April 25, 2024 • 45 mins
In this episode of the Multifamily Innovation® Podcast, part of our Meet the Partner series, we explore the transformative impact of AI in the multifamily industry with Eugene Keplinger from LeaseHawk. With a robust background in engineering and product management, Eugene brings a rich perspective from his experiences at American Express and Invitation Homes.
Eugene and Patrick discuss the strategic use of AI to improve interactions between multifamily operators and their residents. Eugene provides insights into the importance of data in tailoring resident experiences and how LeaseHawk leverages vast amounts of call data to refine customer service strategies and drive resident satisfaction.
Eugene addresses the broader implications of AI in the industry, including the challenges of adopting new technologies and the potential for AI to revolutionize resident engagement. He emphasizes the need for a balanced approach to integrating AI, highlighting its capacity to enhance service delivery without replacing the human touch entirely.
The episode wraps up with Eugene reflecting on the future of AI in multifamily operations, focusing on the ongoing need for innovation and adaptation to meet evolving resident expectations. He underscores the significance of continuous learning and staying ahead in technology to maintain competitive advantage and improve the quality of resident interactions.
Eugene Keplinger's insights underline the critical role of AI in shaping the future of the multifamily industry, from streamlining operations to enhancing resident interactions. His forward-looking approach provides valuable lessons on leveraging technology to build more connected and responsive community environments.
About the Multifamily Innovation® Council:
The Multifamily Innovation® Council is the executive level membership organization that makes a difference in your bottom line, drives a better experience for your employees, and allows you an experience that keeps demand strong for your company. The council is uniquely positioned to focus on the intersection of Leadership, Technology, AI, and Innovation.
The Multifamily Innovation® Council is for Multifamily Business leaders who want to unlock value inside their organization so they can create better experiences and drive profitability inside their company.
To learn more or to join, visit https://multifamilyinnovation.com.
For more information and to engage with leaders shaping the future of multifamily innovation, visit https://multifamilyinnovation.com/.
Connect:
Multifamily Innovation® Council: https://multifamilyinnovation.com/
Patrick Antrim: https://www.linkedin.com/in/patrickantrim/
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
All right, welcome back.
We're here in our studio withEugene Koepplinger.
He is the head of product forLeaseHawk and very well versed
in AI and all the things thatthey're doing to build
interesting and productiveproducts for companies and
multifamily.
This is part of our Meet thePartner series.
Leasehawk has been around doingAI and innovating the
(00:23):
multifamily space for many yearsand we'll get to know Eugene
and what you're working on.
Speaker 2 (00:28):
Yeah, thanks, thanks.
I'm really excited about it,really excited about the
opportunity to talk about AI andhow it impacts multifamily
industry.
Speaker 1 (00:34):
Yeah, you know, I'll tell you at the beginning, and I
want to let those that areexecutives trying to figure out
what to do with this and how toactually bring AI into the
business, because obviouslythere's a lot of conversation
around it and there's manysoftware tools that are
(00:54):
introducing AI that maybehaven't before, and it's going
to be everywhere.
There was an event that Ithought was interesting around
AIR, which is AI is everywhere,just like AIR, but you guys have
been native in AI for many,many years.
I'd love to dive into yourbackground a little bit more and
what inspired you to come toLeaseHawk to make what's next
(01:19):
for what you guys are working on.
Speaker 2 (01:20):
Sure sure so I'm going to step in the way back
machine working on.
Sure sure so I'm going to stepin the way back machine.
I've been in the engineeringproduct management space for
quite some time and I've learnedover the years, through product
management methodologies, thatunderstanding business problems
and solving those businessproblems using technology is
(01:43):
really at the forefront of everybusiness that's out there.
And so I was lucky to getdrafted by a friend a prior
friend and colleague, larry intoLeaseHawk using the same
methodologies that I've beenusing for a long time in product
management to help solve thosebusiness problems.
Speaker 1 (02:03):
Yeah, and going back, that was even Invitation Homes,
correct.
And then, even before that, youspent some time with American
Express.
Speaker 2 (02:11):
Yeah.
So I was at Invitation Homesand then American Express, and
then Larry said let's get theteam back together, and so we
went over to LeaseHawk to keepmoving on the vision that we had
had for a long time, which ishow can we leverage technology
to solve business problems inthe multifamily and
single-family space?
Speaker 1 (02:29):
You know, I'll tell you, american Express is one of
my favorite brands just becausenot only being a member, right,
you're this cardholder memberand that whole experience.
I mean we're using a lot of thestrategies that they've used to
build a business that we use tobuild ours.
I mean you think about how theysupport business owners and
small businesses and you know wethink of them as credit cards,
(02:54):
but really they're more thanthat, right, I mean it's.
And data management.
Speaker 2 (03:08):
You know, I was at American Express for a while and
the number one focus that wehad was making sure that client
data was secure and protectedand accurate.
That was the number one focusfor every department within the
company.
Speaker 1 (03:19):
Yeah, and partnerships too, make me I
think about a lot ofpartnerships, even from venues
and concerts.
And, you know, make me I thinkabout a lot of partnerships,
even from venues and concerts.
And you know, when you look atbrands like data or companies
that respect data in that way,like Tesla, let's say,
innovative brand and easy topick on, right, but no company
is really Tesla.
We don't have like an Elon Muskin our industry.
(03:42):
Arguably you would say MikeMueller would be that right.
Speaker 2 (03:45):
Oh for sure.
He's definitely pushing theboundaries, just like Elon Musk.
Very similar tactics.
Speaker 1 (03:50):
Yeah, and so what I mean by this, though, is, if you
think about data in Tesla afterthat product is shipped, I
guess they know everythingthat's happening after the sale
as much as before the sale,right, how that product is being
used.
But I like the idea of thepartnerships that's happening
with Amex, knowing, like, whenyou look at a Tesla, they're
(04:12):
trying to increase the value ofthe vehicle instead of just the
price, right?
So most of the automakers likehow do we sell more at a higher
price, at a higher margin?
And Elon's thinking like, howdo I make the vehicle more
valuable to the customer?
And I look at Amex or AmericanExpress that way, where there's
a premium for fees on certaincards, but the value with these
(04:37):
partnerships is more valuablethan other cards in its
comparison, right.
So if we can think aboutbringing that back to
multifamily, how do you make thelease more valuable?
Right, using data and thecustomer experience and all
those things.
It just makes me think abouthow important technology
(04:57):
companies in multifamily need to, I think, bring in leaders like
yourself that have this type ofbackground and experience to
then innovate inside multifamilycompanies.
Speaker 2 (05:09):
Yeah, I think you hit on a key point, which is adding
value.
I think that every successfulcompany has grown their success
based on the value that they'rebringing to whatever community
they're serving.
And in Amex you brought up thepoint of partnerships and safety
around data and data management.
(05:32):
They're looking at the data tofind out how can they make the
product more valuable, and thatends up generating a stickier
product.
It ends up increasing customersatisfaction and then it gives
you a landing platform to reallystart innovating and creating
new products that add additionalvalue.
Speaker 1 (05:50):
You know you mentioned adding value.
I got to take me back into thedecision of joining LeaseHawk.
What did you see as a way thatthrough your background and
product and data and theseamazing organizations, you've
helped change and grow?
How do you see that decision?
Like?
(06:10):
Where did you think you wouldadd value at?
Like a company like Leesock?
Speaker 2 (06:14):
Oh, great question.
So, um, I I really learned alot about the industry at
Invitation Homes, um and and,and actually that I was over at
a company called DriveTime.
Drivetime was a major investorin Carvana before Carvana
actually took off and became aprivate company or a public
company.
And when I migrated from that,from the car sales industry,
(06:38):
into the single family industry,what I noticed was how many
different items were exactly thesame.
You're replacing the number onepurchased thing, most expensive
thing, which is a house with acar right, your number two, most
expensive thing.
Sure, you still have the amountof risk for making sure that
your prospects are the rightprospects to buy the vehicle.
(07:00):
And same thing for a house.
You're taking risk on thatconsumer.
And same thing for a houseYou're taking risk on that
consumer.
And so there was so muchoverlay between the two
industries that when I got achance to go over to Invitation
Homes, I learned that.
And then when Larry went overto LeaseHawk, I was jazzed at
taking all of the experiencesthat I learned around Carvana,
(07:21):
drivetime and Invitation Homesand applying that to the
multifamily space.
I would say that themultifamily space is probably
five to eight yearstechnologically behind the car
sales industry.
What I found interesting is whenwe were building out the
Carvana model, we had thisconcept of people can do it
automatically throughself-service.
(07:43):
And I remember thinking tomyself no way, everyone wants to
test drive a car, they want togo see the car, they want to
touch the car.
I was boldly wrong, it was.
It was.
They are 100% right that theindustry is moving towards
self-service and automation asfast as possible.
One thing we can do to reducethe amount of barriers that a
consumer has to go through togain access to their product and
(08:07):
let them conduct business atwhatever time they want to.
If they have to start work at 5am, that means they may want to
wake up a little early andstart doing some shopping for
their next apartment community.
