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Our guest today is Kevin Shtofman. Kevin is the COO of Real Estate Business Analytics, a leading real estate data management platform that connects and distributes all your real estate data for better investments, management, and underwriting decisions. Hi, Kevin. Welcome to the show. Thanks for having me on.

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Also joining us today is Zina. Zina is an AI that's been trained on my work. If you want to further interact with Zina, you can connect with her at brainius.com slash Zina.

00:39
Hi, Zina. Welcome to another episode of our podcast. Please say hello to Kevin.

00:44
Hi, Kevin. It's great to have you join us. I'm really looking forward to hearing your insights and having a thoughtful discussion today. Let's dive in. Likewise here. Yeah.

00:54
All right, very good. So why don't we start by having you tell us a little bit about your career trajectory and how you got into real estate and how you tied that with working with data and AI and all that good stuff. Of course, I'm now in my 22nd year in and around the real estate business, but it has not been a linear journey.

01:20
I started as an analyst, as a grunt on the finance side at two different firms. And I spent a little bit of time at Morgan Stanley before I got my MBA. And while I was getting my MBA, the great financial crisis happened.

01:36
Anything around real estate market values crashed. So the plan had been to U-turn around and come back to Morgan Stanley after, but that obviously was not going to be in the cards. So I needed to pivot. So I went into management consulting on the technology side instead, thinking that that would be a temporary stopgap for a couple of years.

01:56
Instead, I spent nine years across three firms doing that. So I did a lot of system selection, design and implementation of real estate focused technology platforms. And then in the summer of 2019, I left the corporate world and became the COO of a small data visualization startup company. And that really began my journey around real estate data, how to connect it, how to collect it, how to visualize it, how to put AI on top of it. And it's crazy to think, but now I'm at a new company as their COO, and we're focused primarily on the multifamily world of real estate. But all of the same problems I encountered at every property type are here and are paramount and are even more important as... the types of platforms that roll out that likely Zenith sits on top of are interacting with us in a meaningful way.

02:52
Yeah, it's interesting, right, how the issues that we run into in different companies and sometimes even across industries, they're essentially the same, right? So you can carry those skills, those experiences, the way that you go about solving those problems, and you can leverage those capabilities across different enterprises, different companies, different industries sometimes. Yeah.

03:19
So I wanted to ask you about your experience with dealing with change, specifically in the real estate business. As you know, AI is bringing just tremendous transformation to several industries, real estate included. So I'm curious to know, what do you see as the biggest change coming up in the next 12 months as far as real estate goes?

03:47
There's so many, but I think the biggest change is probably just people accepting and getting comfortable with interacting with an AI as they would an intelligent human being. So it's been in the works for a couple of years, but now you're seeing it become more mainstream. The idea that for initial level one support, I need to file a support ticket for a software platform. I need to submit a request because I'm in an apartment building and I need maintenance to help me with something. I'd like to schedule a leasing tour. I'd like to go research available spaces.

04:30
All of that kind of initial interaction level one stuff it's ai first now it's not ai only but it's becoming so mainstream people aren't even thinking about it much anymore that this is just the way you interact instead of search you just work with an ai that's been probably the biggest sea change so far uh and so many more are coming

04:53
And do you think people are getting more comfortable with that? Is it improving the process?

05:01
Or are people resisting, you know, talking to an AI?

05:04
I think that's probably generational. So anyone who and of course, our platform, we serve as primarily the multifamily industry, right? We want to be the foundation data layer for multifamily. And

05:21
obviously in the world of apartment communities and the idea that you would rent instead of own, that's going to skew slightly earlier in age than the average age of a person who owns their own family home, right? There are exceptions to this, right? The senior housing community is all people who are older.

05:41
But in general, if you go across the entire world of multifamily, it's going to skew a little bit younger. And those generation, I would call them mainly millennial and younger, are quite comfortable dealing with having interactions with AI at different aspects of their resident journey.

06:00
And a lot of that is because they've just grown up with screens. So some of them actually prefer an AI to another human because an AI agent is trained a little bit more on empathy and to be less judgmental and to have that more participation trophy mentality in dealing with people. And there's a comfort in that that you might not get every time you interact with another human.

