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

2015-09-25 · 22/93
ClassInterest computedInterest reportedPrincipal computedPrincipal reportedΔ
1-A188,494.1388,494.13426,253.93426,253.930.00
1-AX127,944.44127,944.57——0.13
2-A1196,043.07196,043.07655,398.23655,398.230.00
2-AX194,874.63194,874.59——0.04
B-128,290.5528,290.5628,320.9128,320.910.01
B-218,860.3718,860.3718,880.6118,880.610.01
B-312,759.4712,759.4712,773.1612,773.160.00
B-49,430.189,430.199,440.309,440.300.01
B-512,667.8212,667.820.000.000.00

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.