December 20, 2024 • 40 mins
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EPISODE DESCRIPTION
In this episode, Bekim is joined by Scott Larter. Scott is the VP of Business Development at OPTA Information Intelligence, and is also the VP of Data Solution Sales at FCT (First Canadian Title). OPTA and FCT entered a partnership in 2020 to focus on building data products for the mortgage industry, amassing a database of 18 million residential and 7 million commercial properties across Canada. In 2024, Scott became the Co-Founder and CEO of BrokerBot, a lead conversion and customer engagement solution with monthly real estate market values sent directly to a customer's email, putting a spotlight on the Broker.
Scott is here to discuss: → The path he took to get to where he is today, the lessons he learned from bartending and cold-calling, and what keeps him motivated in his career. → The importance of property data in the mortgage and insurance industries, why individual brokers should be investing in data, and how you can leverage it for a better client experience. → What automated valuation models (AVMs) are, the difference between an AVM and an appraisal, and how hybrid appraisal solutions can lead to faster approvals.
OPTA Information Intelligence Website: www.optaintel.ca
FCT Website: www.fct.ca
BrokerBot Website: www.thebrokerbot.ca
BrokerBot LinkedIn: @BrokerBot
BrokerBot Instagram: @brokerbot_canada
Scott Larter's Email: scott@thebrokerbot.ca
Scott Larter's LinkedIn: @ScottLarter
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Thank you for listening to theLending Thoughts podcast sponsored
(00:02):
by Rocket Pro.
Today, I'm excited to be joined by
my friend Scott Larder.
Scott and I talk about the perfect
career path into the roles that hehas today, as far as I can tell.
We talk about the importance ofdata in the mortgage and insurance
industries when it comes tolending on properties.
And Scott talks about how you as amortgage broker can leverage data
to provide a better clientexperience.
(00:24):
You're not going to want to missthis episode.
Scott, thank you for joining metoday.
Thanks for having me.
Appreciate the time.
Absolutely.
So Scott, can we jump in by you
telling us your life story in 60seconds or less, please?
Oh my goodness.
Okay.
Yeah. 60 seconds.
So I grew up just North of Toronto
(00:47):
in a place called Aurora, youknow, spent my days playing hockey
and golfing all summer.
That was a big passion for me.
There was a lot of time spent onthe golf course, which kept me out
of trouble, which was great.
Ended up going to school down in
the States on a golf scholarship.
Did one year in Florida, three
years in Colorado, and foundmyself with a Bachelor of Science
in Finance.
Wanted to be a stockbroker, if you
(01:08):
can believe it.
Got out of university and turned
pro for a couple of years in golf.
You know, got into the bar world,
bartending for a few years, whichwas great because you could
bartend at night and play golf allday.
And that was my job for a fewyears anyways.
Got into sales working for Rogers,which is a telecom company, as you
know, here in Canada, sellingBlackBerrys to small and medium
business.
(01:28):
You know, made a few changes
through my sales career.
And about 12 years ago, I started
working for Opta InformationIntelligence, which is a company I
work for now.
which sells property data, risk
analytics, valuations, AVMs, allsorts of stuff at the property
level.
We got into a partnership with
First Canadian Title back in 2020,where we combined data sets to
(01:49):
build products for mortgagebrokers, lenders, appraisers, real
estate agents, really anyone thatneeded access to information,
where I've been working for bothFCT and Opta now for about four
years.
I know that's confusing.
Whether you're thinking Opta orFCT, it's really the same data
source.
It's just one product that I'm
selling through a couple ofdifferent channels.
(02:11):
And then recently started astartup about six months ago.
I didn't have enough to do.
I started a startup about six
months ago called BrokerBot, whichuses AVMs or market values for
lead generation, lead conversion.
and customer engagement by sending
updated market values on a monthlybasis on behalf of mortgage
brokers, lenders, real estateagents, so on and so forth.
So I think that was probably 70seconds, but there you go.
(02:36):
That gives you the Coles notesanyway.
Very cool.
So Florida and Colorado are two
not bad places to golf if you liketo golf.
So if you're going to take somescholarships to go play in the
States, those are some prettysweet courses.
So cool.
Can't complain.
Was happy to be there.
Yeah.
Yeah.
Very cool.
Got you out of stockbrokering thenbecause you're like, hey, like
that was the idea.
What was the pivot?
(02:56):
You said, you know, want to be astockbroker, ended up working at
bars, like which, by the way, isgreat for communication skills for
the record.
If you're going to eventually find
your way into sales, some of thebest people we've ever brought
into the company.
Yeah.
Like they're former.
I wanted to get into being a
stockbroker because of a fewmovies.
I wanted to get into being astockbroker because of a few
(03:16):
movies.
You see the Wall Streets, you see
the boiler rooms and that scenewith Ben Affleck when he gets in
front of all those.
junior brokers in training and
just rhymes off what was one ofthe best little scenes.
I mean, that scene alone was like,I think I knew I was going to be
in sales.
I just didn't know in what
industry.
And when you're in sales, you're
trying to think about, well, wherecan I make money?
Where are areas that I've seenother people make money?
(03:38):
And being from Aurora, which is apretty affluent.
area there was a lot ofstockbrokers or people that worked
in finance that both played at ourgolf course and were in the
community so that to me wassomething that i saw around me and
seemed like everybody wassuccessful in it at the time so
you know Got out of school with aBachelor of Science down in the
States, Bachelor of Science inFinance, minored in Economics, but
(03:59):
wanted to give golf a go.
So I took a few years before I got
into stock brokering because Iwanted to give that golf thing a
try.
And, you know, you could bartend
at night, which, again, works on alot of skills that I think you
need in business.
And then, you know, by the time I
decided it was time to give it ashot.
(04:19):
I was making enough moneybartending.
You know, when you're bartending,you're making, depending on the
type of bar, 50, 60, 70 grand ayear.
And a guy in their mid -20sgolfing all day, trying to make it
as a golf pro and making cash atnight, still seeing your friends,
you know, it was really appealing.
So you go in and you start
interviewing.
The jobs that were available for
somebody like me was reallypicking up the phone and cold
(04:41):
calling.
And the starting rate was $28 ,000
a year.
And you'd be in the office from 8
a .m.
until 8 p .m.
dialing for dollars about 200people a day.
So at that point, I decided, youknow, maybe there's other ways
that I can make money because Ihad had a taste of.
you making more than that.
And I think I wanted to figure out
a way to keep going.
(05:01):
But I mean, anyone who's been in
the bar industry for a long time,there comes a time when you've
just had enough, you're missingenough family events.
You know, every night, you know,it feels like Groundhog Day where
you're running tabs and pouringpints and as much fun as that is.
So then I decided, you know,what's similar is commercial real
estate.
So I decided to leave the bar
(05:22):
industry and try my hand atdialing for dollars in commercial
real estate.
And that was in 2008.
And if anyone remembers 2008, thisglobal crash occurred, which
probably wasn't the best time forme to get into commercial real
estate.
But, you know, it was still.
pounding the phones, a hundreddials a day, you know, oftentimes
a hundred no's a day.
Sometimes you get 99 no's a day,
but if there was any sort ofcomfort zone that I was lacking
(05:44):
and picking up the phone after mysix months stint of working in
commercial real estate, that wascertainly behind me.
So from there, you know, kepthoning my sales craft and getting
into a position we're in today.
Hope that helps.
It helps so much and actuallyhelps me understand so much about
you, which is why I like to startthere.
(06:06):
I mean, if I were to be givingcareer advice to a young person,
it would almost sound exactly likethe path that you took, which is
go to school.
Try to be a part of a team.
If it's sports, fantastic.
But it could be other things as
well because I think there are somany lessons to be gained from
that.
Get into the bar industry in some
(06:26):
way, shape, or form to learn theimportance of communication.
Get into cold calling to learn theimportance of just being told no
and losing and day after day beingable to show up and take that and
just have that skill.
try some different things until
you find the thing where you canstart applying those various
skills.
But it's amazing that all of that
has led you to where you aretoday, which is leading sales for
both Opta and FCT and running astartup.
(06:46):
Maybe we'll start on the Optapiece because in some ways it sort
of underlies a lot of what you do,which is really data sales in the
Canadian industry.
And I think from... talking in the
past, it's not just mortgage, it'sinsurance.
At the end of the day, it's realestate, it's housing data.
So can you explain a little bitmore about what that data allows
the industry to do?Sure.
(07:06):
So at Opto, we started in theinsurance industry.
So I think when you look at dataand you look at... the models that
you can build off data.
There's so many different things
that you can do.
But in the insurance world, we
built a database leveraging claimsand leveraging home inspections
that we had done since the mid80s.
(07:28):
The database that we built wasbased on people going inside homes
and capturing information in whichwe built all sorts of different
algorithms to build off that.
Homes within close proximity often
share similar constructionfeatures.
We started going and buying a lotof third -party data.
And today, what Opta would beconsidered is a property data
aggregator.
We own a lot of the data of
(07:48):
ourselves, but we're also goingout in the market and finding
little nuggets of information thatwe can resell and use as part of
our data dictionary or data lake.
There's a number of different ways
of putting it.
But what we have today is a
database of about 18 millionresidential properties across the
country, which...
Hovers over 95%.
We don't do as much in none of it,Northwest Territories and Yukon.
