Stress-Test Your Real Estate Deals with AI: A Practical Walkthrough

We use AI to move from documents to decisions—fast. It flags optimistic assumptions, pressure-tests the model, and standardizes investor communication so we can focus on the calls that matter.

On every deal, there’s a moment when the numbers look fine—until they don’t. AI helps us find that moment early: it challenges rent growth, surfaces expense spikes, and shows what breaks first.

In the first hour on any deal, we:

benchmark the OM against comps,
stress-test taxes, rents, CapEx, occupancy, and exit cap, and
package a one-pager for partners and LPs.

What you’ll get in this walkthrough

A document-to-decision workflow (OM → T-12 → rent roll → insights)
Copy-ready stress-test prompts (taxes, rent, CapEx, occupancy, exit cap)
Pittsburgh presets (Strip District & South Hills) and an interactive toolkit you can use today

1) Turn documents into decisions

We load the OM, T-12, rent roll, inspection notes/photos, and prior reports into a grounded AI (e.g., NotebookLM) and ask it to:

Check rent growth vs. comps – Spot gaps and likely causes.
Find expense anomalies – YoY spikes in taxes, insurance, R&M, admin.
Surface CapEx/deferred maintenance – Rank by severity, timing, and cost band.

This first pass shows where assumptions are aggressive and where we need follow-ups with management, brokers, and vendors.

Pittsburgh angle: Strip District vs. South Hills

Strip District (urban core): Higher asking rents, newer product, active pipeline. Focus on lease-up velocity, concessions, and new-supply sensitivity.
South Hills (Mt. Lebanon / Bethel Park): More price-sensitive demand and steadier occupancy. Bias toward flatter rent growth, tight expense control, and slower absorption after turns.

2) Stress-test before you fall in love with the pro forma

After the baseline check, we run downside cases. Paste these into your AI copilot with your model (or key inputs):

Taxes +15% → Recalculate DSCR by year; flag the first DSCR < 1.25 and the covenant at risk.
Zero rent growth (24–36 months) → Return IRR, EMx, and cash-on-cash by year.
CapEx +20% → Show the hit to stabilized NOI and refinance proceeds.
Occupancy shock → At what economic occupancy does monthly cash flow turn negative? Provide the threshold and a 12-month path back to break-even.
Exit-cap break-even → What exit cap yields LP break-even? Include a ±50 bps table.

Pittsburgh twist:

Strip District: Add a lease-up scenario (longer to stabilize, temporary concessions, slower trade-outs).
South Hills: Add an expense-pressure case (insurance +20%, R&M +10%) and keep rent growth conservative.

3) Build a clean, AI-powered deal room

We use Claude to create a one-pager: asset snapshot, in-place metrics, business plan, top risks/mitigations, and a 10-row “what changes if…” table from the stress tests. We attach the OM, T-12, rent roll, inspection photos, and our model so partners and LPs can validate quickly.

4) Make LP communication effortless (and consistent)

With ChatGPT/Claude, we draft:

a transparent update (Performance → Risks & Mitigations → Next 30 Days),
a short FAQ that answers the obvious “what if” questions, and
a closing checklist (funding status, third-party reports, key dates).

Consistency across deals reduces back-and-forth and builds trust.

5) Drop-in submarket inputs (template included)

Keep these files as a reusable template. They include two rows—Strip District and South Hills (Mt. Lebanon/Bethel Park)—with baseline rent anchors and default stress-test deltas (taxes +15%, insurance +20%, CapEx +20%, vacancy shock +150 bps, exit-cap ±50 bps). Adjust to the OM as needed.

CSV template: Download
JSON template: Download

How we wire it:

Import as a “Submarket_Params” table and select the row matching your subject.
Map *_pct fields to scenario toggles (taxes, insurance, CapEx, vacancy).
Replace rent_growth_base_pct with the OM’s assumption; keep rent_growth_downside_pct at 0% for a flat-rent case.
For Strip District assets, add a lease-up/concessions toggle; for South Hills, add an expense-control toggle.

Copy-ready prompts (download)

To keep this easy for all readers, we moved the prompts into a simple, plain-English file you can paste directly into your AI copilot (no formatting headaches).

Prompts pack (markdown): Copy-Ready_Prompts_RealEstate_AI.md

Interactive Toolkit: Stress-Test Your Deal

For regular audiences, the toolkit now ships as a single HTML file plus a short README. Use it inline or embed it via iframe. The bundle also includes the CSV/JSON submarket presets.

HTML widget: interactive_ai_re_toolkit.html
Toolkit how-to (markdown): Interactive_Toolkit_README.md
All-in-one ZIP (widget + CSV/JSON + both docs): AI_RealEstate_Toolkit_Bundle.zip

Final Thought: The Edge Isn’t Access. It’s How You Use It.

Everyone has access to these tools. The differentiator isn’t whether you have AI—it’s how you think with it.

In the next post, we’ll walk through a full example of how we used AI to vet a deal, build a deal room, and prep LP communication—step by step. We’ll also dive deeper into specific use cases with Claude, NotebookLM, and ChatGPT.

If you found this helpful, consider subscribing to the blog. More detailed examples, prompts, and AI workflows for real estate investors are coming soon.