رونوشت کامل Episode #242: The Mad Scientist of Multifamily: Using Technology to Find Better Real Estate Deals with Neal Bawa

mad scientist of multifamily. Besides one of the most in demand speakers in commercial real estate, he's a data guru, process freak and an outsourced outsourcing expert. He treats his 660 million dollar multifamily portfolio as an ongoing experiment in efficiency and optimization. So I'm looking forward to, you know, a lot of the things that you, you've said before and I, I, I, I really want to just get into it if it's okay. I've got like a, I've got questions and I, and, and, and because you know, a lot of the stuff that we do in multifamily in, in Canada seems a lot, a lot more isolated and it's, there's some differences for sure. 14 00:03:26.000 --> 00:03:44.000 Like. Right. Like I'd say if you're going to talk about market selection that the choices are quite a bit smaller in Canada. Right. Like, yeah, you know, and there is several individual markets but not even close to the opportunities that there are in the states. So it is going to be interesting to hear about this. Yeah. 15 00:03:44.000 --> 00:04:33.000 And we also have like in, in Ontario specifically we have rent control that is kind of like every year we can do up to usually 3% rent increase on our multi family buildings. Which leaves the strategy that I focus on is mostly repositioning older buildings instead of buying new or building new because we can't achieve the, the repositioning and lift that we can without that. And so we're kind of using it to our advantage. But for, for, I mean you've always said data beats gut feel by a million miles. And so for the Canadian investor, you know, you know, we're used to buying in our backyard. What do you consider is like the holy grail of data metrics. 16 00:04:33.000 --> 00:12:10.000 So I, you know, I want to challenge a little bit and maybe this is an interactive conversation, this concept that Canada doesn't have as many markets as the US While theoretically what you're saying is correct. You know, in the US we have 110, 16 cities, 323 metros. Canada has, you know, a few dozen metros. The way we look at metros is, you know, 150,000 people or 100,000 people or more. And I think the U.S. definition is you know, 250,000 for midsize, 500,000 people. For large metros, I guess the Canadian definition would be a little bit smaller. So maybe 100,000 people could constitute a metro. I still think that there are dozens of choices within Canada. Right. Because the way I look at markets necessarily is not Atlanta's a market. The way I look at it is Atlanta is really 14 or 15 different markets, and I only like two or three of them. Even though when people ask me questions, I'll answer questions about Atlanta, but I'm very careful to answer those questions as well. Just understand that Atlanta varies a lot. So there's parts of Atlanta, for example, which is sort of southwest of Atlanta towards the city of Conyers. I don't like that area at all. I see a large amount of delinquency. I see a large amount of crime. So I stay away from the freeway that goes from Atlanta towards Conyers, even though you can live there and you can commute into Atlanta then next. Beyond that, I sort of like the area that's south of the airport, but not the airport, because the airport also has a huge amount of crime. And I'm giving you this example of Atlanta because I think that it applies to, let's say, any of your major cities. To be honest, I still think that your cities are really. They're so huge. I mean, Toronto is what, 5 million, 6 million people somewhere in that range. I don't think Toronto is a market. I think what should be done in the AI age is to basically find appropriate ways of breaking major metros and their suburbs into discrete stories, sub markets. Sometimes they have names. Sometimes you just create a name for them. And you will find that within a single metro, you will find a very substantial difference in terms of profitability, in terms of your ability to create that delta for your Canadian investors. So I would challenge that. I would basically say that this is an issue of the way you look at things. There's this feeling of, well, Canada only has 10 or 20 markets. No, it doesn't. I think it has. In my mind, when I look at the total population of Canada, you are definitely looking at many dozens of markets that you should be looking at. What I would challenge you to do is to do something that was horribly difficult three years ago, but is trivially easy now if you know what to do. And probably somewhere in the middle if you don't know what to do and you need to muddle through it and learn, is to take one of your metros, Toronto and Vancouver might be two good ones, and say, talk to AI and say, what is the right way of dividing this particular metro into sub markets? What is it? Is it along freeway lines? Is it along Certain cities that are part of this particular metro that right outside Toronto. And how do I break up the city of Toronto itself into pieces? This is a conversation with ChatGPT. I usually don't like having this on a computer because when I'm doing it on my phone, I can just basically turn on my voice and, and talk to it and talks back and I ask it questions. And then we basically say, okay, I like this, I don't like this. Yes, this is it, this is a good thing, this is a bad thing. And I'll give you some examples once again, US example, but they definitely apply to Canada. So in the US currently, the biggest single factor that is affecting Delta when it comes to built multifamily, meaning existing multifamily, maybe 10 years, 20 years, 50 years old, the biggest single factor, and if you were to ask like people in multifamily, this, you wouldn't get this answer from 9 out of 10 is property taxes. Right? So I am saying that statistically speaking, as somebody who is an amateur data scientist, I can tell you the biggest single factor that's affecting profitability today is property taxes. Now, does it mean that things like population growth, job growth, income growth, crime

