Ep 109 - Market and Deal Finding from with a Data-Driven Approach with Stefan Tsvetkov

Buckle up for a new episode of Elevate Your Equity podcast with Stefan Tsvetkov, the Founder of RealtyQuant, a company that brings data-driven and quantitative techniques to the real estate industry. On this podcast, Stefan highlighted:

• The start of his real estate investing related career.
• His data analysis on the latest real estate market trends.
 
Stefan is on a mission to add massive industry value through education, investment, technology, and analytics.

Thanks a bunch Stefan Tsvetkov for coming on the show!

Unlock 3+1 degrees of freedom (time, location, financial + health) with our 5 Point Blueprint! https://elevateequity.org/podcastgift

If you really enjoyed this content and are looking for more, you can continue to learn more about us in several different places for free!

• on our website for blogs & other podcast interviews! elevateequity.org
• our YouTube channel! youtube.com/channel/derekclifford
• our book/audiobook! amazon.com/dp/ebook

If you'd like to have a FREE copy of our 7 Ways Commercial Real Estate Syndications Protect and Build Wealth, simply click the link below. We are here and vested in your long-term success! elevateequity.org/7waysEbook

Unlock 3+1 degrees of freedom (time, location, financial + health) with our 5-Point Blueprint! https://elevateequity.org/podcastgift

If you really enjoyed this content and are looking for more, you can continue to learn more about us in several different places for free!

• on our website for blogs & other podcast interviews! elevateequity.org
• our YouTube channel! youtube.com/channel/derekclifford
• our book/audiobook! amazon.com/dp/ebook

If you'd like to have a FREE copy of our 7 Ways Commercial Real Estate Syndications Protect and Build Wealth, simply click the link below. We are here and vested in your long-term success! elevateequity.org/7waysEbook

Transcript

Introduction: 0:03

Welcome to the Elevate Your Equity podcast where we, as married busy professionals, leverage real estate investing to unlock the three plus one degrees of freedom, health, location, time and financial.

Derek Clifford: 0:16

Today, we've got the wonderful Stefan Tsvetkov. He is the founder of Realty quant, which is at www.realtyquant.com, a company that brings data-driven and quantitative techniques to the real estate industry. He is on a mission to add massive industry value through education, investment, technology and analytics. Stefan, great to have you on the show. How are you today?

Stefan Tsvetkov: 0:41

Doing well, how about you?

Derek Clifford: 0:43

We're great, we're great. So let's just go ahead and jump right in. I know that today we're gonna be talking about data driven real estate investing. But generally, for our guests, we'd like to talk about how you got started and interested in investing in real estate and how that led you to today. So if you could give us a little bit of a story about who you are and where you came from. That'd be awesome.

Stefan Tsvetkov: 1:05

Absolutely, yes. So I'm an Eastern European who came here for my master's degree and I did like font style financial engineering in New York City. And then I was working in finance for about a decade. And so the way starting university is first, it's kind of perhaps a little bit trite, in a sense, like with House hacking, so I had just moved to New Jersey, you know, like, right by New York City, and I bought a four Plex, one of the units happened to be like, pretty spacious and nice and had like a nice terrace. And that's how I was pretty happy with it. And I was living, you know, kind of COVID rent free, you know, you have like all kinds of expenses after this. So owning a property, right, but that's how I started it. So it's pretty nice. You get like nice cash flow, or in this case, kind of implicitly, like rent free. And there was some inefficiency to the purchase as well was relatively cheap, you know, compared to its appraisal, you know, it was like a divorced kind of like person going through a divorce selling it. So it was actually a relatively good deal. So that's how I got started first, as far as the house hacking that was back in 2017. Do much after that. Perhaps they still like going to work and, and so forth. Yeah. So in more recently in the recent like, two, three years, I've been like full time in real estate.

Derek Clifford: 2:18

Excellent. Excellent. Cool. So what got you really excited about real estate investing? Was it the house hacking, where you realized, oh, my gosh, like, I could be, I could be living, if I can do this with my private residence, and I can start doing this with rental property? Is that how it all kind of started? Or give us a little bit more background? About what got you excited about real estate in particular?

Stefan Tsvetkov: 2:37

Yes, I mean, and the first thing is, I feel like that many people perhaps feel like buy a house, you kind of get the sense of leverage, if you will. Now, you know, you suddenly you didn't commit that much funds, but you suddenly have a huge house. Right? So that's itself a little bit, a little bit of a mental mental effect, how to say no, it's maybe not like too rational, but but still, you know, that's due to leverage. So, so from there, like I just, to your point, yeah, it's just like realizing, it's, it seems like it has something good investment properties, investment, well, properties is an investment, so to say, you know, like, qualities is an investment and compared to like finance, like traditional finance, stocks, and so forth. So, so I just thought I can, you know, start working for Dios and kind of utilize my skills as coming as a trader in finance, you know, kind of coming, you know, essentially to sort of trade the real estate market in a similar way, in a data driven way. And so I started writing, you know, like, my, like Python scripts, and like doing quotes and like searching for like, different deals, like around New York City. And so that's, that's how I got started.