Let them do it and don't makethem pick up the phone call and
call somebody.
Let them do it online.
Let them do it via text message.
Anything that we can do toincrease self-service and
(08:27):
automation is key for theindustry.
Speaker 1 (08:28):
Yeah, I mean you begin to think like a lot of the
jobs were nine to five jobs thecustomer, the renters, right,
and that whole global workforceis changing, I would suspect,
even more and more as time goeson and it's just not enough to
be able to.
I mean, who said our officesneeded to be open?
Between these times Like thisis, when does commerce actually
(08:49):
happen?
That was before you were ableto do.
You know things online andresearch online and reviews and
all these types of things youknow.
From that aspect, how are youtesting assumptions, like I
would imagine?
One of the things I hear insidethe innovation council is you
know a lot of the um, a lot ofthe organizations have achieved
(09:11):
so much success with the oldmodel, and I would.
I would.
I would relate to theautomotive industry, where you
have the large organizations andnational chains and you know
that are more invested in scaleand franchise model.
Then you have, like the small,you know, dealership rooftop in
Delaware and that owner has donereally, really well with the
(09:35):
old process.
You know, and it seems like thetechnology's here to solve the
business problem.
There needs to be, in manycases, a shift in behavior to
either open your mind tosomething new or test the
assumption about something.
How did you get through thetesting, that assumption of even
(09:58):
yourself, like with the carmodel, even?
Speaker 2 (10:00):
Yeah, were there others that had that feeling and
pushed back.
And what data did we need tofeel like you know what, this is
something that could work atscale, yeah, and then you
(10:21):
monitor what happens afterwardsand that's applicable to every
industry.
And so the whole Carvana modelsimilar right.
They had this idea, they rolledit out in a very small market
and they waited and saw what thedata looked like.
How many people were buyingcars, was it better or worse
(10:41):
than the original benchmark thatthey had using their standard
business problem or process?
We kind of do the same thing atLeaseHawk, where we have this
new concept, this new idea, andwe look for really innovative
companies that want to partnerwith us to apply these new
concepts and theories and let'slet it bake and let's see what
(11:02):
happens and let's compare theoutput of those experiments to
the original benchmark and andwhat we're finding is that
consumers are actually, once youopen up those floodgates,
consumers want to do businessdifferently than the nine to
five model that we originallyhad.
Um, they they may not want tojust on the phone.
(11:24):
They don't want to pick up thephone and talk to somebody
because they have no idea howlong they're going to wait on
hold.
They have no idea what's goingto be on the other side of that.
They have no idea what kind ofmood the other person's going to
be in.
And so, the more that we canallow them to do self-service on
the channel that they prefertext message, phone call, chat
(11:44):
you know those letting themconduct business on the terms
that they want to, at whatevertime that they want to we see a
lot of value add.
You know, when we look at thedata of some of our clients, we
see that people are conductingbusiness outside of the 5 pm
time.
You know the business hours of9 to 5.
(12:05):
We see people conductingbusiness via text and chat at 6
o'clock, 7 o'clock and then onthe chat channel, which is on
websites, that productivitycontinues until 12 or 1 in the
morning.
People are conducting businessat 12 or 1 in the morning, which
is way outside of the benchmarkof 9 to 5.
And you don't want to not havethose customers.
(12:28):
You want to give them theaccess to conduct business at
whatever time they want, usingwhatever tools they want, and so
we're seeing a lot of value inletting the consumer drive how
they want to conduct business.
Speaker 1 (12:41):
Yeah, it makes sense.
I mean, you mentioned data andI start to think about, you know
, the clients sometimes makedecisions based off of their own
confirmation bias or their ownbias, and in many cases it's
they.
They're not, they may not evenbe the customer right.
So when you think about um, theyou mentioned earlier around
(13:07):
forward-thinking brands andinnovative brands that want to,
you know, do this.
The reality is that's not eventhe case anymore.
Right now, interest rates arebeyond something that we can
control.
Insurance costs is out ofcontrol, rising in double digits
.
You've got utilities going up.
(13:27):
You have payroll going up,employee turnover going up,
training, retraining going up.
You have payroll going up,employee turnover going up,
training, retraining, themaintenance costs.
We have to find other paths toincome and this is not only an
assistance program where it cangive you more productivity in
your business, but also, I think, take out some of the expense
(13:47):
that we're spending on missedopportunities and if you
mentioned, sales and that typeof stuff.
But going back to the data, onecompany makes a decision, they
have their data for theircompany and their portfolio, but
you guys have it for a wide,wide net, very long years, and
(14:10):
that seems to be the compellingpiece in AI today is that you
can't catch up with that type ofdata set right 100%.
So when you walk into a meetingwith a client, you can help
them in a more diverse way,knowing that data.
Can we lean into a little bitabout what you're learning with
that data today?
Speaker 2 (14:29):
Yeah, I would say that we've got more data around
properties and consumers ofthose properties than most
companies.
You know LeaseHawk.
We've been around for quitesome time.
We've been using AI for a longtime.
We've recorded over gosh Idon't remember what the last
(14:50):
number was over 100 millionphone calls and that's a ton of
data around consumers andresidents that are calling a
property.
And so now, when we overlay AI,on top of all of this
communication that's coming tothese properties, we can really
get some interesting insightsaround.
What are prospects calling for?
What are residents calling for?
(15:12):
What's driving them to pick upthat call and call you?
What times are they calling you?
Who's answering these calls?
And so we can now give you anextra layer of insights into
your property and your consumersthat most people don't have.
I think that the most powerfuldata set that we get is consumer
(15:33):
questions, because theprospects call in and they
interact with a LeaseHawkproduct of some sort and we
analyze that data.
We're getting an unfettered dataset of what is driving their
demand, what are their buyingdecisions.
I know I've been asked by a lotof marketing people what are
(15:53):
people asking about?
How do I need to adjust mymarketing?
We all know that pricing andavailability is the number one
question that gets asked, but doyou know what's number two?
Do you know what's number three?
Do you know what's number four?
We can give you the list of allof the questions that consumers
are asking, and what's great isit's unbiased.
The consumer doesn't even knowthat they're giving us access to
(16:14):
this data just by asking thequestions that they're asking,
and so we can now helpproperties really adjust their
marketing and positioning orsolve resident-based problems,
because a lot of the calls wetake are residents as well.
They're calling in trying tofind out what the link is for
their work order forms or toschedule amenities or complaints
(16:39):
of some sort, and we can nowtake action on all that data and
do something about it andcreate new workflows, new
automations new workflows, newautomations.
Speaker 1 (16:52):
How has your experience back, even going back
to American Express and theseother brands?
How has that shaped yourmeetings and as you're designing
and working through theseconversations internally with AI
?
Speaker 2 (17:03):
Yeah, great question.
So what's funny is I'm going toactually take the AI part of
that question out of theequation, because the process
for developing new technology isthe same process.
It's been since forever.
It is let's understand thebusiness problems, the really,
really pervasive problems thatcompanies are having, and what
(17:24):
technology can we leverage tohelp solve those problems.
And so when we talk about allthese previous brands of Amex or
Invitation Homes, the processin innovation has always been
the same, which is let's go talkto our clients, let's go talk
to the users of this technologyand let's find out what they're
(17:48):
using it for.
What problems do they have?
What additional problems dothey have that have nothing to
do with our industry?
And once you really understandyour consumers and what problems
they have, that's when you canstart thinking about technical
solutions to help solve thoseproblems.
What I've learned over the pastseven, eight years is that AI
(18:12):
tends to be on the forefront ofmy thought process.
When I'm thinking abouttechnology to solve the problems
, um, but it doesn't have to bethe number one solution to every
single problem.
In fact, we know that's not thecase.
Yeah, um, but uh, but I do findmyself jumping more towards
okay, how can ai help me solvethis problem?
And then I have to work my wayback and say, um, that's not
(18:33):
really the right use case for AI, let's use a different type of
technology to help solve thoseproblems.
But that really helps shape theroadmap and and and build up
our company brand.
Speaker 1 (18:43):
Right, we want our problem to go away.
We don't want a new thing,Right, Right, it's the last
thing we need, Um, and andthat's part of there's a lot of
people that could be intimidatedby these conversations because
they could build a buildingwhich I architectural plan, the
engineers, the disciplines fromelectrical, the infrastructure,
all that stuff for something tostand strong, vertically right,
(19:23):
and you have compliance to that.
You know city inspections andthey also have a long view with
the process, so they respect theprocess.
The process when it comes totechnology, we kind of just want
it right now.
We just want it to work and isas as um, you know, consumer or
business people we want.
Speaker 2 (19:53):
We want the technology to work fast and we
want it immediately and has tobe perfect.
Um, uh, consumers of ourproducts have the same exact
opinion.
They they want access towhatever they want with the
least amount of limitations, asfast as they possibly can.
Um with you know, withouthaving to interact with people,
that self-service and automationis really a key right.
(20:18):
Or I shouldn't even sayself-service and automation,
it's really self-gratification,instant gratification, right.
How do I get what I'm trying toget at as fast as I possibly
can with the least amount ofmistakes?