06:29
Interesting. So it's interesting that you say that because you tend to think of real estate as being very much a relationship-based industry, right? It still is. Okay. So let's talk about that. So where do you see any shift that you see in it being relationship-driven versus data-driven? Where is the intersection of that?

06:55
So I think a lot of the preparation to make an important decision is what is being augmented and accelerated by AI. So the gathering of market data, the gathering of proprietary information or other research that would help you either find a new deal for acquisition, perform the initial underwriting, prepare to find an apartment community for touring, rent, all of the things that are before you make the important decision, AI is so helpful in just pulling together the information you would need to make that decision.

07:33
but oftentimes as we we know when buying a house or even just buying a car anything that we review is a durable good when we're going to outlay that capital there's still an emotional hesitancy to just relying on a machine to tell us what we should do and then following that recommendation blindly we still then rely on

07:54
Brand, personal relationships, domain expertise, previous experience, any bias that goes into that. So relationships still matter hugely when signing a long-term lease or acquiring a building or hiring an executive. But all of that time you spend pulling together information and researching to get to the

08:17
the last mile where your gut matters and your experience matters. I think that is where AI is having the most impact and will continue to do so.

08:25
So from the standpoint of the investor, I understand AI is adding a lot of value, doing the research, providing the analytical aspect of things, making some recommendations.

08:39
But where does the human judgment come into play? When does the person say, okay, AI is telling me this, but my gut feeling is saying this doesn't make sense. Tell me more about your thoughts on that.

08:53
I think as you get more and more local, as you get more and more microeconomic, that asymmetric information, that experience, those gut checks matter a lot more. Because at a macroeconomic level, A, I can pull together a volume of data that would be hard even for the most experienced commercial real estate broker or chief economist to do.

09:14
But there's a nuance in knowing why the southwest corner of an intersection is going to be lower risk or higher return or more profitable general than the northeast corner of that same intersection. That's going to be very hard for AI to deduce. And it might be...

09:35
Not because of an easy to find zoning change that's different between those two corners. It might just be, oh, the left turn access, there's always been issues for years and people accessing this one corner and that extra turn, there's just not as much foot traffic. Or we noticed that there's a grocery in the northeast corner, which would make you think that that's more attractive because grocery-anchored centers are more attractive. But this new concept that seems to be really popular with Gen Z users on TikTok just opened up in the southwest corner, and that's driving a lot more traffic.

10:13
AI might be able to inference that, but the local broker who knows that area is going to know it better

10:20
a lot more often and a lot faster and be able to bring that nuance. And there's hundreds of examples that look like that.

10:26
Yeah, that makes sense. The nuance that the human judgment, the human experience, the human gut feeling, that's difficult for AI to capture all of that. It's really good at capturing

10:39
the big data aspect of things, like you said, the macro side of those decisions. But for that specific location, that specific situation, that specific area of engagement, that human judgment is still very valuable.

10:55
I'll give you one other example to that. There are some people rethinking what fees they charge in multifamily apartment communities. And on its face, you might think to yourself, well, in order to increase our income, we'd like to charge pet fees because that's ancillary income. People that have their pets will spend anything on them. Therefore, it doesn't matter how big of a fee we charge them. Yeah. And AI may recommend that based on cold, hard data.

11:27
But as a growing apartment community owner, you might say, well, look, demographically, we actually want more pet owners because that makes the switching costs going to a different complex without the same amenities higher. And we can charge a little bit more in rent every month to accommodate for that. And then we can advertise, we're pet friendly here. We don't charge pet fees. Right.

11:53
You might be getting the same amount of money net net, but then you have a stickier relationship with your tenant. Is AI going to immediately tell you that? No, that probably not. But someone who's been working in the industry a long time may see where the puck is going and skating toward where the puck is going is removing pet fees entirely and then juicing rents a little bit because you're seen as an apartment community that's specifically friendly toward pet owners who are all by their nature, typically extroverted, friendly people.

