Rich Sarkis  0:00  

We have a number of municipalities, tax assessors that use our system, ironically, to vet their own data because ultimately, because we’re multi sourcing it will have better or more timely data than they have access to. But to take it a step further, which is where you’re going, this has been a hot topic of conversation at a board level with us, because given the fact that we’ve got the system of record the Reonomy, ID, which you use the term backbone, which is exactly how we think about it, then yes, if you go all the way upstream, the natural extension is right at the source, if you can be if the Reonomy ID can be embedded as your recording and mortgage recording and deed transaction etc. Then to your point, it eliminates a lot of the friction, the cost the inefficiencies that are going on and that we’ve seen now during the COVID crisis, right when a lot of these assessors have had to shut down because literally the people can’t go there to record the mortgages because they do it the old fashioned way, right. It’s it’s analog. It’s not digital.

Announcer  1:00  

Welcome to CREPN Radio for influential commercial real estate professionals who work with investors buyers and sellers of commercial real estate coast to coast whether you’re an investor, broker, lender, property manager, attorney or accountant We are here to learn from the experts.

J Darrin Gross  1:20  

Welcome to Commercial Real Estate Pro Networks CREPN Radio. Thanks for joining us. My name is J. Darrin Gross. This is the podcast focused on commercial real estate investment and risk management strategies. Weekly we have conversations with commercial real estate investors and professionals who provide their experience and insight to help you grow your real estate portfolio. Today my guest is Rich Sarkis. Rich is the executive chairman of Reonomy, a leading provider of property intelligence to the commercial real estate industry. And in just a minute we’re going to speak with Rich about Reonomy and how you can utilize that to help grow your business. But first, a quick reminder, if you like our show CREPN Radio, there are a couple of things you can do. You can like, share and subscribe. And as always, please consider leaving a comment we’d love to hear from our listeners. Also, if you’d like to see how handsome our guests are, be sure to check out our YouTube channel. And you can find us on the YouTube at Commercial Real Estate Pro Network. And also there if you would please subscribe that always helps the the algorithm with the YouTube. With that I want to welcome my guest. Rich Welcome to CREPN Radio.

Rich Sarkis  2:42  

Thanks for having me. Although I fear I’m going to disappoint your guests when they go on YouTube. And this is not one of the handsome guests. I’ll try to make up for it with insightful comments. 

J Darrin Gross  2:52  

Well, it’s always kind of a fun, fun, fun thought there but now you’re fine. You’re fine. lighting is good. So we’re good shape. So hey, before we get into this, if you could just take a second and share with listeners a little bit about your background.

Rich Sarkis  3:10  

Sure, sure. So I actually grew up in London in the UK, but came over to the US several decades ago to go to school here and I never left. And it’s actually in school in college in northwestern Massachusetts that I caught the entrepreneurial bug. When I was a 19 year old, Junior, and started my first business many moons ago, the sexy way of saying that one was it was an international arbitrage business. What I found out is that you could find the exact same textbooks that were sold here for hundreds of dollars for a fraction of the cost overseas primarily in the UK. So I set up a whole supply chain and a network of student reps promoting these books. And that’s really was my first business and where I caught the bug as I mentioned, and I started and sold a number of businesses nothing really huge, but enough to sort of make ends meet over the subsequent years. And then I went to the dark side, went to business school because my liberal arts education was fantastic, but a education in business principles, it was not. So I went to Wharton, back in ’05 thinking that I’d meet a new founder or to make a bit of a miscalculation, because back then everyone at Wharton wanted to go into investment banking, sales and trading private equity. And so I was left sort of clutching at straws when it came to entrepreneurial sort of camaraderie for lack of a better term. So pure auction based reasoning, I went to management consulting, went to work at McKinsey in New York. Again, thinking that would be a great you know, not way to not pigeonhole myself into one specific sector or career and that out, after a couple years go and launch a new business.  Ended up staying in McKinsey longer than I thought. The work was very Interesting. They’re good at sort of promoting at the right time and all that stuff back. But after about four or so years there and had just been promoted to associate partner, that’s when I realized I wanted to go back to my entrepreneurial roots. And that’s where, really Reonomy was born in the summer of 2012. So eight years ago now.

