79 0.92 1.06 1.23 1.42 1.64720 0.27 0.32 0.37 0.42 0.49 0.57 0.66 0.76 0.88 1.01 1.17740 0.19 0.23 0.26 0.30 0.35 0.40 0.47 0.54 0.63 0.72 0.84760 0.14 0.16 0.19 0.22 0.25 0.29 0.33 0.39 0.45 0.52 0.60780 0.10 0.11 0.13 0.15 0.18 0.21 0.24 0.27 0.32 0.37 0.43800 0.07 0.08 0.09 0.11 0.13 0.15 0.17 0.20 0.23 0.26 0.30820 0.05 0.06 0.07 0.08 0.09 0.10 0.12 0.14 0.16 0.19 0.22

FICO Alt-A600 3.94 4.53 5.21 5.98 6.86 7.85 8.98 10.25 11.68 13.28 15.06620 2.83 3.27 3.76 4.33 4.98 5.72 6.56 7.52 8.61 9.83 11.21640 2.03 2.35 2.71 3.12 3.60 4.14 4.76 5.47 6.28 7.20 8.24660 1.46 1.68 1.94 2.24 2.59 2.98 3.44 3.96 4.55 5.23 6.01680 1.04 1.20 1.39 1.61 1.85 2.14 2.47 2.85 3.28 3.78 4.35700 0.74 0.86 0.99 1.15 1.33 1.53 1.77 2.04 2.36 2.72 3.14720 0.53 0.61 0.71 0.82 0.95 1.10 1.27 1.46 1.69 1.95 2.25740 0.38 0.44 0.51 0.59 0.68 0.78 0.91 1.05 1.21 1.40 1.61760 0.27 0.31 0.36 0.42 0.48 0.56 0.65 0.75 0.86 1.00 1.15780 0.19 0.22 0.26 0.30 0.34 0.40 0.46 0.53 0.62 0.71 0.82800 0.14 0.16 0.18 0.21 0.25 0.28 0.33 0.38 0.44 0.51 0.59820 0.10 0.11 0.13 0.15 0.17 0.20 0.23 0.27 0.31 0.36 0.42

FICO Subprime460 18.34 20.64 23.14 25.85 28.76 29.30 29.86 30.41 30.98 31.55 32.12480 16.04 18.11 20.39 22.87 25.56 26.06 26.58 27.10 27.62 28.16 28.70500 13.98 15.83 17.88 20.14 22.60 23.06 23.54 24.02 24.50 25.00 25.50

520 12.14 13.79 15.63 17.66 19.89 20.32 20.75 21.19 21.63 22.09 22.54540 10.52 11.98 13.61 15.42 17.43 17.82 18.21 18.61 19.01 19.42 19.84560 9.09 10.37 11.81 13.43 15.22 15.57 15.92 16.28 16.64 17.01 17.39580 7.83 8.96 10.23 11.65 13.25 13.56 13.87 14.19 14.51 14.85 15.18600 6.74 7.72 8.83 10.09 11.49 11.77 12.04 12.33 12.62 12.91 13.21620 5.79 6.64 7.61 8.71 9.95 10.19 10.43 10.68 10.94 11.20 11.46640 4.97 5.71 6.55 7.50 8.59 8.80 9.01 9.23 9.46 9.69 9.92660 4.26 4.90 5.62 6.45 7.40 7.58 7.77 7.96 8.16 8.36 8.56680 3.64 4.19 4.82 5.54 6.36 6.52 6.68 6.85 7.02 7.20 7.38700 3.12 3.59 4.13 4.75 5.46 5.60 5.74 5.89 6.04 6.19 6.34

FOF Frequency of foreclosure. FICO Fair Isaac Corp. LTV Loan-to value ratio. Note: Each sector assumes a loan with the following attributes:full documentation, owner occupied, fixed rate, single family, purchase, no prepayment penalty, 360-month term, 25% debt-to-income ratio (DTI),and $250,000 balance.


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each of the credit sectors. The table assumes thatother dimensions are set to the baseline attributes.Note that the actual FICO and CLTV odds arecomputed on a continuous basis and these points are

shown for illustration.

