You just finished a site tour, and the investor wants to know what’s around the property. Gas stations, grocery stores, urgent care clinics, self-storage facilities, whatever matters for the deal. You know the answer is “a lot,” but you need the data to prove it: names, addresses, ratings, distances. All in a format you can actually hand someone.
The research isn’t hard. It’s just tedious. Searching Google Maps zone by zone, clicking into each listing, copying details into a spreadsheet row by row, deduplicating, sorting by distance. For 50 locations, that’s an hour. For 200, you’re looking at two. And that’s before anyone asks for a map.
That’s exactly what this task is built to fix.
What This Task Does
You give the task four inputs: what you’re searching for, where to search, whether you want a map, and (optionally) a subject property to measure distances from. That’s the entire setup.
From there, the Market Research Associate AI Coworker plans a search strategy using up to 10 Google Maps queries, targeting different zones within your search area to ensure comprehensive coverage. It pulls every matching location with full details (name, address, phone, website, Google rating, review count), deduplicates the results, calculates distances from your subject property if provided, and assembles everything into a clean, formatted CSV. If you requested a map, it generates a branded interactive map with pins for every result.
The whole process takes roughly 5 minutes of your time. The AI does the rest.
Who This Task Is For
Anyone doing site-level research needs to know what’s nearby. This task eliminates the manual assembly so you can focus on what the data actually means for the deal.
This task is built for:
- Acquisitions analysts who need to map the competitive landscape or amenity base around a target property before underwriting
- Market research teams who track specific location types across a metro (e.g., every self-storage facility in Dallas, every urgent care clinic within 20 miles of a site)
- Asset managers and leasing professionals who need to document the surrounding amenities and services for tenant presentations or marketing packages
- Developers and site selectors who are screening locations and need a fast, consistent way to compare what’s around each site
In short: if you already know what you’re looking for and where, this task gives you a comprehensive, formatted dataset in minutes.
Why It Matters
Knowing what surrounds a property is fundamental to almost every CRE decision: acquisitions, development, leasing, asset management. The data shapes your underwriting assumptions, your marketing materials, and your investor presentations.
You already know this. You’ve done the Google Maps research before. You’ve clicked into listings one at a time, copied addresses into spreadsheets, and manually sorted by distance. It’s not complex work. It’s just slow.
The real blocker is bandwidth. When the research takes two hours, it competes with everything else on your plate. So the search gets scoped down (“just grab the top 10”), or it gets deferred entirely (“we’ll add the amenity analysis later”), or someone eyeballs it instead of documenting it. The deal moves forward with an incomplete picture.
This task compresses that two-hour exercise into about five minutes. You get up to 200 locations with full contact details, ratings, and distances, formatted and ready to use. No manual deduplication, no row-by-row copying, no incomplete datasets because you ran out of time.
That’s the multiplier.
What the Output Looks Like
The CSV file generated by this task includes:
- Up to 200 deduplicated locations matching your search criteria
- Name, address (street, city, state, ZIP), phone number, and website for each location
- Google rating and review count to help you gauge quality and popularity
- Distance in miles from your subject property (when provided), sorted nearest first
- An optional branded interactive map with pins for every result and your subject property highlighted
The output is not a handful of top results from a single search. It’s a comprehensive, multi-zone sweep of your entire search area, the kind of dataset you’d expect from a research analyst who spent an afternoon on it.
Frequently Asked Questions About Finding Nearby Locations With AI
Yes, and you should treat this the same way you’d treat a research spreadsheet from a junior analyst: trust the structure, but spot-check the details. The AI pulls real data from Google Maps, so the names, addresses, and ratings are sourced directly. Occasionally a listing may be permanently closed, miscategorized, or duplicated at a slightly different address. A quick scan of the CSV before dropping it into a deliverable takes a few minutes and ensures everything is clean. The goal is to eliminate the two hours of assembly, not the five minutes of review.
The output is a standard CSV with the same data points you’d collect manually from Google Maps: name, address, phone, website, rating, and review count. Nothing about the format signals “AI-generated” unless you tell someone. The data source is Google Maps, which is the same source most CRE professionals use for this research already. The difference is speed and completeness. Instead of grabbing the first 10 results and calling it done, you get up to 200 deduplicated locations across the full search area. That level of thoroughness is what makes the deliverable more credible, not less.
Absolutely. Each run takes about five minutes and produces a standalone CSV for that specific search. If you’re screening five sites in a new market, you can run the task once per site and have a complete amenity or competitor dataset for each one by the end of the hour. The consistent output format makes it easy to compare across locations. Teams doing portfolio-level research or multi-site due diligence get the most value here because every search gets the same depth and the same structure, regardless of how many you need to run.