Derek Clifford: 3:42

Excellent. Well, let's talk about those early days, then, how did you go from getting, you know, being a house hacker and understand that there's this this technology here that you could be leveraging? How did you go from that to actually doing it? Like, I know, you mentioned Python, and writing scripts and looking at data. Let's talk about the early days when you got your business up and running. What did you end up doing with that software that you built in the early days?

Stefan Tsvetkov: 4:07

Yes, so ended up essentially what I would pull, let's say on the on the market and some of markets, you know, like data around New York City to kind of within it was all residential, and I've been transitioning to a commercial like more recently, and like bidding on like different properties in the Midwest and, and that but the time was like all residential, essentially, where they would just pull like about 6000 multifamily, it's like small multifamily properties, two to four units, maybe five, six units, something like that, in the New York City area, and within three hours, like three three state area code as well, and and they will just have them like automatically underwritten so like that's a dream and investing in one component is like just having automated underwriting. You know, if you can underwrite everything in an automated manner, that saves you time that also helps you to do it at scale. That also allows you to sort of invest even on the market, which is a little bit counterintuitive. Since you know, like most investors, they are the reverse to that they only work for deals like off market, but I was able to find, like amazing deals off the MLS like that. Because if you have like 6000 of them, you know, and you look at the top 0.1%, you know, or something like that it's a different thing. So they kind of have it in, you know, just more rigorous, more rigorous approach. And yeah, so what it what it brought is, it was just really different strategies. My second deal was like small like condo building that had I kind of like, purchased the building, and then it had the condos were non warrantable. At the time, that was 2018. And they were to no more integral meaning they don't qualify for conventional financing. But there was kind of the spread between like, what if they were warrantable, like what Congress normally cost, and this sort is kind of package price for the, for the deal. And then I've, I've done like condominium conversions as well like, sort of like searching with data, like the discrepancy between multifamily price and condo prices. And in doing that, also, in New York City, I converted a four unit into a five units, sort of where the fifth unit already existed in sort of doubling its valuation. So that's something else, you can search more efficiently with data, like you can search, like all kinds of like little things, you know, like, even if it's a double address, you know, sometimes or if it's kind of tried to discover something that's classified as a four Plex, but maybe actually has five, five units or, or something like that, and there's being sold residential, but you could, you know, the seller is not realizing that actually, this could have a commercial appraisal that could be worth more, you know, that's another strategy, you know, various cash flow strategies, like looking for, like cash for properties upstate, like, things that will have like 15 cap or 20. Cap, you know, it's more like just to build up your cash flow, and that kind of financial independence, I did that as well. Also, some was doing another thing. It's kind of like small, like renovation projects, and we'll try to discover which ones are undervalued. So like that, it's like a gut rehab, and then sell those with seller financing to another investor or something like this. And so kind of like flip like renovation projects that you don't actually use, oh, very stabilized without renovating anything like myself now in more recent years is switched more to like renovating stuff, but the time was kind of like just finding it's a flip, it's really a flip, but they don't actually want to renovate anything. So just because they use data, because they invest all my time to build up, you know, those processes in scripts and kind of to discover something, it's actually standardized condition, but, but still has some kind of value add to it in a regulatory or so that those are like some examples, and more recent in the commercial space. And I've been doing that in a data driven way, which is quite interesting, actually. And they can talk to that as well.

Derek Clifford: 7:38

Yeah, absolutely. So let's explore that. Let's talk about this. Because what I'm curious if you've given us a lot of examples of what you've used your data, or data, different data driven approach to help find deals for you, right? Let's fill in the details a little bit like how does your process work? Like, what do you do right now? Do you have like a list of properties that you have a whole bunch of like, you know, think of it like a spreadsheet, right, where you've got an address, and then you have you know, the owner, and then a bunch of different fields, like number of units, any leverage if it's on and kind of like what you can get off of a reonomy? Or you can you can lie data, right? First of all, where does your data come from? And then secondly, how are you manipulating the data to find some of these like, either human errors, you know, these human entry errors, or these opportunities? I want to hear more about?