Property Type:The property type dimension consistsof single-family detached (SFD) homes, condominiums,co-operatives, multifamily homes, townhouses, plannedunit developments (PUDs), and manufactured housing(MH). The SFD property type is the baseline.

Condominiums and PUDs exhibited lower defaultsrelative to the SFD baseline by 32% and 18%,respectively. Fitch believes that condos experiencefewer defaults because they are predominantlyconcentrated in heavily populated metropolitan areaswhere demand is high. Also, in high-cost rentalmarkets, condos provide homeownership benefitswith comparable monthly costs, which also fuelsdemand. PUDs also exhibited fewer defaults thanSFD homes. Mortgages secured by either of thesetwo attributes have reduced odds of default.

In contrast, multifamily, townhouses, and MHproperties exhibited higher default rates compared withSFD homes. Multifamily homes are more prone todefault risk since the borrower is relying on incomefrom rental or other sources to help pay the mortgage.Each of these property types is assigned higher oddscommensurate with performance, which reflects the

increased risk of default relative to the SFD baseline.The MH property type has the highest FOF odds penaltydue to the high-risk nature of the property type.

Product Type: Relative odds for adjustable-ratemortgages (ARMs) and balloon loans weredetermined based on their default performancerelative to that of the fixed-rate mortgage (FRM)baseline. Both products exhibited higher default ratescompared with the baseline and, therefore, higherdefault odds are assigned.

The odds penalties for affordability products such as

hybrid ARMs, interest-only mortgages (IOs), and optionARMs were derived based on analysis of paymentincrease potential rather than a logistic regression due tothe lack of substantive performance data available forthese products. FOF regression would produce adistortedly low odds penalty for these products giventhat the short performance history only spans the benigneconomic environment of recent years.

Fitchs analyses of affordability products havedemonstrated the payment increase potential faced byborrowers at the initial rate reset or recast date. TheFOF odds penalties for each of these products are

based primarily on the size of the payment increaserelative to that for ARMs. The payment increases, aswell as other risks associated with these products, arefully described in published Fitch Research.

Loan Documentation: Limited or no documentation(no doc) loans are assigned higher FOF odds basedon the higher default rates exhibited by these loansrelative to the full documentation (full doc) baseline.Fitchs analysis showed that limited doc loans had a24% higher default rate and no doc loans had a 50%higher default rate compared with full doc loans.

Since limited and no doc programs usually omit anincome and/or asset verification, borrowers are morevulnerable to default risk if they overestimate theirincome and assets to qualify for a loan amount theyotherwise would not obtain under a full doc program.

Loan Term: Loans with less than 30-year termsexhibited a 30% lower default rate than the 30-year termbaseline; therefore, loans with maturities less than30 years are given a FOF odds credit to reflect the lowerprobability of default. Borrowers of a 15-year mortgage,in particular, voluntarily assume the higher paymentdespite having a smaller payment option with the30-year mortgage, an ARM, or affordability product.

This voluntary undertaking reflects a premium borrowerselection as opposed to adverse selection, which isassociated with lower mortgage payment options.

As with other affordability products, there is littleperformance history for mortgages with terms of40 years or more to derive an accurate odds penalty(or credit). Therefore, the odds penalties for loanterms of 40 years or more were determined based onFitchs analyses of the payment increase potential,slower amortization, and adverse selection riskassociated with loan terms greater than 30 years.

Based on Fitchs analyses of 40-year FRMs, theincrease in adverse selection risk and lack ofamortization were low relative to 30-year FRMs. TheFOF odds penalty for 40-year FRMs reflects thesmall increase in adverse selection risk. This is alsotrue for longer term hybrid ARMs and option ARMs.However, the hybrids and option ARMs face a 5% and25% higher payment increase, respectively, at the ratereset or recast relative to their 30-year counterparts.Fitch derived an odds penalty for each of these


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products based on the payment increases and, to alesser extent, the adverse selection risk. Products withterms of 40 years and longer are fully described inpublished Fitch research.

Prepayment Penalties: Loans with prepaymentpenalties are concentrated in the subprime sector and,to a lesser extent, in the Alt-A sector. Prime loans

rarely have prepayment penalties. Fitchs analysisshowed that loans with prepayment penalties exhibitedhigher default rates than those without and, therefore,are assigned an FOF odds penalty, regardless of theprepayment penalty term. The baseline FOF formeasuring the increase in default risk was loans thatdid not have a prepayment penalty.

