Modeling and Analytics

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1 25 April 213 Fixed Income Research Modeling and Analytics Research Analysts David Zhang Yihai Yu Taek Choi CS6.8: Agency MBS model update Extend HARP2. program; model updates for conforming jumbo, GN, and ARM products We have released a new agency MBS model CS6.8 into CS+/Locus calculator. HARP2. extension Extend HARP2. model timeline through December 215, and with residual afterwards The model tail prepayment speeds are generally in line with historical speeds of burned out cohorts We discuss potential risks to our HARP speeds projection, especially shortcomings of our existing burnout model component Conforming jumbo model New conforming jumbo mortgage rates based on WAC of wala = 1 pools: about 15bps higher than conforming agency mortgage rates Faster refinance ramp: 6 months vs. 1 months for conforming mortgages Consistent model across loan size, states (CA, NY, etc.) and origination channels (Retail, TPO) A non-agency prime jumbo model is derived from this agency jumbo model with additional 15bps higher mortgage rates GN model updates 1bps higher MIP, announced by GN on January 31, 213 Increased refinance sensitivity to future HPA changes Potential faster prepayment due to the January 31, 213 announcement rescinding automatic MIP cancellation Switching to the newly released pool level MIP data from previously estimated data in CS6.71 Agency ARM model updates Added HARP2. component: HARP speeds are function of ARM product types, FICO, OLTV, CLTV and pre- and post-reset periods Clients can parallel the model runs between CS6.8 and CS6.71 through the model dropdown menu in Locus agency calculators. The model default setting will be switched to CS6.8. We welcome feedback from clients on the new model. ANALYST CERTIFICATIONS AND IMPORTANT DISCLOSURES ARE IN THE DISCLOSURE APPENDIX. FOR OTHER IMPORTANT DISCLOSURES, PLEASE REFER TO CREDIT SUISSE SECURITIES RESEARCH & ANALYTICS BEYOND INFORMATION Client-Driven Solutions, Insights, and Access

2 Nov-11 Jan-12 Mar-12 May-12 Jul-12 Sep-12 Nov-12 Jan-13 Mar-13 May-13 Jul-13 Sep-13 Nov-13 Jan-14 Mar-14 May-14 Jul-14 Sep-14 Nov-14 Jan-15 Mar-15 May-15 Jul-15 Sep-15 Nov-15 Jan-16 Mar April 213 HARP extension: what shall be the tail speeds for HARP cohorts? On April 11, 213, FHFA announced that the HARP program will be extended from December 213 to December 215, while HARP program qualifications (origination date cutoff, pay history requirement, and CLTV requirement) remain the same. Following our existing HARP2. model (see, for example, Short-Term Prepayment Estimates June 212 of June 18, 212), we have updated our HARP2. timeline assumptions. Exhibit 1: HARP2. model timeline is extended to December 215 and with a residual after that Curve represents overall increase in refinance activity due to HARP2.. CS HARP2. model also differentiates HARP performance across CLTV, FICO, OLTV, loan size and servicers. See model documentation for details. Overall % increase of refinance speeds due to HARP2. 25% 2% 15% 1% 5% % CS6.71 CS6.8 Exhibit 1 shows the comparison between the overall HARP2. strength ( HARP model timeline ) assumptions in current CS6.71 and the new CS6.8. CS6.8 assumes the current high HARP efficiency will continue until end of 213, then a gradual HARP burnout will set in, as eligible loans/borrowers get depleted. (In addition to the variable of overall HARP strength shown here, the CS HARP2. model also differentiates HARP performance across CLTV, FICO, OLTV, loan size and servicers. See model documentation for details.) Exhibit 2: CS6.8 prepayment projections for HARP performance Assume forward mortgage rates stay at 3.5%; TBA December 212 actual speeds are based on CS delivery assumption Cohort Dec. 212 (Actual) Dec. 213 Dec. 215 FN FN FN FN FN FN FN FN In addition to the gradual burnout of the overall HARP strength (the solicitation effect ), the existing burnout function in CS prepayment models will also tend to reduce prepayment speeds as borrowers who skip past refinance opportunities/incentives tend to be less responsive to new opportunities/incentives (the responsive effect ). Exhibit 2 shows CS6.8 prepayment projections for sample cohorts under static rates assumptions (future mortgage rates stay at 3.5%). CS6.8 projects tail speeds of around 3 cprs for FN 5s and 23 cprs for FN 6.5s. Are these speeds reasonable? Modeling and Analytics 2