Stefan Tsvetkov: 8:25

Yeah, that's a great question, actually. Yeah. So I mean, you mentioned the reonomy. And I'm sure like, some of your audience is acquainted, like other fields, like prospect now and so forth, especially used in the commercial, like commercial, like direct to owner like direct seller campaigns? And that and yes, you see, what they do is commercial space, I boo rental listings, data from places such as apartments.com, there are different vendors that service this data, one vendor is called Data FINITY. So they would kind of go online, and they would just kind of watch scrape, you know, like a ton of a ton of, you know, like data from apartment to apartment finder, realtor.com, et cetera, et cetera. And so when you're seeing like terms of like, what is the difference versus that behind something like reonomy So if you have your own, I mean, you may have like, you have the inventory, you have like information kind of like the tax County, you know, information you have, perhaps like some sense of the valuation of that property, like what it's approximately worth. And there may be like signals you know, as to like motivated sellers and things like that, or like information for like, the owner is kind of like finding the owner, like the owner address or phone number, email, etc. So this is all useful, but it's a kind of static data that doesn't have a modeling financial modeling to it. So what I do is I kind of more the financial modeling. On top of that, where I would use rental listings data to derive inventories for commercial multifamily, and then model the rental listings data to infer which buildings have the bigger volume on so this kind of a thing, a very good approach and things like So many people can do it if it's underutilized. And so essentially what you get from rental listings data you get are the rents to market compared to the immediate neighborhood immediate the same property class. But also you get kind of an insight into those other revenue streams of a property now, are they charging and all pet fees, administrative fees, like all those other income components, if they're efficient to utilize? And what utilities are they billing to finance is something that gets contained in rental listings as well. And then you can get a sort of estimate, try to estimate based on like, how many postings they're putting out there, you know, perhaps a sense, get a sense into their, like, relative occupancy rate. And, and so this is like high level modeling. But if you think about what's happening in the commercial multifamily investment space, it's you have, you're given an income expense sheet by an agent, and then you kind of work on that income, you know, you just kind of underwrite the property and or like yourself and cook it. Okay, though, all parameters in your try to judge, you know, underwrite like judge if it is a good deal, but you cannot actually do it in a data driven it's K way. Now, if you wanted to do that, that's essentially like what I'm suggesting with rental listing. So it gives you an income expense, a glimpse into the income expense sheet of 1000s, and 1000s of properties. And then I did like a webinar on the topic, actually, at my like, my webinar series, finance meets real estate. And I actually showed actually doing this for like, about 32,000 properties in any market. So I'm like, it just very different way where I would say, build my own marketing list on one side, but those marketing leads they have financial modeling in it. So that's what is unique because if you get prospect now you'll get other vendors, there's typically no financial model, there's no sense like what I'm really doing is like a try to rank all buildings in its Atlanta, Georgia, I try to rank all of market inventory based on which buildings show the biggest potential for Barwon. Now, two investors were very focused within a single occasion. And they say, okay, oh, well, how do you care about this? Because now I can just send direct mail to all the of marketing that are anyways without even financially modeling them, right. Sure. Is like, Okay, I that's comes down to the market side, like that's the other question. Now, if you're so focused on the single market, you know, perhaps that could be due to agent relationships. There are many reasons in real estate to be focused on single market, but in itself is not considered like very data driven, because how about if you were in Florida, let's your Texas which are, grew on a very broad based basis. So let's say like where nearly every county appreciated very strongly, which is not the case, for example, in Georgia, or in North Carolina, but let's say Florida and Texas that have like multiple counties that are a big percentage of their counties appreciate really much. So in that scenario, you can actually do you know, prospecting, where you actually model like, for example, I can pull like all the inventory in Florida, let's say, and literally let go of market inventory and, and kind of find sort of direct to owner, you know, 50 different counties, and let's say, perhaps just the top 25% of the buildings in those counties, you know, like, just as an example, and that's kind of a more potentially, like more intelligent approach to that campaigning, because, okay, whoever picks up the phone, I know, or calls me back or something like that. I know, in the first place. I have some preliminary underwriting for that already. So let's speed up the process. So this is an example in the commercial space. Yeah. And coming to the market side, it's very important, like another so financial modeling is a data driven technique in itself, one can say, you know, automated underwriting we touched on, there are other aspects in for residential investors, there is Automated Valuation, and then there's things like zeros estimate. So one can do that. And also to people who are like very tech savvy, like there's ways to do it in Python yourself to borrow houses, it's not overly complex, like a really basic model to borrow houses, it's probably five lines of code, actually, really, then a much more complicated one will take much more time. Right. But it's quite interesting, but that can help you like discover, like pricing efficiency, like find like properties that even they could be listed by agents, but they're perhaps mispriced. automated underwriting had other like technology side, which is, I would say, like valuable where you need to perhaps condition score properties sometimes. And again, that's also more on the residential side. And in condition scoring gets achieved