Occupancy: Non-owner-occupied second homes andnon-owner-occupied investor properties exhibitedhigher default rates than the owner-occupied baseline.Second homes had higher default rates since borrowersfacing financial difficulty are likely to default on a

second home before their primary residence.

Investment properties exhibited a 24% higher defaultrate than owner-occupied properties. Fitch attributesthe high default rates to the effect of speculativeinvestments and high-risk nature of rental properties.Speculative investing can increase default rates if theproperty does not sell as fast or at the price neededfor an investor to break even. For rental properties,

the borrower is relying on income from externalsources to repay the mortgage. If the property wasfinanced with an ARM, a borrower couldunderestimate the mortgage payment; if the rentalincome is insufficient, the payment increases. Secondhomes and investor properties are assigned separateFOF odds penalties to reflect their distinct, higherodds of default relative to the baseline.

Debt-to-Income Ratios (DTIs): Default rates wereslightly correlated to increases in front-end DTIs, as shownin the chart above. A front-end DTI is the ratio of monthlyinstallment debt to monthly gross income (the mortgagepayment, taxes, and insurance are included in the back-endDTI). For prime and Alt-A loans, a 5% increase in the DTIyielded roughly a 3% rise in default rates.

For subprime loans, there was slightly less sensitivityto default risk when DTIs rose. A 5% increase in DTIyielded a 2.5% rise in default risk. This lowersensitivity is also reflective of the very high default

rates of the subprime sector and the vulnerability of thesubprime borrower to external risk factors.

Closing Balance: Closing balance affects default ratesin a nonlinear way, as shown in the chart on page 9.Both low and high balance loans exhibited higherdefault rates relative to the middle range of the band.The high balance effect is more pronounced in the Alt-Aand prime sectors; default probability rises 5% when the

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FOF Frequency of foreclosure. DTI Debt-to-income ratio.Note: Assumes weighted average of the data samples characteristics.

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loan balance increases from $700,000 to $750,000. Thisis reflective of the higher adverse selection riskassociated with more expensive homes. However,adverse selection risk depends on location. In higherpriced markets, particularly in the coastal states, adverseselection risk is proportionate to the median value.

In the subprime sector, low balance loans incurhigher defaults. Decreasing the loan balance from$100,000 to $50,000 increases the default probabilityby 12%. Very low loan balances are generallysecured by less desirable properties and likely to be

in disrepair, which increases the default risk if theborrower has difficulty selling the home.

Loan Purpose: Refinance mortgages exhibited 12%higher default rates than the purchase loan baseline.

For cashout refinances, in particular, a borrower whoextracts equity from the home is more likely to defaultif he/she is facing financial difficulties or personalhardship such as unemployment or divorce. Also,many borrowers extract cash from accumulated homeequity for debt consolidation purposes, which may bean indication that the borrower is financially strapped.If the borrower reloads the debt after theconsolidation, the debt burden may become intolerableand the borrowers likelihood of default rises.

Loan Seasoning: At deal closing, loans typically

have between two and six months of seasoning.However, in most RMBS pools, there are a numberof loans with 12 months or more seasoning. Theseloans benefit from having survived the earlymonths of loan payments. For prime, Alt-A, and

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subprime loans, having at least six months seasoning

at deal closing results in a decrease in defaultprobability of roughly 8.5%, which results in a FOFcredit for loans seasoned more than six months atdeal closing.

State FOF Dimension: The UFA default multipliersby state, as listed in the table above, are applied as apenalty or credit to Fitchs base FOF derived from theaforementioned collateral risk attributes. Themultipliers for those states categorized as high default

risk are greater than 1.2; states with multipliersbetween 0.8 and 1.2 are at an average risk of default;and those with multipliers of less than 0.8 are low risk.

Regional economic conditions greatly influencemortgage risk. UFAs analysis of regional risk takesinto account each states economic metrics, such aspersonal income and distribution, employmentgrowth, housing construction, and other indicators. Italso factors in a demographic component, whichincludes unemployment rates and population growth,as well as a political component that considers localtaxes and zoning regulations.

