Originally, similar to multiple new professionals in the industry, Oliver entered the finance world needing to learn Excel to map out complex financial deals. As many of our users know very well, the process of creating and modifying financial models can be a hugely time-consuming effort, which may require multiple iterations, which then can create quite a strain on those tasked with that responsibility. Additionally, any changes made to the model create the extra headache of updating all the associated investment memos and project deliverables that are tied to that model.
Due to this experience, Oliver has sought to create a more intuitive, easy-to-use tool specifically catered towards financial modeling. With a whole new engine, Phosphor is in a very simplified language that is nearly identical to Excel with name ranges but sans many of the common pain points experienced by financial modelers. Check out our interview with Oliver below.
Phosphor and Oliver Beavers – Changing the Way Real Estate is Underwritten
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Welcome to the Adventures in CRE audio series. Join Michael Belasco and Spencer Burton, as they pull back the curtain on everything commercial real estate, and introduce you to some of the top minds in the industry. If you want to take your skills to the next level, and be part of a growing community of CRE professionals across the world, this is for you.
Sam Carlson (00:26):
Hello, and welcome back to the Adventures in CRE audio series. Today, we’ve got an awesome guest, Oliver Beavers. And this is going to be an extremely interesting topic, only because it seems like I don’t know how many times, Spencer, you’re going to be able to comment on this, how many times per month, year, whatever, we get hit up on some new black box solution to real estate financial modeling. And they pretty much all commit the same fatal flaw. But today we’ve got something, a story, and a project, and just listen because you’re going to love it. So to frame what we’re talking about here, I’m going to pitch it over to Spencer because Oliver Beavers, who’s our guest. Hi Oliver, how are you doing, buddy?
Oliver Beavers (01:10):
How’s it going, guys? Buenos dias from Buenos Aires.
Sam Carlson (01:15):
There you go. Oliver Beavers has come up with a really awesome unique solution. So Spencer, let’s go ahead and, before we get into the solution, of course, let’s talk about why this particular approach piqued our interest so much.
Spencer Burton (01:32):
Yeah, sure. So first off, we’re talking about Oliver and his company, Phosphor. Interestingly enough, Oliver and I connected for the first time, it’s probably almost been six years by now, Oliver. I think it was 2016, we’re now 2022, depending on the month in 2016. Years ago, he connected with me. Now I get calls, Michael gets calls, emails regularly, from people trying to tackle a collection of problems in real estate financial modeling. We love to hear those, by the way. So, Sam kind of played down those initiatives. I think they’re important. Excel is an inadequate solution to the many problems that come in real estate financial modeling.
Spencer Burton (02:16):
But I haven’t been convinced of any of the solutions, and the reason why is because they’re generally black boxes. What makes Excel so powerful, is that it’s a user-created experience, and there’s sufficient nuance in most of the modeling that we do, in real estate or frankly in any asset class, that the black box solutions don’t work. And the perfect example in real estate is Argus. Now, I shouldn’t say that Argus doesn’t work, because it’s a industry-wide, widely used tool, but what’s ironic about Argus is most firms, they model a portion of their cash flow in Argus, and then what do they do? They export it to Excel, because they need flexibility that Argus and other black box tools don’t create. And so anyway, five, six years ago, Oliver reaches out and he says, “Hey, I think that there’s a better way to model real estate.” And he’s a young guy, a few years out of UVA. I think you had an undergraduate degree in some, was it history?
Oliver Beavers (03:12):
Comparative politics, yeah. My highest math was community college pre-calc, and I had never taken a finance class.
Spencer Burton (03:21):
Right. But clearly a sharp individual. And so, we have this fascinating conversation five, six years ago, he frames this solution. I say, “You should go for it, Oliver. It sounds like something great.” But assuming that that was the end of it. And again, I have these conversations regularly, and I would love to see a solution that replaces Excel, replaces Argus. But the reality is what I’m hearing isn’t… Well, fast forward now five years forward. About, I don’t know, a few months ago, Oliver reaches out again and he said, “Hey, remember me?” And I got to pull that out of my rain from history, and I said, “That’s right, yeah, we had this conversation.”
