Data-Driven Investing and Company Building with Ben Miller of Fundrise

​​Eric Dodds 00:05
Welcome to The Data Stack Show. Each week we explore the world of data by talking to the people shaping its future. You’ll learn about new data technology and trends and how data teams and processes are run at top companies. The Data Stack Show is brought to you by RudderStack, the CDP for developers. You can learn more at RudderStack.com. Kostas, today we’re talking with Ben Miller, who’s the CEO, and one of the founders of Fundrise, which is a really interesting company, it democratizes investment opportunities for assets that are typically, you know, you know, off limits, or very difficult to get access to for your average person, right. So let’s think about, you know, a big corporate real estate development on one of the spectrum, but fascinatingly, venture capital investments in tech on the other end of the spectrum, and you can do both on Fundrise. And there’s more. And I’m really excited to talk with Ben one, because they’re an extremely data driven company. And he’s a CEO. So he’s very technical, but he doesn’t have a day to day role in the data space, but they’re extremely data driven. And one thing that I’m personally extremely curious about is, with a portfolio of assets that diverse you’re dealing with pretty different types of data, pretty different types of data modeling. Very different data formats, right. If you think about the real estate space, there are probably still a lot of PDF documents and other things like that. Right. So I’m interested to ask you about that. I’m also interested in the combination. I mean, how do you run a platform that allows you to invest in an apartment building and Databricks? Which is crazy. So that’s what I’m interested in chatting with him about? How about you?

Kostas Pardalis 01:53
Yeah, I mean, we’ve been, okay, we, we’ve talked like with many people, like all these years, about architectures, technologies, like how we use data, like, how we processed data, you know, like, we then like to focus more on like the, let’s say, like, the technology side of things, and what is like really exciting going on there. And we take for granted, like the connection with how value is, like created out of data. But that’s what like, I find so fascinating about like the episode that we have, with Ben recorded that we literally have like, the one of the best examples here of how data directly connects, and working with data directly connects like with value creation. And of course, like the financial sector in general is like a very good example of that. But here we have, like a person who can take us like, through more unconventional uses of data as like, the business also, it’s like, unconventional in terms of like how, like offers access to investment. And yeah, that’s what I’m really excited about. I really would like to hear more about how we started from the road data and wind up like and creating really tangible value that we can see is dollar signs in our bank accounts. Right. So that’s what really excites me about this episode.

Eric Dodds 03:12
All right, well, let’s dig in and talk with them. Let’s do it. Then Welcome to The Data Stack Show. We have a lot to cover. And we’re going to talk about data in multiple different contexts. But give us your background. Okay, well,

Ben Miller 03:27
so I’m currently CEO and co-founder of Fundrise. But I got here over a long period of entrepreneurship, started in real estate, private equity, venture capital, and worked for a tech startup that went from zero to 100 people back to zero in 36 months. Wow, I’ve worked in real estate development, doing urban mixed use, like huge skyscraper type buildings in the middle of cities, that went through the 2008 financial crisis. And then Fundrise, you know, we started in 2012. We have 275 people, the company, we have 2 million users to think 20,000 apartments on their own through the platform. Wow. And yeah, so basically, I feel like I’ve aged, I’ve lived, I feel like I’ve lived 50 years. And

Eric Dodds 04:24
yeah, it sounds like that. So tell us why did you start Fundrise? I mean, you obviously had a background in real estate, but Fundrise is not just about real estate. So tell us about Fundrise and why you started it.

Ben Miller 04:37
Well, so anybody who went through the 2008 financial crisis came away, very skeptical of mainstream finance. Like it’s kind of a loss of any confidence in the financial system. Yep. And so I wanted to build essentially an alternative to it. That, you know, my dream was to basically build an alternative to Mainstream finance. And that means, like creating a way for people to invest in things that are like, you know, like real estate or like private tech or credit things that basically, super high net worth or institutional investors do. And they know that they’re sort of less correlated or less connected to the stock market, which and the bond market, which are like what normally people invest in?

Eric Dodds 05:24
Yep, yep. Okay, so I just have, like, a question around the mechanics of this. So when you think about private investors, or like high net worth investors, and you can correct me if I’m wrong here, but generally, there’s sort of a and maybe I’m thinking about this more from a deal flow standpoint, right? Like, people fight over deals, and, you know, they’re, you’re trying to, like, make a good investment, you have money to throw at it. And so there’s, you know, it seems like that sort of a closed door network where deal flow happens a lot through like word of mouth or other methodologies, right, where the public soccer exchange is sort of on the other end of the spectrum, where you can go on each trade, and you can, you know, buy whatever you want. How do you bridge that gap? Because it would seem to me and just to be a little bit more pointed in if this is too spicy, let me know that, like, people who are getting good deal flow tend to try to protect their system, right? And so you democratize that, like, how does that work? Yeah,

Ben Miller 06:35
That’s a great question. Well, so partly, you know, I came from that system, so I can go into that system and, and get a deal. So it used to be my job. Yeah. And so that’s part of it. And then there’s a story that those people tell about how they have this deal flow and their proprietary about it, but it’s it that’s mostly the story. The reality is, if you come in with a checkbook, you know, turns out that like, you can

Eric Dodds 07:07
turns out that money talks, yeah, right.