(08:13):
But when you look at thepopulation from Vancouver Island
across to St. John's,Newfoundland, we're the only data
provider that has a consistent,accurate data set that, you know,
whoever the user is can rely on tomake faster decisions without
increasing risk.
So on the insurance side of
things.
pretty much every residential home
insurance quote you do in Canadalikely touches our data one way or
(08:34):
another.
So whether you're going to an
online insurance quota whereyou're asked what your address is,
you type it in, we're populatingall of the data about that
property right in front of thehomeowner or in front of the call
center agent or whoever it mightbe.
The idea there was when you thinkback a year and a half ago, and I
(08:55):
know you're from the States, somay go further back because
obviously data has been a littlemore available before that.
But to get an insurance quote 10years ago, 12 years ago, 15 years
ago, to get one quote, you'd be onthe phone or at your local state
farm agent or whoever it was, andyou would spend an hour and a half
(09:18):
with them.
And they would be asking all sorts
of questions about your house.
And in some cases, you know the
answer, but.
Most people don't know the actual
year built of their own home.
They don't know the actual square
footage.
Do you include the basement?
Do you not?You know, how many bedrooms and
bathrooms?We think people know if their
basement's finished or not.
But there's a lot of really
important information that yougather during that process that
(09:40):
the homeowner may know, but inmost cases doesn't.
So the use case there was toprovide a much cleaner.
customer experience because itshows right away the insurance
company was credible becausethey've got all their information
in front of them and a lot ofinsurance companies today are
pretty dynamic in the way they askquestions so if they feel you're
riskier for you know somethinglike water backup yeah your sewer
backup or whatever it might bethey're going to ask you questions
(10:01):
based on the profile they know intheir home but backup your sewer
backup or whatever it might bethey're going to ask you questions
based on the profile they know intheir home but 15 years ago would
be, what's the percentage ofhardwood to carpet, to tile in
your home?They'd ask you to look out the
front window.
Can you see a hydrant?
How far are you from the closedfire hall?
(10:22):
That's all data now.
You give me an address and I tell
you everything in sub two seconds,99 .9 % of the time.
Regardless of where you go inCanada, to get a home insurance
quote, were the data source beingused.
And in the background, what thehomeowner isn't seeing in
insurance, it's not about marketvalue, because we all know, you
know, in the lending world, it'sabout market value.
In the insurance world, it's aboutrebuild costs.
So if this house were to burn tothe ground, what would it cost to
rebuild it?That is what they are trying to
(10:42):
figure out.
And any Canadian that wants to
look on their coverage A, coverageA tells you roughly how much it
would cost to rebuild the house.
Fast forward to today, you can get
a fully bound insurance policy onmany online experiences.
That's three minutes.
They ask you four questions and
everything is done.
You're a fully bound insurance
policy.
Some people do it in the auto
(11:02):
dealership when they pick up theircar right in front of the finance
manager.
Oh, yeah, here's my policy.
They do it in the waiting room.
So the experience on the insurance
side has come so far.
I'm not going to take all the
blame because of data, but.
You know, technology has come a
long way.
A lot of the policy management
(11:24):
systems available have come a longway.
The data has come a long way, bothabout the homeowner, the driver,
the property itself.
But I think risk tolerance has
come a long way.
The reason why they asked all
those questions was because theywere trying to avoid risk.
Today, they're able to make fasterdecisions that may be more
expensive.
You know, when you look at the
average insurance policy, the costof acquisition, they're not
(11:46):
profitable on that into the secondor even third year.
But they know that in theinsurance world, 90 % of the time,
people are going to renew.
And if they're not a profitable
customer, their prices are goingto go up and they're probably
going to have to go elsewhereanyway.
So it's almost buying the marketto then solve it later.
It's a little different in thelending world, obviously, because
five -year renewals are longerterms than that.
But that's just one use case inthe insurance world of how
(12:08):
insurance improved the quotingworld, the underwriting world, the
risk world, and provides a muchbetter, much cleaner customer
experience today.
Yeah, that's a really helpful
definition, I would say, thathelps us understand how that data
is used.
So one way to think about that
when I'm hearing you say is thatwe actually have more information
than we used to get from theconsumer, despite the fact that
(12:29):
the consumer would spend hourstrying to give us that
information.
It's a much better, much better
experience, creates a much fastertransaction.
Obviously, being that you're inmortgage, Scott, I'm curious to
hear from you.
Why is lending further down the
path in terms of clientexperience?
Why are we not able to deliverthat lending experience as fast as
the insurance?To provide a rocket mortgage?
provide a rocket mortgage?Is that where you're trying to get
to?I would love to provide the rocket
mortgage that is one day, youknow, getting it done really fast.
(12:52):
It sounds like you have the datato do it.
So why aren't we there and maybetalk about some of the things that
need to happen in your mind?Sure.
Talking about we primarily focusedon insurance for a long time.
And back in 2018, when we decidedto make the move over into
financial services, I was excitedabout it.
I had previously been on the salesside of the insurance world.
(13:15):
My wife's a real estate agent herein Toronto.
We were avid home flippers in thebeaches area of Toronto for a
number of years before we hadkids.
It excited me because I knew howmuch easier it was starting to get
to get an insurance policy.
And yet every time it was time to
get a mortgage or time to close ona property or refinance or pull
(13:38):
equity, whatever it was.
There was so many different steps
that you had to go through.
And oftentimes it was not a great
experience.
So I put my hand up very quickly
here at Opta and I said, I think Iunderstand some of the challenges.
Let me dive in on this.
And they welcomed that.
So, you know, immediately where wehad success in the Opta world on
the insurance side was not sellingto one insurance broker at a time.
(13:59):
It was, you know, not selling toone user at a time.
It was finding platforms.
where we could easily fit in or
easily enable that platform to bea reseller.
And very similar to where westarted in insurance, we went with
insurance brokers, which wasinsurance broker platforms.
So there's a number of those verysimilar to the lending world.
There's about four or five of themthat cover pretty much the market
(14:21):
share.
So in Canada, did a similar
approach and started looking atall the POS systems that were out
in the market.
And some being more advanced than
others, some had models that theywould charge the broker or charge
the user versus others that don'tcharge the user, and they would
get their money later on a fundeddeal.
It was a big learning curve.
(14:41):
There was a lot of difference
between the two industries that Ineeded to learn.
It wasn't just turning on data andyou'd get a mortgage like that,
which one day I hope we get there.
But the risk tolerances, I think,
on the lending side, a lot moresteps involved, a lot more people
involved.
A lot less systems seem to be
involved.
Now, that's changed over the years
as well.
But I think when all these new
technologies, POS systems pushingthe envelope on things that they
(15:05):
can do, providing a betterhomeowner experience, a better
broker experience, a better lenderexperience, a few loan origination
systems have popped up that dogive...
Fundmore to be an example, youknow, plumb data in there and it
provides a more efficientunderwriting process through that.
The appraisal process, the legalprocess, and then everything needs
to be almost perfect for thatmortgage to close.
So I look at the two industriesand why they're so similar.
(15:27):
It seems like there's gates to getthrough.
There's a lot more process andapprovals and check marks that you
need on the lending side.
that you don't necessarily need on
the insurance side.
And when I think about the risk
profile of an insurance policyversus a mortgage, the insurance
company is on the hook for theentire home.
If it were to burn to the ground,what would it cost to rebuild it?
(15:47):
Liability.
There's all sorts of stuff that
goes with an insurance policywhere the lender was on the hook
for the difference between whatthe homeowner had put down and
what the house was worth in caseit needed to be sold.
So I always thought there was morerisk involved on an insurance
policy.
And was shocked to see that
there's less process to onboardone versus the other.
(16:09):
And even though I got into thisindustry in 2018, I feel like I'm
learning every single day.
And, you know, you uncover new
challenges and new goals thatcompanies have and new fintechs
and prop techs coming in trying tosolve different problems.
We're just trying to position ourdata as a...
Easy API to connect to aconsistent data source across
(16:30):
multiple channels.
I want to provide consistency,
whether you're marketing, fillingout a mortgage application, pre
-approvals, underwriting,appraisal, even into the mortgage
insurance world.
That to me is where the
opportunity of improving a lot ofthis is, is having consistent data
sources that you can rely on thataren't causing speed bumps
throughout your process.
And then that's when technology
and.
you know, efficient workflows and
(16:51):
underwriting and all the thingsthat are a well -oiled machine,
assuming there isn't roadblocksand speed bumps through that
process.
And I think data is a big portion
of that.
Absolutely agree.
So maybe for explanation inlayman's terms regarding AVMs,
which are something that most ofus in the industry are a very big
fan of and are always seeking andplayers like Opta make that
possible.
Down at the ground level, what is
an AVM?How does Opta support lenders and
(17:13):
insurers and such in obtainingthose AVMs such that we can feel
comfortable lending on propertieswithout the need for full
appraisal?And then my second question to
that would be like, when do youthink a full appraisal is needed
versus an AVM?So for those of you that don't
know what AVM even stands for, forthose of you that don't know what
(17:34):
AVM even stands for, it's anautomated valuation model.
It's a model.
It's a data science driven model
that takes into consideration abunch of information about the
property you're looking at, theproperties in the neighborhood,
the selling experience.
I mean, for those that aren't
familiar with AVMs, Zillow ispopular with the Zestimate.
(17:54):
That is an AVM that they use togenerate leads for real estate
agents.
There is five or six institutional
level AVMs that are being used bythe lending community.