Spencer Burton (04:02):
And he’s had a fascinating trip, I don’t know what you’d call it. A journey, that’s the right term, from that moment to today. And so, let me stop there. Oliver, why don’t you share where you’ve been from five, six years ago until today? And then we’ll get into the problem, which we want to frame this, and then talk about a possible solution to those problems. So, go ahead.
Oliver Beavers (04:27):
Yeah, definitely. Thanks for the introduction. So five or six years ago, I had tried to start a financial modeling consulting company, given the enormously high levels of math and finance. Just kidding. I didn’t really have much in the way of jobs out of college. My first job was managing one of these quotation tank centers, like the sort of mind spas. As like plan D, because everything else fell through. I had a job lined up to be a physical commodities trader in Germany, but the oil market crashed. And so, I graduated in 2014 and was just like, uh oh, what’s next?
Oliver Beavers (05:11):
And I decided I wanted to build a career in clean energy finance, not totally appreciating that in solar specifically, the financing structures around it which we call tax equity, are some of, if not the most complicated structured finance vehicles out there. You’re essentially taking the tax credits that you get from the IRS, and figuring out a compliant way, through a partnership or an inverted lease, to actually earn that tax credit that we as sponsors couldn’t monetize or use, because we didn’t have the tax appetite, into actual capital as a very low cost of capital solution to enabling projects.
Oliver Beavers (05:55):
And so, I essentially picked off the hardest area of finance I could find, figuring that people would find value in that if I could just get into the right doors. And what ended up happening was, you don’t just learn how to build a structured finance model. You have to learn the logic behind it, and then you also have to learn the language of Excel. And if you’re doing anything remotely complicated, you have to go down a pretty hard path for that. And so, I built my first solar model off of an oil terminal model that I got from the prospective company that I was going to work for, as like, “Hey, I’m trying to do this. Can you give me something I can reverse engineer?” And it was a real asset. So it’s similar, based off cash flows.
Oliver Beavers (06:51):
And what becomes immediately apparent, is if you’re trying to learn how to build a model from scratch, you’re not just tasked with translating something into Excel, but rather you have all of these big formulas. And if you want to actually reflect the logic in them, and see what they’re even doing, you have to… And oftentimes they’re so big that you can’t even keep it in working memory. You have to actually write down in words what the logic is, to understand what’s going on. Only then can you actually retranslate it back to Excel, and hopefully you didn’t screw anything up and got the intended result.
Spencer Burton (07:30):
Reminds me of Michael’s S curve formula.
Michael Belasco (07:31):
Yes. I was thinking of the exact same formula, by the way. Where I actually, you’re speaking a language. You’re telling a story that’s just so familiar. So continue, I don’t want to interrupt.
Oliver Beavers (07:45):
Yeah. So, as someone who didn’t go through any formal finance training, when I first approached it I was like, man, I’m going to need to learn calculus. And you very quickly realize that it’s like, huh, I learned exponents in seventh grade, and it’s pretty much it. That’s what we’re doing. And so, the whole world of getting the job at Goldman or the investment banking route, kind of became a pretty big disillusionment for me. Because it was like, okay, pre-calc is more than enough. We’re good here.
Oliver Beavers (08:28):
And then I started, I built what was called Trivium, and I tried to partner with… It didn’t really work out, I partnered with this finance professor who was initially interested but then wasn’t. And I built the website and the marketing, and everything for it and actually got a couple of leads. And I had no job prospects, and so I actually took on these consulting engagements, totally winging it. And it ended up working out, and I kind of had a little bit of a company at that time. And through a pretty lucky series of events, I met this guy named David Busby, who was the former chairman and the lead investor in Sun Edison, which was the largest, one of the largest companies in the world, but the first and largest solar company out there. They kind of pioneered that.
