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Is This The Smartest Pricing Tool for Airbnb Hosts? · Host Planet: Airbnb, Holiday Let, and Vacation Rental Insights

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Welcome to STR Pricing Secrets. This series is powered by host
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Planets and Beyond, the global leader in revenue management for
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vacation rentals. This series is your go to guide
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for mastering pricing strategies that maximize your earnings,
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whether you're a seasoned host or just starting out.
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We'll break down the latest trends, expert insights.
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And actionable tips. To help you boost occupancy,
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increase revenue and stay ahead in the ever changing short term
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rental market. Ready to unlock the secrets to
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smarter pricing? Let's dive in.
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Hello and welcome to. STR pricing secrets.
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In today's episode, we're going to focus on the science behind
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Beyond's data-driven pricing. And joining me to share his
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extensive knowledge on this subject is Ryan Saylor, Beyond's
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Product Marketing Director. How's it going, Ryan?
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Doing well over here, James, how are you?
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Very well, thank you. OK then straight in then let's.
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Start with the. Magic behind the scenes.
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What actually powers Beyond's dynamic pricing?
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Yeah. I mean this is the question.
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I think we could talk about this for the better part of a couple
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hours if we really wanted to. But the short and sweet answer
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is, you know, Beyond was the first dynamic pricing tool for
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short term rentals in the industry.
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We obviously saw what was happening in hotels and
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airlines. They sort of really birthed the
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the revenue management movement and and the broader travel
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industry. But there were some nuances for
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short term rentals, obviously very unique supply, pretty wild
1:36
markets around the world and ultimately pretty fragmented
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ecosystem digitally. So from day one, we really had
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to be the ones, you know, collecting our own data,
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analyzing it, cleaning it and making sure we were confident in
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the data to be able to price somebody's short term rental
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property. So from day one, you know, we've
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been really, really focused on, you know, maintaining that
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source of data and understanding sort of the impacts that it has
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normally on pricing, but just, you know, that the industry and
2:04
individual markets in general. And you know, we, we really
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started before any of the big data aggregators in the, in the
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market as well. So for a while we, we, you know,
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really managed all of that on our own to power the dynamic
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pricing tool. And now we have actual like data
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tools that people can use because we realized we were
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really sitting on this mountain of data that we could use and
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really showcase people. And I think 2020 was a big, a
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big push for us in that, in that arena, there was so much need
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for data that we said, well, we have it all, so why don't we?
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Why don't we share it? But yeah, it really all starts
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with, with day one, you know, 12 / 12 years ago now first
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starting to collect some of that data.
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And I think I want to call out here at the top too.
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Like there is a common misconception I come across a
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lot between hosts, property managers, revenue managers, that
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any sort of revenue management tool, any sort of property
2:59
management tool even that's offering sort of dynamic pricing
3:02
or rates or anything, everybody's using the same data
3:05
set for the, the exact same market.
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And that couldn't be further from the truth.
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Data sets across the board are so vastly different, not only in
3:13
how they're sort of like collected and started, but also
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how they're managed and optimized on a rolling basis.
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It's never never really set it and forget it with the amount of
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data that we're seeing come in. So for us, like owning our data
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pipeline has been very important because we know there are so
3:28
many volatile things that could happen to, you know, a, a data
3:32
source ultimately. So we, we really start with, you
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know, sort of like key core data for beyond, which is going to be
3:40
source connected data from a lot of our partners informing what's
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happening in a market, truly what someone is paying for a
3:46
property, how many people are staying, What's the, what's the
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booking window looking like? That rich data is, is able to,
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to give us a really great picture of any market, any St.,
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any neighborhood, whatever it might be, coupled with some of
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the publicly scraped data we're able to get from online travel
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agencies like Airbnb, VRBO, booking.com.
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That data is ultimately a bit less valuable on the pricing
4:10
side of things just because from that information, we're getting
4:12
posted prices, not booked rates. So it's, it's an assumption of
4:16
what somebody thinks they could be booked out in the market, but
4:18
not necessarily what a guest paid, which I think is, is
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really the core of revenue management.