Some of those are provincial innature.
Some of them handle multipleprovinces.
Some are national in the deliveryof that valuation.
But an AVM relies on a lot ofdata, machine learning, data
(18:14):
science, all the buzzwords thatyou can say on the data science
side.
You need to know enough about the
property.
You need to know enough about the
neighboring properties within thearea.
And you need to have a steadysource for real estate
information.
And by real estate information, I
mean real estate sales, becausesimilar to what an appraiser does,
(18:37):
an appraiser will go into a home.
They'll measure, they'll collect
information, how many bedrooms,how many bathrooms, total square
feet, is the basement finished, soon and so forth.
Lot size, how many car garage,does it have a pool, does it back
onto the water?There's all these things that they
take into consideration.
And then they go and they look,
(18:59):
okay, what are all the otherhouses in this neighborhood that
have sold over the last littlewhile that are comparable?
So are they similar?Are they 2 ,500 square feet?
Do they have that two -car garage?Do they have a pool?
They make some adjustments basedon that.
A data science model is verysimilar.
If you have enough informationabout the properties, if you have
enough information about the salesexperience in that neighborhood,
you're able to put a prettyaccurate valuation on that
(19:21):
property based on the informationthat you have.
When you look at the performanceof AVMs over the last 10 years,
which they've become more and morepopular, they continue getting
better.
Because of data, our AI and
algorithms are getting that muchmore efficient and accurate in the
way they do things.
(19:42):
But typically, you see AVMs are
within 15%, 20 % of the actualsale price of a home.
You'll see that between 75 % and90 % of the time.
That's kind of the... acceptablerange is 1575 within 15 percent 75
percent of the time and then youknow it grows from there some are
more accurate than that but ithink again you have to go back to
(20:03):
you need a steady source of realestate information to be able to
put an avm together i look at anavm as like a recipe you know
anyone that's cooked in thekitchen you may go to italy and
just have the most wonderful mealyou've ever had and you ask them
for the recipe they give it to youyou come back here You go and you
buy the same ingredients from yourlocal grocery store and it just
(20:24):
doesn't taste the same.
I mean, I think you need the
ingredients to spit out the bestpossible recipe.
And that's the one strength thatOpta has, having the ability to
have all of that data right acrossthe country and having access to
it dating back to the mid 80s whenwe started inspecting homes.
The next piece of your questionthere is, you know, when is it
good to use an AVM?versus a full appraisal.
AVMs have other use cases thanjust an alternative to an
(20:47):
appraisal.
It can be used in lead generation.
I think going back to that Zillow,the Zestimate, that they use an
AVM to generate leads and updatehome values for homeowners.
AVMs can be used by mortgagebrokers to see what the
approximate value is so that youcan coach the homeowner through
what.
Their house is actually worth
because a lot of homeowners thinktheir house is worth what it was
(21:14):
in 2001 when the boom was goingon, when the reality is the market
has shifted.
You know, so there's a lot of
different ways and a lot ofdifferent flavors of AVM on how
you can use it, whether it's formarketing, for sales.
Now, in going into aninstitutional AVM, an AVM by
itself is an uninsured product.
It is just based on all the
(21:35):
information we've got.
Here is the value we have on the
property.
Sometimes they have a confidence
rating of high, medium, and low.
There's a couple of different
things that go with that.
But I think throughout the years,
appraisals have been ordered.
Because it relies on a
professional that has access to alot of information, that can go
out and put eyes on a property.
They can see the condition of the
home.
They can take a look at the
(21:56):
finishes, the marble, the winecellars.
You know, think about how farbasements have come in the last
few years.
They're full extensions of our
home.
I'm dating myself here, but
remember back in the late 80s whenI was a young teenager.
It was just panel boarding.
You'd have the cheapest carpet
possible or still concrete.
It was your ball hockey arena.
still concrete.
It was your ball hockey arena.
(22:16):
100 % it was.
And I mean, Sidney Crosby became
who he is because he was shootingpucks at his dryer.
Back then, basements were $125 asquare foot to build out.
They were dungeons.
You would just send your teenage
kids down there because you didn'twant to look at them or hear them.
Now you've got full marblebathrooms and bars and wine
(22:37):
cellars.
So it takes a professional to go
into a house.
and look at everything about the
house and not just data, not justthe year bill, the square footage,
those ingredients that I talkedabout earlier.
So looking at where we are todayin the whole, when do I use an AVM
and when do I use an appraisal?I don't think there'll ever be a
world that will be 100 %appraisals.
I don't think there'll ever be aworld where it's 100 % AVMs.
(23:00):
There are so many differentfactors that come into that.
And one of that is the risktolerance of the lender.
So private lenders, their boardswant everything appraised 100 %
because that's a risk that they'renot willing to take.
Usually in a private lender, youknow, the borrower might be a
little bit more on the risky side.
You know, whether the house is a
little different, whether theircredit score is poor, whether it's
(23:21):
a bad market, like there's anumber of factors that go into it.
So, you know, those propertiesthat don't have those comparables
or they're more rural and you haveto go two kilometers away to find
the nearest comparable.
AVMs aren't going to perform at
their peak in that sort of ascenario.
They love cookie cutter.
They love when every property in
the neighborhood is similar innature.
That's when they perform at theirbest.
So if I'm a lender, I'm trying tofigure out at what point do I set
(23:45):
my risk threshold to go fullappraisal versus an AVM.
There's also the insured AVM.
The insured AVM is provided by
usually an insurance company orinsurance broker that will take
that value that the AVM spits outand they'll put an insurance
policy on it.
So that insurance policy will
cover the bank in case there's anysort of loss.
And banks love to transfer risk.
That is one way that they can
transfer risk is through aninsurance policy on top of an AVM.
(24:08):
But an insured AVM.
You need the perfect scenario for
an eligibility.
You need favorable loan to value.
You need a high credit score.
You need an AVM that performs well
on that property in thatneighborhood.
There's so many things that comeinto play that need to be check
marks in order to get an insuredAVM eligible.
And in that case, it's an instantSLA approved.
That's how fast it can be.
(24:29):
The next wave is hybrid products
that take.
the data, the AVMs, the comps, the
information that's required.
And you put all of that in front
of the appraiser and based on theinformation that they see in front
of them, they approve a value.
That's another form that you can
either get just signed by anappraiser or you can get an
insurance policy added on top ofthat.
(24:50):
So there's different levels ofAVMs.
There's different levels ofappraisals.
You know, you can drive -bys, youcan do a full appraisal, an
appraisal app where you're takingpictures or asking the homeowner
to do those things.
So, you know.
I think if I'm providing any sortof feedback to the lending
community, you got to look atyourself and say, what's my risk
tolerance?And number two, what's the cascade
of things that I want to look at?Is it, if an insured AVM is
(25:13):
available, I want that.
If it's a desktop, because I know
it's a four hour SLA, I want that.
Is it always full appraisals?
Is it drive -bys?How often am I doing them?
Because a house that you broughton four years ago, you're about to
renew.
What do you do there?
Is it another full appraisal?Is it?
pulling AVMs.
Like there's a lot of different
(25:35):
ways that those two products andthe cascading products in between
can be used.
And, you know, no two lenders are
doing the same thing at this pointacross the country.
I hope that answers your question.
It does in any ways.
In fact, side story, just on apersonal level, the last time my
house was appraised was actuallyduring the... lockdowns in like
2020, 2021 during COVID times whenno one was allowed in the house.
(25:57):
And the way that they did it wasthe appraiser had to drive by my
home and they took some outdoorphotos, but they relied on me for
all the indoor photos and sent melike a whole Word document with
instructions of like, here's allthe 30 things I need photographs.
And I'd actually like performedby, in some ways, my own appraisal
on my house, like going around,doing all the things, showing all
(26:20):
these various rooms and mechanicalrooms and whatnot.
It was a very interestingexperience.
And then they came back, they tookall that information and then they
provided the value on the home.
It was really interesting.
And I think it showed society'swillingness to adapt to
circumstances to try to figure outwhat's the best way that I can.
get a value of this home, giventhe circumstances that are at play
right now.
So when I think about AVMs and
appraisals, I actually don't thinkabout the two things as mutually
(26:43):
exclusive, but rather things thatcan work together.
And I think that the better thatwe can make these AVMs, the less
work we can put on appraisals andless work we can put on appraisers
as human beings, and we canactually help them.
do their jobs in a better way.
Because if you talk to appraisers,
they'll tell you like, we wantthat data.
We want more information on aproperty that will actually help
us better value these homes.
(27:04):
And then we can deliver a better
and a faster consumer experience.
So you're right.
It's not one or the other.
It's both.
You know, Scott, I do have to tellyou, you are one of the few people
who can talk to me aboutappraisals and AVMs and actually
get me excited such that I'm like,hey, like the world needs to hear
about this.
You're very passionate about the
topic as well as the world of dataand properties.
(27:26):
What is that that drives ormotivates you?
Because this is, you know, 18-year -old Scott did not say
future Scott is going to be theguy who sells property data to the
world.
But what gets you out of bed every
day to do this?If you're not passionate about
what you're doing, you're doingthe wrong thing, first of all.
So, you know, I think nobodythought they would be in
insurance.
as an 18 year old.
And I thought guys that sold datawore pocket protectors.
(27:47):
And, you know, sometimes I do, Idon't have a pocket on this shirt,
but I remember I got recruited towork for Opta.
