WS1115: Data-Driven Real Estate Investing with Stefan Tsvetkov | #TechandTacticsTuesday

Stefan Tsvetkov is a financial engineer turned multifamily investor. He focuses on data-driven investing. He is the founder of Envvy Analytics and has developed a proprietary enhanced valuation metric that has been calibrated to gauge overvalued real estate markets.



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Stefan goes into detail on how to assess the market that will help you find the right deals or guide you in scouting for opportunities for investing in real estate. This #TechandTacticsTuesday episode is a great way to get exposed to such techniques and information that will enhance your investing strategies.

Key Points From This Episode:   

  • Stefan focuses on data-driven investing.
  • He shares some techniques on finding data for market property selection.
  • He talks about the usage of valuation metrics in real estate.
  • He says house supply ratio drives affordability metrics further.
  • According to his data, Boise Idaho is the strongest price performing city and has a higher downside risk at the moment.
  • One needs to know when the market was fairly borrowed as a time point to know what the market appreciation would be.
  • Price correction, stagnant crisis, income super growth are three scenarios to consider.
  • Stefan gives some data sources that you could visit or look up to predict downside or market appreciation.
  • According to his data, New York state shows strength and momentum.
  • Data-gathering helps him to find opportunities in real estate investing.

Tweet This!

“I’ve been focused on data-driven investing, I talk about it, I host my own webinar as well. And I try to educate people on using data-driven principles in their investment approach, and I can go into that more. [0:04:22.0]

“I like to give back by offering my analytics mindset and offering this capacity to people for free to empower them to think more and use better investment principles.” [0:23:53.0]

Links Mentioned in Today’s Episode:

Envvy Analytics website

Stefan Tsvetkov on LinkedIn

About Stefan Tsvetkov

Former financial engineer (Columbia MSFE) managing ~ $90 billion derivatives portfolio jointly with colleagues.

Multifamily investor across several strategies and real estate analytics speaker, having published own metrics in the field.

Managing Partner at Pepela Capital, a real estate investment firm, and Founder of Envvy Analytics, an investor-geared real estate analytics system. Organizer of Finance Meets Real Estate webinar series.

Full Transcript



[0:00:01.6] ANNOUNCER: Welcome to The Real Estate Syndication Show. Whether you are a seasoned investor or building a new real estate business, this is the show for you. Whitney Sewell talks to top experts in the business. Our goal is to help you master real estate syndication.

And now your host, Whitney Sewell. 


[0:00:24.3] WS: This is your Daily Real Estate Syndication Show. I’m your host, Whitney Sewell. Today, our guest is Stefan Tsvetkov. Stefan, a multifamily investor across several strategies and a Real Estate Analytics speaker, having published his own metrics in the field, he’s a managing partner at The Pala Capital, a real estate investment firm, and founder of Envvy Analytics and investor-geared Real Estate analytics system. He’s also the organizer of the Finance meets Real Estate webinar series. He’s a financial engineer, has a degree in finance, and he started with the Four PLEX and living in it, but then has seen the need for more data and how data can just help shape our decisions to have a much more accurate investing model and just really uses that data to drive the principles for selecting the market, selecting the projects, the specific properties and those things, he goes into great detail around those things today, so I know you’re gonna learn a lot and enjoy the show.

[0:01:26.6] WS: Stefan, welcome to the show. Honor to have you on. I know you have a skill set around data that we all need to be learning more about personally to help our own businesses in real estate in a big way. So, I feel like it’s so important. It’s kind of the wave of the future and it out… It’s been here for a while. But it’s only gonna get more complicated, I think. But the more data that we have, and so I’m looking forward to getting into this conversation, but tell us a little bit more about your business, your current capital business and just around the data stuff, how you got into this.