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And then our our third data pillar internally at Beyond is
4:27
going to be search data. So that's something that we've
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introduced over the past couple years where we're sort of
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understanding what people are searching for out in any given
4:35
market and how that can translate into our perception of
4:38
demand ahead of time. So literally pulling, you know,
4:41
at a granular level, somebody going into a market on a direct
4:45
booking website and saying, I want to stay in this market for
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five days over these specific days for these amenities with
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these guests. So we're able to get sort of a
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an estimate of market demand before anybody books, which is
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super helpful when we have, you know, changes in seasonality,
5:00
crazy things coming out of the past couple years in terms of
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supply, new events and things like that.
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So it's one of those three data pillars are are really important
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and really core for us. And then our team once we
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actually have all of that data internally, we do a ton of work
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with it. So there's a lot of processes
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behind the scenes that go into ensuring that again, any of
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those for our pricing tool, any of the prices that we're pushing
5:24
out are accurate, they're optimized.
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There's nothing wild in the data that's causing us to skew
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different pictures of demand. And internally, we call it our
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our revenue management and data science team.
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And this team literally acts as like revenue managers for entire
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markets for beyond users in that market against everybody else in
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the market. So it's kind of like this like a
5:46
guardian Angel in our algorithm that is looking out for, you
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know, how are the all these Taylor Swift shows impacting
5:52
demand? How is seasonality changing year
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over year? How is shoulder season shifting
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a little bit? Is day of week changing every
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single day? They're the ones really fine
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tuning some of this data. And what's cool about owning our
6:03
own data pipeline is we've been able to set up alerts, many,
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many different types of alerts for all of our markets that we
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price in, where this revenue management team actually sits
6:13
down every single day. And at the beginning of the day
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kind of reviews like red flags in market data.
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So are we seeing like a crazy spike in bookings for, you know,
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a date that we haven't seen before?
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What's going on There isn't an event can be attributed to
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something. How should that inform our
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pricing? Another good example of of the
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work that this team does with our data is we hand launch every
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market that we price in. So even when I first started
6:38
beyond six years ago, we weren't 100% available globally or even
6:42
in the US or even in Europe. We were hand launching markets
6:46
based on the data that we have. And we would say we couldn't
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launch a market if we weren't confident in the data that we
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had actually for that market. So that team is also working on
6:56
building markets, you know, around the world and then
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drilling down into again, sort of those individual hyper local
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clusters or neighborhoods and making sure that, you know,
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those are are making sense of the demand in the market as
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well. We certainly see changes here.
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You know, over the past 12 years, markets have grown,
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they've shrunken a little bit, they've changed, you know, the
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difference between ski and ski out and being in a ski village
7:20
around the lake. All of these different market
7:22
dynamics come into play where you can't just write algorithm
7:26
rules to account for that. You have to have a human eye
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actually sort of building the data literally around like
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physical properties on a map. So I think that's really set us
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apart from day one from from other tools in the industry or
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or any other data aggregator in the industry because we had to
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do it from day one. We weren't going to be confident
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being the first people to do dynamic pricing if we didn't
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have our eyes on these specific markets and actually monitoring
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what's going on in them. So that's, that's a the short
7:54
version of basically our our data philosophy on the on the
7:56
pricing side of things. But yeah, it's it's been so
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important for us to to be fully in control of that data and
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really understand how that shakes out for all of our users
8:06
and making them more money year over year.
8:08
It's pretty fascinating. There was one term that you
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mentioned quite near the start of your answer.
8:12
There you talked about cleaning the data.
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How do you clean the data? Yeah, there's a lot that goes
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into there too. A couple examples of things that
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we've come across, and these are literally things that we learned
8:26
in the early days and beyond. But also just, you know, working
8:28
through the past couple years, things that we've come across.