A good friend of mine who's arecruiter begged me to take the
interview.
And, you know, the more I looked
into the company, the more that Iunderstood it, I'm like, okay,
this sounds like an interestingrole, VP of sales working for this
insurance data company calledOpta.
I went in prepared, but I wasgoing into that interview.
(28:09):
to do a favor for a buddy of mine.
I was in that room for 45 minutes
and left the room and called himand I said, I have to have that
job.
So I don't know what it is.
I mean, Greg McCutcheon and ColinSmith are my two leaders at Opta
and they still are today.
And they did a really good job
getting me excited.
(28:30):
But I just love the story that
data tells.
And the fact that we have a piece
of software where any... addressacross the country, I can plug in
and see pictures of the home, allof the information about the
property, how much it's worth,what permit they pulled on the
property, the rebuild costs, wheretheir local fire department is,
how likely are they to have asewer backup.
While that may sound nerdy tomany, that is exciting to me.
(28:52):
And whether it's the real estateworld and the way that they're
using data or the insurance world.
you know, the house sigmas of the
world and all these new platformsthat are coming out.
I get really excited about data.
I don't know what it is.
Maybe it's because I'm 47 and my18 year old self wouldn't have
loved it.
But, you know, going back to golf,
like golf is an extremelyanalytical sport.
You got to know how far you hityour eight iron.
(29:14):
You need to know how far you hitit in the wind.
You need to know downwind, howmuch spin to put on it, you know,
when to use it.
Oftentimes you're playing a golf
course backwards.
Where's the pin?
Okay.
If the pin's on the left side, I
should hit it on the right.
So being a golfer, I mean,
everything in golf now is drivenby data.
You know, there's a whole industrybuilt on the pros, what they're
(29:34):
playing, what golf clubs they'replaying.
It's included in their marketingspin.
You know, this driver gives youthis much spin versus that
customization.
So I guess with golf, I've always
really been into data.
But, you know, I'm a left brain
guy.
I always love to draw.
I always loved music.
I was more of an artsy guy.
And I try and approach that inselling.
with a very right brain thing,which is data.
And it's maybe not necessarily oneproperty at a time.
(29:56):
It's the dashboard.
It's the overall themes that you
can get from data that talk aboutthe book or talk about, you know,
marketing strategies or whateverit might be.
But there's just something I getmotivated about selling data.
know, just something I getmotivated about selling data.
I don't know.
I love it.
I love it.
I love it.
And as you compare it to golf,you're right.
That is such a healthy comparisonto make.
I guess the one difference is youdon't want to walk up to your ball
(30:21):
and chunk it 10 yards like youtend to do in golf sometimes.
Yeah, you do that in sales too.
Trust me.
I hear you.
So I want to just ask you for a
takeaway.
Our audience is primarily the
lending community and mortgagebrokers.
And as you think about the worldof the mortgage broker, what is
one actual takeaway that you thinkthat they can learn from you, from
(30:43):
Opta, from First Canadian Title orFCT as it's known in the world?
Side note, I hate acronyms forthat reason, AVM, FCT.
It's impossible to keep up.
And now BrokerBot.
Like what should brokers take awayfrom you?
The mortgage brokers that I'veworked with, mortgage brokers that
I've worked with, the platformsthat we've integrated into, I
think a lot of mortgage brokerslook at data as an unnecessary
cost, whether it costs a fewbucks.
And, you know, there's a couple ofdifferent data providers where you
(31:04):
can buy, you know, data on aproperty, depending on where
you're in the country.
But, you know, we've integrated
into Newton Velocity and ScarletNetworks and a few others.
We have a few more that arecoming.
But the benefits to a mortgagebroker is you're the trusted
advisor.
You're working with a homeowner
that, you know, whether this isanother mortgage or their first
mortgage or a refinance, you know,they're coming to you for money
for a reason.
And if I'm trying to put myself as
(31:26):
a salesperson in that trustedadvisor role, I'd love to know
more than less that I can bring tothe table.
Give me the address of your home.
They pump in that address, hit
enter, and everything about theproperty pops up.
You're built, square footage,bedrooms, bathrooms, pictures of
the home, whatever that might be.
That gives you the ability to walk
the homeowner through theirbiggest asset, which is their
home, something that they'reextremely proud of.
(31:47):
So one broker could just say, hey,I'm looking at your home.
It was built in 1986.
It's 2 ,500 square feet.
Hey, looking at a picture of yourhome, what a beautiful property.
Love the gardens.
The third car garage, I have four
hockey bags.
I love the stuff in there.
Amazing.
Pump them up, right?
Yeah.
You know, your house was built in
1986, 2 ,500 square feet.
Looks here like you haven't
finished your basement.
(32:08):
Have you finished it?
Oh, no, we finished it six monthsago.
Great.
Let me just update that.
So through that excitement ofwalking them through their biggest
asset, you're actually validatingany inaccuracies in the data.
Every data provider is not goingto be perfect 100 % of the time.
I tell that to people all thetime.
You know, we're putting what weknow about the property and what
we know about the neighborhood infront of you.
(32:29):
We just ask that you do a quickvalidation process.
Oftentimes, you're not going tohave to touch anything.
But if they finished theirbasement six months ago, and we
just don't know that yet, updatethat as you're having that
conversation.
And by the way, if they haven't
finished their basement, butthey're planning to finish their
basement, you've just unlocked anopportunity.
right, to then maybe refinance orprovide a HELOC later.
(32:51):
So these are things through datawhere you can learn more about.
not only their property, but youcan learn about what their plans
to do with that property.
So after you've done that process,
and oftentimes that integrationwill pre -fill your mortgage
application for you.
So it's not like you're having to
copy and paste and bring it over.
Now your mortgage app is done with
an accurate address, accuratesubject property data.
(33:12):
And now what we're providing is amarket value understanding of the
property.
Depending on where your deployment
is, you could get a range or youcould get a specific value.
If I'm a mortgage broker, I'm notasking the homeowner, what's your
house of birth?And why I'm not doing that is I
think many mortgage brokers,lenders, anyone that's been in
this industry, if the declaredvalue you put on the mortgage
(33:34):
application is based on what thehomeowner says, oftentimes it's
inflated.
And I'll use the example.
Personal pride, Personal pride,yeah.
If my neighbor's house sold duringCOVID for 1 .2 million, yeah.
If my neighbor's house sold duringCOVID for 1 .2 million, they don't
have a finished basement, but Ido.
I'm going to think, well, theirhouse sold three years ago for $1
(33:57):
.2 million.
I have a finished basement.
Mine has to be by this point $1 .5million.
So let's go with $1 .5 million.
Even if they're doing a HELOC and
they have loan -to -values, verysmall percentage, why are they
trying to maximize that value evento one that's too high?
So if we're giving the homeowneror at least the mortgage broker an
(34:18):
idea of the value of the property,Just like the property data, they
can coach the homeowner throughroughly what that value of the
property is and give them a muchbetter understanding of the true
market value of today.
So, hey, it looks here based on
the profile of your home and themarket in your neighborhood that
your house is worth somewhere inthe 1 .5 million to 1 .1 million.
Can we start the process with thatin mind?
(34:39):
Sure.
Okay, let's put 1 .1 million in.
So we put in $1 .1 million.
That mortgage application goes off
to the underwriter.
You've now provided the
underwriter an accurate address,an accurate property data profile
on the subject property for theirapplication, and a much more
accurate value of the home, inwhich case they can take that and
make a much more educateddecision.
Many times, you know, if themortgage broker doesn't know
something, they'll put in anasterisk.
(34:59):
The most widely used postal codein all of the lending community in
Canada happens to be old St.
Nick's HOHOHO, if you didn't know.
Many brokers pump in HOHOHO oranother postal code.
Ask your underwriter how they feelabout that, because I'm telling
you what, that application isgoing to the bottom of the pile.
They're sending an email back toyou and they're going to get back
to that application.
when they have the time or when it
(35:20):
comes to the top of the pile.
So do yourself a favor, whether
it's a couple bucks here, a couplebucks there, pull the data, pull
some accurate information and pullan accurate market value because
you're going to provide a bettercustomer experience.
You're going to get betterreviews, a much more efficient
workflow, and it's not going towaste your time.
You can move on to the next one.
(35:40):
You don't have to worry about that
one that keeps coming back.
Now we go back to appraisals on
this.
Now you've got an accurate
application with an accuratevalue.
Remember that cascade of productsthat we talked about, the insured
AVM, which is an instant SLA.
You've got desktops, you've got
drive -bys, you've got differentflavors in that cascade.
The only way that you're going toget that insured AVM or one that
(36:04):
is a faster SLA is if all the datais correct.
The If the value is 1 .5 million,but the AVMs are coming back at 1
.1, 1 .2.
You're automatically being sent to
a full appraisal, which, you know,looking at what's going to be
going on next year with all themortgage changes.
I think Korea is calling for fiveor six percent increase in home
sales.
Cost of borrowing is coming down.
So the appraiser is very similarto COVID.
They're going to be stretched fortime again.
(36:25):
And, you know, I think at onepoint we were waiting a few days
to a few weeks, depending on whereyou were in the country.
Do yourself a favor.
Spend a few bucks and get that
data accurate up front.
Because it's only going to provide
a better experience for everybodythrough that whole piece.
Mortgage brokers who rely heavilyon Google reviews, these show up.