don't matter? No, I'm just saying that the biggest factor is property taxes. Right? And you can account for the other factors. For example, you can pick a part of Canada that's growing faster than any other parts of Canada. So what I would say is to AI is how do I divide Canada in terms of population growth? What are the fastest growing markets? Sub markets in Canada. That itself is a fantastic thing. So I would say to Claude. Right, I use Claude. You might use ChatGPT. I would say to Claude, I want you to build an interactive map dashboard of Canada. First, I want you to break it up into the provinces. Then I want you to break it up into cities. And then within cities, I want you to break it up further and build me an interactive dashboard. And I want all kinds of demographic data for that. I want population growth, job growth, income growth, home price growth, I want crime, I want school quality. Build me this dashboard and it's going to say, where am I going to get the data from? The answer is, you will figure that out, Claude. So just like we have the bls.gov Bureau of Labor Statistics.gov the Canadians on a province basis actually publish this data. The data is very dense, it's almost unusable. It's in Excel files that are 30 or 40 megabytes in size. But you don't care you're going to say you're going to download this, you're going to slice and dice this data for me and you're going to build me an interactive dashboard. I should be able to drop down any province, any city from a dropdown at the top and you'll give me a visual look at it like a heat map where high population growth is deep red, low population growth is light yellow, right? And I want to slice and dice this and I also want the capability as you build this app, cloud and we'll host it on a server somewhere, right? Some Canadian server. I want you to be able to basically assign weightages, so I might give a 20% weightage to population growth and property taxes, I'll give it 25%. Crime, I'll give it 15%, right. Job growth, I'll give it 20%. And I want to be able to change the weightages. So give me a drop down so I can change the weightages. And based on that, you will rank every single metro, sub metro, and all of these with a ranking. And the moment I change my weightages, your ranking will change in real time. And I want to see it in front of me. Now, what I just described is something that I've already done, right? And it's not a one minute process, it's like a weekend project. It's going to take less time than you think. My only suggestion is if you use Claude, please switch to Haiku. The default cloud model is called Sonnet and in about two hours you will run out of your monthly token limit if you use Sonnet. But if you switch to Haiku, it will still build the model. It'll make some more mistakes, but now you'll be going the whole weekend talking with it and it'll build this magnificent, beautiful map driven interactive dashboard that will give you an understanding of Canada that you never had. I promise it. No matter how many years you've been working in Canadian multifamily, at the end of this you're going to be gobsmacked at how much more you know about Canada than you do now. And then you'll start adding multifamily stuff into it, right? You'll basically say, I want you to read my email and every month where I get this file from Marcus and Millichap or cbre, who are the Marcus and Millichap and CBRE of Canada? Quentin, who are the people that publish data? 17 00:12:10.000 --> 00:12:19.000 We have cbre, so it is similar and we do have Marcus and Millichap, but we have Colliers as one that does a lot of multifamily debt. 18 00:12:19.000 --> 00:17:46.000 Multifamily. Got it. So what you're doing is in the past you could subscribe to every one of their newsletters and these new letters, great content. But the problem is, who has the time to go through this stuff? So people don't do it. Right. So you're going to basically say to ChatGPT or Claude, what are the top 20 resources in Canada for multifamily research and information? Right, Give me those. And it's going to give you a list of 10 or 20. Then you're going to say, I want you to go create a Gmail address for me called research quentinsresearchmail.com right, create that. Then I want you to go to these websites and I want you to subscribe to all of their newsletters to quentinresearchmail.com then I want you to make sure that each month you build me a dashboard of all of the research from all of these sources. You decide what that dashboard looks like. I'll give you feedback on it. And I want a full dashboard and I want to be able to drop down and say, cbre, Colliers, Marcus and Millichap, whatever it is, I want to know what data they're preventing. I want it by vertical. So you've got a tab for multifamily, one for industrial, one for self storage. I want every asset class and yes, I'm a multifamily guy, but the data is already there, man, and cloud is free practically. So you're going to build me tabs for everything in case tomorrow I want to be an industrial guy and so build me this dashboard each month. And then finally when you've built me this dashboard, because it'll take a few months to build, because it needs to read emails and it needs to grab the data and it takes a while for it to get into a cadence because a lot of these places they publish data on a monthly basis, like here in the U.S. yardi sends the monthly newsletter that is universally loved in the US for multifamily. And it's a monthly newsletter, tells us rent growth and occupancy and job growth for all of the metros in the US on a monthly basis. What's going up, what's going down, stuff like that. Right. So it takes a few months for this insane amount of data that you would never even be able to absorb to come in to quentin'sresearchmail.com and once it comes in, you're going to basically say, I want a Dashboard, and then you're going to talk to Claude and say, can you give me a