UFAs analysis is used to derive the quarterly UFAMortgage Report default multipliers by state. Themultipliers represent the level of expected defaultsover the life of a loan relative to the national averageon a constant quality basis. For example, if the UFAdefault multiplier for a state is 0.90, expected defaultsin that state are 90% of those for the average loan inthe U.S.

Fitch tested the UFA multipliers and found their predictivepower to be highly accurate. UFA provided historicaldefault multipliers, by state, dating back to 1996. Themultipliers provided were forward-looking factors; i.e.they were computed as a function of data available up tothe date of the multiplier only, making them appropriatefor out-of-sample testing. To test the calibration, Fitchsubtracted 1.00 from each multiplier and added this factor

to the FOF logistic regression.

Base Loss Severity

LS is computed similarly to FOF through a statisticalanalysis of historical data. This represents a significantdeparture from Fitchs prior approach to LS. In earlierFitch models, LS was calculated using estimatedcarrying costs and liquidation expenses, combinedwith regional home price projections. In building theResiLogic model, Fitch took advantage of theavailability of a robust data set of actual observed lossseverities for defaulted mortgages. This data setallowed Fitch for the first-time to directly model the

relationship between loan attributes and LS.

LS penalties and credits are applied to each loanattribute based on the LS experience relative to thebaseline. However, unlike FOF, where odds penaltiesindicate an increased probability of default, the LSmodel measures each attributes contribution toincidence and amount of realized losses. For thecontinuous dimensions like loan balance, coupon, and

UFA Default Rankings by State(As of September 2006)

State Census Region Default Risk

California Pacific Region HighColorado Mountain Region High

Massachusetts Northeast Region HighMichigan North Central Region HighMinnesota North Central Region HighNew Hampshire Northeast Region HighRhode Island Northeast Region High

Arkansas Southern Region AverageConnecticut Northeast Region AverageDelaware Southern Region AverageGeorgia Southern Region AverageHawaii Pacific Region AverageIllinois North Central Region AverageIndiana North Central Region AverageIowa North Central Region AverageKansas North Central Region AverageKentucky Southern Region AverageMaine Northeast Region AverageMaryland Southern Region AverageMississippi Southern Region Average

Missouri North Central Region AverageMontana Mountain Region AverageNebraska North Central Region AverageNevada Mountain Region AverageNew Jersey Northeast Region AverageNew York Northeast Region AverageNorth Carolina Southern Region AverageNorth Dakota North Central Region AverageOhio North Central Region AverageOklahoma Southern Region AverageOregon Pacific Region AveragePennsylvania Northeast Region AverageSouth Carolina Southern Region AverageSouth Dakota North Central Region AverageTennessee Southern Region AverageTexas Southern Region AverageUtah Mountain Region AverageVermont Northeast Region AverageVirginia Southern Region AverageWashington Pacific Region AverageWisconsin North Central Region AverageWyoming Mountain Region Average

Alabama Southern Region LowAlaska Pacific Region LowArizona Mountain Region LowFlorida Southern Region LowIdaho Mountain Region LowLouisiana Southern Region LowNew Mexico Mountain Region LowWest Virginia Southern Region Low

UFA University Financial Associates, LLC.


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CLTV, sensitivity penalties and credits reflect thepercentage increase or decrease in LS associated withthe possible attributes in each dimension.

The aggregate of the baseline LS percentage and theattribute adjustments produces a base LS percentage foreach loan. This contrasts with Fitchs previousmethodology, in which the loan balance was reduced bya projected market value decline, carrying costs, andliquidation expenses to derive a loans LS percentage.

To determine which credit dimensions interacted closestwith realized losses, Fitch extracted approximately 270,000loans that had experienced a liquidation event and had apayment history dating back to the first payment date. Aftereliminating loans with mortgage insurance (MI), seniorliens, and reperforming and subperforming loans, Fitch hadobserved data for roughly 124,000 loans.

Discussed below and shown in the table on page 3are the 12 dimensions in order of influence on LS.Some of the dimensions affecting FOF did not affectLS and were replaced by others. The dimensionsaffecting LS, but not FOF, include loan coupon andservicer rating. FOF credit dimensions not part of theLS model are prepayment penalty, documentation,and DTIs.