(36:48):
You're saving the underwriter.
Five minutes, six minutes.
I talk to underwriting teams thatuse our data all the time.
Do you know how much money fiveminutes is to an underwriter?
It's worth a lot more than whatyou're paying for the data.
That's my whole point.
Sorry, passion again.
I think you said it so well thatwe land the plane right there,
think you said it so well that weland the plane right there, Scott.
(37:11):
Because that's motivating foreveryone listening.
So some great actionable takeawaysfor all of us.
Some great information.
Even I learned some things.
And I hope everyone learns somethings from this episode.
So thanks for being on with me.
We'll talk soon.
Thanks for having me, Beckham.
Appreciate it.
Thank you for listening to theLending Thoughts podcast sponsored
(00:02):
by Rocket Pro.
Today, I'm excited to be joined by
my friend Scott Larder.
Scott and I talk about the perfect
career path into the roles that hehas today, as far as I can tell.
We talk about the importance ofdata in the mortgage and insurance
industries when it comes tolending on properties.
And Scott talks about how you as amortgage broker can leverage data
to provide a better clientexperience.
(00:24):
You're not going to want to missthis episode.
Scott, thank you for joining metoday.
Thanks for having me.
Appreciate the time.
Absolutely.
So Scott, can we jump in by you
telling us your life story in 60seconds or less, please?
Oh my goodness.
Okay.
Yeah. 60 seconds.
So I grew up just North of Toronto
(00:47):
in a place called Aurora, youknow, spent my days playing hockey
and golfing all summer.
That was a big passion for me.
There was a lot of time spent onthe golf course, which kept me out
of trouble, which was great.
Ended up going to school down in
the States on a golf scholarship.
Did one year in Florida, three
years in Colorado, and foundmyself with a Bachelor of Science
in Finance.
Wanted to be a stockbroker, if you
(01:08):
can believe it.
Got out of university and turned
pro for a couple of years in golf.
You know, got into the bar world,
bartending for a few years, whichwas great because you could
bartend at night and play golf allday.
And that was my job for a fewyears anyways.
Got into sales working for Rogers,which is a telecom company, as you
know, here in Canada, sellingBlackBerrys to small and medium
business.
(01:28):
You know, made a few changes
through my sales career.
And about 12 years ago, I started
working for Opta InformationIntelligence, which is a company I
work for now.
which sells property data, risk
analytics, valuations, AVMs, allsorts of stuff at the property
level.
We got into a partnership with
First Canadian Title back in 2020,where we combined data sets to
(01:49):
build products for mortgagebrokers, lenders, appraisers, real
estate agents, really anyone thatneeded access to information,
where I've been working for bothFCT and Opta now for about four
years.
I know that's confusing.
Whether you're thinking Opta orFCT, it's really the same data
source.
It's just one product that I'm
selling through a couple ofdifferent channels.
(02:11):
And then recently started astartup about six months ago.
I didn't have enough to do.
I started a startup about six
months ago called BrokerBot, whichuses AVMs or market values for
lead generation, lead conversion.
and customer engagement by sending
updated market values on a monthlybasis on behalf of mortgage
brokers, lenders, real estateagents, so on and so forth.
So I think that was probably 70seconds, but there you go.
(02:36):
That gives you the Coles notesanyway.
Very cool.
So Florida and Colorado are two
not bad places to golf if you liketo golf.
So if you're going to take somescholarships to go play in the
States, those are some prettysweet courses.
So cool.
Can't complain.
Was happy to be there.
Yeah.
Yeah.
Very cool.
Got you out of stockbrokering thenbecause you're like, hey, like
that was the idea.
What was the pivot?
(02:56):
You said, you know, want to be astockbroker, ended up working at
bars, like which, by the way, isgreat for communication skills for
the record.
If you're going to eventually find
your way into sales, some of thebest people we've ever brought
into the company.
Yeah.
Like they're former.
I wanted to get into being a
stockbroker because of a fewmovies.
I wanted to get into being astockbroker because of a few
(03:16):
movies.
You see the Wall Streets, you see
the boiler rooms and that scenewith Ben Affleck when he gets in
front of all those.
junior brokers in training and
just rhymes off what was one ofthe best little scenes.
I mean, that scene alone was like,I think I knew I was going to be
in sales.
I just didn't know in what
industry.
And when you're in sales, you're
trying to think about, well, wherecan I make money?
Where are areas that I've seenother people make money?
(03:38):
And being from Aurora, which is apretty affluent.
area there was a lot ofstockbrokers or people that worked
in finance that both played at ourgolf course and were in the
community so that to me wassomething that i saw around me and
seemed like everybody wassuccessful in it at the time so
you know Got out of school with aBachelor of Science down in the
States, Bachelor of Science inFinance, minored in Economics, but
(03:59):
wanted to give golf a go.
So I took a few years before I got
into stock brokering because Iwanted to give that golf thing a
try.
And, you know, you could bartend
at night, which, again, works on alot of skills that I think you
need in business.
And then, you know, by the time I
decided it was time to give it ashot.
(04:19):
I was making enough moneybartending.
You know, when you're bartending,you're making, depending on the
type of bar, 50, 60, 70 grand ayear.
And a guy in their mid -20sgolfing all day, trying to make it
as a golf pro and making cash atnight, still seeing your friends,
you know, it was really appealing.
So you go in and you start
interviewing.
The jobs that were available for
somebody like me was reallypicking up the phone and cold
(04:41):
calling.
And the starting rate was $28 ,000
a year.
And you'd be in the office from 8
a .m.
until 8 p .m.
dialing for dollars about 200people a day.
So at that point, I decided, youknow, maybe there's other ways
that I can make money because Ihad had a taste of.
you making more than that.
And I think I wanted to figure out
a way to keep going.
(05:01):
But I mean, anyone who's been in
the bar industry for a long time,there comes a time when you've
just had enough, you're missingenough family events.
You know, every night, you know,it feels like Groundhog Day where
you're running tabs and pouringpints and as much fun as that is.
So then I decided, you know,what's similar is commercial real
estate.
So I decided to leave the bar
(05:22):
industry and try my hand atdialing for dollars in commercial
real estate.
And that was in 2008.
And if anyone remembers 2008, thisglobal crash occurred, which
probably wasn't the best time forme to get into commercial real
estate.
But, you know, it was still.
pounding the phones, a hundreddials a day, you know, oftentimes
a hundred no's a day.
Sometimes you get 99 no's a day,
but if there was any sort ofcomfort zone that I was lacking
(05:44):
and picking up the phone after mysix months stint of working in
commercial real estate, that wascertainly behind me.
So from there, you know, kepthoning my sales craft and getting
into a position we're in today.
Hope that helps.
It helps so much and actuallyhelps me understand so much about
you, which is why I like to startthere.
(06:06):
I mean, if I were to be givingcareer advice to a young person,
it would almost sound exactly likethe path that you took, which is
go to school.
Try to be a part of a team.
If it's sports, fantastic.
But it could be other things as
well because I think there are somany lessons to be gained from
that.
Get into the bar industry in some
(06:26):
way, shape, or form to learn theimportance of communication.
Get into cold calling to learn theimportance of just being told no
and losing and day after day beingable to show up and take that and
just have that skill.
try some different things until
you find the thing where you canstart applying those various
skills.
But it's amazing that all of that
has led you to where you aretoday, which is leading sales for
both Opta and FCT and running astartup.
(06:46):
Maybe we'll start on the Optapiece because in some ways it sort
of underlies a lot of what you do,which is really data sales in the
Canadian industry.
And I think from... talking in the
past, it's not just mortgage, it'sinsurance.
At the end of the day, it's realestate, it's housing data.
So can you explain a little bitmore about what that data allows
the industry to do?Sure.
(07:06):
So at Opto, we started in theinsurance industry.
So I think when you look at dataand you look at... the models that
you can build off data.
There's so many different things
that you can do.
But in the insurance world, we
built a database leveraging claimsand leveraging home inspections
that we had done since the mid80s.
(07:28):
The database that we built wasbased on people going inside homes
and capturing information in whichwe built all sorts of different
algorithms to build off that.
Homes within close proximity often
share similar constructionfeatures.
We started going and buying a lotof third -party data.
And today, what Opta would beconsidered is a property data
aggregator.
We own a lot of the data of
(07:48):
ourselves, but we're also goingout in the market and finding
little nuggets of information thatwe can resell and use as part of
our data dictionary or data lake.
There's a number of different ways
of putting it.
But what we have today is a
database of about 18 millionresidential properties across the
country, which...
Hovers over 95%.
We don't do as much in none of it,Northwest Territories and Yukon.
(08:13):
But when you look at thepopulation from Vancouver Island
across to St. John's,Newfoundland, we're the only data
provider that has a consistent,accurate data set that, you know,
whoever the user is can rely on tomake faster decisions without
increasing risk.
So on the insurance side of
things.
pretty much every residential home
insurance quote you do in Canadalikely touches our data one way or
(08:34):
another.
So whether you're going to an
online insurance quota whereyou're asked what your address is,
you type it in, we're populatingall of the data about that
property right in front of thehomeowner or in front of the call
center agent or whoever it mightbe.
The idea there was when you thinkback a year and a half ago, and I
(08:55):
know you're from the States, somay go further back because
obviously data has been a littlemore available before that.