I think it's just part of humannature.
Speaker 1 (20:32):
Yeah, I mean you'd think of like a delivery service
and just seeing that this is onyour way, or Amazon, it's 10
steps away.
There's little dopamine, hitsof that.
I'm moving towards the thing Ineed as a customer.
It's pretty interesting, andwhen you leave a voicemail, that
doesn't work.
Speaker 2 (21:19):
You're just wondering like will they call me back?
Because likely they won't rightup that call.
I'm not getting thatself-service that I wanted and
I'm not going to leave avoicemail because I have no idea
who's going to listen to it.
Speaker 1 (21:30):
I have no idea when I'm going to get a call back and
that goes against what I'mtrying to do, which is how do I
quickly accomplish the goal thatI'm trying to accomplish?
Yeah, it's interesting when weback up.
You know and I look here around, maybe you can catch me up,
because last time we sat downwith your team it's been a while
(21:54):
and I know things move quickly.
Tell me how you're using AI tohelp sort of centralize the
relationship with technology,and how we communicate and
engage with residents.
What's new today that you cantalk about?
Speaker 2 (22:10):
Oh yeah.
So you know, when we look atwhat LeaseHawk has been doing
over the last couple of years,at first we started on the
prospect side of things.
There was a really big problemto solve, which was people
wanted to get access to theinformation about the property
using the phone channel, andleasing agents were extremely
busy they were overly busy andthey couldn't address every
(22:33):
single phone call that wascoming in.
While helping this othercustomer go on a tour, while
responding to this residentcomplaint, while working on this
work order, they were just toobusy, and so there's a massive
problem there on the prospectside of things.
So that's where we decided toleverage AI to help answer all
of those calls.
I mean, if you think about it,these property management
(22:56):
companies are spending lots ofmoney to ILS companies like
Zillow or apartmentscom orwhatever to generate leads, and
then the leads are picking upthe phone and calling that
property, but if the propertycan't answer that call, then
that lead falls flat on thefloor, and if you just give them
a voicemail, they're probablynot going to leave a voicemail,
(23:17):
and so you're not even gettingall the leads you're paying for.
So that's where we reallywanted to focus in on the
prospect side of things.
But to go back to your originalquestion, which is how can we
take that knowledge and thattechnology and apply it to the
resident side of things?
We started rolling out residentservices as part of our offering
, which is we'll now takeresident based phone calls and,
(23:40):
using AI and using workflow,we'll let residents that just
want simple tasks accomplished.
What's the link to fill out awork order form?
What time is the gym open?
Can I schedule this amenity?
I have this leaky toilet.
These are all great examples ofhow we could take what we've
already built on the prospectside of things and apply it to
(24:01):
the resident side of things,which increases the resident
satisfaction because they're nowgaining access to the
information they need withouthaving to wait, without having
to talk to a person, withouthaving to leave a voicemail and
not know when they're not goingto get called back.
Sure, it hits that dopaminelevel, like you mentioned,
around.
I just now have access to thisdata that I've been looking for.
Speaker 1 (24:24):
Yeah, and there's more trust there in the process
because they wouldn't be aresident if they didn't go
through that right, withLeaseHawk and that process.
So, you know, I think about andwe've talked about this in our
council meetings is, you know,we're a people business and you
(24:46):
know I was speaking with NVIDIAyesterday as part of our council
stuff and he said look at AI asinstead of artificial, look at
it as like assistance, you know,and I thought that was really
compelling because a lot of whatyou're doing is you're allowing
each leasing agent or manager,whoever's involved in the sales
process to have this assistant,right, but also before the sale
(25:08):
right, paying all the money forthe ads you have the sales
assistant, but then after thesale right, but also before the
sale, right, paying all themoney for the ads, you have the
sales assistant, but then afterthe sale right.
So then it's more likely toconvert on a renewal if things
go well during this day, right.
So that's assisting the wholefinancial process there in
ultimately, what we're doing,which is renting apartments.
But there's been some pushback,even in our meeting around
(25:31):
selling and it's like ifsomeone's not there to know
where the sale fell off or theobjection is then how would you
know how to convert or follow upand that stuff?
And that's where I think aboutyour data is you guys are
listening to all these thingsonline, the questions and all
(25:54):
that stuff.
Are people using that data whenthey're building new
developments and they'rethinking about the ideal
customer?
Do you guys see?
Speaker 2 (26:02):
that already, oh for sure, a hundred percent, so that
data is actually more valuableto us than the data of success.
We have a lot of data thatshows all the successes of AI
and consumers that call in andthey interact with our AI
technology and they scheduletours and they ask their
questions and they get theinformation that they are
(26:25):
looking for.
All that does is support thetheory that AI can be used
productively by consumers, butthat doesn't help us grow any,
and so where the data reallybecomes valuable is in those
scenarios where someone doesn'tget information they're looking
for or immediately wants totransfer and talk to a human.
(26:46):
We can look at that data andfind out oh well interesting.
We didn't know that people wereasking these types of questions.
Let's go ahead and train ourmodels to go ahead and answer
these types of questions.
Or I didn't know that that wasa workflow that needed to be
built out.
The data is telling us thatthis consumer, you know, hit a
roadblock and needed help, andit was a simple workflow
(27:09):
configuration that we had to doto be able to allow them to use
AI to accomplish those things.
So it's that whole Googlemethodology of fail fast fail,
often so that you can get thedata and find out what
adjustments you need to make.
Speaker 1 (27:23):
Yeah, it's interesting Knowing what you
know and knowing what's possiblebecause you're embedded in all
this stuff.
What would you say to amultifamily owner-operator
executive that is looking to you, know, obviously protect cash
flow but also grow the businessin a way that they make all the
(27:46):
right moves right, so it's safe,conservative?
How would you be thinkingthrough all of this in terms of
that prospect journey and andlike what would you?
What would you tell them?
Speaker 2 (28:00):
yeah, um, I think there's a lot of different
metrics that can support the useof ai and automation and
self-service.
Uh, to to help support myportfolio of properties, um, if
I were the owner operator yeah,if you're, you're theoperator,
it's your building.
Speaker 1 (28:16):
Now you have this tool.
Speaker 2 (28:18):
Yeah, the things that I would be looking for are
metrics around how much do Isave by leveraging AI, how much
additional money do I make byleveraging AI?
And we've done a couple casestudies with some of our clients
and what we found is, when AIis in use, we have found that
(28:40):
lead-to-lease time decreases byroughly 15% 20% when AI is in
play, and that's for same-storedata.
So a consumer that uses AIversus a consumer that doesn't
use AI, for the same exactproperty, the consumer that uses
AI tends to convert faster thana consumer that doesn't use AI,
(29:01):
and that is immediate revenueto the bottom line.
I no longer have thosemarketing costs because that
unit is not as vacant or is notvacant for as long as it was.
I don't have the holding costsassociated.
I now have additional revenuecoming in a day, two days, three
days faster using AI, and thoseare really impactful for me as
(29:25):
a property owner.
Furthermore, when we look atwhat AI allows in that
self-service and automation, Iknow that I can grow my business
without growing my human costsat the same clip.
So if I can add anotherproperty without adding a
full-time staff for thatproperty, because I now have AI
(29:48):
to answer 50% of the calls thatweren't getting answered
originally.
That's also impactful to mybottom line.
I have less operational costs,uh to to manage my properties
and and I think both of thoseare really good metrics to to
look into if I was a propertyproperty owner people have and
also the priorities to those.
Speaker 1 (30:08):
So, as you map that out in spoken word, I'm curious
how do you take that back tomeetings and roadmap even your
own product design so that it'saligned with these?
Speaker 2 (30:26):
types of initiatives you just described.
Yeah, I mean, if I look atLeaseHawk's vision, our vision
is how can we increase the speedof leasing and increase the
consumer experience for what wecall our leasing lifecycle?
So that is not just searchingfor a property and touring a
(30:48):
property, it's all the residentside of things as well.
That's part of the leasinglifecycle, and so we're trying
to, and so we're trying toincrease that experience through
self-service and automation,and what I'm finding is that the
ramifications of increasing theconsumer's ability to gain
(31:09):
access and accomplish whateverthey're trying to accomplish
directly impacts the bottom linefrom the property owner side of
things.
So I think it's a win-win forboth sides of the equation.
The consumer's happier becausethey're able to conduct business
at whatever time that they wantto, using whatever channel they
want to, and the owner-operatoris happy because they're able
(31:31):
to lease units faster.
It's truly a win-win.
And so, when we look at ourroadmap and what LeaseHawk's
trying to do, we want to keepincreasing the amount of
workflows that we have using AI.
We want to gain more insightsinto our data so that we can
(31:51):
give that to properties to makebetter business decisions on
their side.
The whole, the whole concept ofa nine to five on Monday
through Sunday was a thing, butusing data, we've been able to
show that you may not have tohave your office open as much,
because we're now able toaddress most of those calls on,
(32:11):
you know, friday, saturday,sundays.
So you now have the ability toreduce your expenses by closing
your office earlier using AI.