Ben Miller 07:11
Yeah. And we’ve done that now. Like, across, like, multiple asset classes, you know, we bought billions dollars real estate, we walked in the room and done hundreds of millions of dollars in like, very sophisticated credit instruments, asset backed securities, and like, you know, we were buying and buying, like 10 of in a 140, for a transactions that we had to have $100 million in net worth. And it’s like a very, you know, talk about rarefied. Yeah.

Eric Dodds 07:46
I mean, that’s, yeah, most people, most people are by air, you know, yeah. Bloomberg

Ben Miller 07:50
terminals and stuff. So we’ve done that. And we’ve also done ventures where, which people will call me, it was the most exclusive and yet we you know, we own, we’ve invested in DBT, labs and Databricks and Canva. And service Titan. Wow. advanta. And so we, so, so yeah, I mean, it takes skills, and it’s not like but it’s not as rarefied ultimately, is it as they want you to believe it is, man, I love it.

Eric Dodds 08:17
That’s sort of a great, sort of a great story of democratization. We’re also just sort of the spirit of like, if you show up with a checkbook. I love that analogy. Actually, it’s not analogy, the reality of that, because that’s sort of what you’re, you know, enabling people to do by proxy. We have a lot of things to talk about from a data standpoint. But of course, our listeners know, I can’t not pick apart the business model a little bit. So, you know, and look, I’m not, this is not a show about investment expertise. And I’m not an expert investor. But I know enough about asset classes to know that real estate and venture are sort of, like, really extreme expenses, you know, pretty extreme ends of the spectrum. Right. And so if you’re investing in commercial real estate, you’re generally looking at, you know, sort of decades long, almost an annuity type approach, when you’re thinking about the asset, like, you’re far more durable, much longer cycles. And then on the venture side we said that you worked for a company that went from zero to 100 employees to zero employees in 36 months, right. Now, of course, if you’re looking at the Databricks of the world, you know, of course, they’ve reached a level of stability, but can you reconcile that, for me in the listeners just to try to understand, generally, investors make money by specializing in a particular asset class. And you seem to have achieved success by democratizing access to multiple different types of asset classes.

Ben Miller 09:51
Yeah, got it. Such a fun question. Well, there’s a great saying that I don’t know if you’ve heard but it basically says In venture, you make 100 investments, you lose all your money in 99. And you make and while back on one. Sure

Eric Dodds 10:09
That’s how the unit DAG, that’s the economics, is the power law. Yeah.

Ben Miller 10:13
And in real estate, you make 100 investments, you make money on 99. And you lose it all on one.

Eric Dodds 10:22
I hadn’t heard the back half of that. But probably because I haven’t been involved in a ton of actual real estate.

Ben Miller 10:28
Because there’s so much leverage, there’s so much leverage in real estate, there’s so much debt. So, so yeah, there’s a lot about them that are really opposites. And if I hadn’t worked for a tech company, and they weren’t, you know, up and down. And if I hadn’t been spent, basically, to learn another 1012 years building company Fundrise, like I, you know, I don’t know if I’d have the background in tech, but after probably 15 years of tech and venture, I have a pretty good sense of like, a least mid stage, mid to late stage venture. Or you might call it growth equity. Sheridan, and one of the ways we generally have been investing at least on the venture side is that we use the tools we use where we use, we have 100, software engineers at Fundrise.

Eric Dodds 11:19
Wow. 100 software engineers. Yeah, yes.

Ben Miller 11:22
You know, we are so, so we just, it’s actually funny, all lots of companies, we got to them, because we are Wow, customers, and we’re like, this is such good technology. We gotta like DBT, right? Oh, my God, we have this company. This company is like a one on one. It’s really, I mean, I think it’s going to end up being the, like, the central nexus of the revolution that’s happening in data. And, we just like hunters, like hunting them down. Yeah. Oh, man.

Eric Dodds 11:56
I love that. I love that. Okay, so that is. Okay, so would you say, just to dig in on that a little bit more? Do you have an investment thesis for a venture as part of Fundrise? Let’s zero in on technology, like a DVT? Or is your investment thesis? Like on the ground value that you can, you essentially have a lab. As an investor, I’m just trying to put my investor hat on like, you have a lab of 100 software engineers who are managing, and we’ll get to the part, I promise to our listeners, we’ll get to the data part. But I can’t imagine that I mean, first party data from apps third party get it? I’m sure it’s insane. I’m sure you have actually pretty severe, like, challenges. Yes. And so you essentially have a lab where you can try a bunch of different technologies. And so you almost don’t necessarily have to have a thesis as much as your team can say. Like, if we had to pick a winner, this would be it, because we’re doing it on the ground.