dropdown here? Can you make this a checkbox? Can you slice and dice this by city? Right? And over time, your dashboards will get far more interesting and powerful. Now at that point you're going to say, ah, now that this dashboard is working exactly the way I want it, which is probably three months or four months from now. Let's combine that with the demographic dashboard that you built for me where you grab data from all the different provinces, you know, job growth, population growth, income, home price, all of the information that your governments are collecting, right? You already built me a dashboard, now build me a hybrid dashboard that connects these two things together. Because what I'm trying to figure out is what part of Toronto, what part of Vancouver, what part of Ontario, what part of any province, state is growing the fastest, right? Then the next thing I'm going to do is basically, I'm going to basically connect it to Wall Street. I'm going to say I want a Wall street connector that shows me all of the articles that affect job growth, population growth and income in my dashboard. And I want you to suck that information in on an ongoing basis. This sounds incredibly hard. And I'll give you a tool to do it called codespace site, right? So anyone that wants to use Claude properly should sign up for a free tool called GitHub Codespace. So you can go into Codespace and it'll ask you to plug in Claude. You go in there and plug in Claude. I like Claude More than ChatGPT, though. I use both on my code space because I run out of tokens for cloud. So something that's a little easier I'll give to ChatGPT. Something that's a little harder, I give to Claude. And so what I'm trying to point out to you is data science is now 100x easier and at least 1000x faster than it used to be three years ago. And these tools, by the way, they were shit 12 months ago for doing this. I still had to do most of it manually because they, they made too many mistakes. That's not true anymore, right? So they make very few mistakes and the mistakes that they make are sort of visible. So I basically say, hey, this didn't work. That didn't work. I'm just talking to it and it fixes it. And it's not frustrating enough for me to give up at any point of time, right? So for my company, Growcapitist.com, you see the Logo back there is the name of my company and that's where investors go and my asset managers go there and stuff like that. But I have a different URL. I won't tell you what it is where hundreds of apps built by my employees, my army in the Philippines, the people that I have in the US are hosted there. And it's using Google authentication. All of it I built using codespace. And these hundreds of apps are basically being modified and improved by people all day long. And so we often have 10, 20 versions of an app in a single week. And they're using data and we're using that data to not just, you know, rank cities because that's what I've been talking about so far.

To be honest, that's low hanging fruit. We're using it to rank our properties, we're using to rank our property managers, we're using it to rank how efficient they are. Like, I'll give you an example of that, but I'll stop here so that, you know, because I've been, you know, going for a while. But does that give you a sense of how you can slice and dice Canada into usable, useful visual dashboards? 19 00:17:46.000 --> 00:18:15.000 Yeah, this is exciting. I, and I love the I, the idea of like being able to pull everything together and then layer data together. My, my, my only concern that I've had is accuracy because of the so many desperate sources coming together. I think you, you can depend on the general theme, but you can't like, the specifics sometimes get, like, can get 20 00:18:15.000 --> 00:20:39.000 inaccurate because it will be inaccurate initially. Right. But I think my point is this is a journey. It's not a destination. It's a journey that you never get to the end of. Right. So your data, when you initially create the dashboards will be disparate. So you might say all the data from Colliers, let's just build it in one app. All the data from Marcus and Millichap will build in our CBRE will build in a separate app because I don't think this data relates to each other. That's fairly good criticism. But over time you'll learn what are the top 10 trends that Colliers is seeing in multifamily? What are the top 10 trends that CBRE is seeing? Those it can give to you and say this unquestionably, are the top 10 trends like Collier sees. Unquestionably, these are the top five markets in the US these are the five that it doesn't like. Like it's got issues with these. Just reading that information makes you better at Your job, right? So you should be able to do all this stuff. And again, I do this on the weekend. I code 10, 12 hours a day on my code space. And by coding, I mean I talk to it, Right. I'm not actually. I haven't written a single line of code in over 20 years. Right. The last time I actually wrote code was 2002. I'm writing tens of thousands of lines of code each weekend for my business and for my life. Before we started, I showed you an app that I wrote for my neck, right? So for my neck and back, it sort of tracks how well I'm doing. That's trivial. Like, it took two or three minutes to write that app. And I think that the world that we live in, we have access to incredible amounts of information. If you want, I'll volunteer an hour of my time, right. Separate from this podcast where we can go build this app. Because my code space is already built, so I can build it in my code space, and by the end of 60 minutes, you'll have a functional app. And then I'll do what is known as a git. I'll give you a git, and I'll tell you what to do with it. You just have to put it into your cloud and say a few words, and it'll create your code space, and you'll have the app. And then you keep improving it over time because it's a Canadian app. So I

think that these are all things that are trivially easy to do. They take dozens of hours. But I think that's still trivial compared to the fact that you, Rob, or Quentin, just couldn't do these things. Two or three years ago, you could. You'd have trouble even dreaming it up, let alone actually doing it. 21 00:20:39.000 --> 00:20:52.000 Well, it is interesting because it takes, you know, somebody like you for me to come on and just give all those ideas of what you can do and like the possibilities, and we're probably just scraping the surface as well. 22 00:20:52.000 --> 00:22:37.000 It's so incredible. So this, you know, one of the things I like to do is I have lost the ability to read books. And I feel very bad about that, but I've lost that ability. And so my books are YouTube. Right? So, you know, I. You know, I have, you know, over time, talked with Claude about my inability to read books. And I said, I want to keep learning. And so basically, it asks me questions every week about what's exciting to me, what, what do I want to read about, what do I want to know about, And I just give it a list, right? This seems an interesting thing. I'm very deeply interested in battery technology and