Automate RE Underwriting with Marc Rutzen

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Marc Rutzen  0:00  

It's not going to be the first or the second or the third or maybe even the hundredth but if you keep looking you keep trying you're going to find something that works and you know it's it's true and real estate is true and tech to

Taylor   0:12  

welcome to passive wealth strategies for busy professionals. Today our guest is Mark Rutten from in noto Inc, this is a discussion for the real estate center, syndicators and lenders out there who need a faster way and a more effective way to analyze more deals in less time. Anybody that's been out there spending time investing time, looking at deals, getting deals sent to them by brokers, spending the time looking at them, and just to go back to the broker and, you know, negotiate or try to break things down or just, Hey, I'm not interested or pursue it further put out an LLC and close on the deal. 

Anybody who's done that, you know, how much time it takes to sort through all of those deals, especially if you're really Getting into the really running the numbers. It's it's brutal. We get into the time in this episode that they've quantified how much time that their analytics software saves. So listen for that it's huge. But if you're in that world, you're in the syndication world, you know how much time it takes. This knows technology can save you a lot of time on analyzing your deals and do a better job because they're working with more data. So Mark is going to tell us all about how that works, how much time it can save, what their analytics software does, how it works, where it pulls the data from, all that great stuff. 

So it's a very, very futuristic conversation, I guess, in many ways. So I hope you enjoyed the episode. If you are please leave us a rating or review on iTunes. Very big help, subscribe wherever you get your podcasts. And without further ado, here's our interview with Mark Rutzen. Mark, thank you for joining us today.

Marc Rutzen  1:57  

Absolutely. Thanks for having me.

Taylor   2:00  

So can you tell us a little bit about your background before we get into the platform?

Marc Rutzen  2:06  

Yeah, so I started in real estate now, eight years ago, and I got in and not the ideal time to get into real estate 2011. So trying to do real estate development back then. And I quickly saw now, you know, aside from the whole market being terrible thing, real estate was very inefficient. You know, I saw how much manual data manipulation and manual calling and emailing to get data was going on. I thought, Man, there's got to be a better way to do this. Right. 

So I started getting into tech, learned from and development myself, you know, sort of teaching myself enough to be dangerous, really, I'm not going to program it myself. But, you know, I know how to find the right people to do the job of building tech platform. And then I combined the real estate knowledge with that technical g knowledge and and built a noto out of out of that

Taylor   3:06  

great, perfect, perfect introduction and perfect transition into and noto and it's a it's a very unique platform. Can you tell our listeners about kind of the basics of what your analytics platform does?

Marc Rutzen  3:20  

Yeah, so what we do, it's kind of twofold. On one side, we're workflow automation. So your rent rolls your key 12. When you were analyzing a deal is very painful to get a PDF and not and have to manually go through and transcribe that to excel, check every number, what we do, we take the process of doing that whole manual data entry. We do it in seconds, you upload your PDF rent roll, your T 12 operating statement, we're going to parse out all the relevant data, pull that into Excel, pull it into online interface to show you trends, with leases and floor plans and all that and really give you solid insights. 

I'm the operations of a property you're looking at. But the beautiful part is we take that data from everyone who's running through the workflow automation component, and we train on it to predict. So we apply machine learning to it to predict what your rent actually should be relative to the market, what your expenses should be. And if you're doing a value add, you know, what are you going to get for those value, add amenities? Should I do the granite countertops, hardwood floors, what's going to be the bang for my buck, so to speak, and we can do all that with machine learning applied to that data we get from essentially making people's lives easier. And that's that's kind of how the platform works.

Taylor   4:40  

Cool. There's a lot of promise in that and and machine learning is I don't want to say a buzzword or anything like that, but most people don't really know much about it, if anything if they've heard the term before. Can you describe what that is what the machine learning process Concept means

Marc Rutzen  5:02  

Yeah, yeah. So um, it's it's kind of straightforward. I mean, so when you average you go into a market, you're looking for comps, right? You want to see red car to see what rents do in the market. You're looking at things like the year built, the number of units, the proximity to the subject, what amenities are present or not present in the in the subject property. So basically the process that you go through of finding what is really a red cap, what is actually to be used as a rent comp, you're looking at the year built, the number of units proximity to subject the amenities that the property has or does not have relative to the comps. 

All of that is a judgment call based on what you know about the subject and what you know about the comps. We just automate that process right we know that you haven't done it one way we know to wait those decisions based on the way you You've done it in the past. And the way every other underwriter has done it in the past, what importance to the place on those variables when they're underwriting using our platform? 

We basically take that information and you've coded into a set of rules that allows you to automatically underwrite it. So it's, it's encoding human experience into a computer and then spitting out the result for you, without you having to manually update all those assumptions, if that makes sense. Hmm,

Taylor   6:30  

okay. And you have a fairly significant data set I figure to work is when

Marc Rutzen  6:38  

this point, we're looking at about 2 million properties nationwide, that are updated on a weekly basis in our database.