Ben Miller 13:06
Yeah, I mean, that’s so it’s funny, I have this view, I repeat it constantly internally, which is when trying to analyze something, you want to analyze it from the bottom up, and then from the top down. And you need to do both, and you actually need to iterate on both. But often when you do one than the other, you find they don’t actually match and you’re like, well, the bottom up and top down shouldn’t give you different answers. As data people, you start to, you know what I mean? And so we have top down because basically one of those sort of, I think, mythologies in investment is that there’s all this alpha, but actually think is mostly beta, which basically means that the macro, you know, if you’re invested in cloud, over the last decade, you know, probably did pretty well investing in like, you know, crypto is enabling technology basically didn’t work like so. So like, as good as a company picker. You might have been like this, the macro is such a big driver of returns, like you just bought Yang for 10 years, you’re like, Oh, you look so smart. Right? Yeah. so and so. That’s like, I think it’s very free real estate. Like if you just had bought apartments, and, you know, the Sunbelt, like everybody moved there, and everybody lived there and what and inflation drove up tons of rents. So, that’s like, half of my belief, the other half is a bottom up and the bottom up is like it with the software. You’re using it. We’re like, we’re, you know, like right now we’re in the process of picking a we call it but basically reverse ETL so we’re looking at all the companies and like like, Wow, sorry, not to throw any shade here but like wow segment and really is in trouble. Everybody talks about segments being so great, like, teams like, Oh, my hand segments likely, like doesn’t have a future. And so, I just believe you have to do both. And that’s how we do it. So we have a strong man, our thesis has been modern data infrastructure. We’ve done a little bit of Prop tech to investments, not much because we know a lot about property and technology. But our top down thesis has just been the modern data infrastructure. And now an AI combined with modern data infrastructure is just, it says it’s a wave, like as big as the computer was. And so then we’re not venturing and like having to pick three people and a dog, right? We’re like we can. That’s really hard. But if you pick like, no our investments like, like, Fivetran, we want to invest in Fivetran. That doesn’t take away the imagination. Yeah. Yeah. And so, and I, and if I were, you know, I’m going to pitch you, I’m going to say, Hey, you’re in the data industry, like, shouldn’t you be able to invest in Fivetran? retool, and DBT and Databricks? Like, yeah,

Eric Dodds 15:55
yep. Yep. super interesting. It’s refreshing to hear. And again, this is sort of like a total armchair investor. But the logical conclusion with those two things would be that you would like to invest, and then property technology, right management, all that sort of stuff. But it’s actually great to hear that you’re just sort of following the things that you are finding, like actual value in and not trying to wedge something in with these two, like, sort of core competencies, which is great. Well, one question on segment, because segment, in many ways, is like a really great product, but you’re evaluating reverse ETL. Like, why do you why do you have that perspective? And I’ll direct the question a little bit in terms of do you think, because they’re a pretty large business, actually. Do you think the innovation has slowed down just because they got acquired? And it’s like, much more difficult to innovate from a product standpoint?

Ben Miller 16:55
Oh, okay. Well, so we like that we have our head of Chief Product Officer, and one of our senior engineers has been looking at it, and so that I could tell our story, our data story is, could we basically just probably live the story many companies did. But we, what we don’t, what we want are three different, like, so we looked, we’re looking at Hightouch, census and RudderStack. So it’s up. And so I’m not sure I can do it justice to basically explain to people who are really technical, but the difference is basically like, we want to have control over our own data. We don’t want to shove our data somewhere else. Yeah. Right. And that’s probably the main problem. Yeah, I think that’s why we were looking at companies that allow that. Yep.

Eric Dodds 17:46
Yep. Certain more like a warehouse native, like the sort of, you have control on your, you’re sort of building an infrastructure around your Snowflake or Databricks environment, right,

Ben Miller 17:57
exactly. And be able to, like create audiences, and shove more data, you know, like, use it or Iterable, we can shove in an interval. We use Zendesk for customer service, we have Tableau and looker. And we just basically need to be able to take the data and we want to control the way we want and shove it to where we want and yeah, not be constrained by like, you know. Sure. So my antiquated approach? Yeah, no, I mean,

Eric Dodds 18:25
I think I was actually a guest on a completely different podcast recently. And we were sort of talking about how abstraction is the way of the future, right? Because the big challenge is that if you get into a situation where you’re beholden to, let’s put it this way, anytime that you’re storing data in a third party cloud system, you’re beholden to, to what I believe. And actually, I’m interested in your opinion, there’s sort of two major problems that I see with that. One is that they have to choose a data model or a schema, right, you can’t store the data without having an opinion on that. And in most environments, you don’t have control over that, unless it’s your own data warehouse or data lake, in which case, you can define your own data schema that matches your business logic to a tee. Right. But if it’s another system, like they have to decide how to store their data, and it rarely matches, that’s a huge pain point. The other big pain point, which is, you know, maybe not equally as painful, like from a fundamental standpoint, but from a practical standpoint is that you’re beholden to their API’s, right? So like interacting with your data, you inherently are subject to a gatekeeper, right, as opposed to having access to the raw data. Those are two major things that I was you in, like, if you’re not building towards that or thinking about that, it’s going to be hard because companies just are becoming, they have way less of an appetite. for dealing with that, it’s just not acceptable anymore. Yeah,

Ben Miller 20:03
and like, it’s an aggregate into the data, which is, like I’ve become obsessed with. So the data model, that abstraction is so essential to be able to have the right downstream business conclusions and business like abilities. And so like, actually, I’ll start with like, even though, we built our web apps and our mobile apps, first, we had to, like scale on that side, you know, before we got to scale and the real estate side, it’s the real estate side, where we really got I got more sophisticated about data. And, and it was that we went about creating a data model that we thought reflected all of how real estate works. So it wasn’t constrained to. So like, this is true for companies, too. So but like, so you have operational data, you know, data, data, and you have like, kind of sometimes you have various financial data, and all those things in different places.

