PriceLabs’ Minimum Stay Recommendation Engine Algorithm

Latest: Dynamic Min-Stay is Now Live!

You can now fully automate your minimum stay rules with Dynamic Min Stay, a powerful new feature that applies our data-driven recommendations directly to your listings. These recommendations adjust automatically based on seasonality, market trends, and listing performance—no more guesswork required.

This article dives into how our model behind Dynamic Min Stay has evolved, what’s powering the latest recommendations, and how they help you drive more revenue with less effort.

Improved Min-Stay Recommendations

Minimum stay rules are powerful levers for maximizing both revenue and occupancy. That’s why we developed our Minimum Stay Recommendations to help you set the right rules for your listings. With the launch of our new Dynamic Min Stay feature, you can now automatically apply and update these recommendations in real time.

In this post, we highlight how we have continually improved our model over time. While the rest of this post will end up being a peek under the hood of the model and a bit more on the technical side, the TL;DR summary is:

• Improved data and algorithms make the model stronger and the generated recommendations better
• Dynamic Min Stay led to an increase in revenue and decreased operational costs when compared to using flat Min Stay rules

Background

In 2017, we introduced our dynamic minimum stay settings, which quickly gained popularity. The ability to adjust minimum stay requirements based on lead time and automatically open up availability for shorter stays was a novel revenue optimization feature. It was a natural progression for revenue management systems, and we took the lead in introducing it.

In February 2022, we expanded these settings, offering even more flexibility with additional layers, adjacent night settings, and the option to set them differently for various seasons.

However, a common concern voiced by our customers was the challenge of determining the optimal settings. Questions like “What should the minimum stay be for bookings far in advance?” and “How should these settings change based on lead time?” often perplexed users. Suppose you want to prioritize mid-term rentals—how should you adjust your settings accordingly?

Our data science team has been diligently addressing these questions since last year, and we’re thrilled to share PriceLabs’ Minimum Stay Recommendation Engine – the World’s first and only intelligent minstay engine.

Why use dynamic minimum stay restrictions

There are two primary reasons to use dynamic Minimum stay restrictions:

1. Operational: Check-ins and Check-Outs come with additional operational costs and overhead, which cut into profit. Another case is that one-night weekend stays usually signal a party, which most short-term rentals want to avoid.
2. Revenue maximization: Taking a booking negatively affects the probability of neighboring dates getting booked. Set your Min Stay too low and you can find your calendar full of unbooked gaps, set it too high and those dates might not get booked at all!

Coming up with the right Minimum Stay rules is a balancing act between the value of Guaranteed Revenue now and Opportunity Costs associated with neighboring dates being less bookable. Here’s a deep dive on how it works:

The methodology: “opportunity cost” vs “guaranteed revenue”

At the core of our minimum stay 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 booked, the chances of getting booked reduced 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-night stay.

Let’s focus on the previous night (the 14th) and, for 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, many week-long stays that would have previously been booked on the 11th will now be unable to.

The question remains – how many of these longer bookings 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 any longer bookings are expected, the opportunity cost might be high enough to where there’s a benefit to holding out with a longer minimum stay requirement and not taking that guaranteed revenue!

The example above illustrates that one part of calculating the “guaranteed revenue” vs “opportunity cost” tradeoff is easy:

1. Everyone knows the guaranteed revenue if you were to get that short booking.
2. 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 minimum stay recommendation.

With the above examples and context, you’ll notice a few things about our minimum stay recommendations:

1. They tend to suggest having longer minimum stay requirements far out. Generally speaking, a lot of demand is yet to book, and you’ll still have a good chance of booking those nights later, even if you turn down demand for shorter stays.
2. As a date gets closer (e.g., last minute), we’ll generally recommend reducing the minimum stay 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.
3. In markets where many longer bookings do happen last minute, you will see that the last-minute recommendations might not drop as much. This is because the recommendations look at the localized booking patterns of similar properties in the area (more on that below)!
4. In markets with low overall demand, the recommendations would tend towards lower minimum stay restrictions (take whatever bookings you can get).
5. High-demand months might be recommended in the seasonal suggestions for increasing the minimum stay.

What Powers Our Min Stay Recommendations Today

When attempting to calculate these values, there are 3 main categories of factors we need to consider. These factors are continuously being tuned and improved on here at PriceLabs

1. Market Trends
   • Historic bookings to get typical occupancy levels, distributions of Booking Window, and corresponding Length of Stays (LOS) for bookings.
   • From this, we can calculate the probability of getting booked on a given date for a given LOS during a given Booking Window for a typical market listing
   • We can also do a first estimate of the negative change in booking probability for neighboring dates from removing those other market bookings that are no longer possible
2. Listing Performance
   • The values we get from Market Trends do a good job of estimating values for a typical listing; however, your listing is unique, and we need to make adjustments due to how your listing performed for past and future dates
   • We will look at the overall occupancy level compared to the market; if we see you are falling behind, we want to encourage more bookings by being less restrictive with Minimum Stay Rules
   • We will also use your prices to tweak the value functions. If you have a more weekend-heavy strategy than the market and have more variation in your DOW prices, that will be picked up here. The model will want to prioritize longer min stays on weekdays to make sure a booking that may split up the weekend dates is worth it.
3. Risk Factor – Dealing with Uncertainty
   • Especially at low Length of Stay, increasing the Minimum Stay rule by a single night can greatly increase the possible revenue, but greatly decrease the chance of getting booked at all.
   • Consider a 2-night minimum changing to a 3-night minimum, potential revenue has increased by +50% from the extra night. That would be worth the risk as long as the probability of booking those nights hasn’t decreased more than -33% from being more restrictive.
   • But -33% is a large drop in the probability of getting a booking, and our calculation of that change in probability is only an estimate and comes with inherent uncertainty in the value. Our model may come back with the booking probability decreasing by -30% but the true value might lie in the range of -35% (not worth increasing the minimum stay) to -25% (definitely worth increasing the minimum stay). Due to this uncertainty, we want our final recommendation to err on the side of taking a slightly less optimal booking than taking no booking at all
   • While overall our model is looking to maximize revenue, for close calls and edge cases like the example above, we do add a factor that will choose the less restrictive Minimum Stay Rule

Length of stay

Two modes: short-term vs mid-term rentals

Many of our customers (especially in urban locations) 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 sees a lot darker 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, the recommendations might still allow short stays if there’s not enough mid-term demand. Mid-term suggestions incorporate the demand for stays longer than 14 nights, but the recommendations might still be lower if such demand isn’t enough.

Stay restrictions in seasonal markets

For very seasonal markets (e.g. ski or beach markets), annual minimum stay settings do not work. Using our Minimum Stay Profiles in combination with Custom seasonal profiles.

However, the challenge of finding optimal and revenue-maximizing minimum stay restrictions for each season becomes even more complicated.

To help, we also run the opportunity cost optimization for each month’s 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 create special requirements for these using Minimum Stay Profiles.

Why this matters for your business

PriceLabs’ enhanced Min Stay Recommendations leverage deeper insights into Market Trends, your unique Listing Performance, and a sophisticated approach to Risk Factor uncertainty.

By continuously refining our data and algorithms, we’ve built a model that intelligently balances guaranteed revenue against opportunity costs, even in complex edge cases. This results in stronger recommendations proven to boost your revenue while reducing operational overhead, moving you beyond the limitations of static rules.

With Dynamic Min Stay, harnessing this powerful optimization is now effortless, letting our smarter model work automatically to maximize your bookings and profitability.

Back to building,

PriceLabs Data Science Team