Taylor   6:47  

Now that's, that's very helpful. Do you like where are you getting that information from? Is it mainly from your users? Is it from outside sources like costar where's that information? Coming to you from.

Marc Rutzen  7:01  

So we get it mostly from our users. So we've got some great lender partnerships across the country. And, you know, as we were talking about a little bit before the show, like lenders are a great source of data, because they're underwriting so many deals for, you know, every deal that's thrown in front of them. 

There's like five people approaching them to underwrite that deal for four different you know, borrowers, and then those borrowers are going to five other lenders at the same time. So you know, the level of underwriting that occurs for any deal that's out there, lenders are doing it like crazy. And so that data is really good source from those partnerships that we've got. Then we've got integrations with the Yardi page that we've pulled data in from directly from the owners, which is great. So we get those updates every single day. And we've got some really big national portfolios integrated that way. 

And then the rent rolls and t 12. People are just uploading every deal that they do every syndicator every lender who's on the platform. All that goes in and trains our algorithms and helps us get better and better.

Taylor   8:13  

Big Data and real estate investing, it had to catch us at some point. It's good that it has now. Can you walk me through the process? From the syndication standpoint or the lender standpoint of actually using the platform and the benefit that you either syndicate or the lender would get out of using your platform as opposed to Yeah, XL model we might have developed?

Marc Rutzen  8:40  

Yeah, so what we're going to do, so say you've got a deal in front of you. There's two cases one you don't have a deal and you're just perusing the market to is you have a deal and funny. The more common use case for us is you have a deal. You don't know if it's a good deal or not. So what we do will parse your rent roll and t 12. 

You just upload them to the platform. go through a few simple confirmations will tell you what the red trends and the expense trends look like at the property very quickly, you'll get to not only what is the income, but is that income stream reliable, right? 

Am I going to get hit with a ton of lease renewals in a month, and then all of a sudden my income doesn't look the same, right? So we'll pull that information up front, then what we do is we put that information in the context of the market. So we use the parameters that the data on your property to surface the best rent comes the most statistically comparable rent comes. So we're looking at things like your built number of units the same as, as anyone who's looking at rent comes with, but we're doing it automatically and we're doing it for every single property in the market simultaneously. 

And so we do we serve up the most statistically comparable comps, you select them in the interface, you go through and look at expense benchmarks in the interface, you can see are they expensive The property historically on par with other properties that are similar in terms of size, age, etc. And then once you've you've judged Okay, this is actually checking the boxes for me this looks like a good deal. Then you can export and you've got a full export with a waterfall model. You can look at you know, different syndication terms and see how it's gonna affect your returns. So it's, it's a pretty streamlined process, start to finish it takes a little bit of learning just because you know, there's a lot to it. But you know, the people that use it to swear by it, use it on all other deals.

Taylor   10:39  

Well, you know, I can see that because messing around with Excel has been trying to underwrite properties. Yeah, probably the worst part of buying real estate is just the punching stuff into Excel.

Unknown Speaker  10:53  

Now, if I,

Taylor   10:55  

how can I incorporate her I could an investor out there using the platform in corporate Their own judgment into some of these things. Maybe I see that the hypothetically at the platforms, recommending these three comps in the area. 

And I'm confident that based on my understanding of the area that one of those three is not a valid cop, because it's on the wrong side of the tracks to use that. Yeah, turn a phrase. But can I incorporate that judgment into the numbers?

Marc Rutzen  11:26  

Oh, yeah. So what's cool about it we so we, for some context, we learned that lesson hard right up front, we're like well, this serve the expense comps and or the red cops and they'll just go with it. It'll be fine. That did not happen. That's not how that played out. Not even close. So people are they said right off the bat. That's not a comp. That is a comp. You missed this one. You need this one. Get rid of that one. 

I would never come to that. And then we learned Hey, we have suggested comes and we The suggestion and then you pick which ones you actually want to include in the analysis. And if we missed one, you could actually search that address and pull that data in for that comp, even if it's not in our suggested comps, that really helped adoption because people were no longer pissed off. They were more, hey, you know, I'm in the driver's seat. This is, you know, you're kind of cool with your predictions. They're great. But mindset is still what's driving the analysis. And I think that was a big, big factor for people, huh?

Taylor   12:33  

Yeah. Now, I mean, in anything like this, we need to understand the benefit to us. And it takes a lot of time to mess around with Excel and interpret, you know, offering memorandum and gather comps and all this, all these things. 

So, you know, are we saving? Are we getting a lot of time savings out of using the platform or is it quality of data thing, we're getting better data out of the platform that we might collect on our own, or what I would like to cover the top benefits that? Yeah.

Marc Rutzen  13:06  

So it's it's both. So it is the quality of the data and it is time savings. And so we actually just a week ago, we we completed a time study among our lenders, which again, they're super high volume so that they have to move fast. By default to cover how many loans are they're actually looking at. So 50 minutes across the board was the average time to spread a moderately sized PDF rental. So this is like an average of 200 units. It takes 15 minutes to get that from something you got to transcribe manually into Excel. That part is terrible. Yeah. That is that is the worst like entry level analyst job and sometimes you just don't have a team of people to send it off to, to, you know, manually do that. 

