Join Dr. Rachel Gainsbrugh in this episode of The Luxury Short-Term Rental Doctor as she chats with Anurag Verma, co-founder of PriceLabs. Learn advanced strategies for maximizing rental income through dynamic pricing. Anurag shares insights from his journey at United Airlines to applying revenue management algorithms to the short-term rental market. Discover how to leverage AI-driven insights and market data to optimize pricing, attract high-value bookings, and stay ahead of the competition. This episode covers setting the right base price, using market dashboards, and customizing pricing strategies for short-term and mid-term rentals.
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Episode Transcript
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Rachel (00:00):
Hello.
Hello.
My name is Dr.
Rachel Gainsborough, and I am obsessedwith all things, short term rentals,
revenue streams, and helping you navigateyour career, real estate, and your busiest
and most wonderful seasons of life.
So grab your coffee, get comfortable asyou get ready to learn and grow with me.
This is the Luxury ShortTerm Rental Doctor podcast.
(00:26):
Hello.
Hello everyone.
Welcome to another LuxuryRental Doctor podcast for show.
I think this episodehas been long overdue.
I have the amazing AnuragVerma here with us.
He is the co founder of Price Labs.
Let me let you know alittle bit about Anurag.
So before Price Labs, he usedto work for United Airlines.
(00:48):
Designing and improving revenue managementalgorithms and systems back in 2014.
He brought that experience fromrevenue management automation
that's prevalent within airlinesand hotels into the vacation rental
in short term rental space as well.
Now he has a background in mathematicsand statistics, and he is also in,
(01:11):
uh, PhD in operations and research.
So fellow doctor in the room, whichI'm super, super excited about on Iraq.
I've got so much to sharewith you, my friend.
I know, I know you fromseeing you at live events.
You are someone whosupports our community.
We love to send our members.
Your way.
We are actual utilizers of price labsourselves, and I've got an interesting
(01:35):
story to share with you, but withoutfurther ado, I would love you to
introduce yourself to the people and we'lldive right into this, to the episode.
Anurag (01:44):
Yeah.
I think we, we already gave you a monthto say, uh, but no, like, uh, pretty
plain, uh, underdog, uh, one of theco founders at price labs, I mainly.
We work on data science andproduct management at Price Labs.
Um, so, how to improve our algorithms,what data to use, how to use it, um,
(02:10):
how to, how should the UI be so thatthe customers can interact with the
algorithm, or like, what else can wemake with the same data that we already
have, and those kinds of things, right?
Um Yeah.
Uh, also unlike you, Ithink not a real doctor.
Uh, so yeah, yeah,
Rachel (02:32):
yeah, yeah.
And so you mentioned somethinga little bit earlier.
I want to acquaint you to our audience.
Our audience are busy doctorswho are looking to leverage, uh,
using short term rental or midtermrental as their investing strategy.
So not very, um, I would say tech, um,aware for on the, I guess, the, um,
product management roadmaps, so to speak.
(02:53):
So what is UI and why does it matter,
Anurag (02:58):
right?
So, um, UI is anytime UIstands for user interface.
Uh, and then the cousinis user experience, right?
So we have a dynamic pricing algorithm.
We have a lot of data.
We want to show thatdata to our customers.
We want to, uh, we want our customersto have the right pricing strategy
(03:22):
in place and automate that, butit's, uh, it's at the end of the day,
you are logging into our system andinteracting with our system, how can
we make those interactions, uh, simple.
But powerful, uh, that'ssort of the crux of it.
Anybody can make simple stuffthat doesn't do anything.
(03:44):
You just, you can put an onand off button at anywhere.
And, uh, if it doesn't doanything, there is no complexity.
Uh, but if you want it to do somethinguseful, then you need to have, uh, the
customer doing some interaction with it.
Think of a car, for example, right?
Um, 30, 40 years back, uh, you would keyit in, you would shift from neutral to
(04:11):
first, uh, you would leave the clutchand start on the accelerator or which
way, I don't know anymore because nowuser interface has improved so much.
You don't worry about gears.
So much, right?
There is no touch anymore.
So that's, uh, for some people that'slike worse user interface, but for
(04:32):
a lot of people, cars have justbecome more accessible and easier.
You don't need to be a professional,uh, even earlier, you didn't
need to be professional drivers,but like a lot more people can
now use cars than before, right?
So how can, how can we make, um, anysoftware better so that more people
can figure out these conflicts?
(04:52):
Yeah.
Rachel (04:58):
And I, I love that.
And I feel like, you know, the more, uh,technology advances, the more access, uh,
we're able to have to certain resourcesand we'll tap into that in a moment.
So what is Price Labs, Anurag?
Anurag (05:14):
Yeah.
Uh, so anybody who has booked.
Flight tickets hasprobably experienced it.
I used to work for United Airlines.
Um, and my role was primarilyimproving our algorithms that
determine those prices, right?
So, uh, when, if, if you're comingto February, Chicago in February, uh,
(05:37):
Chicago is super cold in February,uh, Very few people show up.
Um, planes are runninghalf empty sometimes.
What can be done?
So like as, as an airline, for example,you have a plane that is flying.
Um, you can't just cancel the flightbecause if you cancel too many flights,
(05:58):
eventually customers think of you asunreliable and then things like that.
So just because the flight isempty, you don't want to cancel it.
You shouldn't cancel it.
No.
You're already incurring, uh,like the cost to fly the plane
from one city to another.
The cost of the crew, thepilots, the flight attendants,
(06:19):
that's not going anywhere.
Uh, so whether you fly theplane half empty or you fill
up a few more seats at a time.
even if at a discounted rate,that is all incremental.
Uh, the additional people on the planedon't cost too much because most of
the cost is this fixed cost, right?
(06:39):
So the thought is, howcan we set up pricing?
One way is like to discount theprices all the way to the floor.
And now you might get the planefully booked at 10 a ticket.
And you have actually made less moneythan, than with the half empty plane.
The second way is to say, okay.
We can't give everyticket out for 10 bucks.
(07:01):
In fact, we should not giveany ticket out for 10 bucks.
But how can we The 50 people whoare willing to pay the full price,
is there something about them?
Uh, maybe they tend to book very late.
Maybe these are business travelers, right?
Uh, so we want to protect 50seats for the business travelers
who will pay the full price.
But can we discount the earlybookers, like maybe 20 or 30 tickets?
(07:24):
So that we get something more thanwhat we would have otherwise gotten.
Um, so that's what we do in airlines.
Now, how does it translateto short term rentals?
Like, uh, the story basically goes,I was doing this for airlines, and
one of my very close friends wasstudying at that time in Chicago.
And he's like, I'm listing myroommate's empty room on Airbnb
(07:45):
while he's on his internship.
And he's like, Airbnb is not suggestingwhat price do I put it saying?
Like, do I put a hundred everywhere?
Should I have a tire in summer?
Are there some dates where itshould be higher than others?
And he and I got discussing and we werelike, okay, like there are plenty of
Airbnb hosts around the world that areprobably thinking about the same thing.
(08:08):
How can we help them set the right price?
Um, and to start off, wedidn't know the answer.
And so we said, okay, we'll,we'll experiment a little.
So like the whole price labsname came from that to say,
okay, what, what will work?
What will not work?
So we found a bunch of things over time.
Um, most locations havewhat's called seasonality.
There is a high season.
(08:29):
There is a low season.
You should increase prices, uh, oryou should be able to charge more in
high season and low in low season.
Um, There is, uh, in most placesthere is a day of week pattern.
If you're in a vacation destination,you're likely going to have a lot more
demand during weekends than midweek.
(08:50):
If you're in a business hub, youpotentially have a high demand during
midweek, uh, because of business travel.
Yeah.
Um, even if you are in, not inshort terms, but in midterm, uh,
you see similar patterns, right?
For example, when summer internshipsare going on, a lot more people
are looking for two to threemonths long, uh, housing, right?
(09:13):
Yeah.
Um, there are events and holidays thatare very, holidays are predictable,
uh, but events are very unpredictable.
Uh, there are times like when we started,we, we knew about Chicago Marathon and
we knew a ton of people come there.
One of the craziest findings we hadwas the weekend after Chicago Marathon.
Every year, more or less, there'ssomething called Chicago open house,
(09:37):
which
is like a lot of buildings in Chicagoopen their doors to say, even if you
don't work there or live there, you'rewelcome to come in and have a look.
This is like, um, this is kind ofa thing that Chicago does and we
realized that has almost the samedemand spike as the marathon and
(09:57):
almost no one, like, If you're newto Chicago, you don't know about it.
If you're not in Chicago, you don'tknow about it, but there are definitely
these architecture enthusiasts who areinto it, who are flying and staying in
short term rentals and hotels, right?
So, we were like, okay, as anindividual host, like, how do
you keep track of these things?
And we said, okay, can we keep track ofthe data so that our, uh, we can update
(10:22):
prices for our customers on their behalf?
They still get control to say what'sthe minimum you want to go to, what's
the maximum, and there's plenty ofbells and whistles and knobs to say,
I never want to take a short booking,uh, like less than two nights long,
or, uh, when it's far out, do this.
But how can we build all ofthat so that, uh, a busy host or
(10:47):
manager doesn't have to spend.
extra hours just researching themarket all the time and yet can
make more revenue out of that.
So like the parallel to the plane hereis if you have a vacation home, you
likely have a mortgage on that, right?
Rachel (11:05):
Yeah.
Anurag (11:06):
So, uh, that is going out regardless of whether
somebody stays there or not.
Uh, so how can I make, it's okayduring low months to not make enough
revenue to meet their mortgage.
Um, As long as over the courseof a year, uh, you make it in, in
the long run, you make it right.
(11:27):
So peak months are going to makea lot more, uh, and slow months
are going to make a lot less.
Just because slow months aregoing to make less doesn't mean
that they should make zero.
Uh, that's sort of the thought becausewhatever little the low months make,
they help cross the annual thresholdto say, okay, my total mortgage was
(11:47):
this much, uh, And even though 90percent of that got covered in the
peak months, the slow months helped me,uh, help me actually make some profit.
Rachel (11:58):
I love that.
Yeah.
Yeah.
And I believe that this isexactly what we had experienced.
So now it's story time.
So on your back in, um,I want to say 2019 ish.
I had launched one midtermrental in my market.
It's a residential subdivision marketin the South of Atlanta market and, um,
(12:20):
that property was doing really well.
Uh, we launched it without price labs,but then we learned about dynamic
pricing and I tell my community, ifyou're not using price labs, you're
leaving a lot of money on the table.
Right.
And, uh, so once I launched, um, thesecond property, it was a larger property
and I said to myself, okay, uh, since it'slarger, the cleaning team, um, was having
(12:44):
a little bit of a transition period.
I was going to, uh, onboard a newcleaning team, but for the first
month I pre marketed the property.
I wanted a 30 day minimum stay just tostart and then we're going to adjust it
to two night, three night stay minimum.