via machine learning. It's kind of the only way so it's kind of like reading photos via like computer vision algorithms. So a company in Europe who does that does it is folksy AI, for example, I don't do it to the article on specifically as far as the computer vision but they do natural language processing for property descriptions can if you don't need to read like 1000s of descriptions, perhaps you can kind of categorize you know, the data in in a more kind of more intelligent way. So that is, those are like other like data driven techniques, just kind of mentioning putting out there for your audience at very high level and market side. So just like briefly on the market side, since we mentioned like the perspective where real estate is very vocal, right and most investors they kind of focus in build either the upper house versus They integrated or, you know, like they build their team at a single place right? In itself. Like there could be a different approach. It's always hard to scale in real estate in either across properties or across markets in different ways, like with different challenges, but still on the market side, it's very important. So what do it on the market? So one thing is to compute downside risk measures. So this is something I think that I know several people who do it so in go insert welcome market monitors from new power users. welcome Mark Norris is one person who does it, their studies, Dallas Fed, I guess, or something like that. There is for the Atlantic University has a study, and there is a study for different countries by Bloomberg economics. So those are like different sources that give you like market valuations, data COVID. And my company related quantities actually, I would say like there as well. So we publish, where we are actually the only company that publishes market valuations data for like 2700 us counties, to what market relations means, again, like in speaking to the data driven investing side and on the markets, and on the property side, it really means what is your estimator of downside risk? And where is the market county relative to fundamentals? Is it 10% overvalued, 20%, overvalued this kind of perspective. And fundamentals are the fundamentals of income population and housing supply. And so that is another thing. So that's another thing that they do. And they do some of my appreciation forecasting as well. And so that market data like for forecasting, as well as downside risk for almost every county in the country is actually is available. relative quantity is something that is provided to others as well, since like all of the other data driven methods are actually kind of in house. You know, I just used them in, in my own investment process. Yeah, so it's very interesting market valuations a very interesting topic, actually, because it changed kind of like, it was a different narrative before 2021. Okay, was it events like the beginning of COVID. And that's when they started doing like some of those studies of like, market valuation in real estate. And in the beginning of COVID, it was the narrative was always use real estate is fairly valued. And that continues actually into the first quarter of 2021, which is quite interesting. Some investors if you will, because like many investors thought it's already overwhelmed with them, right. And many investors are always concerned real estate explant is expensive. It wasn't really the case, it was the case in a few Western states of Idaho was the most notable example already getting of COVID was actually around 25% over borrowed in kind of relative matters. And that's again, like this measure is if someone thinks they cook a word does this really mean, after the global financial crisis, this same metric deviation from population income housing supply through like 85% Correlation the state level versus the subsequent actual declines? So for example, like the big declines, were in California, Florida, Nevada, Arizona, like 40, to 60%, and 40 to 56%, excuse me, like for Nevada, the higher number, and so they were actually overwhelmed in the same in this measure, like 40 to 60%. And then the undervalued there were around 10, states that were undervalued at the time, negative negative market valuation, and they actually dropped only 4% On average, which is quite interesting. And 4% was the median income drop in the US at the time. So you can say, if you think in kind of like affordability like price to income terms, we can say that valuations like price to income valuations COVID, they stay the same in those states. So it's, which is very interesting, very interesting observation at the time. So bottom line, it's fundamentals do seem predictive of price decline, when it comes to real estate, it's harder in the stock market, I came out of like a trading background in finance, it's harder in the stock market to have the technology sector. In real estate, it is very fundamental, easier to model. And so that's why I kind of do the trout as far as like producing this data. And that's very much useless in my investing. So this is like, having measures of downside risk is extremely important. We'll have people going now in some of like, the very trendy market, those trending markets, they could as well be further devalued. It just happens to be already now they're not not some of them were okay until 2021, and so forth, but inflation changed, change the state of market valuations, and they kind of really rallied up because simply incomes didn't, you know, didn't respond to inflation. And other you know, like, even like, population housing supply, sort of less dollar value, you know, sort of like other fundamentals, they didn't move accordingly as well. And so that is kind of, you know, so the today's another like, that's the one technique, I feel it's, I know like a few people who use it, I mean, there's people around me who use it and obviously like people who use our data use it as well. Generally, like most investors feel they're getting the market so they're always like, what if the market hit a peak is it you know, what if should they sell my property and so forth? And and so for me, this was like a big step is like a professional investor to get like, again, to wrap my head around the market side. It's much less time consuming than properties, I would say. But it's a very useful thing. It's relatively easy. If your audience if they want to reach out to me, it's actually I can guide them through even like doing some of those studies themselves. They are so inclined, they're kind of like financing client and so forth.