[0:01:57.0] ST: Hi, Whitney. Thanks for having me. So, I’m a financial engineer by background. Just to give a little background for your audience, I used to be an assistance in finance, so I used to manage 90 billion before joint with colleagues for a large insurance company. It was a derivatives portfolio, so my background was in mathematics and then financial engineering, and in the recent years, I’ve switched to being a real estate investor, so I could do my own projects. I haven’t syndicated so far, I do different flips buying homes in the New York City area, so I started by purchasing a four PLEX, sort of house hacking pretty standard living in one unit renting out the other is… Although that’s pretty nice. And then I do some finance person, I told Well, it’s great marketing efficiencies, and I was actually at the end of my finance career, I was thinking… should they go into crypto currencies for their, not for their ability, but for their marketing efficiencies, is that there’s a contingencies there, and I was working on… I probably have a few thoughts on lines of code on trading cryptocurrencies programmatically between markets, and that was on around… Then I took… And then I was a real estate, and then I do, okay.

[0:03:19.5] ST: The ease as for capturing the marketing efficiency, interested in the magnitude that was I so much bigger and it’s just so much easier for the effort, it seems that it just seems like a no brainer, far as a turn on effort goes, versus a more trading system, where in finance where you would actually need to build up your tactics in an extremely good way in an extremely seamless went work, and you would be like capturing small market efficiency differences versus the elsewhere… Percentages can be really high. So that was like felt like as a finance person, Okay, I’m going into an asset, that makes me scary than the physical assets, there’s a lot of hassle and with programs and etcetera, but in terms of its financial properties, I really felt it was kind of like a finance person’s dream later on if you will. So that I really liked. So that’s how I got into it. And yes, you mentioned, so I’ve been focused on data-driven investing, I talk about it, I host my own webinar as well, and I try to educate people on using data-driven principles in their investment approach, and I can go into that more.

[0:04:36.6] WS: Yeah, you mentioned many hassles around this real estate investing… Right. Or owning rentals, but you said it was a financial person’s dream. Is that right?

[0:04:45.7] ST: Yes, I think the finance property is something like… I was always very arbitrage oriented, like your audience is different seeking, consider riskless profit, but sort of this kind of times zero inefficiencies, like purchasing a real estate asset having a 10%, 20% spread at times zero or something like that. So that’s kind of my focus like as an investor, I wasn’t so much waiting for market appreciation, and so this thing works really well and easier in terms of capturing this and having also some of the income component to a trade or like myself, I was a trade or pretty much a trader like myself. That was really, really attractive. And even felt like, why aren’t more people doing anything…

[0:05:34.6] WS: Yeah, why aren’t more people doing this? That’s right, (yeah, exactly). What… No doubt about it. It seems like the more I get into it, I feel the same way. It’s like, Oh, I gotta tell more people about this amazing thing, if you haven’t been in real estate before, it just seems like there’s no, Oh, I don’t want tenants, I don’t wanna have to clean toilets those things, right. But they obviously have not seen the bigger picture of real estate investing and having that mindset, but I wanted to pop into your specialty around data, of course, and let’s jump into… I’m happy to take this any way that works best is how you’re thinking about it, but obviously the market property selection, those things, I know you have some techniques around finding that data and how to use it and… let’s jump in. 

[0:06:13.8] ST: So two things, I like to stress on a little bit. One thing I talk about is the usage of valuation metrics in real state. Valuation metrics give a perspective. So, in Finance, there is a manager John Hussman, so he is a PhD in Finance or Economics, something like that, so John Hussman …is called Hussman Investment Trust. So, they publish certain metrics where they try to assess if the stock market is on the top , and that’s harder to be done in finance, especially now, since it’s as you know heavily driven by tech, especially the big tech companies and they don’t have a clear evaluation, nobody knows what they’re worth. So there’s no one evaluation whatsoever, but there has been like an attempt… So he has a metric that is really intricate, how he built it, but it’s sort of an improved version of what would otherwise the attempt in finance be price… but because price… doesn’t work as well. Since we have all that additional staff and complication or anything experimental, distorted, etcetera, so he had his own metric, so what they did… In Forrest was trying to see, okay, after 2007, there were states like California, Arizona, that dropped 40-50%, and then there were the states like North Dakota, even taxes that drove like one to 4% only.