8:31
Some examples are, you know, like 8 years ago, all of a
8:34
sudden we started detecting massive spikes down in Florida
8:38
for like February 1st. And so that that starts to skew
8:41
our demand and say, Oh my gosh, the first day of February is
8:44
always super, super high demand for some reason.
8:46
Isn't the event what's going on? It ultimately was snowbirds
8:49
setting their arrival dates for February 1st.
8:51
So yes, it was demand, but it wasn't just demand for that day.
8:55
And that's one thing that we learned.
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We have to understand when we're seeing that in certain markets
8:59
to sort of lessen maybe the daily demand for that arrival
9:03
date when we know it's more long term like seasonal travelers
9:07
coming into a market. That's one example of what of
9:10
what we see on sort of the even like the connected source
9:13
partner data, a scrape data example might be the fact that
9:17
we're, you know, scraping booking.com, Airbnb and VRBO.
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If we have one property that's listed on, you know, all three
9:24
of those online channels in any given market, we want to make
9:27
sure we're not counting that as three different properties.
9:29
So we have to de duplicate, you know, each of those properties
9:32
and kind of mash them into one to really understand what's
9:36
happening for one specific property and to not over
9:39
allocate demand. So that's one thing that we're
9:41
constantly monitoring and saying, OK, we, we don't want to
9:43
be saying that there's too many listings or too many properties
9:46
in this market just because they're coming from different
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data sources. And there's a number of nuances
9:52
with that. Do you use photos, the listing
9:54
title, the coordinates? So it's a, it's a combination of
9:58
a ton of different data points that we can use.
10:01
But that's, that's, yeah, a lot of the, the cleanliness that we
10:03
see come in. One other example, just to give
10:07
you a concrete example, is we're able to look at basically like
10:11
channel sources. So in any given market, are we
10:14
seeing the majority of bookings come in on direct booking
10:16
websites or directly over the phone?
10:19
Or is it a market like Miami where we're seeing a ton
10:23
actually come in on Airbnb and maybe a little bit on VRBO?
10:27
What's interesting is we have, you know, I think over 70 PMS
10:30
channel connection partners, plus we're getting all this
10:32
additional data from those online travel agencies.
10:35
It's wild what people what like reservationists, what even
10:39
guests can put in as far as their their channel where they
10:43
actually book their reservation. Literally like the data is so
10:46
messy that if somebody booked through Airbnb it could be AB
10:51
and B as the source or Airbnb or airspace BNB.
10:55
Like literally cleaning millions of data sets on just like how do
10:59
we make sense of this data and categorize it into maybe like 10
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channels instead of 50,000. So that's another example that
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we've come across over the years, but there's many more.
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And we've got, you know, I think over 80 full time employees
11:14
working on like the product design and engineering side of
11:16
things. We're actually looking out for
11:18
nuances in this data depending on the market, the source,
11:22
whatever might be, and ultimately how that impacts our
11:24
ability to price and support users in these markets.
11:28
You mentioned this before as well, that there is an
11:30
assumption perhaps in the industry that the big dynamic
11:33
pricing platforms are all quite similar and they're all getting
11:37
the same data and stuff like that, but just.
11:39
Explain to us how. Different they are, and what
11:42
really marks beyond doubt what really is your What are your
11:45
USPS? Yeah.
11:48
I think on specifically on the data piece, one thing we've
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we've really highlighted over the past couple years is going
11:53
back to that scrape data in our assumption of market demand and
11:57
then ultimately how we should price every single day for every
12:00
unique listing in a market. We're really not using that
12:03
scrape data mostly for the reasons that I mentioned before.
12:07
We like scraping a website literally means if I'm a human
12:11
going to a website and I write down that this property is
12:13
available for 200 lbs on this, you know, specific 2 day range,
12:18
that's what we're scraping. So it's, it's the posted price,
12:21
it's whether the calendar is available or not available.
