One piece of feedback we heard from customers often - it's not always easy to figure out what those settings should look like. What should your far out min-stay be? How much should they change by lead time? What if you want to focus more on mid-term rentals? Our our data science team has been working on answering this question since last year, and we have something exciting to share! But before we go into how we come up with min night recommendations, it is important to understand what min-night settings help with.
Why use dynamic min-night restrictions
Minimum night restrictions are one of the key controls you have to create rules to decide which reservations you're okay taking, and which ones you're not fine with. There are be two primary reasons to use dynamic min-stay restrictions:
- Operational: The easiest example of this is one night stays on weekends usually signal a party, which most short-term rentals want to avoid.
- Revenue maximization:
- If you set your min-stay too low, you might get many short bookings far out. Taking a short booking precludes you from being able to take longer bookings on the days around it. So even though you got "guaranteed revenue" from the days that got booked, the dates around it now have a smaller chance of booking.
- If you set your min-stay too high, you might be able to protect your calendar to get long bookings, but any unsold days will see lesser demand, or if there are gaps shorter than min-stay setting, none at all.
Our dynamic min-stay settings allow you to have your min-stay settings change by lead time, gap length, adjacent days and more! With this new update, we are able to also recommend what those settings might be to maximize revenue! But before we get there, let's look at the operational aspects of this.
Your operational preferences are why our recommendations are editable
While we do know a lot about revenue maximization and booking patterns in your market, we don't know everything about your operations and preferences. Our methodology focuses on revenue maximization, and we make our recommendations editable so you can change or skip certain settings that don't work well with your operations!
For example, in the suggestions below you are able to uncheck the "last minute" setting that was lowering min-stay to 1 night within last 6 days. Note that you may still get a 1 night booking when there's a gap - if you don't want those either, you can edit the orphan night setting after clicking "Apply and Customize" as well!
Our recommendations can be edited before you apply to a listing
The methodology: "opportunity cost" vs "guaranteed revenue"
At the core of our minimum-night recommendation engine is "opportunity cost". In simple terms, selling a couple of nights 11 months out brings some "guaranteed revenue" (the revenue from those two nights). This feels great, and barring a cancellation, you are now guaranteed certain income for that month. However, for the dates surrounding the two nights that got booked, the chances of getting booked reduces pretty drastically. That drop in potential revenue from nights around the booked dates is the "opportunity cost".
To illustrate this, consider the example below showing a calendar with 10 days and 2 nights (15th and 16th of the month) booked with a 2 nights stay.
Now let's focus on the previous night (the 14th) and as an example, overlay the possible 4 night reservations that could book the 14th night:
Because the 15th isn't available, the last 3 of those potential bookings aren't really possible anymore.
Once the 2 nights (15th & 16th) are booked, it's not just the 14th that experiences a drop in potential demand, but also other nights around it. For example, a lot of week long stays that would have previously booked the 11th will now not be able to.
The question remains - how many of these longer bookings that could potentially bring larger revenue (by also booking the 14th and other adjacent nights) do we forecast in the market?
- If not many longer bookings are expected, the opportunity cost is low and we should take the guaranteed revenue from that short booking.
- If a lot of longer bookings are expected, maybe, the opportunity cost might be high enough to where there's a benefit to holding out with a longer min-night requirement and not take that guaranteed revenue!
The example above illustrates that one part of calculating the "guaranteed revenue" vs "opportunity cost" tradeoff is easy:
- Everyone knows the guaranteed revenue if you were to get that short booking.
- Estimating opportunity cost is trickier. Our data science team has been working on this since last year, and we've come up with a nifty way to incorporate local demand patterns and booking probabilities into an optimization framework to find that tipping point where taking a shorter booking will be worse for the overall listing revenue. That tipping point becomes your min-night recommendation.
With the above examples and context, you'll notice a few things about our min-night recommendations:
- They tend to suggest having longer min-night requirements far out. Generally speaking, a lot of demand is yet to book, and you'll still have a pretty good chance of booking those nights later on even if you turn down demand for shorter stays.
- As a date gets closer (e.g., last minute), we'll generally recommend reducing the min-night restrictions to take those shorter bookings. When there's relatively little demand left to book, it's better to take that "assured revenue" from that short booking instead of holding out for "potential revenue" from longer bookings that might not materialize.
- In markets where a lot of longer bookings do happen last minute, you will see that the last min recommendations might not drop down as much. This is because the recommendations look at the localized booking patterns of similar properties in the area (more on that below)!
- In markets with low overall demand, the recommendations would tend towards lower min-night restrictions (take whatever bookings you can get).
- High demand months might be recommended in the seasonal suggestions for increasing min-stay.
The demand data feeding our methodology
If you've been using our Market Dashboards
in the last couple of years, you've probably already seen the kind of detailed booking pattern analysis that we have built over the years for markets around the world (the image below shows the demand by LOS for various stay dates in a ski market in the US).
When running the min-stay recommendation algorithm for any listing, we look at demand patterns by length-of-stay (LOS) and booking window (BW) for various seasons for similar properties around your listing to suggest the min-night settings for your listings!
Length of stay patterns in an example Ski market (Big Sky, MT, USA)
Two modes: short-term vs mid-term rentals
Many of our customers (especially in urban locations) do see a significantly higher proportion of mid-term bookings on their properties. The image below shows data for 2 bedroom properties in Chicago (our HQ!) - you'll see that compared to the ski market above, Chicago see's a lot more dark gray (15+ night stays).
Length of stay patterns in Chicago, IL, USA (an example urban market) show weekend heavy short term demand, but also a large portion of mid-term stays.
We created these two modes based on observations that many customers prefer one over the other for operational reasons.
- If you select "Prefer short-term", we'll remove the 15+ night bookings from the market demand (and related supply) and show recommendations
- If you select "Prefer mid-term", we'll consider all the demand in the market. Note that even with all the demand included, if there's not enough mid-term demand out there, the recommendations might still allowing short stays.
Mid-term suggestions incorporate demand for stays longer than 14 nights, but if such demand isn't enough, the recommendations might still be lower.
For very seasonal markets (like the ski market above, or many beach markets), our annual recommendations might not be the right fit for all the varying demand patterns. To account for this, we also run the opportunity cost optimization for each months' demand in isolation to see if for a given month, the recommendations deviate from the overall recommendations.
These "exception" months are called out with our recommendations, and you can easily create special requirements for these using our recently released seasonal profiles
. As an example, for the recommendations for a market where July is high season, you might see that we recommend increasing min-stay for July.
In seasonal markets, you might see a call-out for which months might need a higher or lower minstay.
How frequently should you update these settings
Though the data we gather updates daily, the recommendations don't change as often (the long term booking patterns don't change as frequently). You don't have to check the recommendations more than once a month - if your property is under-performing the market, it might be helpful to edit the min-night recommendations and lower them to boost the performance and gain reviews that can help long term.
As always, please reach out to our support team with feedback, thoughts and suggestions on this at email@example.com