Want to up your underwriting game? Nelson Lin, a data scientist and multifamily syndicator, shares his strategies for analyzing data, including how to create your own database, ways to deduce value-add opportunities, rent pricing, and more!
Nelson Lin | Real Estate Background
- Multifamily syndicator, data scientist, and founder of Subtle Asset Management, which consults data science for other commercial real estate firms to help them organize data and apply machine learning algorithms.
- GP of 164 units (72 in Chicago, 92 in Jacksonville)
- LP of 3,000+ units and two industrial properties
- Based in: Austin, TX
- Say hi to him at:
- Best Ever Book: The Gap and The Gain: The High Achievers’ Guide to Happiness, Confidence, and Success by Dan Sullivan
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Slocomb Reed: Best Ever listeners, welcome to The Best Real Estate Investing Advice Ever Show. I'm Slocomb Reed and I'm here with Nelson Lin. Nelson is joining us from Austin, Texas. He's the founder of Subtle Asian Management which does data science consulting for commercial real estate firms to help them apply machine learning algorithms. He's also a GP in 164 units in two deals in Chicago and Jacksonville, Florida, and he's an LP on over 3000 units and two industrial properties. Nelson, can you start us off with a little bit more about your background, and then tell us what you're currently focused on?
Nelson Lin: Yeah. I am a data scientist, I live in Austin, Texas right now. My background - I basically build machine learning models and algorithms for mostly hedge funds. My biggest client is Brookfield Asset Management, I help them with their residential division. They have 10,000 single-family homes, for example. What I do is I crunch numbers and I output either projections for the future, or sort of help guide on both asset management and on acquisitions. My background is mostly around robotics originally, but I've transitioned from the tech space where I used to work for Apple and Microsoft in Columbia, where I graduated from. Eventually, I went into real estate and I found a niche where there's a combination of large firms that own a lot of real estate, and have a big need for managing, organizing, and finding data within their massive pool of properties.
Slocomb Reed: Gotcha. Give us some specifics on the things that larger real estate firms do that you're building machine learning algorithms for, so that they can improve their processes.
Nelson Lin: I can't reveal too much data, obviously. One example is [unintelligible 05:12] of about 10,000 single-family homes to their subsidiary contracts. On the 10,000 single-family homes, you actually have a lot of data now to work with. Everyone knows about Zillow rents, and Zillow estimates. The hard part with Zillow rents is that any Joe Schmoe landlord could put their listing as a rental. When you don't have good data, you don't really have good models. So when you're a professional landlord with 10,000 single-family homes, you have a lot more data to work with, cleaner, and overall, you can get a much more accurate model. So the "Zillow rent model" I built for them is about half the error rate compared to the actual Zillow model that they use.
A few things about that as well - they focus on internal data, and a lot of proprietary sources that Zillow probably doesn't have access to. On top of that, the company is very vertically integrated. So other than just acquisitions and finding what the Zillow rent price should be, we're tracking down tenant management issues. For example, if you have a tenant coming in, you can give a ballpark estimate based on certain information, like how likely are they to default on rent. Machine learning and data science are these large buzzwords, but they're very slowly being adopted into the real estate space, mostly because of how hard it is to get organized and clean data. And really, only commercial hedge funds and large firms right now have access to that information.
Another way I've been helping is there's a fund out in Dallas, I'm building them a lead generation model. One thing I've been doing is putting an ear and scraping from different websites sources to figure out which companies are going bankrupt. In Texas, for example, these are called war notices. War notices will tell people, "Hey, this company fired 100 employees recently." You can use that to track and figure out, for that state, which company that is, and then on top of that which properties they own, and you can get to them ahead of time before they go on the market, because usually after a layoff, they also try to divest themselves of the asset.
So there's more than one way to define machine learning in commercial real estate, but lead generation, asset management, and acquisitions - there are quite a few ways to implement data to scale up and build your business, which is also stuff that I'm applying to my own GP business.
Slocomb Reed: I want to get to that Nelson, and I want to talk about how you're applying this to your own GP business, but correct me where I'm wrong on what you were just sharing with us, Nelson. It sounds to me clearly that in order to build quality machine learning algorithms, you have to have a large, accurate data set. In your experience, it's the much larger companies, it's the hedge funds, it's the ones that own tens of thousands of rentals that have a dataset large enough for you to work with, so that you can create models for them to learn from their own data. Let's say I just bought my first single-family rental. I'm not coming to you to ask me how much to rent it for, I need to just go to Zillow, don't I?
