Ask a VP of maintenance how many technicians a community needs and the answer usually starts with a ratio. One tech per 100 units. It is the number budgets are built on, the number staffing plans default to, and the number that quietly determines how much backlog a team can absorb before residents feel it. The problem is that the ratio was never a capacity model. It was a cost-control convention, and the evidence behind it has thinned considerably.

The 100-Unit Rule Was Built for Budgets, Not Workloads

For at least 20 years, one team member per 100 units has been treated as the industry standard for onsite staffing, sustained primarily because payroll dominates operating budgets and a flat ratio made expense forecasting simple . NAA's own staffing guidance describes one office employee and one maintenance employee per 100 units as the age-old general rule that most owners, operators, and developers use to calculate personnel expenses, while explicitly noting that many factors beyond the formula matter in any staffing situation .

The data underneath the rule tells a messier story. The 2019 NAA Survey of Operating Income and Expenses found an average of one full-time employee for every 45 units across office and maintenance combined, less than half the spacing the rule implies . A luxury Denver asset staffed one office and one maintenance employee per 61 units and credited that extra capacity with operations that regularly exceeded resident expectations . Industry voices have been direct about the flaw: demand varies with property size, resident needs, and a long list of nuances that a single ratio cannot see, and the model should not survive simply because it has always been done that way .

The takeaway is not that the correct ratio is 45, 61, or 100. It is that units per technician is an output of your operating context, not an input you can copy from another portfolio.

Workload Benchmarks Explain What Ratios Hide

If two properties both run at 1:100, the ratio tells you they spend similarly on labor. It tells you nothing about whether either team is drowning. Workload benchmarks do.

Industry data from 2023-2024 shows technicians completing an average of 109.5 work orders per month, roughly 1,314 per year . Work-order saturation, calculated as open work orders divided by units, averages 5.98%, with 10% or less considered acceptable and 5% or less exceptional . Average completion time runs 3.88 days from creation to close, 66% of work orders are finished within 24 hours, and emergencies make up 6.67% of volume . Callback rates average 2.01% overall, and HVAC is both the most common work order category and the most common callback category .

Service mix shifts these numbers dramatically. A Ball State University survey found maintenance teams spend roughly 18% of their time on preventive maintenance and 12% on turns, with most time consumed by immediate repairs . Separate research across 385 communities and more than 73,000 units found service teams spending an estimated 19 hours per week on scheduled work like grounds and trash, and 87% of properties outsourcing at least part of their preventive maintenance . When operators formalized PM, the demand curve itself moved: one year after implementing a preventive maintenance plan, service request volume dropped an average of 13.4%, repair and maintenance expenses fell 8.8%, and completion times improved 17.1% . A 244-unit HVAC-focused case cut AC service calls by 26% and improved AC work order completion time by 60% .

Same units, same headcount, very different demand. That is why a ratio comparison across portfolios, or even across properties in the same portfolio, is close to meaningless without workload and service-mix context.

The Labor Market Will Not Rescue a Flat Ratio

Whatever ratio you target, filling it is getting harder. NAA's Q4 2025 labor report shows maintenance technician postings down 4.1% year over year and supervisor postings down 5.2%, even as leasing postings grew, suggesting operators are leaning on existing staff and contractors rather than expanding maintenance headcount . Advertised technician salaries still rose 2.2% and supervisor salaries 3.1% over the same period . Meanwhile, construction trades pay $40 to $50 per hour against traditional technician rates in the high teens to high $20s, trade school attendance is near all-time lows, and a large cohort of experienced techs is approaching retirement . The staffing difficulty is persistent enough that companies routinely budget knowing 10% to 20% of maintenance expenses will go unspent .

In that market, hiring to the ratio is not a plan. Capacity has to come from how the existing team is deployed.

Deployment Models Are the Real Variable

Operators pushing past the ratio are changing structure, not just spreadsheets. A fixed technician count per property is suboptimal by design because workload peaks and troughs with turns, seasons, and days of the month . Maintenance is also harder to centralize than leasing because different buildings require specialized equipment and skills, so it demands more deliberate redesign than simply moving tasks offsite . Podding is one documented response: technicians specialize in a discipline like plumbing, work orders, or turns while covering several communities, and practitioners frame it explicitly as skills-based recruiting and pay, not headcount reduction . Centralizing maintenance triage and coordination has similarly enabled some operators to move beyond the classic one-per-100 model .

The common thread is coordination. Shared technicians, specialized pods, and centralized triage all fail without a system that routes the right work to the right person and keeps execution consistent across properties. This is where Accolade fits: as a system of action that connects your existing systems of record to centralized maintenance coordination, standardized execution, and portfolio-level visibility into workflows. Not a dashboard that reports the backlog after the fact, but the layer that makes cross-property deployment operable day to day.

How to Benchmark Yourself Honestly

Drop the single ratio and build a small comparative set. Measure work orders completed per technician per month against the 109.5 average, saturation against the 5.98% norm, completion time against 3.88 days, and callbacks against 2.01% . Then segment by property: PM share of hours, turn load, scheduled work, and outsourced scope, because those mix differences explain far more of the variance between teams than headcount does . If your numbers lag, the first question should be how work is triaged, routed, and standardized, not how many technicians you are short. The ratio was never the benchmark. The work always was.