FAQ
How should a CRE team use this article?
Use it as a checklist for the questions to ask during underwriting, not as a substitute for source-backed deal review. The final model still needs document citations, market checks, review states, and clear assumption ownership.
Where does Cactus fit in this workflow?
Cactus reads deal-room materials, checks assumptions against market context, surfaces conflicts, lets users approve the facts that drive the model, and preserves the logic as Proprietary Memory for the next deal.
AI is changing CRE underwriting when it is wrapped in source trails, deterministic math, reviewer approvals, market checks, and Excel-ready outputs.
AI is changing commercial underwriting because the work is full of repeatable friction. Analysts spend too much time extracting rent rolls, cleaning T-12s, reconciling OMs against historicals, pulling comps, checking market assumptions, and rebuilding the same model logic from scratch.
But speed is not the full answer. A fast model with weak source discipline is just a faster way to make the wrong bid.
Where AI helps
- Extracting structured facts from OMs, rent rolls, T-12s, leases, PDFs, and spreadsheets.
- Normalizing income and expense lines into the team's underwriting format.
- Finding conflicts between seller materials, leases, operating history, and market context.
- Preparing assumptions for human review instead of burying them in a black box.
- Drafting memos, lender packages, BOVs, and presentation outputs from approved work.
Where AI needs controls
CRE teams should be skeptical of any workflow that skips citations, formula tie-outs, market checks, approval states, or editable assumptions. The more polished the output, the more important the audit trail becomes.
Cactus treats AI as part of the workflow, not as the final authority. The user decides which document facts, market checks, and custom assumptions drive the model.
How Cactus turns this into defensible underwriting
The workflow matters because the model is only one artifact. Cactus connects the deal package, market evidence, reviewer decisions, Excel outputs, templates, and Proprietary Memory so the work survives handoffs instead of disappearing into a one-off spreadsheet or chat thread.
- Extract relevant facts from OMs, rent rolls, T-12s, leases, PDFs, spreadsheets, and customer templates.
- Check rents, expenses, growth targets, cap rates, sales comps, and site context against market intelligence from premium data providers, public records, and firm history.
- Surface conflicts, confidence states, reviewer comments, and assumption overrides before the model becomes the memo.
- Populate Excel or Cactus models, then store approved facts, templates, comps, and decisions in Proprietary Memory for the next deal.
The point is not to make the model less sophisticated. The point is to make the source, market check, assumption owner, review state, and output path visible before the number reaches a partner, lender, client, or investment committee.
Defensible underwriting
Defend every number before it reaches IC.
Cactus gives CRE teams ARGUS-grade underwriting intelligence with document extraction, market checks, source trails, reviewable assumptions, Excel-ready outputs, and Proprietary Memory around the workflow.
- Rent rolls, T-12s, OMs, comps, and assumptions live in separate files.
- Market evidence gets copied into the model without a durable source trail.
- Reviewer decisions disappear after the memo, email thread, or spreadsheet version changes.
- Extract deal facts from OMs, rent rolls, T-12s, leases, PDFs, spreadsheets, and customer templates.
- Check rents, expenses, growth targets, sales comps, and other assumptions against market intelligence.
- Populate Excel or Cactus models and preserve approved logic as Proprietary Memory.
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