How Dealyze Reads Your Documents
96% field extraction accuracy across real offering memos.
Two-Stage AI Extraction
Every PDF goes through a multi-pass pipeline powered by GPT-4o vision.
We render your first few pages and identify the document type: Offering Memo, T12, Rent Roll, or Mixed. This tells us exactly which fields to look for.
A targeted prompt extracts 27+ financial fields from up to 10 rendered pages. If key fields are missing, a fallback pass scans the full document for financial tables.
What We Extract
- Property info — name, address, unit count, year built, lot size
- Income — gross potential rent, vacancy rate, other income, effective gross income
- Expenses — line items (taxes, insurance, maintenance, management, utilities) and totals
- Financing — asking price, loan amount, interest rate, loan term, amortization
- Rent roll — unit-level rents, square footage, bed/bath counts
- Valuation — cap rate, price per unit, GRM
Accuracy by Document Type
Summary-only OMs may be missing line-item expenses or detailed rent rolls. You can fill in any gaps manually.
Confidence Indicators
Every extracted value includes a confidence signal so you know what to trust.
What To Do When Extraction Is Wrong
Every value in your report is editable. Open the assumptions editor, change any number, and the underwriting model re-runs instantly. No need to re-upload — just correct and go.
Our Test Results
Tested across 10 real offering memos from CBRE, Marcus & Millichap, and local brokerages. Properties ranging from 10 to 79 units across California and Washington markets. The three-pass extraction pipeline achieves 96% field-level accuracy across all 27+ extracted fields.
See for yourself — upload any OM and we'll show you what we find.
Try Free Analysis