[0:07:31.7] ST: I was trying to see what was the driver of that. And so I was trying to see, Okay, what has been the biggest predictor of down transposed to be in the post-2007 scenario, which is a big market correction scenario, and so I was looking at different things, I was looking at foreclosure rates, and if someone, you know there’s a vendor called data solutions, so they have the best foreclosure rates in the US. So I was looking for foreclosures, I was looking at simple market votility, I was trying to do in finance, they call sharp ratio like risk-adjusted returns, maybe do some markets or disadvantages and risk-adjusted turns, and they tend to maybe do well when times, but then they really crash it because they’re not … robust and I was trying to work and things like that, and so what I found was really simple, actually, and it was that for affordability deviations versus well-selected moving average historical window, but essentially really affordability, like price income ratio deviations is what seems to drive down…

[0:08:39.3] WS: Say that again, what drives…

[0:08:41.0] ST: Affordability deviations, so like price income ratio deviations. What it means, it’s not due to price income ratios themselves, but it’s everything else, and if you take deviations versus the moving average one on five, and that means it’s not even everything else other than just pricing, the reason it’s everything else that are in pricing comes and some other… For the mandate, the soldiers Bento, this moving average. And that can be the theban study for the wants are… And then some studies of, Well, cities, the experience housing shortage, like so far as is Ontario, etcetera, suggests the locates changes in population to housing supplier Asia drive affordability metrics further, so it’s kind of a line with people’s situation where you have a pricing comprises of a… Your house is worth five times the median house with income in the one… Right now, if you have some name Safran or in the past, Rory, they don’t know the exact figure for some Francisco, I’d say the pricing tortious five. And then it’s a point in the future, or it 20 that does not make a market like this overall, it’s only changes versus the top that’s gonna make it over ability.

[0:09:56.8] ST: Okay, that’s already reflective of the housing sorcerer, maybe it’s not advantageous to a… There’s a expensive for people to live there, but the fact that in upstate terms, not to for the ball, it doesn’t mean… So we’re monitoring these kind of studies to… So what I… That’s… And it’s another comparison. So before 2007, so this has been… That’s not always something I… Having backed, it’s a simple procedure with my Bottas calibrating it to the crash postnatal showing that this was very predictive at the state level for the ability deviations versus a point a year, a historical time moving or join on were predictive to around 84, 28, 70% per one correlation. So that was a predictor, Bonar, supposed to pay… So what this means, and then at the county level to about 74%, so it would be worse, it’s not so good, but still counties that we were doing like 3000 counties in the US for 2080 with a 17 to 20900 county Sinhalese percentage. Deviation in affordability, and then that’s how much they draw in the poster Financial Crisis, and the correlation of the two is being like 74%, so that’s extreme to us who beat something like some of the metrics that processing is like John grow all the time.

[0:11:22.7] ST: Operation roaster, if you think of population, how well it reprises tote State, I was fake know that taxes and further the big markets that have done amazingly, but how predicts population alone being of prices and it’s actually 40% correlation. And then when you meet to the… That’s much higher and it crashing something that has your downside risk predicted in a completely forward basis, the… The global financial crisis as a Sanford, the counties, very strong, and then the state level is at like safe others kind of my bottles, this has been done by other people before, so there is… If you guys watch, I’m actually hosting you about it my way, we are hyper… And one of the detours to her, this is all market monitor, so all the market monitors is a service by engineers, the guys who before 2007 was actually on CNN and hearing house… Sure. Markets in Peoria for the center, the interest over Borland, he was essentially doing pricing compressors and he was saying that… So that was the one at the time, because if one works at the Historia before transpiration metrics, and that’s Cantonese just have like a history of other metrics at every time point, and if one does, it is one…

[0:12:50.0] ST: We can see that in 2002, 2003, the market was super, barely voted, and then over the course of three years, it became impact over about… By 2005, 2096 was already super over above course of just two, three years, that was something that I know now, and it doesn’t know the timing of when you know it’s gonna happen, but tortfeasor of her dancers and every time point is away in senior way available now and I would argue quite a fairly bare the market, even though governmental data is pretty logging at the part, it’s pretty much what they have in a 2002 anywhere. I have a different Barbican for cream, but generally the current picture, just for the audience, at least in my study, like what I have seen has been different, so if we speak of stays, there are a few states that roster or they are also the really well-performing states, and they’re not at, well, I do. Who is the most over? Otitis about 25%. And then in this framework, it’s over one then about 25% I do. In terms of just its pricing coton, now, of course, either now has also been very strongest price performer to say like boycotts is the strongest performing City of The 1050s in America, it disciple the way one knows this is, but one needs to know when the market was fair Department in north start to time horizon, even know what appreciation to be otherwise, doesn’t even know what market price appreciation is, when you start when it’s…

