Commercial Real Estate is the Largest Asset Class in the U.S.

An avid entrepreneur, Richard Sarkis has launched and run numerous businesses and most recently, he was an Associate Partner at McKinsey & Company specializing in Financial Services. In 2013 he co-founded Reonomy, a commercial real estate data and analytics platform he hopes will democratize the world of commercial real estate data, bringing CRE into the 21st century and in line with other capital markets. Richard Sarkis joins former SEC chairman Arthur Levitt to discuss his company Reonomy

Richard Sarkis joins former SEC chairman Arthur Levitt to discuss his company Reonomy, which launched its first platform in New York City in 2013, and in 2017 they launched a second nationwide platform. 

Host: Arthur Levitt Producer: Madena Parwana 

See omnystudio.com/listener for privacy information.

Episode Transcript

Speaker 1 (00:00):
This is a closer look with Arthur Levitt. Arthur Levitt
is a former chairman of the u S Securities and
Exchange Commission, a Bloomberg LP board member, a senior advisor
to the Promontory Financial Group, and a policy adviser to
Goldman Sachs. An have An entrepreneur, Richard Sarkas has launched

(00:21):
and run numerous businesses, and most recently was an associate
partner at mckenziean company specializing in financial services. In two
thousand and thirteen, he co founded Rehonomy, a commercial real
estate data and analytics platform that he hopes will democratize

(00:43):
the world of commercial real estate data, bringing commercial real
estate into the twenty first century in line with other
capital markets. Reonomy launched its first platform in New York City,
and in two thousand and seventeen they launched a second
nationwide platform. He joins me now for a closer look.

(01:07):
You consider the disruption realomy brings to the commercial real
estate industry is in transparency and the democratizing of data.
Rich What does this actually mean? Well, if you look
at commercial real estate, it's um by all intents and purposes,
the largest asset class in the US and yet it's

(01:27):
been one where you've had asymmetric information advantage, and it's
that information that insight knowing that a building is a
good site for redevelopment, or that a building is listed
for sale or maybe under leverage. That type of insight
and information has basically been held in the minds of
a few, and trading on that information has been how

(01:50):
folks have made money. But increasingly we're seeing that the data,
in the information that's available in either the public domain
or in the private domain through third party vendors, can
be used and harnessed to help inform those decisions, and
as such, you're giving access to a much wider birth
and wider population of folks to help trade on that information.

(02:13):
Who would be your principal customer UM? So when we
first started, we essentially had three main UM stakeholders or
food groups that we cater to. On the one side,
we had those on the equity side of the transaction,
So those are the large institutional investors, developers, some hedge funds,
private equity that are essentially buying buildings or portfolios of assets.

(02:35):
On the other side of that trade, you have folks
on the debt side who are looking to secure the
mortgage or refinancer property. Those are big banks like Chase, Wells, Fargo,
et cetera. And then the third big group are the
ones who are trying to broker those transactions. So the
brokerage firms, large firms like a cbr E and new Mark,
Knight frank At j l L. Yeah, in a sense,

(02:55):
they view us like the Bloomberg for buildings, where we
cover the information ation on an asset by asset, of
building by building level, the same way you'd go into
Bloomberg to get information about a company before trading that stock.
But how do they pay you for that information? Um, Well,
so we've got a pretty wide suite of products on
on one end of the spectrum, we've got an easy

(03:16):
to use, lightweight web application. So it's almost like Zillo
if you know that for residential. So it's the Zillo
for commercial real estate, where they pay us a monthly
fee and acts in exchange for access to our website
and so they can search a building, they can search
a market, a specific asset type, and so that's a
recurring monthly fee that they pay us, ranging from anywhere

(03:37):
from forty bucks a month to several hundred dollars a
month depending on the features and the data that they have.
And then we've got some larger clients, so the banks
to insurance firms, brokerage firms that actually not only by
the data from us, and that's volume based based on
how much data they're licensing on an ongoing basis, they're
also leveraging our core applications on our algorithms to help

(03:59):
cleanse and enriched their own data. Which is your principal
market the first go the second and larger institutional group
that you've just described. Great question. So when we started,
we only had one and and and as you said
in the intro, we were only focused on New York
City and we had that one um zeloesque product, if