Oliver Beavers (09:21):
And I helped him with a couple of things, and he wound up introducing me to his head of project finance when he was CEO for a year or two at Sun Edison. And amongst a bunch of other people just because he liked me and he knew I was interested in it, and put in the effort, can actually use these weird tax structures and speak to them. And they’re like, “All right. Yeah, it’s cool.” And so, around that time I began trying to actually learn Python, because I didn’t always have clients. And immediately it was like, oh, I can do some pretty cool stuff with this. Let me see if I can build a financial model, and solve some of these weird circular reference issues.
Oliver Beavers (10:06):
But you could read it, you could read it. And I was like, wait a second, this makes a lot of sense. And so, when I went out, before being officially employed by 38 Degrees North, I went out as a consultant and I had built this project finance modeling thing. And I was like, “Guys, this is what I’m trying to do as a business. Can we test this live? I’m happy to do the Excel stuff that you want, but let me test it.” And so, first week or two on the job, clearly wasn’t going to cut it. I had modeling experience, but I didn’t have deal experience, and I didn’t know what actually went into running and managing a transaction, like all of the hackiness. And so, what I had done unknowingly, was I had built a black box. I had built the project finance logic directly into the coding.
Oliver Beavers (10:59):
And so fast forward over a three and a half to a four-year career there, on the side, I rebuilt Phosphor probably four times. Not really knowing, just incrementally getting better. There was a pause for about eight months, and then finally it clicked. It was like, I just need to rebuild Excel’s whole engine. And that’s what I ended up doing, and then you wind up layering in a very simplified language that’s almost exactly the same as Excel with name ranges, but without having to paste it across a whole bunch of columns, and something pretty awesome came out of it. And so, COVID crushed me pretty hard there, and I was just ready for the next thing. Tried to work at a high-speed train startup for a little bit, but that just didn’t pan out for various reasons.
Oliver Beavers (11:52):
And so, I’ve been full time on taking this language and engine that I’ve built, and turning it into a product that’s built to not just solve the modeling stuff, but built to essentially automate all the process work around the model. You’re taking model inputs and outputs and translating them into investment memos and decks, all this copying and pasting from different formatted stuff, how you manage things. I found that when I was at 38, so much of what I did was just process. It wasn’t value add, I wasn’t learning deal work and how to be a… I wasn’t learning the commercial skills because there was always too much in the way. And so the ability to automate that just kind of clears up all this room to actually think about what the deal is, and how you’re structuring things, and how you’re actually solving problems. And that’s the initial thing that I’m building with this.
Michael Belasco (12:51):
So, a lot of our listeners know very well, all these pain points. And you’ve alluded to some of them. And we’ve had conversations, there’s some things with Excel that are necessary, and then there’s others like you said, that are unnecessary and burdensome, but there’s no better solution. Would you be able to speak a little bit more about Phosphor in context of the major pain points? You talked about copy and pasting and things like that. What other pain points that we all experience as Excel nerds, and financial modeling monkeys that live in our Excel spreadsheets, and we have a lot of pain points, I’d love for you to just elaborate other areas that you solve for. I’d love to hear more about that and how Phosphor works a little more.
Oliver Beavers (13:44):
Yeah. So, how I’ve been presenting this to potential investors who don’t necessarily have these finance skills, is as an Excel wiz and someone who does that for a living, you’re kind of a programmer to a degree. You’re building software, but with like a no-code, low code sort of interface. But you’re not able to use any of the tools that any software engineer would be without today. The most basic version control, gone. The most basic ability to test your stuff, gone. Not repeating stuff when you’re making a new model and just templating stuff, it just doesn’t exist. I think most fundamentally, it doesn’t exist because Excel is a language that doesn’t really have any semantic meaning. You could create a formula for EBIT, or EBITDA, which would be EBIT minus depreciation minus amortization. And in Phosphor, that’s actually-
Michael Belasco (14:43):
NOI in real estate. Yep, NOI in real estate.