12:25
And then maybe some information on amenities, maybe minimum
12:28
stays, but nothing super detailed like we're getting from
12:31
actual guest reservation information.
12:34
So we wouldn't want to necessarily say that, OK, six
12:37
months from now in London, for example, we're seeing a huge
12:42
price increase on Airbnb for the specific day in the specific
12:46
neighborhood. That might be an indication that
12:49
other people in the market are sensing something's going on.
12:52
Obviously, we're gut checking this with maybe events or
12:55
holidays or anything else going on, but we don't want to
12:58
necessarily take, you know, some other hosts in the market and
13:00
say well, they're pricing high, therefore we should too.
13:03
So we don't really use scrape data in our algorithm
13:06
day-to-day. It has certainly been used over
13:09
the years to sort of inform our perception of maybe supply in
13:12
the market or high level like seasonality trends based on
13:16
availability and then also pricing swings and curves.
13:19
But because we, we really brought dynamic pricing first to
13:21
the industry, we always said you don't want to just copy off your
13:24
competitors off of, you know, the house down the street or
13:27
whatever it might be because who knows what they're using the
13:29
price. Maybe there's flat pricing and
13:30
they don't really care or they, they don't, they're not that
13:32
involved with, with pricing their property.
13:34
So why would you just want to look to them and what they're,
13:37
what they're posting? So that really does set us
13:40
apart. I think most other pricing tools
13:43
rely a lot on scrape data. That also comes from, you know,
13:47
again, we hand launch every single market, but I'm pretty
13:50
sure every other option available in the industry today
13:53
really launched the world in a day.
13:56
So they basically wrote rules around that scrape data to say,
13:59
OK, let's scrape and sort of cluster these 500 listings
14:03
together. We'll look at prices and then
14:05
we'll be able to price other listings based on the curve that
14:08
we're seeing. That certainly helped them
14:10
expand and support, you know, every market around the world.
14:13
But ultimately, then you have to add on a ton of customizations
14:16
at the user level to say, ooh, I'm not actually, you know,
14:19
getting as much of A price increase for Christmas this year
14:22
that I would have expected. So those are some of the things
14:24
that users have come to us and say, like, can you fix this?
14:27
Can you help? And over the past couple years,
14:29
we really have shifted away from using a lot of that scrape data
14:33
and using better data sources that we have.
14:36
So that's like the biggest example I have.
14:38
And I, I think capping that off with sort of again, the
14:41
ownership of the data pipeline, because we own our data
14:45
scrapers, the connected source data, the search data, we're
14:48
able to like pivot and really, you know, shift our, our
14:52
strategy for the algorithm if anything were to happen.
14:56
And what I mean by that is on the scrape data side of things,
14:59
this was a couple years ago now, but Airbnb actually made a huge
15:03
change for everybody that scrapes their data where you
15:06
weren't able to get like the daily posted prices and
15:09
availability. You had to essentially go in and
15:12
actually like make a fake booking for 3-4 or five days,
15:14
whatever to get that daily level data.
15:18
And you know, we had some non beyond customers come to us and
15:21
say, Hey, I was just made aware that my pricing tool hasn't
15:25
updated prices for my market or data for the past couple days.
15:28
Because of that, because they had to rely so much on scrape
15:31
data, everything was paused for a couple days, if not a couple
15:34
weeks until they were able to figure out a fix on the scrape
15:36
data side of things. So truly, like our size of the
15:39
database is able to help us pivot in those moments where we
15:42
can still factor in demand and understand what's going on while
15:45
we build our own solution for scrape data.
15:47
And we don't have to pause all updates and, and really, you
15:50
know, hold out to get a picture of the market.
15:53
That was like the biggest example the past couple of years
15:55
that a lot of people had some frustrations with.
15:57
But because we've we've built and owned all that since day
16:00
one, we were able to pivot a lot, a lot quickly.
16:04
It's really interesting what you mentioned there where if you're
16:06
comparing yourself to another rental that's not using dynamic
16:11
pricing, I've never really thought of that before.
