Once lease obligations are extracted, Firststreet encodes them as precise financial models, every escalation formula, recovery structure, and calculation rule expressed in machine-executable logic.
Extracting the text of a lease obligation is not sufficient for governance. A provision that says "base rent shall escalate annually by the greater of 3% or CPI" contains multiple conditional branches, a data dependency on an external index, and a calculation rule that must be applied at a specific interval. Representing that provision accurately as an operational model requires encoding all of those elements, not just summarizing the clause.
Firststreet's structured financial logic layer takes the obligations identified during lease interpretation and converts them into models that can be independently calculated, compared against operational data, and verified for accuracy. Each model captures the calculation inputs, the formula structure, the applicable period, and the expected output, expressed in a form that the monitoring engine can evaluate against actual billing records.
This is what separates Firststreet from lease abstraction. An abstract can tell you that a CPI escalation clause exists. Structured financial logic can tell you exactly what the rent should have been billed at in Q3 of last year, and compare that against what was actually charged.
Commercial lease portfolios are not homogeneous. A single 200-lease retail portfolio may contain triple-net leases with annual fixed-step escalations, modified gross leases with base year expense stops, gross leases with no recovery rights, and hybrid structures that don't fit any standard category cleanly.
Firststreet's structured logic layer is built to handle this complexity without flattening it. CAM recovery structures with tenant-specific exclusions and administrative fee caps are modeled with full fidelity. The platform does not round off the edges to make modeling simpler.
Structured financial logic is the reference layer for the entire Firststreet governance stack. When the execution monitoring engine evaluates whether billing is correct, it compares actual operational data against the expected output derived from the structured logic model. When the discrepancy detection layer identifies a gap, it quantifies that gap using the same model. When evidence-based findings are generated, they cite the specific logic that was applied.
This means the quality of financial modeling directly determines the quality of every downstream finding. Firststreet invests heavily in modeling fidelity because imprecise models generate false positives, missed discrepancies, and findings that don't hold up under tenant scrutiny. The goal is not to produce a rough estimate of what rent should be, it is to produce the exact number the lease requires, supported by traceable logic.
Request a demo to see how Firststreet structures the financial logic inside your lease portfolio and what that makes possible for governance.