And again, the consumer's happybecause they're getting access
to what they're trying toaccomplish on a Sunday without
human intervention.
Speaker 1 (32:27):
Yeah, we've talked about this even in our meetings
around.
Even the experience, theemployee experience too, like
giving them more flexibility inhow they want to show up to work
so you could shut down on aTuesday but still have fast
leasing speed to lease all thatstuff.
I love how you described that.
You know the word automation.
I think of automation and thenI think of autonomous.
(32:48):
Autonomous is like complete.
You know hands-off automationwhere people begin and end the
process.
You have this sort of turnoverto.
You know there's still thehands on the steering wheel.
I was.
Have you been on a Waymo rideyet?
Speaker 2 (33:02):
I have not.
No, I'd be all for it, thoughas a technology guy, I'd be in
no problem.
Speaker 1 (33:11):
I did a video with it .
It's pretty cool.
I hope I'll be sharing it soon,but the steering wheel's still
there, you know.
So we're in this state ofchange where a lot of executives
worked in a way that thesethings weren't possible.
So you know, we get a job, weget promoted, we tell others how
to do a job, and we make greatmoney and success and people
(33:34):
tell us we're doing great.
So there's this bias that wehave to the path of success or
yields.
And yet here we're in thistransition.
I've said this before.
We're in moments of and AI isprobably accelerating it faster
than anything else which is thecordless phone, the self-driving
(33:55):
car, right, you know, you lookat motion pictures, all these
things that the process changed,but we described it as
something that it used to be forus to just kind of have context
over it.
You know, what do you think thefuture of leasing is?
You know we don't makepredictions.
I don't expect you to makepredictions, but where do you
(34:17):
think things are going with thisprocess?
Speaker 2 (34:20):
Yeah.
So I think that you've hit on acouple of components that are
really important.
Number one is that wholesteering wheel.
When it comes to automation, Ithink that that's part of the
FUD factor the fear, uncertaintyand doubt and you know, waymo
puts that steering wheel inplace because it makes humans
feel better that they're in acar that has a steering wheel
(34:41):
versus not in a car.
It makes them trust thetechnology better.
I think that in this industry,we deal with the same kind of
FUD factor.
Now I've noticed over the lastfive years of being in AI in the
multifamily industry that thatFUD factor is reducing every
single year and people arebecoming more familiar with this
(35:05):
technology and, okay, withdeploying it and trying out new
use cases of this technology.
I think there's a lot ofsynergies between that Waymo
experience that you had andusing AI in our multifamily
industry.
Speaker 1 (35:22):
Yeah, you know, I wrote down a few things here.
Going back to my ride in Waymo,when I opened the app I thought
first I'm like, okay, I'm gonnaget into this cool technology
thing.
But when I opened the app Ithought, oh my gosh, that's the
most brilliant marketing messageI've ever seen.
And when I opened the app itsaid I summoned the car and it
(35:43):
said the most experienced driveris on the way.
And I'm thinking about all thedata and all the conversations a
hundred million, you said yeah,over a hundred million.
You said, yeah, like the mostexperienced leasing agent is is
here, right, you know, like thedata I I thought, like wait a
minute, my car is sitting outthere right now not driving.
(36:05):
This thing has been drivingmillions of miles, all it's
driven more than me.
You know what I mean.
And so we, the fear,uncertainty and doubt thing, uh
it thing.
It's more of a personalmovement of self changing
behavior that I think plays outin these experiences.
Speaker 2 (36:25):
Yeah, for sure, I think one of the things that
we've had to do with ourtechnology.
Because AI is again soimpactful and so powerful to
assist our clients in gettingover that FUD factor, we've had
to put configurations in placethat say, okay, we can do this
as much as you want.
So we've got the ability to putAI up front and our technology
(36:49):
just picks up the phone and sayshow can I help you?
Right, there's no IVRs in place, there's no, you know, press
this if you're that.
It is just someone picking upthe phone call every single time
consistently and saying how canI help you?
Now we can deploy it in thatcapacity.
(37:09):
Some of our clients are all forit.
They want to reduce theiroperating expenses as much as
possible, and that is a big wayto do.
It is putting AI up front.
In the experience, otherconsumers they still like that
white glove approach of having ahuman.
That's there, right, that'sthat steering wheel in the Waymo
, and so we also have theability to deploy our AI
(37:31):
technology as just a fallback,if the agent doesn't answer the
phone, let's go ahead and takethat.
Call back, um, and we've we'vedone this so that we can get
through all this, thesedifferent variations of fud
factor is that like the crawlwalk run kind of yeah, approach?
Speaker 1 (37:49):
yeah, that's right.
Just some more clients want tojust run.
Speaker 2 (37:52):
Someone want to run and we're all for it.
Those, those are great, greatclients of ours.
In fact, they ended up beingthe most successful clients
because they reduced their costs.
Their consumers have a greatexperience, a consistent
experience, and we get a ton ofdata out of it which helps us
train our models even furtherand grow our product offering.
Speaker 1 (38:13):
What are some challenges you're excited about
solving?
I can imagine AI is not doneright.
Speaker 2 (38:20):
No, it's in its infancy.
It really has, even thoughwe're one of the leading vendors
for AI in this space.
Even we see that it's in itsinfancy infancy.
I think the most excitingchallenge that I see coming up
is what we call crossing thechasm.
(38:41):
If you're familiar with thatbook, we've seen over the last
five years early adopters ofthis technology and people that
are willing to experiment anddeploy and test and see what
happens.
Those are what we call theearly adopters.
Now we're kind of in thiscrossing the chasm moment where
we're going to start getting aninflux of the majority of people
(39:03):
now getting comfortable usingAI technology and deploying AI
technology across theirportfolio and I think there's so
much growth that we'll gain outof it.
But also the amount of datathat we'll collect out of 80% of
properties starting to deploythis technology and seeing what
(39:25):
comes of that data.
How much more automation can weinput?
We're actually looking at usingAI now to make recommendations
so we can analyze calls and sayyour leasing agent did great at
the introduction side of thingsbut kind of fell flat on the on
their close of of these calls.
(39:45):
Here's some recommendation.
Here's some training material,that that we can offer to that,
to that agent to help them close.
So we're now letting AI makehumans better in doing their job
.
I think that's a huge area ofopportunity.
Speaker 1 (40:01):
Yeah, and I'm excited for the residents.
Honestly, with that data, Imean, you start to think about
just the way I live my life andI know people have my data
because things that are moreinteresting are coming to me
more naturally instead offriction in the process, even
from purchasing e-commerce stuff.
Right, I mean, the buyers haveall been trained on the
(40:26):
expectations of this stuff.
We've covered a lot here.
I want to make sure that Idon't miss anything.
What you know about the abilityfor you to come into an
organization, bring AI, bringtechnology and solutions to
lease faster what, what?
What did I?
What?
What did I miss?
Is there something that Ishould have asked that I didn't
(40:46):
ask?
Speaker 2 (40:47):
Um, no, I don't think so, but but I do.
I do want to leave onestatement, which is um, you know
, ai is, is, is.
Ai is great technology, butit's not the answer to
everything by itself.
It's no different than a Swissarmy knife.
It's got a lot of different usecases, but it's also limited in
its use cases.
(41:07):
So I guess my messaging wouldbe don't be afraid of AI
technology.
Also, don't try to apply AItechnology to every problem that
you have, because it's got itsuse cases where it's great and
some that it's not the mostimportant technology to use.
And the third thing I'd say is,when you're going out and
(41:30):
looking at AI and how to deployAI, talk to your vendors.
Make sure your vendors areexperienced.
They've been in the game for along time.
We've been in the game for along time, so we can tell you
all of the great use cases of AItechnology.
But also there's some areasthat that technology is not the
most impactful and we've gotother recommendations for it.
(41:52):
So I think leveraging thatconsultative approach with AI
experienced vendors is key tothe industry.
Speaker 1 (42:03):
Yeah, and we're going to bring you guys into the
council and help our membersunderstand more of these things.
Because you know, like again Igo back to the analogy of
building apartments, becauseuntil you, you know, we have
these white walls in our studiohere.
I always call them like themuseum walls, like if you put
art on it, you know that's itincreases the value of the thing
(42:25):
, right.
But to me, building apartments,I can see through the, I can
almost see through the drywall,right, because I know what's
behind it.
You know, because I know what'sbehind it.
You know, and I think asexecutives listen to you know
people like yourself and otherexperts that have actually built
things then this isn'tsomething to just like consume.
(42:48):
You have to play with it, youhave to see it used.
It's like when I went in theWaymo, like I experienced it,
and I just encourage anybody ifthey're in that what did you
call it?
Fud, fear, uncertainty anddoubt.
State the way through.
That is jumping on a call,getting in touch with someone
like yourself and tryingsomething like using it, seeing
(43:11):
the result, reflecting on thedata, and that's why we're
bringing in, you know, someonethat's leading product.
You know you're not here tosell.
You're here to help usunderstand this stuff so we can
create the value.
I like also the way that yourvision is.
It's not like you're here totalk about AI.