16:14
It's quite an interesting thing, thing to think about.
16:17
I mean, how important is it to compare properties that are
16:22
using dynamic pricing as opposed to those that are not?
16:26
And they've just got maybe a seasonal rate, they've got one
16:29
for summer, one for winter. Is it much more important to
16:31
look at the rentals that are actually using dynamic pricing?
16:35
I think it's important to understand, I think the
16:39
percentage of your market or at least get a good idea of how
16:42
many people in your market are using some form of dynamic
16:45
pricing. But even that definition of
16:47
dynamic pricing is hard. We kind of have an internal one
16:50
where we say if you have like more than 20 unique pricing
16:54
seasons throughout a given calendar year, we call that
16:56
dynamic pricing. Nobody's really managing 20
16:59
static seasonal strategies. That's just crazy.
17:02
Anything fewer, you're probably doing high season, low season
17:05
shoulder and then events. My, my recommendations for
17:10
property managers are are really to, instead of looking at direct
17:14
competitors and sort of what they're doing, put yourselves in
17:17
the shoes of the guest and understand how one of your
17:21
guests or an average guest in your market is actually going
17:23
about booking in your market. The reason why I don't like to
17:27
use com sets or you know, anything that's calling out like
17:31
1 property against 5 or 10 properties is because guests
17:34
aren't doing that. If I'm going to book a stay in
17:37
New York City, I'm not going through and picking out 10
17:40
listings, getting them all up and looking at all of them.
17:43
I'm going to an aggregator like Google or maybe a direct booking
17:46
website that I know or Airbnb. And I'm typing in the broad
17:49
level market where I want to go, the dates, maybe a bedroom
17:52
count, things like that. And then I'm getting all my
17:54
results, probably hundreds, if not, if not thousands of results
17:57
from there. I'm kind of whittling down
17:59
pricing, photos, all that good stuff.
18:02
And that journey, I think is really you where you can have
18:05
the most amount of impact as a revenue manager.
18:08
So instead of working your way backward from that list with
18:10
like competitors, encourage everybody like audit your own
18:14
properties within that flow for a guest, because that's
18:16
ultimately how most people are booking.
18:18
Sure, there's maybe repeat guests who come directly to you
18:20
or, you know, do know, maybe some of your competitors and
18:24
kind of pull up those websites. But the majority of people are
18:26
are starting with a very broad search and then, you know, going
18:30
down that funnel to where they ultimately land on on a property
18:33
in the market. So it's better to understand,
18:35
OK, how am I pricing compared to all the two bedrooms in my
18:39
market or in the specific neighborhood?
18:41
Two-bedroom 1 bathrooms, 2 bedroom with certain amenities
18:45
like that's going to help you better understand how to really
18:48
effectively market your your properties to some of these
18:51
guests versus having to worry about, you know, 5 specific
18:55
competitors when you might just be spinning your wheels there
18:58
and completely missing the bigger picture that guests are
19:00
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19:03
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19:43
What percentage of property managers are actually using
19:46
dynamic pricing tool? Yeah.
19:49
It's we, we did a survey I think about a year, a little over a
19:54
year ago now and we were a little bit shocked at the
19:56
results. I believe we were still looking
19:59
at like less than 20% of the global supply using that
20:03
definition that I explained earlier of dynamic pricing.
20:07
There are still a lot of people out there who are even like flat
20:11
pricing. Maybe a weekday weekend rate,
20:14
maybe a high season, low season, but literally for half the year
20:17
being at $200, the other half of the year being at $150.00 a
20:20
night. That is still very prevalent
20:23
even for some property managers. You know, I think we talked a
20:26
lot about the professional property management aspect of
20:30
our industry, but there still are a lot of property managers
20:33
and revenue managers who are more confident in sort of
20:36
manually setting their own prices and, and really honing in
20:39
on that pricing strategy without using dynamic pricing.
