A gym's revenue is its member count multiplied by what each member spends. The location decides the first number and the operator decides the second, so a new club should be forecast as members first: households in reach, the share that join, and how long they stay. Revenue follows from the brand's pricing plan, and a miss can be traced to the input that caused it.
Many site forecasts in fitness go straight after revenue, because revenue is the number on the pro forma and the one the lender reads. That's usually reasonable for retail and restaurants, since each sale is a separate decision made near the store. A gym runs on a recurring relationship instead, and that relationship comes with a count attached to it. That count is the part of the business a location actually controls, and it is also the unit the industry itself measures: the Health & Fitness Association counted 81 million Americans with a fitness facility membership in 2025, or 26.1% of the population age six and older.
Why a revenue forecast hides the site in a gym
A gym revenue forecast blends two decisions that belong to different people. The location sets how many people can reach the club, join and stay. The brand sets how much each member pays, through pricing, tier structure, promotions, and personal training and class sales.
The split matters because the second decision runs on its own schedule. One low-cost gym franchisor's 2025 annual report shows average monthly dues per member rising from $17.63 at year-end 2021 to $19.51 at year-end 2025. That climb came from system-level pricing and tier mix, and no individual club's address had anything to do with it.
Now look at how a revenue model learns. It takes existing clubs as analogs and ties their revenue to the characteristics of their sites. When the analog clubs raised dues or sold more premium tiers, revenue went up, and unless price and tier mix are explicit inputs, the model can't tell that apart from a better location. It ends up crediting the site variables with a pricing decision. The reverse happens too: a club in a strong catchment that ran an aggressive low-price promotion looks like a weak location in the training data.
Dues are also where most of the money sits. A large premium club operator reported in its fiscal 2025 10-K that membership dues and enrollment fees made up over 72% of total center revenue. When most of the revenue is a recurring payment per member, the member count carries most of the forecast, so it deserves to be modeled as its own quantity.
The three inputs a member forecast is built from
A member forecast for a new gym comes down to three things: catchment households, a join rate and a retention rate. Each one is a separate assumption a team can write down, check against the existing fleet, and argue about in committee on its own terms.
Catchment households. This is the count of households matching the brand's member profile inside the drive time members will realistically make. The drive time should come from the road network at the hours members train, typically early morning and after work, and the profile should come from the home addresses of the brand's existing members rather than a generic demographic target. How trade areas get drawn covers why a radius circle overstates reach in the suburban corridors where many clubs lease.
Join rate. This is the share of those households that sign up. The honest source is the brand's own clubs: members per matching household in comparable catchments, adjusted for the clubs already operating nearby. Rival clubs take part of that pool, and so do the brand's own units, which is where cannibalization analysis earns its place in a fitness forecast. Moving members from one of a brand's clubs to another and booking it as growth is one way a fleet overstates a new site.
Retention. This is how long members stay once they join, which turns a stream of gross joins into a standing member count. The Health & Fitness Association's 2025 benchmarking report, covering 175 companies and more than 17,000 facilities, put average member retention at 66.4% for the year. That number is an industry average across formats and countries, so it works as a sanity check on the model. The real input is the retention the brand already sees in its own clubs, broken out by how far members live from the club they joined.
Boutique studios add one more wrinkle. A studio's room caps how many members the schedule can serve, so its forecast has to take the lower of the catchment number and the capacity number. The boutique fitness franchise breakdown works through that ceiling in detail, and the member forecast is what makes the comparison possible, because capacity is also measured in members.
Where the site shows up in each input
Catchment, join rate and retention are all site inputs, which is exactly why they get forecast separately. A gym member makes the join decision at home, weighing a few clubs against the drive, so the variables that predict a gym's member base come down to reach and competition.
Walk-by counts don't carry much weight for this kind of trip, as the foot traffic analysis for destination categories explains. The drive time sets the ceiling on catchment households. Road barriers, turn-in from the main road, parking and the evening congestion pattern all change how many households are realistically inside it. The competitive set decides how much of that ceiling is up for grabs, and it includes independents and studios in adjacent modalities that often get left off a franchisor's site packet. Retention depends partly on the club experience, which the site doesn't control, and partly on how inconvenient the club is to reach once the January resolve fades, which the site does decide.