[0:14:33.9] ST: Well, to other voting points, if we did, for example, for it, and let’s say for the… And we decided maybe uniting a robust market exceeds its prior pen, we’re fairly forward in the current business cycle, the one to think for the being argue with the inter-investors like the only super strong state in the Eastern half of the US interest. Then one thing that they have exceeded their being from 2007 and they sort other pretty much right there, they maybe just exceeded it, but the Akihito, ’cause they were heavy overlay 40-50%. And so the one in daycare motivations, and you have a very clear picture of what the price appreciation is, so Boise, strongest, very strongest price performer in the country, and simultaneously as areas of higher donors in the moment, does not mean that there are other estate level than less over-molestation, maybe around 15% and 105% like this crane State of Texas is 10% for either is around 9%, and that’s in the end of 2000 points I began to share because the governmental dates are logging on probably would change and I Beathard but the broad you extend market has been fairly valued to 0% impact in other countries, and their studies like near there are state published in visual capitalist share different metrics and then in those like us, possibly Bowery, with what they see as well within Canada, very overalls, canine, very overlaid newsreader much as well, and the UK, one extent for us now, and that has been the intrusion of most investors as well, so a, with inflation maybe are you inflation, etcetera.

[0:16:37.7] ST: And it could change, it’s gonna be interesting to see it does change or not over next two years, but the cascade has been had an to some of the really well-strongest markets are a little bit over-oldies, a bit more… Overall, it’s a smaller state and it has had incredible… Or no question about it. Another thing is like if a market is or state or county, or let’s say more difficult, is over about… Does it mean that it’s going to correct it? No, they would be three scenarios, one can say oil, so one is a price correction, so that’s what happened at 20007. The second scenario would be, Well, sort price is growing not so strong in the future, and incomes growing faster than other… In catching up. So sorties, if you go in, and then a third scenario would be, Well, income, super growth, so dire for the over-market could be the prices, in fact, the detour strong income there going in a super way, and so the over-valuation resources… Those are different scenarios. So again, it doesn’t mean if Ontario is there for a market, doesn’t mean it’s not actually up on now, but that’s kind of a direction scenario that is the best testament, a good correlate to the state level five is pretty high as where they’re seeing historian.

[0:18:05.7] WS: Yeah, that’s so much great information. And I hire looking at the data, and we could talk about this alone probably all day, and I was gonna ask you about… You said the crash was predictable in 2007, I was gonna say, what about now, and you highlight it on some of those things or compared to some of the current state of the market or current market anyway.

[0:18:23.6] ST: Right, it’s very different.

[0:18:25.3] WS: If you could bring this more elementary to the listener and myself, just like where to find some of this data, just a couple of key things that we should be watching right now as far as the data…

[0:18:35.8] ST: So I do public these metrics, I have an analytical and B Analytics can go to get the free report and realities dot com get sample report, but as far as doing your own study, it’s free data, it’s available, so I use prices from a triage of Better Housing Finance Agency for price histories and incomes are from Bureau of Economic Analysis, and then for population and housing supply data, use senses. So those are the data sources, is Shabaab online, anyone can do it. What they should look for is just trying themselves to get an intuition with the data, trying to get a sense of what has been there, just trying to get genuine predictive-ness for what they’re trying to predict, and also if you’re trying to… Britons de and those are your areas, if you’re trying to predict depreciation and that will be different…

[0:19:34.5] WS: Sure. With all the data that you know, Give us your best prediction for the real estate market over the next six to 12 months, 

[0:20:19.3] ST: what’s my best prediction, if you say like this, so you say shows to creation of the coat is chosen and momentum. So my espresso for the next 60 to 22 attest, 6 12 months, their strength… So my best prediction for this year’s growth in prices and fire last year being prices, so it can sound silly, but it works well in the state for all the correlation or this year price performers last year, price performance that correlated to 77%. So that’s pretty destruction of…

[0:20:19.3] ST: That’s what I would say as far as appreciate your one can do more complex machine where they in transforming or those people in the finance and things like that, and try to do that, but really, if I just take the time series of prices in any market and just like does to correlation on them, and that’s a really decent way and doesn’t even need to know the job Action Center in markets. And

[0:20:42.1] WS: Just a few final questions to find any daily habits that you have, that you’re disciplined about that have helped you achieve success.

