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

2020-03-25 · 76/93
ClassInterest computedInterest reportedPrincipal computedPrincipal reportedΔ
1-A124,318.9124,318.911,339,600.611,339,600.610.00
1-AX36,254.0936,254.09——0.00
2-A179,968.8379,968.83793,286.41793,286.410.00
2-AX78,333.2078,333.21——0.01
B-120,600.9320,600.93165,859.25165,859.250.01
B-213,733.9513,733.95110,572.83110,572.830.00
B-39,291.339,291.3374,805.0474,805.040.00
B-46,866.986,866.9855,286.4255,286.420.01
B-512,710.8012,710.790.000.000.01

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.