what the Chinese do with battery technology, for instance. And so what it does is it goes into my YouTube channel and logs into my YouTube channel and finds recent stuff that is relevant to the things that I like for personal life and for health and for work, and creates a YouTube list for me so I don't have to sort anymore. I get up in the morning, the YouTube list is sitting there and. And I just go through the list. So this morning I found a free GitHub repository, which is a trading tool. And it allows you to use any account like E Trade or whatever it is that you're using Robinhood and put in your philosophy. And this tool uses dozens of different very smart agents. It gets hundreds of megabytes of data every day from different sources, gathers it all together and gives you recommendations. I only use the tool for 20 minutes, guys, but it took me about three minutes to install, 20 minutes to check. There was a YouTube video that I watched. I am blown away. I would not before AI be able to spend 1000 hours to do what this tool has done. It's completely free. I can give you the name for it if you want it. I've only been running it for a few hours. It is stunning. 23 00:22:37.000 --> 00:22:38.000 Wow, that's incredible. 24 00:22:38.000 --> 00:23:00.000 Can I ask you what are some of the improvements you've been able to make on your business with this method that you've been doing and like having the programs that you're creating rank your buildings and your markets and your property managers, like you said, what are some of the big changes that you've been able to make from that? 25 00:23:00.000 --> 00:23:39.000 I'm going to give you a very specific example. And everything I give you applies to Canada exactly the way it does for the U.S. like, this is a hundred percent application that applies. Every property manager uses a different property management software, right? So here in the US we have a smorgasbord and we can't convince our property managers to use what we want because, you know, our properties are all over the US and some parts of the US like Entrada and some parts of the US like Yard Matrix, and some parts of the US like, you know, other app Folia. Right. So I'm assuming you have the same problem in the US where you don't have one universal property manager property management software that everybody uses. 26 00:23:39.000 --> 00:23:41.000 We have Buildium. We have Yardi. 27 00:23:41.000 --> 00:26:18.000 Yeah, Buildium. We've got Buildium here as well. So here's the problem. Your property managers are forcing you to use the reports that they're pulling out of Buildium, the reports that they're pulling out of Yardi, and these reports, they don't actually meet your needs. You think that they do, but they don't meet your needs. So if I had a free codespaces account with my cloud connected, I would say these are my properties and these are the property management software that each of my properties uses. I want to build a dashboard where I want real time ranking of how well or poorly each property manager is doing in a variety of things that they do. Property managers all do a set of things. They manage tenants, they manage delinquency, they manage contracts, they manage incoming calls, they do maintenance stuff. So that list is trivial for Claude to give you. What are the top 10 things that a property manager needs to do? Ask that question, will give you a list. Then say, I want a dashboard that allows me to track what every property manager is doing and have an apples to apples comparison to see which one is the most efficient, which one is the least efficient. I want to know, for example, and these are real examples. I want to know how long it takes for every property manager at every property that I have to pick up the phone and respond to an incoming lead. How many minutes and what is the average across my portfolio? So imagine a dashboard, it's got very nice boxes up at the top and one of the boxes says 46 minutes. Okay, 40, 46 minutes is the average amount of time for an incoming lead to get a call back. Right. This is a tenant leads, right? Somebody who wants to be a tenant. As you know, with the tenant leads, they're very fickle. In 48 hours, a tenant lead is completely worthless because the tenants already filled out three forms. Somebody's already talked with him, he's already scheduled two appointments. Now he doesn't want to talk with you. Right. But the lead in the first hour, we call it the golden hour, is worth five times more than a lead at the end of 24 hours. Did you know that your conversion ratio to a appointment at your property is 5x in the golden hour? Then it is 24 hours later and 48 hours later the lead is worthless. It's actually not worth calling because

you're wasting your property manager's time. Does that make sense? Right? 28 00:26:18.000 --> 00:26:19.000 Yep. 29 00:26:19.000 --> 00:27:40.000 All of the information about how long each property manager takes to respond by email, by text and by phone is in the property management systems. They're integrated now. Right. And some of them, there's a phone system that's non integrated but that phone system also has its own login, right? So what you're saying to your AI tools, you're saying, build me a dashboard that sucks all of the information out of the property management software and it doesn't need to be real time, right? I mean, we're not talking about real time stuff. This is reporting. So even if it sucks it once a month, it's good enough, right? So each month, here's my login for the property management software for my building in West Toronto. Here's my other login for a different property management, you know, software for my building in East Toronto. Get the data, get to the point where you've built an internal database. Just so you know, Claude can, especially if you're using the free code spaces, it can very trivially, in a second, build a database server. And what it can do is it can take all the data from your property manager 1 and your property manager 2. The data basically doesn't look the same. Right. It can rationalize the data so that everything matches up and they're apples to apples, and then it can stick them into its own internal database. Make sense? 