But to get an insurance quote 10years ago, 12 years ago, 15 years
ago, to get one quote, you'd be onthe phone or at your local state
farm agent or whoever it was, andyou would spend an hour and a half
(09:18):
with them.
And they would be asking all sorts
of questions about your house.
And in some cases, you know the
answer, but.
Most people don't know the actual
year built of their own home.
They don't know the actual square
footage.
Do you include the basement?
Do you not?You know, how many bedrooms and
bathrooms?We think people know if their
basement's finished or not.
But there's a lot of really
important information that yougather during that process that
(09:40):
the homeowner may know, but inmost cases doesn't.
So the use case there was toprovide a much cleaner.
customer experience because itshows right away the insurance
company was credible becausethey've got all their information
in front of them and a lot ofinsurance companies today are
pretty dynamic in the way they askquestions so if they feel you're
riskier for you know somethinglike water backup yeah your sewer
backup or whatever it might bethey're going to ask you questions
(10:01):
based on the profile they know intheir home but backup your sewer
backup or whatever it might bethey're going to ask you questions
based on the profile they know intheir home but 15 years ago would
be, what's the percentage ofhardwood to carpet, to tile in
your home?They'd ask you to look out the
front window.
Can you see a hydrant?
How far are you from the closedfire hall?
(10:22):
That's all data now.
You give me an address and I tell
you everything in sub two seconds,99 .9 % of the time.
Regardless of where you go inCanada, to get a home insurance
quote, were the data source beingused.
And in the background, what thehomeowner isn't seeing in
insurance, it's not about marketvalue, because we all know, you
know, in the lending world, it'sabout market value.
In the insurance world, it's aboutrebuild costs.
So if this house were to burn tothe ground, what would it cost to
rebuild it?That is what they are trying to
(10:42):
figure out.
And any Canadian that wants to
look on their coverage A, coverageA tells you roughly how much it
would cost to rebuild the house.
Fast forward to today, you can get
a fully bound insurance policy onmany online experiences.
That's three minutes.
They ask you four questions and
everything is done.
You're a fully bound insurance
policy.
Some people do it in the auto
(11:02):
dealership when they pick up theircar right in front of the finance
manager.
Oh, yeah, here's my policy.
They do it in the waiting room.
So the experience on the insurance
side has come so far.
I'm not going to take all the
blame because of data, but.
You know, technology has come a
long way.
A lot of the policy management
(11:24):
systems available have come a longway.
The data has come a long way, bothabout the homeowner, the driver,
the property itself.
But I think risk tolerance has
come a long way.
The reason why they asked all
those questions was because theywere trying to avoid risk.
Today, they're able to make fasterdecisions that may be more
expensive.
You know, when you look at the
average insurance policy, the costof acquisition, they're not
(11:46):
profitable on that into the secondor even third year.
But they know that in theinsurance world, 90 % of the time,
people are going to renew.
And if they're not a profitable
customer, their prices are goingto go up and they're probably
going to have to go elsewhereanyway.
So it's almost buying the marketto then solve it later.
It's a little different in thelending world, obviously, because
five -year renewals are longerterms than that.
But that's just one use case inthe insurance world of how
(12:08):
insurance improved the quotingworld, the underwriting world, the
risk world, and provides a muchbetter, much cleaner customer
experience today.
Yeah, that's a really helpful
definition, I would say, thathelps us understand how that data
is used.
So one way to think about that
when I'm hearing you say is thatwe actually have more information
than we used to get from theconsumer, despite the fact that
(12:29):
the consumer would spend hourstrying to give us that
information.
It's a much better, much better
experience, creates a much fastertransaction.
Obviously, being that you're inmortgage, Scott, I'm curious to
hear from you.
Why is lending further down the
path in terms of clientexperience?
Why are we not able to deliverthat lending experience as fast as
the insurance?To provide a rocket mortgage?
provide a rocket mortgage?Is that where you're trying to get
to?I would love to provide the rocket
mortgage that is one day, youknow, getting it done really fast.
(12:52):
It sounds like you have the datato do it.
So why aren't we there and maybetalk about some of the things that
need to happen in your mind?Sure.
Talking about we primarily focusedon insurance for a long time.
And back in 2018, when we decidedto make the move over into
financial services, I was excitedabout it.
I had previously been on the salesside of the insurance world.
(13:15):
My wife's a real estate agent herein Toronto.
We were avid home flippers in thebeaches area of Toronto for a
number of years before we hadkids.
It excited me because I knew howmuch easier it was starting to get
to get an insurance policy.
And yet every time it was time to
get a mortgage or time to close ona property or refinance or pull
(13:38):
equity, whatever it was.
There was so many different steps
that you had to go through.
And oftentimes it was not a great
experience.
So I put my hand up very quickly
here at Opta and I said, I think Iunderstand some of the challenges.
Let me dive in on this.
And they welcomed that.
So, you know, immediately where wehad success in the Opta world on
the insurance side was not sellingto one insurance broker at a time.
(13:59):
It was, you know, not selling toone user at a time.
It was finding platforms.
where we could easily fit in or
easily enable that platform to bea reseller.
And very similar to where westarted in insurance, we went with
insurance brokers, which wasinsurance broker platforms.
So there's a number of those verysimilar to the lending world.
There's about four or five of themthat cover pretty much the market
(14:21):
share.
So in Canada, did a similar
approach and started looking atall the POS systems that were out
in the market.
And some being more advanced than
others, some had models that theywould charge the broker or charge
the user versus others that don'tcharge the user, and they would
get their money later on a fundeddeal.
It was a big learning curve.
(14:41):
There was a lot of difference
between the two industries that Ineeded to learn.
It wasn't just turning on data andyou'd get a mortgage like that,
which one day I hope we get there.
But the risk tolerances, I think,
on the lending side, a lot moresteps involved, a lot more people
involved.
A lot less systems seem to be
involved.
Now, that's changed over the years
as well.
But I think when all these new
technologies, POS systems pushingthe envelope on things that they
(15:05):
can do, providing a betterhomeowner experience, a better
broker experience, a better lenderexperience, a few loan origination
systems have popped up that dogive...
Fundmore to be an example, youknow, plumb data in there and it
provides a more efficientunderwriting process through that.
The appraisal process, the legalprocess, and then everything needs
to be almost perfect for thatmortgage to close.
So I look at the two industriesand why they're so similar.
(15:27):
It seems like there's gates to getthrough.
There's a lot more process andapprovals and check marks that you
need on the lending side.
that you don't necessarily need on
the insurance side.
And when I think about the risk
profile of an insurance policyversus a mortgage, the insurance
company is on the hook for theentire home.
If it were to burn to the ground,what would it cost to rebuild it?
(15:47):
Liability.
There's all sorts of stuff that
goes with an insurance policywhere the lender was on the hook
for the difference between whatthe homeowner had put down and
what the house was worth in caseit needed to be sold.
So I always thought there was morerisk involved on an insurance
policy.
And was shocked to see that
there's less process to onboardone versus the other.
(16:09):
And even though I got into thisindustry in 2018, I feel like I'm
learning every single day.
And, you know, you uncover new
challenges and new goals thatcompanies have and new fintechs
and prop techs coming in trying tosolve different problems.
We're just trying to position ourdata as a...
Easy API to connect to aconsistent data source across
(16:30):
multiple channels.
I want to provide consistency,
whether you're marketing, fillingout a mortgage application, pre
-approvals, underwriting,appraisal, even into the mortgage
insurance world.
That to me is where the
opportunity of improving a lot ofthis is, is having consistent data
sources that you can rely on thataren't causing speed bumps
throughout your process.
And then that's when technology
and.
you know, efficient workflows and
(16:51):
underwriting and all the thingsthat are a well -oiled machine,
assuming there isn't roadblocksand speed bumps through that
process.
And I think data is a big portion
of that.
Absolutely agree.
So maybe for explanation inlayman's terms regarding AVMs,
which are something that most ofus in the industry are a very big
fan of and are always seeking andplayers like Opta make that
possible.
Down at the ground level, what is
an AVM?How does Opta support lenders and
(17:13):
insurers and such in obtainingthose AVMs such that we can feel
comfortable lending on propertieswithout the need for full
appraisal?And then my second question to
that would be like, when do youthink a full appraisal is needed
versus an AVM?So for those of you that don't
know what AVM even stands for, forthose of you that don't know what
(17:34):
AVM even stands for, it's anautomated valuation model.
It's a model.
It's a data science driven model
that takes into consideration abunch of information about the
property you're looking at, theproperties in the neighborhood,
the selling experience.
I mean, for those that aren't
familiar with AVMs, Zillow ispopular with the Zestimate.
(17:54):
That is an AVM that they use togenerate leads for real estate
agents.
There is five or six institutional
level AVMs that are being used bythe lending community.
Some of those are provincial innature.
Some of them handle multipleprovinces.
Some are national in the deliveryof that valuation.
But an AVM relies on a lot ofdata, machine learning, data
(18:14):
science, all the buzzwords thatyou can say on the data science
side.
You need to know enough about the
property.
You need to know enough about the
neighboring properties within thearea.
And you need to have a steadysource for real estate
information.
And by real estate information, I
mean real estate sales, becausesimilar to what an appraiser does,
(18:37):
an appraiser will go into a home.
They'll measure, they'll collect
information, how many bedrooms,how many bathrooms, total square
feet, is the basement finished, soon and so forth.
