14 Sep Airbnb Pricing: Wheelhouse Vs Beyond Pricing
In this post, we’re going to dissect the optimal Airbnb pricing strategies as well as two of the most popular dynamic pricing tools in the market today.
If you’re currently using any pricing tools, then there’s definitely something actionable for you to take away.
I’m going to try and cover these two pricing tools in depth. But before I start, I’d like to take a minute to set the stage.
I’ve been using Beyond Pricing (BP) for about 18 months before a member gave me the idea of trying out Wheelhouse (WH) and actually compare these two pricing tools’ algorithms.
So, I framed this as an experiment to try for two weeks starting in the middle of August.
August has been a historically busy month for me (the highest average daily rate in peak-season), so a great time to get some REAL pricing data. I was hesitant to mess with this too much so I only turned on WH for one of my properties, a 1-bedroom unit.
I’m going to share with you my findings, but first, let’s talk about optimal Airbnb pricing strategies.
How to Optimize your Airbnb Pricing
While it’s nice to think that there’s a one-size fit all solution, but all of the solutions have its drawbacks.
There are really only 3 viable options as a host on Airbnb: 1) do it yourself, 2) Airbnb Smart Pricing, 3) third-party pricing tools like WH or BP.
What is optimal Airbnb Pricing
Optimal Airbnb Pricing is the sweet spot where the price you charge maximizes your occupancy rate as well as cost per night. Over-quoting or under-charging both represent missed opportunities with Airbnb.
There are a few terminologies that I’m going to use. I’m borrowing it from the hotel industry since our business model is pretty much the same as theirs.
The terminologies that I’d like to highlight are Revenue per available room (RevPAR), Best available rate (BAR), Occupancy rate (OR), and Average daily rate (ADR).
If you don’t already know these terminologies, please head over to this glossary.
The Pros and Cons of Doing it Yourself
Socrates once said, “to know thyself is the beginning of wisdom.” One of the pros of doing it yourself is that you get 100% control of your own nightly rate. Perhaps the ancient wisdom is very applicable if you know the ins and outs of your own market. If you have 10+ units in the same market, then perhaps it’s also wise to manually adjust your pricing.
Here are a couple of examples of what I mean by doing it yourself. This guy Kevin said it best, so I’m not going to attempt to paraphrase.
This guy named Jaquo said it best. This method works if you know your market the best. But it also takes the most precious and the most non-renewable resource from you, TIME!
I’d imagine this is not scalable if you have multiple units in multiple cities. Can you imagine the time it’d take for you to master each unique location…
The Pros and Cons of Airbnb Smart Pricing
I’ve written numerous blog posts about my POV on this. I personally think Airbnb’s smart pricing is deeply flawed. I guess I’m not the only one feeling this way.
I’d never recommended this method at its current state of affairs.
The Pros and Cons of Third-Party Pricing Tools
- It’ll save you time, which is to me is the most precious resource that you have
- Set it and forget it.
- It’s dynamic because it pulls in different data points to determine the best price for your market
- It’s made me 33% more money than I could have on my own.
- It doesn’t know your markets as intimately
- It charges 1% of your booking fees.
The math goes something like this:
- 300 dollar booking for two nights
- Airbnb takes 3% host fee: 300 – 9 = 291 dollars
- Beyond Pricing takes 1% of that 291 = 2.91
Total fees = 4% (3%Airbnb host fee + 1% BP Fee).
In summary, you have to know thyself in order to figure out what works best for you and what you lean towards to.
Research Before You Buy
Now, we’re going to transition a little bit because you’ve been waiting so patiently for my little experiment. Don’t worry, I’ll get to that very soon! 🙂
Before I talk about the actual numbers behind my experiment, I’d like to include a few other considerations into my evaluation of these services. In this case, it’s Wheelhouse and Beyond Pricing.
Most users will never do this type of analysis, but since I came from the world of selling software services, I inherently knew what to look for. Clues that are deeper than what meets the eyes (I guess selling software in the tech world did teach me something after all).
Below are the 4 extra factors that I took into my consideration:
- The company’s founders background
- The company’s current state of affairs
- How much money they’ve raised
- Market share on the industry
- The CEO of BP comes from the hotel revenue management business, so his background will help BP create a superior pricing tool
- The company is still very much focused on its pricing algorithm and improving its core product.
- BP raised $3.5 million from VCs.
- As far as I can tell, they’ve signed with a number of large property management companies as well as hotels. They were also the first-to-market pricing tool.
- The CEO comes from a marketing background that had nothing to do with hotel management. Hence their website’s design feels easier to use.
- The company is no longer focused on its dynamic pricing tool, which raises a big red flag. This means their product will not improve, no more support, and no more focus.
- WH raised $19.1 million from VCs.
- See my 2nd point. Wheelhouse’s business model pivoted their focus to now providing premium short-term rentals. Now they compete with companies like Sonder. Another thing to note, Sonder recently raised $135 million. WH is significantly under raised and will be under-resourced.
I mean doesn’t it seem strange to you when a business all of the sudden pivots to a different business? It just shows me that they weren’t what they claimed to be. I also suspect their shift is due to Airbnb’s increasing investment in luxury markets.
In all honesty, raising a lot of money sometimes can backfire because of the increasing amount of pressure from your investors to meet, at times, ridiculous and unattainable revenue goals.
Airbnb Pricing tools: Wheelhouse Vs. Beyond Pricing
Now, let’s talk about my experiment.
I’m going to introduce three datasets below; 2 predictions and 1 actual revenue booked for the same period last year.
Airbnb Pricing tool: Wheelhouse
My base price was set at 180 dollars. To keep the experiment somewhat standardized, I also had set my base price at $180 for BP.
Again, this is only one dataset for my 1-bedroom unit in Mountain View. To make this experiment 100% foolproof, I’d test two identical units simultaneous within the same time frame. Obviously, that wasn’t possible in my case.
Airbnb Pricing tool: Beyond Pricing
If we do a day to day comparison, BP’s predictions are consistently less.
If my unit was booked based on WH’s pricing, I’d have gotten $1,250 and $922 from BP. That’s a difference of 328 dollars, at least in theory.
Well, that’s a shit ton of money for only 6 nights. You”re probably thinking, why the hell are you still using BP, Sam?!
Let’s look at what actually happened.
During those 7 days, I ONLY got 1 booking, which was from August 21st to the 22nd. TOTAL REVENUE WAS $232 excluding my cleaning fee.
I got so discouraged from WH that I actually had to cut my experiment short. Instead of full-14 days, I could only stomach for 6 days.
A noticeable difference between these two pricing tools is that WH charges more for last-minute reservations, while BP is the opposite. WH maximizes your nightly rates while BP maximizes your occupancy rate.
Luckily, I have some historical data. So, let’s see how much money I’ve lost compared to the same period last year. From the same period last year, I charged an ADR of $171.55.
From this, I can say that while my ADR from BP is lower than WH, but my RevPAR is much higher. My actual booking from WH was $232 and my last year’s booking was $1200.86.
My net loss was $968 (I took a hit so hopefully, you don’t have to).
There’s really no one-size fits all solution. In an ideal world, yes, I can expect these pricing tools to deliver with 100% accuracy, but we don’t live in that world. We live in a world where we have to make choices. Choices that will have consequences.
In my case, I made a choice to do this experiment and lost $968.