30 00:27:40.000 --> 00:27:41.000 Yeah. 31 00:27:41.000 --> 00:32:43.000 Right. Now, once it's done, that data is comparable. You don't care about any of this stuff. You've never even seen the data. Right. What you know is you've got one set of PDFs coming from one source, another set of PDF from one source, and what it's doing is it's rationalizing it and cleaning the data. Right? What it's doing is it's taking the data from the PDFs and it's cleaning it up and putting it into rows and columns, and it's saying, okay, for property management 1, which column matches to the other one and does it really match properly? And it's rationalizing it. Once it's rationalized, that data is sitting on your free database on codespaces. Now you're saying, I want to start comparing. How can I compare this data? How can I compare their phone calls? How can I compare how many text Messages Property Manager 1 sends to Chase a lead, and how many does Property Manager 2 does, and how quickly do they do it? Now, when you ask it these questions, often the answer will be, I don't know. Claude loves to give you I don't know answers. What I say to Claude is, how can you figure it out? Oh, you can go do this. No, I don't want to do this. How can you figure it out? Every time Claude gives me work, I give the work back to Claude over Time, it says. Well, if you were to give me this login to this phone system. You mentioned that they have an AT&T phone system at Property One in west Toronto. Could you give me a login to that? Sure. I can call my property manager and give you a login. Property manager is going to be mystified. Why do you need to log into my phone system? Because I want to track how often you make phone calls. Oh, okay, sir, here's the username and password. Right? So now I'm logging in and it's grabbing the phone information and the text information that my property manager is using and sucking it into the database, rationalizing and cleaning it. And now at the top of the database, I see something like 46 minutes. This is a real number, by the way. 46 minutes is what it takes my property managers across my 4,000. Sorry, not now. 2,000 units. I've sold so many. 2,000 units. And then below that is a table which shows my top 10 properties. So I have one property that is about 25 minutes. It's the Idaho Falls property. And then I have a really shitty property that I'm still trying to fix in. It is in Chattanooga, Midtown. And it takes about five hours to respond, right? So I've already fired the property manager and hired a new property manager. And they're just as bad, so I now have to fire them as well. So now I have, by every property, I can see what their response rate is. Now, everything that I've just described has just been about one thing that you measure, which is how long does it take my property manager to call my tenant leads back, Right? Now imagine dozens of those metrics, all in a dashboard. Very visual. Speed to lead, which we talked about. But then how many text messages, how many phone calls do you call back? If you get 10, let's say you get 100 tenant leads, however you get them. Incoming phone call form. What percentage of those people. What percentage of those people do you respond to? You might think it's 100%. Actually, I have no properties ever that I've had where it's 100%. Okay? So these are all things that we sell ourselves on. Nonsense. That we sell ourselves on. That 100% of incoming leads are going to be responded to. No one is that efficient. No property manager is doing that. Right? So if you get to, like the high 90s, pat yourself on the back. Right. But you don't know. You have no freaking idea. Right? Because as far as I know, no syndicator has a dashboard that tells Them that and nobody has a time to go figure it out. Right. So then the next thing is what out of these hundred leads, remember this property got 100 tenant leads, how many appointments were set? Right. This is called L2A. The system is called LASEL. Right. So LASL is leads, appointments, shows, applications, leases. Right. I'll give you again, Leads, appointments, shows, applications, leases, LaSalle. Each one of these has a ratio in between them. L to A, A to S, S to A and A to L with lease being the final. These four ratios, no one tracks them. But these four ratios are the basis of your success in multifamily. The reason nobody tracks them is that nobody has the time to find the data. But now we are all programmers. What we should

be doing is talking to Claude and having Claude find this. Tell Claude, Neil Bauer's LASEL system. I want to track for all of my properties. Let's figure out how we are going to do it. You're going to log in, you're going to save the logins. This is going to take you some time. I'm telling you, it's not magic. The first time you're going to do it, you're going to be pulling your hair out. But over time, you'll get better and better at it. Because the truth is Claude doesn't need to get any better. It's Rob that needs to get better. It's Quentin that needs to get better. 32 00:32:43.000 --> 00:32:46.000 What questions to ask it and when it's. 33 00:32:46.000 --> 00:32:49.000 When it answers you. How to push back. 34 00:32:49.000 --> 00:33:12.000 Yeah, yeah. And you keep pushing back really quickly though. Here's another interesting thought. I mean, I'm sure that you've done this already, but then you, like you say you're comparing your own properties to each other, but then you could kind of say, okay, now in this market, how does my property compare to others in the market? 35 00:33:12.000 --> 00:33:53.000 Yes. Right. And we do that through a COSTAR reports. Right. So the COSTAR submarket report has dozens and dozens of different data points for that submarket. The submarkets occupancy, the submarkets vacancy, the submarkets concessions, the submarkets. Well, at least those are the top three that we would start with. Right. So how is the submarket doing? Average rents on a one bedroom basis, average rents on a bedroom basis, and average rents on a square foot basis. Right. How is my property doing? So we use COSTAR here because it's sort of our fundamental tool. I don't know if COSTAR is available in. 36 00:33:53.000 --> 00:33:55.000 Yeah, it is. 37 00:33:55.000 --> 00:36:25.000 So essentially the answer is, Rob, that if you have a COSTAR subscription, maybe you do, maybe you don't. Then you have it log in every month, pull a property, sorry, a sub market report for each of your properties, have it rationalized. Remember the whole database thing where it takes this stuff