Stayover vs checkout cleaning time: two jobs, one room

A stayover refresh and a checkout clean are not the same job done at different speeds. They are different jobs, with different task lists, that happen to take place inside the same four walls — and the commonly published planning figures put the checkout at roughly twice the stayover. So the single number most properties plan with, minutes per room, is an average of two things that never belonged in the same average. Two days at identical occupancy can differ by most of a shift’s worth of housekeeping labor, and a property tracking one number will not see either one coming.

The difference is the task list, not the room

The room is the same. The work is not.

A stayover is a service call: trash and linens out, towels replaced, bed made rather than stripped, bathroom wiped down, amenities topped up, a vacuum if the floor needs it. The guest’s belongings stay put, which both removes work and adds it — you clean around things you cannot move.

A checkout is a reset. The bed is stripped and remade from bare, the bathroom is done properly rather than freshened, and then come the tasks that exist only on a checkout: the walk-through for damage and left-behind items, drawers and fridge and microwave opened and checked, the amenity setup returned to standard, and — at most properties — an inspection before the room can be sold again.

That last list is why the gap is structural rather than a matter of effort. A 2021 stopwatch time study published in the Journal of Industrial Engineering and Management found that task-related variables had a statistically significant effect on total cleaning time while the employee-related variables it tested did not, and that five tasks accounted for over two-thirds of the total. It was run in a single four-star Spanish hotel, so its minute counts do not transfer to a 60-room exterior-corridor motel off an interstate. The mechanism does: cleaning time is driven by which tasks are on the list, not by who is holding the cart.

Worth saying plainly, because the alternative explanation is the one properties reach for first. When Thursday runs long, the instinct is that someone was slow. Usually Thursday just had more checkouts. Per-person variance is real too — a written cleaning standard is how you tell a slow week from a heavy one — but it is the second thing to check, not the first.

Stayover vs checkout cleaning time, on two identical days

Take an invented 62-room property, and two Thursdays in the same month. Both run at 74% occupancy — 46 occupied rooms, the same number on both days. Suppose this property has measured its own two numbers and they came out at 28 minutes for a checkout and 13 for a stayover.

Same occupancy, same building, same rates. Five and a half hours apart — most of an extra shift.

Now plan both days with one number. Say this property runs about 60% checkouts across a full month, which blends its two numbers to 22 minutes a room. A schedule built on 46 rooms × 22 minutes budgets 16.9 hours — 4.2 hours too many on the first Thursday and 1.4 hours too few on the second.

The over-staffed day costs money quietly and nobody reports it. The under-staffed day ends in overtime, or in rooms that are not ready when the 3pm arrivals walk in. And the month reconciles perfectly, because the errors cancel: the grouping was wrong, not the sum, which is why it survives so long. A labor cost per occupied room figure built on a blended average inherits the same flaw — MPOR is a genuinely useful number, but only as good as the mix assumption you fed it.

If your own two numbers are a guess today, start a free trial — every clean gets recorded with its own start and finish time, which is the part that otherwise needs a stopwatch.

The published figures disagree with each other

This is the part most articles skip: the reference ranges do not agree.

Common planning figures put a checkout clean at 25–30 minutes and a stayover refresh at 15–25. Elsewhere you will find checkouts at 30–45 and stayovers as low as 12, depending on whose standard you are reading. The spread is not sloppiness — it reflects genuinely different buildings. A four-star hotel, an all-suite property and a select-service brand standard are measuring three different jobs, and none of them was measured at yours.

So treat any published figure as a sanity check, never a target. If your checkout clean comes out at 34 minutes against a published 28, the useful question is not “why am I six minutes over” — it is whether your 34 is stable and which way it is moving. A number drifting from 28 to 34 has a findable cause. A number simply higher than someone else’s average usually means a different building.

We know of no benchmark dataset specific to US economy and midscale independents at 20–120 rooms. If someone quotes you one, ask how many properties it covers and what segment they sit in.

Measuring your own two numbers in about two weeks

Be disciplined about one thing: never derive a per-room time by dividing a shift total by a room count, because that just rebuilds the blended average you are trying to get rid of.

1. Split the assignment sheet by type before anyone starts. Checkouts and stayovers get recorded separately from the first room.
2. Capture start and finish per room, not per shift.
3. Run it for two full weeks, including a heavy-checkout weekend and a quiet mid-week stayover day. You want a few dozen observations of each type.
4. Discard the first two days. Being timed changes the time; people settle.
5. Take the median, not the mean. One 70-minute room caused by a broken toilet moves a mean and tells you nothing about tomorrow.
6. Recheck twice a year, and after anything that changes the work — a refurbishment, a linen change, a new amenity setup, a different vacuum.

Most properties never get past step two, because a stopwatch is one more thing for a supervisor to carry. In ProfitClock, housekeeping tasks are generated nightly from room status in the property’s own timezone and completed on the clock, so each individual clean is recorded with its own start and finish time as a by-product of work that is happening anyway. Splitting those records by type and taking the two medians is still your arithmetic rather than the software’s — but the stopwatch part goes away. Where the morning’s room counts come from is its own question — the room report you already pull is usually the answer.

Where this is genuinely hard

Two numbers is better than one. It is not right, and these are the places it breaks:

• A third-night stayover often is not a stayover. Plenty of properties do a full clean on a set interval. That room is a checkout-shaped job with a guest’s belongings still in it, which can run longer than a checkout. It deserves its own number.
• Do-not-disturb rooms move work rather than removing it. A DND stayover at 2pm becomes an evening service or tomorrow’s problem, and those minutes land on a day nobody planned them for.
• Small samples are noisy. A 62-room property gets a handful of checkouts on a slow Tuesday, and a median built on nine rooms is an anecdote.
• Room type matters too. A kitchenette suite and a standard king are not the same checkout. Whether that earns a third and fourth number depends on how mixed the building is; for one room type in two bed configurations, usually not.
• None of this cleans a room. If the constraint is that you cannot hire, better numbers do not fix it. What they do is tell you honestly whether the schedule you wrote was ever achievable — a smaller claim than most of this category makes.

What actually changes

Once the two numbers exist, tomorrow’s staffing stops being a judgement call and becomes arithmetic on the departure list: checkouts times one number, stayovers times the other, divided by productive hours per shift. That is the arithmetic behind how many housekeepers a small motel needs, run on the day in front of you instead of on an average month.

And it makes the heavy Thursday visible on Wednesday afternoon, which is the only moment when anything can still be done about it.