TGCI 167: Financial Engineer using Machine Learning and Data Analytics to Analyse

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Episode 167: Financial Engineer using Machine Learning and Data Analytics to Analyse

Copy of EP #18 - 2 Guests

Summary

In today’s show, Pancham interviews Stefan Tsvetkov – founder of RealtyQuant, multifamily investor, analytics speaker, and live webinar host.

Achieving financial independence is a natural goal for real estate investors but for Stefan, making more equity was his stepping stone to start his own company and make his own investments! With his financial engineering background, he was able to provide value in the real estate industry through data analytics, technology, and quantitative techniques!

In this episode, learn how data can be the key to successful investments as he shares the concept of data-driven investing. He’ll also share his analytical skills as he breaks down the data-driven system for investing, how you can see and predict the valuation of the properties, and how analyzing the market is very useful.

 

Listen and enjoy the show!

PanchamHeadshotTGCI
Pancham Gupta
Screen Shot 2021-12-06 at 1.14.01 PM
Stefan Tsvetkov

Tune in to this show and enjoy!

Copy of Quote #00 - 1 Guest

Timestamped Shownotes:

  • 0:41 – Pancham introduces Stefan to the show
  • 1:49 – His natural transition from financial engineering to real estate investing
  • 9:08 – Data-driven investing and being more than just figures
  • 13:35 – The benefit of using Automated Valuation Model when investing
  • 16:10 – How data analysis helps identify potentially good investments
  • 20:14 – Market analysis and why its data is very significant
  • 25:37 – The best-performing markets to look out for based on market analytics
  • 31:44 – Taking the Leap Round
  • 31:44 – His 1st alternative investment outside of Wall Street
  • 32:57 – Overcoming his fear by studying each investment details
  • 33:58 – How his property investment didn’t go as expected
  • 34:33 – Why investors should understand the investments and its market evaluation
  • 36:26 – How you can enroll in his Data-driven Real Estate Investing Course

3 Key Points:

  1. Data-driven investing is a system that imports data into quantitative methods and is able to pull big amounts of data to process and that can be analyzed.
  2. By collecting these numbers and being able to do the algorithm for data-driven investing, you can now have an estimate and track whether it has good valuation and value-add.
  3. Analyzing the market is driven by real estate fundamentals such as income, housing supply, and operations. Thus, knowing and comparing the data from different states helps.

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Welcome to The Gold Collar Investor Podcast with your host, Pancham Gupta. This podcast is dedicated to helping the high paid professionals to break out of the Wall Street investments and create multiple income streams. Here’s your host, Pancham Gupta.

 

Hi, this is Joe Fairless if you want to diversify out of Wall Street investments, then listen to The Gold Collar Investor Podcast.

 

Pancham Gupta  Welcome to The Gold Collar Investor Podcast. This is your host, Pancham. Really appreciate you for tuning in today. I have a person with a very analytical and financial engineering background today. His name is Stefan. He’s the founder of reality quant.com, a company that brings data driven and quantitative techniques to the real estate industry on a mission to add massive industry value through education, investment technology and analytics, financial engineer turned multifamily investor, analytics speaker, and live webinar host. He holds a master’s degree in financial engineering from Columbia University and during his finance career managed about $90 billion worth of derivatives portfolio jointly with the colleagues. He has been featured on multiple podcasts. And he also quit his full time job as you will hear and started his own company and his own investments. Hey, Stefan, welcome to the show. 

 

Stefan  Hey, Pancham. Thank you for having me.

 

Pancham Gupta   Pleasure to have you on the show. Before we get started, are you ready to fire up my listener break out of Wall Street investments? 

 

Stefan  Absolutely. 

 

Pancham Gupta  Looking forward to it. So, before we get started, why don’t you go over your background with the listeners and tell us more about the person behind that background.

 

Stefan  I studied Financial  Engineering. So, I came to the States at 22. I’m Eastern European originally I came at 22. I studied Master’s in Financial Engineering at Columbia and I worked as a derivatives portfolio manager for 10 years. That was my career before so used to manage like a large like $90 billion portfolio to jointly with colleagues in finance.

 

Pancham Gupta  What kind of instruments did you manage? And I have the similar background like, you know, financial, I was in FinTech. I did do bit of financial engineering, like the CQF, and all that. So, a little bit more interested that’s why. So, what kind of instruments did you manage?