Eric Dodds 21:12
Yep. And, and are like, pretty different in and of themselves, right? Like, oh, yeah, they’re

Ben Miller 21:17
different. I mean, there’s the sort of pipe challenge of getting it and joining it, and cleaning and all that stuff. But it’s also just like, you then you have to have, but that goes back to the data, all you have to have a sense of how they all relate to each other in a way that’s as abstracted as possible. So that you basically don’t have made the mistakes we made over the past 10 years. If we baked into the wrong place opinions. Hmm. Right?

Eric Dodds 21:44
Can you just give an example of like, what is an opinion that you baked in? And specifically, where did you bake it? And why was that problematic? Like, at what point in the pipeline? Did you bake it?

Ben Miller 21:58
I’ll give you a couple. I’ll give you one that sort of data on this business. So when we first started, we have like, you know, 1000s of apartments, and every apartment has millions and millions of transactions, like, you know, you’ve paid 15 cents for water that day. And so we have all the data and the

Eric Dodds 22:16
utility, right? Because everything? Well, yeah. Okay, so yeah, sure, you know, you had to have a plan that you’re gonna tie the unit level. So you’d have like, almost a building level, and then a unit level. So we’re talking about like, and then you have a tenant level. And then now there’s, there’s so it’s solely house. Yeah. Yeah. This is like I was saying to, like earlier, some of them, like, basically,

Ben Miller 22:41
like, everything in the world is data. Yeah, like, right, the whole universe is data. And just, and so if you’re in an industry, and you care about outcomes, basically, you want to figure out how you can model that subject of the universe, so that you can start to get good at predicting what’s gonna happen, and understand what’s going wrong. And so in a property, right, when you have, there’s, you know, they’re like, oh, city, you know, 1000, people went to live there, all sorts of things happening. And you’re trying to basically get good outcomes. At the end, that’s the combination of people feeling good. And people paying the rent and people having like, everything fixed. And, and then like, next door, they maybe fill the whole foods, or they’re, you know, they’re they shut down the street, because there’s traffic, because they modify the road, there’s all these things happening in the built environment. And that build environment also has things like, things having virtually you find an apartment, by looking on the internet, like that lease and traffic comes through, there’s just all these flows of data around Nexus, which ultimately, is the property. And if you can get really good at capturing all that data and modeling that data, right, you can actually just make better investments and you can build infrastructure that ultimately everybody wants to use.

Eric Dodds 24:05
And when you say infrastructure, you’re talking about physical?

Ben Miller 24:10
Well, I’m a data infrastructure. Okay. Well, you can it’s both I mean, you can get to both the adults where the physical infrastructure comes from a data decision. Yes. An insight that you get from information. And

Eric Dodds 24:25
when you say, data infrastructure that people like to use, there are multiple potential audiences that you could mean there. So like, Who are you thinking about? Where are you thinking about, like, people who are data professionals? Are you thinking about the tools that the actual tenant uses to streamline their, you know, sort of engagement with you as a landlord?

Ben Miller 24:52
Or Bryce? Yeah, so Okay. When I’ve listened to your show, right, you usually have people who are technically building pools. And what’s happening in the data and infrastructure industry is that there’s tools or technology being built that really, I mean, like, to me, they’re like revolutionary. I mean, it’s just from Yeah.

Eric Dodds 25:12
We talked about DBT. Like, yeah, yeah, I think of an example. Sure. But

Ben Miller 25:17
I mean, if you go back, I mean, let me just digress for a bit, and I’ll come back. Yeah, sure, of course, it is almost like you have to answer it in the top down. And so. So 10 years ago, we built Fundrise. We have 100. We currently have 100. engineers, software engineers, one is a data edge.

Eric Dodds 25:36
No way. Are you serious? Because yeah, you said, what? 200 To 300 employees, right? Got a couple 100 employees, 100 software engineers in one day to engineer, that’s shocking.

Ben Miller 25:50
Yeah, because the organization is like data engineering, your data is like a separate, it’s like a separate segment. And mostly Application Engineering. And we basically process billions of dollars. And we push information to our likes, our web application or iOS, we, yep, a whole part of a whole team that maintains the performance of your real time performance of your portfolio. And that’s like we do like I’m doing data points that processes with like

Eric Dodds 26:23
Windows or data products, like, yes, you’re delivering a data product inside of an app, essentially. Yeah.

Ben Miller 26:30
But they’re not, you know, the idea of like a separate, we have a cloud ops team, right? Yeah. And identity team, but we didn’t put it there . We didn’t have data engineering, like as a as like, its own, like, resource parsing. Yeah.

Eric Dodds 26:48
Interesting. That is just one quick perspective on that. And tell me if I’m off here. It’s interesting, because you’re delivering a bunch of data products, which is almost like a software subsidiary in the way of your apps, right? You sort of have like an end user, and you’re delivering different features and products within the experience that they have. Which is interesting, right? Like centralized data engineering isn’t necessary to deliver a data product to an end user, right? Data is just an input that sort of feeds like a user experience. And that’s fascinating, actually, like, that’s sort of the layering there.

