Google Maps Data for Commercial Real Estate

Commercial real estate has always run on local knowledge. The broker who knows which units on a high street are struggling, which landlord has a vacancy coming, and which tenants are expanding wins mandates that a better-resourced competitor without that knowledge never sees coming. For decades that knowledge lived in people's heads and in expensive proprietary databases that were out of date the day they were compiled.

A surprising amount of it is now sitting in public. Every operating business keeps a Google Maps listing because it has to: that is how customers find it. Collected across a district, those listings describe the commercial fabric of an area in detail — who occupies what, what kind of business it is, how well it is doing, and, crucially, which units have stopped trading. This guide covers how brokers, landlords, investors and property managers use that data, which fields carry the signal, and where its limits are.

What Google Maps knows about a commercial area

A pull of every business in a district returns, for each one, its name, category, full address broken into street, city and postal code, coordinates, phone and website, rating and review count, opening hours, and a status field saying whether it is operational, temporarily closed or permanently closed. Put together across a few hundred listings, that is a tenant map of the area with performance signals attached.

Three things make it useful for property work specifically. It is current, because businesses update their own listings and customers update them with reviews. It is granular, down to the individual unit and the exact coordinates. And it is comparable: the same fields come back for every district, so a high street in one town can be laid next to a retail park in another and read the same way.

Five ways property teams use it

1. Finding vacancy before it is advertised

This is the most direct use and the one people tend not to expect. The business_status field marks listings that Google considers permanently or temporarily closed. A permanently closed listing at a retail address is, very often, an empty unit — or one that will be empty soon, because listings tend to be marked closed by customers who turn up and find the shutters down, sometimes before any agent has instructed on the space.

For a letting agent, that is a list of potential instructions to pursue. For a landlord's asset manager, it is an early read on a parade's health. For an investor, a rising count of closures in one district against a stable count in another is a leading indicator worth more than any lagging vacancy report. One caveat matters here: a closed listing is a lead, not a fact. A business may have moved within the same building, rebranded under a new listing, or been closed in error. Treat closures as a shortlist to verify, ideally by cross-checking whether a new listing has appeared at the same address.

2. Reading tenant mix and anchor strength

Pulling every business around a property and grouping by category tells you what the location actually is, as opposed to what its marketing brochure says it is. A parade that is half takeaways and betting shops trades very differently from one anchored by a supermarket, a pharmacy and a GP surgery, and the category breakdown makes the difference visible in a table rather than an impression.

Ratings and review counts then show strength within that mix. A high-street anchor with thousands of reviews and a strong rating is pulling footfall. An anchor whose rating has been sliding for a year may be a tenant at risk, and for a landlord that matters long before the lease does.

3. Finding tenants for a vacant unit

Turn the question around. You have a vacant unit; who should be in it? The businesses most likely to take space are operators already succeeding in comparable locations nearby, and the data shows exactly who they are: same category as the use class you can offer, strong ratings, high review counts, and — the tell for an expanding operator — several listings under the same name across the region.

That last signal is cheap to compute. Group the pull by business name or website domain and count locations. An independent with three well-reviewed branches opened over the last few years is the classic expansion tenant, and it will rarely be on a national agent's radar. Our guide to finding multi-location businesses covers the grouping in detail.

4. Underwriting a location for acquisition

An investor looking at a retail or mixed-use asset wants to know whether the micro-location is improving or decaying. Business data gives three readings that complement the usual demographic and transaction data. Density: how many trading businesses sit within walking distance, and how that compares with similar locations. Quality: the rating distribution across those businesses, which separates a busy, well-run area from a busy, declining one. Churn: what has opened and closed recently, which needs two pulls a few months apart but is the most telling of the three.

None of this replaces a valuation. It does make the location section of an investment memo specific rather than adjectival, and it is fast enough to run across a long list of candidate assets before deciding which deserve proper diligence.

5. Prospecting occupiers for representation

Tenant-rep brokers need to know which businesses are likely to need new space. The signals overlap with the expansion case above, plus a few specific to over-crowding or distress: very high review velocity in a small unit, opening hours stretched to the limit, or a rating declining in a pattern that suggests capacity problems. A shortlist built that way is a far better starting point for outreach than a purchased list of every company in a postcode.

The fields that carry the signal

One practical note on located_in: it is only returned when you look up places individually rather than through a category search. For a shopping centre tenant schedule, run the category search first to get the list, then an individual lookup on the place IDs you care about. Our field reference lists which columns behave this way.

How to collect it for a district

The Google Maps Data Scraper takes categories and locations and returns every listing with the fields above. For property work the useful pattern is to search broadly rather than narrowly: you want the whole commercial fabric, so run the categories that make up a high street — shops, restaurants, cafés, services, health, leisure — across the postal codes in your area, rather than a single use class.

Two settings matter. Keep the closed businesses in the output rather than filtering them out, because for property they are the point. And keep the place ID on every row, because the most valuable reading, churn, comes from comparing this pull with the next one, and that comparison has to match listings precisely. A quarterly re-pull of a district you care about costs very little — a parade of four hundred businesses is under a dollar at $0.002 a record — and turns a snapshot into a trend.

Where it falls short

Being straight about the gaps saves a bad decision. Google Maps knows about businesses, not about buildings: it will not tell you lease terms, rents, landlord identity, floor areas or service charges. Offices are under-represented, because many office occupiers do not maintain customer-facing listings, so it is strongest for retail, leisure, food and beverage, and services. Upper floors and back-of-house units are often invisible. And closure flags lag reality in both directions, as covered above.

The sensible framing is that Maps data is the occupier layer. It sits alongside land registry and lease data, footfall counters, and planning applications, and it answers the questions those sources cannot: who is actually trading here, how well, and what has changed recently.

A worked example

A regional agent wants to win more retail instructions in a market town of about 60,000 people. They pull every business in the town's three central postal codes across the high-street categories — just under five hundred listings — and re-run the same pull three months later.

The first pull alone produces a tenant map and an initial list of fourteen permanently closed listings. Cross-checking against new listings at the same addresses removes five, which had relocated or rebranded, leaving nine units that look genuinely empty. Several are not yet on any agent's books. That is nine conversations with landlords who may not have instructed anyone yet.

The second pull adds the trend. Six more closures, four of them on one secondary street, and two strong independents that opened second branches in neighbouring towns over the same period. The agent now has three things to sell: a list of vacancies, an evidence-based view that one street is softening, and two expanding operators to introduce to landlords with space. None of it came from a paid database, and all of it is refreshable every quarter for the cost of a coffee.

Conclusion

The commercial fabric of any town is described, listing by listing, on Google Maps, and collected as data it answers questions property teams have always paid dearly for: where the vacancies are, how the tenant mix trades, who is expanding, and which locations are improving or slipping. The business_status field turns closures into leads, category and rating data turn a district into a readable tenant map, and repeated pulls turn snapshots into churn. Its limits are real — no lease data, weak on offices, closures to be verified — but as the occupier layer of a location analysis it is current, granular and inexpensive. Livescraper collects it across whole districts in one task, closed listings included, so the local knowledge that used to take years on the ground can start from a spreadsheet.

Related reading: Retail Site Selection Using Google Maps Business Data, How to Scrape Closed Businesses from Google Maps, Business Location Data for Market Intelligence.