How commercial brokers find the right tenant for an empty shop unit

A commercial broker finds a tenant for an empty unit fastest by working out what kind of business the surrounding area is missing, then approaching operators of that kind who are already expanding nearby. Count the categories within a short walk of the unit, set them against the people who live and work there, and the gaps appear: a residential street with no gym, an office cluster with one lunch spot, a family area with no pharmacy. The prospect list is then short and specific, and every call opens with a reason the operator should care.

This is for retail and leisure brokers, letting agents for landlords, and in-house leasing teams at shopping centres and mixed-use schemes. It covers how to profile a unit's area, how to turn gaps into prospects, a worked example, and an AI employee that does the legwork for every unit on your books.

Begin with the street, not the tenant database

The usual approach is to mail the unit's particulars to every retailer in the database and wait. It produces silence, because most operators are not looking in that street and the email does not say why they should. Operators decide on an area first and a unit second. A broker who arrives with the area's case already made (who lives here, who works here, what they currently have to travel for) is answering the operator's first question before it is asked.

Profiling a unit's area

The few figures that make a unit's case to an operator.

Use a Travel area on foot for a high-street unit, or by car for a retail park, so the profile describes the people who can actually reach the door. Population figures come from the Demographics block in about 100 countries, including Canada, France, Australia and India; there are none for the United Kingdom or the United States, where offices, schools and homes nearby carry the case instead.

A worked example: a corner unit in Toronto

Gyms show how chain-led a category can be. xMap's places database lists 246 gyms within 10 km of the centre of Toronto (November 2026); 72 belong to a chain, and the two largest chains have 23 and 15 branches in that circle. That makes the category a good hunting ground for a broker: there are several operators with a track record of opening one branch after another.

Now a made-up unit: a 300 m² corner space in a residential neighbourhood west of downtown. Within a 10-minute walk the flow finds about 14,000 residents, many aged 25 to 44, four cafes, two pharmacies, a supermarket and no gym or fitness studio at all. The nearest branch of each of the three biggest gym chains is more than a 10-minute walk away. These numbers are invented to illustrate the method.

The broker's list writes itself: the gym chains with branches nearby but none in this pocket, and the boutique studio brands that opened recently elsewhere in the city. Each gets a one-page note: the residents, their ages, no fitness operator within the walk, their own nearest branch and its distance. A landlord who expected a cafe gets a better-fitting tenant, and the operator gets a site they had not seen.

From a gap to people to call

• Brands already near. Nearby places across the city, kept to chains of the missing category, shows who has branches close by and where their closest one sits.
• Brands opening now. Set Opened to the last 12 months to find operators adding branches this year.
• Brands hiring managers. Job postings for store or club managers is often a sign that openings are planned.
• Who decides. Apollo, through your own account, adds the expansion or property contact where the company is listed.

The unit profiler as an AI employee

1. New rows: starts when a unit you are letting is added to a sheet in Google Sheets or a table in My data.
2. Travel area: 10 minutes on foot (or by car) around the unit.
3. Demographics: residents and ages inside it, where available.
4. Nearby places, counted, with up to 12 types: gyms, cafes, restaurants, pharmacies, clinics, nurseries, groceries, salons and the like.
5. xMap LLM, on the whole list: name the three categories the area most lacks for its population, using only the counts, and say why in a sentence each.
6. Make a report: a branded PDF of the area profile, ready to attach to the particulars, emailed to the broker.

What lands in the inbox is a profile per unit and the categories to chase. A second flow can then search the city for operators in those categories, as above, and drop them into a sheet for the week's calls.

Mistakes that keep a unit empty

• Mistaking an absence for a gap. No butcher in a street of offices is not demand. Check the gap against the people who are there.
• Ignoring weak incumbents. A category present but poorly rated can be as good an opening as one that is missing.
• Sending the same particulars to everyone. Operators answer notes about their own network and their own customers.
• Forgetting what is being built. New housing nearby changes the case within a year; mention it if the landlord knows.
• Quoting residents where there are no figures. Say clearly when population was not measured.

Templates to start from: retail tenants for my vacant units, tenant mix for my new mall, malls where my category is missing and rivals hiring store managers. More for property teams on the real estate page.