Ben Miller 27:28
Yeah, well, I mean, I, I could be all wet on this one. But I just think the evolution we’re going through is like happening, writ large at other companies like we just like we like if you go back to 10 years ago, we had a monolithic code base, and we ended up with micro services. And that’s sort of what’s happening in data. industries, like he should have we the first phase is you build out pipes with Fivetran, hit the empty in place. And then now we’re going to do the reverse yells, you’re going to work your way through, like natural evolution. And so like, but where I sit is at the application layer, the business layer, where we’re going to use the tools that are being invented by companies like Databricks and DBT. To build applications for people who are not data professionals.

Eric Dodds 28:22
Yep. Right now is that the end consumer or like, when you say not data professionals, like define that, because that could be someone who like is on the Fundrise app, and they want to make an investment and like, I don’t care, or I or that person needs the data to make an informed decision, or you’re sort of presenting them with like, the ability to make informed decision, right?

Ben Miller 28:43
Well, there’s so there’s lots of different ways to do that. We have to we’re definitely like, what is essentially it’s just an internal user, who can like, make better marketing campaigns. And yes, decisions about products and just have the data. And we are the way we did it before we had our database, and we just dumped it directly into Tableau in terms of every like, which is like, here we go. And then we had like, by splitting that up, and using the logic added Tableau, like, we can, like there’s no there’s like things that we didn’t know we’re like, kind of learning, but then, but I’m like the I go to real estate, and I think about real estate thinking about finance, real estate, commercial real estate, apartment buildings, and, you know, industrial buildings. And that is a form of finance mostly. Yeah. And so I gathered this data before this for this podcast. So there are 100,000 data analysts in the country.

Eric Dodds 29:40
In the United States, and you know, it’s their job title data and who they work for.

Ben Miller 29:44
As I think it said, I think it sends 90 to 93,000. So okay. And there are 6.5 million people in the financial services industry.

Eric Dodds 29:56
Working what

Ben Miller 29:59
So I think that those two professions converge. Yep. And in the same, like, basically in the same kind of pattern, which is that, for us, I think for most people to get the data we had originally go to engineers, like, give, can you get around this? Yeah, this data, custom report. And then we could have gotten to a place where now a data analyst can write a sequel or, you know, get the data for us. Yep. And then I think we’ll, we want to get to a place where then like, a person like me or person, the marketing department can get the data. Yeah. Without knowing SQL. Yep. And then, you know, what’s gonna happen is that, like, so? Then that person who’s like me is like an Excel user, like Excel spreadsheets, basically, yeah, how do they think about data? And then what’s going to happen is, that’s gonna, that it’s gonna be done by AI with natural language. Yeah, I agree with that. So that progression is happening. And that progression is the progression of all technologies, which is that it becomes a mass mass user. So it gets really cheap. Yeah, and it gets really easy. Yep. And so like, the data infrastructure industry is sort of linear, like, a mid phase, like sort of, is now graduating from it being, you know, deeply technical to being sort of a business person starting to get at it. Yep. And that’s what I want to do. And for real estate, in finance, that’s where we’re going for finance. So if a financial professional should basically know it, you know, in five years, they shouldn’t be using Excel SugarCRM rarely? Yeah, so clunky.

Eric Dodds 31:46
Yeah, you know, okay, so a couple of thoughts there. This is a conviction that I’ve held for quite some time. So I think this statistic that you mentioned, is I think it’s true. But I think it’s actually sort of a gross Miss labeling problem. And let me tell you what I mean, by that, my guess would be just based on hearing your background, that you have built things in Excel that on paper are essentially software, I mean, probably like, essentially built software in Excel, right, whether that’s the, you know, the ability to sort of model something, or analyze data you’re using, you know, sort of like macros. And you know, and some of the best software developers that I’ve worked with actually came out of that and it was back background, quant background, and are just excel masters, right? And they, that gives them a fundamental understanding of the relationship of data and how to express things through logic, which is really interesting. The gap has been a tooling problem, which I think you highlighted really well, right. Like you have an analyst who’s really good at SQL, and they can wrangle Tableau right? And so they’re like labeled an analyst and then you have, right, someone who is trying to build multiple scenarios across multiple connected Excel files to try to predict, you know, sort of margin on a large commercial real estate investment that has, you know, 1000s of inputs into the model by like, well, where’s the skill set difference, right? There isn’t, I mean, there’s certainly flavor differences, but like, you’re just talking about different tools, you’re talking about people who are sort of producing similar types of work, they’re just using very different tools. And I agree that it’s so exciting, that’s starting to converge, where it’s like, well, those should not be separate and really excel, like, you know, should sort of fade into the background. So I agree, like, that statistic is fascinating. But I also think that that’s a tooling gap. And it’s starting to close. I think it’s probably a bit earlier than you, you sort of said like we’re in the middle phase. I think we’re like, still on like, free middle phase, like pretty early. But you know, maybe that’s just my perspective.

Ben Miller 34:22
Well, I guess my goal is to try to move us to the next phase, like we’re going to build a product or products that like start to leverage the more technical Yeah, products that like DVT or are getting Databricks, both of which we invested in.

Eric Dodds 34:42
Oh wow. Okay, so you Okay, so Fundrise you gave people the ability to invest in both of those companies?

