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

2014-12-26 · 13/93
ClassInterest computedInterest reportedPrincipal computedPrincipal reportedΔ
1-A1111,342.70111,342.701,104,168.661,104,168.660.01
1-AX162,064.52162,064.56——0.04
2-A1254,013.95254,013.953,802,757.583,802,757.580.01
2-AX255,552.69255,552.84——0.15
B-129,189.4329,189.4426,920.8326,920.820.01
B-219,459.6219,459.6317,947.2217,947.220.01
B-313,164.8813,164.8812,141.7012,141.700.00
B-49,729.819,729.818,973.618,973.610.00
B-512,731.9312,731.830.000.000.10

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.