We're talking about speed tolease, we're talking about those
efficiency programs, theprofitability of that company,
(43:34):
and that can be exciting.
So I appreciate your time.
You know, I know that this isprobably more of a series and,
again, you know the stuff thatwe'll do inside the council will
be useful in answering some ofthe questions.
Or, if you have a question, ifyou're listening or you're
watching, send us a message andwe'll get it to Eugene and we'll
(43:55):
talk about what's next for youand your company.
But thanks for coming on.
Any final thoughts you want toleave our viewers with?
Speaker 2 (44:02):
No, no.
I'm very grateful to be in thistechnology space, in this
industry that has so manydifferent use cases for
technology, and it's just agreat experience for myself.
I love watching the marketchange and I really look forward
to where we go over the next 10years.
Speaker 1 (44:21):
That's awesome.
It's great.
I love what you said about thedue diligence when that change
is coming.
Stick with those that have beenthrough it.
Speaker 2 (44:29):
Yeah, for sure.
Well, thanks so much for havingme.
Speaker 1 (44:30):
Yeah, great to have you on.
We'll see you in the next one.
All right, welcome back.
We're here in our studio withEugene Koepplinger.
He is the head of product forLeaseHawk and very well versed
in AI and all the things thatthey're doing to build
interesting and productiveproducts for companies and
multifamily.
This is part of our Meet thePartner series.
Leasehawk has been around doingAI and innovating the
(00:23):
multifamily space for many yearsand we'll get to know Eugene
and what you're working on.
Speaker 2 (00:28):
Yeah, thanks, thanks.
I'm really excited about it,really excited about the
opportunity to talk about AI andhow it impacts multifamily
industry.
Speaker 1 (00:34):
Yeah, you know, I'll tell you at the beginning, and I
want to let those that areexecutives trying to figure out
what to do with this and how toactually bring AI into the
business, because obviouslythere's a lot of conversation
around it and there's manysoftware tools that are
(00:54):
introducing AI that maybehaven't before, and it's going
to be everywhere.
There was an event that Ithought was interesting around
AIR, which is AI is everywhere,just like AIR, but you guys have
been native in AI for many,many years.
I'd love to dive into yourbackground a little bit more and
what inspired you to come toLeaseHawk to make what's next
(01:19):
for what you guys are working on.
Speaker 2 (01:20):
Sure sure so I'm going to step in the way back
machine working on.
Sure sure so I'm going to stepin the way back machine.
I've been in the engineeringproduct management space for
quite some time and I've learnedover the years, through product
management methodologies, thatunderstanding business problems
and solving those businessproblems using technology is
(01:43):
really at the forefront of everybusiness that's out there.
And so I was lucky to getdrafted by a friend a prior
friend and colleague, larry intoLeaseHawk using the same
methodologies that I've beenusing for a long time in product
management to help solve thosebusiness problems.
Speaker 1 (02:03):
Yeah, and going back, that was even Invitation Homes,
correct.
And then, even before that, youspent some time with American
Express.
Speaker 2 (02:11):
Yeah.
So I was at Invitation Homesand then American Express, and
then Larry said let's get theteam back together, and so we
went over to LeaseHawk to keepmoving on the vision that we had
had for a long time, which ishow can we leverage technology
to solve business problems inthe multifamily and
single-family space?
Speaker 1 (02:29):
You know, I'll tell you, american Express is one of
my favorite brands just becausenot only being a member, right,
you're this cardholder memberand that whole experience.
I mean we're using a lot of thestrategies that they've used to
build a business that we use tobuild ours.
I mean you think about how theysupport business owners and
small businesses and you know wethink of them as credit cards,
(02:54):
but really they're more thanthat, right, I mean it's.
And data management.
Speaker 2 (03:08):
You know, I was at American Express for a while and
the number one focus that wehad was making sure that client
data was secure and protectedand accurate.
That was the number one focusfor every department within the
company.
Speaker 1 (03:19):
Yeah, and partnerships too, make me I
think about a lot ofpartnerships, even from venues
and concerts.
And, you know, make me I thinkabout a lot of partnerships,
even from venues and concerts.
And you know, when you look atbrands like data or companies
that respect data in that way,like Tesla, let's say,
innovative brand and easy topick on, right, but no company
is really Tesla.
We don't have like an Elon Muskin our industry.
(03:42):
Arguably you would say MikeMueller would be that right.
Speaker 2 (03:45):
Oh for sure.
He's definitely pushing theboundaries, just like Elon Musk.
Very similar tactics.
Speaker 1 (03:50):
Yeah, and so what I mean by this, though, is, if you
think about data in Tesla afterthat product is shipped, I
guess they know everythingthat's happening after the sale
as much as before the sale,right, how that product is being
used.
But I like the idea of thepartnerships that's happening
with Amex, knowing, like, whenyou look at a Tesla, they're
(04:12):
trying to increase the value ofthe vehicle instead of just the
price, right?
So most of the automakers likehow do we sell more at a higher
price, at a higher margin?
And Elon's thinking like, howdo I make the vehicle more
valuable to the customer?
And I look at Amex or AmericanExpress that way, where there's
a premium for fees on certaincards, but the value with these
(04:37):
partnerships is more valuablethan other cards in its
comparison, right.
So if we can think aboutbringing that back to
multifamily, how do you make thelease more valuable?
Right, using data and thecustomer experience and all
those things.
It just makes me think abouthow important technology
(04:57):
companies in multifamily need to, I think, bring in leaders like
yourself that have this type ofbackground and experience to
then innovate inside multifamilycompanies.
Speaker 2 (05:09):
Yeah, I think you hit on a key point, which is adding
value.
I think that every successfulcompany has grown their success
based on the value that they'rebringing to whatever community
they're serving.
And in Amex you brought up thepoint of partnerships and safety
around data and data management.
(05:32):
They're looking at the data tofind out how can they make the
product more valuable, and thatends up generating a stickier
product.
It ends up increasing customersatisfaction and then it gives
you a landing platform to reallystart innovating and creating
new products that add additionalvalue.
Speaker 1 (05:50):
You know you mentioned adding value.
I got to take me back into thedecision of joining LeaseHawk.
What did you see as a way thatthrough your background and
product and data and theseamazing organizations, you've
helped change and grow?
How do you see that decision?
Like?
(06:10):
Where did you think you wouldadd value at?
Like a company like Leesock?
Speaker 2 (06:14):
Oh, great question.
So, um, I I really learned alot about the industry at
Invitation Homes, um and and,and actually that I was over at
a company called DriveTime.
Drivetime was a major investorin Carvana before Carvana
actually took off and became aprivate company or a public
company.
And when I migrated from that,from the car sales industry,
(06:38):
into the single family industry,what I noticed was how many
different items were exactly thesame.
You're replacing the number onepurchased thing, most expensive
thing, which is a house with acar right, your number two, most
expensive thing.
Sure, you still have the amountof risk for making sure that
your prospects are the rightprospects to buy the vehicle.
(07:00):
And same thing for a house.
You're taking risk on thatconsumer.
And same thing for a houseYou're taking risk on that
consumer.
And so there was so muchoverlay between the two
industries that when I got achance to go over to Invitation
Homes, I learned that.
And then when Larry went overto LeaseHawk, I was jazzed at
taking all of the experiencesthat I learned around Carvana,
(07:21):
drivetime and Invitation Homesand applying that to the
multifamily space.
I would say that themultifamily space is probably
five to eight yearstechnologically behind the car
sales industry.
What I found interesting is whenwe were building out the
Carvana model, we had thisconcept of people can do it
automatically throughself-service.
(07:43):
And I remember thinking tomyself no way, everyone wants to
test drive a car, they want togo see the car, they want to
touch the car.
I was boldly wrong, it was.
It was.
They are 100% right that theindustry is moving towards
self-service and automation asfast as possible.
One thing we can do to reducethe amount of barriers that a
consumer has to go through togain access to their product and
(08:07):
let them conduct business atwhatever time they want to.
If they have to start work at 5am, that means they may want to
wake up a little early andstart doing some shopping for
their next apartment community.
Let them do it and don't makethem pick up the phone call and
call somebody.
Let them do it online.
Let them do it via text message.
Anything that we can do toincrease self-service and
(08:27):
automation is key for theindustry.
Speaker 1 (08:28):
Yeah, I mean you begin to think like a lot of the
jobs were nine to five jobs thecustomer, the renters, right,
and that whole global workforceis changing, I would suspect,
even more and more as time goeson and it's just not enough to
be able to.
I mean, who said our officesneeded to be open?
Between these times Like thisis, when does commerce actually
(08:49):
happen?
That was before you were ableto do.
You know things online andresearch online and reviews and
all these types of things youknow.
From that aspect, how are youtesting assumptions, like I
would imagine?
One of the things I hear insidethe innovation council is you
know a lot of the um, a lot ofthe organizations have achieved
(09:11):
so much success with the oldmodel, and I would.
I would.
I would relate to theautomotive industry, where you
have the large organizations andnational chains and you know
that are more invested in scaleand franchise model.
Then you have, like the small,you know, dealership rooftop in
Delaware and that owner has donereally, really well with the
(09:35):
old process.
