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

2014-06-25 · 7/93
ClassInterest computedInterest reportedPrincipal computedPrincipal reportedΔ
1-A1124,177.23124,177.232,252,196.462,252,196.460.00
1-AX183,660.26183,660.19——0.07
2-A1268,386.57268,386.571,945,386.051,945,386.050.01
2-AX270,814.06270,814.06——0.00
B-129,812.9329,812.9326,457.3426,457.340.00
B-219,875.2919,875.2917,638.2317,638.230.01
B-313,446.0813,446.0811,932.6611,932.660.01
B-49,937.649,937.648,819.118,819.110.01
B-512,789.7512,789.840.000.000.09

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.