and puts it into tables, blah, blah, blah, and then basically say, now I want to compare my properties to others. Now you will not be able to compare them for the Lasel system because that is information that COSTAR does not publish. Leads, appointments, shows, stuff like that. That's internal performance. But external performance is things like occupancy, vacancy, concessions. Right? Price per bedroom, price per square foot. That's published data. So you want to be able to compare. Let's say that there are two properties and Rob and Quentin are competing with each other. They both have properties, they're next to each other and both properties, the two bedroom units are 1,000. Thousand dollars. Sorry, 1,000 square feet. So Quentin's is 1,000, Rob's is 1,000. They're next to each other, Right. And let's assume that they're the same age, whatever, Right. For the moment, hypothetical, right. Over the last year, which one of the two has gotten more rent per square foot? Which one of the two has gotten more rent per bedroom? And then you might say, well, that's because Quentin's property is prettier looking, or it's five years older or whatever it is. Right, but that's just, that's just an out. That's you saying I can't do this. No, the answer is trivially simple. The answer is, okay, find me five other properties and not Rob's properties that are like mine. Same age, same look. Go find pictures of these properties inside and outside. Go find their websites. Now I have a comp set that exactly matches my property. Maybe it's not Rob's property, really. Right. I'm looking the wrong one. I'm going to go find my comp set. Now, in this comp set, how am I performing? By looking at the monthly COSTAR report, which is going to have all of those comps in there. Because if you, you know that if you have a COSTAR subscription, you can tell it. This is a one time process. You can say, my five preferred comps are these for this property that I own right. Now, every time it pulls a report once a month, it's going to pull your comps. So you're going to have their data so you can compare their data. Right. What I'm trying to say is these are all things that people have been teaching at multifamily schools and webinars for decades and almost nobody really does them because they're so hard to do. But now by comparison, they're trivially easy to do. Right. 38 00:36:25.000 --> 00:36:26.000 It's crazy. 39 00:36:26.000 --> 00:36:31.000 You can even apply the data to properties you're looking to buy. 40 00:36:31.000 --> 00:36:56.000 There you go. That's the next step, right? First have a dashboard of what it is that you're doing with the property, then apply it to what anybody else is doing. And again, these dashboards that you can now build the amount of data that you can gather. You can gather data now as a, as a medium sized company with one virtual assistant working on this, you can gather data for the whole freaking country. 41 00:36:56.000 --> 00:36:58.000 Yeah, that's right. 42 00:36:58.000 --> 00:37:55.000 Now you can't sell this data because if you do, you are going to get sued the next day. Right? But you can gather it and use it in a way that you build this AGI, you know, basically an intelligence that has an incredible understanding of your country. Start with the submarket, forget about the country. But I challenge you that if you start this process with the goal of I want to understand everything about my submarket, every good property, every bad property, what's happening in the submarket. I want to know every employer, every good thing, every bad things that are happening. And I want to automate all of it and have a wonderful dashboard for it. That's a great learning process because at the end of it you would have basically learned all of the things that I've learned over time. I don't know if it's in a podcast if you're

allowed to screen share, but I can show you one of mine on the screen. So you can see some of the stuff that I do in code spaces with cloud. 43 00:37:55.000 --> 00:37:59.000 Yeah, absolutely. And then if you send it to us, we can just put it in the show notes. 44 00:37:59.000 --> 00:40:02.000 Yeah. So I'm going to click on this and I won't go into details because there's some stuff here that you shouldn't see. But essentially this is codespaces. It's free, by the way, for your use. And this is, I have like 60 or 70 of these code spaces. This one is basically full company setup backup system so that I never lose anything. Right. So it configures everything. This is the one that I did this morning, remember the one that I were telling you that there's this incredible system that basically allows you to track the stock market and you know, if you have a philosophy or if you have a, like a stock, like Nvidia, it basically allows you to do this. Notice that all of this stuff, everything that it did, it did in about three minutes. This whole string is about three minutes. So I said, go to the web, find Torick Research trading agents, git and install and configure it. Give me a summary of what it does. So it's a blah, blah, blah, blah, blah. And then it, you know, it's lazy, right? So it said, hey, can you give me the link? No, I don't have the freaking link. Right? But it's public. I heard about it on a video. They're giving it away. So I refused to have it do work. Have me do work. Okay, it did that. It did a web search. Found it. Yay, I found it. Great. Let me clone and set it up for you. Blah, blah, blah. All kinds of junk, junk, junk, junk. Roughly two minutes later, it says, I installed and configured what it does. Blah, blah, blah, blah, blah, right? So now I'm having conversations about building a dashboard for it using the specific keywords that I want. Like I want to track Nvidia or I want to track tsmc, my favorite Taiwanese company. And now basically, I have a dashboard. And here's the cool thing. I didn't pay anything for this, right? And there's no bias in this. Nobody's trying to sell me anything. This is basically a tracking dashboard that has 50 or 60 different agents. So every time I use this, those 60 agents go out and find hundreds of megabytes of information, churn it, and give me that distilled knowledge of those people. I have no doubt that this is better than my average stockbroker agent. Right. Again, three minutes. 45 00:40:02.000 --> 00:40:10.000 Yeah. That's incredible. What is the dashboard there? I don't see the actual dashboard. 