Derek Clifford: 20:20

Yeah, no, that's, that's great. A lot of interesting stuff here. And I love the approach of the downside risk. And some of the, one of my questions that I was going to ask you was, what variables do you look at, right, because I have a data driven approach as well. When I start underwriting properties, at least I like to think about it in a certain way. Like, for example, one method that I use, is whenever I'm looking at multifamily, I will look at Zillow Rentals, or Craigslist, or, you know, some place for single family homes, because that data seems to be more available since there's more management companies that need that outreach and Zillow Rentals or premium rental, whatever, right. And so you can set assumptions based on how much a single family home is going for it with similar finish and similar square footage and you know, a yard and things like that, you can make an assumption for how much a rental unit inside of an apartment building would go for by looking at that data, right. And so you can kind of correlate the two. So I like to zero in on like what the upper limit is by saying if a single family house is going for 1000 bucks, we're sure as heck not getting 1000 bucks for a rental unit, or we shouldn't be right. And that's certainly a variable and assumption I'd like to make. And so I think he touched on a lot of those variables that you look at in market and in the property driven approach. That's awesome. And I think you'll be worth going back and listening to again, at least that's what I'm going to do. But one thing I want to ask you was given where we are right now, I have a couple of questions. The first is what markets do you think right now are undervalued and which ones are overvalued? As we record this in the middle of quarter two? In 2022?

Stefan Tsvetkov: 21:59

That's a great question. One thing is to actually I have the data at year end 2021. So that's as far as it goes. The reason, though, is lagging by one quarter. So it's lagging by one quarter, I can get the prices anywhere, you can get the prices from zero, and so on and so forth. But the real fundamentals, you know, things like income in our housing supply population, or they come from governmental agencies, so it's always like this least a quarter to a quarter and a half behind. So were things if your end, urine 1021 Really Well, broadly, the West, Western markets get more overvalued with inflation. So specifically the five smaller Western states of Idaho, Arizona, Utah, and Colorado. So Idaho is like at the very top, nearing 50%. And again, like just the most negative story, negative, it's, it's actually they're also the best performing ones, right. So they're also tremely performing. So it's like, I run appreciation for cats, and my highest appreciation for cats are in Idaho as well, something that is all overvalued. That's not a good predictor of appreciation for this a good predictor of downside only because real estate, the test for like momentum in real estate like via, like autocorrelation and other ways. In real estate has water momentum. So actually, for example, like in Florida, it's like close to 80% like momentum compared to like last year, let's see, it's this year versus last year, I think. So it's actually interesting that if something is overrated, it still gets the highest depreciation forecast if around like kind of like forecasting. So because it's that's the only thing the trend is going to continue until it stops. But this is your measure whenever we reached an uncertain timing, so timing, completely uncertain this year, a measure of downside risk afterwards, assuming we've had kind of like a global financial crisis kind of correction. So Idaho is at the top, and then it gets like Nevada and Arizona or like Canada, like 25 30% range, and then Colorado, Colorado, Utah, right after this. And so like these five states, like five more western states, they are the top 10, the southern states have like big multifamily states of Florida and Texas, they reach like around the building, like around 21%. So there was kind of like bigger states. And to clarify, like all these measures were like at least in half or less at the beginning of 2021. So just a year ago, all these measures where if we take a result 30% It was at 14% only at the beginning at the very recent inflation development over a very short time. We were just like fairly well before. That's basically the story and that's like consistent with Bloomberg economics consistent with actually like some of the other studies that are out there. Use like different relatively different methods, but But Tyler go in line. And so yeah, which are the undervalued, undervalued at the current point in the cycle given we're like already, like 1012 years into the market cycle. It's only the depressed one. So it's only really the northeast, broadly speaking is underwater. So the Northeast if a big decline happens, is it going to drop probably no. It's going to drop like 4%. And so generally there can be exceptions there. came you know, your neighborhood is really booming and something you know happens in your specific neighborhood. Of course, it's more local than that. But high level speaking, another big drop there, and the Midwest is fairly valued, where smaller states kind of very overvalued. California is further divided, actually. So there you go. So that's like the narrative of interesting research.

Derek Clifford: 25:19

It's so bad that they have that they have such terrible tenant landlord laws, though. policy issue. Yeah, exactly.

Stefan Tsvetkov: 25:27

Exactly. I agree. No, no, I'm not. And again, that's I'm saying that's not an appreciation predictor. It's not doesn't mean I'm bullish on performance. I'm just saying that's what their measure of downside risk and, and so it's quite interesting, cuz, you know, like, people will say California is expensive. Nevada is cheap. Well, depends. In absolute terms, California is expensive, but it's relatively cheap steel, but but again, compared to the fundamentals, they're going over the long run. At the moment, Nevada is expensive, California is well fairly valued. And something like New Jersey is cheap. And so that's kind of how it ends up. Again, it's reflective of their relatively poor appreciation. So other data, like places like New Jersey, there are exceptions. So water, like good markets invest in my opinion. So my general opinion is it's relatively risk versus I feel like most investment managers should start rebalancing their portfolio towards places like the Midwest, I just feel it's the time for that. That is just my sense. There is a private equity fund actually am I'm speaking for like later today that they did like this kind of strategy where they actually did change the send to their investor, this, we're going to be investing on the secondary and tertiary markets in the Midwest. And that's considering some of the yield curve inversion and other considerations there. And again, like we know, like, what fin investors in places like Phoenix, Arizona, they're kind of making a fortune right now. And it's really great. But it's just again, like, even if they are the question is for new acquisitions, what do you do? And so for new acquisitions, it seems to me like it's the strategy is kind of to Okay, to places like Indiana, Kentucky states, you know, and we can talk like more specific markets as well.

