Looking Beyond the Base Rate

March 04, 2021 |

This is a guest post by Tim Speicher, Co-Founder at Buoy, a revenue management and reporting tool for vacation rentals and boutique hotels.

Think back to the first listing you created. Faced with a blank calendar, you probably thought to yourself “I just want to track a few rentals in my area, price competitively, and change rates when the booking pace changes.” It doesn’t seem complicated, but pretty much everyone in the industry still uses a base rate algorithm or just prices by hand.

There is a better way.

Those first vague inklings you had when you made your first listing can actually become a fully automated, truly dynamic rate setting algorithm. Buoy is built on those same principles. This post will show you how we translated those instincts into an algorithm.

Let’s Talk About Base Rates

Base rates are simple enough. Set a rate, let the system push the rate up on weekdays, down on weekends, up in peak months, down in off-peak, way up on New Year’s Eve, way down just after ski season ends, etc. Base rate models layer five or six rules on top of a number you set, then use it to generate rates for your whole calendar.

Their great strength is their ability to work independent of market inputs. Unfortunately, the same characteristic makes them inflexible around peak dates and sluggish amidst abrupt market changes. Once, when managing listings in Louisville, I had to inflate my rate 5x to match my comps during the Kentucky Derby. We received reservations, and I was perplexed as to how the base rate’s demand predictors missed a big event so badly. When Covid onset the following year, I had to drop rates by hand to match market conditions. Wouldn’t a truly dynamic system have done that for me?

Base rates are born of a time when robust market data was scarce or nonexistent. They are intrinsically restrictive and tough to use. We can do better. Let’s make our own algorithm, starting with that first instinct: “I just want to track a few rentals in my area, price competitively, and change rates when the booking pace changes.”

Where do I track competitors? 

Everywhere you can. At Buoy we use data from Airbnb, Expedia, Booking.com, and Trip Advisor. Our data supplier, Transparent, puts it into a simple spreadsheet for us. They even throw in hotels for good measure. 

Take your spreadsheet and sort by  competitors. When doing this think to yourself “Which other accommodations do my guests consider before booking my listing?” Make sure to trim the data down to a manageable amount so it’s not overwhelming(Think fifty, not five hundred). You can sort however you want, but I prioritize 

  • Distance: by radius, because zip codes are arbitrary and neighborhoods are disputed, 
  • Size: This includes bedrooms, bathrooms, capacity, and square footage in that order 
  • Quality: Review score and review count and photos are helpful too
  •  Performance: Average length of stay, occupancy, earnings, etc. 

The opportunities are endless. Go crazy with it!

How to Use the Competitive Set

Now that you have a competitive set, track their rates 12-18 months into the future. Use the resulting rate range to establish a reasonable range for your own rates. Generally, you want more rate variance than your competitors, but you should get more aggressive with high rates than with low ones. If you are struggling with how high to push your rates for peak nights, take a look at hotels. While less relevant from a competitive standpoint, hotels tend to be more accurate demand predictors than vacation rental managers. When their rates are through the roof, follow suit. They probably know something you don’t.

In light blue: The rate range for a given compset. Layered on top, we can see the listing’s rates graphed on a line. (Green: booked. Dark gray: blocked. Light gray: available) Note how the rate drops relative to the compset as the booking window closes.

Adjusting According to Booking Pace Change

Now for the other part of the equation: “I want to change rates when the booking pace changes.”

First ask yourself “when do travelers to my area tend to book their stays?” Look at reservations in your competitive set over the past few years. Now narrow those reservations down to just the relevant dates: the ones you are pricing. If you are pricing a big weekend like Labor Day in the US, look at Labor Day reservations for 2020 and 2019 and 2018 and so on until you run out of data. Specifically look at the booking window: the length of time between when the stay is purchased and when the stay begins.

You will end up with an array of purchase dates. They tell a story of reservation volume slowly rising at the end of June, creeping up all summer, then dramatically accelerating a week before the stay begins. Note how there is almost no purchase activity in the rest of the year.

For our algorithm, we would keep the rates high until bookings pick up pace in July, then drop rates in line with our competition as August draws on, and finally drop rates again in the last few days of the booking window if our listings are still available.

Think about it, why adjust rates down before bookings pick up? We should aim to be higher than our competitors, then neck-and-neck with our competitors, then slightly lower than our competitors just before time runs out.

Listen to Your Instincts

The last element is you. Having automated the rest of our algorithm to match market conditions, you may still want to apply your own subtle modifications. That’s totally fine. You may also want to negotiate your own rates with guests that’s lower than what your algorithm recommends. Not to worry. Your algorithm is an outgrowth of your instincts, so don’t be afraid to deviate from it if the circumstance dictates. Rest assured that any decision you make is backed up by millions of data points and the unique, irreplaceable instincts you bring to the table.

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