Uh, since it was a larger property, Iwas anticipating it would make anywhere
around eight or 9, 000 per month.
(13:07):
The smaller property wasmaking about six to 7, 000.
So I said eight or 9, 000 on average.
There's no way, I was like, dare I think10, 000, but my, Self limiting belief.
I'm a little girl from Haiti.
I was like, who had 10,000 a month to spend?
That's just outrageous.
There's no way, um, that thatmarket could, um, I would say,
(13:28):
uh, you know, have that level ofdemand, uh, absorbed in that market.
So I turned on the dynamic pricingtool, price labs, and I know sometimes
it requires some adjustments.
Sometimes it's a little bit higher thanI would expect or a little bit lower.
So it was going to play around with it.
But when I turned it on expecting abouteight or 9, 000, it listed my property
(13:49):
for 28, 000 for the next 30 days.
And I was like, uh, no, themortgage is 2, 500 a month.
There's no way.
So let me go ahead and adjust it.
Cause you know, Price Labs has AI.
I'm not sure why, um, it went so high.
So let me go ahead and turn it offand adjust it before I turn it off.
It got booked.
(14:11):
It got the next month.
It was for 15, 000 to 21, 000and it was just gangbusters.
I had to take a step back andthink to myself, where else
am I playing small in my life?
I was going to adjust it based on, youknow, my limiting beliefs, my thought of,
(14:32):
okay, it's going to be a family traveling,but turns out it was attracting,
uh, the film industry, the insurancecompanies, and so more of corporations.
Whose pockets are a little bit deeperbecause I know I'm not spending 28, 000
as an individual, but the corporations,it was a whole different avatar.
Business travelers would cast and crewand groups, and it just blew my mind.
(14:56):
And I tapped into a new audience.
That I wasn't aware of a few monthslater, air DNA and a casting company
called mystics arts reached out for usto be featured on a Netflix TV show.
And so I say, listen, I know thatyou talked about UI user interface,
user experience initially.
(15:18):
I felt that, uh, pricelabs now it's much better.
It was even a little clunkier.
Then if you're like,you should try this too.
You should try that.
No.
I'm not trying any tool.
I'm going to stay the course withPriceLabs and I'm just so excited that,
you know, you guys continue to offernot only, um, updates and, and you're
implementing things that will improve theuser interface, but you're offering, you
(15:41):
offer a ton of training when people ask mehow to use PriceLabs, they say, Hey, hop
on the training, there's training for you.
All the time.
There's a whole library of training andfor me, that's where I see the value, um,
for, you know, is in, in price lives isjust the support, the level of support,
the level of training, but I wantedto share that little story with you on
your, I don't know if you're aware of it.
Anurag (16:03):
I did not.
So there are multiple things.
I have so many questions.
What kind of a large house isthis that has a 2, 500 mortgage?
Like, uh, that, that itself seems,uh, quite something, but then it's,
it's the, there are two parts tolike, when something gets priced, uh,
(16:26):
that high one, if you had sent us aquestion, we would have said, Hey, we
need to look at the base price, right?
The base price, if it is too high, andwe come up with a base price, we show
market recommendations, like, becausewe look at data from Airbnb and like
(16:46):
scrape data from there, uh, we say,okay, like similar houses are listed
at, let's say, uh, 700 a night, right?
Uh, but then at the end of theday, like, This is true in the
real estate industry in general.
Every house is unique.
Like, uh, I think plenty of people havetried, Zillow had tried at one point
(17:08):
to say, can we predict the home price?
Right.
And it's very hard to quantifythe quality of a home.
Uh, two homes can be inthe same subdivisions.
Uh, one of them could be facing thelake and the other one could be facing,
I don't know, maybe the parking lot.
Right.
And.
And so generally we would have said,Hey, look, you know, your home,
(17:30):
they'll, uh, look at the data, but thenalso know the reality of your home.
So like maybe the base price is too high.
Let's reduce that.
Um, but then the flip side is also, uh,and generally speaking, if it hadn't
gotten both that day, we would have said,Hey, look, after seven days, you, our
algorithm would have said, Hey, look,your base price is probably too high.
(17:51):
You're not getting any bookings.
Like what's happening, right?
Yeah.
Um, So, and then there is that limitingbelief kind of thing where, uh, there are
times, so like generally speaking, it'snot just about increasing prices, right?
It's also reducing pricesduring lean periods.
(18:11):
But, uh, sometimes it's, youthink that it, it doesn't make
sense for me to 300 bucks a night.
Yeah.
But then we say, look, All your datesin low season are sitting at 300.
You still haven't gotten booked.
Let's try going down to 250 and seeif you can get some things booked.
Yeah.
And then on the flip side, thebiggest event in town is happening
(18:33):
and you have said, I never wantto go above 800 bucks a night.
And you're like, this thing, TaylorSwift, you would have gotten it
booked at 2x the price becausethere is just so much more demand.
So
Rachel (18:46):
she's a market maker.
Anurag (18:49):
Yeah.
So like, uh, sometimes like we, we tellour customers one absolutely true about
training, like we, it is any pricing tool.
It's not, um, all knowing.
And, uh, you know, like, uh, itdoesn't know everything, you know?
(19:11):
So like, it is important toknow how to drive this thing.
Yeah.
And then again, like.
Very important to like attendthe trainings to see, okay, what
bells and whistles exists becauseeverybody has a different process.
Um, so for example, some folks aremore risk averse than others, right?
(19:34):
So, um, we have customers who say, Hey,look, if I, if my next month is not like
70, 80 percent booked, I'm panicking.
Uh, and they might be in alocation where like half the
bookings come in the last month.
So like, our thought is like, okay, one,uh, we provide a lot of data to say,
okay, it's okay not to panic, uh, ifyou have 30 percent vacancy in the next
(19:58):
30 days, because most of the bookingsare still yet to come in your market.
Uh, it's totally fine.
But then if you, if you are someone whosays, Hey, it just helps me sleep better.
I don't care about what the data says.
You can go and adjust yoursettings to say, okay, I want
more farther out bookings.
Uh, yeah.
Okay.
Taking on slightly lower pricesand it will help me sleep better.
(20:19):
Uh, all of these bells and whistles exist.
So, it's
Rachel (20:22):
so customizable.
You're absolutely right.
I was just speaking, um, with our groupand One of the questions that popped
up is an insurance company reached outand she had Instabook on and, you know,
she's still in the negotiation phases.
Should she leave Instabook on furtherout all and accept those bookings, which
(20:43):
will impact her ability to, you know,generate revenue from the insurance.
And it's two sided, you know, if youfeel, you know, Like you won't sleep
at night knowing that, you know, youare impacting your ability to get these
quick bookings versus if your overallstrategies to have hold out hope for
these insurance companies, right?
(21:05):
There's very, very, um,I think customizable.
And so my recommendation to her, um, was,you know, for me, I would hold out for the
insurance since I'm having a conversationwith them and I'll know within the next 48
hours or so what direction we're going in.
And then we could, you know,return to the Instabook strategy.
So it's, it's a two edges sword for sure.
(21:26):
And so what we're seeing, excuse me,Anurag, is insurance policy holders.
Um, You know, we're workingwith the corporation.
We have the film industrythat we're serving.
We have, um, data centers that arebeing built in some of our markets.
We have a political campaigngoing on right now in the U.
S.
So we have political campaignstaff that need housing.
(21:48):
We have medical professionals.
There are so many midterm mentalstrategies that are out there.
What I love about one of theslides that you shared a couple
of years ago, uh, Show that.
Well, you know what?
This midterm rental demand, althougheveryone's talking about travel nurse,
travel nurse, travelers, it was herebefore the big travel nurse buzz.
And so whether it's climate migrants,snowbirds, there are such a variety of
(22:11):
midterm rental guests that are out therethat need a place to stay for 30 days,
60 days, 90 days, so on and so forth.
So I use inside of price lab, acustomization, which is the stay
restrictions for my far out bookings.
I like increase it so much tode incentivize people to block
(22:33):
my calendar for two or threenights days because I want.
To, uh, incentivize those 30 day staysfurther out, but closer to my, you know,
recent checkout, I like to use, you know,a two night, three night in, in my market.
Am I doing it right on your end?
This is what we teach our community.
Yeah,
Anurag (22:51):
that is the right way to go.
Generally speaking.
Um, yeah, like you said, if you takea two night booking a month out, now
somebody who is going to show up ina week's time and want to stay for a
month or two months or three months.
Can't, can't book your place anymorebecause there is a two night booking
(23:13):
sitting, sitting in the middle andgenerally speaking, uh, so you want
to say, okay, I want to keep mycalendar a little open to, to absorb.
And again, this doesn't workin every location, right?
So you have to, uh, find out if yourmarket is a midterm rental market or
not in some ways, in some ways, right?
Rachel (23:34):
Well, I get that question all the time, and I don't mean
to interrupt you, but I thinkthis is a good actual question.
So, um, outside of having thatintimate knowledge of your market,
what are some ways that our memberscan identify if their market is a
midterm rental friendly market or not?
Anurag (23:49):
Right.
So there are, uh, I can think ofthe price labs answer in some ways.
Okay.
I'm trying to also think if thereis a non price labs answer for this.
So, for example, there are websiteswhere you list when you, uh, that you
(24:10):
want to do mid term rentals, right?
Rachel (24:12):
Mm hmm.
Anurag (24:13):
Um, Furnished Finder is one.
Yeah.
Traveling Nurses.
Mm hmm.
Many of these places.
Uh, go find out, like, dopeople in your city list there?
Like, if nobody's listing there,uh, maybe it's not as popular.
Um, my hope here is that if a lotof people were coming for midterm
(24:35):
stays, then the enough supplywould have shown up there, right?
You could be onto something ifnobody's listening, you could be
like, maybe this is an open market.
Like, how do I find out?
Um, the way we answer it is we havea product called market dashboards
(24:57):
and it costs 10 bucks a month,but to answer this question, you
don't need the per month part.
You just use it once and thenyou're done kind of a thing.
Um, where we will show you the length ofstay patterns that, uh, that tend to book
on Airbnb over the course of the year.
(25:18):
So like, okay, the occupancy in thismarket is maybe 80 percent of on average,
but like what portion of that comes from30 plus night rentals, what quotient
comes from 14 to 30 night rentals.
The way we do this is by essentiallyscraping publicly available data,
uh, on, on Airbnb and looking ateach property on Airbnb and their
(25:41):
calendars to say what dates areavailable, what dates are not available.
And then you do it frequently tosee what, uh, these dates that were
available now became unavailable.
Uh, so.
Quite likely it is like if your calendarwas empty and then there are two night
(26:02):
booking showed up your, your calendaron Airbnb to every other bookers
will, or to every other guest willhave those two dates as unavailable.
And that gives you a sense that, okay,maybe those two dates are booked.
Now, instead of two days becomingunavailable together, if 30 days
become available, we say, okay,maybe those 30 nights got booked.
Or if 45 nights got booked, then45 nights become unavailable.