 

Stefan  Yeah, cause you know, Pancham, you went to CMU, right. and they know that they have they have a computation or finance program there. 

 

Pancham Gupta  Exactly. 

 

Stefan  Then there’s also certificate they think separate, like independent one. 

 

Pancham Gupta  That is right., 

 

Stefan  Yeah, so that’s, well, options and futures and they’re just both equities and interest rates side. So, options futures, pretty much interest rate swaps, you know, pretty much a wide range, we were pretty broad, kind of broad position desk.

 

Pancham Gupta  Got it. So yeah, go ahead. So, you’re managing that $90 billion portfolio for investment company? And then yeah, 

 

Stefan  Got it. So, it’s pretty interesting. So, you were in fun. You did you came from Eastern Europe. And then you did your masters,. you said, right here and then you went into fund like, you know, managing endure derivatives division managing big portfolio. Did you quit your job? We’re with a team of several people. Yes, several people. So that was my job, then. Now, I always wanted to do my own investments, I was always leaning to the alternative investment side. So, I kind of always liked like, private markets, private investments. So, it led to like a bought a house, you know, it’s kind of a little bit cliche, bought a fourplex kind of house hacked into it, you know, lifted, lived in one unit, and rented out the others. And it was oh, that’s pretty nice. It’s working out well. So, it seems like you have like a good opportunity for cash flow over some pricing efficiency, etc. So, so that’s how I, that’s how I got started. I bought my primary residence in 2017. And I’ve done a few deals in the New York City area since then. So, it’s primarily kind of flips and condo conversions and like basically seeking inefficiency, and like being a private investor, so I have my own portfolio. I do not presently manage funds for others.

No, I no longer work, yes. So, I pretty much finished with my work earlier this year. And I’ve been very happy to be able to do that and be able to transition to a new field and being a private investor and being an entrepreneur. I quite like the dynamic versus kind of working for income, you know, like where you’re sort of equity optimizing versus income optimizing. So that’s been interesting for me.

 

Pancham Gupta  Wow, this is very Interesting, I have the similar journey, but you know, you talk about it so coolly that you just quit your job and you want to be an entrepreneur. And for a lot of people, including me, it was very hard transition. You mentioned you always wanted to do that. So was it very easy for you to quit and start your own thing. Did you prepare a lot to get into this?

 

Stefan  Well, I mean, well, you can say we had some differences with my employer. So, it’s kind of naturally transition to that, but they wanted, I would say for like, several years, it was kind of preparing, you know, like getting ready for that and expecting it. And so, it just had, like, I was doing, like, you know, like, my real estate portfolio on the side. So, I thought, okay, have some cash flow, like, I can make more equity, I think the trigger that made it for me, like in terms of like people trying to reach financial freedom, mentally, for me is okay, if I, even if I’m going to make less cash for that my job, obviously, in an income sense, I would nevertheless make more equity easily. And it’s like, pretty difficult to be an entrepreneur and equity space, right? I mean, you know, so it’s like, if you try to compete to an entrepreneur in an equity space, it’s gonna be super, super hard, you need to have like a million dollar salary. And you’re then going to beat an entrepreneur who makes like a small income and some equity.

 

Pancham Gupta  So, you had at least enough cash flow to quit your job, is that fair, or 

 

Stefan  Yeah, I had some cash flow, then I thought, okay, I can kind of, I got a real estate license in New Jersey, actually, because I thought, okay, I can serve as my own buyer agents and my own views and sort of mimic an acquisition fee. So that was, so the thought process, okay, can have like 2% on each of everything that they buy, which is sort of somewhat comparable to, you know, acquisition phase in the in the investment management, syndication space. So, effectively, so it was okay, I can, I can have this kind of infrastructure where I have a little bit of cash flow and, and I have, like acquisition fees and everything that they buy, and, and I’m still not going to make probably my salary or be below that. But I’m going to make much more equity in this kind of system of doing that. And so, the, I think the equity was a big driver, for me, it wasn’t so much pushing cash flow is like people they think like, okay, in real estate, you can find cash flow and be financially independent, I feel the bigger benefit is like time zero, equity gain captures, like inefficiency, with every single deals, sort of entering a position where you open, like, you know, a certain amount of equity that you keep accumulating. And so that was the total, that’s what I didn’t have at my job because you know, when you work, you have to save and, and then if so you get taxed, you have to save, and then a same portion you invest, and it’s not an equity optimal approach. And so, I thought I can have less income, be able to kind of sustain myself still, and nevertheless do better in what I always wanted to do better, which is kind of investments in equity.