12:23
Yeah. Now, that's a great example. It's about having that foresight and being able to include your experience in the industry and in the particular market you're dealing with to really make those long-term decisions. Now, I'd like to get Zina in on the conversation and have her ask you a question. So, Zina, given Kevin's expertise in real estate and data management, what question do you have for him? Kevin, with real estate becoming more data driven, I'd love to hear how do you think the role of human intuition in real estate investing will evolve when we have increasingly sophisticated data platforms guiding decisions?

13:05
Yeah, I think, Zena, you're going to see the bar just gets elevated, right? It's like the availability of data and AI tools will just make everything a little bit faster and y'all you'll have a reversion to the mean on how sophisticated everybody who's looking at deals gets to so while there may be a bit of a gap today eventually all of the firms that are trying to find and acquire real estate assets will have similar tools to work with so it really becomes about what type of data

13:43
It seems counterintuitive to an investment decision or in a management decision that will actually increase profitability at an asset without undermining the underlying tenant experience. so um a good example of this probably not anymore because now everyone's using it but a couple years ago very few people were looking at hiring data they were looking at demographic movement but they weren't looking at the the job openings that existed in a particular market and what industries those job openings were for or even more specific companies that was a leading indicator

14:22
in certain markets and a leading risk in another. So like when a lot of openings disappeared in San Francisco,

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for one company and then appeared in another market for that same company, you could inference that that company was about to relocate their corporate headquarters.

14:42
Things like that, right? There's going to be some examples of data that not everyone in the industry is using. And when you have enough experience, your intuition and nuance will tell you, I need to be looking at this data set that my competitors are not looking at because it's going to matter.

15:00
And it's just amazing how much data is publicly available out there. And we may not be leveraging all of it, but there's a lot of data out there.

15:10
And I wanted to give a simple example and just get your thoughts on this. So at a very basic level, right? If you just want to know what your neighbor's house is worth, you can just go to Zillow, right? And it will give you an estimate and you have a pretty good idea

15:26
of how much that house is worth. Now, that can be a little bit creepy, right? So how do we deal with this issue of privacy versus leveraging data in real estate?

15:39
So I'm going to say something that might be controversial, but I think that in most aspects of our lives, privacy is dead. Hmm. uh and that we we have you know as a country and maybe even just as a wider global culture we've all started making the decision that convenience matters more than privacy there are some exceptions um especially around like healthcare data but

16:09
For the most part, we are choosing that our lives are going to be easier if we share information so that information can be accessible by more to collaborate with us. You see the ads that you're being fed are definitely probably more relevant to your life. And you're like, how is this possible?

16:28
I'm inclined to buy everything that I'm getting served up because they know so much more about you. So for people that are extremely privacy-minded...

16:39
I go back and forth on whether I am. It depends on the day you ask me. But I think you need to recognize that if you want to prioritize data privacy in your life, you're going to have to deal with more friction versus people who don't.

16:54
And that's just going to be a life reality moving forward. Yeah. Yeah, I mean, we see this in our everyday lives, right, with things as simple as, you know, when you do a Google search on yourself, you know, sometimes you see all this stuff that you don't necessarily want to have that information out there, you know, information about health.

17:16
where you went to school or where you lived and what houses you bought and so on and so forth. And there are ways for you to have that data removed, but you got to go through that friction, right? So we have to be really intentional about removing that data or even with, you know, phone calls. I get these spam phone calls all the time.

17:38
And the only way to get rid of it, you know, you should just block it once. It doesn't matter because they use a different number and the calls come through anyway.

17:45
But there are ways for you to go out there and remove your listing from the databases that they're using. But you have to be very good about researching how to do that and intentional about removing your data. So, yeah, that is definitely a challenge.