Ben Miller 34:50
Yes, we know, both Databricks DBT. We’ve invested in and in our through one of the in our Its technology strategy. And, both of those companies are enabling technologies. That if you mean, it’s if you can basically get the right data into their, you know, yeah, into their tooling. Yeah. And then get that into the right. UI. So yeah, like, that’s basically I mean, there’s a lot more than that happening, right. But there’s, that’s where you can start to basically like, democratize access to their, to the innovations.

Eric Dodds 35:36
Yep. Yeah. Okay, I have a funny question about this is a quick sidebar, but then I want to ask about third party data, because the real estate aspect of that is really fascinating to me. I’m just thinking about a Fundrise user. And I’m thinking about investing in an apartment building. And I’m thinking about investing in Databricks, right. And I’m thinking about the number of people I know, who have an appetite to invest in both things. Because traditionally, when you think about the way that people want to allocate capital, like an individual who wants to allocate capital, you’re going to play to your strengths, right? And so you have people who were like, you know, they invest primarily in real estate, right. And then the other end is venture or whatever you want to call it, right angel investment, you have a large appetite for risk, and you have, you know, your sort of portfolio like affords for you to just sort of make a wide investment in a number of things that are like, asymmetrical, and so you have a larger volume of investments, but you know, and you’re willing to lose, because one of them might be asymmetrical in terms of returns. It seems like Fundrise is a place where you have that, like, you’re presenting an opportunity for someone to sort of do both things. But is that the actual user? Like, how was your user base segmented? Because I, I’m just thinking about myself, actually, it’s like, Well, I actually would kind of be interested in making both of those investments for different reasons as part of my portfolio. But maybe your average investor isn’t thinking about investing in commercial real estate and Databricks. Right, and your traditional commercial real estate investor, you know, not to stereotype but may not know, like, why Databricks is technology is, you know, creating so much value for their company.

Ben Miller 37:26
Yeah, that’s fine. We need high touch for one of these companies so we can get a better audience.

Eric Dodds 37:36
Yeah, so get the audience and Databricks you know, whatever.

Ben Miller 37:39
And they could want to build heads and build more personalized experiences.

Eric Dodds 37:47
Yeah, but in terms of just your gut sense of like your users, like, it’s, it’s such an interesting dynamic of extremes of the spectrum on a single platform.

Ben Miller 37:55
Yeah, I mean, it’s so weird, we have a lot of investors. And so what happens is, there’s a lot of their law use cases, I don’t like personas, because there’s just too many, when you have, you know, a million and a half million investors. And you people invest this is one of the things they invest, like, what they’re doing 2021 The middle of the pandemic when printed, money being printed. And what they’re doing right now, is so different.

Eric Dodds 38:28
Oh, yeah, even the same persona is a different persona.

Ben Miller 38:31
Or they’re just because their behavior is much less consistent than they believe, or any kind of like, you know, archetype modeling would believe. Interesting. And I think that, like, if you put the we right now, everybody’s, I’m an AI. And a year ago, Noah was talking about AI. Yeah. And so like, you know, we’ve invested in some pretty good AI companies too, which I haven’t we haven’t announced yet. But, and if I were to start talking about like, like, you know, Wragge, and you know, and why this company, so central, open AI uses them, and people a year ago would have just been totally not interested. Sure. And not persona driven, right. Yeah. So there’s like, I mean, this goes the middle of the top down, like the macro, high interest rates, oh, I’m making 5% on my savings. Like maybe I’m not going to invest in anything. And there’s so many things. That investor that as it’s not as driven by the persona or psychographic demographic stuff, and that like the problem with the analyst, is they really want to believe that it’s analyzable . Yeah, yeah, they seek signals desperately, which is sure like Peter P hacking, let’s just be there. That’s there. Yeah. And, and so like, that’s like, usually my experience with seeing data is It is either dead obvious in the data, no signal and look for it. You’re basically P hacking.

Eric Dodds 40:06
Yeah, yep. Yeah. Unless you have a pretty stable data set.

Ben Miller 40:13
Yeah, but what makes the world stable these days? That’s

Eric Dodds 40:17
great. Okay, this is maybe a little bit more of a personal question for you. But I can’t resist. And if you don’t want to answer, that’s totally fine. Did you have millions of investors on the platform? A lot of people believe that investment is an inherently emotional decision. And we convince ourselves that it’s a quantitative decision. What’s your take on the individual investor? Like? And the reason I asked that is because I’m trying? And really, that’s a mirror question for me, because I think about investing, you know, when there were, you know, interest rates, was zero pandemic, like, and am I a different person? Now, I am a different person. A lot of people would say, “Well, you shouldn’t have an emotional response to this, right?” Like, you have a plan, you stick to it, but I made decisions based on, you know, my perception of things. And a lot of that is emotional.