J Darrin Gross  5:20  

Got it? Well, you did. I mean, I don’t know that much about McKinsey. But they’re always cited and all the big studies and kind of a finger on the pulse of what’s going on in business. So,

Rich Sarkis  5:34  

Yeah, it was a lot of interesting stuff I started is pretty eclectic. My first ever project was doing a revenue assistance model for the National Basketball Association. So that was pretty cool. I get to hang out and they’re in their HQ Fifth Avenue and sort of ogle the stars and stuff like that. So it was it was fun.

J Darrin Gross  5:53  

Yeah, no, but that’s a little bit different scene today. The COVID stuff Well, great, I appreciate you sharing a little bit about your background there. And so what I’d like to do is kind of talk a little bit about Reonomy. And and kind of if you could, could you basically just start with describing, you know what it is? 

Rich Sarkis  6:18  

Sure, sure.  So maybe if I describe what it is today, it’ll be helpful for the listeners to hear what it was and what it started off as because there’s obviously been a big evolution and a, an increase in the number of products and features that we have. But initially, my thesis was that we needed to do for non single family homes, what others like Zillow and others had done for this single family home discovery experience. So when you and I would want to research our dream home or find out whether we should live in this town or that town we turn to Zillow, and unbeknownst to us because it’s a very easy to use tool. Use your Map based, searching your filters, there’s a lot of heavy computational work that goes on, not just with the zestimate and giving you an assessment of value, but also being able to bring all those myriad of different data points across all the MLS and all that stuff. So and that didn’t really exist at a ubiquitous level nationwide. And so that was the thesis. can we leverage technology on the back end to crunch all this data? leverage modern, elegant, intuitive software on the front end, so it is not heavyweight and burdensome to use? Right? It feels just that modern, right? 

And so we set about doing that in New York. And lo and behold, after about a year of development, we had built a better mousetrap and the reason we knew that is because big lenders like JP Morgan, big brokerage firms like Cushman Wakefield developers, like Tishman Speyer, all their users were using our platform in New York City. To find information about properties, do some research on a sub market. A drill down and find out who actually owns the property. Who holds the debt, is there note holder all that stuff and it was very easy for them to use. So they used it day in day out.   We used to like joke back then because we would pour over the the usage metrics that we could actually tell when people get up from their desk and go to the bathroom because there would be a 10 minute gap in their usage. That’s how much they’re using it right off the bat. 

And so basically, that has evolved. And now we’ve got a platform that for the on the web application side, again, that the commercial version of Zillow is now available available nationwide coast to coast. And so we have scores of not just the brokers, lenders and investor developers, but we’ve got a lot of field service providers. We do have a lot of insurance brokers using our platform, as you know. And so it’s become this ubiquitous web portal where you can find out information on anything that’s not a single family home. And as the as the months have become quarters have become yours. We’ve also set up a slew of different products for medium sized companies and even large fortune 50 fortune 100 companies where they can access our data, our analytics, our insights by API and other means.

J Darrin Gross  9:15  

Got it. So the when you started off at sound like you were the whole idea was basically kind of the Zillow for the commercial property. Was that that basically the

Rich Sarkis  9:26  

Yes. On the on the web application side. Exactly. Yes. And one thing that was very interesting, and it was pure happenstance, and because we stumbled on this by doing that, because if you think about it, you can sort of wrap your hands around New York City, right? It’s the five boroughs. There’s a common data standard, borrow block, and lot. There’s something called the BIN to building identification number so you can take all these disparate datasets across these different government agencies and make them relational, but we really had a huge struggle when we have to try and replicate that nationwide because Now you’re talking 3000 counties plus 20,000, plus municipalities, each with its own data model. And so whatever we have built in New York sort of threw up on itself when we try to go to other markets. And what we ended up doing is creating our own data standard called the Reonomy. ID, which is a unique identifier the same way you and I have a social security number, which is our unique identifier. The same way in other areas of capital markets. There’s something called the CUSIP that all broker dealers, all lenders, all investors agree is the unique identifier for any company. We established that for the properties, right. And we built a huge machine learning knowledge graph, basically, that takes all of that data and is trained to resolve any record in a public or private data set to that unique ID. And that’s really our unfair competitive advantage and how we’ve been able to have not just a massive ubiquitous web application that covers every time every municipality No matter if it’s a, you know, one of the largest NFL cities or one of the secondary, tertiary, or even smaller towns in the middle of nowhere, but also how we’ve been able to stand up those enterprise products that do a lot of that entity resolution for our customers, again, fully automated, but then are able to layer on all these data sets on top of that.

