Equipment Utilization Rate: Why YoY and MoM Disagree
Monthly utilization rate is the number most often quoted when the construction equipment market is described as heating up or cooling down. It gets forwarded as a single percentage, and readers draw a conclusion from it. The reality is messier: in the same month the month-over-month figure can climb while the year-over-year figure falls, and the headline number can rise while individual machine categories move in different directions. Reading these figures correctly starts with separating three pairs: utilization rate from working hours, year-over-year from month-over-month, and headline numbers from machine-type detail.
1. Utilization Rate and Working Hours Are Different Metrics
| Metric | What it measures | Unit | Typical use |
|---|---|---|---|
| Monthly utilization rate | Share of tracked machines that logged work in the month | % | Whether machines are moving |
| Average monthly working hours | Mean operating hours per tracked machine | Hours | How much each machine works |
Utilization rate counts how many machines are working. Working hours measure how long each one works. When a large share of the fleet sits idle, utilization falls, yet the machines still in use may log very high hours. The reverse also happens: utilization can recover because equipment has spread across more job sites, while hours per machine stay flat. Read together, the two numbers prevent a fleet from mistaking movement for productivity.
Utilization alone overstates or understates performance; working hours alone hide idle capacity. The two belong in the same report.
2. Why Year-over-Year and Month-over-Month Diverge
Because utilization is a ratio, changes should be stated in percentage points. Subtracting one month from another gives points; dividing that difference by the base gives a percentage. The two conventions get mixed up in retelling, and a 16-point gain written as a 16% gain means something else entirely.
The monthly figures from February to April 2025 show both directions at once:
| Month | Monthly utilization | Year-over-year | Month-over-month |
|---|---|---|---|
| February 2025 | 44.6% | +12.3 points | -6.22 points |
| March 2025 | 60.8% | -3.38 points | +16.3% (as reported) |
| April 2025 | 62% | -4.29 points | +1.17 points |
February fell month over month but rose year over year; March and April rose month over month but turned negative year over year. The crossover is not a contradiction. Month-over-month readings are distorted by the Lunar New Year window, when site shutdowns and restarts shift by weeks between years. Year-over-year comparisons use a different base, so direction can differ. What the data describes is the seasonal position of a single month, not an inflection point in the cycle.
The March row also supplies a ready example: 60.8% and 44.6% differ by 16.2 percentage points, and the reported figure is a month-over-month increase of 16.3%. The two describe different quantities, and read side by side they suggest different levels of urgency. Keeping the original unit when republishing data is more reliable than converting it into a more striking percentage.
A conclusion needs at least three consecutive months moving the same way, checked against working hours. One month is not enough.
3. Machine Types Diverge More Than the Headline
A blended utilization rate erases structural differences between categories. In February 2025 the reported split was excavators at 47.3%, wheel loaders at 50.9%, truck cranes at 58%, and crawler cranes at 53.6% — a spread of more than ten points.
The gap follows project type. Lifting equipment concentrates in factory construction, bridges and wind installation, where long schedules keep machines on site and operating on a steady rhythm. Excavators and loaders follow earthworks and foundation work, more exposed to weather, start dates and site sequencing. When assessing rental demand for a specific machine type, the category figure sits much closer to reality than the headline.
4. Association Data Cannot Substitute for Fleet Metrics
Association data aggregates major product categories nationally by month and describes the market. A rental fleet faces a different question: how many machines are on rent, how many are idle, for how many days, and where the idle units are parked.
Building an in-house metric takes four steps:
- Define "working" — agree on how many operating hours in a day count as a working day, and fix that definition once
- Capture hours — install a T-BOX to read engine running hours from the CAN bus, with positioning to confirm the machine is on a site
- Aggregate monthly — fleet utilization = machines that worked ÷ machines available for rent
- Benchmark — place your figure beside the industry number and see whether the fleet is ahead of or behind the market
Machine-reported hours remove the error and lag of manual logs. For how the metrics interlock, see rental fleet utilization analytics; for how hours and fuel are captured, see fuel and hour meter monitoring.
5. Four Checks Before Quoting a Number
- Is this figure a utilization rate, or average working hours
- Is the change stated in percentage points, or in percent
- When year-over-year and month-over-month disagree, has the base or the seasonal timing been identified
- Behind the headline, which machine type is driving it, and does that match the fleet's own mix
Industry data sets the backdrop; a fleet's own definition drives decisions. To design an hour-capture and utilization reporting setup for a specific fleet size, contact us.
FAQ
What is the difference between utilization rate and average working hours?
Utilization rate is the share of tracked machines that recorded any work in the month. It answers whether machines moved at all. Average working hours is the mean operating time per tracked machine, which answers how much each machine worked. Reading both together separates wide deployment from genuine intensity.
Why are utilization changes sometimes quoted in points and sometimes in percent?
A utilization rate is already a percentage, so the difference between two months is expressed in percentage points, not percent. Moving from 44.6% to 60.8% is a rise of 16.2 percentage points. Writing it as a 16.2% rise describes a different quantity, and this mix-up is one of the reasons year-over-year and month-over-month readings appear to contradict each other.
Can association-level utilization data be applied to a single fleet?
No. Association figures aggregate major product categories nationwide and describe the overall market. A single fleet needs its own numbers: machines on rent, idle days, and billable working hours. Build that metric from equipment-captured hour data first, then compare it against the industry figure.