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

2016-10-25 · 35/93
ClassInterest computedInterest reportedPrincipal computedPrincipal reportedΔ
1-A165,225.2465,225.243,043,817.103,043,817.100.01
1-AX95,401.0595,401.10——0.05
2-A1146,247.61146,247.612,902,689.272,902,689.270.00
2-AX145,028.83145,028.93——0.10
B-127,200.1327,200.1430,242.8430,242.840.01
B-218,133.4218,133.4320,161.8920,161.890.01
B-312,267.6712,267.6813,639.9813,639.980.01
B-49,066.719,066.7110,080.9510,080.950.00
B-512,695.4212,695.430.000.000.01

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.