3 25 April 213 Exhibit 3: Sample prepayment performance for seasoned cohorts going through burnout Attributes as of Apr. 213 Avg. prepayment (CPR) Cohort Loan Size CLTV Factor FN , % FN CHASE 183, % FN , % FN CHASE 131, % FH , % FH CHASE 15, % FN , % Exhibit 3 shows sample prepayment speeds for seasoned cohorts going through burnout. For FN 5s/6.5s, cohort speeds were burned out at 27 and 2-22 cprs in 211 before the new HARP2. started the prepayment increase in 212. On the other hand, the Chase cohorts for FN 5s and 6.5s have been running at around 4 cprs and over for more than 3 years. With Chase and other high efficient HARP servicers constituting a much smaller portion of FN 5s and 6.5s, and as the HARP program works off remaining eligible loans/borrowers, tail speeds around 3 cprs for FN 5s and 2 cprs for 6.5s seem to be reasonable, in our view, assuming the refinancing efficiency of these remaining servicers is not drastically increased. Note the prepayment speeds are not sensitive to loan size difference in burned out FN and FH 6.5s across 26 and 21 vintages. The 21 FN 6.5s, at less than 2% average pool factor and 8K loan size, were at around 2 cprs from 29 to 212, and have increased to on average 27 cprs in 213. While tail speeds will be determined by the slowest servicers and the most unresponsive borrower cohort, projecting the path to these tail speeds is subject to two main risk factors in CS6.8: the solicitation effect : if relatively slower servicers increase refinancing efficiency to Chase s level, then the projected HARP burnout in 213 and 214 will be pushed back, and the HARP model timeline will be more front loaded the responsive effect (the existing model burnout effect): current model burnout was fitted to pre-crisis data when loans in pools or cohorts were much more diverse in terms of credit quality, and refinancing willingness and ability. Given the much higher mortgage underwriting standards instituted after the crisis, successful mortgage borrowers were much more uniform, hence we should expect much less burnout, in our view. For example, FN 28 5s are one of the strongest vintage/coupon cohorts, with uniformly large loan size, high FICO score, and low OLTV. They have been running high prepayment speeds since 21, with little evidence of burnout. CS6.8 s burnout for this cohort is likely too high. We will improve the burnout function in our next model iteration. Given these uncertainties, we plan to improve our HARP assumptions with new information going forward and to add dials so that clients can express their own views. Exhibit 5 shows the impact on prepayment projections and model valuations. OAS for TBA and IOS are substantially reduced and durations are much shorter, due to much higher prepayment speed projections in 214 and 215, in CS6.8. Modeling and Analytics 3

4 25 April 213 Exhibit 4: Prepayment and valuation impact on seasoned TBAs and IOS CS 6.71 CS 6.8 Security Price(4/1 9/213) OAS OA Dur 1Y CPR 3Y CPR LT CPR OAS OA Dur 1Y CPR 3Y CPR LT CPR FN FN FN FN FN IFN358 IO IFN3553 IO IFN3555 IO IFN3558 IO IFN368 IO IFN36567 IO estimates Since market participants have generally expected the HARP program will be extended over 213, the market responses to the FHFA announcement were mild. In order to back populate various valuation matrices, CS6.8 assumes that the market started pricing in the HARP extension from 9/1/212. The model uses the old (CS6.71) HARP timeline assumption on 9/1/212, and then gradually switches to the new (CS6.8) HARP assumption till April 11, 213, when a 1% new assumption is used going forward. Conforming jumbo model: new jumbo mortgage rates model, faster refinance ramp, and layered risk of high loan size, states (CA), and origination channel (TPO) Both conforming jumbos (FNCK, FNCJ, FHT6, FHT4) and non-agency prime jumbos have seen growing issuance. Their prepayment speeds have also exhibited large volatility in recent years. CS6.8 models jumbo prepayment with 3 components: new jumbo mortgage rates shorter refinance ramp prepayment s-curve based on loan size, geographic/states (CA, NY, etc.) and origination channel (TPO, Retail) Prevailing mortgage rates for jumbo loans are needed to estimate the refinancing incentive. Exhibit 5 shows spreads between conforming 3 year mortgage rates (Freddie Mac survey) and various jumbo mortgage rates (surveyed by CS, HSH, Bankrate.com). While the overall trend shows a tightening spread since the crisis, the rates show very large volatility and discrepancies with each other. Modeling and Analytics 4