(04:20):
you will, And so that was everything we had. And
we didn't know that we had a second market, so
to speak, until we stumbled on it. Until we realized
that we were pulled frankly by our clients to say, Hey,
this stuff that you've developed and that has enabled you
to scale that product that all our brokers, are lenders,
our loan originators use, uh, that same technology would be

(04:43):
very useful to us for our own data. Would you
be willing to make it available to us as a product,
and that's when we about twelve eighteen months ago, realized
that there was a big there there and that since
then has really taken off as a huge area of
growth for us. So they're roughly at this point probably
about fifty fift either equal uh and both are growing rapidly.

(05:04):
But the adoption on the enterprise side has has certainly
been very acute in the last twelve months or so.
How does your approach to data differ from traditional processes?
So traditionally in this space, and you've got a few
large publicly traded legacy incumbents that have collected the data
that again has resided in in the broker's heads, if

(05:27):
you will, or or or the lenders, because they're the
ones doing the transactions. They collected that data the quote
unquote old fashioned way. They had call centers full of people,
and they still do to this day, calling the brokers,
calling the landlords, calling the lenders, getting information about a transaction,
in putting it into a database and then selling it
back to the people ironically who gave them the data

(05:49):
to begin with. And so if that is a human
driven approach, we have a diametrically opposed business model that
leverages machines, algorithms cod computing to gather there and cleanse
that data. So it's pretty diametrically opposed to what's been
out there in the past. Property ownership is often obscured
by opaque ll c s. How do you cut through

(06:11):
this and get information? Yes, that's one of the And
to me, as somebody who was not a commercial real
estate insider when I started this company, that was somewhat
counterintuitive that here you've got these massive buildings and yet
it was very difficult for folks to actually ascertain who
owns them. Because you know, one to three Main Street
would be owned by one to three Main Street LLC.

(06:32):
That doesn't really tell you much as a potential equity
player or a debt player, who's behind that and importantly
what else they own and might be exposed to. So
what we do is we have these algorithms that collect
all the data that's out there in the public domain
mortgage filings, violations, permits, UH, tax filings, and they take
all that data and our machines and our algorithms are

(06:54):
able to stitch all those data points together and follow
the digital bread comes if you will uh to go
from one LLC to the next and ultimately to the
parent companies and the principles associated with them. It's a
series of rules if you will that do that rich
in the beginning, Why was commercial real estate the industry
that had particular interest for you as being ready for disruption?

(07:18):
Where did the idea come from? So my co founder
and I met or were introduced actually by one of
the venture capitalists that I had known earlier in my
career as an entrepreneur, and I was really struck by
three things. One is the sheer size of the asset class. Again,
it's the largest asset class in the US, about fifteen
trillion dollars of value at stake on the dead inequity side.

(07:39):
I was also struck by the fact that there was
a lot of inefficiencies and pain points where people weren't
able to collect or analyze data in a way that
they were used to in other areas of capital markets
and trading securities and derivatives, etcetera. And last, but not least,
I was very excited by the fact that there hadn't
really been much technological in a vation, if at all,

(08:00):
in this space. So I thought Taking those three things together,
we're a recipe for a potentially very large outcome and
and and a valuable company. Well, I can see where
this data would be extremely valuable. I think people in
our audience would like to know whether we're talking about
a niche set of big players or whether there's diversity

(08:23):
in terms of your customers and how they use reonomy.
There's very wide diversity. So on one end of the spectrum,
we've got lightweight web application tools like a Zillo, and
they are used from anyone from core real estate professionals
that you'd expect to be users like brokers, lenders, uh
those loan originators, developers, but also anywhere from a roofer

(08:45):
contractor and HVAC installer. What we have what we call
storm chasers, which is very topical with a hurricane Michael,
folks who literally want to follow the path of a
storm and find every single building of a certain size
within that so that they can contact the owners of
those buildings, get in touch with them, and help them
with the repairs post storm. Uh. So it's a very

(09:07):
wide array of users, some of which are are relatively unexpected.
I certainly didn't you know, I think that I was
going to be building reonomy for storm chasers when I
started the company six years ago, but we've seen a
very wide adoption across a wide birth of of users.
Tell me more about that. Are you suggesting that after

