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

2016-01-25 · 26/93
ClassInterest computedInterest reportedPrincipal computedPrincipal reportedΔ
1-A183,635.1883,635.18339,411.79339,411.790.01
1-AX121,044.56121,044.59——0.03
2-A1183,841.37183,841.373,713,645.113,713,645.110.00
2-AX182,288.87182,288.84——0.03
B-127,931.4827,931.4828,894.7028,894.700.01
B-218,620.9918,620.9919,263.1319,263.130.00
B-312,597.5212,597.5213,031.9513,031.950.01
B-49,310.499,310.499,631.579,631.570.01
B-512,662.1812,662.190.000.000.01

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.