A revenue model folds all of this into one coefficient per variable. A member model keeps each piece somewhere a real estate team can inspect directly: a household count the team can see on a map, a join rate the team can compare against its existing clubs, and a retention assumption the team can test against its own cancellation data.
Member forecasts can be checked early
A new gym takes a while to mature, which is why early checkpoints matter. The same premium club operator states in its annual report that its new centers have taken three to four years on average to ramp to expected performance. A forecast stated only as mature-year revenue can't be checked until then.
By that point, pricing changes, a new competitor and a remodel have all blurred the answer to whether the site call was right in the first place. A member forecast produces numbers that are observable much sooner. Presale sign-ups, and the home addresses attached to them, arrive before the doors open. Monthly gross joins arrive in the first few months. Cancellations by distance and tenure arrive through the first renewal cycle. Each one tests a specific assumption, so a shortfall points somewhere useful.
The last row of that table is the one revenue-only forecasting can't produce. When membership is on plan and revenue is below it, the location did its job, and the gap sits in pricing, tier mix or ancillary sales. When membership is short, the conversation moves to the catchment, the competitive set or the retention assumption. Both conversations are worth having, and they involve different people.
What this means for the committee
A real estate committee approving a club lease is really approving a member count, whether or not the pro forma is written that way. Presenting the forecast as households, join rate and retention gives the committee three assumptions to challenge one at a time, which is a stronger position than defending a single revenue figure.
Defending a site forecast to the committee covers what that room tests, and the same principle applies here: every number should open into the inputs behind it.
Breaking the forecast into members also keeps it honest after approval. Revenue forecasts built on analog revenue tend to drift as pricing changes across the fleet, and a model trained on last year's dues quietly goes stale when the brand reprices. A member forecast holds up better through a pricing change, because the operator can multiply the same member curve by the new dues and revisit the join rate only when a price move is large enough to change demand. How retail revenue forecasting works covers analog selection and model types in more depth; for fitness, the adjustment is to choose members as the quantity the model predicts.
GrowthFactor builds forecast models on an operator's own performance history through GrowthFactor Labs, and teams can also load a model built elsewhere into the platform and toggle between several models on the same site. Either way, a fitness team can flip inputs on and off and watch what each one does to the forecast. For brands with 40 or more locations, Discovery is a free 30-day analysis of the brand's own data that identifies who the member really is, which variables matter for the brand and what its true trade area looks like, which is the raw material for the catchment input of a member forecast. The real estate team still makes the call on the lease; the forecast's job is to show which assumption the call depends on.
Frequently Asked Questions about Forecasting Gym Membership
How do you forecast membership for a new gym location?
Start with the households inside the drive time members will realistically make, filtered to the profile of the brand's existing members. Apply a join rate taken from comparable clubs in the fleet, net of rival clubs and the brand's own nearby units, then apply a retention rate to get from gross joins to a standing member count. Revenue comes last, by multiplying that count by the dues and ancillary spend the brand plans to charge.
Why is revenue a weak target for a gym site forecast?
Revenue mixes two decisions. The location controls how many people can join and keep coming, while dues, tier mix, promotions and personal training sales are set by the operator. A revenue model trained on analog clubs absorbs every pricing change in those clubs as if it were a site effect, so it can rate a site highly because its analogs raised prices, not because they signed more members.
How long does a new gym take to reach mature membership?
Often several years. One publicly traded premium club operator states in its 2025 annual report that its new centers have taken three to four years on average to ramp to expected performance. Low-cost and boutique formats can ramp on different curves, so the useful benchmark is the operator's own fleet, measured in members per month since opening.
When can an operator tell whether a new club is on track?
Well before revenue matures. Presale sign-ups and where those members live are visible before opening, monthly gross joins are visible within the first few months, and cancellations by distance and tenure are visible through the first renewal cycle. Each one tests a specific site assumption, which a single revenue number cannot do until the club is years old.