(09:29):
the storm, investors went to your site and then requested
information on specifically which homes were damaged and which parts
of where the storm hit exactly. So they used what's
called our interactive MAC feature where they were able to
overlay the path of of a storm. And they do
this not just with the major hurricanes, but hail storms

(09:49):
throughout the Midwest. And they basically want to then layer
on every single rental building of a certain size so
that they can get in touch with those building owners.
They those people work with the insurance companies and can
conduct repairs on behalf of those insurance companies. So there's
obviously great benefits for the community. UH and up ontel reonomy,

(10:11):
they didn't really have a way of doing that at scale.
They would literally be driving around looking for properties with damage,
trying to figure out, well, how am I going to
find out who owns this building? In some cases almost
staking them out for hours on end before they can
get in touch with somebody and and try to get
to repair their property. Who are your largest clients now?

(10:33):
So our largest clients arranged from large brokerage firms like
a cbr E, Akushman, Wakefield, a new Mark, UH two
large lenders like Chase, and then we even have other
occupiers like we work as a very large client of
ours UH and they and they use a lot of
our data to help inform some of their decision making processes.
What kind of information can you provide a potential real

(10:57):
estate investor? For example, how could you help me and
identifying properties that are likely to sell or in need
of being refinanced. Yeah, and I think that's sort of
the holy grail as to how do I get the
predictive analytics to know that something is an opportunity before
everybody else does. I'll give you one very topical example.
Everybody's talking about these opportunity zones that were created the

(11:20):
ladder half of the latter part of last year, about
these zones that if you invest as a developer, you'll
get some pretty substantial tax breaks. The problem is that
it's very difficult to actually find which properties lie within
those UH with the within those opportunity zones, and then
get in touch with the owners and developers to try

(11:41):
and do an assemblage or build something. So with our platform,
you're actually able to overlay the opportunity zones UH to
the the actual assets, and as a developer, you'd then
be able to contact the owner of the current site
and make an offer and then try and build something
over there and get the huge tax breaks are associated
with that, and then ultimately do what they're designed to,

(12:03):
which is spurred development and economic growth within those regions.
Is this going to put brokers out of out of jobs?
What's fun? You say that? When I first started, I
thought that the brokers would sort of rebel against me,
that they were going to feel that there was going
to be a distant disintermediation of of their job right
Because if all the lenders have it on the one hand,
and all the developers have access to all this data

(12:25):
on the other, what good is a broker going to be?
So to speak, What I found is the polar opposite
is that brokers don't want to just take money because
their toll takers and they just have to be there.
They want to add value to their existing clients. They
want to find new perspective clients and they want to
establish a true trust based partnership with those users, and
so they are using rehonomy now to preemptively and proactively

(12:48):
find the right development sites for their developer clients and
find opportunities. So we've actually seen some of the strongest
adoption of reonomy with the brokerage community. Rich Will you
innovate in house only or are you looking for acquisitions? Well, primarily,
to date, we've really done a lot of our or

(13:08):
most or if not all, of our R and D
and our development in house, and we've developed our own
proprietary algorithm algorithms and the data engine. But as we've
now started to scale and become a more prevalent data
platform nationwide, we are starting to look at making uh
strategic acquisitions, either as potential channel partners or some smaller

(13:31):
data players in certain specific niche markets or areas. Is
there a any human customer service at Reeonomy. Absolutely, that's
a big part of what we do. We're huge believers
in and having the machines do the grunt work and
the manually comb and crunching the numbers and and running

(13:51):
the algorithms. But that just as important as that is
having that human touch, that human presence, and so we've
got a world class what we call our Clients Success
Team that is dedicated to the success of our clients.
Each one of our users, whether they're John Doe developer
in Albuquerque, New Mexico, or Chase Manhattan Bank, UH, they

(14:12):
have access to a dedicated person who's in charge of
ensuring that they're using the platform, that they have their
questions answered. So there's a big human element from a
CS perspective. You said that starting a company and scaling
it are two very different things. And when you did
start to scale and expansion to Los Angeles proved to