Subprime CES are treated the same as subprime firstlien mortgages for those LS dimensions affected bycredit sector. However, several of the 12 first liencredit dimensions affecting LS were insufficientlysignificant to be incorporated in the CES losscalculation. The LS dimensions that constitute the

CES model include closing balance, CLTV, loancoupon, occupancy, and loan seasoning

Closing Balance: The original loan amount (orclosing balance) exhibited an inverse relationship toLS. Larger loan balances exhibited lower relativelosses than low balance loans, which Fitch believes isattributable to several factors. Foremost, theforeclosure and liquidation costs as a percentage of alarge loan balance are lower than that of a smallbalance loan. Second, properties securing a large loan

may be secured by more marketable properties thatmay be in better condition, requiring less reparationand expense. Servicers are likely to prioritize theliquidation of these properties before a propertysecuring a smaller loan amount.

Subprime loans have lower balances than prime andAlt-A loans. Thus, more subprime loans incurredlosses and the loss percentages were higher. Thisfurther demonstrates the impact that the closingbalance has on LS.

CLTV:As shown in the chart above, CLTV affects LSbecause it reflects the amount of homeowner equity thatis available to absorb market value losses. LS ispositively correlated to CLTV; loans with high CLTVshave little equity in the first loss position and, therefore,incur higher losses in a soft or declining real estatemarket. The LS penalty is based on the positiverelationship between losses and CLTV.

Loan Coupon: The loan coupon or mortgage ratedirectly affects losses since it drives interest carrying

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costs. Interest accrues at the note rate on the outstandingloan balance from the time the borrower makes the lastpayment through the time liquidation proceeds arereceived. Carrying costs can be significant for hard tosell properties or in states with lengthy foreclosuretimelines or onerous eviction laws. The eviction processcan take several months even in moderate states. The LS

penalty is based on the positive relationship between themortgage coupon and losses.

Property Type: As with default performance, condosand PUDs incurred fewer losses than the SFD baseline.As a result, condos and PUDs are applied a LS creditdue to their low loss experience relative to the baseline.

Multifamily homes, townhouses, and MHexperienced higher losses relative to SFD homes. Theloss experience is attributable to the lower marketdemand and property condition declines associatedwith multifamily and MH properties. Townhouses

usually experience steeper market value declinessince they compete with SFD properties and havemonthly association dues, which negatively affect theresale value. The LS penalties applied to multifamily,townhouse, and MH properties reflect their higherloss experience relative to the SFD baseline.

Occupancy:Non-owner-occupied second homes andinvestor properties exhibited higher losses than the

owner-occupied baseline. Investor properties arevulnerable to loss if there is a rise in housing supply,which can affect resale values and the period to sellthe property. Carrying costs and liquidation expensesdrive up losses if the borrower is unable to rent or sellthe property. To expedite a sale, the price of theproperty is likely to be reduced.

Loan Purpose: Cashout and rate term refinancesexperienced higher losses than purchase loans, whichis the baseline for the loan purpose LS dimension.Losses on cashout refinances are due to the loweramount of homeowner equity available to absorbmarket value declines. As a result, loan refinances areapplied a LS sensitivity penalty to account for thehigher loss potential, as evidenced by past experience.

Credit Sector: The baseline for the credit sectordimension is subprime. LS for prime loans that went intoforeclosure was very low; few loans incurred a realized

loss of over 35% of the original balance. A largepercentage of the sample, almost 34%, incurred no loss,as shown in the chart above.

More Alt-A loans incurred a realized loss, about 16%,as shown in the chart on page 13. Most of the Alt-Aloans incurred losses in the 10%30% range, thoughmore loans than prime incurred losses in excess of 35%.Prime and Alt-A loans are applied a LS credit since they

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incur lower losses than the subprime mortgages. Primeloans have a larger credit than Alt-A due to the very lowloss experience of the prime loans.

About 8% of the subprime sector experienced no losses,as shown in the chart on page 14, much less than thepercentage of prime and Alt-A loans. In addition, over

1.5% of the sample incurred a loss of over 100%. This islikely due to the higher carrying costs and liquidationand foreclosure expenses.