J Darrin Gross  11:22  

Got it. And so that the starting point, I guess I would ask, because I’m familiar with, you know, different municipalities and the way they they show the data and you’ll specifically look like where I’m at in Portland, Oregon, and the county I think it’s called Portland maps comm has a fairly rich program. It’s gotten I think it’s called Portland maps calm that’s that’s the name of it, but it it basically it goes to the lot, you’ve got a map and it’ll show the ownership and then it will show permits, it’ll show all the stuff but if you go across the county line to any of the other counties, it’s all like, you know, a whole new language. Yeah. All hidden in different places and all that. So I’m curious is you, you mentioned, you know, if you start in like one city, I could see how it’d be fairly easy from a standpoint of familiarity and create that model is you grew to nationwide. Yeah. The challenge is, how did you overcome the challenges? I mean, you mentioned the Reonomy ID number.

Rich Sarkis  12:31  

Yeah, that and that’s how we did it. I mean, look, I sort of fast forward through all the pain and then the fact that I lost all my hair doing it, and but it took years to do and 10s of millions of dollars of R&D, because initially, frankly, we did not realize what you just articulated, which is there’s a huge heterogeneity in the data, accuracy in the data availability across county lines and across municipalities, right. And there’s no federal oversight to say this is how you should Should I record a mortgage? This is the data standard or anything like that. And so as a result, what we did is we were naive. And we said, okay, well, let’s do what we’ve done really well in New York and the second largest city, which is LA. And that’s where we realized that’s where we had our oh shit moment pardon my French, which is the fact that LA LA County is made up of over 80 different cities and municipalities, right.  Encino, Beverly Hills, Calabasas, the list goes on and on. And each one of those is its own unique data snowflake. And so that’s where we really realize the size of the task that was ahead of us. And that’s where we went back to the R&D drawing board. And we were able to, luckily, thankfully come up with this idea to say, well, let’s build our own identifier because none existed out there. 

Each one of those municipalities had a different way of recording and a different unique identifier, but let’s build our own one nationwide. And that’s truly turned to machine learning and AI Because we could not come up with all the rules ourselves across all these datasets, because when you zoom out and you look at all the non single family home, your your properties you’re talking about including vacant land about 50 million commercial assets, tax lots. So even if you’re just comparing two datasets, that’s 50 million times 50 million, that’s a really big number, a number of permutations on how you can match. And so we for the first time, this is about four years ago really turned to AI and machine learning and we’re able to crack the code doing that. So to answer a question, we returned to technology, truly not just from a marketing gimmick, point of view, to be able to to crack that code.

J Darrin Gross  14:44  

Got it. So you you start out with an idea you you have a plan and to work into one area, you go to the next market, you realize, you know, one market doesn’t work like the other market. In each case, are you basically scraping data from public sites?  Is that kind of were the starting point?

Rich Sarkis  15:04  

Good question.  Good question. So what we what happened is when we realized we had hit paydirt with this Reonomy, ID, and this machine learning classifier that enabled us to not just light up the top markets, but every market in the US, we said, well, let’s get as much data as we can, right? Because now we sort of eliminated the barrier to entry to stand up additional markets, as well as the barrier to entry to normalize and validate and cleanse data sets. Right? So what we did is yes, we collect at the source, but we supplement and that source being the local municipality, the tax assessor, the Secretary of State, but we’ve supplemented that with large third party partnerships that we have many of them exclusive. We’re really the only ones in commercial real estate that get access to that data. So these are companies the likes of Dun and Bradstreet, Corelogic, Black Knight, First American, Experian, the list goes on and on. We have these What I call our raw materials or ingredients, right? We’re like the chef, we have these very unique recipes, where we know how to take all these amazing ingredients, combine them in a way that makes the best cakes available. And that’s really what our end users are buying from us or licensing, whether it’s through our web application or API’s or data feed products. It is the reonomy dataset, which is derived, which takes all this raw materials across public and private data sources, as we mentioned, and compiles them in a way that is informative and valuable.

