Structured data extraction is the process of turning unstructured documents into typed fields that fit a predefined schema. In commercial real estate, it converts a lease, rent roll, or offering memorandum into named values such as base rent, commencement date, and square footage. The output is a table a system can query, not prose a person must read.
How Does Structured Data Extraction Work?
Structured data extraction works by defining a target schema first, then compelling a model to fill it. The schema names each field and its type: base rent as a number, commencement date as a date, tenant as a string. The model reads the document, locates values, and returns them in the schema's shape.
Modern systems enforce the shape with constrained decoding. A JSON Schema is compiled into a state machine, and at each generation step only tokens that keep the output valid are allowed, which OpenAI's Structured Outputs (2024) and comparable features from Google, Anthropic, and open-source runtimes use to guarantee the format. This guarantees valid structure, not correct content. A field can be perfectly typed and still wrong, which is why extraction accuracy is measured per field, not per document.
Component | Function |
Schema | Names each target field and its data type |
Locating | Finds where in the document each value lives |
Constrained decoding | Forces the output to match the schema's shape |
Validation | Checks types and ranges before the value is accepted |
Why Structured Data Extraction Matters
Structured data extraction matters because everything downstream in a deal needs structure, and the source documents have none. A pro forma cannot read a 90-page lease; it needs base rent as a number. Structured extraction is the bridge, and its accuracy sets a ceiling on every calculation built on top of it.
The adoption signal is clear. Over 66% of commercial real estate firms have shifted toward automation for document processing and lease tracking, with 59% reporting significant decreases in manual data-entry time, per industry reporting compiled by Extend. Leading extraction platforms report 95%+ field-level accuracy on standard fields. The remaining percentage is not evenly spread: it concentrates in amended, cross-referenced, and narrative clauses, which is where structured extraction must be paired with review.
Example
Structured data extraction is clearest on a single lease against a schema. An analyst defines a five-field schema and runs a retail lease through an extraction model, which returns typed values with a source page for each.
Schema field | Type | Extracted value | Source page |
tenant_name | string | Riverside Outfitters LLC | 1 |
base_rent_annual | number | 612000 | 41 |
commencement_date | date | 2026-09-01 | 3 |
rentable_sqft | number | 8500 | 2 |
escalation_pct | number | 3.0 | 12 |
From these typed fields, a system computes base rent per square foot directly: $612,000 divided by 8,500 square feet equals $72.00 per square foot per year. That figure feeds a comparison against market rent with no re-keying. Because each value carries a source page, the escalation_pct of 3.0 can be verified against page 12 before it enters a pro forma. The document went in as prose and came out as five queryable numbers plus a trail back to their source.
Variations and Edge Cases
Structured data extraction spans several techniques, and the right one depends on how variable the documents are. The variants below trade rigidity against flexibility as layouts and clause language change across a portfolio.
Variant | Treatment |
Template extraction | Fixed field positions; fast but breaks on layout changes |
Schema-guided LLM extraction | Model fills a schema from varied layouts; handles amendments |
Constrained decoding | Guarantees valid output shape via a compiled grammar |
Function calling | Model targets a schema as a strong hint, not a hard guarantee |
Table extraction | Specialized handling for rent rolls and financial schedules |
Structured Data Extraction vs OCR
Structured data extraction is often confused with OCR, but they solve different problems. OCR, optical character recognition, converts an image of a page into raw text, with no understanding of what any value means. Structured data extraction takes that text, or a digital document, and maps specific values to named, typed schema fields.
OCR answers "what characters are on this page." Structured extraction answers "what is the base rent, as a number, and where did it come from." OCR is often an input step to extraction, not a substitute for it, which is why an OCR-only pipeline still leaves an analyst reading prose to find the fields that matter.
Frequently Asked Questions
What is structured data extraction?Structured data extraction is the process of turning unstructured documents into typed fields that match a predefined schema. In commercial real estate it converts a lease or rent roll into named values such as base rent and commencement date that a system can query directly.
How accurate is structured data extraction on leases?Leading platforms report 95%+ field-level accuracy on standard fields, per industry reporting. Accuracy drops on amended, cross-referenced, and narrative clauses, so the remaining error concentrates where it is hardest to read, which is why field-level review is paired with extraction.
Is structured data extraction the same as OCR?No. OCR converts a page image into raw text with no understanding of meaning. Structured data extraction maps values to named, typed schema fields such as base rent as a number. OCR is often an input step to extraction, not a replacement for it.
Related Terms
Retrieval-Augmented Generation
AI Hallucination
Field Extraction
Intelligent Document Processing
Human-in-the-Loop