Unit 3 · Creating Your Pricing Strategy · Lesson 3.12
Variable minimum stays
Variable minimum stays apply different length-of-stay rules to different booking windows: stricter, longer requirements far from check-in, relaxing as the date approaches to fill gaps in the calendar. The rules should come from your own comp set’s booking data, not a pricing tool’s defaults, which tend to run conservative. This lesson works through a full four-tier example and compares it against those defaults.
The full lesson text below is an edited transcript of the video, published 2026-08-24. The complete course is free at the playbook.
A question of opportunity cost
Length-of-stay restrictions are easiest to understand through opportunity cost: taking one opportunity means forgoing another. Set a minimum stay and you are choosing which reservations you want, favouring one stay length over another across a specific stretch of the calendar.
The trade runs in both directions. Relaxed rules produce more short stays at the expense of longer ones. Stricter rules produce longer stays, and at the same time cut your visibility online, because a guest searching for a two-night trip never sees a home that requires three. Neither side is right by default; the point is to strike a balance between what suits you and where the market’s trends sit, which makes this a data decision rather than a preference.
Strict far out, relaxed close in
A variable minimum stay strategy applies different rules across different time frames. The time frames are booking windows, the lead times between booking and check-in, and each window carries two rules: one for weekdays and one for weekends.
The typical practice in short-term rentals is stricter rules further out, requiring longer stays while the date is still distant, then relaxing the restrictions as check-in approaches to fill any gaps of availability left in the calendar.
The graph to read
The data comes from the length of stay versus booking window graph on the market dashboard, the same graph the length of stay strategy uses, with one change: the graph is filtered to show bookings by length of stay and booking window. That filter breaks the activity down by the frequency of materialised bookings instead of by nights booked, and frequency is the better lens when the question is how often each stay length actually books at each lead time.
As with the rest of this pricing framework, the graph should be reading your own comp set, loaded on the dashboard, so that the trends you act on belong to your actual competition.
A four-tier worked example
In one sample comp set, the filtered graph showed shorter stays booking in real volume, especially close to check-in. That supports relaxed rules over the near term: inside the 0-6 day window, a one-night stay allowed on weekdays and a two-night stay required on weekends.
In the 7-14 day window the two-night counts picked up, which argues for a two-night requirement across both weekdays and weekends. From two weeks out to two months, three-night stays showed a decent count while two-night stays stayed significant, so the rule there became two nights on weekdays and three on weekends.
Beyond the two-month mark, the majority of bookings ran three nights or longer, so the final tier requires three nights across the board. Four windows, eight rules, each one recorded in the strategy sheet as it was decided, and every one of them traceable to a visible pattern in the comp set’s bookings rather than to a guess.
Why default suggestions fall short
PriceLabs generates its own recommended minimum stay settings for a listing, and it is tempting to skip the analysis and take them. The reason not to comes down to whose data the recommendations describe. Everything in this pricing framework runs on a comp set you defined yourself. PriceLabs draws on its own sample of listings, one you can neither see nor control, and its suggestions tend to be conservative, leaning toward more bookings rather than better ones.
The comparison on the sample listing made the point. PriceLabs recommended allowing one-night stays on both weekdays and weekends within a 6-day booking window, despite the decent two-night activity sitting in the comp set data. Its far-out default was the same very relaxed rule, one night on weekdays and two on weekends all the way out, despite the clear three-night booking counts further from check-in.
For this comp set, the recommendations were too conservative, so they were set aside and the four-tier strategy stood. That is the general conclusion in miniature: since you have no control over which listings a tool bases its recommendations on, it is always better to know the actual trends of listings similar to yours, set the requirements from those, and treat the suggestions as a benchmark to compare against rather than a strategy to adopt.