Oliver Beavers (14:44):
Okay. Well, in Phosphor you’d actually type that out in the words, but in Excel that would be A7, minus B7, minus D7, all in the same column. And so, once you move from something that’s just a bunch of cells being referenced together, to something that has real meaning, this whole world of opportunity opens up for clarity, error reduction, but then also integration with just the most basic modern web tools, that save time and hassle, and make life a lot better as someone who has to manage and own that kind of thing every day.
Michael Belasco (15:26):
Awesome. Spencer, you were about to say something?
Spencer Burton (15:29):
Yeah. I have a lot of thoughts on this. Let’s stick to the problems that are so common. So let me give one right now: we have 60 some odd real estate financial models that we’ve shared to the ACRE library. You’re aware of those, Oliver, I think you’ve even rebuilt one of them into your tool. And we update those regularly. Some of the models update monthly, some update every three months, six months. Here’s the problem with updating an Excel model: if you’re working on an old version, Michael, and I roll out a new version, the only way for you to update yours is to throw away the current version you’re using, start with the new version and then re-input all the inputs. There’s no way to roll… And that was version control that Oliver was talking about. But in a very real sense, if there’s an error in the model that we share together, and I make an update, that update does not roll to yours. And that’s a big problem with Excel models.
Spencer Burton (16:28):
Let me use another one. And Oliver kind of alluded to this, so we teach in our accelerator, this concept of building modules. So you’ve got your investment cash flow module, you’ve got your operating cash flow module, and you might have a module for multifamily, you might have a module for retail, you might have a module for industrial. The operating cash flows of each property type are distinct. Then you’ve got a reversion cash flow module, which could vary, whether it’s a for sale or a for-rent product. Then you’ve got your return modules, that’s going to roll up either your un-levered or your levered cash flow. And then you’ve got your partnership level modules, and those are going to vary depending on the partnership structure, whether it’s single-tier, multi-tier, whether it’s IRR hurdles, cash on cash hurdles, whether you’re using equity multiple, whether it’s an American waterfall or a European waterfall.
Spencer Burton (17:14):
And so, you can imagine a scenario where… And one of the reasons why we have 60 some odd models in our library, is because you have to have all these different variations of what are essentially modules. What needs to exist is a tool where I could build 60 modules, and then simply say, “I’m going to drop in my development and investment cash flow module, my retail operating cash flow module, my for rent reversion cash flow module, and I want a module for this type of partnership cash flow,” and in a few seconds, or however long that takes, you’ve got a model. And what I hear heard from Oliver when he came back five, six years later, is that’s what he figured out, in a no-code way. So, that was the other thought I had.
Spencer Burton (18:02):
Now the final thought, and I’m sorry I’m getting a little long-winded, is we had Brandon [Tallman 00:18:09] on our Moneyball Solutions to Real Estate Analysis episode. Remember that guys, a few weeks ago?
Michael Belasco (18:17):
Yeah, that was great.
Spencer Burton (18:19):
And the very last question, Oliver, that we asked Brandon… So Brandon, Oliver, for your sake is former data analytics out of baseball, ran an analytics team for a Major League Baseball team. And he came into real estate, and he’s incorporating a lot of the techniques that he used in data science, and in baseball, in real estate. Fascinating episode, it was one of the more interesting episodes we had. But the very last question we asked him at the end is, “If you were an undergrad right now, and you were learning a new skill that would change, or would have the most impact on your career in real estate, what would it be?” And he said, “Learn Python.”
Spencer Burton (18:57):
And that’s great, but the problem is not all of us have a coding mind, and not all of us are going to learn Python. And therefore, there’s this gap between the subject matter experts, those who know actually what should be in the modules, how the cash flows run, and those who create the code that then run those cash flows. And therefore, some solution needs to be found in between. Not a black box solution, a no-code solution, but far more efficient than Excel. And that’s what I hear that you’re building with Phosphor.