 

Pancham Gupta  That’s awesome and this is -inaudible –  at the, it’s always a different mindset, the mindset that you’re talking about, and the mindset of a person who’s talking about building cash flow, they might love their job, it’s a very slow and steady, you know, climb to get to a level where they have enough cash flow multiplied by the equity gains over the years where they may still want to do their job until the age of 60, or 50. But you know, spending time for 20 years in investing for them, it works out perfect. But in your case, like you said, you wanted to multiply using equity rather than the cash flow, just to sustain you did the cash flow. So, do you still live in that fourplex? And where is that four lex?

 

Stefan  No, I live in a rental building now. It’s kind of I wanted to, you know, like most of the properties in northeast are kind of boulders. I want to move into something new. And I do have it, it’s a holiday rental property. It’s doing well. It’s doing normal.

 

Pancham Gupta  Is it in New York City? 

 

Stefan  No, it’s in Hudson County, New Jersey. 

 

Pancham Gupta  Got it. Okay. Awesome. So that’s great. So now that you’ve quit your job, now you’re doing investing full time being an entrepreneur and with this financial engineering background, quant background, you have focused your time, really in analyzing using your skill set to analyze all this data and making sense out of it for people who are in the real estate space, right? So, tell us about the data driven investing and also, what do you see today in the market based on that data?

 

Stefan  Okay, yeah, so to me like this. So, here’s what data driven investing is to make sure to meet this neck in real estate specifically, so it has like several like major pillars one can say so, the first one being automated underwriting So to make the underwriting on market, and an off market is kind of different. So, let’s say, all markets that would be kind of like pooling, I would say on market is some of the applicable to the residential space model, there is always the mind to do it, or you have to find the off market, but when doing it data driven in residential, small houses, and etc, in all small properties that’s actually very effective I have found.

 

Pancham Gupta  So, like, what does like people who don’t know what this means? Can you explain what is data driven real estate? What is data driven investing mean?

 

Stefan  Yes, so data driven investing means employing data science, employing machine learning employing quantitative methods to pull big amounts of data or comparatively big amounts of data and study it and also search for analytical purposes, for those who kind of automate the human function in both repetitive tasks as well as automate in depth human intelligence, where that would be, let’s say, like, you know, if you have to retrieve images, you can use computer vision for that, if you have to read through textual descriptions of listings, you can use like, natural language processing, and sort of like automating the human analysis process end to end. So, if you have like, a system that works end to end that is to me, like, data driven system for investing. Similar approach, one can apply to, you know, other aspects of one’s business, like I do it in my marketing, about like online marketing, and I would kind of like write like browser, various browser automations and try to like, kind of scale everything this way.

 

Pancham Gupta  So basically, to summarize what you just said that data driven investing is to take all the data that’s available for all the on market listings that are available, put them in some kind of 

 

Stefan  Off market data as well of market as well 

 

Pancham Gupta  Off market as well, and you can consolidate, get all of that data into some system and then you consolidate that, run it through some algorithms, machine learning algorithm, try to make sense out of it, and then outcomes, the result for you, whatever that result, or parameters happen to be. And based on that, you will make a decision of whether this is overvalued, undervalued, or, you know, at market value, is that fair?

 

Stefan  Evaluation, like if property evaluations your target, then yeah, then you’re like various strategies, that’s if you want to, I don’t know, like, say example like just to the audience, I don’t like comic convergence in the New York City area. So, let’s say I would do like my last property that’s being condominium conversion in downtown Jersey City. And then for that, I would kind of pull like, maybe 6000 will earn for multiple strategies within maybe 6000 multifamily in the primarily small and some larger multifamily is in the around New York City within like three, four hours’ drive from those like 6000 I would kind of have like data feeds that are like pricing them on different criteria. So, I’m going to converge strategies will kind of Bronco the best ones in that so it’s like this in distance kind of reviewers in okay, it’s just automation and kind of like computations and like getting what your cap rates are some estimates for equity gain, etc. On one can run his or her as AVM. So, to say so like you can do like with zero does I mean, zero? Didn’t very well.

 

Pancham Gupta  What is AVM?