Ben Miller 41:16
Yeah, I mean, my conclusion is that there’s the emotional reality. First, the facts are gathered to fit the emotional reality. And that’s how people are. And then it doesn’t matter if you’re an individual, or you’re an institutional investor, like, the facts are second. And the motions are first. Yep. And that’s like, you know, why the passive investing movement has been, I think, really constructive. And a little bit of our investment philosophies, like, you know, we’re not trying to be smarter than Sequoia or Blackstone, we’re trying to basically just create, access and index what our reserves would hopefully be obvious, macro drivers of companies. But it is frustrating, because in 2021, people are shoving money at us. And I’m like, no, just got to just sit at hay out. So hold on, hold your horses. And now, it’s like people are much more reticent, much more concerned. Everything’s going down, everything’s gonna keep going down, I think, generally, broadly, at least. And that’s a great time to be investing. Prices are much lower than they were, and everybody’s much more reluctant. And so it’s hard. It’s, I mean, investing in stock markets, or mass psychosis business. I mean, it’s just people look at the markets and think that reflects quantitative analysis, but it mostly reflects us psychology. Yeah. Yep. Yeah. And, and they can’t really get outside your Zeitgeist. I mean, that’s why it is one of the hardest parts of my business. Now,

Eric Dodds 43:07
I would think, though, and I don’t want to be, I don’t want to read too much into it. But one thing that’s interesting to me about having a platform like yours is that if you think about an individual making investments, it’s not like they’re getting data back from the systems that they use, again, like, let’s just think about someone buying a stock on E trade, or even someone who makes an angel investment in a technology company, right. They’re, like we, as humans, we will automatically bias ourselves to confirm that the decisions that we made were good, especially when it’s our personal money, right? It’s very difficult to break out of that. What’s really interesting to me about the dynamic that you provide is that you can provide data back that doesn’t care about how you feel, right? And so you, in some ways, can potentially stem a little bit of the confirmation bias that we all struggle with, by providing sort of an objective perspective with data. Is that true?

Ben Miller 44:16
I think that the way we think about it is slightly different, but that we try to provide more content, like more like, like, if you are, if you’re an investor, you’re looking at our app, it would, it’s a news feed. It looks and you’re getting, like investment strategy memos about the investments like oh, we invested in this company. Why is coming from because reason why people don’t invest in things that often don’t have confidence and they build my knowledge. But the world I mean, the world today, in the last four years, it’s just been an absolute roller coaster. Yeah. And, you know, clearly if you look back, like, it’ll be fine. You make your way through it, it goes up, it goes down. Mostly if you do the right stuff, it does pretty well. But that volatility people hate that. Yeah. Yeah. And that’s just a challenge to, again, it goes back to the surf, that volatility is just an emotion, it’s an emotional reaction to the volatility, yes, things are gonna go down. But like it is, I think maybe if you go to enough cycles, you get sort of like less affected by that up and down.

Eric Dodds 45:36
Yeah, yeah, I think, you know, my big takeaway there is, data itself is not education. I think that’s a, I think that’s really wise. I want to get some specific data questions here. This has been such a fun conversation, and I have a million more questions. But can we talk about the data inputs for real estate investment, because, you know, there’s, you know, I’m just thinking about sort of what you have to deal with as a company, what you expose to your users, and I’m just thinking about the factors here, right. And so let’s start with maybe, and I’m sure I’m gonna be way wide of the mark here. So keep me honest. But let’s think about like, simple, like, reasonably stable data, which would be maximum number of tenants capacity, average rent, sort of like average ongoing maintenance costs, things that you have a, you probably have a lot of historical data that serves as a proxy, where you can probably get that within a pretty accurate margin in terms of predicting what the cost basis is going to be from that standpoint, right. So you have that side of it. But the value of real estate is, on some level, highly subjective, and is influenced significantly by market conditions. changes in the environment, you mentioned construction, you know, the ability to incorporate new technology into some sort of large building, which is a capital investment, there are sort of all these variables that feel much more subjective as inputs into an investment model. What does that look like for you? What does that look like for your end user? And then, of course, you have, I would assume, an immense amount of third party data that you’re sort of putting in this model is just publicly available, right? I mean, prices, tax information, you know, all those sorts of things.

Ben Miller 48:01
Yeah, I mean, this is the opportunity. And we’re in the process of attacking it. But the way it is done today is by hand manual, really, you go gather data, mostly by the data, there’s a, there’s a handful of companies that are primary supplier suppliers, that would be you go get that data, and then you manually input it into a spreadsheet was brutal. And there’s like, I mean, we have 100 real estate people at the company. And, and, and that’s just like, they spend their days taking stuff out of PDFs, and out of, you know, spreadsheets and putting them into other spreadsheets. And then like trying to figure out kind of what, it’s a layer and a bunch of assumptions, as you said, and then figure out what you should do. And this is like, kind of what you said earlier is like a big insight, mostly the work is getting the information, and then getting that information sort of organized and cleaned. And it’s not actually once you have it, you know, joined and cleaned and aggregated and everything else like that. Usually, the conclusion is pretty straightforward. Yeah, the work hours are actually going into that front end part. And you’re like, Well, what if like, that was done with data infrastructure, rather than by people doing it by hand, but the entire you know, whether it real estate or venture or private equity or whatever, the go down the list of all the financial industry, all lending, the lending, and if you’re, you know, I work with all these banks, like banks, I’ll do it by hand. And so that’s crazy. But that’s how that’s basically where the industry is. And then the part that like, like, I kept, I wanted to do this, we’ve done you know, 1000s of underwriters 1000s. And like, if you took all those underwrites and back tested them, you would see that they’re like, they’re no more predictive than like random.

Eric Dodds 50:01
Yeah, no more protective than a venture capitalists call on who’s gonna?