Oliver Beavers (19:32):
Yeah. Yeah, that’s the goal. The goal is really just to take… You can’t build a model in Python, and then send it to someone in capital markets and be like, “Hey, can you guys size debt against this?” It’s just not going to work. It’s not going to work. The big reason that I’d spent so much time at 38 as I refined this, was just, what am I actually solving for here? what are the commercial realities that need to fit into this? And so, I don’t expect to ever be able… Even Phosphor, it’s super clear, you’ll be able to read it. But the whole idea, before even building a product, the first thing that I made sure it could do was actually compile a live Excel model. So you can take Phosphor, click a button, you’ve got a fully linked up spreadsheet.
Oliver Beavers (20:31):
And so, the whole idea here is that if I’m a sponsor and I’m going out to capital markets, I can send a link to the foster thing. If you as the bank or the lender, don’t know what that is and aren’t comfortable with it, you click a button, you get the Excel file. Eventually though, like two or three turns between each other, and it’s like, “Well, wait? Where did the version control go?” We can just do that. And, “Oh, I can read this. This makes sense as I understand it. Okay.” And so, that was sort of like, a lot of these black box solutions don’t have that off-ramp concept. Because you can’t just sell total buy-in to seven different deal counterparts on financing
Oliver Beavers (21:13):
And so, that was day one, what I was solving for. But to Brandon’s point, if I were trying to get into finance, I guess five or six years ago, the whole reason that I built this as like a language was I don’t need to learn Excel and modeling at the same time, and get the two conflated. I can just read the logic, and if I want to build something in Excel, I just rebuild it. And it’s there for me, and I can access that. And I might just have to create inputs in a slightly different manner, but there you go. Now you understand how a financial model works, and go forth and conquer. That was the intention behind the education part of this.
Sam Carlson (21:59):
I want to jump in here for just a second, and I’m just a simpleton listening to a bunch of nerds talk about financial modeling and all this kind of stuff. But I have found that in life and in business, there’s always this very checkers approach to a problem, and then a very chess approach to a problem. Was it maybe Thomas Edison that talked about first-order principles? I think that is who is quoted for saying that. But there’s all these different ways of thinking, and when you look at this approach to the black box, I totally understand it because it follows the broad pattern of human thought. Okay, we see a problem, we’re going to create this solution.
Sam Carlson (22:48):
What I’m hearing here, is we see this problem, we’re going to look at it in reverse. Instead of trying to create a one-touch solution, we’re going to simplify the components so it solves itself. If we build this simplified language, if we build all these pieces, and all these things that interact together, therefore it solves all of these downstream problems. And the inverse of that is being, I’m looking at all of these downstream problems, and I’m going to create the one thing that solves all of it. And while that might be the most tempting approach, it’s not the one rooted in the fundamental logic that will actually solve problems in second, third, and fourth-order consequence.
Sam Carlson (23:40):
And really, any time you solve a problem, you need to say, “Well, is this really a problem? Am I actually looking at the problem?” Because usually the problem is not the problem. Usually the problem is something that happened upstream of the problem, and so then you got to break it into its parts. So what I’m hearing, and again as you guys are all talking from your own experiences, as you create the tool, Spencer, as you’ve been modeling, Michael, you guys have been in the deal doing. As I’m hearing all this I’m like, well, really this is just a deconstruction of the fundamental logic upstream from doing a model.
Michael Belasco (24:22):
You know what this is? Everybody knows there’s a problem. We’re all working in Excel, we’re all working in finance, we know there’s a problem. But the real challenge is articulating the right question. And knowing that what the right… It’s not the answer, it’s the right question. So when we talk about these black box solutions, everybody thinks the problem is, “Oh, it’s complicated. Plug something in here and trust us, the creator of this black box, and you are going to get the right solution.” That’s never really been the problem. And what Oliver’s come across and realized, is that the problem is the step in between. The people are craving the logic, and need to be able to follow that.
