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.

1) Turn Documents into Decisions

We load the OM, T-12, rent roll, inspection notes, and prior reports into a grounded AI tool like NotebookLM and ask it to surface what matters:

• Check rent growth vs. comps — Spot gaps and likely causes.
• Find expense anomalies — Year-over-year spikes in taxes, insurance, R&M, and admin.
• Surface CapEx and deferred maintenance — Ranked by severity, timing, and estimated cost.

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. Here are the exact prompts we paste into our AI copilot along with the deal model or key inputs:

• Taxes +15% — Recalculate DSCR by year; flag the first year DSCR falls below 1.25 and identify the covenant at risk.
• Zero rent growth for 24–36 months — Return IRR, equity multiple, and cash-on-cash by year.
• CapEx +20% — Show the impact on stabilized NOI and refinance proceeds.
• Occupancy shock — At what economic occupancy does monthly cash flow turn negative? Provide the break-even threshold and a 12-month path back.
• Exit cap break-even — What exit cap rate yields LP break-even? Include a ±50 bps sensitivity table.

Pittsburgh-specific additions:

• 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 generate a one-pager covering the asset snapshot, in-place metrics, business plan, top risks and mitigations, and a sensitivity table showing what changes under each stress scenario. We attach the OM, T-12, rent roll, inspection photos, and our model so partners and LPs can validate quickly without back-and-forth.

4) Make LP Communication Effortless and Consistent

With AI, we draft three documents for every deal update:

• A transparent performance update structured as: Performance → Risks & Mitigations → Next 30 Days
• A short FAQ that pre-answers the obvious investor questions
• A closing checklist covering funding status, third-party reports, and key dates

Consistency across deals reduces back-and-forth and builds investor trust over time.

5) Submarket Inputs We Use for Pittsburgh Deals

For every Pittsburgh deal, we anchor our stress tests to two submarket profiles. You can recreate these as a simple reference table in your own underwriting model:

Strip District — Baseline rent growth: 2.5–3.5% annually. Stress-test deltas: taxes +15%, insurance +20%, CapEx +20%, vacancy shock +150 bps, exit cap ±50 bps. Add a lease-up concessions toggle for newer product.

South Hills (Mt. Lebanon / Bethel Park) — Baseline rent growth: 1.5–2.5% annually. Stress-test deltas: same as above. Add an expense-control toggle and keep rent growth at the conservative end.

Adjust these anchors to the specific OM assumptions on each deal — the goal is to have a consistent starting point so you're not building from scratch every time.

Copy-Ready Prompts You Can Use Today

Here are the core prompts we use across our deal workflow. Paste these directly into Claude, ChatGPT, or NotebookLM with your deal documents attached:

Document review: "Review this OM, T-12, and rent roll. What rent growth assumptions are made and are they supported by the submarket comps? Flag any expense categories with unusual year-over-year movement."

Stress test: "Using the inputs from this model, run the following scenarios and show results by year: (1) taxes +15%, (2) zero rent growth for 36 months, (3) CapEx +20%, (4) occupancy drops to 88%. At what occupancy does monthly cash flow turn negative?"

LP one-pager: "Using this deal data, write a one-page deal summary for limited partners. Include: asset overview, in-place metrics, value-add business plan, top 3 risks with mitigations, and a sensitivity table showing IRR under base, downside, and stress scenarios."

LP update: "Turn these deal notes into a quarterly LP update. Structure it as: Performance this quarter, Risks and mitigations, Plan for next 30 days. Keep the tone clear and confident without hype."

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 Pittsburgh deal, build a deal room, and prep LP communication — step by step.

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