A broker blast hits your inbox with a single-tenant net lease retail deal. The tenant looks solid, the cap rate is in range, and the location checks a few boxes. You want to move fast, but first you need to know: does this actually fit your buy box?
So you open the OM, start pulling lease terms, look up the tenant’s credit profile, check demographics, and cross-reference it all against your criteria. By the time you have a clear answer, 30 minutes are gone, and there are four more OMs sitting in your inbox. The deals that slip through the cracks are rarely bad deals. They’re the ones you never got to.
That’s exactly what this task is built to fix.
What This Task Does
You upload an offering memorandum (or a broker one-liner or deal summary) and fill in your buy box criteria across four dimensions: tenant and credit quality, lease terms and structure, investment strategy and pricing, and market and location characteristics.
From there, the Real Estate Analyst (with Memory) takes over. It extracts every screening-relevant detail from the OM, runs a deep location analysis on the property address, and checks for gaps. If your criteria reference demographics, traffic counts, parcel details, or tenant financials that the OM doesn’t cover, the AI pulls in the right tool to fill the gap: Precisely for parcel data, Google Maps for proximity checks, web research for tenant credit profiles, and a demographics report for trade area thresholds. Nothing runs unless it’s needed.
The whole process takes roughly 10 minutes of your time. The AI does the rest.
Who This Task Is For
If you’re acquiring single-tenant net lease retail, you already have a buy box. The challenge is not defining what you want. It’s screening every deal that crosses your desk fast enough to keep pace with deal flow.
This task is built for:
- Acquisitions analysts who screen 5-10+ OMs per week and need a consistent, repeatable process for each one
- Acquisitions directors who want their team spending time on deals that fit, not on deals that don’t
- Independent investors and syndicators who operate without a dedicated analyst but still need disciplined screening
- Brokerage teams who want to pre-qualify a listing against a buyer’s known criteria before making the intro
In short: if you already have a buy box and a stack of OMs, this task gives you a clear pass/fail on each one in minutes.
Why It Matters
The manual version of this process is not complicated. You read the OM, pull the key terms, check the tenant, look up the location, and compare it all to your criteria. You’ve done it hundreds of times.
You already know how to screen a deal. That was never the problem.
The problem is doing it fast enough, consistently enough, across enough deal flow to never let a good one slip. When each screen takes 30 minutes and you’re juggling LOIs, site visits, and closings, the screening backlog grows quietly. Not because you forgot, but because there aren’t enough hours.
What happens is predictable: you skim instead of screen, you pass on deals you never actually evaluated, and you lose the discipline that makes a buy box useful in the first place. A 10-minute screen versus a 30-minute screen is not just a time savings. It’s the difference between screening every deal and screening some of them.
That’s the multiplier.
What the Output Looks Like
The fit check generated by this task includes:
- A 2-3 sentence deal summary covering the tenant, location, lease structure, and investment thesis
- A four-column criteria comparison table (Criteria Name, Target, Actual, Pass/Fail) with a final conclusion row
- A written rationale paragraph explaining the reasoning behind each determination
- A linked Deep Location Analysis for the property address
- A final recommendation: Investment Passes, Investment Fails, or Investment Needs Further Review
The output is not a vague summary with a “looks promising” at the end. It’s a structured, data-backed screening memo, the kind you’d expect from an analyst who actually read the OM and checked the numbers.
CRE Agents is a platform built for commercial real estate professionals who want to move faster without cutting corners. Task #[TASK_NUMBER] is just the beginning.
Frequently Asked Questions About Screening Net Lease Retail Acquisitions With AI
Yes, and the output is designed to make that review fast. Every pass/fail determination is tied to specific data points, not inferences, so you can see exactly what the AI found and how it compared to your criteria. If a criterion comes back as “Inconclusive,” the task tells you what data was missing so you know where to dig deeper. Think of it as your analyst’s first pass: thorough, structured, and ready for your judgment call.
The task is built to be conservative. It only marks a criterion as “Pass” or “Fail” when it has supporting data from the OM, location research, demographics, or verified external sources. When the data is ambiguous or unavailable, it flags the criterion as “Inconclusive” rather than guessing. That means you’re never getting a false green light. The fit check is a screening tool, not a replacement for full underwriting, and it’s transparent about what it knows and what it doesn’t.
Absolutely. The task takes roughly 10 minutes per deal, and your buy box criteria stay consistent across every screen. That means you can run five deals through in under an hour and get a structured comparison across all of them. For teams screening 10, 20, or 50 deals a week, this is where the time savings compound. Every deal gets the same rigor, whether it’s the first one on Monday morning or the last one on Friday afternoon.