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.
The assumed occupancy level used to extrapolate variable operating expenses.
Also known as: Gross-Up Provision
By Angel Campa, Founder · Updated March 2026
Without a gross-up provision, existing tenants in a partially occupied building subsidize the vacant space. If a building is only 60% occupied but tenants pay pro rata shares of actual expenses, each tenant effectively pays 167% of their fair share. A gross-up to 95% occupancy normalizes expenses to reflect a stabilized building, preventing cost spikes as vacancies fluctuate.
Found in the "Operating Expenses" or "Additional Rent" section. Look for language about "adjusting expenses as if the building were X% occupied" or "gross-up to 95% occupancy." May also be in a definitions section.
Lextract uses a combination of AWS Textract OCR and Claude AI to identify and extract the gross-up % from your lease PDF. The AI searches for the field name and common aliases like "Gross-Up Provision" across all pages of the document, then assigns a confidence score based on OCR quality and extraction certainty. Fields with lower confidence are flagged for human review.
Lextract automatically checks this field against its 15-rule red flag engine. Issues detected for gross-up %:
Lease Structure
The categorization of expense sharing.
Pro Rata Share
The tenant's fractional responsibility for total building operating expenses.
Base Year
The foundational year used to calculate operating expense increases in gross leases.
CAM Cap %
The maximum allowable annual increase for controllable operating expenses.
CAM Cap Type
Specifies whether the CAM cap is cumulative and compounding or non-cumulative.
Management Fee Cap
The maximum allowable percentage of gross revenues charged for property management.
Gross-up adjusts variable operating expenses to reflect what they would be if the building were at a specified occupancy level (typically 95%). This prevents tenants from absorbing costs attributable to vacant space. Only variable expenses (like utilities and janitorial) are grossed up; fixed costs (like insurance and taxes) are not.
The standard gross-up assumes 95% occupancy, meaning variable expenses are calculated as if 95% of the building were occupied. Some leases use 100%, but 95% is the market standard because even fully leased buildings maintain some operational vacancy.
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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