J Darrin Gross  16:36  

Right. And I think one of the things is a user just having the uniform, the uniform presentation, yeah, data is, you know, just saves a lot of brain cells. When you know that you look at one property and you can look at another property and you’re going to get the information in a similar way. You don’t have to become a programmer to try and understand 

Rich Sarkis  17:00  

Exactly, you don’t need to you don’t need a PhD to figure it out. And look, that is that is a very important point you make because that is something in general, but especially I found in commercial real estate that gets overlooked is the UI, the user interface, the experience the the consistency of it, and making sure as I said, at the onset, it is modern, you know, my iPhone is essentially a supercomputer, but a three year olds can use it, right? And so getting to that level of sophistication and heavyweight analysis on the back end, but to some extent, shielding the end user from that and making sure it is consistent, elegant, intuitive and usable. is a very important flip side of the coin of machine learning and AI, it’s the delivery how you deliver it is crucial as well.

J Darrin Gross  17:47  

Got it, so is your business. Would it be fair to kind of call it a data aggregator is it I mean as far as you’re going out, and again, 

Rich Sarkis  17:58  

Part of what we do that that that is Part of what we do I view that as means to an end the end being, what are the insights we can give? Really, it’s it’s an intelligence platform, right? Because we are providing intelligence, property intelligence, but also intelligence on companies and people and how they relate to those properties. As you and I were chatting before the show, right? But before we can do any of that, you’re absolutely right, we need to be able to aggregate disparate data sets that are messy, fragmented, and, and make them relational, basically put them in order, again, the same way as chef, you know, prepares all the ingredients before they figure out how to how to cook the dish. That is a necessary means. And that is a big part of our core IP. And our core competency is we’ve developed a system that’s just really good at aggregating data.

J Darrin Gross  18:50  

Gotcha. And as far as the the data that you’re collecting, is there any I guess Quality control for lack of a better word?

Rich Sarkis  19:02  

Yeah. 

J Darrin Gross  19:03  

To recognize, I guess, kind of boots on the ground to confirm the data, or how do you guys police that?

Rich Sarkis  19:10  

Yeah, there’s multiple tiers of that. Really three main legs of that stool one is programmatically, those machine learning systems have built within. And that’s where the learning part of machine learning, we’re constantly retraining it with training sets, whether it’s new data sets, or training sets that come from the usage of our platform, the users themselves using the platform, and basically being able to signal an incorrect or incomplete data set. It feeds back into the system and it gets smarter over time and gets better at identifying what the correct value for an individual data point and we’re not perfect. We never will be but we’re constantly getting better. And every day that goes by as a data we are iterating or the system iterates and evolves and becomes stronger. So that’s the sort of technological component. 

We do. amplify that with our own in house research team, it’s not a big research team, but it’s sort of we call it the SWAT team that goes deep and does two things. One is they help curate those training sets that are so valuable to the system. But they also help to crack the code on some of the really hairy problems, the edge cases that perhaps are not as well suited for the system. So that’s number two. And number three, and I alluded to this is we’ve got this unfair competitive advantage, where we have 100,000 plus users on our platform, that web app, the Zillow like experience, and their usage, and we never take, you know what one user sees or does and share it or everything, but in aggregate, when you can take all that usage data that is really valuable from a learning and data sanctity perspective, and so we leverage that heavily as well.

J Darrin Gross  20:46  

On the uniformity of the information, I’m just kind of curious. All of these municipalities, I mean, it doesn’t matter whether it’s a state or company or county or whatever there, everybody invest so much money into creating a data collection system and maintaining that. And on and on. I’m just wondering, have you been approached? Or have you had any conversations with any mean municipalities about being the backbone, or the kind of the back office support for that? Because I could see that. As you know, every municipality is stretched for funding, for sure. And, you know, I would think that if there’s a better way, that’s a lesser cost, that would be of interest. And so is that anything like that?