Michael Belasco (25:06):
The big gripe with Argus is that you don’t know, you can’t vet the background. You can’t see the background, you have to put it into Excel and then you have to go, and if you want to verify it, you go and you rebuild your formulas based on your Excel knowledge. So it’s finally, somebody has asked the right question, in my mind, and it has been answered. Or the attempt now is to answer the question, Oliver, as well on the path, is why this whole team, the Adventures in CRE team is so excited to have heard what Oliver’s been up to. Because I think he’s really, maybe there are others that have asked the right question, but he’s asked it and now is on a path to solve the right question.
Spencer Burton (25:44):
Let me frame the problem in a way that the more senior people listening to this, who quite frankly, you don’t understand the problem in most cases. The MDs, the senior directors at the firms that I’ve interacted with. And this isn’t putting them down. They don’t understand the problem because they’re not the ones doing the work. The problem is you’re paying high, intelligent, high octane people six figures to do things, 90% of the work that they’re doing, and you’re talking hours and hours and hours and hours, they should not be doing.
Spencer Burton (26:24):
I can’t tell you, and again, I’m not putting down phenomenal firms that I’ve worked for. But you spend 75, 80% of your time fingers on the 10 key, making adjustments to models, complete… Not a complete waste of time, but largely a waste of time. When the real value is in the 10%, when you’re thinking about the assumptions, you’re vetting the assumptions, you’re identifying the way that your analysis is different. But no, 90% of the time, and again, Sam, the firms are paying six figures to high octane people to do-
Oliver Beavers (27:02):
Middle school math.
Spencer Burton (27:03):
Work [crosstalk 00:27:05].
Oliver Beavers (27:04):
Well, you know what? It’s not only that. There’s a lot of, these models become beast, they become complex, they become legacy within organizations.
Spencer Burton (27:15):
No one understands them.
Michael Belasco (27:16):
And it takes you, the brainpower is just kind of following it and trying to solve it. I can’t tell you how many models I’ve seen at companies I’ve worked at, where… And these are hundreds of millions of dollars, one project, couple hundred million dollars, and I’m finding hard codes out in the field. I’m finding like all kinds of stuff, and you can’t believe it. So it’s not just the math, there is a complexity around maintaining stuff, and an absence of a solution. Which I don’t believe is here yet, and what Phosphor is trying to solve. Is that, yeah, you’re paying these six-figure people to do that, and there’s no other option at the moment. And hopefully something comes down the rodeo, which is [crosstalk 00:28:02].
Sam Carlson (28:01):
There’s two things I want to talk about. One, I want to talk about potential outcomes. And then two, I do want to step on the brakes. I want to tap the brakes just a little bit. Let’s actually talk about… I think everybody knows why. We’re all excited. Clearly, if you’re listening to this podcast, if you’re watching the podcast even more so. But Spencer, you talked about high octane people, 90% of their job being put into these tests that are repetitive, redundant, could be totally simplified. Okay, well, what happens if Oliver, if this is successful, if that happens, then what does that mean as far as output potentially? We don’t know, obviously, it’s the future. But what could that mean for small family shops and large shops like-
Spencer Burton (28:58):
Yeah. I think Oliver’s done more work in terms of the types of efficiencies that could be gained here. I personally don’t see this as an exercise in let’s cut the staff by 50%. Even though that is one opportunity. But what it means is, let’s unleash, it reminds me of a Seinfeld episode. I shouldn’t even go there.
Michael Belasco (29:21):
Now I want to know, you can’t leave us hanging.
Spencer Burton (29:24):
Where George is only using one little piece of his brain, and his brain gets unlocked. It feels like, you have these high-octane people, let’s unleash their potential to work on higher-value tasks. Imagine if, instead of doing eighth-grade math and auditing formulas, they were doing data research to identify the right markets, and studying out the comps in a more complete way. But instead, 90% of their time is getting the model right. And then it’s like, I’ve spent all this time, I’ve spent 20 hours on this model, you might spend two or three hours on the inputs. It should be the reverse. And imagine if you spent the same amount of time, but 20 hours on the inputs and two hours on the model. You’d have a higher quality of output and product. Oliver, we’ve been talking a lot. What’s your view on the potential here, and this particular problem as it relates to untapped potential in individuals?