 

Stefan  Yeah, so AVM is the Automated Valuation Model on the property side. So that is typically like a machine learning regression. That typically gradient because we know some of your audience, this audience is in tech. So, it tends to be like a collection of gradient boosting regressions or just a single gradient boosting regression that is taking like the various inputs of houses and just outputting you know, what the property value should be? It’s a very difficult field. It’s zero recently, we’re in this Well, and, and they will be right buying division. So that’s not and it’s a challenging field. But that said, it doesn’t 

 

Pancham Gupta  They should have hired you, right? They should have used your more. I’m just kidding.I don’t know what exactly failed in there. I don’t know exactly what is. I’m sure there’s model itself was good but they do not care to the other business dependencies, etc. But yeah, in terms of AVM, it’s just well, it’s just like pricing properties in an automated manner. The benefit of this, you can now seek inefficiency, because to my perspective for real estate investing, and why like real estate is because it’s an inefficient market. So, the way to capture this well is with data, it can be done on the commercial side as well. So, on the commercial side, so I built a model. I have it on my channel I run a webinar called Finds Me Through Estate we had you over there Pancham so that was great. So, I did like a lecture for finance mutual estate where I showed like a model for commercial multifamily. So that model uses, like, kind of scrapes public data, which is like has ownership issues one needs to consult like an internet attorney, etc, but assuming like with various vendors, one can assemble a similar set of data, one can model like big, like 100 units, 200 unit buildings in an automated manner, where one models specifically for their Balwant and tries to find, okay, we have or 1 to 4 with our various other markets, from the inventory of 300 buildings in 1 to 4 -inaudible- this is the vector of buildings that is most likely to this the ranking of buildings, it’s just most likely to give us to correlate to the ones that would ultimately perform best environment space where you can raise the appraisal 20%. Or you can raise the appraisal by just raising the rents. So that’s achieved by having like inventory feeds such as can be prospect now or others, this is what every syndicator does. But on top of this, on top of it, adding rental listings data

 

Pancham Gupta  on what you’re explaining that makes a lot of sense, right? But for you to build this model, on top of the algorithms and all that the data is the key. So, you have to get either data in a similar format available from many, many different sources feeding into your system. Or if they’re available in two different formats, you have to normalize all that data in certain format so that your system can understand it. So, is that something that you are doing on your side? Or are there services available off the shelf at a paid services where you can get this data in a certain format?

 

Stefan  Yeah, well, the inventory of commercial multifamily is available in like services like Costco or yardie, met matrix prospect now, etc. That’s as far as the inventory goes to get a glimpse into the potential variant for those buildings. I think one needs to kind of like, sort of like, estimate, like, what is their income expansion trade, so we don’t know. And to get like a small preliminary glimpse into their income expense, I think the best way is rental listings data. So, I mean, what I have been doing for my prior personal investing and is using apartments.com. Now that’s Terms of Service question and like when they speak to his authority, and etc, but you know, like for personal use, I would just like refer to the rental listings data. And that’s kind of my source for that, because then you can get for all those different buildings have like 100 units, 15, or 20 units, whatever you can get, whether the rents are below market relative to the immediate neighborhood, you can get, whether their other income components are in place, or below market or relative to, you know, the immediate neighborhood, other income combines meaning like administrative fees, pet fees, etc, etc, you can get whether their utilities are in line with the market, whether they’re building like utilities, relative to other immediate neighborhood buildings. And by this collection of numbers, if you do like your algebra, to combine them, you can say, Okay, this is a building, but you don’t know what it’s worth, you’re not even pulling data on what it’s worth. It’s just 100 unit building new seats, rental listings data, but you can see relatively what percentage improvements you can make, by sort of like doing estimate, this is should ought to be the share of their electric bill within overall utilities. And this ought to be the share of UT over utilities within the total and just kind of having a simple model. And coming out at the end with, okay, this is how much you can raise the volume, 10% 5% 20%, etc. And just modeling that across many buildings. And that can be potentially a more useful commercial monitoring strategy where one can say, now I can pick a set of markets, let’s enforce it. Now I can pick like various counties, who the buildings in those counties, and just maybe target the best months, or let’s say, or if maybe I’m just working my walk on market and I know it really well, in a physical sense, I can focus on Okay, now there are those five kind of golden buildings that I have seen in the day to day they have rent to market, their bed fees, etc, are not in place, you can raise some of those fees and administrative fees, the utilities are not optimal. And then you can say one can say, okay, that’s, that can keep tracking those over several years, just because they have this kind of information.