Derek Clifford: 26:53

You know, what's interesting is, I've actually been so I'm deeply involved in Indianapolis, in some of the surrounding areas, and also Louisville, and Evansville, some of those like tertiary and secondary markets. Over the last year and a half, I have seen purchase prices go for, you know, see class product, that's maybe I don't know, it'd be a stretch to call them stabilized, I guess they're stabilized at lower rents than market. So I don't know if that's actually stable or not, I'd say no. But those properties, I used to see those coming in at 6570, a door for C class, or maybe even 55, like in that in that realm 5055. Now, it's trading for 75. And that's only been happening in the last year or two. And there was like an A B plus A Class A minus asset that I was looking at in a wealthy suburb of Indianapolis, and it was 500 units. And I was going to take this thing down and a fair price for it at the time back in 2020, or 2021, was, like 95,000, a door, that seemed to make sense. And the hedge fund came in and bought at 125 a door at the time. And that's for B plus A minus stabilize assets. And that surprised me when I saw that now I'm seeing a class product in Indianapolis going for like 150, you know, 141 50, a door, and C and D class going for somewhere between 70 and 90, and then up around 100, and newer construction, even at 130 in the suburbs. And so I think that the fact that you said about you know, being fairly valued, it's true because the rents, they're equitable, like the 1% rule of saying, hey, you know, whatever the market rent is, if you can get your monthly rent is 1% of the per door purchase price, then you're in good shape, and that's still achievable in the Midwest in the multifamily space, it's getting harder, but it's still achievable. So that all that all makes sense there and I think that I'm really understanding what you're saying here

Stefan Tsvetkov: 28:52

Well, I mean just like one caveat, just to say like to to respond to like what you're mentioning and I've been myself like this in my most recent bid like again on the kind of risk averse side like speaking was like a 48 unit in the mall by Des Moines in Iowa. So that's, you know, a fairly kind of, you know, not like the super booming market right but again, like it's that specific market was like really like one or 2% undervalued in this kind of perspective. And so it's again, like this is my personal like, risk tolerance to the content. But one thing like is the caveat to what you said is really multi commercial multifamily to your earlier point it's, it's harder even with a costar subscription so for it's harder to have this kind of amazing data where you can model or test appreciation and model downside risk and so this just discovered this is the broad like FHFA federal housing phising Finance Agency data which is really kind of like more like ties to single family and small multifamily space. That's a very important did a study where like the commercial commercial multifamily cost and well in a REIT index by costar commercial multifamily index versus FHFA was actually 91% correlation kind of over the long run price terms in price terms. So, you know, like it's over the long run, it should kind of wash out. But it's really different. Of course, it's different valuation methods. So like some of the things you're seeing in Indianapolis and so forth. It's just very hard to gauge like specifically commercial, commercial multifamily valuations, but but it is on some level moving in line, because like, if we take, let's say, some of the rizona overvaluation that happens in the residential space, and then we observe it in commercial space, Kansas, well, with all the demands, rent growth happened, and like people like exiting, and doing like amazingly profitable, like exits, right, which is great, but over a very short timeline. And so it does seem to kind of intuitively be somewhat aligned. So I don't think it's like really too different. But of course, he has for a very specific market, zip code and urban city, things can deviate. You can have commercial, perhaps commercial market multifamily go really high and then single family homes or not, and the other way around, and definitely possible, but the broad trends are still like, it's fundamental. It's still the same assets, right? It's just places for people to live, right? Well, whether it's income approach, whether it's comparables approach, fundamentally, it's based on, you know, incomes, population housing supply. So it's just kind of a short term, little bit noise to it, that noise can be extremely impactful to you to your investment, because you're just there for a short time, right? You're there for five years, years, and so forth. From a broader market perspective, the 202 ought to converge at some point more or less over the year. It's very, very interesting what you mentioned, as far as like, we view in Indianapolis in that.

Derek Clifford: 31:41

Yes, absolutely. So I've been, like I said, I've been seeing evidence of what you're saying. And I understand the caveat that the data is not completely one to one, but I can just tell you from what it looks like from an investor buyer standpoint, and from what my tenants are doing, right, so at least from my sample set of working in Indianapolis in that part of the Midwest, I have one last question for you before we head into the Rapid Round. And that is probably as a curious question for many people out there. What trends are you seeing right now in data? At least, you know, delayed back a couple of quarters, right? That you think we as investors, as all investors should be paying more attention to? Like, are you seeing any red flags like too much overvaluation, too much money printing, or you know, how the overall macro economics of our policy or government policy is tying into what you're seeing in the data, anything that you can extract from your, you know, from all your number crunching and combing through these mountains of data and research?