Rich Sarkis  21:46  

It is, it’s very topical. I would say two things. One is it’s something that is already in flight. We have a number of municipalities, tax assessors, they use our system, ironically to vet their own data because ultimately Because we’re multi sourcing it will have better or more timely data than they have access to. But to take it a step further, which is where you’re going, this has been a hot topic of conversation at a board level with us. Because given the fact that we’ve got this system of record, the Reonomy, ID, which you use the term backbone, which is exactly how we think about it, then yes, if you go all the way upstream, the natural extension is right at the source, if you can be if the Reonomy ID can be embedded as you’re recording a mortgage recording a deed transaction, etc, then to your point, it eliminates a lot of the friction, the cost, the inefficiencies that are going on, and that we’ve seen now during the COVID crisis, right when a lot of these assessors have had to shut down because literally the people can’t go there to record the mortgages because they do it the old fashioned way, right. It’s it’s analog, it’s not digital. And so yes, that is a stream of work that that is ongoing with us now. As you know, right elephant in the room, working with local government agencies is never, you know, fast. And so a lot of these things are going to take quarters, if not years to come to fruition. And you’re talking a lot of different agencies, but certainly it is something that that we believe is valuable, and, frankly, will benefit the overall market.

J Darrin Gross  23:24  

Yeah, I’ve had a little bit of experience with just different public agencies. And you know, it’s never it’s, it’s amused me just how protective they can be about their position. based on historical This is the way we do it. Right kind of thing. Regardless of there’s an example. Local example I was on a committee and, and the the committee was, is responsible for one thing, and then there’s a whole nother branch. government that does a different thing, but calls on the exact same people for the exact same thing. And I was going like, that’s like two cars driving down the street will stop and at the same place, you know, taking up the consumers time twice. Once I’m gone how does that not? Does not does anybody else see that? You know, and they’re like, we do it totally differently our concerns are totally gone. You know, as a user, I just don’t see it. And I just but it was just it was a real eye opener just how territorial and I think it you know, as you look at politics and stuff you can see that real easy that it’s you know, if anybody’s gonna lose is not gonna be me it’s gonna be the other guy kind of thing. So I wish you well with that, but it does seem to make sense if if you know, the government’s are are stretched if there was a way that you can become the the backbone and they could feed your your platform. It seems like that’d be a win win for for you, because you would have the, the data, at least at the source, they’d have the backbone. So I wish you well with that. But I know I know, as a user, you know, there’s nothing more frustrating than if you buy, you know, a data set. I mean, I’ve done this with marketing before where you buy a list. And, you know, maybe 5% of it’s accurate. You know, you’re just I know that data is it’s just always a challenge. That’s why I’m kind of curious, like you, you know, we’re talking about some sort of verification or quality control that. Question I have for you, going forward, Are there additional data points that you recognize that would be useful that you could set on the platform, or that other people have, have approached you about?

Rich Sarkis  25:52  

Yeah, so I alluded to this so so when we started off, and that’s where the system of record really we were very property centric, right? We were really They’re good at telling you most everything there is to know about a property. Right? Who owns it? Who owns the debt? How much dead, what zoning is, what the owner addresses are, etc, etc, etc.

J Darrin Gross  26:12  

So, let me ask you that before it’s time to just say when you say that the the basics ownership great lender great, which would be to me kind of like a transactional stuff at a title. County is kind of a starting point. What about the property details, the square footage you’re built, and I’m thinking more about like the the permit all the county stuff that you know that that is an insurance professional that the the questions I always am trying to answer are, you know, when was this updated, you know, and if there’s permits, etc, kind of thing? Are those types of data points are those ones that you guys

Rich Sarkis  26:54  

We do? We do but as you alluded to, it varies right and to some extent, we’re at the mercy What is recorded and how and when right? And that goes back to your whole question around? Well, if we could be the system of record, wouldn’t that be better for everyone? Certainly better for us, but better for you the end user, right, because we have a more timely and minimize the latency etc. So there are some counties that are pretty abysmal in terms of when and how they update their records. And unfortunately, we’re at the mercy. There are others where they’re at the forefront and it’s digitized. It’s available via API, they update it intraday multiple times a day, not just once a day, right? And there’s everything in between now, where we strive to be the best, but not perfect. And again, the bar varies by county is that we multi sources, we never just take one source and say, Okay, well, this is where we’re getting the data for county x, and this is where we’re getting it for county, why we get it from many redundant sources. And a lot of time you’ve asked, well, what’s the point because to your point, it’s like the same data set and you’ll get the same value for The same square footage point, but oftentimes you won’t and right. That’s where the algorithms come into play. And they’re learning, okay, for this type of property in this county, for this type of record this data point, what is the most accurate source? And that’s constantly iterating. And trying to figure out, you know, who wins in that scenario across our data set, and that’s the sophistication that goes in the background to try and assess what the most complete, most accurate picture is for any given property. As it relates to the data points that you’re saying.