18:01
I think luck every day that I graduated from college just as Facebook was becoming a thing. So just social media just kind of wasn't a thing until my senior year of college. So all of those... parties and events that we went to that like i'm sure it's a lot harder to be uh a teenager or at least 20 something right now because just everything you do gets digitally recorded and you're just not developed to know when to say no to something or where or when to resist the temptation of like turning the camera on i'm just glad that that was not a thing because i i you know i'll just leave it at that

18:46
Yeah, no, definitely things have changed. And, you know, the other side of this equation is people are, at least it appears to me, people are becoming more comfortable sharing things that, you know, maybe 30, 40 years ago, we were much more private about some of the things that, you know, that we post or that we want people to know about. And people seem to be a little bit more open to sharing. And I don't know if that's necessarily good or bad, but it's just the way the world works today. Yeah.

19:14
Now, going back to the data conversation, I wanted to get your thoughts on what challenges do you see with data quality? Because real estate, as I understand, is a very fragmented industry. So how do you deal with data quality in the real estate environment?

19:31
Yeah, there's a very, very tired and cliche saying garbage in, garbage out. And I've tried to refine that statement because there's actually a lot of information that gets entered into a system, whether that's an accounting system or a leasing system or a CRM or a treasury tool, et cetera, that at the time it's entered into the system,

19:57
is accurate. It was the correct information. The problem is that information gets stale and dated and certain sets of information you might only enter once a quarter and certain sets of information you might need multiple times daily. And so the cadence of that information matters a lot in decision making. So if people are not entering information,

20:27
at a pace and timeliness that are key to operational decisions in the company, that is when you encounter a lot of problems. So when you're closing the books at the end of the month, you're going to take a snapshot at the end of the month on lease ups and trade outs and unit turns, make readies, all that stuff.

20:51
But if you weren't forecasting forward what's coming in the pipeline and you don't have that context, it could be 24 hours later that the financial or operational picture looks very different in your portfolio, but you reported on a specific date and time.

21:07
The same can be said for underwriting decisions for acquisition. You may be saying, look, I'm going to have a go-no-go investment committee meeting on this date. And if you don't have the context that two applications have just gone in front of city council to get zoning changes and permitting approvals near you for buildings that would show themselves as competitive supply, have you been tracking that forward-looking information and are accounting for that in your underwriting model?

21:38
So the ability to gather all of the data that you need and to understand the nuance and context necessary to make these decisions is usually what separates the people that are outperforming their benchmarks in NOI or IRR or unlevered yield on cost, whatever metric you're going to use to gauge yourself against your benchmark or your competitors. That is what becomes important. That makes sense.

22:04
Now, I'd like to get Zena's thought on this as well. So Zena, what are your thoughts on where you see the biggest opportunities and perhaps the biggest challenges from a data standpoint in the world of real estate?

22:22
The core opportunity is in turning scattered, often siloed property data into actionable intelligence that can fuel smarter, more transparent investment decisions. But with that comes the challenge of ensuring data quality, privacy, and avoiding over-reliance on automation. The human element, context, relationships, and ethical decisions remains critical to balance all that data power.

22:46
Absolutely. And I think it just reinforces everything that we just talked about.

22:51
If I can take that as a opportunity to do like a 45 second commercial on the company, because it's so core to our ethos.

23:01
Reba is the data foundation for multifamily housing. If you don't get the foundation level, and there's a reason we call it foundation, right? We're a real estate company. software company at the end of the day, if you don't have a good foundation on a building, the building gets shoddy, it's risky. So all of the other products that then sit on top of that foundation layer. So think REBA rent, which is our revenue management tool, REBA budget, which is our budgeting tool, REBA amenities, which will help you think through, well, if I'm optimizing for monthly rent,

23:35
how do I then think through the amenity mix and additional revenue I could get from amenities? Plus, given our recent acquisition of Marker, how might I take some market data and infuse my operating information with how I might want to change my business going forward? The key to all of that is getting the foundation right at the lower level. And then you can provide that, you can extrapolate that across other property types and other tools. If you don't get the data foundation right,

24:03
Turning that into a dashboard is problematic, but low risk. But the minute you put AI on top of it and it tells you to prioritize a specific work order or execute a tenant refund or flag somebody for eviction because they're late, like if you do that and you have bad data, you're now making very risky, very consequential decisions on top. So that data needs to be really, really solid. Yeah, no, I think you hit on something that is a core belief that I have is your foundational data is really, really important. And if you just put analytics or AI on top of bad data, you know, you're going to get the wrong results. So that's right. Absolutely.