46 00:40:10.000 --> 00:41:20.000 I haven't built the dashboard yet because I had to start work today, right? So it started to build the dashboard. I haven't yet finished. Finished building the dashboard. But there are so many dashboards that I have, right? I'm obsessed with dashboards because I find that, you know, what is a dashboard? The term was invented because it was a car's dashboard. And a dashboard was something where you could take your eyes off of the road for 1/5 of a second, understand something useful, and get back to the road. Because if you took one second, you could be dead, right? So a dashboard needed to give you information very, very quickly. And today you can build insane dashboards. Like, you can build dashboards that you couldn't possibly imagine. And I have about a couple hundred of them, but on this server, the one that's open right now, Here, I'm going to see if I can. Right here, this is the core dashboard. And again, weird looking URL, but let's see if it works. Oh, the server is probably not running. It shuts down. It's probably going to give me an error message, see? Oh yes. All right, so hang on right there. So it just took a second to come up. So remember the trading agent? 47 00:41:20.000 --> 00:41:21.000 Yeah. 48 00:41:21.000 --> 00:42:17.000 Not only did it install it for me, it went and built a card for me. Trading agents. I didn't do this. It already knows because I've trained it right. YouTube transcripts. I love getting transcripts on YouTube and asking Claude to do stuff, but it's so annoying to go and download the video, then upload it to descript, then get the transcript and do something with it. So I built a software that basically I can just copy paste any YouTube URL and it goes in and does all the stuff in the background and gives me the transcript. So again, these family finances, these are Idaho Falls townhomes. These are. Well, again, the server is not running. Sorry. GitHub is free, but if you don't touch it for like an hour, the server will shut down. If you want to pay more, it won't shut down. So these are all dashboards, but I think you get an idea about what can be done. Right? So altogether my company has about 300 of these. 49 00:42:17.000 --> 00:43:03.000 That's insane. I, you know what? I, I'm, I'm just so blown away by the whole process and the way that you're layering public data and private data in order to help better make decisions within your own business and within all the different levels of, of your organization. Which is, is really impressive because the more personal data or data that you create and you, you get that isn't publicly available, the, the more the public data has a greater impact because now you're layering in with knowledge that that is personal and that that's created that isn't available anywhere it will be available. 50 00:43:03.000 --> 00:45:25.000 We're in an age where basically data is going to become like Wikipedia, right? So let me give you an example. You remember that whole thing you said about Buildium, right? You're using Buildium. So once I train Claude on how to take Buildium data and rationalize it, you know what's the next thing I tell it? Turn this into a skill and call it Buildium. And now share this skill with everyone in my company that's doing this. Now the next guy never has to go to the web to understand how to rationalize Buildium

data because Claude remembers that I've built a skill called Buildium and it automatically uses that skill. This means that for you to become an expert at property management software, you, you just have to have a list of property management software that you subscribe to that you have for your properties. One time it has to go in and basically figure out how to grab the data, which is frustrating. It takes a while and then it has to rationalize it. But when you're done, you never want to lose the skill. Quentin, so you say, build me a skill and share it now. And by the way, there are tens of thousands of skills shared for free on GitHub. Tens of thousands. Like this trading thing, somebody did it for themselves and then they said build it into a skill and share it with the world. So my point is, once you have built this for a property management software, you'll never lose it. Right? Just the last step should always be build me a skill and share it amongst my company, amongst my company. So this stuff gets easier. Like for example, I don't tell it to connect any new project to Google Drive because I've already done that and I've already shared the connection to Google Drive as a skill. So now it applies, it automatically just goes and finds Google Drive. It doesn't ask me. Right. So I think that when you start with this process, it's unique. AI is such an insane revolution in the way we do everything that I think that people who are thinking they're AI users, they're just chatgpt chatters, they're not AI users yet. That's low hanging fruit. You're using about 1% of AI and you're feeling really good about yourself. But then you're not taking AI to its potential, which is limitless, and to your potential, which is limitless. 51 00:45:25.000 --> 00:46:02.000 Okay, real quick, I wanted to get back to the whole idea that the, that the markets that we invest in are really the most important thing for us to focus on. Right. So if you were going to give some advice here to people on choosing the market, what would, what would like the core aspects be that you would look for? Like even just like the top three, let's say, you know, that would help someone decide, throw this into, you know, chat or whatever they're using to help decide on what markets would be best. 52 00:46:02.000 --> 00:47:02.000 So there's about six metrics that I'd like to mention. You can pick and choose. Right. So there's for any market. Population growth, job growth, home price growth, Crime, Incoming supply as a percentage of inventory. This one's technical because it's not demographic in nature. It's real estate. Incoming supply as percentage of inventory and then last 12 months rent growth. None of these were available for free before. Now they are, or they can be computed or imputed. So these are all metrics that I would use when ranking a submarket and comparing it to other submarkets. 53 00:47:02.000 --> 00:47:21.000 Yeah, very good. I, I think that this is pretty fascinating.