J Darrin Gross  28:36  

No, I think that the multiple sets I mean, that’s, that’s a real challenge I see just based on you know, if you start with the county record, and you assume that’s kind of the, I mean, assume, loosely, that that’s kind of the good starting point. But I’ve found, you know, many, many times that either it’s, it’s absent or incorrect? Or maybe the the assessor whoever’s in the field is having a bad day or maybe the property owner did some things that didn’t ever get recorded.

Rich Sarkis  29:12  

Absolutely. There’s miriade of reasons.

J Darrin Gross  29:14  

Yeah, yeah.  Well, no, I think that it’s all fascinating and and the you know, I think the other thing that I’m really kind of recognizing is that for all these years that I’ve been putting data in the computer in the machine that I couldn’t understand, you know, what does this have to do with anything, you know, it feels like the era of big data and the ability or I guess, data science is really coming to life. And that all that information has been collected. Now there’s there are people that are able to comb through it and and whether it be you know, apply it in a situation like this, use it to forecast or to see trends or, or what have you. It’s it is kind of fascinating when you look at it from that standpoint. So let me ask you this that the obviously Reonomy you guys are a big player. I think I mentioned you have used Yardie and Costar before is this do you see this as like? Is that the field? Is it I guess I’m trying to understand the competition cuz I guess in your business, I would think probably the biggest potential challenger in this field would be Google itself.

Rich Sarkis  30:41  

That’s true for any field, right?

J Darrin Gross  30:43  

Yeah. No,

Rich Sarkis  30:44  

As I chat with a lot of my founder friends is Google can basically wake up one day and go into any field and they’re going to make a dent.

J Darrin Gross  30:53  

Yeah, I mean, insurance included. I’m just kind of curious. Do you, when looking at the the the current field, are there some distinctions that you see that Reonomy has that that sets you apart? In a way?

Rich Sarkis  31:10  

To some extent what most, if not all, the legacy incumbents that you talked about? Right have been around for decades, right? And so and they’ve built great businesses, I mean, Costars market cap is north of 25 billion at this point, right. And so, and they are used by a lot of folks, but they have been built across several decades. And so it goes back to what I’m talking about modernity modern, what is the modern solution? So we like to think of ourselves and our users. More importantly, think of us as the modern solution to commercial real estate data and analytics and intelligence and insights, because we’ve had the benefit of hindsight and looking at what others do well and don’t do well. But we’ve also had the benefit of building our system from scratch on bleeding edge technology. Leading Edge databases, cloud computing, machine learning AI, and a lot of the software and tools we use just didn’t exist, even when we started the company, right? So that is our sort of differentiation when you look at the landscape. And then when you look at the new entrance, right, because there has been a proliferation of cre tech, prop tech companies over the last five or so years, to your point around reaching the critical mass getting to a certain point, we feel like we are one of very few companies out there that has managed to reach that critical mass, that adoption, and not just in terms of the fundraising that we’ve done right. With this series D last year, a lot of the banks participated because of the risk and underwriting that you can do with our data, but also just in terms of being in market and the validation we’ve got from our end users themselves. So we differentiate with the legacy incumbents by being more modern and we feel like we differentiate with somebody A new are your companies by being sort of more established and more validated and more trustworthy solution then, than those.

J Darrin Gross  33:11  

Gotcha.  And  I ment to ask you  earlier, in the beginning, when you guys started, were you Have you always been a subscription based?

Rich Sarkis  33:20  

Yes. We’ve always been a subscription based? Yes. Whether it’s on our web application where it’s every user has a license, and it’s recurring and they renew and all that stuff.  And on the data, you’re licensing our data on a recurring basis. Correct. It’s subscription based.

J Darrin Gross  33:36  

Gotcha. I’m kind of curious, being somebody that’s so familiar with the data, like you are and just the commercial real estate. Today is what is today July 22nd, I believe 20 when we’re recording in the middle of a COVID, or the COVID-19 situation. I’m kind of curious if you can speak to or if you see any. Are you seeing anything? Are you able to identify anything that’s changing? Or do you have any kind of a crystal ball button on the application that says, you know, this is changing, just across the commercial real estate asset classes? 