Product Type: ARMs and balloon mortgagesexperienced higher realized losses than the FRMbaseline. ARMs incurred higher losses than balloonmortgages due primarily to a higher fully indexedmortgage rate, which increases the carrying costs.

To derive LS penalties for affordability products, Fitchleveraged the LS experience for products with similaramortization schedules. This approach allows for

consistency in the application of a sensitivity penaltydespite the lack of loss experience data during adeclining economic environment. The hybrid ARMsare treated similarly to the FRM baseline since theamortization terms are similar.

IOs are treated similarly to balloon mortgages giventhe lack of amortization that both products feature.IOs with five-year interest-only periods are applied

the same penalty as a five-year balloon and so forth.Fitch does not apply a sensitivity penalty to IOs withinterest-only periods of fewer than five years, sincethe amortized amount during the early years of amortgage life is small and, therefore, would have anegligible impact, if any, on LS.

Given that no other products in the data sampleresemble an option ARMs negative amortizationfeature, Fitch determined that a higher CLTVassumption for the option ARM would serve as anappropriate proxy for applying a sensitivity penalty. Agrowing loan balance resulting from negativeamortization reduces homeowner equity, which, inturn, increases the original CLTV. Fitch raises theCLTV by five percentage points when applying theCLTV sensitivity penalty to an option ARM, which isconsistent with Fitchs prior published research onoption ARM risk. Option ARMs also are assigned thesame product type LS penalty as a short-term ARM.

Seasoning, FICO Score, and Loan Term: Loanswith more seasoning at the RMBS closing dateexperienced lower losses than those without anyseasoning. Fitch applies a LS credit based on thedecrease in LS relative to a six-month increase inloan seasoning at deal closing.

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FICO scores had a minor impact on the incidence andamount of losses realized. Still, Fitchs analysisshowed that loans with high FICO scores incurredfewer realized losses than loans with low scores.

Loan terms of less than 30 years also exhibited lowerdefaults and losses than the 30-year term baseline.

Loans with 15-year maturities pay down principal ata much faster pace, which significantly can reduceloss exposure. A LS credit is applied to loans thathave a term less than 30 years.

Servicer Rating: Fitch rates servicers on a scale ofRPS1 (highest quality) to RPS5 (lowest quality).Fitchs analysis of historical LS performance foundthat LS is inversely correlated to servicer quality, asevidenced by the Fitch rating.

The quality and stability of a servicers operation havea direct impact on its default management capabilities

and ultimately on LS, regardless of product type.Fitchs servicer ratings serve as an added measure ofany additional risk or benefit directly associated withthe servicer of an RMBS transaction.

Because differences in servicing practices andexecution can affect loan performance, Fitch analyzesroll rates, resolution rates and methods, timelinemanagement, and expense controls, all of which

directly impact LS. Fitchs evaluation of eachservicer process as it relates to collections, lossmitigation, bankruptcy and foreclosure, and realestate owned (REO) assets, together with a review ofits loan administration process and use of technology,determines the servicers rating.

The LS credit was derived based on Fitchs analysis ofloss performance in relation to servicer ratings. Thecredit reflects a positive correlation between theincidence of realized losses and the servicer rating;loans serviced by servicers rated RPS1 by Fitchexperienced fewer losses and lower loss severities thanthose serviced by RPS3 rated servicers.

Since this has a negligible effect on prime and Alt-Apools, ResiLogic will apply a minimum credit to theAAA base loss amount for highly rated servicers.

Primary Mortgage Insurance

Fitch applies a LS sensitivity credit to loans that haveborrower- and lender-paid primary MI. The LScredits are directly proportional to the MI coveragepercentage; LS sensitivity to MI was not derivedbased on a regression. Fitch gives full credit or applies adiscount to the mortgage coverage percentage,depending on the insurers financial strength rating andthe proposed RMBS class rating.

