Square Footage Explains 9% Of What A Building Spends On Mechanical Service

A 14x Range Is Not A Benchmark

Go looking for what a commercial building spends on mechanical service and you will be handed a dollars-per-square-foot figure. Depending on the source it will be somewhere between $0.15 and $2.15 per square foot per year.

That is a fourteenfold spread. Nobody would accept it for labour rates or material costs. It survives here for a simple reason: almost nobody holds real per-building revenue, and $/sqft is computable from a floor plan. It is the number you can produce without the data, so it is the number that got published.

We hold the data. Across 25,912 commercial buildings, here is what floor area actually explains.

Nine Percent

Regress annual revenue per building against floor area and the r-squared is 0.093.

Nine percent of the variation in what a building spends is attributable to its size. The other 91% is something else.

This is not a statement that size does not matter. It does, and we publish the gradients: in offices, the largest third of buildings bill about 2.8x the smallest third, and in multifamily the spread is wider still. Size orders buildings within a type reliably enough that it was the one enrichment field worth paying to join, and the only one that survived a paired within-contractor sign test at p=0.002.

The claim is narrower and more useful: size works as a band, and fails as a rate.

Dividing By Area Makes The Estimate Worse

This is the part that should end the practice.

If you convert revenue to a per-square-foot rate, you have not normalised the number. You have introduced a second measurement, with its own error, into the denominator of the first. We measured the result: dividing by area adds about 19% more variance than it removes.

The per-square-foot form is not a simplification of the underlying number. It is a worse version of it.

There is a structural reason. The relationship is not proportional. Mechanical load does not scale with floor plate, it scales with what is happening inside: a restaurant's kitchen sets its load, not its dining room, which is why restaurant spend is flat across floor area entirely. We measured that gradient and it is not even monotonic, so a size multiplier applied to restaurants would be confidently wrong on one of our largest samples.

Warehouse space, by contrast, is mostly unconditioned volume. Two buildings of identical area, one a clinic and one a distribution shed, are not remotely comparable per square foot.

Where The Other 91% Lives

We tested most of the obvious candidates. Almost all of them died, and the dead ends are worth publishing because each one is an enrichment cost you can now avoid.

Building type is the real driver. Contractor-entered building type explains more of the variance than any physical attribute we could join, and grocery and convenience stores bill roughly 3.3x what restaurants do per building per year.

Climate looked like a 1.7x driver and was not. As a group mean it produced a clean gradient. Then we compared each contractor against his own book, and it vanished. It was telling us where contractors are located, not what buildings cost to run. The original r-squared of 0.004 was right.

Building permits looked promising and did not survive. 2.17x as a group mean, then a paired test at p=0.067. Suggestive, not established, and probably a size proxy.

A 205-code property-use taxonomy explained less than the contractor's own free text. Zero of 43 adjacent steps separated. Fragmentation, not signal.

Occupant firmographics explained nothing. Revenue and employee count at the occupying business did not predict the building's mechanical spend. The occupant-load belief is falsified.

The sharpest signal we found anywhere was rooftop equipment presence, at 3.32x with standard errors between 1.5% and 2.6%. It has one problem: it lives in an asset record, which means you only know it after you already service the building.

The Honest Answer About The Remaining Variance

Some of the 91% is not knowable from any data source, and pretending otherwise is the mistake.

Within one contractor's own book, of one building type, the per-building band is still about 4.7x. Across the whole population, 59.6% of buildings move more than 2x year over year, and last year's actual invoices explain only r-squared 0.349 of next year's.

That is not a data gap. It is the year-to-year randomness of what breaks. No enrichment API closes it, which is why any honest per-building estimate is a range and never a point.

What To Use Instead

Price off building type first, then size within type. The type sets the tier. Size moves you within it. That ordering matters because the type effect is larger than the size effect and is free to obtain.

Quote a range, and let its width mean something. A band that is tight where the data is dense and honestly wide where it is thin carries information. A single number smells like false precision and invites a customer to test whether it is exactly that.

Stop benchmarking yourself against a $/sqft figure. If a published rate spans 14x, then whatever you compute lands inside it, which means it can never tell you that you are wrong. A benchmark that cannot fail you is not a benchmark.

How This Was Measured

One mechanical contractor's billed revenue from one building over a year, contractor-weighted so no single firm can carry a figure, on a six-month minimum activity window annualised on the actual window. Non-operating tenants and records averaging under $300 a job are excluded.

Floor area comes from assessor records for the regression, and from FEMA/ORNL USA Structures (CC BY 4.0) where we need a per-building rather than per-parcel measurement. That distinction matters more than it sounds: 19.4% of commercial addresses carry more than one structure, up to nine on a single address, and a contractor bills per building.

These coefficients are HVAC and mechanical only. Segment ordering is measurably trade-specific, so they should not be carried into another trade.

The full per-segment table with the size gradients, the held-out validation, and the cells we measured and chose not to publish is in the revenue per building by segment dataset, free to cite under CC BY 4.0. To get the figure for one building, the building revenue calculator takes an address.