How to Choose a Franchise Territory Using Demographic Data

Most franchise territory decisions still get made on gut feel — a city name people recognize, a spot on the map that's conveniently close to another location. That works until it doesn't. Here's a framework for screening candidate territories with actual demographic data before you sign anything.

Population alone tells you almost nothing

The most common mistake is ranking candidate markets by population and stopping there. Two territories with nearly identical population can have completely different ceilings depending on who actually lives there and how they're spread out. For most franchise concepts — and especially for home services (roofing, HVAC, lawn care, pest control) — four signals matter more than raw headcount:

• Population — the size of the addressable market. Necessary, but not sufficient on its own.
• Median household income — a proxy for spending power and average ticket size. A market with lower income can still work for value-priced concepts, but it caps how much you can charge.
• Homeownership rate — for anything tied to a home (roofing, HVAC, landscaping, pest control, remodeling), a renter usually isn't your buyer. A territory that looks big on population but is mostly renters has a much smaller real market than it appears.
• Density / land area — how spread out your customer base is drives service radius, drive time between jobs, and cost per lead for local marketing. A territory with the same customer count packed into a smaller area is cheaper to operate.

A screening framework

1. Define your ideal customer. Rough income range, homeowner vs. renter, and (if relevant) age bracket. This is a business decision, not a data one — but it determines every filter after this.
2. Screen candidate markets at the state or city level. Compare states or cities on population, median household income, and homeownership rate to build a shortlist. This is the fast, cheap pass — eliminate markets that fail your income/homeownership bar before doing any deeper work.
3. Narrow to specific ZIP codes or counties within a shortlisted market. A city-level average can hide a lot — a city can have a respectable median income overall while your actual target customer is concentrated in a handful of ZIP codes. County and ZIP-level data lets you see that variation instead of averaging it away.
4. Check the density. Two territories with the same customer count aren't equally easy to serve if one is packed into a few square miles and the other is spread across a county. Land area and housing-unit density (both in the data) tell you which is which.
5. Layer in what the data can't tell you. Competitive saturation, local permitting/regulatory quirks, and existing franchisee territories aren't in any demographic dataset, including ours — they still need boots-on-the-ground or brand-side research before you finalize anything.

A worked example: two cities, same population, very different territories

Take two real US cities with almost identical population — Orland Park, IL (116,302 people) and Elizabeth, NJ (137,302 people). If you screened these two on population alone, you'd treat them as roughly comparable-sized markets. They're not:

For a home-services franchise, Orland Park is the far stronger territory despite the smaller population: nearly 9 in 10 households own their home (the decision-maker for a roof, HVAC system, or lawn contract), and household income runs almost 70% higher. Elizabeth's larger population is real, but with only about a quarter of housing owner-occupied, the actual addressable market for a home-services concept is smaller than the population number suggests — and much smaller than Orland Park's, despite Elizabeth's higher headline population. Elizabeth is also nearly 5x denser (12 sq mi vs. 56 sq mi for a similar population), which matters more for concepts where density is an asset — delivery, foot traffic, or anything walkable — than it does here.

None of this makes Elizabeth a bad market in general — it likely suits a different kind of concept well. It illustrates why population alone can point two very different-looking franchise businesses at the same number and reach opposite conclusions.

Where to get this data

Every number above — and the same four signals for every US ZIP code, county, and city — is in the zipcodemax dataset. Browse by state, county, or city to screen candidate markets, or see the full data dictionary for every column in the file.