Rich Sarkis  34:23  

Yeah, yeah. So we’ve actually got a whole part of Reonomy, our Reonomy research and we’ve got a bunch of very smart folks that leverage our data that put out a whole bunch of reports, analysis datasets, a lot of it dynamically update, etc. encourage everyone to go to Reonomy.com and click on our research section. And they’re putting out a lot of leading edge stuff that’s being used and picked up by government agencies and stuff. So yeah, we’re seeing a lot. I think some of the highlights we are seeing if I you know, rewind a few months is that we’re seeing a huge slowdown, not surprisingly, when you think about it on in terms of transaction velocity, right? Just the number of transactions that are happening or haven’t happened in Q2 certainly was a prodigious fall from where we were in Q1, but even, you know, Q2 last year, and that’s felt all the more acutely within Office, Retail and Hospitality. 

Interestingly, multifamily has been somewhat resistance. Although, you know, questions remain as to how long they can be resistance. And equally interesting, we have seen some asset classes that have actually outperformed compared to where they were historically, specifically industrial, warehouse. Again, with a move to a lot of work from home, you need all these data centers, they take room and all that stuff. And so there has been a lot of interest on there. So yes, you can see a lot of that stuff on the platform. And again, I’d encourage everyone to go to the research section where a lot of that cutting edge data is being leveraged and shown in a very easy to digest way but it’s very very enlightening.

J Darrin Gross  36:02  

I appreciate that. Typical user, obviously we’ve talked about the commercial real estate, people mentioned the, you know, a fair amount of insurance people use it. Is there a target user?

Rich Sarkis  36:22  

The four main user groups if you will, our lenders so typically the loan originators we mentioned that a lot of the big banks and their originators use our platform to find which properties are in your refinance window, drill down under loans them and but they also use it increasingly for risk underwriting, compliance, etc, to find out information about who they’re lending to what is the portfolio of properties associated with any given person or company, right? So you’ve got lenders on the one hand, you’ve got a lot of institutional investors, developers, either people who buy the properties and they don’t just have to be the largest They can also be small local folks. But these are folks who do this day in, day out. These are the professionals who look for that assemblage that they can combine. Look for that repositioned opportunity from an asset point of view. Third is you have the brokers, not surprisingly, brokerage firms have widely adopted our platform our products for a number of years now. So these are typically of capital markets, folks, investment sales brokers, chief among them. 

And then last but not least, and this is the one where we alluded to are the field service providers, we have a lot of roofers, solar folks, energy folks who use our platform from a lead gen perspective, because to your point around buying a list from a marketing perspective, typically those lists are pretty coarse and it’s the analogy is it’s a shotgun scatter approach and say somewhere in that list is a property owner or two that might have a need for your service. But good luck finding it right because you don’t have the ability to Take that list and filter it, right. And so if you think about what our platform really does for those field service providers and allows them to dial in the location, right, I’m interested in this area of the country, and they can literally draw on the map. I’m interested in this type of property multifamily only. And this size, and I’m only interested in ones that are old, right? Haven’t been renovated in the last 20 years or whatever. And then you go from 50 million assets throughout the US to the 13 that really are in your strike zone, and then you can tell me, okay, who owns those and what’s their phone number and all that stuff. That’s the type of lead list that you would want that the field service providers wants versus you know, 10s of thousands of people and to your point, only a very small percentage are even relevant, let alone accurate.

J Darrin Gross  38:49  

Right.  No, I’ve spent a lot of money on on lists that are you know, I mean, it’s it’s frustrating. I mean, there’s always a couple of golden nuggets in there that you figure it out, which I mean, but that’s just kind of the that’s no different than cold calling, you know, law of big numbers. If you, you know, whatever your numbers, are you if you call enough people, there’s somebody that needs to buy or somebody needs your product kind of thing. But no, I appreciate you sharing it. 

