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

2015-07-27 · 20/93
ClassInterest computedInterest reportedPrincipal computedPrincipal reportedΔ
1-A193,337.6493,336.841,743,145.241,743,145.240.80
1-AX135,327.60135,326.42——1.18
2-A1200,442.74200,442.74870,354.00870,354.010.02
2-AX200,006.20200,006.20——0.00
B-128,480.9028,481.5728,044.2528,044.250.68
B-218,987.2618,987.7118,696.1718,696.170.45
B-312,845.3212,845.6112,648.3812,648.380.30
B-49,493.639,493.869,348.089,348.080.23
B-512,676.7412,677.070.000.010.34

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.