You know, and it seems like thetechnology's here to solve the
business problem.
There needs to be, in manycases, a shift in behavior to
either open your mind tosomething new or test the
assumption about something.
How did you get through thetesting, that assumption of even
(09:58):
yourself, like with the carmodel, even?
Speaker 2 (10:00):
Yeah, were there others that had that feeling and
pushed back.
And what data did we need tofeel like you know what, this is
something that could work atscale, yeah, and then you
(10:21):
monitor what happens afterwardsand that's applicable to every
industry.
And so the whole Carvana modelsimilar right.
They had this idea, they rolledit out in a very small market
and they waited and saw what thedata looked like.
How many people were buyingcars, was it better or worse
(10:41):
than the original benchmark thatthey had using their standard
business problem or process?
We kind of do the same thing atLeaseHawk, where we have this
new concept, this new idea, andwe look for really innovative
companies that want to partnerwith us to apply these new
concepts and theories and let'slet it bake and let's see what
(11:02):
happens and let's compare theoutput of those experiments to
the original benchmark and andwhat we're finding is that
consumers are actually, once youopen up those floodgates,
consumers want to do businessdifferently than the nine to
five model that we originallyhad.
Um, they they may not want tojust on the phone.
(11:24):
They don't want to pick up thephone and talk to somebody
because they have no idea howlong they're going to wait on
hold.
They have no idea what's goingto be on the other side of that.
They have no idea what kind ofmood the other person's going to
be in.
And so, the more that we canallow them to do self-service on
the channel that they prefertext message, phone call, chat
(11:44):
you know those letting themconduct business on the terms
that they want to, at whatevertime that they want to we see a
lot of value add.
You know, when we look at thedata of some of our clients, we
see that people are conductingbusiness outside of the 5 pm
time.
You know the business hours of9 to 5.
(12:05):
We see people conductingbusiness via text and chat at 6
o'clock, 7 o'clock and then onthe chat channel, which is on
websites, that productivitycontinues until 12 or 1 in the
morning.
People are conducting businessat 12 or 1 in the morning, which
is way outside of the benchmarkof 9 to 5.
And you don't want to not havethose customers.
(12:28):
You want to give them theaccess to conduct business at
whatever time they want, usingwhatever tools they want, and so
we're seeing a lot of value inletting the consumer drive how
they want to conduct business.
Speaker 1 (12:41):
Yeah, it makes sense.
I mean, you mentioned data andI start to think about, you know
, the clients sometimes makedecisions based off of their own
confirmation bias or their ownbias, and in many cases it's
they.
They're not, they may not evenbe the customer right.
So when you think about um, theyou mentioned earlier around
(13:07):
forward-thinking brands andinnovative brands that want to,
you know, do this.
The reality is that's not eventhe case anymore.
Right now, interest rates arebeyond something that we can
control.
Insurance costs is out ofcontrol, rising in double digits
.
You've got utilities going up.
(13:27):
You have payroll going up,employee turnover going up,
training, retraining going up.
You have payroll going up,employee turnover going up,
training, retraining, themaintenance costs.
We have to find other paths toincome and this is not only an
assistance program where it cangive you more productivity in
your business, but also, I think, take out some of the expense
(13:47):
that we're spending on missedopportunities and if you
mentioned, sales and that typeof stuff.
But going back to the data, onecompany makes a decision, they
have their data for theircompany and their portfolio, but
you guys have it for a wide,wide net, very long years, and
(14:10):
that seems to be the compellingpiece in AI today is that you
can't catch up with that type ofdata set right 100%.
So when you walk into a meetingwith a client, you can help
them in a more diverse way,knowing that data.
Can we lean into a little bitabout what you're learning with
that data today?
Speaker 2 (14:29):
Yeah, I would say that we've got more data around
properties and consumers ofthose properties than most
companies.
You know LeaseHawk.
We've been around for quitesome time.
We've been using AI for a longtime.
We've recorded over gosh Idon't remember what the last
(14:50):
number was over 100 millionphone calls and that's a ton of
data around consumers andresidents that are calling a
property.
And so now, when we overlay AI,on top of all of this
communication that's coming tothese properties, we can really
get some interesting insightsaround.
What are prospects calling for?
What are residents calling for?
(15:12):
What's driving them to pick upthat call and call you?
What times are they calling you?
Who's answering these calls?
And so we can now give you anextra layer of insights into
your property and your consumersthat most people don't have.
I think that the most powerfuldata set that we get is consumer
(15:33):
questions, because theprospects call in and they
interact with a LeaseHawkproduct of some sort and we
analyze that data.
We're getting an unfettered dataset of what is driving their
demand, what are their buyingdecisions.
I know I've been asked by a lotof marketing people what are
(15:53):
people asking about?
How do I need to adjust mymarketing?
We all know that pricing andavailability is the number one
question that gets asked, but doyou know what's number two?
Do you know what's number three?
Do you know what's number four?
We can give you the list of allof the questions that consumers
are asking, and what's great isit's unbiased.
The consumer doesn't even knowthat they're giving us access to
(16:14):
this data just by asking thequestions that they're asking,
and so we can now helpproperties really adjust their
marketing and positioning orsolve resident-based problems,
because a lot of the calls wetake are residents as well.
They're calling in trying tofind out what the link is for
their work order forms or toschedule amenities or complaints
(16:39):
of some sort, and we can nowtake action on all that data and
do something about it andcreate new workflows, new
automations new workflows, newautomations.
Speaker 1 (16:52):
How has your experience back, even going back
to American Express and theseother brands?
How has that shaped yourmeetings and as you're designing
and working through theseconversations internally with AI
?
Speaker 2 (17:03):
Yeah, great question.
So what's funny is I'm going toactually take the AI part of
that question out of theequation, because the process
for developing new technology isthe same process.
It's been since forever.
It is let's understand thebusiness problems, the really,
really pervasive problems thatcompanies are having, and what
(17:24):
technology can we leverage tohelp solve those problems.
And so when we talk about allthese previous brands of Amex or
Invitation Homes, the processin innovation has always been
the same, which is let's go talkto our clients, let's go talk
to the users of this technologyand let's find out what they're
(17:48):
using it for.
What problems do they have?
What additional problems dothey have that have nothing to
do with our industry?
And once you really understandyour consumers and what problems
they have, that's when you canstart thinking about technical
solutions to help solve thoseproblems.
What I've learned over the pastseven, eight years is that AI
(18:12):
tends to be on the forefront ofmy thought process.
When I'm thinking abouttechnology to solve the problems
, um, but it doesn't have to bethe number one solution to every
single problem.
In fact, we know that's not thecase.
Yeah, um, but uh, but I do findmyself jumping more towards
okay, how can ai help me solvethis problem?
And then I have to work my wayback and say, um, that's not
(18:33):
really the right use case for AI, let's use a different type of
technology to help solve thoseproblems.
But that really helps shape theroadmap and and and build up
our company brand.
Speaker 1 (18:43):
Right, we want our problem to go away.
We don't want a new thing,Right, Right, it's the last
thing we need, Um, and andthat's part of there's a lot of
people that could be intimidatedby these conversations because
they could build a buildingwhich I architectural plan, the
engineers, the disciplines fromelectrical, the infrastructure,
all that stuff for something tostand strong, vertically right,
(19:23):
and you have compliance to that.
You know city inspections andthey also have a long view with
the process, so they respect theprocess.
The process when it comes totechnology, we kind of just want
it right now.
We just want it to work and isas as um, you know, consumer or
business people we want.
Speaker 2 (19:53):
We want the technology to work fast and we
want it immediately and has tobe perfect.
Um, uh, consumers of ourproducts have the same exact
opinion.
They they want access towhatever they want with the
least amount of limitations, asfast as they possibly can.
Um with you know, withouthaving to interact with people,
that self-service and automationis really a key right.
(20:18):
Or I shouldn't even sayself-service and automation,
it's really self-gratification,instant gratification, right.
How do I get what I'm trying toget at as fast as I possibly
can with the least amount ofmistakes?
I think it's just part of humannature.
Speaker 1 (20:32):
Yeah, I mean you'd think of like a delivery service
and just seeing that this is onyour way, or Amazon, it's 10
steps away.
There's little dopamine, hitsof that.
I'm moving towards the thing Ineed as a customer.
It's pretty interesting, andwhen you leave a voicemail, that
doesn't work.
Speaker 2 (21:19):
You're just wondering like will they call me back?
Because likely they won't rightup that call.
I'm not getting thatself-service that I wanted and
I'm not going to leave avoicemail because I have no idea
who's going to listen to it.
Speaker 1 (21:30):
I have no idea when I'm going to get a call back and
that goes against what I'mtrying to do, which is how do I
quickly accomplish the goal thatI'm trying to accomplish?
Yeah, it's interesting when weback up.
You know and I look here around, maybe you can catch me up,
because last time we sat downwith your team it's been a while
(21:54):
and I know things move quickly.
Tell me how you're using AI tohelp sort of centralize the
relationship with technology,and how we communicate and
engage with residents.