5 Jumbo Spread Sep-9 Nov-9 Jan-1 Mar-1 May-1 Jul-1 Sep-1 Nov-1 Jan-11 Mar-11 May-11 Jul-11 Sep-11 Nov-11 Jan-12 Mar-12 May-12 Jul-12 Sep-12 Jumbo Spread 25 April 213 Exhibit 5: Jumbo spreads noises in survey results 3 year Jumbo mortgage rates spread to Freddie Mac conforming survey rates CS Survey HSH Bankrate Source: Freddie Mac, HSH, Bankrate.com, Credit Suisse A better approach, in our view, is to utilize the loan rates data of newly originated jumbo loans/pools. They represent the actual mortgage rates that borrowers and lenders have managed to transact on. (We used a similar approach to model GN primary mortgage rates for our GN prepayment model. See Modeling and Analytics: Agency MBS Model CS6.6 of August 19, 212.) Exhibit 6: Jumbo spread using "wala = 1" pools.8 Conforming Jumbo Spread (implied by pools age=1) Dec-8 May-1 Sep-11 Jan-13 lnsz>18k ltv<7 fico >76 No credit control Exhibit 6 and Exhibit 7 show the importance of control for mortgage credit and loan size when constructing the jumbo/conforming mortgage rate spread index. Overall, among conforming mortgage loans, mortgage rates tend to be lower for larger loan size and better credit (high FICO, lower OLTV) borrowers. Agency jumbo loans, on the other hand, tend to carry higher mortgage rates compared with similar credit-worthy conforming loans, due to reduced liquidity and a higher prepayment/convexity profile. Modeling and Analytics 5

6 CPR 25 April 213 Exhibit 7: Conforming mortgage rates across loan size Mortgage Rates for different loan sizes (oltv <8 fico >75) /28/25 1/1/26 2/22/28 7/6/29 11/18/21 4/1/212 8/14/213 Exhibit 6 shows the jumbo/conforming spread constructed by using wala = 1 loans, controlling for best credit profile in the conforming sector. CS6.8 uses this spread to the Freddie Mac survey rate as the starting jumbo rate, and mean-reverts this spread to the historically long term spread of 15bps. Exhibit 8: Refinance age ramp jumbo vs. conforming 6 Age Ramp for Jumbo and Conforming Jumbo Conforming Age (month) Exhibit 8 shows that jumbo loans have a much shorter refinance ramp vs. conforming loans. This accounts for the large prepayment speeds difference between 212 and 213 FNCK 3s and 3.5s pools. Modeling and Analytics 6

7 CPR CPR CPR CPR 25 April 213 Exhibit 9: Prepayment s-curve across loan size, states, and loan channel Retail, loan size > TPO, loan size > 45 CA NY Non-CA/NY CA NY Non-CA/NY incentive Incentive Retail, 3 < loan size < 45 CA NY Non-CA/NY incentive TPO, 3 < loan size < 45 CA NY Non-CA/NY Incentive Exhibit 9 shows the layered prepayment risk for jumbo loans: TPO loans are generally faster than Retail channel loans; California loans are faster than NY loans. The TPO CA loans with a loan size higher than 45k can hit almost 7 cprs with a 3bps incentive, while similar NY loans are not refinancable due to the cost of local recording tax. The CS conforming model has components that differentiate performance across states ( geo model ) and origination channels (TPO vs. Retail). The CS6.8 jumbo model combines these components with the higher incentive caused by high loan size, and is able to mimic the rich performance patterns shown in Exhibit 9. In addition, we also apply the agency jumbo model to new issue non-agency jumbo loans, by adding another 15bps mortgage rates spread between non-agency and agency jumbo loan rates. Exhibit 1 shows the comparison of prepayment projections and valuations between FNCL, FNCK and non-agency jumbo deals. Modeling and Analytics 7

8 25 April 213 Exhibit 1: FNCK, FNCL and Non-agency prime jumbos Pricing date: 4/9/213, assuming 7bps OAS; Prepayment projections are based on static mortgage rates assumption Security Coupon WAC WALA FICO LTV Loan Size Price OAS OA Dur OA Cvx 1Y CPR 3Y CPR LT CPR FNCL , FNCL , FNCL , FNCK , FNCK , SQMT133 A , CSMTH1 A , GN model updates: 1bps MIP increase, higher sensitivity to HPA due to appraisal requirement in order to roll in closing cost, potential higher speeds in order to cancel MIP, using newly disclosed pool level MIPs The CS6.8 GN model update includes the: 1bps MIP increase Higher sensitivity to CLTV/HPA due to the appraisal requirement if borrowers want to roll in the closing cost Potentially higher prepayment due to the new FHA policy that rescinds the automatic cancellation of annual MIP collection at 78% LTV after 5 years new pool level upfront and annual MIP disclosure Exhibit 11: GN model update 1bps increase in MIP Except for loans with MIP-exemption Ginnie Mae announced on January 31, 213 an across-the-board 1bps MIP increase, except for the loans qualified for MIP-exemption (Exhibit 11). This increase is now updated in the CS6.8 GN model. Modeling and Analytics 8