Stefan Tsvetkov: 32:42

Absolutely. That's a great question. When we mentioned some of the valuations in the West, I think that's something people should be paying more attention to, specifically, like I mentioned, regards to perhaps rebalancing portfolios for new acquisitions, you know, as far as that perspective, so that is just my personal view, again, like, I know how well those markets are doing. That's, you know, something also seen in the data. But again, like, this is one another thing actually, I can mention from them. So used to be like a derivatives trader and finances. So like another thing to watch. On the I'm sure, like people saw like the some of the yield curve inversion, in like in the treasury bond market. So not everything I want to say, like, since many people are concerned about mortgage rates. So now, and that's interest rates are very hard to predict. So this is sort of with a very big, you know, uncertain, much more uncertainty than valuation, you know, and that sense statement, but generally, like the trend, to me where we seem to be is, for example, 2018, we had a Fed hiking rates, so mortgage rates spiking in 2018, as well, then inversion and curve inversion, and she does on LinkedIn, right rates actually going down into a bond rally bond rallies when people are buying too many bonds, essentially, so rates going down. And then there is a huge bond rally at the beginning of recession. So when the COVID, quote unquote, recession, very short recession that was started, I was trading, okay with a Bloomberg screen and how it work. And there was like a huge bond rally when the recession started. And so that is generally speaking, if you like one dynamic to watch now, because Okay, we have the rate, we have the rate hikes, mortgage rates rally to pretty much, but they feel like people are expecting mortgage rates to go like much, much higher. And that is not the baseline scenario, I feel because the baseline scenarios, okay, mortgage rates, mortgage rates correlate to the 10 year Treasury rates, people don't hold their mortgages for 30 years, kind of like more like 10 years or something like this. So yeah, so it's kind of driven by the 10 year treasury to 10 year Treasury rate increased a lot with the reflecting like the Fed hike expectations, okay, but the general trend, at least or what we saw, like after she doesn't understand what to expect now. So then to go into a bond rally, and if we experienced a recession, which could be a very short recession could be a longer run, we don't know. But the very short recession seems a very high likelihood, like just by the yield curve inversion like probabilities and that and so we expect This virtual recession thing is actually the baseline scenario is a big bond rally when this happens. So mortgage rates tend to go down. So this like one thing, just to mention since here, like what's the interest rate concerns, they're relatively varied. I just feel that, you know, recessions, they correlated with low interest rates. So that's, we're just at the earlier stage, and people kind of panic about that, because there's like all the rate hikes, but they're already reflected in the 10 year Treasury rate or us like mortgage rate. So that's one thing to kind of keep in mind that is the market cycle progresses, we ought to or like kind of one baseline or one expectations to see a bond rally first. And then to see that and then like some comments on them, like perhaps like stock market is like stock market has like volatility index, like bigs and overtime, actually, now there again, and yeah, so let's get back to like the volatility index in the stock market and to VIX is not at that high levels. Now, even with some of the decline in the stock market. And unless the market structures changed, so I'm just looking for like, kind of signals for even from the stock market, like some weakening it could not may not affect real estate, like we don't know, right, but just the sort of like the general economy. So in the stock market, so VIX is around 30. Right now today. So it's not like a particularly high level of volatility, the stock market, and during the COVID actually spiked way, way higher. And so unless the market structure, the stock market structure changed, vix ought to be much higher for us to be like in a more affordable stage, because it's actually was observed at the time and that, like speaking as a trader in finance at the time, was that there was words of like volatility control funds in the market that were kind of like dumping equities when the market starts going down. And we found seen that yet, and that was like really exploding the big, so that just hasn't happened yet. So that's what I'm trying to say. So we so that would be so car spiking next stock market volatility and hires in the bond rally or two, if we kind of fall like the the general trend that we seem to be in, it might change, we never know right, then that the road to fall and only then have a recession. And so that's kind of kind of some expectations. So just like tying to like people's concerns about like, mortgage rates, and so on, and so forth. And your current version generally has like, it's considered federal 98% Probability of recession within two years, that doesn't mean that the recession could be very small. And also, the other recession could be no drop in real estate whatsoever, like it was with COVID, as well. And then things going well, so so it's pretty hard to say like very specifically, but these are just like some like data points that I consider and market valuation extremely important. Because again, like there's no timing to where the clients could happen. But this is your measure of risk and in real estate, I kind of really kind of urge people to healthy like to pay attention that style, because it's not the stock market this not like crazy, uncertain stuff. It's it's very fundamental. So you can kind of have a sense of your downside risk.