(26:25):
So we track all of these changesand are able to paint a picture
to say, hey, look, in this market,a lot of the dates that become
unavailable, uh, are from longer stays.
Uh, so maybe this is amid term rental market.
Now, important, uh, thing here isalways, like, when you're looking
at data, Never rely on one source togo look at multiple sources, right?
(26:49):
And that's where I'm trying to say like,okay, uh, I know the price of answer.
Like I want to find out whatother answers exist there.
Um,
Generally speaking, you, you'dspoke about a few things.
If it is nurses, is therea big hospital there?
Because if there is likely that, uh,that that kind of demand might exist,
(27:09):
are there large companies around?
Um, I know.
We have customers in Seattle and SanFrancisco who mainly cater to Amazon
and Microsoft and Facebook employeeswho, uh, when they move there, the
company provides housing for the firstthree months, uh, kind of a thing.
And they have relationships withthese folks to say, okay, I have
(27:31):
a house, three months, sure,like gladly taking it, right?
Uh, so some of it could just belike, if you're in that market, get
a feel of, of things around you.
And then some of it could belike external sources like us.
Rachel (27:45):
I love that.
And I know from the beginning, Ifeel like from the beginning of
time, Price Labs was already hip tousing and leveraging AI insights.
Can you speak to that a littlebit and what that progression has
looked like over the last few years?
Anurag (27:59):
Yeah.
So this is actually played into thewhole UI user interface question.
Um, so we, We process a lot ofdata, uh, like I said, we scan
every property on Airbnb or booking.
com to say how are they changingtheir prices, how is the occupancy
(28:21):
changing and things like that.
Uh, and we use that data toforecast what do we think will
happen in the future, right?
If we see May as being a very strongmonth in Atlanta, Uh, we'll say,
okay, maybe we can increase pricescompared to, uh, other months.
Uh, and if June was going to bevery, like much lower than last
(28:41):
year's, you will say, okay, likeJune is not shaping up really well.
Uh, you haven't booked either.
Let's try to reduce your bookings,no, prices to get you some bookings.
All of this in the, these are all,um, mathematical models that come
up with the price recommendations.
(29:02):
The flavor of AI that has become verypopular operate is, is generative AI
or, uh, LLS language models where, uh,their usefulness is almost complimentary.
Uh, okay, so chat, GPT or similarmodels are not going to come up with
(29:23):
price recommendations, uh, for you.
Like they don't know what'shappening in the market,
Rachel (29:27):
right?
Anurag (29:29):
Where they end up being helpful is we show a lot of data to our customers.
Uh, we have a tab called table of datawhere we say, okay, here's your property.
Here are recommendations.
You can also go and see, okay,what's happening in my market.
One of the feedback we hear is, Hey,look, if I'm a professional revenue
manager, or if I'm, uh, into, uh, dataand charts, This all makes total sense.
(29:55):
I can read the data.
I can figure out what's happening.
But a lot of times I don't necessarilyhave the time to figure out what
each of these lines means and, uh, oreven to interpret like, okay, what's
the right way to interpret thesetwo lines being close to each other
versus being away from each other.
Yeah.
Uh, and so we created thisAI insights button that says,
(30:18):
Hey, look, here's the data.
Uh, Can we provide you a textsummary of what you're seeing?
And we want to addresstwo things with this.
One, there is a lot of data that weshow, but once you learn how to read
it for a few dates, it becomes alot easier to realize that, Oh, this
(30:41):
is actually pretty straightforward.
But can we give you those like two, three,four, five insights that help you realize
that, Oh, this is what I'm looking at.
Like this is straightforward.
Uh, so a lot of the data we show isnot terribly complex, but then, uh,
the AI insights or the LLM modelsare helping make sense out of it so
(31:04):
that then you're, then you get morecomfortable with data essentially.
I
Rachel (31:10):
love that.
That makes a whole lot of sense.
So for me, um, what I love usingchat, GBT and AI for as well.
is you're interpreting largeamounts of information.
So, for example, if I'm, um, doing amasterclass and I have, uh, 200 people
attending on Iraq and they all havea variety of questions, but at the
(31:32):
end of the day, some of the questionsmay fall into the same category.
So part of, um, instead of me manuallygoing through and say, okay, this is a
question about how to analyze markets.
This is a question about how to setup a property for a midterm rental.
Well, if I can take that, you know,database of questions and add it to
(31:53):
chat GPT, it helps me to categorizeand identify how many times this
type of question is showing up.
And it helps me to then create apicture of, you know, what it is our,
our community, we need to be teachingabout next week, because this question
keeps showing up time and time again.
So that's how I use it.
And I hope, you know,someone listening could.
(32:13):
See the value of, you know, the timethat it can save in terms of how to
process large amounts of data andhelp with category categorization.
I think it does that pretty well,
Anurag (32:25):
I'll say it's still, uh, that kind of stuff.
It does pretty well.
Like, when you ask it for factsis when sometimes it can go, so,
so I do want to add a disclaimer,like, don't, Don't fully trust it.
Like, do you want to, uh, like, especiallyon subjects that you don't know too
(32:47):
much about it, you can say things that.
With a lot of confidence.
Right.
And it's just not right.
Rachel (32:55):
A hundred percent.
A hundred percent.
Yeah, it's been a great tool forme to process information, but
yeah, I don't trust it very much.
You're absolutely right.
So you mentioned the market dashboards.
I find that to be very interesting.
Um, I actually used it a while back withone of the members of our community.
For a market that is right outside ofwhere I live, I actually went, um, and
(33:16):
I looked at it based on the dashboard.
It didn't feel very promising atall, but the numbers seem so great.
I went out there, I drove out there andlooked at this market and it was so rural.
I kid you not.
And it had construction happening.
And when I looked to see, well,what did, what did they build?
And maybe there's something, itwas a tractor supply, which is a
(33:38):
store that just doubled down on.
This is so rule.
This is so I told him, no, no, no, no, no.
I don't feel comfortable.
This would be high risk andit was the market dashboard
that helps us to identify it.
And then when we use some othertools as well, we had one more
tool that kind of corroborated.
that.
So I think that's very, very handy.
(34:00):
So I get the question onyour rock a lot of times.
Well, what's the differencebetween price level?
What's the differencebetween Airbnb smart prices?
What's the difference between otherdynamic pricing tools that that is in
the category of where price lab sits?
Um, would you mind givingus a little highlight?
Because I have my own answer, but Iwould love your perspective on how
(34:20):
is price labs Uh, different from theother dynamic pricing tools as well
as yeah, the smart pricing on Airbnb.
Anurag (34:28):
Right?
So the Airbnb answer is a littleeasier in some ways, right?
Mm-Hmm.
. Um, at one level you would think, um,you want to say Airbnb has a lot of
data available about every bookingthat happens on their platform.
Uh, the.
(34:50):
The place where this slightly falls apartis that a lot of people rightly list,
not just on Airbnb, but also on booking.
com.
Yeah.
So for a particular home, uh, Airbnbdoesn't even know how much it's,
it's making, uh, because many of thedates are getting booked on booking.
(35:11):
com or sometimes people have directbookings as well where like they create
their own website and things like that.
But even then, Airbnb has a lot of data.
Now, one, I think their expertisepotentially is just not in data or, uh,
(35:33):
the platform is designed to cater toevery Airbnb host in some ways, right?
So, where we excel is just inproviding customers with a lot more
bells and whistles to say, Hey, look.
The, the hosts that reallycare about finding out how can
(35:54):
they maximize their revenue.
Uh, those are the ones that sort of weend up catering to, to say, okay, uh, you
don't just want to turn on a switch, whichis what a lot of times in smart pricing,
Airbnb smart pricing, there's a switch,and then there's a minimum and maximum.
Um, and then.
And that's about as muchcontrol you get, right?
And we say like, that's, thatshouldn't be the end of it.
(36:14):
Like you should be able to do a lotmore with your pricing strategy.
Yeah.
And a lot of customers to beginwith, uh, will still do the switch
and then maybe set their base price.
We'll show them data.
But then over time, as they learnmore, they'll want to do more about it.
So they might say, Hey, look, uh,I want to cater more to midterms.
(36:36):
Uh, how do I do that?
And then Your strategy comes upto say, okay, uh, I want to take
short bookings only last minute andthen longer bookings further out.
Uh, somebody says, um, how can I do sothat, um, when I have a gap between two
bookings, um, I might have a minimum ofthree nights, but if there's a two night
(36:57):
gap, um, I don't want it to be wasted.
What can I do?
And there are a fewthings that you can do.
You can say, Okay, if it's atwo night gap, I want to reduce
my minimum nights to two.
Take something.
And then you can say, But I alsowant to make sure my revenue, like,
from a booking is at least something.
Like, I don't want to sellthose two nights for cheap.
(37:18):
Then you can say, Okay, when there's atwo night gap, let's bump up the price.
If somebody books, uh, you at leastmake something meaningful, right?
So those kinds of bells and whistles andcontrol and how much market data that we
surface that's truly relevant for somebodywho's Looking to maximize their revenue.
Uh, so that's that's one the secondone is uh partly because of the
(37:43):
control that we provide a lot of timeswhat I hear is Airbnb smart pricing
will drop the rates to the floor.
And, um, which, uh, I don't fully knowwhy that would happen, uh, but that's
the feedback that we get a lot of times.
(38:05):
And so then, and maybe that'salso a part of control, right?
Uh, where we give our customers thecontrol to say like, okay, you, you
have the full control of the base price.
So.
If you want to price things higher,uh, you just bump up the base
price, and then that's how it works.
It'll give you feedback to say, Heylook, with these higher prices, maybe you
(38:26):
are overvaluing your, your home in thiscase, and nobody seems to be booking,
so maybe you should reduce it back.
Uh, but, but you stillhave the full control.
Uh, so I would say, generally speaking,it boils down to how much, Um, compared
to other providers, uh, I thinkthe answer is again on the similar
lines where we tend to provide a lotmore of these, uh, customizability,
(38:54):
uh, compared to other providers.
And then also like the kind ofdata that we show about the market.
Uh, I think we rolled it out almost.
A year and a half back, but I thinkwe are the only provider even now that
shows how is your market and by market.
I don't mean all of Atlanta, but ifyou're located in a particular part
(39:18):
of Atlanta, how is that sub market?
We call it the hyper local market,like the closest homes around you.
How are those performingcompared to the to a year back?
Uh, is in real time.
So like, for example, uh, Juneis still 14 or 15 days away.
(39:39):
And right now, maybe in Atlanta,June is booked, let's say 25%.
And last year, you can easily goand look, uh, in different places.
Last year, June wasmaybe 45 percent booked.
I'm just making this up.
Yeah.
Yeah.
Yeah.
(40:00):
But the difference is thatlast year's June is completed.
All the bookings have happened,and then it is 45 percent book.
This year's June is still ongoing.
Right now it is 25%.
But eventually, maybe it willcross 45%, maybe it won't.