(14:33):
be more of a challenge than you expected. Did you
find that any part of your business model needed to change. Absolutely.
That was a big lesson learned because when we started
in New York, we had one way of compiling the data,
and when we took on additional capital from BING Capital
with a view to scale our platform nationwide, we naturally thought, well,

(14:55):
we're going to do exactly what we did in New
York and the second largest CR market and the West,
which is l A. The issue is that the l
A data ecosystem was completely different than New York, so
it just simply didn't translate, and essentially we had to
go back to the drawing board and find a different
business model or a different way to scale. How long

(15:16):
did it take you to do the national launch? Well,
in hindsight, I can almost chuckle and say, you know,
it took um exponentially longer. It took almost three years
to do of real heavyweight R and D and developing
what is now a machine learning driven data engine capable
of ingesting data across all three thousand plus counties in

(15:36):
the US. UM. The reason I chuckle is because when
I was first presenting my idea to ban capital back
in it was I was telling them that it would
take a quarter maybe two at the outset long shot
to be able to scale it, and and it proved
to be um anything but as straightforward as that. Recent

(15:58):
data suggests that the majar of high growth startups fail
due to premature scaling. Are you going to avoid this faith, Well,
it's something that I'm very mindful of because what happened
when we were getting ready to scale, and we were
successfully launched in New York and looking to do l A,
I committed, frankly the cardinal sin of scaling before I

(16:22):
was ready to and what you just asked about, we
started hiring folks. We even hired sales folks in l
A before we had launched, and then when we saw
that it wasn't gonna work, we have to make the
unfortunate decision to shut down that part of the business
in order to be able to redevelop a platform that
was gonna work. So now having gone through that, it's
something that I'm very mindful, I don't want to make

(16:44):
that same mistake twice. What do you offer an enterprise
client like we work? For example, what we offer is
essentially two things. Our platform enables them to weaponize their
own data. We Work is sitting on top of a
treasure trove of data that they're generating on all the
spaces that they're at leasing, buildings that they're buying, and

(17:05):
even things that they're not transacting on and just looking at.
We have a series of applications that allows them to
normalize and stitch together all that data so that it
becomes much more usable and informative to them. And the
second thing is we can enrich that data with all
the other data that we bring to bear across the
properties that they're tracking and other properties and markets that

(17:26):
they care about. Rich, how did you pick your board? Well,
in some instances, the board picks you. So when our
investors like Bain Capital, Soft Bank, s ap UH invest
in US, they usually as a precondition of investing, will
take a board seat. So that's why you need to
vet your investors really well, because you're not just taking

(17:47):
their money, You're really signing up to work side by
side with them. And in other instances, we've got independent
board members who come from such companies s s NP
and FORRNADO who bring tremendous subject matter expertise and help
from a business point of view, UH answer some of
the harrier business problems and questions that we have. Rich

(18:08):
consruption of the real estate industry has only just started.
So what will the future bring and when will contracts
start moving to the blockchain. That's a great question and
one that I get asked quite often. I'm a big
proponent of crawling before you walk, before you run. I

(18:28):
think blockchain has a lot of great potential applications. I
don't think that commercial real estate is in a position
where it can necessarily unlock a lot of that value,
for the reason that a lot of the data right
now is still very fragmented, even within a company. I
think we're trying to help companies overcome that problem. And

(18:49):
once they have nice, clean, normalized data, the blockchain is
a great way for them to uh then have the
transactions have a great audit trail. UM. But I think
there's a little bit of work that needs to be
done in the coming year or two to be able
to get the industry in a place where it can
really take advantage of what the blockchain has to offer.

(19:10):
Is it easy to raise money now? And is attempting
to over build the company? It is? And UM one
VC that I respect a lot. UH said that, you know,
most startups fear that they're going to die of famine,
but a lot of them die of gluttony because they
raised too much money. And when you have a lot
of money in the bank, you feel compelled to spend it,

(19:31):
and then you overspend and that can read to some
very bad decisions and spending gets out of hand. Uh
and next thing you know, UH, you know you've raised
that a high evaluation, but you don't have the metrics
to justify that. So it's something we've tried to be
very mindful of and from the time where we were
trying to figure out how to scale our company from
New York to national It took us over three years,

