Unit 3 · Creating Your Pricing Strategy · Lesson 3.15
Cleaning fees and cancellation policies
Cleaning fees and cancellation policies are the last two inputs to the pricing strategy, and both are verification jobs rather than invention jobs. Market data at comp set level shows what your competition charges to clean and which cancellation terms their guests actually book under. Set cleaning to cover your costs and stay inside the market range; default to strict, and loosen only when the data says to.
The full lesson text below is an edited transcript of the video, published 2026-08-24. The complete course is free at the playbook.
Where the numbers come from
Both of these are verification exercises: you are not inventing a cleaning amount or choosing a cancellation policy from preference, you are checking that the choices you have already made sit sensibly inside the market you compete in. The data sits in the fees and policies section at the bottom of a market dashboard in a tool such as PriceLabs, and it only means anything when the dashboard is filtered to your comp set rather than to the market as a whole.
Before looking at it, have your own numbers to hand: what your cleaner currently charges you, or the quotes you have collected from local cleaners. Without them the market distribution is just a picture; with them you can place yourself on it immediately.
Markets that clean for almost nothing
The hardest case to handle is a market where cleaning is priced at almost nothing. It usually means the market is dominated by owner-operators who clean their own properties. Some of them set the amount at $10 or $20, enough to cover soap, shampoo and the supplies bought at Costco every few weeks, because the labour is their own and costs them nothing on paper.
You have a choice: mirror them, or run your own process. The better answer is usually your own, which means hiring a cleaner who is genuinely good at the job and delivering a higher standard of stay. There is one downside worth naming: a cleaning fee well above what the rest of the market shows can weigh slightly on where your listing ranks on Airbnb.
The alternative, absorbing the cost of cleaning into your nightly rate, sounds simpler and makes pricing much harder. A one-night stay sold at $100 against cleaning costs of $80 leaves $20. Two nights leaves considerably more, three nights better again, and every rate decision now drags an arithmetic problem behind it: what does this leave if it books for one night, or two, or five? Setting the cleaning amount to cover the cost of cleaning removes the problem and leaves you free to move nightly rates without the mental gymnastics.
When your cleaning cost is the outlier
The opposite problem looks similar and is not. Say the market clusters around $150 and your listing sits at $350. That is a red flag, and it is a supplier problem before it is a pricing problem: if $350 is genuinely what cleaning your property costs while other operators run professional services for less than half of that, the likely explanation is that you are being overcharged.
There are exceptions. A cleaner who is exceptional, or who is quietly doing other work for you on top of the turnover, may be worth what they cost. Short of that, the move is to find a different operator who can do the job at a competitive rate and bring your cleaning fee back toward the middle of the market.
If you genuinely cannot find anyone cheaper and the listing is struggling for bookings, there is a fallback: leave the cleaning amount where it is and be more conservative with your nightly rates, so the total the guest pays stays competitive. It is the weaker of the two answers, so exhaust the supplier route first.
Reading the distribution
The chart is a histogram. The horizontal axis is a series of bands covering the range the market spans; the vertical axis carries two readings per band, the share of listings sitting there and the share of bookings those listings took. In one market, roughly 17% of listings sat in the $193 to $224 band, with a second, lighter bar beside it for the booking share.
What you want is simple: your own cleaning amount somewhere inside the range the market occupies, and not off the chart entirely, in territory where almost nobody else sits. If you are down at the very bottom and quietly absorbing part of the cost yourself, you have room to move up without doing damage. If you are out past the top band, at $400 where the market stops at $321, that is the signal to go and find a different cleaning provider.
The policy that protects your calendar
On cancellation, the strongest position for a host is a non-refundable rate: once the booking is made, the night is sold and the money is coming whatever happens next. The catch is that booking channels generally require you to discount in exchange, so the protection is bought with revenue you have already given away.
Without discounting, strict is the next best thing and the policy worth defaulting to. It gives a host the most protection: a booking sitting on the calendar is very likely to still be there, and still to be revenue, at the end of the month.
A flexible policy pushes the risk back onto you, and how much risk depends on your booking window. Under Airbnb’s flexible terms a guest can cancel free of charge until 24 hours before check-in. If your average booking lead time is one or two months, that is a long stretch in which it can rain, someone can catch a cold, or the trip can simply lose its appeal, and you are left with a near-term empty night you have very little chance of selling again. Plenty of guests on flexible terms turn up anyway; the point is that the exposure runs one way.
Checking strict against your market
The ideal policy protects your revenue and stays attractive to guests, which is why strict cannot simply be imposed. Where strict is rare in a market, running it can cost you bookings. The market data settles it: the cancellation chart sits immediately beside the cleaning data and splits the comp set into flexible, moderate, strict, super strict 30 and super strict 60, with the same two readings for each.
In the market used as the worked example, strict dominated: more than 60% of listings ran it and just under 60% of bookings were taken under it. That is strong evidence you can run strict without hurting your own bookability, because the guests in that market are demonstrably booking under those terms. Read the same chart in a market where flexible or moderate carries the larger share of listings and bookings, and the honest conclusion is the opposite one.
So the rule of thumb is to default to strict and follow the market when the market clearly disagrees. The exception is a long booking lead time. If most of your competitors run flexible terms but your bookings arrive a month or more ahead, matching them means carrying cancellation risk on nights that are close to impossible to re-sell, and the stricter policy may still be the right call.
There is no formula for that decision, only a trade with a cost on both sides. On one side is the booking you lose because a guest reaches your listing page, sees strict terms, and takes the property down the road with moderate terms instead. On the other is the revenue you lose because a guest booked on flexible terms, cancelled late, and left you a night you cannot fill. Both outcomes happen. Choose the side you would rather be wrong on and make peace with it up front, because the decision is made once and lived with all season.
Where the market data stops
One limitation is worth knowing before you lean on that chart. Airbnb carries far more policy combinations than market data reflects: strict, strict with non-refundable options, firm, the super strict variants, and the permutations between them. Market dashboards do not resolve those individually, and firm does not appear at all, so what you get is a general guide to whether your market leans strict or flexible rather than an instruction on which exact variant to select. That last step is a judgement call: pick the variant that suits your listing and gives a guest on your listing page the least reason to hesitate.
Then record both decisions in the strategy sheet, the cleaning amount you are working with and the cancellation policy you have settled on. In the worked example the cleaning figure recorded was $275, comfortably inside the market range and not scraping the bottom of it, alongside the strict policy the data had just justified.