Oliver Beavers (30:28):
Yeah. I think that at the most fundamental level, what I’m trying to do is enable. That’s the goal. It’s not like cut head count, it’s like the… The first deal I worked on at 38 was with this developer, and our shared, levered pricing model was about 80 megabytes big. And that’s what we used to communicate value, because each of the projects was priced to a levered return. And so, when you do that, whether it’s real estate or solar, if you want to price 30 projects to a levered return project by project, you’re breaking out production, you’re breaking out revenue, you’re breaking out every single line item that might be a single project, into like 30 different versions of that. It makes the modeling so much more complicated.
Oliver Beavers (31:25):
And so, I came back to my counterpart there two or three months ago, and he was one of the first investors, and I started showing him what I was doing. And the reaction that gave me the feels the most was I might actually be able to think on what I’m doing. If you can get rid of all of this mindless process stuff, that prevents you from actually learning commercial skills, it really changes the game in terms of how you approach markets, how you approach underwriting, how you develop skills, how you integrate that with the contracts, and the broader deal skills that you need to learn.
Oliver Beavers (32:15):
Sam, I think you actually really hit it on the head with the first principles. That’s something that I spent a lot of time on, and the first principles this area of transaction finances, that at the most fundamental level what we do for a living as deal people, is it’s inherently structured The legal contracts, the acquisition agreements, the credit agreements, they are remarkably well structured, and they follow this sense of definition that’s only really enabled through semantics. You have the words and the logic to express that. Whereas Excel on the other hand, is completely unstructured. It’s just a bunch of cells referenced together. So, the question that I eventually started to ask, was what happens when you take structure and tie it to structure in the language itself?
Oliver Beavers (33:08):
And all of a sudden, you’ve got these abilities to integrate the model directly into the deal documentation, so that your outside legal council on the deal is no longer at arm’s length away, and they constantly know what deal they’re driving. You can integrate it directly into the investment memos and docs, and just click a button and update stuff. But there’s this whole world of having something where logic reflects reality and semantics, that can enable… Just to go a little bit more in the tech world, you can turn that into a smart contract, and all of a sudden you’re in the whole decentralized finance space, because you can. And so, what originally started out is something that was like, I can really make deal work better for people, and that’s still the absolute goal, has turned into, well, wait a second. Excel kind of acts as like this gate or this door to a much broader integration into the rest of the finance world and these insane opportunities, because it has no structure.
Oliver Beavers (34:20):
And so, if I provide that structure, it kind of just opens up these opportunities. And what you start realizing is that it’s not just about team efficiency, but there is a clear path to just reducing transaction costs on deals in a meaningful way, that give you pricing value in a competitive market. And so, it’s been fun to explore that. The focus is still on making this easier for everyone as soon as possible, just to make deal work not suck as much as it sometimes does.
Spencer Burton (34:56):
So, what’s the roadmap for Phosphor? You have a prototype at this stage, you’ve shared it with us, it’s really cool. A lot of work, I know, still needs to go into it before you have something that’s ready for, call it market. But what’s your roadmap, or at least call it one year plan here for Phosphor?
Oliver Beavers (35:18):
Yeah. The one-year plan, because I spent like five years on the actual engine and language itself, a lot of it is coming together pretty quickly. You’re just adding this front-end product layer, and that will be refined over the next six months or so. I’m hoping to have an actual, not an MVP, but an alpha where all of its features are complete for the most part. And people can start, like early customers and investors can start testing it out and identifying major areas to work through. Some of that stuff I’m excited to work with you guys on. But then by the end of this year, the real goal is really to take that core modeling product, and expand it into this GitHub of finance concept. Where all of this information on how to do deal work, how these various financial structures is so closed off right now. Just because there are a whole bunch of firms that make a pretty serious amount of money keeping that closed sourced, as we call it.