 

Pancham Gupta  Got it. So, you know, that makes a lot of sense, right? For people who are listening who are more of engineering mindset, they would appreciate what you’re talking about, right?. So, all we’re trying to that Stephen is trying to do is taking all this data right and making sense out of it and putting it out and based on whatever you’re trying to track, whether it’s valuation, whether it’s income expense, all that he can figure out whether it is a value add or not a value add whether it’s a good buy or not a good buy. So, based on what you’ve just done, historically speaking with your models, people who are listening, let’s say they are investing in single family homes, and which markets would you say, are overvalued, given where we are today?

 

Stefan  The markets, that’s a very interesting topic, just the market analysis, you know, like a few has taken me like 2% of the time, but it’s been like extremely useful. And like, I’ve spoken like at different events on it and has had like really high interest, because the market side is driven by, you know, the real estate fundamentals, like incomes and housing supply and population changes, right? Those fundamentals, they’re kind of simple to track. So, we have all the data on them. And what I found, so he did a study before he doesn’t back study for like the 2007 market crashes. So, my study was the following. So, it took like, oh, the like, cause like 3000 counties in the US. And I also like the state level for different states. And in those different regions, they wanted to see what predicted the crashes after 2007. And so, what they found is affordability, deviations relative to kind of well selected moving average window are very good at predicting downside risk. So, at the state level, affordability deviations predicted to 85% Pearson correlation, the downturns pose the peak, at the county level for 3000 counties, you lose about 75%. So much worse, right? 75% is much worse.

 

Pancham Gupta Can you explain what that 75% number is again?

 

Stefan  Yes, to what their races. One can build like more detailed models, let’s see, but in the simple sense, which anybody can do, and I’ve been kind of like trying to inspire other investors to do it and stop worrying about the market is to just take the history of prices from FHFA, or zero, whatever, you need have, like at least maybe like 20 years before a major drop, like 2007 that you can kind of study and calibrate to that drop. So, let’s say since 87, you know, or something, something like that, right histories. So, you have the price histories, and you have the income histories, income histories from Bureau of Economic Analysis. So that’s all governmental free data, anybody can use it to school, their CSV files and study to that on 20 years kind of moving average window, if one uses that, and one takes the how much price income ratio, the big in each region in every single county was relative to the either median or average, I need to see over that 20 year period, like some kind of average metric of the moving average metric. And so, you know, that deviation at the state level correlated 5%, to the actual drugs to dry phi, this, this is like exact time period state. And so, every single county is going to have its peak at a different date. So, you know, some counties are going to pick it cute in Q2 of 2007. Others, or maybe got, you know, continuing to like 2008, or others new peaked in 2005. But those different dates, and then subsequently, they’re going to have drop that, on average lasted about four years in the US. So, the peak nationwide was Q2 2007. And then the drop was like continue like four years later, or four years in one quarter later. But in different regions, in some regions maybe took six years for the full, like bleeding out like swarm leading out to the prices, you know, like in in some regions was like two, three years on. So that’s peak to bottom drop, the magnitude of it was correlated at the state level 85% to the affordability deviation. Now, when I say this, like sometimes people think that okay, it’s price to income is what is actually predicting the drugs, that’s not correct. It’s that you need to have the prices, you do want to definitely normalize them by incomes. That in itself is not sufficient, because I know like many people will get Okay, our affordability in San Francisco is maybe like 20, like prices, or 20 times income or something like that. And so, it’s extremely not affordable, it has to be overvalued. That’s not true. It will only be overvalued, if that affordability shifts above a moving average level. And only then it’s gonna be predictable. And the reason is when it takes kind of moving our level as a denominator is a basis. It’s going to reflect other real estate fundamentals there that are not income, which is for example, housing supply to population like housing shortage developing. So, let’s say San Francisco is the affordability of 60 in the past, but then it shifted up to an affordability of 20 it became extremely not affordable, that say, that may or may not be overly dependent on like the kind of the part of, you know, like how it’s the move relative to the moving hours and how quickly it happened and etc. And so, so that’s like that was the station, it’s very easy how, like a few like it has some sort of people want to go on my website is rubicon.com. So how, like analytics and analytics staff, they can sign up for some data there and, and in to use rate how this works.