Nelson Lin: More or less. Most of these companies, I have NDAs with them, and they're not going to be sharing their proprietary data. If they do sell it, usually only other hedge funds are the ones buying it, because the license will be, say, $10,000 a month to source data from another company that owns several thousands of units. So it does feel unfair in a lot of ways trying to compete, but there are still ways for you to keep up and track, mainly through census information. FRED for example is also really useful.
Slocomb Reed: What is FRED?
Nelson Lin: FRED is the federal database that the government keeps. There's a lot of economic data, for example, Case-Shiller and different rent CPI changes that you can use to track how the prices have changed, and how inflation is doing in a specific city. It's a pretty good historical model, but if you just type in "FRED economic data" in Google, it should pull up that website pretty quickly.
Slocomb Reed: Okay, gotcha. You are in two general partnerships - 72 units in Chicago and 92 units in Jacksonville. Within your general partnerships, are you the entire general partnership, or do you have a specialization within those partnerships?
Nelson Lin: Yes. Obviously, being a data scientist, my role in the group is largely doing the math underwriting, but I also raise capital. My experience being on both east and west coast, in the finance and tech industry, a lot of my network is in both of those industries. So they generally have capital invest, but they would, say, never leave Brooklyn, ever. They would never want to get out of the Bay Area, California. But if I am somebody who's in Chicago, a place they might visit once or twice in a lifetime, they'll gladly put money in places that cash-flow, that have a cheaper per dollar cost, and overall, there's easier access that I can provide them by being a boots on the ground person.
Slocomb Reed: Gotcha. Two questions for you, Nelson. The first question is how are you using your experience as a data scientist to inform your underwriting beyond what the rest of us typically do? Nelson, the real question is how can I learn how to use data science to improve my underwriting? Tell us what you're doing, but let us, the Best Ever listeners and especially me, know how it is that we can improve our own underwriting by doing it more like how you're doing it.
Nelson Lin: Got it. So you don't have to go as advanced as, say, building a regression model. They tend to be expensive jobs to hire for, and they also are often above and beyond what's necessary to do well in real estate. One way to really improve your underwriting is - I don't think that most syndicators are keeping a good track of OMs that they've already seen. One major aspect that they could really use to benefit themselves is to take a large sample size and use the estimates to figure out how conservative can I be on that.
I'll give an example. In Chicago, I have a list of dozens of OMs in the last year that I've seen for apartment complexes. I have the age listed, but more importantly, I have all the expenses listed. So when you take enough samples, you can figure out what's the average, but on top of that, you can figure out what is the tail end of the properties that might seem interesting or totally off. So if you have, say, 30 properties, you'll get an average that you can use as your underwriting. But you can go the conservative case, which is say the maximum 33rd percentile for what you would pay in water expenses. I've actually gone through OMs now and seen, "Hey, this property is actually at the 99th percentile for water expenses per unit." Why is that the case? It becomes pretty evident that there's a leak somewhere in the building.
Slocomb Reed: Nelson, let's stay on that example. When you're looking at an offer memorandum and you see 99th percentile water expense, the first alarm bell in my mind is, "Hey, if I find that leak, my operating expenses should go down dramatically. There's value." Is that the kind of thing that you're doing?
Nelson Lin: Exactly. Usually, I check on the water expenditure side, "Hey, there's a leak," or maybe there's an opportunity to replace with low-flow water items. At the same time, we also look at electrical expenses. These are super high; probably they either haven't separately metered out or they haven't switched to all LEDs. On the HVAC side, maybe they're using all older boilers, something of that nature. Or you can check, "Hey, within this parabola or within this range that's normal, where are the numbers off on my expenses?" And from there, you can figure out a value-add, very quickly, without even having to visit the property, that it stays outside of the normal range of what these expenses should be.
But you can't really do that until you have a database of maybe 20 or 30 OMs that you've probably already looked at, but you've never kept track of in, say, an Excel sheet. Something like that, you don't need a programmer who costs six figures a year to run your business for. You could keep track of it yourself, hire a VA. Whatever is your underwriting, you should be accumulating a list of expenditures that you're spending, and then based on that, you could figure out where does this property land on the average just for each dollar amount.