[0:20:48.5] ST: So what is self… Me on the investment side is, this is just like market broad stuff, it doesn’t help the invests in helping people, but what they’re gonna help them in investment side is the web scrape being… I use it in my online marketing as well, I use it to do what, creating the together in data mining, etcetera, both for marketing, for my webinar as we as for in the investment selection of properties. So what they would do it, for example, so when I kind of started deviated from the peer residential space and being switching towards, but in the research space and around the New York star who like 6000 slow multi-family properties, and I would kind of have the more Bristol at my computer. So that would be my approach to what a goatee investing, and so that has Keneally award in my own best fund to find opportunities to have outside returns in either transact or in just… Valletta has really, really helped me. Another thing like you kind of try to stress to others, when one takes this approach that is not just like boring data, it’s more like you can actually…

[0:22:04.5] ST: The mindset, Hitman function, but also human in Taras, if you have… If you’re gonna be underwriting properties and moving and the focus of those properties or creating the actual descriptions of those properties, so that is something that machine and can do now their companies like… And no associated. Anyway, there is for CAI company in New York, and that’s like image precipitation, conditions coring for us. The images, for example. So that’s a good way. So let’s say if you have conditions scoring no longer maybe need to look at these entirely Atwater, you will, but at least when you need to start to of place things, you can have that automated and similarly for the Trois cremation, as you can ping conditions… Course of simply having your co-read those descriptions.

[0:22:56.1] WS: Eventually we’ll be able to automate all the data enough, we want… Have to think about it, right?

[0:23:00.5] ST: But it’s very good. I mean, you’re not a physical asset, you’d say there as many changes, not easy to actually achieve a thing where it’s achievable is the preliminary and a stage, and that’s where you can kind of like you can scope parts of what more properties like that. And that’s very doable in a market commercial multi-fomented by the way, and I have a model, I present it for my webinar, if people wanna get up for or how to scroll of markets, commercial and properties and try to assess which are the ones that have the most father without even, there’s no pricing, petite are not to market, you don’t know anything about it, you don’t know what they cost you, and you try to assess percentage-wise what’s gonna be the volute you’re gonna make 20% price increase or any percentage increase based on their andesite us how you like to give back, I like to give back by this podcast by offering my analytics mindset and offering this capacity to be able for free, and so empowering them to think more and you special investment principles. In many cases, it’s not even… Not necessarily so time efficient for me to do it, or so project a commercial to do it, but it’s just…

[0:24:19.8] ST I think it’s a good way to have quality content, and I do LinkedIn posts on some of those topics as well, it center, so I kind of try to have quality content and people will actually benefit from

[0:24:34.0] WS: Awesome pleasure to meet you have on the show. And I just think it’s helpful for the listeners and myself to just… To be exposed to all the data that’s available. I think oftentimes, you even know some of that’s available until you hear somebody talk about it like yourself that’s on all this research or maybe think about data differently than you have in the past, depending on what it is and the accuracy of it and those things. So just grateful for you helping us think through that and just being a financial engineer and degree in finance, I think it’s right, if you’re all to help people like us to think about these things in a better way and how to use the state of the best tell the musters how they can get in touch with you and learn more about you.

[0:25:11.0] ST: Yeah, so they can get a… Essentially, is on the phone, so I… By that will be analytical and I do some… You wanna wear more about what they do, I try to tell people on the education side, mentoring wise as well, so they can reach to me there for that, I try to mentor people in analytics and data, the were investing, they can operate LinkedIn, the onset coming.

[0:25:39.5] ANNOUNCER: Thank you for listening to The Real Estate Syndication Show, brought to you by Life Bridge Capital. Life Bridge Capital works with investors nationwide to invest in real estate while also donating 50% of its profits to assist parents who are committing to adoption, Life Bridge Capital, making a difference one investor and one child at a time. Connect online at for free material and videos to further your success.


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