 

Pancham Gupta  So, can I ask you one question based on your analysis? Which market is right now the best performing or let’s call it overvalued, the top overvalued, most overvalued market or undervalued market?

 

Stefan  Yeah, well, the best performing is a different thing. So, the best performing is the ones like if we take fair valuation when they were fairly valued in the past. And if we know that starting points correctly, then we know what the price performance was. Because if we actually if we don’t know, valuation, we cannot even know price performance well. And you know, because we just let’s say we take Florida, for the prices are like roughly around their 2007 level. Now that kind of looks weird, like a little bit. Now, they went a bit above now since COVID, let’s say and let’s say is that a strong market or in you know, investors know, it’s a very strong market, right, investors have the market intuition. They know that Florida and Texas are some of the best markets, they have that intuition. But how do you see that in the data? Well, Florida, you have to take the proper fair valuation and was fair, because those sharply over words in 2007, but 50%, it’s a distorted starting points for your appreciation. And so, but if it takes inspiration, you get used to the strong markets. And so as far as well, performing markets, I think it’s gonna come as no surprise, but I mean, that’s gonna be like Idaho is the top performing state. And, you know, Colorado, and so, you know, it’s just all the western states. And it’s gonna come to this no surprise to any real estate investment managers, who is aware that basically, the whole set of Western states except California performed extremely strong. And then Texas, and Florida and Tennessee, let’s say, on the eastern, more central, and eastern part performed well, and so that’s first of all, as far as overborrowed, they actually have in front of me right now. So as far as overvalued, the currently, we’re currently actually that’s my data is behind center. And this is one of the income data, government data is very slow to come out. So, the most overvalued states are Idaho. 25% overborrowed, the state of Nevada, so that’s another 25, Idaho 25, Nevada 16, Arizona, 14, Coral 14, the District of Columbia 12, Texas 10, Washington 10. So, these are the only more than 10%, so your – inaudible- is very fairly borrowed  There were studies studied by Wombok, Wombok Economics in Russia. it showed that like Canada and Scandinavia and like Oceania, so say like Australia and New Zealand are sharply overborrowed, on you know, like different use, like several different metrics, they show U.S fairly borrowed, and Dahlia was correct as of 2020. So once the data comes pretty transparent may go a little bit, probably more in the overborrowed territory will expect, you know, the current developments, but you know, as a standard 2020 was still fairly wide with the market. Idaho is not fairly borrowed. I know like many investors have spoken in Boise before. And there was a study that came out three some things in Fortune was published another by Billy for the Quantic University or something like that. And their study also showed Boise is the most overborrowed city in America. So that’s, you know, like one place that is okay, that’s like sharply the only one that is kind of definitely overborrowed, by not Leto. And in other cities, I mean, some of the very well performing cities so to Boise, Spokane, Washington, Quad, Texas, Phoenix, Arizona, are kind of at the peak. But even Phoenix, I see it in my model at least 15% overborrowed, so it’s not that much. Boise you see is 37%. So that’s kind, very, that’s the only sharp on.

 

Pancham Gupta  I want to move to the second round. Yes, I want to wrap this up. Is this data available for people to look at? Or is this like on your website?

 

Stefan  Yeah, you can go to realtyquant.com/analytics. They have the data there for like 3000 us counties.

 

Pancham Gupta  Got it. Awesome. Thank you, Stefan. We’ll be back after this message…

 

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Pancham Gupta  Stefan, this is the second round, we call this taking the leap round. I asked these four questions to every guest on my show. My first question for you is when was the first time you invested outside of Wall Street is that the fourplex?

 

Stefan  No. The first time outside of Wall Street, I invested most of my savings in the banking system of like Caucasian countries in Eastern Europe, between Europe and Asia, which happened to be paying very high yield. And so, I was able to find like some banks that had like, very clean foreign investment in their like ownership structure was very, like straightforward. And they were right rated in fact, like, so the one I the financial institution invested in was investment grade rating and, and that and it was paying like a very high kind of like double digit yield. And I thought, I know that’s a good deal in there, like stock market did even better. But that’s where I invested time, I always like to hang on to like more certainty. And it may sound strange to some people, but they did feel that Eastern European will classify as investment grade like financial institution would be safer in stock market. So that’s kind of how I felt at that time. So that was the first I would say like, kind of paternity alternative investment that I’ve done, like quite a few quite a bit of like cryptocurrencies afterwards as well.

