# The Square Footage Lie {% hint style="info" %} This is a living document. It is maintained and updated without notice. It's best to refresh pages to ensure delivery of the latest iteration from the CDN Content Delivery Network in your area {% endhint %} PaintScout presents time-based estimation as an alternative to square-foot pricing. On the surface, that sounds like a meaningful shift. But when you slow down and examine what’s actually being taught, the underlying assumption hasn’t changed. Square footage hasn’t been removed from the equation—it’s been converted into a different unit. Throughout the guide, time-based estimation is defined as calculating how long a task takes and multiplying that time by an hourly rate. But the hours themselves are derived from production rates expressed as square feet per hour, linear feet per hour, or hours per item. That is square-foot pricing translated into time. The math is identical. Only the label has changed. This distinction matters because the entire system depends on a single assumption: that time scales predictably with surface area. If that assumption were true, converting square footage into hours would stabilize estimating. But if the assumption is false, the method fails regardless of how clean the spreadsheet appears. What’s missing from PaintScout’s explanation isn’t a feature—it’s a disclosure. For their software to work, it would have to account for every variable imaginable. The list includes both common and uncommon variables. All variables that impact time on a jobsite must be considered. That's impossible. To support the model, PaintScout relies on frictionless examples. Their 12x15 bedroom sample assumes no setup time, no patching, no sequencing costs, and no condition variability. It’s a controlled illustration designed to make linear math appear reliable. In that environment, almost any estimating method will seem to work. Real production doesn’t behave that way—and the failure shows up even in environments where variability should be minimal. New-construction painting provides one of the cleanest datasets available in the trade. The substrate is consistent. The products are consistent. The methods are consistent. The quality standards are consistent. These controls isolate architectural variables and remove many of the excuses typically used to explain estimating error. If square-foot-based time rates were ever going to work, they would work here. They don’t. Field data from controlled new-construction work shows that the smallest house required the most time, while the largest house required the least. A home that was 45 percent larger was completed faster. When square-foot-per-hour rates were applied across those homes, estimating errors ranged from significant underestimates to nearly 60 percent overestimates. Averaging the rates didn’t fix the problem—it guaranteed error on every job. This is the flaw that never gets addressed in time-based estimating models. Time is not a transferable constant. Production does not scale linearly with size. Layout efficiency, architectural repetition, access, sequencing, and interruption dominate outcomes long before square footage does. Treating time as a rate that can be averaged across conditions is mathematically indefensible. With that framework established, we can now look at how PaintScout actually implements time-based estimation—and why the problems aren’t incidental but structural. **Here is the data.** 1,336 sq ft, **54.5 hrs**\ 1,937 sq ft, **50.0 hrs**\ 1,830 sq ft, **47.0 hrs**

The scatterplot titled 'Home Size vs. Completion Time' illustrates the relationship between the size of a house and the time it takes to paint

The data show that the largest house required the least time to paint, whereas the smallest house required the most time. Estimating software assumes a linear relationship between square footage and time. **The field data proves the opposite.** 1,336 sq ft = 54.5 hours\ 1,937 sq ft = 50.0 hours (**45% larger, 8% faster**)\ 1,830 sq ft = 47.0 hours **The smallest house took 16% longer than the largest house.** **If they estimated these homes using sqft/hr rates:** Let's say they calculated an "average" rate from the first house:\ 1,336 sq ft ÷ 54.5 hrs = **24.5 sq ft/hr** **Applying the same rate to the larger homes yields the following result:** **1,937 sq ft house**\ Estimate: 1,937 sq ft ÷ 24.5 sq ft/hr = **79.1 hours**\ Actual: **50.0 hours**\ Error: **+58% overestimate** **1,830 sq ft house**\ Estimate: 1,830 sq ft ÷ 24.5 sq ft/hr = **74.7 hours**\ Actual: **47.0 hours**\ Error: **+59% overestimate** **Or if you used the largest house rate:**\ From the 1,937 sq ft house: 1,937 sq ft ÷ 50 hrs = **38.7 sq ft/hr** **Applying to smaller houses:** **1,336 sq ft house**\ Estimate: 1,336 sq ft ÷ 38.7 sq ft/hr = **34.5 hours**\ Actual: **54.5 hours**\ Error: **-37% underestimate** **1,830 sq ft house**\ Estimate: 1,830 sq ft ÷ 38.7 sq ft/hr = **47.3 hours**\ Actual: **47.0 hours**\ Error: **+0.6% (accidentally close)** You'll often hear people in the industry teaching others to use **averages** as the basis for estimating. **Let's take a look at how using averages plays out.** If you averaged those three homes: 1,336 sq ft in 54.5 hrs\ 1,937 sq ft in 50.0 hrs\ 1,830 sq ft in 47.0 hrs **Total:** 5,103 sqft in 151.5 hrs = **33.7 sqft/hr "average"** That average is **worthless.** The small home actually ran at 24.5 sq ft/hr The larger homes ran near \~38-39 sq ft/hr The "average" rate of 33.7 sq ft/hr would be **wrong on every single house**—underestimating small homes by 27% and overestimating large homes by 13-16%. They’re building their entire methodology on averaging across conditions that have fundamentally different relationships between size and time. These insights are barely recognizable in the highly inconsistent residential repaint market, where the number of variables is significantly higher. In repaint work, both complexity and surface condition vary wildly. If estimating software can't handle the simpler case of new construction, where only architectural variables differ, how can it possibly handle repaint work, where surface conditions, product requirements, and complications multiply the variables? The answer is that it can't. Contractors using these systems win the bids they lose money on and lose the bids they would profit from—a perfect inverse selection that guarantees either unprofitable volume or business failure. This pattern has been observable since the early 1990s. The fundamental mathematics hasn't changed. Yet current estimating software in 2025, such as PaintScout, still treats painting as if variables don't exist, fixed elements scale linearly, and every square foot takes the same time regardless of job-site variables. PaintScout is designed for painting contractors, and I am, in fact, a painting contractor—and PaintScout is useless for my business. *** **Related:** [Why Square Foot Pricing Doesn't Work](https://jackpauhl.gitbook.io/archive/field-notes/business-strategy/why-square-foot-pricing-doesnt-work) **Also read:** [*The Flaw of Averages*](https://jackpauhl.gitbook.io/archive/field-notes/industry-analysis/the-flaw-of-averages-in-cost-estimating) expands on the math behind this problem, showing why estimating methods built on averages—whether square feet, time-based rates, or production speeds—produce error even when the data looks clean.