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Economic Stress and Rating

Category Loss Coverage

Fitchs modeling of loan-level risk attributes providesinsight into the relative risk of default and loss among

loans. Long-term historical analysis determines theexpected case loss. However, the RMBS rating processrequires determination of the appropriate absolute levelof loss protection associated with each rating category.AAA rated bonds should be ensured of full principaland interest return even if losses are many timesgreater than the expected case. ResiLogic is designedto project loss levels reflecting the impact of nationaland regional economic stress on FOF and LS.

The determination of sensitivity to economic stress isbased on analysis of historical mortgage performance.After accounting for the risks associated with the

various loan attributes described earlier, Fitch stillfound unexplained default and loss behavior. Asshown in the charts on pages 3 and 4, there weresubstantially higher actual FOFs than predicted by themodel for the 2000 vintage. Statistical analysis ofmortgage performance across a variety of economicconditions shows that mortgage default and loss levelsare highly sensitive to economic stress penalties anddegree of state concentration. Adding sensitivity toeconomic conditions to the model accounted for much ofthe additional default and loss not captured by loanattribute analysis.

Once the sensitivity to economic stress wasestablished, Fitch ran extensive Monte Carlosimulations of economic stress on the model loansample. The simulation included variation in bothnational and state-level economic stress. The state-level stresses reflect the historical volatility ofeconomic stress in each state as well as theconcentration of risk by state. The model sensitivityto economic stress was increased relative to thehistorical experience for all loans and, to a largerdegree, for subprime loans than for prime and Alt-Aloans. With the increased sensitivity to stress, moresevere loss levels were generated in the simulation.This was a conservative adjustment to the model

intended to reflect the limited availability ofperformance data, particularly for subprimemortgages, under conditions of severe stress.

The simulation generated a distribution of loss results foreach credit product (prime, Alt-A, and subprime). The Brating loss expectations are associated with the highestprobability outcomes from the simulation, with eachhigher rating category representing a lower probability,

higher loss point on the distribution, culminating in thelow probability AAA loss expectations.

The distribution of loss levels generated by the

simulation was used to determine the appropriateFOF and LS stress multiples and incorporate theminto the model for each rating level. In defining stressmultiples, Fitch has sought to maintain consistencywith its long-established approach relatinginvestment-grade loss protection to severe historicaleconomic scenarios. Prior iterations of Fitchs modelhave tied required loss coverage to the severe Texasrecession of the late 1980s and the Californiarecession of the 1990s. In both instances,unemployment rates rose rapidly by around 4%.

The basis for Fitchs prior AA loss coverage levelsassumed a national level of stress as severe as theseregional recessions, with AAA losses representingan even more unlikely scenario. The AA multiplesused for each product type in the new model arebased on similar levels of stress, encompassingsimulations of approximately 4% unemployment ratechanges on the national and state level. The AAAstresses are even more severe, based on simulationresults equivalent to 5%6% of national and stateunemployment stress.

Fitchs simulation stresses can also be related tohome price declines. The AAA rating stress equatesto rapid, sustained declines in nominal home prices of

25% or more, depending on the state where theproperty is located.

Impact of ResiLogic on Fitch Loss

Coverage Expectations

To evaluate how ResiLogic compared to Fitchsexisting loss coverage indications, Fitch tested theResiLogic model against over 700 pools that had beenevaluated in 2006. This analysis showed that, whilethere was broad agreement between ResiLogic andFitchs loss coverage expectations, some differences didappear. The most notable differences are due toResiLogics relatively heavy weighting of FICO versusLTV when computing FOF, as well as the impact of thenew LS model. Additionally, ResiLogic does not giveas much benefit to fixed-rate subprime mortgagescompared with hybrid ARMs. These differences canresult in either increases or decreases in ResiLogicsloss coverage levels relative to Fitchs current model,depending on pool characteristics.


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The change in relative weighting of FICO versusLTV is most noticeable at the extreme ranges of thesubprime mortgage spectrum. Very low FICO loansare assigned higher FOFs, even if LTV is low, and

high LTV loans are not treated as harshly ifaccompanied by relatively high FICOs.

ResiLogics LS model generates higher averageseverities than previously and, in particular, does notdifferentiate to the same degree between fixed andARM loans as the prior model. Similarly, the FOFsfor fixed and hybrid ARMs with similarcharacteristics are also less disparate. Thecombination of these changes results in less benefitfor fixed pool loss coverage relative to ARM pools,particularly in the subprime credit sector.

