lev-os Analyzing Mortgage Backed Securities
Evaluates MBS structures with prepayment modeling (CPR/CDR), collateral analysis, and tranche-level credit risk assessment.
When To Use
- Analyzing agency (Ginnie Mae, Fannie Mae, Freddie Mac) or non-agency RMBS deals
- Modeling prepayment and default scenarios on residential mortgage collateral pools
- Evaluating tranche-level credit enhancement, subordination, and cash flow waterfall mechanics
- Comparing MBS tranches for relative value across spread, WAL, and convexity profiles
- Assessing seasoned pools for re-REMIC structuring or secondary market trading
- Reviewing offering documents, prospectus supplements, or trustee reports for deal-level risk
Inputs To Gather
- Deal documents: Prospectus supplement, pooling and servicing agreement (PSA), trustee reports
- Collateral tape: Loan-level data including FICO, LTV, DTI, loan age, geography, occupancy type, documentation level
- Pool statistics: Current balance, WAC, WAM, WALA, average loan size, delinquency buckets (30/60/90+), REO pipeline
- Structure details: Tranche map (senior/mezzanine/subordinate), credit enhancement levels, OC/XS triggers, step-down dates
- Prepayment and default assumptions: Base-case CPR, CDR, loss severity, recovery lag; stress scenarios if applicable
- Market context: Current mortgage rates, refi incentive, HPA trends, [VERIFY] agency guarantee status and program eligibility
- Rating agency criteria: Applicable S&P, Moody's, Fitch, or DBRS loss/stress frameworks if rating-dependent analysis
Workflow
Map the deal structure
- Identify all tranches: senior (A classes), mezzanine (M classes), subordinate (B classes), IO strips, residual
- Document the cash flow waterfall: sequential vs. pro-rata pay periods, trigger events (delinquency/loss triggers), clean-up call provisions
- Record credit enhancement: subordination percentages, overcollateralization targets, excess spread capture mechanisms
Profile the collateral pool
- Stratify loans by vintage, FICO band, LTV bucket, geography (state/MSA concentration), loan purpose (purchase/refi/cash-out), and property type
- Identify adverse selection risks: high LTV concentrations, low-doc loans, investor properties, geographic clustering
- Calculate weighted-average characteristics and compare against benchmark pools for the same vintage/program
Model prepayment scenarios
- Set base-case CPR using historical analogs (e.g., seasoned conventional 30yr at current coupon spread to market)
- Run sensitivity across CPR vectors: slow (e.g., 6 CPR), base (e.g., 15 CPR), fast (e.g., 35 CPR), and ramp scenarios (PSA multiples)
- For each scenario, compute WAL, yield, spread to benchmark, and effective duration for each tranche
- Assess negative convexity exposure on premium-priced tranches and extension risk on discount tranches
Model default and loss scenarios
- Set base-case CDR and loss severity using collateral characteristics and historical performance curves
- Run stress scenarios: 2x CDR, elevated severity (e.g., 40%–60% on non-agency), delayed recovery timelines
- Determine tranche-level loss absorption: at what CDR/severity combination does each tranche experience principal write-down
- Evaluate trigger mechanics — will delinquency or cumulative-loss triggers divert cash flow from subordinate tranches
Assess credit enhancement adequacy
- Compare current subordination levels to original levels and to rating-agency loss benchmarks
- Evaluate OC build/release mechanics and whether excess spread is sufficient to maintain targets under stress
- For seasoned deals, assess whether step-down conditions have been met and whether senior tranches benefit from de-leveraging
- [VERIFY] Check for any amendments, modifications, or servicer advances that may affect collateral performance
Synthesize relative value and risk conclusions
- Rank tranches by spread per unit of WAL risk, credit risk, and convexity exposure
- Flag tranches with asymmetric risk profiles (e.g., thin mezzanine with cliff risk, IO strips with high prepay sensitivity)
- Compare to similar deals in the market for relative value context
- Identify key monitoring triggers: delinquency thresholds, cumulative loss benchmarks, servicer performance metrics
Output
Produce a structured MBS analysis report containing:
- Deal overview: Issuer, shelf program, closing date, original/current balance, servicer(s), trustee
- Collateral summary: Pool composition table with stratifications, weighted-average metrics, delinquency and loss performance to date
- Structure summary: Tranche map with current balances, coupons, credit enhancement levels, and priority of payments description
- Prepayment analysis: Table of WAL, yield, and spread across CPR scenarios for each evaluated tranche
- Credit analysis: Loss absorption capacity by tranche, stress-test results, trigger proximity assessment
- Relative value assessment: Spread comparison to benchmark deals, convexity-adjusted return analysis
- Risk flags and monitoring points: Concentration risks, servicer concerns, trigger events approaching thresholds
- Appendix: Key assumptions, data sources, model methodology notes
Quality Checks
- Verify that all tranche balances sum to total deal balance and that waterfall logic is internally consistent
- Confirm CPR/CDR assumptions are sourced from stated methodology, not arbitrary — cite historical analogs or agency benchmarks
- Ensure loss severity assumptions match collateral type (e.g., non-agency subprime vs. agency conforming have materially different severities)
- Cross-check credit enhancement percentages against trustee reports, not just offering documents, for seasoned deals
- [VERIFY] Rating agency criteria versions used — S&P, Moody's, and Fitch periodically update RMBS loss frameworks
- [VERIFY] Regulatory considerations: risk retention rules (Reg RR), QM/ATR status of underlying loans, Volcker Rule implications for trading book holdings
- Flag any data gaps in the collateral tape (missing FICO, undisclosed LTV) and note their impact on model reliability
- Mark all forward-looking projections as estimates subject to rate, HPA, and macroeconomic assumptions