Rich if we could, I’d like to shift gears here for a second. As I mentioned to you, by day, I’m an insurance broker. And I work with clients to assess risk and determine what to do with the risk. Yeah, and there’s a couple of different strategies we typically consider. The first is we look at can we avoid the risk? If that’s not a possibility, we look to see if there’s a way we can minimize the risk. And then if if neither of those are an option, we look to see if we can transfer the risk. And that’s essentially what an insurance policy is. And I like to ask my guests if they can take a look at, you know, their, their business their clients somewhere in that spectrum, and if they can identify what they consider to be the BIGGEST RISK. And, you know, again, for clarity, I’m not necessarily limiting this to a an insurance related answer. But if you’re willing, I’d like to ask you, Rich Sarkis, what is the BIGGEST RISK?

Rich Sarkis  40:28  

So from my point of view, the BIGGEST RISK right now that folks within our industry have is the risk of complacency, the risk of getting crippled by what’s going on with COVID-19 and obviously, the situation is fluid, it’s evolving it’s it’s, to some extent, getting worse in many areas of the country and that is not good news. And there’s you know, no real light at the end of the tunnel because yes, there’s there’s vaccines in flight and therapeutics etc, but nothing really concrete. that’s readily available and where we can say, okay, by this date, you know, business as usual, right. And so in the face of such huge and unprecedented uncertainty, the BIGGEST RISK right now for companies and individuals alike is to almost freeze and to not make decisions that they ought to be making.  In our world. That means specifically embracing data technology, information insights, to be able to when things do pick back up beyond the front foot and not get caught, shortchanged, basically.  And to probe a little bit deeper, one of the things that recessions tend to do, right, and you don’t know if we’ve seen the last of the, you know, big dip that we had in March, April, and whether it’s a true V, you know, recovery, but there are going to be headwinds, at the very least, and we’re even seeing you know, job numbers come back and we’re was picking up now because states are having to shut down again, it’s going to create some more negative outlook, they’re one of the one of the benefits, perversely enough of a of a shock to the economic or financial system is it shines a light on inefficiencies. And whereas when the sun’s out and everyone’s making, hey, you can sort of throw more bodies at the problem and say all hire more people, and we’ll do this do that. When times are tough and belt tightening happens, it’s like, well, what are these people doing? Do I really need this? This? This is how we do that. And so when you are critically looking at those inefficiencies, there’s this almost this, this cleansing mechanism where you then say, Okay, well what is the most efficient way to do this function or to prospect to risk to assess risks to underwrite to whatever it is to conduct an appraisal? And that’s where invariably all roads lead to data technology, systems and machines doing what a lot of folks were doing.  And don’t get me wrong, I’m not saying machines will do everything, but it’s really the highest best use and making sure that the appraisers, the brokers, the investors, the lenders, the insurance agents are focusing on their highest best use, which is doing bringing their local knowledge, their insights, their judgment. And that’s things that are very difficult to train a system to do, right? And let the computers and the machines and the data crunch through all the data and do the dirty work, frankly, right, the 80% of it, and let the people do the 20% of their highest best use. And so the BIGGEST RISK is to not embrace that not realize that there is this huge opportunity ahead of all of us to really sort of shift the paradigm and go to a much more efficient and I believe sustainable, long term outlook for the industry.

J Darrin Gross  43:55  

Yeah, no, I think the future has a few more surprises in store.  And I agree with you, I think one of the one of the healthy things that does come with these challenges is, you know, people get lean and and recognize that maybe there’s a better way more efficient way to do things and and she usually does it that’s kind of what propels the growth in the end. Yeah, yeah, that’s good. Hey, Rich, where can the listeners go? If they would like to learn more or connect with you?

Rich Sarkis  44:31  

Absolutely, they should go to a Reonomy.com. As I mentioned, that is not only where they can find out more about the products and data that we offer, but also where there’s a bunch of free resources that folks can, can sign up for and and enjoy, whether it’s research on what the COVID impact has been on, on our markets or research at large about different asset types and markets and what we’ve seen historically.

J Darrin Gross  45:02  

Awesome.  Rich can’t say thanks enough for taking the time to talk today. I’ve enjoyed a greatly learned a lot, and I hope we can do it again soon. 

Rich Sarkis  45:12  

Likewise, thanks for having me. 

J Darrin Gross  45:14  

All right. For our listeners, if you like this episode, don’t forget to like, share and subscribe. Remember, the more you know, the more you grow? That’s all we’ve got this week. Until next time, thanks for listening to Commercial Real Estate Pro Networks. CREPN Radio.

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