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

2014-01-27 · 2/93
ClassInterest computedInterest reportedPrincipal computedPrincipal reportedΔ
1-A1133,140.87133,140.871,071,642.011,071,642.010.00
1-AX198,251.07198,251.04——0.03
2-A1271,820.78271,820.78305,425.83305,425.830.01
2-AX274,296.32274,296.33——0.01
B-130,258.0530,258.0525,783.8625,783.860.01
B-220,172.0420,172.0417,189.2417,189.240.01
B-313,646.8413,646.8411,628.9111,628.910.00
B-410,086.0210,086.028,594.628,594.620.00
B-512,809.7012,809.720.000.000.02

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.