Derek Clifford: 37:49

Yeah, very interesting. I think there's something about the stock market that correlates well with the overall economy, which of course reflects on real estate, because if people aren't working, then they can't pay their rent, and then that reflects back. And it's like this big cycle, everything is all interconnected. So I appreciate this. We could be talking about this for a really long time. And I appreciate all this information is really good stuff. In the interest of time, what I'm going to do now is I'm going to move to the Rapid Round, which is the same five questions that I asked every one of our guests, and we're going to ask them to you and they're meant to be answered about 32nd timeframe or less, if possible, and we're going to rapidly ask them to you real quick if you're ready for it. Are you ready? Yep. Sounds great. Cool. Question number one, what book would you say has had the biggest impact on you and why? And hopefully, it's not Rich Dad, Poor Dad or the Bible? Because we get those a lot.

Stefan Tsvetkov: 38:43

Yes. Well, I don't know. I guess When Genius Failed was a book I read in undergrads with campaign finance book about Hedge Fund Long Term Capital Management during the 98 Russian debt crisis. So it's kind of like an introduction to like, I guess like finance and arbitrage and it has affected me, perhaps it has affected me continuing that sort of like what I try to do and the mindset that I have now in real estate as well.

Derek Clifford: 39:06

Sure. Excellent. Love that. All right, we'll put that in the show notes. Number two, if people wanted to emulate your success, what do you think is the first actionable thing that they could do to follow in your footsteps?

Stefan Tsvetkov: 39:20

Go to my website.

Derek Clifford: 39:25

Learn all the data learn how to how to look at all this data this way. I love that awesome. Number three, what is one tool process or hack in the last three months that's helped you personally save time and or effort?

Stefan Tsvetkov: 39:39

Working for like help online kind of like virtual assistant help, that has helped me a lot?

Derek Clifford: 39:45

Excellent. Is that happened in the last three months or so?

Stefan Tsvetkov: 39:48

Yeah, yeah, definitely. Philippines. Well, okay, now that one hasn't fully realized yet. I've been working for an acquisition side, It's actually kind of remote or remote acquisitions analyst and But yeah, that one not fully saved me time.

Derek Clifford: 40:03

Yeah, but I love it though. I love that you're thinking like that already. And it's in progress. So that's definitely something we recommend for everyone. All right, number four, if the people you know, had to describe you with one word, what do you think that word would be?

Stefan Tsvetkov: 40:17

Analytical.

Derek Clifford: 40:20

I definitely can see that. All right. And then number five, what small thing do most people not know about to?

Stefan Tsvetkov: 40:27

Most people don't know I'm Bulgarian. My nationality, I guess. They didn't know I'm not from America.

Derek Clifford: 40:33

That's cool. Or do you go back there every once in a while or obviously, not right now?

Stefan Tsvetkov: 40:39

Yeah, yeah, after COVID kind of reduce but I used to, I would go like every other year, usually something.

Derek Clifford: 40:45

Awesome. And I was cool. It's good to see you have extended family there, right?

Stefan Tsvetkov: 40:49

Yeah, of course.

Derek Clifford: 40:50

Awesome. Well, hey, you know, I'm Stefan, thank you so much for coming on the show. What I want to do now is I want to give you a space to be able to tell our listeners how they can find out more about what you have going on, so.

Stefan Tsvetkov: 41:03

Yeah, absolutely. So, RealtyQuant.com, I mentioned earlier, that's my website. So I'm starting a data-driven real estate investing course later this year. So it kind of on the education side and also there we have market valuations data to some of the downside risk and like appreciation discussion we had for like 2700 us counties. So that's something people feel can benefited, you know, the current time yet on our investor list, as well as Finance Meets Real Estate on YouTube so that's my to my name and finance mutual aid. So that's my YouTube channel.

Derek Clifford: 41:37

Awesome. And we'll be linking to all those inside the show notes so that people if they want to get ahold of you, they just go to the show notes, no matter where they're listening or watching this and they can they can do that. So Stefan, thank you so much for coming on the show and sharing your wisdom with us. It's really great stuff.

Stefan Tsvetkov: 41:52

Thank you, sir.

Derek Clifford: 41:53

Yeah, absolutely. And for your listeners out there who have listened all the way to this point, I want to thank you guys, wherever you're watching this or listening to it. Please make sure that you THUMBS UP, YOU LIKE, SUBSCRIBE and you comment so that we can get more interaction and we can appease the algorithm gods and find more and more people to listen to the show and bring on awesome guests like Stefan as well. So want to thank you, dear listener, thank you very much for your listenership. And you guys take care. Have an awesome rest of the week. We'll see you guys next time. This is Derek signing off.