How do I know?
Like, how do I know right now?
Because I don't want to make decisionsto reduce June prices after June is done.
(40:23):
I want to make that decision right now.
Right.
So we, uh, we have this data inour product and the AI insights
actually show this data and talkabout it as well to say on May 14th
in 2023, how occupied was June?
And maybe at this point of time, lastyear, June was 20 percent booked.
(40:49):
And right now it's 25 percent booked.
And now you say, Oh, the zoom is actuallygoing to be stronger than last year.
Uh, so that kind of data, the depth ofdata, uh, and then because of that, also
the depth of the recommendations generallyis something we pride ourselves on.
Uh, so yeah, in terms of differentiation,I think that depth of how much data we
(41:12):
show and use and how localized it is, andthen how customizable the platform is too.
To execute your particular strategieswhen you graduate to the point where
you say, okay, I'm making, I'm doingwell, but I want to do something more.
How can I do that?
Rachel (41:29):
I love it.
I love it.
I love it.
And you're absolutely right.
You can be.
Uh, you know, at the basic levelwhere you're just getting started or
you could elevate yourself to nextlevel where you're doing real revenue
management, uh, and, and, you know,pricing management, which is amazing.
So question for you on your, thisis a basic question for those
(41:50):
who are very new and they're notas familiar with a price lab.
What is base price?
What's that?
What does that mean?
Does that mean my lowest price?
Does that mean an average price?
What does that mean?
around my max price.
What does the base price meanand why is it so important?
Because it's one of thefirst things we want to set.
And I know it's one of the first thingsthat price labs gives a recommendation on.
Anurag (42:13):
Yeah.
So the base price is theaverage price across the year.
Um, so this includes low season whenyou may not get booked high season
when you're very likely to get booked.
midweek, weekend, everything included.
So think of if your base price is ahundred based on the location that you're
(42:34):
in, we will come up and say, Hey, look,July is high season and it's high season.
Like we need to increase the prices by30 percent during July and in August.
It is high season but slightlylower than July, maybe it's 25
percent increase on base price.
(42:55):
So then 130 becomes youraverage price, uh, for July
because 100 was your base price.
Now that doesn't meanevery date in July is 130.
Uh, maybe July 4th a lot of people aretraveling in, and that, we say, Hey
look, there is a spike in demand, pumpit up further, and that becomes 175.
Uh, but then midweek a week later,maybe it ends up being 110, uh,
(43:21):
because, uh, not, not that muchdemand is out there, even though
it's high season, it's midweek, andthat 110 might also reduce over time.
Uh, even July 4th will reduce overtime to say, right now it might be 175,
but in a month's time, if it's stillnot booked, we will say, okay, let's,
let's tone it down and see if it getsbooked now, kind of a thing, right?
(43:43):
So the base price is in some ways arepresentation of your home's quality.
Now, I'm sorry, not your home'squality, your listing's quality.
Uh, there, there's a difference here.
A listing is the onlinepersona of your home.
Uh, so the home may be great.
(44:06):
But if the listing is not, if thelisting has poor pictures, a poor
description and bad reviews, then,uh, people are not going to book it
if it is priced above the market.
Like, in fact, like even in lowseason, it might be tough to, uh,
(44:27):
even in high season, it might be toughto get it booked at a good price.
So you generally, This istrue across everything.
If the product is bad,it's hard to sell it.
The price stops mattering, right?
But a lot of times the differences inproduct are not like, it's not all bad.
Like the reviews are bad, pictures are badand everything, everything can't be bad.
(44:50):
Like you are going tohave decent pictures.
You are going to have good reviews.
But even then there are slight perceptibledifferences where some, a lot more people
are choosing another home over yours.
Even though they are both inthe same subdivision, maybe
it's how you decorated it.
And then over time you keepimproving on these things.
And as you improve, we will noticethat, Hey, look, you started off with
(45:14):
this a hundred bucks a night, butyou're getting quite a lot of bookings.
We think you can bump up yourprice because maybe you've
made improvements to the home.
Maybe you didn't anything,don't do anything.
Having these algorithmsfor whatever reason is now.
ranking you better than it was before.
Maybe it's a string of goodreviews that came through, right?
A lot of things impact theperceived quality of your listing.
(45:41):
Uh, and, and that's the, that's whatthe base price tries to reflect.
Rachel (45:46):
That's amazing.
I love that.
And as far as the minimum andmaximum, I usually set a minimum on
your eye, but I don't set a maximum.
Am I doing it wrong?
Anurag (45:56):
This is a question.
There's no right or wrong on this one.
Okay.
Like we talked about, uh, if TaylorSwift shows up, you don't want to cap
your prices at twice the base price.
On the other hand, there are, thereis a genuine reason to say, look, I
(46:17):
do want to cap my price at something.
Even if the biggest thing ishappening in town, if I raise my
prices 5x, I might get a booking.
But the guests might come and say, Hey,look, I, I know why the price was so high
because there was this thing going on.
Uh, I booked it because I needed aplace to stay, a good place to stay.
(46:40):
And this was a good place to stay.
But when reviewing, they might sayon value for money, maybe they give
you four stars or three stars, right?
Uh, generally speaking, wedon't see that very often.
Uh, people do understand that.
Okay.
With Spikes in demand prices are goingto be high, but there are folks who
(47:00):
are generally concerned about like,okay, I don't want to get a bad review.
Uh, and for that reason, I put amax price to say, okay, whatever
happens, uh, I'm, I'm happy with thismuch increase on the price during
peak season or peak trial periods.
Rachel (47:20):
Got it.
Got it.
So I know that Price Labs has launcheda new, um, I guess what is it called?
An update, so to speak, or aproduct inside of the platform,
the revenue estimator pro.
Can you talk to us alittle bit about that?
Anurag (47:35):
Yes.
So, uh, remember how I talkedabout market dashboards?
That's something werolled out in 2020, 2020.
And those are essentially to like get areally deep dive into a market to say what
kind of length of stay patterns exist.
Is it a good mid term market or not?
All kinds of things.
(47:56):
One of the things we kept seeingour customers do is that when they
were interested in purchasing aproperty in a certain market, they
would go order a market dashboard tofigure out how much can this make.
But market dashboards have so muchinformation there, uh, and for that
reason, they are priced a little higher.
(48:19):
They were priced at 10 bucks for a smallmarket and 20 bucks for a big market.
Uh, for a market with 5,000 listings and so on.
And the request that our customers keptmaking is like, Hey, I get a lot of,
uh, if, if you're a property manageror if you're evaluating, I am not
looking at the same market every time.
(48:39):
Like, I want different, differentlocations, uh, to evaluate different
locations in different homes.
Can you, can we have a subscriptionwhere I can try out many different
locations and not pay 20 bucks perlocation or 10 bucks per location.
Uh, so we said, okay, if, if all onthe first part, if all, uh, potential
(49:05):
owner or property manager is thinkingabout is what's the revenue potential.
Uh, we, we don't need the entiredepth of market dashboards.
We only need the revenue, occupancy, andADR, these three pieces of information.
Yeah.
And we can offer that for cheap.
And then once you have sort of narrowedin on, okay, out of these 10 locations,
(49:26):
this is the one I really want to doubledown on, then you go maybe do a market
dashboard to see, okay, give me all thein depth information about this market.
Rachel (49:38):
That's awesome.
And how much did you say that one was?
Anurag (49:42):
That one is, um, let me look up, it has different subscription levels.
Rachel (49:48):
No worries.
We can actually look it up andshare it in the show notes.
And the reason I asked that question,honestly, is because one of the reasons
that I signed up for Price Labs at thetime, back in 2018, 2019, Anurag was,
it was one of the only platforms thatwas offering a flat fee per month.
Uh, to be honest with you, tech creep.
(50:09):
Is something that, you know, I'mconcerned about for my community
because everyone has a new techtool they want you to sign up for.
And before you know it, your techbill is a thousand dollars per month.
And if you're a new or short term rentalhost, I want you to keep those costs
down and only really use technology.
That's going to impact your decisionmaking, your ability to make life easier.
(50:32):
You don't need 800 tools and youdon't need a tool eating away
at your, um, a percentage ofyour revenue every single month.
And so I was really appreciate,appreciative that Price
Labs was under 20 a month.
Very affordable, very cost effective.
And it gave me theinformation that I need.
It has the customizations that I need.
(50:53):
And so that's really one of thereasons why I signed up for it.
And I've, I've been happy ever since.
And, and, and that'sreally important guys.
So be aware of tech creep.
There's someone right nowworking in their basement.
Um, that's going to launcha new product for you.
That's going to help you hostmore intelligently or in a way
that you've never hosted before.
(51:15):
And.
You know, I get thatquestion all the time.
So many tech tools, but all youneed to get started is a channel
manager and a dynamic pricingtool that did anything else.
Um, say, you know, your HVAC and all ofthat, all of those integrations, that's
fine and dandy, but it really adds up.
I also use another toolfor market analysis.
(51:37):
I like to keep my technologystack less than, I would say 75
to 50 per month per property.
So if you're seeing that you'reexceeding that, you're, you're bloating
your business technology wise as a newinvestor, if you're like enterprise,
that's a whole different story.
So I just wanted to put that out thereto our members who are getting started.
(51:58):
Any opinions about that at all Anurag?
Anurag (52:02):
No.
Uh, again, like you said, it dependson how many properties you're managing.
Right.
We also have customers withone property that don't, don't
even use a channel manager.
Um, yeah, you probably don't need
Rachel (52:15):
it.
Yeah, absolutely.
Anurag (52:17):
Um, like, but then if you're on just Airbnb, you probably
don't need a channel manager.
Exactly.
But when you do as a second step, youdo want to look at webo and booking.
com as well, because, uh, that helpsyou sort of diversify a little bit.
Yeah.
If, uh, suddenly I stopped gettingbookings from Airbnb, do I have
(52:40):
something else to fall back on?
Exactly.
And that's when you startneeding, so like, uh, a channel
manager and things like that.
A hundred percent.
So yeah, on, on day one,uh, like start leaving.
Rachel (52:50):
Yeah.
Exactly.
And I do find like, like I said, thereare so many technology tools out there.
I think Airbnb is such a hot topicthat there are people creating
tech tools for the communityand you don't need all of it.
You just, you don't need all of it.
As a matter of fact, I saw atool that could estimate it
estimates for you on this night.
If you're booked at, if you'repriced at say 300 a night.
(53:15):
The probability that you'regoing to get booked is 32%.
And I was like, okay, but if you'rebooked at, if you're priced at 400 night,
the probability of you getting booked.
And so it was going night to night tonightand your probability getting booked.
And it just felt likethat, that sounds cool.
And I'm a, if I see a spreadsheet,I'm in heaven, but sometimes
(53:36):
it's overdoing it a little bit.
So.
Anurag (53:41):
It's like you have to hop off in
Rachel (53:43):
a minute.
Yeah, no worries.
All right.
So Anurag, thank you.
Thank you.
