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

2016-09-26 · 34/93
ClassInterest computedInterest reportedPrincipal computedPrincipal reportedΔ
1-A167,982.5467,982.542,281,902.452,281,902.450.00
1-AX99,913.8499,913.87——0.03
2-A1152,077.91152,077.913,771,621.213,771,621.210.00
2-AX150,857.11150,857.14——0.03
B-127,324.0227,324.0230,214.4630,214.460.00
B-218,216.0118,216.0220,142.9720,142.970.01
B-312,323.5512,323.5513,627.1813,627.180.00
B-49,108.019,108.0110,071.4910,071.490.01
B-512,710.4112,710.340.000.000.07

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.