9 bps LT CPR Change wrt base HPA assumption LT CPR Change wrt base HPA assumption 25 April 213 Exhibit 12: GN model update steep CLTV/HPA curve for new originations Refi Speeds Sensitivity for GN 4 36 (age=22 oltv=97) 2 Refi Speeds Sensitivity for GN (age=37 oltv=13 cltv=117) CS6.71 CS CS6.71 CS HPA Shock (%) HPA Shock (%) In addition, we have increased the FHA prepayment sensitivity to CLTV/HPA. FHA borrowers are required to re-appraise their properties if they want to roll in the closing costs, which are generally a significant barrier to refinancing by FHA borrowers. Exhibit 12 shows two examples. For a hypothetical GN 4 loan with 97% CLTV, given the current mortgage rates incentive, if HPA were to be down by 1%, prepayment would be reduced by 6 cprs in CS6.8. On the other hand, for a GN 6 pool with 117% CLTV, a 1% HPA may increase the prepayment by 5 cprs in CS6.8, almost double the response in CS6.71. On January 31, 213, FHA announced a new policy to rescind the automatic cancellation of the annual MIP collection at 78% LTV after 5 years amortization. This may increase borrowers incentive to refinance to get rid of the annual MIP when LTV is reduced due to amortization and/or HPA. CS6.8 adds an elbow shift (as a function of LTV) as an extra incentive to refinance to reduce the annual MIP. Exhibit 13 shows an example of this elbow shift over the lifetime for a GN 3.5s pool, with OLTV = 95, and under flat HPA assumptions. Exhibit 13: GN model update potential impact from the new FHA MIP cancellation policy On January 31, 213, FHA announced the rescinding of the automatic cancellation of the annual MIP collection at 78% LTV after 5 years amortization. This may marginally increase future prepayments as borrowers may refinance to get rid of annual MIP Elbow shift Month Modeling and Analytics 9

10 Estimated in CS 6.7 Estimated in CS April 213 GN has also started to disclose pool-level upfront and annual MIPs since October 212. Previously, without this information, CS6.71 estimates the pool level MIPs using the MIP table in Exhibit 14, on the assumption that 6% of the refinanced loans qualified for the grandfathered low MIP. Exhibit 14: GN MIP table CS6.8 will switch to use the actual pool level MIP data, instead of the estimated data. We examine potential valuation changes due to the gap between actual and previously estimated pool level MIPs. Exhibit 15 shows a comparison of upfront and annual MIPs between actual and previously estimated values for pools issued after 6/212. For upfront MIPs, estimated values are, on average, about 1.7bps higher than actual value, with an error standard deviation of 19.8bps. For annual MIPs, estimated values are, on average, about 8.9bps lower than actual value, with an error standard deviation of 13.5bps. Overall, the estimated values were accurate, hence valuation jumps due to this issue should be small in general. Exhibit 15: GN model update pool level MIP data Estimated pool MIPs used by CS 6.7 are close to Actual pool MIP used by CS 6.8 for pools issued after 6/1/ Weight Average Error Weight Average Error Standard (CS 6.8- CS6.7) Deviation (CS 6.8- CS6.7) Upfront MIP Annual MIP Upfront MIP Actual Pool Level 14 Annual MIP Actual Pool Level Modeling and Analytics 1

11 25 April 213 Exhibit 16 shows a sample of GN pools that have much lower MIPs than previously estimated and used in CS6.71. Most of these pools have a much higher percentage of loans with grandfathered low MIPs than the 6% value used by the CS6.71 estimation method. Exhibit 16: GN MIP data release example of pools with much lower MIPs than previously estimated in CS6.71 Issuer Series UPB Issue date WAC Refinance pct CS 6.8 Pool Level MIP Upfront MIP Annual MIP CS 6.7 Estimated MIP CS 6.8- CS 6.7 Upfront MIP Annual MIP Upfront GN AC644 1,664, /1/ GN ,93, /1/ GN AC2916 1,287, /1/ GN ,25, /1/ GN AD1887 1,226, /1/ GN AD595 2,539, /1/ MIP Annual MIP Exhibit 17: GN model update valuation impact CS 6.71 CS 6.8 Security Price (4/19/213) OAS OA Dur 1Y CPR 3Y CPR LT CPR OAS OA Dur 1Y CPR 3Y CPR LT CPR GN GN GN GN GN GN GN GN GN IG2341 IO IG23451 IO IG2351 IO Exhibit 17 shows the sample impact on prepayment projections and valuations for CS6.71 and CS6.8. Overall, the lower coupons are affected mainly by the 1bps MIP increase as well as the higher sensitivity to HPA/CLTV, while the higher coupons are affected by the higher sensitivity to HPA/CLTV and the extra elbow shift/incentive due to the rescinding of the automatic cancellation of MIPs. Modeling and Analytics 11