Thank you.
We're going to share with thecommunity how to connect with
you and share it in the show.
And I so appreciate youspending time with us.
This was a lot of fun.
Anurag (53:56):
Yeah.
Same here.
Rachel (53:58):
All right.
Bye bye for now.
Anurag (54:00):
Thank you so much.
Hello.
Hello.
My name is Dr.
Rachel Gainsborough, and I am obsessedwith all things, short term rentals,
revenue streams, and helping you navigateyour career, real estate, and your busiest
and most wonderful seasons of life.
So grab your coffee, get comfortable asyou get ready to learn and grow with me.
This is the Luxury ShortTerm Rental Doctor podcast.
(00:26):
Hello.
Hello everyone.
Welcome to another LuxuryRental Doctor podcast for show.
I think this episodehas been long overdue.
I have the amazing AnuragVerma here with us.
He is the co founder of Price Labs.
Let me let you know alittle bit about Anurag.
So before Price Labs, he usedto work for United Airlines.
(00:48):
Designing and improving revenue managementalgorithms and systems back in 2014.
He brought that experience fromrevenue management automation
that's prevalent within airlinesand hotels into the vacation rental
in short term rental space as well.
Now he has a background in mathematicsand statistics, and he is also in,
(01:11):
uh, PhD in operations and research.
So fellow doctor in the room, whichI'm super, super excited about on Iraq.
I've got so much to sharewith you, my friend.
I know, I know you fromseeing you at live events.
You are someone whosupports our community.
We love to send our members.
Your way.
We are actual utilizers of price labsourselves, and I've got an interesting
(01:35):
story to share with you, but withoutfurther ado, I would love you to
introduce yourself to the people and we'lldive right into this, to the episode.
Anurag (01:44):
Yeah.
I think we, we already gave you a monthto say, uh, but no, like, uh, pretty
plain, uh, underdog, uh, one of theco founders at price labs, I mainly.
We work on data science andproduct management at Price Labs.
Um, so, how to improve our algorithms,what data to use, how to use it, um,
(02:10):
how to, how should the UI be so thatthe customers can interact with the
algorithm, or like, what else can wemake with the same data that we already
have, and those kinds of things, right?
Um Yeah.
Uh, also unlike you, Ithink not a real doctor.
Uh, so yeah, yeah,
Rachel (02:32):
yeah, yeah.
And so you mentioned somethinga little bit earlier.
I want to acquaint you to our audience.
Our audience are busy doctorswho are looking to leverage, uh,
using short term rental or midtermrental as their investing strategy.
So not very, um, I would say tech, um,aware for on the, I guess, the, um,
product management roadmaps, so to speak.
(02:53):
So what is UI and why does it matter,
Anurag (02:58):
right?
So, um, UI is anytime UIstands for user interface.
Uh, and then the cousinis user experience, right?
So we have a dynamic pricing algorithm.
We have a lot of data.
We want to show thatdata to our customers.
We want to, uh, we want our customersto have the right pricing strategy
(03:22):
in place and automate that, butit's, uh, it's at the end of the day,
you are logging into our system andinteracting with our system, how can
we make those interactions, uh, simple.
But powerful, uh, that'ssort of the crux of it.
Anybody can make simple stuffthat doesn't do anything.
(03:44):
You just, you can put an onand off button at anywhere.
And, uh, if it doesn't doanything, there is no complexity.
Uh, but if you want it to do somethinguseful, then you need to have, uh, the
customer doing some interaction with it.
Think of a car, for example, right?
Um, 30, 40 years back, uh, you would keyit in, you would shift from neutral to
(04:11):
first, uh, you would leave the clutchand start on the accelerator or which
way, I don't know anymore because nowuser interface has improved so much.
You don't worry about gears.
So much, right?
There is no touch anymore.
So that's, uh, for some people that'slike worse user interface, but for
(04:32):
a lot of people, cars have justbecome more accessible and easier.
You don't need to be a professional,uh, even earlier, you didn't
need to be professional drivers,but like a lot more people can
now use cars than before, right?
So how can, how can we make, um, anysoftware better so that more people
can figure out these conflicts?
(04:52):
Yeah.
Rachel (04:58):
And I, I love that.
And I feel like, you know, the more, uh,technology advances, the more access, uh,
we're able to have to certain resourcesand we'll tap into that in a moment.
So what is Price Labs, Anurag?
Anurag (05:14):
Yeah.
Uh, so anybody who has booked.
Flight tickets hasprobably experienced it.
I used to work for United Airlines.
Um, and my role was primarilyimproving our algorithms that
determine those prices, right?
So, uh, when, if, if you're comingto February, Chicago in February, uh,
(05:37):
Chicago is super cold in February,uh, Very few people show up.
Um, planes are runninghalf empty sometimes.
What can be done?
So like as, as an airline, for example,you have a plane that is flying.
Um, you can't just cancel the flightbecause if you cancel too many flights,
(05:58):
eventually customers think of you asunreliable and then things like that.
So just because the flight isempty, you don't want to cancel it.
You shouldn't cancel it.
No.
You're already incurring, uh,like the cost to fly the plane
from one city to another.
The cost of the crew, thepilots, the flight attendants,
(06:19):
that's not going anywhere.
Uh, so whether you fly theplane half empty or you fill
up a few more seats at a time.
even if at a discounted rate,that is all incremental.
Uh, the additional people on the planedon't cost too much because most of
the cost is this fixed cost, right?
(06:39):
So the thought is, howcan we set up pricing?
One way is like to discount theprices all the way to the floor.
And now you might get the planefully booked at 10 a ticket.
And you have actually made less moneythan, than with the half empty plane.
The second way is to say, okay.
We can't give everyticket out for 10 bucks.
(07:01):
In fact, we should not giveany ticket out for 10 bucks.
But how can we The 50 people whoare willing to pay the full price,
is there something about them?
Uh, maybe they tend to book very late.
Maybe these are business travelers, right?
Uh, so we want to protect 50seats for the business travelers
who will pay the full price.
But can we discount the earlybookers, like maybe 20 or 30 tickets?
(07:24):
So that we get something more thanwhat we would have otherwise gotten.
Um, so that's what we do in airlines.
Now, how does it translateto short term rentals?
Like, uh, the story basically goes,I was doing this for airlines, and
one of my very close friends wasstudying at that time in Chicago.
And he's like, I'm listing myroommate's empty room on Airbnb
(07:45):
while he's on his internship.
And he's like, Airbnb is not suggestingwhat price do I put it saying?
Like, do I put a hundred everywhere?
Should I have a tire in summer?
Are there some dates where itshould be higher than others?
And he and I got discussing and we werelike, okay, like there are plenty of
Airbnb hosts around the world that areprobably thinking about the same thing.
(08:08):
How can we help them set the right price?
Um, and to start off, wedidn't know the answer.
And so we said, okay, we'll,we'll experiment a little.
So like the whole price labsname came from that to say,
okay, what, what will work?
What will not work?
So we found a bunch of things over time.
Um, most locations havewhat's called seasonality.
There is a high season.
(08:29):
There is a low season.
You should increase prices, uh, oryou should be able to charge more in
high season and low in low season.
Um, There is, uh, in most placesthere is a day of week pattern.
If you're in a vacation destination,you're likely going to have a lot more
demand during weekends than midweek.
(08:50):
If you're in a business hub, youpotentially have a high demand during
midweek, uh, because of business travel.
Yeah.
Um, even if you are in, not inshort terms, but in midterm, uh,
you see similar patterns, right?
For example, when summer internshipsare going on, a lot more people
are looking for two to threemonths long, uh, housing, right?
(09:13):
Yeah.
Um, there are events and holidays thatare very, holidays are predictable,
uh, but events are very unpredictable.
Uh, there are times like when we started,we, we knew about Chicago Marathon and
we knew a ton of people come there.
One of the craziest findings we hadwas the weekend after Chicago Marathon.
Every year, more or less, there'ssomething called Chicago open house,
(09:37):
which
is like a lot of buildings in Chicagoopen their doors to say, even if you
don't work there or live there, you'rewelcome to come in and have a look.
This is like, um, this is kind ofa thing that Chicago does and we
realized that has almost the samedemand spike as the marathon and
(09:57):
almost no one, like, If you're newto Chicago, you don't know about it.
If you're not in Chicago, you don'tknow about it, but there are definitely
these architecture enthusiasts who areinto it, who are flying and staying in
short term rentals and hotels, right?
So, we were like, okay, as anindividual host, like, how do
you keep track of these things?
And we said, okay, can we keep track ofthe data so that our, uh, we can update
(10:22):
prices for our customers on their behalf?
They still get control to say what'sthe minimum you want to go to, what's
the maximum, and there's plenty ofbells and whistles and knobs to say,
I never want to take a short booking,uh, like less than two nights long,
or, uh, when it's far out, do this.
But how can we build all ofthat so that, uh, a busy host or
(10:47):
manager doesn't have to spend.
extra hours just researching themarket all the time and yet can
make more revenue out of that.
So like the parallel to the plane hereis if you have a vacation home, you
likely have a mortgage on that, right?
Rachel (11:05):
Yeah.
Anurag (11:06):
So, uh, that is going out regardless of whether
somebody stays there or not.
Uh, so how can I make, it's okayduring low months to not make enough
revenue to meet their mortgage.
Um, As long as over the courseof a year, uh, you make it in, in
the long run, you make it right.
(11:27):
So peak months are going to makea lot more, uh, and slow months
are going to make a lot less.
Just because slow months aregoing to make less doesn't mean
that they should make zero.
Uh, that's sort of the thought becausewhatever little the low months make,
they help cross the annual thresholdto say, okay, my total mortgage was
(11:47):
this much, uh, And even though 90percent of that got covered in the
peak months, the slow months helped me,uh, help me actually make some profit.
Rachel (11:58):
I love that.
Yeah.
Yeah.
And I believe that this isexactly what we had experienced.
So now it's story time.
So on your back in, um,I want to say 2019 ish.
I had launched one midtermrental in my market.
It's a residential subdivision marketin the South of Atlanta market and, um,
(12:20):
that property was doing really well.
Uh, we launched it without price labs,but then we learned about dynamic
pricing and I tell my community, ifyou're not using price labs, you're
leaving a lot of money on the table.
Right.
And, uh, so once I launched, um, thesecond property, it was a larger property
and I said to myself, okay, uh, since it'slarger, the cleaning team, um, was having
(12:44):
a little bit of a transition period.
I was going to, uh, onboard a newcleaning team, but for the first
month I pre marketed the property.
I wanted a 30 day minimum stay just tostart and then we're going to adjust it
to two night, three night stay minimum.
Uh, since it was a larger property, Iwas anticipating it would make anywhere
around eight or 9, 000 per month.
(13:07):
The smaller property wasmaking about six to 7, 000.
So I said eight or 9, 000 on average.
There's no way, I was like, dare I think10, 000, but my, Self limiting belief.
I'm a little girl from Haiti.