 

Pancham Gupta  Did you have to overcome any fears? When you did that?

 

Stefan  I did. Yes, I had to, especially on that many one of the country’s like, it’s the banking system is extremely corrupt. So, you have to actually like do a world of study. So, I had to like really to study in very detail like, oh, institutions and find those like exceptional ones, you know, there exceptions really, that have like good governance systems, etc. Yeah, and then there was a situation that so their financial systems kind of function of the oil price a little bit. So, then oil was crashing, and, like non-performing loans were like we’re sending so it’s like the, the published metrics were sending over time, but that was sort of counting yeah okay, takes time, as long as they have a good governance and like good, you know, like, the character of the institution is good. It’s probably gonna, you know, not go bankrupt so quickly, so it kind of was like keeping a bait went well. So, it went well. But that was there were fears for sure. 

 

Pancham Gupta  Got it. My third question for you is, can you share with us one investment that did not go as expected?

 

Stefan  Well, so I have a property we are working now for condominium conversion, and we’re going through like, it wasn’t a rent registered, we’re going through like some issues with the local rent leveling board. It’s a case and that definitely has not gone as expected as far as working with, I guess, northeast municipal government officials is extremely fun. 

 

Pancham Gupta  Got it. Okay. So, my last question for you is what is one piece of advice would you give to people who are thinking of investing in Main Street that is outside of Wall Street?

 

Stefan  One piece of advice I would give to them is trying to spend more time learning how to generate alpha and then maybe you will not do it. And just try to understand private equity investments, understand technology, how you can generate alpha yourself in a way that depends on you. 

 

Pancham Gupta  Got it? By alpha you mean, yield and generally what you’re meaning here is

 

Stefan  Market advantage. Alpha in finance is yeah. So, to your audience, so alpha in finance to have like, like a regression or something has like beta times, you know, times the number plus alpha. So alpha is like the shifting factor. So, it’s like the factor by which you’re beating the market, you know, through your knowledge, which inherently is more of a private equity concept. And so that’s what they would say, I mean, they want then they can think of how to apply this maybe in the news, new finance were like the center of the spinal fluid, where the security tokens, etc, now are able to generate offer, there are which security tokens reflect that in some way than that. But that would be my advice, you know, rather than I’ll just buy this stock, and not know. And the second piece of advice, actually, second piece of advice, no market valuation. So that has been for me, like me, what I mentioned a little bit earlier, has been, I believe, it hasn’t realized itself yet, in the sense of we haven’t had downs during the real estate market, let’s say, but knowing market valuation is going to come very useful to me in the coming years. And I’m pretty confident in that interest, as far as you know, whenever like, harder times dance,

 

Pancham Gupta  basically, what you’re saying is educate yourself really in this space and make your decisions based off that. So, well. Thank you, Stefan. How can listeners connect with you? I know you mentioned your website, realtyquant, you also have a course that you’ve put together based on your experience, you’re learning that people can find out more about can you talk about that and how can they get to that course?

 

Stefan  Yes. So, I have a course on data driven investing, which is starting in January, January 15 will be the starting date. You know, it’s data driven investing with Python, and we’ll probably put like some no code to swell and like try to kind of make more very than useful to different audience. So that’s realtyquant.com/academy. That’s my course. Best way to reach to me again, my website, realtyquant.com or on LinkedIn Stefan’s Tsvetkov on LinkedIn.

 

Pancham Gupta  Great. Well, thank you Stefan for your time here today. 

 

Stefan  Thank you, Pancham.

 

Pancham Gupta  I hope you learned something from Stefan’s analytical podcast. He’s very analytical. I know we’ve got a little bit too much into the weeds, but this is really good information. If you’re of the analytical mind, as an engineer, you would appreciate what he was talking about and how he’s using data analytics and machine learning to get ahead of where the trends are going. So really reach out to him at realtyquant.comIf you are more interested in learning about it. Thanks for listening. If you have questions for me, email at p@thegoldcollarinvestor.com This is Pancham, signing off. Until next time, take care.



Thank you for listening to The Gold Collar Investor Podcast. If you love what you’ve heard and you want more of Pancham Gupta, visit us at www.thegoldcollar investor.com and follow us on Facebook @thegoldcollar investor. The information on this podcast are opinions. As always, please consult your own financial team before investing.

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