12 25 April 213 Agency ARM model update: adding HARP2. component CS6.8 adds a HARP2. component to the agency ARM model. Exhibit 18: FN ARM HARP 2. performance across terms, vintages, and coupons Speed change defined as average speed of Oct, Nov, and Dec 212 minus Jan 212 speed Type Year Coupon UPB (in billions) Loan Size FICO OLTV CLTV (Jan. 212) Speed Change , , , , , , , , , , / , , , , , / , , / , Exhibit 18 shows the prepayment speed changes from January 212 to Q4 212, as a proxy for HARP2. speed increases, across ARM product types, vintages and coupons. The 27 5/1 ARM is an exception, as this cohort went through fixed-to-arm resetting in 212, hence the potential HARP2. speed increases were cancelled by the lower prepayment after resets where borrowers enjoyed a much lower mortgage rate indexed on 1-year Libor or CMT rates. The overall HARP2. behavior is Pre-reset ARMs in general responded strongly to the HARP 2. program. For a given vintage and coupon, ARMs with longer initial fixed-period responded stronger. For a given type and vintage, for example, 7/ % vs. 7/ %, the level of response is correlated to the level of credit worthiness, i.e. higher FICO and/or lower CLTV. Post-reset ARMs, e.g. 5/1 24,25, and 26 vintages, still responded to HARP 2. at moderate levels by potentially refinancing into fixed-rate mortgages to lock in the historically low fixed long term rate. Using similar structures as in fixed rate model, CS6.8 ARM model uses these drivers to model HARP2. response. Exhibit 19 shows a sample model results for FN 5/ % cohort. Modeling and Analytics 12

13 CPR 25 April 213 Exhibit 19: FN 5/ % error tracking CS6.8 tracks HARP 2. speeds 5 Actual v HARP 2. Jan-8 Jan-9 Jan-1 Jan-11 Jan-12 Jan-13 Modeling and Analytics 13

14 GLOBAL SECURITIZED PRODUCTS RESEARCH Roger Lehman, Managing Director Global Head of Securitized Products Research Eric Miller, Managing Director Global Head of Fixed Income and Economic Research RESIDENTIAL MORTGAGES CONSUMER ABS Mahesh Swaminathan, Managing Director Chandrajit Bhattacharya, Director Group Head Group Head AGENCY MBS NON-AGENCY MBS Gaurav Singhania, Vice President, CFA Marc Firestein, Analyst Mahesh Swaminathan, Managing Director Chandrajit Bhattacharya, Director Group Head Group Head CDO / CLO Qumber Hassan, Director Gaurav Singhania, Vice President, CFA David Yan, Director Senior Strategist qumber.hassan@credit-suisse.com gaurav.singhania@credit-suisse.com david.yan@credit-suisse.com Vikram Rao, Vice President Marc Firestein, Analyst vikram.rao.2@credit-suisse.com marc.firestein@credit-suisse.com CMBS Roger Lehman, Managing Director Serif Ustun, Vice President, CFA Sylvain Jousseaume, Vice President Tee Chew, Vice President Group Head serif.ustun@credit-suisse.com sylvain.jousseaume@credit-suisse.com tee.chew@credit-suisse.com roger.lehman@credit-suisse.com MODELING AND ANALYTICS David Zhang, Managing Director Group Head david.zhang@credit-suisse.com Tony Tang, Director tony.tang@credit-suisse.com Yihai Yu, Director yihai.yu@credit-suisse.com Taek Choi, Vice President taek.choi@credit-suisse.com Oleg Koriachkin, Vice President oleg.koriachkin@credit-suisse.com Jack Yu, Vice President jie.yu@credit-suisse.com Joy Zhang, Vice President joy.zhang@credit-suisse.com LOCUS ANALYTICS Brian Bailey, Director Locus Analytics Specialist brian.bailey@credit-suisse.com Shana Bornstein, Vice President Locus Analytics Specialist shana.bornstein@credit-suisse.com LONDON JAPAN Carlos Diaz, Vice President Tomohiro Miyasaka, Director carlos.diaz@credit-suisse.com Japan Head tomohiro.miyasaka@credit-suisse.com

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