I was like, who had 10,000 a month to spend?
That's just outrageous.
There's no way, um, that thatmarket could, um, I would say,
(13:28):
uh, you know, have that level ofdemand, uh, absorbed in that market.
So I turned on the dynamic pricingtool, price labs, and I know sometimes
it requires some adjustments.
Sometimes it's a little bit higher thanI would expect or a little bit lower.
So it was going to play around with it.
But when I turned it on expecting abouteight or 9, 000, it listed my property
(13:49):
for 28, 000 for the next 30 days.
And I was like, uh, no, themortgage is 2, 500 a month.
There's no way.
So let me go ahead and adjust it.
Cause you know, Price Labs has AI.
I'm not sure why, um, it went so high.
So let me go ahead and turn it offand adjust it before I turn it off.
It got booked.
(14:11):
It got the next month.
It was for 15, 000 to 21, 000and it was just gangbusters.
I had to take a step back andthink to myself, where else
am I playing small in my life?
I was going to adjust it based on, youknow, my limiting beliefs, my thought of,
(14:32):
okay, it's going to be a family traveling,but turns out it was attracting,
uh, the film industry, the insurancecompanies, and so more of corporations.
Whose pockets are a little bit deeperbecause I know I'm not spending 28, 000
as an individual, but the corporations,it was a whole different avatar.
Business travelers would cast and crewand groups, and it just blew my mind.
(14:56):
And I tapped into a new audience.
That I wasn't aware of a few monthslater, air DNA and a casting company
called mystics arts reached out for usto be featured on a Netflix TV show.
And so I say, listen, I know thatyou talked about UI user interface,
user experience initially.
(15:18):
I felt that, uh, pricelabs now it's much better.
It was even a little clunkier.
Then if you're like,you should try this too.
You should try that.
No.
I'm not trying any tool.
I'm going to stay the course withPriceLabs and I'm just so excited that,
you know, you guys continue to offernot only, um, updates and, and you're
implementing things that will improve theuser interface, but you're offering, you
(15:41):
offer a ton of training when people ask mehow to use PriceLabs, they say, Hey, hop
on the training, there's training for you.
All the time.
There's a whole library of training andfor me, that's where I see the value, um,
for, you know, is in, in price lives isjust the support, the level of support,
the level of training, but I wantedto share that little story with you on
your, I don't know if you're aware of it.
Anurag (16:03):
I did not.
So there are multiple things.
I have so many questions.
What kind of a large house isthis that has a 2, 500 mortgage?
Like, uh, that, that itself seems,uh, quite something, but then it's,
it's the, there are two parts tolike, when something gets priced, uh,
(16:26):
that high one, if you had sent us aquestion, we would have said, Hey, we
need to look at the base price, right?
The base price, if it is too high, andwe come up with a base price, we show
market recommendations, like, becausewe look at data from Airbnb and like
(16:46):
scrape data from there, uh, we say,okay, like similar houses are listed
at, let's say, uh, 700 a night, right?
Uh, but then at the end of theday, like, This is true in the
real estate industry in general.
Every house is unique.
Like, uh, I think plenty of people havetried, Zillow had tried at one point
(17:08):
to say, can we predict the home price?
Right.
And it's very hard to quantifythe quality of a home.
Uh, two homes can be inthe same subdivisions.
Uh, one of them could be facing thelake and the other one could be facing,
I don't know, maybe the parking lot.
Right.
And.
And so generally we would have said,Hey, look, you know, your home,
(17:30):
they'll, uh, look at the data, but thenalso know the reality of your home.
So like maybe the base price is too high.
Let's reduce that.
Um, but then the flip side is also, uh,and generally speaking, if it hadn't
gotten both that day, we would have said,Hey, look, after seven days, you, our
algorithm would have said, Hey, look,your base price is probably too high.
(17:51):
You're not getting any bookings.
Like what's happening, right?
Yeah.
Um, So, and then there is that limitingbelief kind of thing where, uh, there are
times, so like generally speaking, it'snot just about increasing prices, right?
It's also reducing pricesduring lean periods.
(18:11):
But, uh, sometimes it's, youthink that it, it doesn't make
sense for me to 300 bucks a night.
Yeah.
But then we say, look, All your datesin low season are sitting at 300.
You still haven't gotten booked.
Let's try going down to 250 and seeif you can get some things booked.
Yeah.
And then on the flip side, thebiggest event in town is happening
(18:33):
and you have said, I never wantto go above 800 bucks a night.
And you're like, this thing, TaylorSwift, you would have gotten it
booked at 2x the price becausethere is just so much more demand.
So
Rachel (18:46):
she's a market maker.
Anurag (18:49):
Yeah.
So like, uh, sometimes like we, we tellour customers one absolutely true about
training, like we, it is any pricing tool.
It's not, um, all knowing.
And, uh, you know, like, uh, itdoesn't know everything, you know?
(19:11):
So like, it is important toknow how to drive this thing.
Yeah.
And then again, like.
Very important to like attendthe trainings to see, okay, what
bells and whistles exists becauseeverybody has a different process.
Um, so for example, some folks aremore risk averse than others, right?
(19:34):
So, um, we have customers who say, Hey,look, if I, if my next month is not like
70, 80 percent booked, I'm panicking.
Uh, and they might be in alocation where like half the
bookings come in the last month.
So like, our thought is like, okay, one,uh, we provide a lot of data to say,
okay, it's okay not to panic, uh, ifyou have 30 percent vacancy in the next
(19:58):
30 days, because most of the bookingsare still yet to come in your market.
Uh, it's totally fine.
But then if you, if you are someone whosays, Hey, it just helps me sleep better.
I don't care about what the data says.
You can go and adjust yoursettings to say, okay, I want
more farther out bookings.
Uh, yeah.
Okay.
Taking on slightly lower pricesand it will help me sleep better.
(20:19):
Uh, all of these bells and whistles exist.
So, it's
Rachel (20:22):
so customizable.
You're absolutely right.
I was just speaking, um, with our groupand One of the questions that popped
up is an insurance company reached outand she had Instabook on and, you know,
she's still in the negotiation phases.
Should she leave Instabook on furtherout all and accept those bookings, which
(20:43):
will impact her ability to, you know,generate revenue from the insurance.
And it's two sided, you know, if youfeel, you know, Like you won't sleep
at night knowing that, you know, youare impacting your ability to get these
quick bookings versus if your overallstrategies to have hold out hope for
these insurance companies, right?
(21:05):
There's very, very, um,I think customizable.
And so my recommendation to her, um, was,you know, for me, I would hold out for the
insurance since I'm having a conversationwith them and I'll know within the next 48
hours or so what direction we're going in.
And then we could, you know,return to the Instabook strategy.
So it's, it's a two edges sword for sure.
(21:26):
And so what we're seeing, excuse me,Anurag, is insurance policy holders.
Um, You know, we're workingwith the corporation.
We have the film industrythat we're serving.
We have, um, data centers that arebeing built in some of our markets.
We have a political campaigngoing on right now in the U.
S.
So we have political campaignstaff that need housing.
(21:48):
We have medical professionals.
There are so many midterm mentalstrategies that are out there.
What I love about one of theslides that you shared a couple
of years ago, uh, Show that.
Well, you know what?
This midterm rental demand, althougheveryone's talking about travel nurse,
travel nurse, travelers, it was herebefore the big travel nurse buzz.
And so whether it's climate migrants,snowbirds, there are such a variety of
(22:11):
midterm rental guests that are out therethat need a place to stay for 30 days,
60 days, 90 days, so on and so forth.
So I use inside of price lab, acustomization, which is the stay
restrictions for my far out bookings.
I like increase it so much tode incentivize people to block
(22:33):
my calendar for two or threenights days because I want.
To, uh, incentivize those 30 day staysfurther out, but closer to my, you know,
recent checkout, I like to use, you know,a two night, three night in, in my market.
Am I doing it right on your end?
This is what we teach our community.
Yeah,
Anurag (22:51):
that is the right way to go.
Generally speaking.
Um, yeah, like you said, if you takea two night booking a month out, now
somebody who is going to show up ina week's time and want to stay for a
month or two months or three months.
Can't, can't book your place anymorebecause there is a two night booking
(23:13):
sitting, sitting in the middle andgenerally speaking, uh, so you want
to say, okay, I want to keep mycalendar a little open to, to absorb.
And again, this doesn't workin every location, right?
So you have to, uh, find out if yourmarket is a midterm rental market or
not in some ways, in some ways, right?
Rachel (23:34):
Well, I get that question all the time, and I don't mean
to interrupt you, but I thinkthis is a good actual question.
So, um, outside of having thatintimate knowledge of your market,
what are some ways that our memberscan identify if their market is a
midterm rental friendly market or not?
Anurag (23:49):
Right.
So there are, uh, I can think ofthe price labs answer in some ways.
Okay.
I'm trying to also think if thereis a non price labs answer for this.
So, for example, there are websiteswhere you list when you, uh, that you
(24:10):
want to do mid term rentals, right?
Rachel (24:12):
Mm hmm.
Anurag (24:13):
Um, Furnished Finder is one.
Yeah.
Traveling Nurses.
Mm hmm.
Many of these places.
Uh, go find out, like, dopeople in your city list there?
Like, if nobody's listing there,uh, maybe it's not as popular.
Um, my hope here is that if a lotof people were coming for midterm
(24:35):
stays, then the enough supplywould have shown up there, right?
You could be onto something ifnobody's listening, you could be
like, maybe this is an open market.
Like, how do I find out?
Um, the way we answer it is we havea product called market dashboards
(24:57):
and it costs 10 bucks a month,but to answer this question, you
don't need the per month part.
You just use it once and thenyou're done kind of a thing.
Um, where we will show you the length ofstay patterns that, uh, that tend to book
on Airbnb over the course of the year.
(25:18):
So like, okay, the occupancy in thismarket is maybe 80 percent of on average,
but like what portion of that comes from30 plus night rentals, what quotient
comes from 14 to 30 night rentals.
The way we do this is by essentiallyscraping publicly available data,
uh, on, on Airbnb and looking ateach property on Airbnb and their
(25:41):
calendars to say what dates areavailable, what dates are not available.
And then you do it frequently tosee what, uh, these dates that were
available now became unavailable.
Uh, so.
Quite likely it is like if your calendarwas empty and then there are two night
(26:02):
booking showed up your, your calendaron Airbnb to every other bookers
will, or to every other guest willhave those two dates as unavailable.
And that gives you a sense that, okay,maybe those two dates are booked.
Now, instead of two days becomingunavailable together, if 30 days
become available, we say, okay,maybe those 30 nights got booked.
Or if 45 nights got booked, then45 nights become unavailable.
(26:25):
So we track all of these changesand are able to paint a picture
to say, hey, look, in this market,a lot of the dates that become
unavailable, uh, are from longer stays.
Uh, so maybe this is amid term rental market.
Now, important, uh, thing here isalways, like, when you're looking
at data, Never rely on one source togo look at multiple sources, right?