Oliver Beavers (36:27):
And so, the thing that I’m trying to do as someone who worked so hard to enter the area of the industry that I did, was make that available. Make that available not just to companies, which will be awesome, because all of a sudden you get something that you would have paid a lot of money for, almost for free. But for people who were actually trying to learn, all of a sudden… One of the things that I’ve seen just as I’ve gotten into the software engineering world, is that as finance people, most of what we do is resume and LinkedIn-driven. But in the software world, what people do to build up their resume is they publish their own work on GitHub to present publicly.
Oliver Beavers (37:18):
So it’s like, well, what if I can do that for incoming senior, people looking for a job? Whether you’re in college, in business school, in masters of real estate, whatever, you’ve taken the time to think through a good business idea, or an acquisition or whatever. You can actually publish that out there, so that people can see your work and maybe expand on it. But it’s a different way to build up this kind of resume and profile, that I think I would have had a much easier time entering the market if I had the ability to do that.
Spencer Burton (38:02):
Yeah, this is an important point. This is actually what got me most excited about Phosphor is, and if you’re listening into this and you’re familiar with the ACRE mission, it very much aligns with our mission. Which was when we started ACRE, it was all about essentially open sourcing real estate financial models. They were closed prior to that, it frustrated the heck out of Michael and I. It’s like, how are we supposed to learn this skill, if we can’t even see what an institutional quality model looks like? Well, let’s go out and share it with the world for free, and that’s what we did. And Excel, as inadequate as it is, is the vehicle that we use now. And so, we’ve published this library over the years.
Spencer Burton (38:44):
I’m excited about the prospect of, and if you’re unfamiliar with GitHub, it’s essentially a repository of code. So if you build code, you can either share with an organization, or you can just keep it for yourself, or you can share it with the world. And the world can go and see your code, can use your code in their own programs. Well, imagine, in the smaller context of a model, Spencer Burton puts out a single-tier cash-on-cash European-style waterfall. And I put it out, but in a Phosphor module, that can then be used by anyone.
Spencer Burton (39:22):
Or to echo Oliver’s point, you’re a second year in your MBA program, and you want to prove to employers that you are proficient. You build a model, make it public, publish it as Oliver said, and now you’ve got a work there, that you can put on your LinkedIn resume, that you can share. In fact, that’s one of the reasons Michael and I both were able to get our jobs out of grad school, is we had this public body of work that showed that we had the technical skills to do this work. So anyway, I think it’s really exciting. I know we’re running out of time, Sam.
Sam Carlson (39:56):
Yeah, no, this has been fantastic. Again, as a person who doesn’t do financial modeling in my day to day, and really doesn’t quite understand the details or the nuance of it, it’s been fascinating. I absolutely see the implication. I think people who are listening, they’re probably going to be very excited. So, the next steps for Oliver, for the Adventures in CRE team here is, we’re helping Oliver develop this, and consulting in the niche where we have proficiency, as he builds this tool. And we’re looking forward too, to see what comes of it. And we’ll absolutely let the audience know how it’s developing and what’s going on, and bring Oliver back on when things are, the next step. So, thank you Oliver for coming, and it’s been a lot of fun. I think people, again, have really enjoyed it. So, thanks everybody and we will see you on the next podcast.
Thanks for tuning into this episode of the Adventures in CRE audio series. For show notes and additional resources, head over to www.adventuresincre.com/audioseries. Would you like to learn real estate financial modeling in a matter of weeks, and do it with zero guesswork? If so, the ACRE Accelerator is for you. The Accelerator is a step-by-step case-based program, designed to teach you exactly what you need to know, and in the order you need to know it, so you can gain both the knowledge and experience to take your career to the next level. To see if the Accelerator is right for you, go to www.adventuresincre.com/accelerator.