(19:53):
and we didn't raise a penny in between that time
because we knew that our business wasn't ready to support
that additional capital and allow us to scale. Now you're
tracking CHECH growth to find which market one should be
considering investing in next. Where are the new tech hubs
you've identified? So what we're seeing is a lot of

(20:15):
activity in southern California. L A, certainly in l A
over the past few years, has a lot of well
publicized UH tech activity with Facebook, Google coming into such
markets as Santa Monica and Venice. What's interesting to see
is that further migration UH south is now starting to
touch San Diego, and that's starting to be a very

(20:36):
UH fertile and and um big market from a tech
growth perspective. Is the commercial real estate market in the
western part of the country more attractive than New York
right now? Right now, it seems to be. The data
suggests that l A is white hot UH in some
asset types. In New York, you're seeing a bit of

(20:56):
a slow down others are are are still doing well,
but l A and southern California in particular, UH seems
to be very bullish right now from a CR investment perspective.
You've also mentioned Philadelphia as a booming commercial market. Why, yes, Well,
what's interesting with Philadelphia. There's a lot of vacant land

(21:17):
in Philadelphia that's ripe for development. So what you're seeing
is a lot of developers move in, UH, snap up
some of that vacant land. Ideally assemblages of vacant land
that's uh contiguous and develop because what we're seeing is
and in some cases it's almost becoming a commuter city
to New York, where some folks are are living in

(21:38):
Philadelphia and working in areas around there. And then it's
also developing its own more robust economy, and there's a
lot of opportunity to build and develop from scratch over there,
given the prevalence of vacant land. You've done a remarkable
job and it's a great story. Did you always set
out to be your own boss or did you the

(22:00):
ideas that needed development and that's how it happened. I've
always been enamored with the notion of building something out
of nothing. I went to a French high school growing up,
and I remember when I was about eight or nine
years old, the teacher was trying to explain to us
what an artisan was, and the way she framed it
as an artisan is somebody who takes great satisfaction and

(22:20):
derives a sense of purpose from building something out of nothing,
from seeing a finished, beautiful wood chair from a pile
of twigs. So I've carried that analogy forward with me
in my business career, and that's the part of being
an entrepreneur, UH that I find very intoxicating and fulfilling.
Reonomy isn't your first startup. What mistakes did you make

(22:41):
in the past, it helped you with this new venture.
I've made a ton of mistakes, and I like to
joke that I've lost my hair as a result of
the stress that it can cause. UM part of those
mistakes is being defensive and maybe not being ego less
about the decision making process and admitting that it's okay

(23:02):
to make mistakes and in fact, you're invariably will make mistakes.
It's how you react to those mistakes and how you're
able to course correct that is going to make a
company and you successful versus not making mistakes to begin with,
who told you that the idea would never work and why?
Um So, we got quite a few rejections early on

(23:22):
from vcs that said, look, if it ain't broke, don't
fix it. A lot of folks in commercial real estate
are making money handover fest. Certain people are going to
feel threatened, uh, which are all very good reasons. But
I go back to the fact that it's just such
a big asset class that is ripe for disruption that
I felt very compelled and I had that courage of

(23:43):
the conviction, so to speak, that there was a there there,
And that's part of the folly, if you will. The
madness of an entrepreneur is you've got to be a
little bit crazy to persevere in the in the face
of adversity or or data that suggests that it can't
be done. Rich is the company profitable at this time.
So we're growing very uh robustly on the top line

(24:04):
in terms of profitability, we are not cash flow positive
or profitable. We're choosing to reinvest all of our dollars
back in the company because we're in this hyper growth
mode and we really want to keep growing from a
customer brace perspective versus necessarily optimized for profitability. At this point,

(24:24):
he is a serial entrepreneur, former associate partner at McKenzie
and Company, and his new venture, Realomy seeks to democratize
the world of commercial real estate by making all the
relevant data transparent, bringing real estate information and data into
the twenty one century in line with other capital markets.

(24:48):
Richard Sarcas, thanks for joining us. By the way, if
you have comments about the show or suggestions for topics,
please email me at a Closer Look at Bloomberg dot net.
That's a closer Look one word at Bloomberg dot net
and follow me on Twitter at Arthur Levitt. This is

(25:08):
a Closer Look with Arthur Levitt.