24:46
Now, I would like to take us back to a macro view of real estate. I know that real estate tends to be a local kind of conversation, but what is your overall view of the real estate market in the next 12 months or so? And what advice would you have for people interested in investing in real estate at this point in time?

25:11
depends on your balance sheet i think is the first kind of uh piece of advice there are if you're like a very very early days thinking about the market uh you want to make your first purchase investment property a home something the the best place to start is just using publicly available data

25:37
and an llm to be your thought partner so a lot of people they go on to claude or chat gpt or gemini or co-pilot they just ask a question get an answer and if it's not what they're looking for walk away instead you need to think of these people as like really really intelligent collaboration partners where you tell the AI partner to ask you what clarifying questions need to be answered before they would confidently make a recommendation to invest or avoid a particular investment in a certain property. And that back and forth is actually where you learn where your knowledge gaps are. And that's a good exercise, whether you've been in the business for six months or 16 years, because that's how I use.

26:26
We use Claude's Sonnet 4.6 model, which is quite amazing. And we pay for Claude Enterprise to protect information inside and have more of a history and be able to collaborate across users.

26:39
When you're first getting into the business, the world of real estate data is so vast, you just don't know what you don't know.

26:47
And so start by saying, and be honest with the model. Hey, I'm trying to get into real estate investing.

26:55
I have X dollars to work with in my budget.

27:00
What questions do you need me to answer before you make a recommendation? And then all of a sudden it'll fire off. you know where do you live what full-time job gives you potential other income or do you have a family to take care of uh do you have free time to go visit a site outside your market do you want something completely local in your own neighborhood so you can go check on it after you buy it like all those types of questions and then once you've answered those questions it will give you some advice and then and only then do you think about well like is that the actual budget i have

27:34
Am I so comfortable with risk, I'm going to go take out a loan to make my budget bigger? Am I going to go ask friends to co-invest? You introduce all this new complication.

27:43
Start with AI as a thought partner. And by doing that, not only do you get a baseline level of understanding, when then you go reach out to your network of experts, you may know you have friends who are brokers or estate agents or attorneys or title people.

27:58
you're now coming to that conversation more prepared and it's going to be a more productive conversation with them. So they're not thinking of you as just some newbie. You've been thoughtful in how you approach this. So they're excited to engage with you. Yeah, no, this is fantastic, right? That we have these amazing tools at our disposal these days that can help us think through some of these things, make us more knowledgeable, ask better questions, help guide us in our thinking process. I just think this whole thing is truly amazing. And if you use correctly, right, you have to know how to use it. But once you learn how to use it correctly, it can be very, very helpful. So, Kevin, this has been a fantastic discussion. Thank you so much for being a guest here on the show. I'd like to ask you a final question. Where can our audience find you? And what final message would you leave for our audience?

28:55
Yeah, just for those interested in talking about real estate or data, or quite frankly, anything else, I just love talking to people. Come find me on LinkedIn. I'm the only Kevin Stoffman there. And if you are in the multifamily space, and you'd like to learn more about the platform that we provide, it's www.getreba.com. My final thought, final piece of advice.

29:22
Just get started. A lot of people get paralysis by analysis and get afraid just to make the jump. The most learning you can get is by doing and by doing something wrong. I learned so much more by messing up than I did by succeeding. And I messed up a lot. So I've learned a lot. Just do it with a small amount of your time and a small amount of your money to start. So your risk is low. And as you learn more and you build up a muscle and a habit, that's when you can increase your pool of capital and your risk.

29:55
That's great advice. You know, that's how we learn. It's through that friction, right? And sometimes that means we mess up, but that's okay. That's part of the learning process.

30:05
All right. Thank you, Kevin. And thank you, Zena, for being a great co-host here on our podcast.

30:12
It's my pleasure. I'm glad to be part of the conversation. All right. Thank you both. Thank you.