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 numerical multiplier expressing the relationship between a building's total rentable area and its total usable area, typically expressed as a decimal (e.g., 1.15), representing the add-on factor for common areas allocated to tenants.
The load factor ratio (also called the "add-on factor" or "common area factor") is calculated by dividing total rentable square footage by total usable square footage for a building or floor. A ratio of 1.15 means for every square foot of usable space, a tenant is billed for 1.15 square feet of rentable space — the additional 15% representing the tenant's allocated share of common areas. Ratios vary significantly: efficient floor plates in modern buildings may have ratios near 1.10–1.12, while older buildings with large corridors and inefficient cores may reach 1.20–1.25 or higher. Tenants should compare load factor ratios across competing buildings to understand the true cost per occupiable square foot.
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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Free AI lease abstraction tools are fast and easy — but they have real limitations. Here is what free tools deliver, what they miss, and when you need structured output instead.
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