20:42
So I was a little bit surprised on on those results ultimately
20:46
means there's still plenty of room for growth.
20:48
And I, I would still consider most people using a dynamic
20:53
pricing tool to, to be kind of like an early adopter in the
20:55
industry, depending on the sophistication.
20:58
I think there's still a lot of, a lot of opportunity for
21:01
property managers to use dynamic pricing and stand out in their
21:04
market, not only for raising rates in periods of high demand,
21:08
but the other half of it is making sure you're competitively
21:11
priced when there isn't a lot of demand.
21:13
So you can book occupancy, you can drive drive additional
21:16
revenue through just getting extra nights booked versus like
21:19
what I think most people go to is dynamic pricing is going to
21:23
increase my rates a ton. Yes, that happens during high
21:26
demand, but we're also just trying to get you booked and
21:28
make sure your prices are are optimized for when the market's
21:31
not going to be anywhere near a sellout. 100%, yeah.
21:34
I think I started using dynamic pricing about 20-19 ish.
21:39
So I feel like a very, very early adopter now and and take
21:42
it from me people, if you're not using dynamic pricing, give it a
21:47
try because you might be shocked at the amount of extra revenue
21:50
that you can earn. I certainly left a lot of money
21:53
on the table. Couple of things before we
21:55
finish then Ryan, I just want to touch on AIA little.
21:58
I mean, how much does AI power what you're doing and beyond?
22:04
Yeah, Certainly our favorite buzzword over the past couple
22:08
years. Now at this point, I think AI
22:11
has been super helpful on a couple things that we've
22:13
launched recently. I think from a revenue
22:16
management standpoint, especially like a software
22:18
standpoint, it's been really helpful in helping our users
22:20
understand again that mountain of data, all the charts we're
22:24
showing, all the different types of reports, being able to
22:26
consolidate sort of performance summaries or an analysis for
22:31
users day-to-day to share with owners use it to sort of inform
22:36
their own investment strategy. That's been a very helpful, you
22:40
know, like generative AI aspect of the tool that we've used.
22:44
I will say, like since day one, machine learning has been more,
22:48
more powerful for us on the back end.
22:50
So that's typically what we talk about when it, when it comes to
22:53
again, like cleaning a lot of that data automatically, not
22:56
having to do it manually with, with like an engineer or
22:58
something like that. And, and being able to use
23:01
machine learning to identify and, and help us really comb
23:04
through a lot of this data, but also, you know, be able to go
23:08
through different levels of tests within our algorithm.
23:11
You know, we're, we're not working with the same beyond
23:13
pricing algorithm that launched 12 years ago.
23:16
It's probably on, it's like thousandth iteration at this
23:19
point. And so we use a lot of machine
23:21
learning to basically test ultimately, if we're rolling out
23:24
a new algorithm feature like occupancy based pricing or
23:28
negative event factors, using a lot of machine learning to
23:31
understand what impacts that could have for some of our users
23:35
before we even launch it. So that's usually how how our
23:38
team uses machine learning to really test out certain features
23:42
that we're adding to the algorithm.
23:43
And if it makes sense to move forward with that for every
23:46
single market and every single user.
23:48
That's really been our core over the past, you know, really 10
23:51
years. And then more recently, adding
23:53
in more AI features in the tool just to help people understand
23:57
where we're coming from from a data standpoint and then a
24:00
recommendation standpoint on the pricing side and just making
24:03
sense of, you know, all of the data that's available.
24:06
That's one thing I want to call it to like since 2020, since
24:08
2019-2020, there's been an explosion of data available in
24:12
this industry. And that's great.
24:14
But the other half of it is making sure you can understand
24:17
what to do with that data. Just having it, looking at it
24:20
maybe makes you feel good, but if you're not able to translate
24:23
that into strategic updates, then you're you're really kind
24:27
of missing out on on a big piece there.