(26:49):
And that's where I'm trying to say like,okay, uh, I know the price of answer.
Like I want to find out whatother answers exist there.
Um,
Generally speaking, you, you'dspoke about a few things.
If it is nurses, is therea big hospital there?
Because if there is likely that, uh,that that kind of demand might exist,
(27:09):
are there large companies around?
Um, I know.
We have customers in Seattle and SanFrancisco who mainly cater to Amazon
and Microsoft and Facebook employeeswho, uh, when they move there, the
company provides housing for the firstthree months, uh, kind of a thing.
And they have relationships withthese folks to say, okay, I have
(27:31):
a house, three months, sure,like gladly taking it, right?
Uh, so some of it could just belike, if you're in that market, get
a feel of, of things around you.
And then some of it could belike external sources like us.
Rachel (27:45):
I love that.
And I know from the beginning, Ifeel like from the beginning of
time, Price Labs was already hip tousing and leveraging AI insights.
Can you speak to that a littlebit and what that progression has
looked like over the last few years?
Anurag (27:59):
Yeah.
So this is actually played into thewhole UI user interface question.
Um, so we, We process a lot ofdata, uh, like I said, we scan
every property on Airbnb or booking.
com to say how are they changingtheir prices, how is the occupancy
(28:21):
changing and things like that.
Uh, and we use that data toforecast what do we think will
happen in the future, right?
If we see May as being a very strongmonth in Atlanta, Uh, we'll say,
okay, maybe we can increase pricescompared to, uh, other months.
Uh, and if June was going to bevery, like much lower than last
(28:41):
year's, you will say, okay, likeJune is not shaping up really well.
Uh, you haven't booked either.
Let's try to reduce your bookings,no, prices to get you some bookings.
All of this in the, these are all,um, mathematical models that come
up with the price recommendations.
(29:02):
The flavor of AI that has become verypopular operate is, is generative AI
or, uh, LLS language models where, uh,their usefulness is almost complimentary.
Uh, okay, so chat, GPT or similarmodels are not going to come up with
(29:23):
price recommendations, uh, for you.
Like they don't know what'shappening in the market,
Rachel (29:27):
right?
Anurag (29:29):
Where they end up being helpful is we show a lot of data to our customers.
Uh, we have a tab called table of datawhere we say, okay, here's your property.
Here are recommendations.
You can also go and see, okay,what's happening in my market.
One of the feedback we hear is, Hey,look, if I'm a professional revenue
manager, or if I'm, uh, into, uh, dataand charts, This all makes total sense.
(29:55):
I can read the data.
I can figure out what's happening.
But a lot of times I don't necessarilyhave the time to figure out what
each of these lines means and, uh, oreven to interpret like, okay, what's
the right way to interpret thesetwo lines being close to each other
versus being away from each other.
Yeah.
Uh, and so we created thisAI insights button that says,
(30:18):
Hey, look, here's the data.
Uh, Can we provide you a textsummary of what you're seeing?
And we want to addresstwo things with this.
One, there is a lot of data that weshow, but once you learn how to read
it for a few dates, it becomes alot easier to realize that, Oh, this
(30:41):
is actually pretty straightforward.
But can we give you those like two, three,four, five insights that help you realize
that, Oh, this is what I'm looking at.
Like this is straightforward.
Uh, so a lot of the data we show isnot terribly complex, but then, uh,
the AI insights or the LLM modelsare helping make sense out of it so
(31:04):
that then you're, then you get morecomfortable with data essentially.
I
Rachel (31:10):
love that.
That makes a whole lot of sense.
So for me, um, what I love usingchat, GBT and AI for as well.
is you're interpreting largeamounts of information.
So, for example, if I'm, um, doing amasterclass and I have, uh, 200 people
attending on Iraq and they all havea variety of questions, but at the
(31:32):
end of the day, some of the questionsmay fall into the same category.
So part of, um, instead of me manuallygoing through and say, okay, this is a
question about how to analyze markets.
This is a question about how to setup a property for a midterm rental.
Well, if I can take that, you know,database of questions and add it to
(31:53):
chat GPT, it helps me to categorizeand identify how many times this
type of question is showing up.
And it helps me to then create apicture of, you know, what it is our,
our community, we need to be teachingabout next week, because this question
keeps showing up time and time again.
So that's how I use it.
And I hope, you know,someone listening could.
(32:13):
See the value of, you know, the timethat it can save in terms of how to
process large amounts of data andhelp with category categorization.
I think it does that pretty well,
Anurag (32:25):
I'll say it's still, uh, that kind of stuff.
It does pretty well.
Like, when you ask it for factsis when sometimes it can go, so,
so I do want to add a disclaimer,like, don't, Don't fully trust it.
Like, do you want to, uh, like, especiallyon subjects that you don't know too
(32:47):
much about it, you can say things that.
With a lot of confidence.
Right.
And it's just not right.
Rachel (32:55):
A hundred percent.
A hundred percent.
Yeah, it's been a great tool forme to process information, but
yeah, I don't trust it very much.
You're absolutely right.
So you mentioned the market dashboards.
I find that to be very interesting.
Um, I actually used it a while back withone of the members of our community.
For a market that is right outside ofwhere I live, I actually went, um, and
(33:16):
I looked at it based on the dashboard.
It didn't feel very promising atall, but the numbers seem so great.
I went out there, I drove out there andlooked at this market and it was so rural.
I kid you not.
And it had construction happening.
And when I looked to see, well,what did, what did they build?
And maybe there's something, itwas a tractor supply, which is a
(33:38):
store that just doubled down on.
This is so rule.
This is so I told him, no, no, no, no, no.
I don't feel comfortable.
This would be high risk andit was the market dashboard
that helps us to identify it.
And then when we use some othertools as well, we had one more
tool that kind of corroborated.
that.
So I think that's very, very handy.
(34:00):
So I get the question onyour rock a lot of times.
Well, what's the differencebetween price level?
What's the differencebetween Airbnb smart prices?
What's the difference between otherdynamic pricing tools that that is in
the category of where price lab sits?
Um, would you mind givingus a little highlight?
Because I have my own answer, but Iwould love your perspective on how
(34:20):
is price labs Uh, different from theother dynamic pricing tools as well
as yeah, the smart pricing on Airbnb.
Anurag (34:28):
Right?
So the Airbnb answer is a littleeasier in some ways, right?
Mm-Hmm.
. Um, at one level you would think, um,you want to say Airbnb has a lot of
data available about every bookingthat happens on their platform.
Uh, the.
(34:50):
The place where this slightly falls apartis that a lot of people rightly list,
not just on Airbnb, but also on booking.
com.
Yeah.
So for a particular home, uh, Airbnbdoesn't even know how much it's,
it's making, uh, because many of thedates are getting booked on booking.
(35:11):
com or sometimes people have directbookings as well where like they create
their own website and things like that.
But even then, Airbnb has a lot of data.
Now, one, I think their expertisepotentially is just not in data or, uh,
(35:33):
the platform is designed to cater toevery Airbnb host in some ways, right?
So, where we excel is just inproviding customers with a lot more
bells and whistles to say, Hey, look.
The, the hosts that reallycare about finding out how can
(35:54):
they maximize their revenue.
Uh, those are the ones that sort of weend up catering to, to say, okay, uh, you
don't just want to turn on a switch, whichis what a lot of times in smart pricing,
Airbnb smart pricing, there's a switch,and then there's a minimum and maximum.
Um, and then.
And that's about as muchcontrol you get, right?
And we say like, that's, thatshouldn't be the end of it.
(36:14):
Like you should be able to do a lotmore with your pricing strategy.
Yeah.
And a lot of customers to beginwith, uh, will still do the switch
and then maybe set their base price.
We'll show them data.
But then over time, as they learnmore, they'll want to do more about it.
So they might say, Hey, look, uh,I want to cater more to midterms.
(36:36):
Uh, how do I do that?
And then Your strategy comes upto say, okay, uh, I want to take
short bookings only last minute andthen longer bookings further out.
Uh, somebody says, um, how can I do sothat, um, when I have a gap between two
bookings, um, I might have a minimum ofthree nights, but if there's a two night
(36:57):
gap, um, I don't want it to be wasted.
What can I do?
And there are a fewthings that you can do.
You can say, Okay, if it's atwo night gap, I want to reduce
my minimum nights to two.
Take something.
And then you can say, But I alsowant to make sure my revenue, like,
from a booking is at least something.
Like, I don't want to sellthose two nights for cheap.
(37:18):
Then you can say, Okay, when there's atwo night gap, let's bump up the price.
If somebody books, uh, you at leastmake something meaningful, right?
So those kinds of bells and whistles andcontrol and how much market data that we
surface that's truly relevant for somebodywho's Looking to maximize their revenue.
Uh, so that's that's one the secondone is uh partly because of the
(37:43):
control that we provide a lot of timeswhat I hear is Airbnb smart pricing
will drop the rates to the floor.
And, um, which, uh, I don't fully knowwhy that would happen, uh, but that's
the feedback that we get a lot of times.
(38:05):
And so then, and maybe that'salso a part of control, right?
Uh, where we give our customers thecontrol to say like, okay, you, you
have the full control of the base price.
So.
If you want to price things higher,uh, you just bump up the base
price, and then that's how it works.
It'll give you feedback to say, Heylook, with these higher prices, maybe you
(38:26):
are overvaluing your, your home in thiscase, and nobody seems to be booking,
so maybe you should reduce it back.
Uh, but, but you stillhave the full control.
Uh, so I would say, generally speaking,it boils down to how much, Um, compared
to other providers, uh, I thinkthe answer is again on the similar
lines where we tend to provide a lotmore of these, uh, customizability,
(38:54):
uh, compared to other providers.
And then also like the kind ofdata that we show about the market.
Uh, I think we rolled it out almost.
A year and a half back, but I thinkwe are the only provider even now that
shows how is your market and by market.
I don't mean all of Atlanta, but ifyou're located in a particular part
(39:18):
of Atlanta, how is that sub market?
We call it the hyper local market,like the closest homes around you.
How are those performingcompared to the to a year back?
Uh, is in real time.
So like, for example, uh, Juneis still 14 or 15 days away.
(39:39):
And right now, maybe in Atlanta,June is booked, let's say 25%.
And last year, you can easily goand look, uh, in different places.
Last year, June wasmaybe 45 percent booked.
I'm just making this up.
Yeah.
Yeah.
Yeah.
(40:00):
But the difference is thatlast year's June is completed.
All the bookings have happened,and then it is 45 percent book.
This year's June is still ongoing.
Right now it is 25%.
But eventually, maybe it willcross 45%, maybe it won't.
How do I know?
Like, how do I know right now?
Because I don't want to make decisionsto reduce June prices after June is done.
(40:23):
I want to make that decision right now.
Right.
So we, uh, we have this data inour product and the AI insights
actually show this data and talkabout it as well to say on May 14th
in 2023, how occupied was June?
