Waterfall Forensics.

Waterfall Forensics

We ran the contract forward for eight years. It matched to the penny.

Sequoia Mortgage Trust 2013-1: deconstructed from its prospectus into an executable encoding, then replayed against every monthly distribution the trustee ever reported — 93 periods, February 2013 through final termination.

$363M

of actual distributions reproduced

99.7%

of 2,139 comparisons within $1

$0.08

worst principal delta, ever

Every period, every class — computed from the contract vs. reported by the trustee

2014-10-27 · 11/93
ClassInterest computedInterest reportedPrincipal computedPrincipal reportedΔ
1-A1114,657.61114,657.611,814,162.311,814,162.310.01
1-AX167,416.72167,416.80——0.08
2-A1259,177.02259,177.021,968,793.841,968,793.840.00
2-AX261,287.32261,287.30——0.02
B-129,391.6829,391.6926,833.0626,833.070.01
B-219,594.4619,594.4617,888.7117,888.710.00
B-313,256.1013,256.1012,102.1212,102.120.01
B-49,797.239,797.238,944.358,944.360.01
B-512,748.8012,748.720.000.000.08

drag through 93 distribution dates — Δ is the summed absolute difference between the contract-computed and trustee-reported figures for the period

Or see all 93 periods at once: the tie-out heatmap ›

How

The governing documents are deconstructed into a provenance-linked encoding — every waterfall step, trigger, and floor provision cites the clause that defines it. A deterministic interpreter executes that encoding, period by period, against the collateral cashflows the trustee reported. State rolls forward on computed values only; an error anywhere would compound and surface. It doesn't.

The same engine then runs Monte Carlo counterfactuals — what should have happened under different prepayment, default, or servicing behavior — and exports the whole model as readable Python your own analysts run and defend. Every encoding is open to clause-by-clause verification, and every verification lands in an append-only ledger.