Ben Miller 50:06
Yeah, I mean, it’s because the reality is nobody predicted the pandemic in 2019. No one predicted inflation and 2020. No one predicted the Ukraine invasion. 20. Yeah. Why? No one predicted like, and so, you know, no one predicted that, like, climate change would basically wreck. Florida, he kind of predicted Florida, but you wouldn’t actually think that insurance costs, but they wouldn’t. But it happened in Texas, like with inflation, our rents went up 20% In a month. Typically, that would happen over a decade. Yep. And so that was unpredictable. And so there’s just all these things that have been buffeting the models. And I think so I just and so the industry is obsessed with this analysis. And it’s because I believe that it’s really a narrative. It’s a sales tool. Yeah, it’s actually not really to drive. It’s not really data driven. It’s like, what do I need to put on this piece of paper? That gets us to? Yes. And that’s how the investment business works. Yep. And I believe you get to a place where it’s gonna happen. Because once you have good data infrastructure, right, you can then start attacking it with ML and AI, and you can start to get to a place where it’s where, you know, it’s pretty, not objective, because ultimately, you know, is what the next shock is going to be. But it’s not like you’re not going to pretend that this person is a genius. Because there’s so much chance in it. Yeah,

Eric Dodds 51:44
I think about that as like, there’s a mental model I love called, like, sorta, like, beware of the fat tails, you know, like the, you could be tracking things so closely, right? And then like, a depression hits, right. And it’s really unexpected, right? Or, to, to your point, I mean, there’s sort of all these things that, you know, the pandemic, right, that’s like a very fat tail thing, where it’s like, okay, well, in the bell curve, like, most things work, but there’s an outlier in the fat tail that hasn’t really happened or manifested in the way that it occurs in history. And so it’s a novel experience. And that’s just part of the fat tail, right? Where it’s like, the tail is not actually close to the x axis, it’s pretty far. And so there’s a lot of things that could happen in there that are impossible to predict. And so do you sort of view data infrastructure as like getting the tail a little bit closer to the x axis so that we can have more accuracy?

Ben Miller 52:42
Or like, how do you write better, do it right, right. So basically, part of the reason that we’re so bad at the tail risk is psychological. Because we have a we have a bias to extrapolate the present into the future. And so we have the sort of the expectation that the future will be like a bell curve. So that’s partly a human dynamic. And then as part of the tooling problem, Excel is limited. At modeling, the kind of multivariable kind of outcomes like where it’s just it’s a very linear structure. Yes. And so if you get to a place where on the output, you have better tooling, and on the input you have, you know, not just people but like, you know, AI, essentially, that’s basically helping, like, Hey, this is where the data is saying, you’re saying this, but like it was just a fat tail event happens every four years. Yeah. How fat is it? How much of a bell curve is this? So? So I think that this is what’s going to happen in the financial industry. It’s, it’s going to happen, I’m hoping to be part of it. And that’s basically like, again, the application of the tooling that you’d normally cover, is being able to get this more sophisticated approach to data into the hands of the business analysts and the business person who today basically is getting, making bad poor decisions as a result.

Eric Dodds 54:12
Yep, yep. All right. Well, we’re at the buzzer, as we like to say, but I’ll end with, again, sort of another little bit of a personal question. Okay, so if you had to completely switch careers, and you couldn’t be involved in real estate, and you couldn’t be involved in an investment, you know, sort of the investment world, what would you do?

Ben Miller 54:38
I probably would be a teacher. What would you teach? Oh, man, history. Maybe, you know, it doesn’t, it almost doesn’t matter. Science. I’d love to ask your astrophysics, something like, like physics would be fun. But I feel like I just get a lot of joy and figuring things out and then teaching it. Hmm. And so, like, I guess, like my fallback is to go via teacher somewhere.

Eric Dodds 55:06
Yeah. I hope you have a gigantic exit. And then, you know, you go teach kids in high school who are like, who are you? And you’re like, Well, I built a huge company and sold it, but I’m gonna teach. I’m gonna teach you about, like, how the government works.

Ben Miller 55:24
That’d be so fun. That would be I don’t teach. I just don’t want to teach MBAs. Yeah, yeah. I’ll teach anybody which is that, you know, like, as, in bring, like, real I want to teach entrepreneurship, because I don’t think it’s like, teachable, but I think I agree with that. Yeah. But I think that there’s like stories and lessons and stuff from it. That I want to teach to people who are not MBAs.

Eric Dodds 55:50
Yeah. Yeah. And I would argue this is a whole other episode. But I would argue that, like physics and history are probably much more closely related subjects than a lot of people would like, take it at face value. Right, like,

Ben Miller 56:02
Wow, okay. Wait a second. You have to tell me some points. What you mean.

Eric Dodds 56:08
Yeah, we’ll have you back on and we can talk about it. All right. Well, it’s been such a good show to learn so much. Congrats on the success of the company. Good luck, wrangling the data pulling out of PDFs. And we’ll have you back on sometime soon.

Ben Miller 56:21
Beautiful! Thanks for having me.

Eric Dodds 56:24
We hope you enjoyed this episode of The Data Stack Show. Be sure to subscribe to your favorite podcast app to get notified about new episodes every week. We’d also love your feedback. You can email me, Eric Dodds, at eric@datastackshow.com. That’s E-R-I-C at datastackshow.com. The show is brought to you by RudderStack, the CDP for developers. Learn how to build a CDP on your data warehouse at RudderStack.com.