And maybe at this point of time, lastyear, June was 20 percent booked.
(40:49):
And right now it's 25 percent booked.
And now you say, Oh, the zoom is actuallygoing to be stronger than last year.
Uh, so that kind of data, the depth ofdata, uh, and then because of that, also
the depth of the recommendations generallyis something we pride ourselves on.
Uh, so yeah, in terms of differentiation,I think that depth of how much data we
(41:12):
show and use and how localized it is, andthen how customizable the platform is too.
To execute your particular strategieswhen you graduate to the point where
you say, okay, I'm making, I'm doingwell, but I want to do something more.
How can I do that?
Rachel (41:29):
I love it.
I love it.
I love it.
And you're absolutely right.
You can be.
Uh, you know, at the basic levelwhere you're just getting started or
you could elevate yourself to nextlevel where you're doing real revenue
management, uh, and, and, you know,pricing management, which is amazing.
So question for you on your, thisis a basic question for those
(41:50):
who are very new and they're notas familiar with a price lab.
What is base price?
What's that?
What does that mean?
Does that mean my lowest price?
Does that mean an average price?
What does that mean?
around my max price.
What does the base price meanand why is it so important?
Because it's one of thefirst things we want to set.
And I know it's one of the first thingsthat price labs gives a recommendation on.
Anurag (42:13):
Yeah.
So the base price is theaverage price across the year.
Um, so this includes low season whenyou may not get booked high season
when you're very likely to get booked.
midweek, weekend, everything included.
So think of if your base price is ahundred based on the location that you're
(42:34):
in, we will come up and say, Hey, look,July is high season and it's high season.
Like we need to increase the prices by30 percent during July and in August.
It is high season but slightlylower than July, maybe it's 25
percent increase on base price.
(42:55):
So then 130 becomes youraverage price, uh, for July
because 100 was your base price.
Now that doesn't meanevery date in July is 130.
Uh, maybe July 4th a lot of people aretraveling in, and that, we say, Hey
look, there is a spike in demand, pumpit up further, and that becomes 175.
Uh, but then midweek a week later,maybe it ends up being 110, uh,
(43:21):
because, uh, not, not that muchdemand is out there, even though
it's high season, it's midweek, andthat 110 might also reduce over time.
Uh, even July 4th will reduce overtime to say, right now it might be 175,
but in a month's time, if it's stillnot booked, we will say, okay, let's,
let's tone it down and see if it getsbooked now, kind of a thing, right?
(43:43):
So the base price is in some ways arepresentation of your home's quality.
Now, I'm sorry, not your home'squality, your listing's quality.
Uh, there, there's a difference here.
A listing is the onlinepersona of your home.
Uh, so the home may be great.
(44:06):
But if the listing is not, if thelisting has poor pictures, a poor
description and bad reviews, then,uh, people are not going to book it
if it is priced above the market.
Like, in fact, like even in lowseason, it might be tough to, uh,
(44:27):
even in high season, it might be toughto get it booked at a good price.
So you generally, This istrue across everything.
If the product is bad,it's hard to sell it.
The price stops mattering, right?
But a lot of times the differences inproduct are not like, it's not all bad.
Like the reviews are bad, pictures are badand everything, everything can't be bad.
(44:50):
Like you are going tohave decent pictures.
You are going to have good reviews.
But even then there are slight perceptibledifferences where some, a lot more people
are choosing another home over yours.
Even though they are both inthe same subdivision, maybe
it's how you decorated it.
And then over time you keepimproving on these things.
And as you improve, we will noticethat, Hey, look, you started off with
(45:14):
this a hundred bucks a night, butyou're getting quite a lot of bookings.
We think you can bump up yourprice because maybe you've
made improvements to the home.
Maybe you didn't anything,don't do anything.
Having these algorithmsfor whatever reason is now.
ranking you better than it was before.
Maybe it's a string of goodreviews that came through, right?
A lot of things impact theperceived quality of your listing.
(45:41):
Uh, and, and that's the, that's whatthe base price tries to reflect.
Rachel (45:46):
That's amazing.
I love that.
And as far as the minimum andmaximum, I usually set a minimum on
your eye, but I don't set a maximum.
Am I doing it wrong?
Anurag (45:56):
This is a question.
There's no right or wrong on this one.
Okay.
Like we talked about, uh, if TaylorSwift shows up, you don't want to cap
your prices at twice the base price.
On the other hand, there are, thereis a genuine reason to say, look, I
(46:17):
do want to cap my price at something.
Even if the biggest thing ishappening in town, if I raise my
prices 5x, I might get a booking.
But the guests might come and say, Hey,look, I, I know why the price was so high
because there was this thing going on.
Uh, I booked it because I needed aplace to stay, a good place to stay.
(46:40):
And this was a good place to stay.
But when reviewing, they might sayon value for money, maybe they give
you four stars or three stars, right?
Uh, generally speaking, wedon't see that very often.
Uh, people do understand that.
Okay.
With Spikes in demand prices are goingto be high, but there are folks who
(47:00):
are generally concerned about like,okay, I don't want to get a bad review.
Uh, and for that reason, I put amax price to say, okay, whatever
happens, uh, I'm, I'm happy with thismuch increase on the price during
peak season or peak trial periods.
Rachel (47:20):
Got it.
Got it.
So I know that Price Labs has launcheda new, um, I guess what is it called?
An update, so to speak, or aproduct inside of the platform,
the revenue estimator pro.
Can you talk to us alittle bit about that?
Anurag (47:35):
Yes.
So, uh, remember how I talkedabout market dashboards?
That's something werolled out in 2020, 2020.
And those are essentially to like get areally deep dive into a market to say what
kind of length of stay patterns exist.
Is it a good mid term market or not?
All kinds of things.
(47:56):
One of the things we kept seeingour customers do is that when they
were interested in purchasing aproperty in a certain market, they
would go order a market dashboard tofigure out how much can this make.
But market dashboards have so muchinformation there, uh, and for that
reason, they are priced a little higher.
(48:19):
They were priced at 10 bucks for a smallmarket and 20 bucks for a big market.
Uh, for a market with 5,000 listings and so on.
And the request that our customers keptmaking is like, Hey, I get a lot of,
uh, if, if you're a property manageror if you're evaluating, I am not
looking at the same market every time.
(48:39):
Like, I want different, differentlocations, uh, to evaluate different
locations in different homes.
Can you, can we have a subscriptionwhere I can try out many different
locations and not pay 20 bucks perlocation or 10 bucks per location.
Uh, so we said, okay, if, if all onthe first part, if all, uh, potential
(49:05):
owner or property manager is thinkingabout is what's the revenue potential.
Uh, we, we don't need the entiredepth of market dashboards.
We only need the revenue, occupancy, andADR, these three pieces of information.
Yeah.
And we can offer that for cheap.
And then once you have sort of narrowedin on, okay, out of these 10 locations,
(49:26):
this is the one I really want to doubledown on, then you go maybe do a market
dashboard to see, okay, give me all thein depth information about this market.
Rachel (49:38):
That's awesome.
And how much did you say that one was?
Anurag (49:42):
That one is, um, let me look up, it has different subscription levels.
Rachel (49:48):
No worries.
We can actually look it up andshare it in the show notes.
And the reason I asked that question,honestly, is because one of the reasons
that I signed up for Price Labs at thetime, back in 2018, 2019, Anurag was,
it was one of the only platforms thatwas offering a flat fee per month.
Uh, to be honest with you, tech creep.
(50:09):
Is something that, you know, I'mconcerned about for my community
because everyone has a new techtool they want you to sign up for.
And before you know it, your techbill is a thousand dollars per month.
And if you're a new or short term rentalhost, I want you to keep those costs
down and only really use technology.
That's going to impact your decisionmaking, your ability to make life easier.
(50:32):
You don't need 800 tools and youdon't need a tool eating away
at your, um, a percentage ofyour revenue every single month.
And so I was really appreciate,appreciative that Price
Labs was under 20 a month.
Very affordable, very cost effective.
And it gave me theinformation that I need.
It has the customizations that I need.
(50:53):
And so that's really one of thereasons why I signed up for it.
And I've, I've been happy ever since.
And, and, and that'sreally important guys.
So be aware of tech creep.
There's someone right nowworking in their basement.
Um, that's going to launcha new product for you.
That's going to help you hostmore intelligently or in a way
that you've never hosted before.
(51:15):
And.
You know, I get thatquestion all the time.
So many tech tools, but all youneed to get started is a channel
manager and a dynamic pricingtool that did anything else.
Um, say, you know, your HVAC and all ofthat, all of those integrations, that's
fine and dandy, but it really adds up.
I also use another toolfor market analysis.
(51:37):
I like to keep my technologystack less than, I would say 75
to 50 per month per property.
So if you're seeing that you'reexceeding that, you're, you're bloating
your business technology wise as a newinvestor, if you're like enterprise,
that's a whole different story.
So I just wanted to put that out thereto our members who are getting started.
(51:58):
Any opinions about that at all Anurag?
Anurag (52:02):
No.
Uh, again, like you said, it dependson how many properties you're managing.
Right.
We also have customers withone property that don't, don't
even use a channel manager.
Um, yeah, you probably don't need
Rachel (52:15):
it.
Yeah, absolutely.
Anurag (52:17):
Um, like, but then if you're on just Airbnb, you probably
don't need a channel manager.
Exactly.
But when you do as a second step, youdo want to look at webo and booking.
com as well, because, uh, that helpsyou sort of diversify a little bit.
Yeah.
If, uh, suddenly I stopped gettingbookings from Airbnb, do I have
(52:40):
something else to fall back on?
Exactly.
And that's when you startneeding, so like, uh, a channel
manager and things like that.
A hundred percent.
So yeah, on, on day one,uh, like start leaving.
Rachel (52:50):
Yeah.
Exactly.
And I do find like, like I said, thereare so many technology tools out there.
I think Airbnb is such a hot topicthat there are people creating
tech tools for the communityand you don't need all of it.
You just, you don't need all of it.
As a matter of fact, I saw atool that could estimate it
estimates for you on this night.
If you're booked at, if you'repriced at say 300 a night.
(53:15):
The probability that you'regoing to get booked is 32%.
And I was like, okay, but if you'rebooked at, if you're priced at 400 night,
the probability of you getting booked.
And so it was going night to night tonightand your probability getting booked.
And it just felt likethat, that sounds cool.
And I'm a, if I see a spreadsheet,I'm in heaven, but sometimes
(53:36):
it's overdoing it a little bit.
So.
Anurag (53:41):
It's like you have to hop off in
Rachel (53:43):
a minute.
Yeah, no worries.
All right.
So Anurag, thank you.
Thank you.
Thank you.
We're going to share with thecommunity how to connect with
you and share it in the show.
And I so appreciate youspending time with us.
This was a lot of fun.
Anurag (53:56):
Yeah.
Same here.
Rachel (53:58):
All right.
Bye bye for now.
Anurag (54:00):
Thank you so much.
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