Slocomb Reed: Nelson, this is really helpful. That makes so much sense. For everyone who's in acquisitions, reaching out to brokers, getting themselves in front of deals, we could be collecting data from all of those offer memoranda to figure out, as you said, the bell curve of each expense, water expenses that are way too high more likely than way too low, or things that are being underestimated. Often, especially in a Midwestern market like mine, like Cincinnati, with a smaller property, you're talking about an unsophisticated seller... And sometimes the broker has to create a proforma because they're not getting sophisticated actual expenses from the owner.
If you're building that dataset, it makes sense that you can be using it as a cross-reference for the expenses on the deals you're currently underwriting. To your point, 99th percentile water bill means there's a leak. I find the leak, I stop the leak, I've immediately added value. Where else in deal analysis is your work background proving itself out to be very advantageous?
Nelson Lin: In any business there are two ways to make money. Your profit is entirely determined by revenue minus expenses. I've talked about estimating your expenses; you can also do the same thing with your revenue specifically. If you're estimating your revenue, it's very difficult to take just three comps in the area and say, "This is it. This is the whole number." Usually, within a statistical sample, you'll want, say, for example, 30, to get to a normal distribution. That looks like a curve, that makes sense. For example, if you're in a classroom and you only have three kids in the classroom, it's hard to say, "Hey, the average height for all kids of this age is six feet. For fifth graders. These are the really tall fifth graders." Ideally, the more samples you have, the more of a curve it looks like, the more balanced that you can get for a number; you can figure out what the normal range is.
In real estate, when you're acquiring new properties, a lot of people will just take the three best, highest rental comps in the area to show, I guess, investors. It's hard sometimes, because there isn't a lot to work with. But if you give them a range, that gives them an idea of how conservative do I want to be. Maybe you want to, say, take the lowest range and give them an idea of a worst-case scenario, versus something that's a best-case scenario on the high end. You shouldn't be sharing deals based on only the high-end scenario, of course, you want to be as conservative as possible.
There's a website called rentometer.com that I like a lot, because it splits up based on that number. It has a 50% mark, a 25% mark, and a 75% mark. That means I know what my high target is, what my likely average target is, and what's my conservative rent case. How do I compete against, say, all these other properties in this area and all these other syndicators in the area? I want to be conservative; I want to target that 25th percentile for the rent. Maybe not 99th, because you don't want to be too conservative and price yourself out of the market. But on the acquisition side, you can also space it apart and then figure out from there how should I be properly purchasing.
If you look on rentometer.com as well, they actually show you which properties are in the green, which means it's cheaper than the average. They also show you the red, which is what's more expensive than the average. On the rentometer.com map, you can actually see "Oh, there's a group of red properties", and when you look through it, you figure out oh, this is totally new construction, A class neighborhood of apartment complexes. The one I'm buying in is largely green in this area. Even though the average within a neighborhood might be high, I am in the cheap neighborhood that's a few blocks away.
This might not happen in most markets, but in Midwest, like Chicago, it changes within three to four blocks. A whole street could be class A storefronts, and then you have four blocks later some of the roughest neighborhoods in town. The one particular I'm thinking of is Oak Park and Austin in Chicago. When you are taking estimates, you want to look at the area, but you also want to figure out what is my range. I would say that data acquisition side would be really helpful when underwriting.
Break: [00:18:19] - [00:20:15]
Slocomb Reed: A couple of other things here. I pronounce it Rentometer, but I don't know how it's supposed to be pronounced. It's an affordable service. As a real estate agent, I compare it to sold comps for real estate sales, because typically what you're seeing in Rentometer is something that has already leased, or that was advertised for lease several months ago. I also use a combination of Zillow and apartments.com in other places to figure out the "active comps". As an operator, Rentometer usually has a larger data set, especially now, than the active lease listing websites. But the active lease listing websites give me a good idea of what my competition is right now.
Nelson Lin: Agree, yeah.