Lot size, how many car garage,does it have a pool, does it back
onto the water?There's all these things that they
take into consideration.
And then they go and they look,
(18:59):
okay, what are all the otherhouses in this neighborhood that
have sold over the last littlewhile that are comparable?
So are they similar?Are they 2 ,500 square feet?
Do they have that two -car garage?Do they have a pool?
They make some adjustments basedon that.
A data science model is verysimilar.
If you have enough informationabout the properties, if you have
enough information about the salesexperience in that neighborhood,
you're able to put a prettyaccurate valuation on that
(19:21):
property based on the informationthat you have.
When you look at the performanceof AVMs over the last 10 years,
which they've become more and morepopular, they continue getting
better.
Because of data, our AI and
algorithms are getting that muchmore efficient and accurate in the
way they do things.
(19:42):
But typically, you see AVMs are
within 15%, 20 % of the actualsale price of a home.
You'll see that between 75 % and90 % of the time.
That's kind of the... acceptablerange is 1575 within 15 percent 75
percent of the time and then youknow it grows from there some are
more accurate than that but ithink again you have to go back to
(20:03):
you need a steady source of realestate information to be able to
put an avm together i look at anavm as like a recipe you know
anyone that's cooked in thekitchen you may go to italy and
just have the most wonderful mealyou've ever had and you ask them
for the recipe they give it to youyou come back here You go and you
buy the same ingredients from yourlocal grocery store and it just
(20:24):
doesn't taste the same.
I mean, I think you need the
ingredients to spit out the bestpossible recipe.
And that's the one strength thatOpta has, having the ability to
have all of that data right acrossthe country and having access to
it dating back to the mid 80s whenwe started inspecting homes.
The next piece of your questionthere is, you know, when is it
good to use an AVM?versus a full appraisal.
AVMs have other use cases thanjust an alternative to an
(20:47):
appraisal.
It can be used in lead generation.
I think going back to that Zillow,the Zestimate, that they use an
AVM to generate leads and updatehome values for homeowners.
AVMs can be used by mortgagebrokers to see what the
approximate value is so that youcan coach the homeowner through
what.
Their house is actually worth
because a lot of homeowners thinktheir house is worth what it was
(21:14):
in 2001 when the boom was goingon, when the reality is the market
has shifted.
You know, so there's a lot of
different ways and a lot ofdifferent flavors of AVM on how
you can use it, whether it's formarketing, for sales.
Now, in going into aninstitutional AVM, an AVM by
itself is an uninsured product.
It is just based on all the
(21:35):
information we've got.
Here is the value we have on the
property.
Sometimes they have a confidence
rating of high, medium, and low.
There's a couple of different
things that go with that.
But I think throughout the years,
appraisals have been ordered.
Because it relies on a
professional that has access to alot of information, that can go
out and put eyes on a property.
They can see the condition of the
home.
They can take a look at the
(21:56):
finishes, the marble, the winecellars.
You know, think about how farbasements have come in the last
few years.
They're full extensions of our
home.
I'm dating myself here, but
remember back in the late 80s whenI was a young teenager.
It was just panel boarding.
You'd have the cheapest carpet
possible or still concrete.
It was your ball hockey arena.
still concrete.
It was your ball hockey arena.
(22:16):
100 % it was.
And I mean, Sidney Crosby became
who he is because he was shootingpucks at his dryer.
Back then, basements were $125 asquare foot to build out.
They were dungeons.
You would just send your teenage
kids down there because you didn'twant to look at them or hear them.
Now you've got full marblebathrooms and bars and wine
(22:37):
cellars.
So it takes a professional to go
into a house.
and look at everything about the
house and not just data, not justthe year bill, the square footage,
those ingredients that I talkedabout earlier.
So looking at where we are todayin the whole, when do I use an AVM
and when do I use an appraisal?I don't think there'll ever be a
world that will be 100 %appraisals.
I don't think there'll ever be aworld where it's 100 % AVMs.
(23:00):
There are so many differentfactors that come into that.
And one of that is the risktolerance of the lender.
So private lenders, their boardswant everything appraised 100 %
because that's a risk that they'renot willing to take.
Usually in a private lender, youknow, the borrower might be a
little bit more on the risky side.
You know, whether the house is a
little different, whether theircredit score is poor, whether it's
(23:21):
a bad market, like there's anumber of factors that go into it.
So, you know, those propertiesthat don't have those comparables
or they're more rural and you haveto go two kilometers away to find
the nearest comparable.
AVMs aren't going to perform at
their peak in that sort of ascenario.
They love cookie cutter.
They love when every property in
the neighborhood is similar innature.
That's when they perform at theirbest.
So if I'm a lender, I'm trying tofigure out at what point do I set
(23:45):
my risk threshold to go fullappraisal versus an AVM.
There's also the insured AVM.
The insured AVM is provided by
usually an insurance company orinsurance broker that will take
that value that the AVM spits outand they'll put an insurance
policy on it.
So that insurance policy will
cover the bank in case there's anysort of loss.
And banks love to transfer risk.
That is one way that they can
transfer risk is through aninsurance policy on top of an AVM.
(24:08):
But an insured AVM.
You need the perfect scenario for
an eligibility.
You need favorable loan to value.
You need a high credit score.
You need an AVM that performs well
on that property in thatneighborhood.
There's so many things that comeinto play that need to be check
marks in order to get an insuredAVM eligible.
And in that case, it's an instantSLA approved.
That's how fast it can be.
(24:29):
The next wave is hybrid products
that take.
the data, the AVMs, the comps, the
information that's required.
And you put all of that in front
of the appraiser and based on theinformation that they see in front
of them, they approve a value.
That's another form that you can
either get just signed by anappraiser or you can get an
insurance policy added on top ofthat.
(24:50):
So there's different levels ofAVMs.
There's different levels ofappraisals.
You know, you can drive -bys, youcan do a full appraisal, an
appraisal app where you're takingpictures or asking the homeowner
to do those things.
So, you know.
I think if I'm providing any sortof feedback to the lending
community, you got to look atyourself and say, what's my risk
tolerance?And number two, what's the cascade
of things that I want to look at?Is it, if an insured AVM is
(25:13):
available, I want that.
If it's a desktop, because I know
it's a four hour SLA, I want that.
Is it always full appraisals?
Is it drive -bys?How often am I doing them?
Because a house that you broughton four years ago, you're about to
renew.
What do you do there?
Is it another full appraisal?Is it?
pulling AVMs.
Like there's a lot of different
(25:35):
ways that those two products andthe cascading products in between
can be used.
And, you know, no two lenders are
doing the same thing at this pointacross the country.
I hope that answers your question.
It does in any ways.
In fact, side story, just on apersonal level, the last time my
house was appraised was actuallyduring the... lockdowns in like
2020, 2021 during COVID times whenno one was allowed in the house.
(25:57):
And the way that they did it wasthe appraiser had to drive by my
home and they took some outdoorphotos, but they relied on me for
all the indoor photos and sent melike a whole Word document with
instructions of like, here's allthe 30 things I need photographs.
And I'd actually like performedby, in some ways, my own appraisal
on my house, like going around,doing all the things, showing all
(26:20):
these various rooms and mechanicalrooms and whatnot.
It was a very interestingexperience.
And then they came back, they tookall that information and then they
provided the value on the home.
It was really interesting.
And I think it showed society'swillingness to adapt to
circumstances to try to figure outwhat's the best way that I can.
get a value of this home, giventhe circumstances that are at play
right now.
So when I think about AVMs and
appraisals, I actually don't thinkabout the two things as mutually
(26:43):
exclusive, but rather things thatcan work together.
And I think that the better thatwe can make these AVMs, the less
work we can put on appraisals andless work we can put on appraisers
as human beings, and we canactually help them.
do their jobs in a better way.
Because if you talk to appraisers,
they'll tell you like, we wantthat data.
We want more information on aproperty that will actually help
us better value these homes.
(27:04):
And then we can deliver a better
and a faster consumer experience.
So you're right.
It's not one or the other.
It's both.
You know, Scott, I do have to tellyou, you are one of the few people
who can talk to me aboutappraisals and AVMs and actually
get me excited such that I'm like,hey, like the world needs to hear
about this.
You're very passionate about the
topic as well as the world of dataand properties.
(27:26):
What is that that drives ormotivates you?
Because this is, you know, 18-year -old Scott did not say
future Scott is going to be theguy who sells property data to the
world.
But what gets you out of bed every
day to do this?If you're not passionate about
what you're doing, you're doingthe wrong thing, first of all.
So, you know, I think nobodythought they would be in
insurance.
as an 18 year old.
And I thought guys that sold datawore pocket protectors.
(27:47):
And, you know, sometimes I do, Idon't have a pocket on this shirt,
but I remember I got recruited towork for Opta.
A good friend of mine who's arecruiter begged me to take the
interview.
And, you know, the more I looked
into the company, the more that Iunderstood it, I'm like, okay,
this sounds like an interestingrole, VP of sales working for this
insurance data company calledOpta.
I went in prepared, but I wasgoing into that interview.
(28:09):
to do a favor for a buddy of mine.
I was in that room for 45 minutes
and left the room and called himand I said, I have to have that
job.
So I don't know what it is.
I mean, Greg McCutcheon and ColinSmith are my two leaders at Opta
and they still are today.
And they did a really good job
getting me excited.
(28:30):
But I just love the story that
data tells.