What's new today that you cantalk about?
Speaker 2 (22:10):
Oh yeah.
So you know, when we look atwhat LeaseHawk has been doing
over the last couple of years,at first we started on the
prospect side of things.
There was a really big problemto solve, which was people
wanted to get access to theinformation about the property
using the phone channel, andleasing agents were extremely
busy they were overly busy andthey couldn't address every
(22:33):
single phone call that wascoming in.
While helping this othercustomer go on a tour, while
responding to this residentcomplaint, while working on this
work order, they were just toobusy, and so there's a massive
problem there on the prospectside of things.
So that's where we decided toleverage AI to help answer all
of those calls.
I mean, if you think about it,these property management
(22:56):
companies are spending lots ofmoney to ILS companies like
Zillow or apartmentscom orwhatever to generate leads, and
then the leads are picking upthe phone and calling that
property, but if the propertycan't answer that call, then
that lead falls flat on thefloor, and if you just give them
a voicemail, they're probablynot going to leave a voicemail,
(23:17):
and so you're not even gettingall the leads you're paying for.
So that's where we reallywanted to focus in on the
prospect side of things.
But to go back to your originalquestion, which is how can we
take that knowledge and thattechnology and apply it to the
resident side of things?
We started rolling out residentservices as part of our offering
, which is we'll now takeresident based phone calls and,
(23:40):
using AI and using workflow,we'll let residents that just
want simple tasks accomplished.
What's the link to fill out awork order form?
What time is the gym open?
Can I schedule this amenity?
I have this leaky toilet.
These are all great examples ofhow we could take what we've
already built on the prospectside of things and apply it to
(24:01):
the resident side of things,which increases the resident
satisfaction because they're nowgaining access to the
information they need withouthaving to wait, without having
to talk to a person, withouthaving to leave a voicemail and
not know when they're not goingto get called back.
Sure, it hits that dopaminelevel, like you mentioned,
around.
I just now have access to thisdata that I've been looking for.
Speaker 1 (24:24):
Yeah, and there's more trust there in the process
because they wouldn't be aresident if they didn't go
through that right, withLeaseHawk and that process.
So, you know, I think about andwe've talked about this in our
council meetings is, you know,we're a people business and you
(24:46):
know I was speaking with NVIDIAyesterday as part of our council
stuff and he said look at AI asinstead of artificial, look at
it as like assistance, you know,and I thought that was really
compelling because a lot of whatyou're doing is you're allowing
each leasing agent or manager,whoever's involved in the sales
process to have this assistant,right, but also before the sale
(25:08):
right, paying all the money forthe ads you have the sales
assistant, but then after thesale right, but also before the
sale, right, paying all themoney for the ads, you have the
sales assistant, but then afterthe sale right.
So then it's more likely toconvert on a renewal if things
go well during this day, right.
So that's assisting the wholefinancial process there in
ultimately, what we're doing,which is renting apartments.
But there's been some pushback,even in our meeting around
(25:31):
selling and it's like ifsomeone's not there to know
where the sale fell off or theobjection is then how would you
know how to convert or follow upand that stuff?
And that's where I think aboutyour data is you guys are
listening to all these thingsonline, the questions and all
(25:54):
that stuff.
Are people using that data whenthey're building new
developments and they'rethinking about the ideal
customer?
Do you guys see?
Speaker 2 (26:02):
that already, oh for sure, a hundred percent, so that
data is actually more valuableto us than the data of success.
We have a lot of data thatshows all the successes of AI
and consumers that call in andthey interact with our AI
technology and they scheduletours and they ask their
questions and they get theinformation that they are
(26:25):
looking for.
All that does is support thetheory that AI can be used
productively by consumers, butthat doesn't help us grow any,
and so where the data reallybecomes valuable is in those
scenarios where someone doesn'tget information they're looking
for or immediately wants totransfer and talk to a human.
(26:46):
We can look at that data andfind out oh well interesting.
We didn't know that people wereasking these types of questions.
Let's go ahead and train ourmodels to go ahead and answer
these types of questions.
Or I didn't know that that wasa workflow that needed to be
built out.
The data is telling us thatthis consumer, you know, hit a
roadblock and needed help, andit was a simple workflow
(27:09):
configuration that we had to doto be able to allow them to use
AI to accomplish those things.
So it's that whole Googlemethodology of fail fast fail,
often so that you can get thedata and find out what
adjustments you need to make.
Speaker 1 (27:23):
Yeah, it's interesting Knowing what you
know and knowing what's possiblebecause you're embedded in all
this stuff.
What would you say to amultifamily owner-operator
executive that is looking to you, know, obviously protect cash
flow but also grow the businessin a way that they make all the
(27:46):
right moves right, so it's safe,conservative?
How would you be thinkingthrough all of this in terms of
that prospect journey and andlike what would you?
What would you tell them?
Speaker 2 (28:00):
yeah, um, I think there's a lot of different
metrics that can support the useof ai and automation and
self-service.
Uh, to to help support myportfolio of properties, um, if
I were the owner operator yeah,if you're, you're theoperator,
it's your building.
Speaker 1 (28:16):
Now you have this tool.
Speaker 2 (28:18):
Yeah, the things that I would be looking for are
metrics around how much do Isave by leveraging AI, how much
additional money do I make byleveraging AI?
And we've done a couple casestudies with some of our clients
and what we found is, when AIis in use, we have found that
(28:40):
lead-to-lease time decreases byroughly 15% 20% when AI is in
play, and that's for same-storedata.
So a consumer that uses AIversus a consumer that doesn't
use AI, for the same exactproperty, the consumer that uses
AI tends to convert faster thana consumer that doesn't use AI,
(29:01):
and that is immediate revenueto the bottom line.
I no longer have thosemarketing costs because that
unit is not as vacant or is notvacant for as long as it was.
I don't have the holding costsassociated.
I now have additional revenuecoming in a day, two days, three
days faster using AI, and thoseare really impactful for me as
(29:25):
a property owner.
Furthermore, when we look atwhat AI allows in that
self-service and automation, Iknow that I can grow my business
without growing my human costsat the same clip.
So if I can add anotherproperty without adding a
full-time staff for thatproperty, because I now have AI
(29:48):
to answer 50% of the calls thatweren't getting answered
originally.
That's also impactful to mybottom line.
I have less operational costs,uh to to manage my properties
and and I think both of thoseare really good metrics to to
look into if I was a propertyproperty owner people have and
also the priorities to those.
Speaker 1 (30:08):
So, as you map that out in spoken word, I'm curious
how do you take that back tomeetings and roadmap even your
own product design so that it'saligned with these?
Speaker 2 (30:26):
types of initiatives you just described.
Yeah, I mean, if I look atLeaseHawk's vision, our vision
is how can we increase the speedof leasing and increase the
consumer experience for what wecall our leasing lifecycle?
So that is not just searchingfor a property and touring a
(30:48):
property, it's all the residentside of things as well.
That's part of the leasinglifecycle, and so we're trying
to, and so we're trying toincrease that experience through
self-service and automation,and what I'm finding is that the
ramifications of increasing theconsumer's ability to gain
(31:09):
access and accomplish whateverthey're trying to accomplish
directly impacts the bottom linefrom the property owner side of
things.
So I think it's a win-win forboth sides of the equation.
The consumer's happier becausethey're able to conduct business
at whatever time that they wantto, using whatever channel they
want to, and the owner-operatoris happy because they're able
(31:31):
to lease units faster.
It's truly a win-win.
And so, when we look at ourroadmap and what LeaseHawk's
trying to do, we want to keepincreasing the amount of
workflows that we have using AI.
We want to gain more insightsinto our data so that we can
(31:51):
give that to properties to makebetter business decisions on
their side.
The whole, the whole concept ofa nine to five on Monday
through Sunday was a thing, butusing data, we've been able to
show that you may not have tohave your office open as much,
because we're now able toaddress most of those calls on,
(32:11):
you know, friday, saturday,sundays.
So you now have the ability toreduce your expenses by closing
your office earlier using AI.
And again, the consumer's happybecause they're getting access
to what they're trying toaccomplish on a Sunday without
human intervention.
Speaker 1 (32:27):
Yeah, we've talked about this even in our meetings
around.
Even the experience, theemployee experience too, like
giving them more flexibility inhow they want to show up to work
so you could shut down on aTuesday but still have fast
leasing speed to lease all thatstuff.
I love how you described that.
You know the word automation.
I think of automation and thenI think of autonomous.
(32:48):
Autonomous is like complete.
You know hands-off automationwhere people begin and end the
process.
You have this sort of turnoverto.
You know there's still thehands on the steering wheel.
I was.
Have you been on a Waymo rideyet?
Speaker 2 (33:02):
I have not.
No, I'd be all for it, thoughas a technology guy, I'd be in
no problem.
Speaker 1 (33:11):
I did a video with it .
It's pretty cool.
I hope I'll be sharing it soon,but the steering wheel's still
there, you know.
So we're in this state ofchange where a lot of executives
worked in a way that thesethings weren't possible.