Slocomb Reed: I'm putting myself in the mind of a prospective tenant... They're not going to Rentometer to see what rents were six months ago in the neighborhood; they're going to Zillow, Hubzu, Dwellsy, whatever, and seeing, "Okay, which apartments can I see this weekend?" Another thing that helps, to your point about the high red rents and the low green rents - it helps to have a street by street, block by block understanding of neighborhoods as you were saying. But also, that can be an indicator that if you're buying in the "green area" where rents are low, that rents could be rising, because amenities are being added to the neighborhood.
Most often, at least in Cincinnati, we're seeing that that new construction, those new construction apartments that would have the higher-than-average rents, the first floor is entertainment, retail, restaurant, bar, the kind of things that the people living in the green, more affordable apartments are going to be attracted to, and you should see rent growth in the area.
The other thing that I use it for is trying to figure out which amenities actually increase rent rates, and which don't. I assume you're doing something similar, Nelson, when you're analyzing for renovations. How much does it help to put laundry in the units, how necessary is it to have off-street parking, those kinds of things? When did you guys buy your deals in Chicago and Jacksonville?
Nelson Lin: Chicago was actually beginning of 2021, I believe, and then Jacksonville was like 2021. It was COVID buys.
Slocomb Reed: Yeah. Tell us more about the Chicago deal, what attracted you to the market first, what attracted you to this property, and with what projections did you buy it in early 2021?
Nelson Lin: Got it. Chicago, the one thing that everyone will tell you and talk about is that it's a terrible market to invest in.
Slocomb Reed: That's why I wanted to talk about that one. Yeah, Nelson, let's go.
Nelson Lin: Across the board, all of the multifamily syndicators and large investors you'll talk to will say, "Why are you investing in Chicago? What are you doing?" To that, my response is that "Most people don't invest in Chicago." Good luck finding something at market price in Austin, where I live right now. In Chicago, the thing is we can pick up properties at roughly 40%-50% off the stabilized basis value. We are not necessarily competing with the whole country for properties in Chicago, unlike say in Austin, or pretty much anywhere in Texas right now. Sp we are buying in specific submarkets, so we're not just buying in Chicago anywhere.
Chicago is the third biggest city in the country. What does that mean? Certain neighborhoods are the sizes of cities themselves. One particular area we're buying in, it's called Bronzeville. If you look at Rentometer, if you have a pro account, and pretty much any sort of historical trend site, there is a lot of creative appreciation that you can have, mainly because rents have increased by double-digit amounts even before the pandemic, for the last three or so years; prices have doubled since 2017. This is a small sub-market that is outperforming most places in the US that I've seen. But at the same time, it's big enough for the city that you'll get, for example, tier 1 lending, because you're in Chicago; it's a major gateway city. When we got CBRE to fund the loan, we got some pretty good deal terms that it would be harder to do in, say, Chattanooga, Tennessee.
Slocomb Reed: Let's dive into some of these numbers now. 72 doors in Chicago - when was it built, what's the unit mix? And then tell us, how much did you pay for it and what kind of lending terms did you get?
Nelson Lin: Got it. This is actually split across many buildings. One was 18 units, and then a bunch of smaller six units. Deal terms are 3.75%, with a bridge, I believe, a construct loan of a few hundred thousand... The numbers for that one, I have to actually pull them up real quick. But I think the Jacksonville one might be more interesting, just because it's more recent. Can I explain that one instead?
Slocomb Reed: Let's stick with Chicago, since we've already started there.
Nelson Lin: Okay. No worries.
Slocomb Reed: We're also running a little short on time.
Nelson Lin: I'll probably do that then. In Chicago, the deal terms are 3.75%, tier 1 lending, it's a gateway city in Chicago. The deal was split across multiple properties. We bought it all-in; I want to say the first property was 1.5, and then the other one was two, so all-in the value I believe it will be around 6.5 million, when it's all said and done. The first property, all-in costs are around 1.5 million, should be worth 2 million when it's done. The other property we bought all-in for 2.5 million, but we believe it'll be worth around four when we're done. So the other packages, we are selling some of the smaller six-units. And then the deal size, I don't have it off the top of my head, unfortunately.
Slocomb Reed: Gotcha. What attracted you to Chicago though, is that Chicago is a large enough market that it's composed of submarkets. One of the things that I find true in Cincinnati, which is much smaller, a third the size of Chicago I believe, is that even within an urban area the size of Cincinnati, when you have a lot of new redevelopment attracted to one particular neighborhood or one part of a neighborhood, the tide rises in the surrounding neighborhoods as well. If you wanted to say Cincinnati is an X market, it's a seven cap market or something like that, you're missing out on what's happening at a more micro level.