24:29
And I think we've been able to use AI to help people get to
24:31
that point and translate it into, you know, content that's
24:35
easier to digest. Now we're going to finish by
24:38
talking Tofu, which is your pretty exciting analyzer tool,
24:44
which is on the website, so. Tell us about this, especially
24:47
for those. Hosts out there who maybe want
24:49
to try beyond or give dynamic pricing a try, just tell us how
24:53
important and interesting this tool is.
24:56
Yeah. So we've launched a listing
24:59
analyzer that makes it super easy for you to understand what
25:02
Beyond could do for your property.
25:04
So you could drop in a link from vrbobooking.com, Airbnb directly
25:08
in there and we'll automatically show you how much more money you
25:11
could be making with Beyond and how we do it.
25:14
It's not just tossing up a big number.
25:15
It really walks you through, OK, we're going to optimize your
25:18
calendar here. Here's what we're seeing in your
25:20
market. And then we can, we can even get
25:22
you into more market data for your specific market.
25:25
So it's been super helpful and showing people, especially hosts
25:29
early on, you know, with the, the power of dynamic pricing and
25:32
what it can do for your business.
25:33
And it also helps us really show people, you know, before we even
25:37
take over a pricing strategy, which we understand is very,
25:41
very, it's a big deal for for anybody managing a property,
25:44
your pricing strategy is so core to your business.
25:48
And ultimately what you're trying to do with your business,
25:50
whether that's grow, maintain, add owners, whatever might be
25:53
taking over a pricing strategy is, is a lot of work work.
25:56
And we, we don't take it lightly.
25:58
So what this tool really helps us do is show people beforehand
26:03
what will happen before they actually enable, before they
26:06
even create an account with beyond what's going to happen
26:08
with their pricings and ultimately what they can see
26:11
from that. So it's been super helpful, but
26:13
it's also really great if you're, you know, wanting to buy
26:16
a 2 bedroom in a market, find a 2 bedroom on Airbnb that looks
26:19
similar, plug it into the tool and we'll show you how much, how
26:21
much it can make in like less than 20 seconds.
26:24
So it's, it's a quick analyzer for you to be, you know, seeing
26:27
some of that revenue with some of the market data that we see.
26:30
And then we even open it up past that.
26:32
If you're interested in more data, later this month, we'll be
26:35
launching a free version of our market insights for, for folks
26:39
to use. So that will be even more data
26:41
for for people to access coming out of that listing analyzer
26:44
tool. So that's available today on our
26:46
website. Anybody can go check it out.
26:48
And like I said, you just got to plug in a listing.
26:50
We keep it easy and we'll show you how much money you can make.
26:54
I've tried it myself. It is brilliant.
26:56
If you're doing any research on if you want to buy a property in
26:59
a certain location or you just want to check out what your
27:01
property could learn, could earn next, really, really useful.
27:04
So head to the Beyond website and check it out.
27:08
Ryan, it's been a. Real pleasure, thank you for
27:10
explaining. All about the science.
27:12
That's going on that behind the scenes at Beyond, it's been.
27:15
Great to chat to you. Thank you, James.
27:17
Happy to be here. If you want to talk for five
27:19
more hours on data, I'm happy to.
27:21
We'll find some more time to do so.
27:23
Awesome. We will.
27:24
We will put your LinkedIn in the show notes so that people can
27:27
contact you directly and organize those chats.
27:29
Thanks, Ryan. Love it.
27:31
Thank you. Great chats of Ryan Saylor
27:33
there. Make sure you give that property
27:35
analyzer tool a try. All right, that's it for today's
27:38
episode of STR Pricing Secrets. Remember, this series is
27:42
sponsored by Beyond. And if you'd like to try their
27:45
dynamic pricing tool, head to beyondpricing.com.
27:49
For more free education tips and trends from the STR industry,
27:53
visit Houseplanet Club, where you can sign up for our
27:56
newsletter, listen to our podcasts, and download a suite
27:59
of ebooks for hosts and property managers.
28:02
That's it for today's episode. Until next time, happy hosting.