Role of ResiLogic Model in Rating

Process

Mortgage pool analysis using the ResiLogic model isa core component of Fitchs RMBS rating process.However, Fitchs RMBS rating criteria encompass amuch more extensive analytical process in addition toloan-level modeling. While a detailed explanation ofthis process is outside the scope of this document,key components include:

Extensive review of origination and servicingoperations and practices.

Review of legal structure. Analysis of cash flow structure.

Rating committees will consider these and otherinputs, along with model results, when determiningRMBS ratings. Fitch will carefully consider anychanges to loss coverage levels indicated byapplication of the ResiLogic model.

Loss Coverage and Credit

Enhancement

For most prime and many Alt-A securitizations, creditenhancement for a given rating is equal to the loss

coverage level (subordination level). However, forstructures that employ excess interest as creditenhancement, additional cash flow modeling is requiredto determine the subordination and overcollateralizationnecessary for each rated class. Fitch makes use of theindustry standard INTEX DealMaker technology forcash flow modeling and provides all of its cash flowmodeling assumptions on its web site atwww.fitchratings.com. These assumptions are detailedin previous Fitch Research.

ResiLogic Model 1.0 SoftwareWith the introduction of the ResiLogic model, Fitch

will be making its RMBS model software available tomarket participants for the first time. Originators,issuers, underwriters, and asset managers can nowhave access to Fitchs loan-level default and lossanalysis. After a beta test period, following Fitchsconversion to ResiLogic, the model will be availablefor licensing. The ResiLogic software will beprovided both as a desktop software system and anapplications programming interface (API). Thedesktop software will allow a user with a properlyformatted data file of mortgage information togenerate indicative loss coverage numbers and obtaindetailed risk reports stratified by risk dimension or at

the loan level. Originators and purchasers ofmortgages can use the API tools to integrate Fitchsanalytics into their own software systems to aid inreal-time risk analysis and pricing.

Copyright 2006 by Fitch, Inc., Fitch Ratings Ltd. and its subsidiaries. One State Street Plaza, NY, NY 10004.Telephone: 1-800-753-4824, (212) 908-0500. Fax: (212) 480-4435. Reproduction or retransmission in whole or in part is prohibited except by permission. All rights reserved. All of theinformation contained herein is based on information obtained from issuers, other obligors, underwriters, and other sources which Fitch believes to be reliable. Fitch does not audit or verify thetruth or accuracy of any such information. As a result, the information in this report is provided as is without any representation or warranty of any kind. A Fitch rating is an opinion as to thecreditworthiness of a security. The rating does not address the risk of loss due to risks other than credit risk, unless such risk is specifically mentioned. Fitch is not engaged in the offer or sale ofany security. A report providing a Fitch rating is neither a prospectus nor a substitute for the information assembled, verified and presented to investors by the issuer and its agents in connectionwith the sale of the securities. Ratings may be changed, suspended, or withdrawn at anytime for any reason in the sole discretion of Fitch. Fitch does not provide investment advice of any sort.Ratings are not a recommendation to buy, sell, or hold any security. Ratings do not comment on the adequacy of market price, the suitability of any security for a particular investor, or the tax-exempt nature or taxability of payments made in respect to any security. Fitch receives fees from issuers, insurers, guarantors, other obligors, and underwriters for rating securities. Such feesgenerally vary from USD1,000 to USD750,000 (or the applicable currency equivalent) per issue. In certain cases, Fitch will rate all or a number of issues issued by a particular issuer, or insuredor guaranteed by a particular insurer or guarantor, for a single annual fee. Such fees are expected to vary from USD10,000 to USD1,500,000 (or the applicable currency equivalent). Theassignment, publication, or dissemination of a rating by Fitch shall not constitute a consent by Fitch to use its name as an expert in connection with any registration statement filed under theUnited States securities laws, the Financial Services and Markets Act of 2000 of Great Britain, or the securities laws of any particular jurisdiction. Due to the relative efficiency of electronic

publishing and distribution, Fitch research may be available to electronic subscribers up to three days earlier than to print subscribers.

Documents

RMBS Loss Model 2007-08