What you've found in Chicago was a particular neighborhood that has seen a lot of appreciation recently, and an opportunity to buy a collection of smaller properties that don't hit the inbox of a lot of syndicators, A, because it's in Chicago, B, because they were smaller properties, that gave you this opportunity to take advantage of... They're not just rent growth trends in places like this, they're re-urbanization trends, all things considered. That's pretty exciting. You guys underwrote this to a five-year hold, I would imagine?
Nelson Lin: We actually are at the seven-year hold, and we're estimating at 22% IRR. The big reason was that most of the value came from the beginning. It's hard to do the math on it exactly, but across 72 units, 18 of them we bought at 90k a unit, because those are a lot of three beds, and we actually have some four beds in there as well so they're bigger units. Another 54 we bought were a bit smaller, and we bought them for around 50,000 per unit. So the all-in math is we're buying at around 60% of what the stabilized value should be, putting another 20%, which is probably 20-30 grand even per unit.
We're all in for 80% of the full value, based on today's market comps. So if we were to sell, we could make that 20% profit, or we could actually refinance out and do a major BRRRR. The cap rate in Chicago right now for the areas we're in is still around 6%. Everyone likes a BRRRR deal, but imagine doing that on 72 units in Chicago, for example. It's very hard to pull that off in a city like Austin, just because it's so competitive.
Slocomb Reed: Yeah. Being so competitive in Austin is one of the reasons why you're able to pull it off in Chicago, because everyone is paying attention to Texas in the southeast. Nelson, are you ready for our Best Ever lightning round?
Nelson Lin: Yes, I am.
Slocomb Reed: Awesome. What is your Best Ever way to give back?
Nelson Lin: I run a group called Subtle Asian Real Estate. I give back a lot of information and teach people how to invest in real estate. A lot of them are starting from zero to one. I used to put a lot of time into a food pantry when I was living in Chicago, but since I moved, I have mostly been focusing on educating and helping people get started in real estate.
Slocomb Reed: What is the Best Ever book you recently read?
Nelson Lin: I'm currently reading The Gap and The Gain. I had a bit of a mindset shift recently, and I've been getting help a lot. People point towards the gap and the gain to sort of pick up on what it's like to be an entrepreneur and do better in my business.
Slocomb Reed: What is the Best Ever skill you've developed in commercial real estate, outside of your data science background?
Nelson Lin: I think the Best Ever skill I've developed is raising capital; it's something that I didn't expect to do. But for example, the Subtle Asian Real Estate group is now that 13,000 members. It was a pandemic project and it's kind of taken a life of its own. I've recently had a lot of people reach out not just to hear how to start investing but also to be an LP in my groups.
Slocomb Reed: What is your Best Ever advice, Nelson?
Nelson Lin: My Best Ever advice - don't dive into something without doing the research first. I made a mistake on a flip recently, which would probably be, if I remember correctly -- there's a section on what's your worst deal? I'll save it for that if we're going to that next.
Slocomb Reed: We're not, no. But tell us about it.
Nelson Lin: I went into a flip without knowing the flip business very well. I got caught up in the market, it was hot, and we ended up holding it for 11 months. We didn't lose money, we still bought it decently cheap, but I did not pick the best renovation stuff for it.
Slocomb Reed: Did you have high-interest debt?
Nelson Lin: No, luckily, I [unintelligible 00:30:32.05] But it was low-interest, and luckily, even though we paid off the debt, it was still a harrowing situation to have an empty, vacant unit for 11 months.
Slocomb Reed: Gotcha. Nelson, where can our Best Ever listeners get in touch with you?
Nelson Lin: You can either find me by googling Nelson Lin or subtleassets.com, those are my websites. You can also find me on my Facebook page, Subtle Asian Real Estate. Those are probably the best ways you can reach out to me.
Slocomb Reed: Great. Well, Best Ever listeners, thank you for tuning in. If you've gotten value from this episode, please subscribe to our podcast. Do leave us a five-star review and share this episode with a friend whom this conversation with Nelson can add value to. Thank you and have a Best Ever day.
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