And the fact that we have a piece
of software where any... addressacross the country, I can plug in
and see pictures of the home, allof the information about the
property, how much it's worth,what permit they pulled on the
property, the rebuild costs, wheretheir local fire department is,
how likely are they to have asewer backup.
While that may sound nerdy tomany, that is exciting to me.
(28:52):
And whether it's the real estateworld and the way that they're
using data or the insurance world.
you know, the house sigmas of the
world and all these new platformsthat are coming out.
I get really excited about data.
I don't know what it is.
Maybe it's because I'm 47 and my18 year old self wouldn't have
loved it.
But, you know, going back to golf,
like golf is an extremelyanalytical sport.
You got to know how far you hityour eight iron.
(29:14):
You need to know how far you hitit in the wind.
You need to know downwind, howmuch spin to put on it, you know,
when to use it.
Oftentimes you're playing a golf
course backwards.
Where's the pin?
Okay.
If the pin's on the left side, I
should hit it on the right.
So being a golfer, I mean,
everything in golf now is drivenby data.
You know, there's a whole industrybuilt on the pros, what they're
(29:34):
playing, what golf clubs they'replaying.
It's included in their marketingspin.
You know, this driver gives youthis much spin versus that
customization.
So I guess with golf, I've always
really been into data.
But, you know, I'm a left brain
guy.
I always love to draw.
I always loved music.
I was more of an artsy guy.
And I try and approach that inselling.
with a very right brain thing,which is data.
And it's maybe not necessarily oneproperty at a time.
(29:56):
It's the dashboard.
It's the overall themes that you
can get from data that talk aboutthe book or talk about, you know,
marketing strategies or whateverit might be.
But there's just something I getmotivated about selling data.
know, just something I getmotivated about selling data.
I don't know.
I love it.
I love it.
I love it.
And as you compare it to golf,you're right.
That is such a healthy comparisonto make.
I guess the one difference is youdon't want to walk up to your ball
(30:21):
and chunk it 10 yards like youtend to do in golf sometimes.
Yeah, you do that in sales too.
Trust me.
I hear you.
So I want to just ask you for a
takeaway.
Our audience is primarily the
lending community and mortgagebrokers.
And as you think about the worldof the mortgage broker, what is
one actual takeaway that you thinkthat they can learn from you, from
(30:43):
Opta, from First Canadian Title orFCT as it's known in the world?
Side note, I hate acronyms forthat reason, AVM, FCT.
It's impossible to keep up.
And now BrokerBot.
Like what should brokers take awayfrom you?
The mortgage brokers that I'veworked with, mortgage brokers that
I've worked with, the platformsthat we've integrated into, I
think a lot of mortgage brokerslook at data as an unnecessary
cost, whether it costs a fewbucks.
And, you know, there's a couple ofdifferent data providers where you
(31:04):
can buy, you know, data on aproperty, depending on where
you're in the country.
But, you know, we've integrated
into Newton Velocity and ScarletNetworks and a few others.
We have a few more that arecoming.
But the benefits to a mortgagebroker is you're the trusted
advisor.
You're working with a homeowner
that, you know, whether this isanother mortgage or their first
mortgage or a refinance, you know,they're coming to you for money
for a reason.
And if I'm trying to put myself as
(31:26):
a salesperson in that trustedadvisor role, I'd love to know
more than less that I can bring tothe table.
Give me the address of your home.
They pump in that address, hit
enter, and everything about theproperty pops up.
You're built, square footage,bedrooms, bathrooms, pictures of
the home, whatever that might be.
That gives you the ability to walk
the homeowner through theirbiggest asset, which is their
home, something that they'reextremely proud of.
(31:47):
So one broker could just say, hey,I'm looking at your home.
It was built in 1986.
It's 2 ,500 square feet.
Hey, looking at a picture of yourhome, what a beautiful property.
Love the gardens.
The third car garage, I have four
hockey bags.
I love the stuff in there.
Amazing.
Pump them up, right?
Yeah.
You know, your house was built in
1986, 2 ,500 square feet.
Looks here like you haven't
finished your basement.
(32:08):
Have you finished it?
Oh, no, we finished it six monthsago.
Great.
Let me just update that.
So through that excitement ofwalking them through their biggest
asset, you're actually validatingany inaccuracies in the data.
Every data provider is not goingto be perfect 100 % of the time.
I tell that to people all thetime.
You know, we're putting what weknow about the property and what
we know about the neighborhood infront of you.
(32:29):
We just ask that you do a quickvalidation process.
Oftentimes, you're not going tohave to touch anything.
But if they finished theirbasement six months ago, and we
just don't know that yet, updatethat as you're having that
conversation.
And by the way, if they haven't
finished their basement, butthey're planning to finish their
basement, you've just unlocked anopportunity.
right, to then maybe refinance orprovide a HELOC later.
(32:51):
So these are things through datawhere you can learn more about.
not only their property, but youcan learn about what their plans
to do with that property.
So after you've done that process,
and oftentimes that integrationwill pre -fill your mortgage
application for you.
So it's not like you're having to
copy and paste and bring it over.
Now your mortgage app is done with
an accurate address, accuratesubject property data.
(33:12):
And now what we're providing is amarket value understanding of the
property.
Depending on where your deployment
is, you could get a range or youcould get a specific value.
If I'm a mortgage broker, I'm notasking the homeowner, what's your
house of birth?And why I'm not doing that is I
think many mortgage brokers,lenders, anyone that's been in
this industry, if the declaredvalue you put on the mortgage
(33:34):
application is based on what thehomeowner says, oftentimes it's
inflated.
And I'll use the example.
Personal pride, Personal pride,yeah.
If my neighbor's house sold duringCOVID for 1 .2 million, yeah.
If my neighbor's house sold duringCOVID for 1 .2 million, they don't
have a finished basement, but Ido.
I'm going to think, well, theirhouse sold three years ago for $1
(33:57):
.2 million.
I have a finished basement.
Mine has to be by this point $1 .5million.
So let's go with $1 .5 million.
Even if they're doing a HELOC and
they have loan -to -values, verysmall percentage, why are they
trying to maximize that value evento one that's too high?
So if we're giving the homeowneror at least the mortgage broker an
(34:18):
idea of the value of the property,Just like the property data, they
can coach the homeowner throughroughly what that value of the
property is and give them a muchbetter understanding of the true
market value of today.
So, hey, it looks here based on
the profile of your home and themarket in your neighborhood that
your house is worth somewhere inthe 1 .5 million to 1 .1 million.
Can we start the process with thatin mind?
(34:39):
Sure.
Okay, let's put 1 .1 million in.
So we put in $1 .1 million.
That mortgage application goes off
to the underwriter.
You've now provided the
underwriter an accurate address,an accurate property data profile
on the subject property for theirapplication, and a much more
accurate value of the home, inwhich case they can take that and
make a much more educateddecision.
Many times, you know, if themortgage broker doesn't know
something, they'll put in anasterisk.
(34:59):
The most widely used postal codein all of the lending community in
Canada happens to be old St.
Nick's HOHOHO, if you didn't know.
Many brokers pump in HOHOHO oranother postal code.
Ask your underwriter how they feelabout that, because I'm telling
you what, that application isgoing to the bottom of the pile.
They're sending an email back toyou and they're going to get back
to that application.
when they have the time or when it
(35:20):
comes to the top of the pile.
So do yourself a favor, whether
it's a couple bucks here, a couplebucks there, pull the data, pull
some accurate information and pullan accurate market value because
you're going to provide a bettercustomer experience.
You're going to get betterreviews, a much more efficient
workflow, and it's not going towaste your time.
You can move on to the next one.
(35:40):
You don't have to worry about that
one that keeps coming back.
Now we go back to appraisals on
this.
Now you've got an accurate
application with an accuratevalue.
Remember that cascade of productsthat we talked about, the insured
AVM, which is an instant SLA.
You've got desktops, you've got
drive -bys, you've got differentflavors in that cascade.
The only way that you're going toget that insured AVM or one that
(36:04):
is a faster SLA is if all the datais correct.
The If the value is 1 .5 million,but the AVMs are coming back at 1
.1, 1 .2.
You're automatically being sent to
a full appraisal, which, you know,looking at what's going to be
going on next year with all themortgage changes.
I think Korea is calling for fiveor six percent increase in home
sales.
Cost of borrowing is coming down.
So the appraiser is very similarto COVID.
They're going to be stretched fortime again.
(36:25):
And, you know, I think at onepoint we were waiting a few days
to a few weeks, depending on whereyou were in the country.
Do yourself a favor.
Spend a few bucks and get that
data accurate up front.
Because it's only going to provide
a better experience for everybodythrough that whole piece.
Mortgage brokers who rely heavilyon Google reviews, these show up.
(36:48):
You're saving the underwriter.
Five minutes, six minutes.
I talk to underwriting teams thatuse our data all the time.
Do you know how much money fiveminutes is to an underwriter?
It's worth a lot more than whatyou're paying for the data.
That's my whole point.
Sorry, passion again.
I think you said it so well thatwe land the plane right there,
think you said it so well that weland the plane right there, Scott.
(37:11):
Because that's motivating foreveryone listening.
So some great actionable takeawaysfor all of us.
Some great information.
Even I learned some things.
And I hope everyone learns somethings from this episode.
So thanks for being on with me.
We'll talk soon.
Thanks for having me, Beckham.
Appreciate it.
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