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

2016-12-27 · 37/93
ClassInterest computedInterest reportedPrincipal computedPrincipal reportedΔ
1-A161,222.2861,222.281,092,820.221,092,820.220.00
1-AX90,239.8790,239.90——0.03
2-A1141,465.69141,465.691,754,259.211,754,259.210.01
2-AX140,184.19140,184.16——0.03
B-127,057.1427,057.1430,938.3630,938.360.01
B-218,038.0918,038.0920,625.5820,625.580.01
B-312,203.1812,203.1813,953.6713,953.670.00
B-49,019.059,019.0510,312.7910,312.790.01
B-512,715.3912,715.400.000.000.01

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.