All this stuff that you told us and most of it can actually be used really for anything. You're. We're just here applying it for real estate. You know, I think that, yeah. That it's very valuable. You could probably throw it into every aspect of your life. 54 00:47:21.000 --> 00:47:25.000 So like my, my neck and back tracker. 55 00:47:25.000 --> 00:47:26.000 Exactly, exactly. 56 00:47:26.000 --> 00:48:11.000 Whatever you need. I was totally, I'm totally. I mean I'm gonna have to spend a lot more time I think going down this rabbit hole. I just, I never, I think I, I never thought of the potentials and now I'm starting to see all the different potentials kind of come in and I'm looking forward to it there. I, I have like a hundred other questions that I had planned on asking you and we're just not going to have the time. There's no way virtual assistants. I wanted to see how you would do that with, you know, people who are small investors because you have a background in the virtual assistants. That's how I kind of connected with you for the very first time. And I wanted to see how that could help, you know, five to ten unit people survive till 25. 57 00:48:11.000 --> 00:49:16.000 There's a bunch. When I have an answer for that one, I mean it's a very simple answer. So I get that question a lot about how do I hire and manage and virtual assistants because it's a painful process and difficult process. So we built a course on our website. It's multifamilyyou.com club. Multifamilyyou, don't forget the you.com club. And then basically look in there and you'll find a course for virtual assistants. It's about 50 minutes long. It'll give you all of the things that We've learned over 10 years of managing up to 25 full time virtual assistants. And there's a lot to learn there. But all of it is in there. It's very specific, it's very metrics. It basically tells you what to do, what not to do, what are the gotchas. So take that course. I don't know exactly what it's called. I think it's called Search for Virtual assistants. You'll see all of our presentations stored there. One of them is clearly about virtual assistants or using remote workers. So it's about an hour long, and I think it'll give you a great start. Okay. 58 00:49:16.000 --> 00:49:17.000 And that's the best place for people 59 00:49:17.000 --> 00:50:10.000 to go to learn about you is multifamilyu multifamilyyou.com club. It's a free website. We publish about eight webinars about all things related to real estate and also things that catch our fancy, like climate change or A.I. you know, like anything that affects real estate. And then we talk about lots of things, not just multifamily. We'll do webinars on Airbnb and industrial and self storage and student housing, even though we're primarily multifamily folks. But we like looking at all this other stuff and doing webinars about them. So about 10,000 people a year sign up for our webinars and take them. 1% of them become investors with us. The remaining 99, they're just here for the free info. We don't have a educational, structured program. We don't have an upsell to, hey, it's not a subscription. It's just free. The data is all free. 60 00:50:10.000 --> 00:50:22.000 Wow, this has been fascinating. I really want to thank you for coming on again. You know, I, this is, I've got, I've got three pages of notes. That's not common for me when I'm talking to somebody. 61 00:50:22.000 --> 00:50:44.000 Yeah, I've got a bunch of notes, too. It's gonna be, it's gonna be a good weekend for me. I can't wait to, to get going on this. And I, I, I'm so happy that we were able to get you on our, you know, podcast and, and chat with you, Neil. This has been really, really good. I'm, I'm really hoping we could do this again sometime. 62 00:50:44.000 --> 00:50:56.000 Just, I mean, we just scratched the surface. We didn't ask, like, a lot of the questions that we had for you. But for those listening, what is the best way to get in touch with you? 63 00:50:56.000 --> 00:51:33.000 Definitely multifamily you.com club join our ecosystem. We send out all kinds of crazy stuff. We're actually going to do a webinar where we show 50 ways in which we use AI inside of our company, and it applies to any kind of company. So you could be running a company that has nothing to do with real estate. You could be, you know, running a janitorial business, and all 50 would apply. So the best way is to basically join that ecosystem. Multifamilyu.com club and keep learning. We've had people in our database for 11 years. They've never bought anything from us. They're just here for every webinar. 64 00:51:33.000 --> 00:51:38.000 I got to admit, I've been on your database for probably 10 years. 65 00:51:38.000 --> 00:51:41.000 There you go. Quentin's in the list. Yeah. 66 00:51:41.000 --> 00:51:51.000 Well, that's cool. Well, we'll definitely have you back if you. If you're open to it and appreciate everything you've done today. So thank you again. Quinton. How can people get in touch with you? 67 00:51:51.000 --> 00:52:14.000 Oh, yeah. If you want to talk real estate, I'm happy to talk. 15 minutes. Quinted souza.com you can get in touch with me there or visit Durham rei. Come out to one of our monthly meetings. We have, you know, 50, 60 people come out every month and chat real estate and have fun. How about you, Rob? 68 00:52:14.000 --> 00:52:33.000 Just email me, you know. You guys know I'm in Costa Rica. So if you have any questions about real estate down here, if you just want to come visit, that would be fun. You can reach me at rob, Mr. Breakthrough. Ca. And thank you, everyone, for listening. We'll see you next time. 69 00:52:33.000 --> 00:52:36.000 Thank you.