So you know, we get a job, weget promoted, we tell others how
to do a job, and we make greatmoney and success and people
(33:34):
tell us we're doing great.
So there's this bias that wehave to the path of success or
yields.
And yet here we're in thistransition.
I've said this before.
We're in moments of and AI isprobably accelerating it faster
than anything else which is thecordless phone, the self-driving
(33:55):
car, right, you know, you lookat motion pictures, all these
things that the process changed,but we described it as
something that it used to be forus to just kind of have context
over it.
You know, what do you think thefuture of leasing is?
You know we don't makepredictions.
I don't expect you to makepredictions, but where do you
(34:17):
think things are going with thisprocess?
Speaker 2 (34:20):
Yeah.
So I think that you've hit on acouple of components that are
really important.
Number one is that wholesteering wheel.
When it comes to automation, Ithink that that's part of the
FUD factor the fear, uncertaintyand doubt and you know, waymo
puts that steering wheel inplace because it makes humans
feel better that they're in acar that has a steering wheel
(34:41):
versus not in a car.
It makes them trust thetechnology better.
I think that in this industry,we deal with the same kind of
FUD factor.
Now I've noticed over the lastfive years of being in AI in the
multifamily industry that thatFUD factor is reducing every
single year and people arebecoming more familiar with this
(35:05):
technology and, okay, withdeploying it and trying out new
use cases of this technology.
I think there's a lot ofsynergies between that Waymo
experience that you had andusing AI in our multifamily
industry.
Speaker 1 (35:22):
Yeah, you know, I wrote down a few things here.
Going back to my ride in Waymo,when I opened the app I thought
first I'm like, okay, I'm gonnaget into this cool technology
thing.
But when I opened the app Ithought, oh my gosh, that's the
most brilliant marketing messageI've ever seen.
And when I opened the app itsaid I summoned the car and it
(35:43):
said the most experienced driveris on the way.
And I'm thinking about all thedata and all the conversations a
hundred million, you said yeah,over a hundred million.
You said, yeah, like the mostexperienced leasing agent is is
here, right, you know, like thedata I I thought, like wait a
minute, my car is sitting outthere right now not driving.
(36:05):
This thing has been drivingmillions of miles, all it's
driven more than me.
You know what I mean.
And so we, the fear,uncertainty and doubt thing, uh
it thing.
It's more of a personalmovement of self changing
behavior that I think plays outin these experiences.
Speaker 2 (36:25):
Yeah, for sure, I think one of the things that
we've had to do with ourtechnology.
Because AI is again soimpactful and so powerful to
assist our clients in gettingover that FUD factor, we've had
to put configurations in placethat say, okay, we can do this
as much as you want.
So we've got the ability to putAI up front and our technology
(36:49):
just picks up the phone and sayshow can I help you?
Right, there's no IVRs in place, there's no, you know, press
this if you're that.
It is just someone picking upthe phone call every single time
consistently and saying how canI help you?
Now we can deploy it in thatcapacity.
(37:09):
Some of our clients are all forit.
They want to reduce theiroperating expenses as much as
possible, and that is a big wayto do.
It is putting AI up front.
In the experience, otherconsumers they still like that
white glove approach of having ahuman.
That's there, right, that'sthat steering wheel in the Waymo
, and so we also have theability to deploy our AI
(37:31):
technology as just a fallback,if the agent doesn't answer the
phone, let's go ahead and takethat.
Call back, um, and we've we'vedone this so that we can get
through all this, thesedifferent variations of fud
factor is that like the crawlwalk run kind of yeah, approach?
Speaker 1 (37:49):
yeah, that's right.
Just some more clients want tojust run.
Speaker 2 (37:52):
Someone want to run and we're all for it.
Those, those are great, greatclients of ours.
In fact, they ended up beingthe most successful clients
because they reduced their costs.
Their consumers have a greatexperience, a consistent
experience, and we get a ton ofdata out of it which helps us
train our models even furtherand grow our product offering.
Speaker 1 (38:13):
What are some challenges you're excited about
solving?
I can imagine AI is not doneright.
Speaker 2 (38:20):
No, it's in its infancy.
It really has, even thoughwe're one of the leading vendors
for AI in this space.
Even we see that it's in itsinfancy infancy.
I think the most excitingchallenge that I see coming up
is what we call crossing thechasm.
(38:41):
If you're familiar with thatbook, we've seen over the last
five years early adopters ofthis technology and people that
are willing to experiment anddeploy and test and see what
happens.
Those are what we call theearly adopters.
Now we're kind of in thiscrossing the chasm moment where
we're going to start getting aninflux of the majority of people
(39:03):
now getting comfortable usingAI technology and deploying AI
technology across theirportfolio and I think there's so
much growth that we'll gain outof it.
But also the amount of datathat we'll collect out of 80% of
properties starting to deploythis technology and seeing what
(39:25):
comes of that data.
How much more automation can weinput?
We're actually looking at usingAI now to make recommendations
so we can analyze calls and sayyour leasing agent did great at
the introduction side of thingsbut kind of fell flat on the on
their close of of these calls.
(39:45):
Here's some recommendation.
Here's some training material,that that we can offer to that,
to that agent to help them close.
So we're now letting AI makehumans better in doing their job
.
I think that's a huge area ofopportunity.
Speaker 1 (40:01):
Yeah, and I'm excited for the residents.
Honestly, with that data, Imean, you start to think about
just the way I live my life andI know people have my data
because things that are moreinteresting are coming to me
more naturally instead offriction in the process, even
from purchasing e-commerce stuff.
Right, I mean, the buyers haveall been trained on the
(40:26):
expectations of this stuff.
We've covered a lot here.
I want to make sure that Idon't miss anything.
What you know about the abilityfor you to come into an
organization, bring AI, bringtechnology and solutions to
lease faster what, what?
What did I?
What?
What did I miss?
Is there something that Ishould have asked that I didn't
(40:46):
ask?
Speaker 2 (40:47):
Um, no, I don't think so, but but I do.
I do want to leave onestatement, which is um, you know
, ai is, is, is.
Ai is great technology, butit's not the answer to
everything by itself.
It's no different than a Swissarmy knife.
It's got a lot of different usecases, but it's also limited in
its use cases.
(41:07):
So I guess my messaging wouldbe don't be afraid of AI
technology.
Also, don't try to apply AItechnology to every problem that
you have, because it's got itsuse cases where it's great and
some that it's not the mostimportant technology to use.
And the third thing I'd say is,when you're going out and
(41:30):
looking at AI and how to deployAI, talk to your vendors.
Make sure your vendors areexperienced.
They've been in the game for along time.
We've been in the game for along time, so we can tell you
all of the great use cases of AItechnology.
But also there's some areasthat that technology is not the
most impactful and we've gotother recommendations for it.
(41:52):
So I think leveraging thatconsultative approach with AI
experienced vendors is key tothe industry.
Speaker 1 (42:03):
Yeah, and we're going to bring you guys into the
council and help our membersunderstand more of these things.
Because you know, like again Igo back to the analogy of
building apartments, becauseuntil you, you know, we have
these white walls in our studiohere.
I always call them like themuseum walls, like if you put
art on it, you know that's itincreases the value of the thing
(42:25):
, right.
But to me, building apartments,I can see through the, I can
almost see through the drywall,right, because I know what's
behind it.
You know, because I know what'sbehind it.
You know, and I think asexecutives listen to you know
people like yourself and otherexperts that have actually built
things then this isn'tsomething to just like consume.
(42:48):
You have to play with it, youhave to see it used.
It's like when I went in theWaymo, like I experienced it,
and I just encourage anybody ifthey're in that what did you
call it?
Fud, fear, uncertainty anddoubt.
State the way through.
That is jumping on a call,getting in touch with someone
like yourself and tryingsomething like using it, seeing
(43:11):
the result, reflecting on thedata, and that's why we're
bringing in, you know, someonethat's leading product.
You know you're not here tosell.
You're here to help usunderstand this stuff so we can
create the value.
I like also the way that yourvision is.
It's not like you're here totalk about AI.
We're talking about speed tolease, we're talking about those
efficiency programs, theprofitability of that company,
(43:34):
and that can be exciting.
So I appreciate your time.
You know, I know that this isprobably more of a series and,
again, you know the stuff thatwe'll do inside the council will
be useful in answering some ofthe questions.
Or, if you have a question, ifyou're listening or you're
watching, send us a message andwe'll get it to Eugene and we'll
(43:55):
talk about what's next for youand your company.
But thanks for coming on.
Any final thoughts you want toleave our viewers with?
Speaker 2 (44:02):
No, no.
I'm very grateful to be in thistechnology space, in this
industry that has so manydifferent use cases for
technology, and it's just agreat experience for myself.
I love watching the marketchange and I really look forward
to where we go over the next 10years.
Speaker 1 (44:21):
That's awesome.
It's great.
I love what you said about thedue diligence when that change
is coming.
Stick with those that have beenthrough it.
Speaker 2 (44:29):
Yeah, for sure.
Well, thanks so much for havingme.
Speaker 1 (44:30):
Yeah, great to have you on.
We'll see you in the next one.
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Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
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