AI Lease Abstraction Accuracy: Benchmarks and What to Expect
What accuracy can you realistically expect from AI lease abstraction tools? We break down field-level accuracy rates, where AI excels, where it struggles, and how to validate output.
Lextract extracted 126 fields from a $52M/year REIT master lease: absolute net structure, 20-year term, portfolio-level financials, and REIT entity classification.
By Angel Campa, Founder · Updated March 2026
Location
Multiple US locations
Size
Multi-property
Annual Rent
$52.0M/yr
Term
240 months
Tenant
Service Properties Trust (SVC)
Landlord
RMR Group
This is a REIT master lease covering a portfolio of service properties at $52 million annual rent on a 20-year absolute net structure. Master leases require extracting portfolio-level terms while flagging that individual property details are not embedded — a fundamentally different structure from single-tenant leases.
Lextract correctly classified this as a master lease, extracted the portfolio-level financial terms ($52M/yr, 20-year absolute net), identified the RMR Group management structure, and flagged the absence of individual property detail as expected for this lease type. The absolute net structure was correctly distinguished from NNN.
126
Fields Extracted
Under 3 minutes
Extraction Time
| Field | Extracted Value | Why It Matters |
|---|---|---|
| Lease Type | Master Lease — Absolute Net | Portfolio-level obligation; tenant responsible for ALL costs including structural |
| Annual Rent | $52,000,000 | Largest annual rent in extraction corpus — portfolio-level obligation |
| Lease Term | 240 months (20 years) | Very long-term REIT master lease — limited near-term refinancing risk |
| Portfolio Structure | Multiple US properties | Master lease covers entire portfolio; no per-property breakdown |
| Rent Escalation | Fixed 2% annual | Below-CPI escalation common in long-term REIT structures |
| REIT Entity | Service Properties Trust | REIT tenant classification affects credit analysis differently than operating company |
In a triple net (NNN) lease, the tenant pays taxes, insurance, and maintenance. In an absolute net lease, the tenant additionally pays for structural repairs and replacements (roof, foundation, etc.), making it the most tenant-responsible lease structure. Lextract distinguishes these and flags the difference.
Lextract extracts all portfolio-level terms (total rent, term, escalation, renewal options) and notes the master lease structure. Per-property details are flagged as not present in the document — not as extraction failures.
What accuracy can you realistically expect from AI lease abstraction tools? We break down field-level accuracy rates, where AI excels, where it struggles, and how to validate output.
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