You gotta do it yourself. Oh, yeah. We take that time from 15 minutes to five and a half minutes. Right and That's, that's including the upload to upload to the system, you check boxes to confirm things. And then we spit it out and you can download an Excel. That's all the way getting it from a PDF into Excel. So you could work with five and a half minutes. And with with 12 operating statements, similar but potentially bigger savings, depending on how many you're uploading. Our lenders would do four at a time because they are going to do the 12. 

And then they get to do three years of historical at five minutes was the average it was taking them before. Now it's not a 17 minutes with a note. So that's that's real time saving, especially when you're doing high volume you're looking at you're not closing every single deal you look at you're looking at 100 and closing one, right if you look at 100 deals, you're going to spend hours and hours and hours reading read rules and t 12. And we're helping you do it in a few minutes.

Taylor   14:56  

Yeah, that's a huge savings me when you think that The time savings on just one potential deal. And for both syndicators, you having to look at 100 deals to actually close on one or two. It's a huge amount of time. Definitely. Yeah. And these lenders, I can imagine, I don't know what the average salary is of one of those even low level analysts that's transcribing all this information, it gets expensive. It still gets expensive still gets expensive, so Well, that's great. I mean, so I mean, what kind of adoption Are you seeing amongst the investor crowd? I mean, is it? Is it more popular with bankers or investors? I mean, what kind of your balance of clientele, really,

Marc Rutzen  15:45  

we're, we're kind of a two tiered market. So we've got the lenders that are using it as like a key component of their workflow and they're running all their deals through it. And we've really found that like, we resonate with the top of the market lenders We have very, very high volume. So like the top 10 lender crowd in like the country for multifamily. They eat this up. They love it, because the benefits in terms of time savings are just so large. But we also resonate with the syndicators because the syndicators, they don't have a team of analysts to give this stuff to. It's just it could be like a one or two man army. And that means they have to manually process those rent rolls. And they have to look at they have to go gather comps and they have to get the data. 

So for the syndicators, it's more, yes, it processes the stuff faster. But then you can also look at where do I sit relative to the market, you're getting, you're looking at your expense benchmarks, you know, so how is your property performing relative to similar properties in the market? In terms of expenses, you're looking at your rent comps, how is my property performing relative to similar size, your built amenity profile properties in the market and if I want to look at a different level, like more highly a monetized property. 

I can actually pick the more highly a monetized comps the recently renovated comps and actually see what my new price points going to look like in terms of of that kind of step up, right so syndicators love it for that. And we've got you know, the Neil bowels of the world we get tons of multifamily us students on we've got Michael blocks on the platform, Charlie Dobbins within a couple podcasts and presentation, Charlie. So there's a lot of different groups and the syndication realm that we're working with and we're only continuing to expand there.

Taylor   17:38  

Yeah, some of the some of the names and you mentioned, Neil, I mean, he's a he's a very tech guy himself. So

Marc Rutzen  17:46  

that's a natural fit, I'd say because he from day one, we demo it for me. He's like, I get it. I get it. You don't need to keep selling me on it. I get this. All right. Let's work together. Pretty Pretty cool. Pretty cool stuff. There. doing those guys are they're expanding really rapidly,

Unknown Speaker  18:03  

actually. Yeah.

Taylor   18:06  

Yeah, he's grown. He's grown very quickly. And I'm curious, are you keeping track of the number of deals that say your syndicators or your bankers are actually closing having underwritten them on your platform? I think that would be interesting to follow.

Marc Rutzen  18:24  

Yeah, we need to pull our new numbers because they're changing all the time. But we've had like, we're more tracking it at the document level and we don't necessarily follow it through and survey them afterwards and say that did you close it or not? But you know, we had like, couple thousand documents like rent rolls and t 12. uploaded in the last two weeks. We're doing kind of like a two week cycle on our KPI reports. So I mean that that number is growing to you look at the beginning of the year was a couple hundred our total couple thousand 2019 would have really taken off in terms of the volume that's going through the system.

Taylor   19:06  

That's cool. And how many employees do you have?

Marc Rutzen  19:10  

At this point? We're just 12 it's not a huge team with 12 we can get. I mean, that's, that's 10 programmers and data scientists, and just me and one other person that are not. Well, I'm kind of technical, but I'm non technical. 

So, yeah, it's a lot of r&d. It's a, it's a lot of trial and error and figuring out what's going to work, how stuff's going to resonate with the market. And then, for me, I do my best to provide real estate expertise and and make sure we're building something relevant.

Taylor   19:43  

That's awesome. Very, very product driven and not, you know, I, you know, I'm a salesperson, you're not a bunch of sales people. It's more right product development. Definitely. Yeah, that's great. So we're gonna take a quick break for our sponsor. We've got three questions we ask every guest on this Are you ready?

Marc Rutzen  20:01  

I'm ready.

Taylor   20:02  

Great. First one, what is the best investment that you've ever made?

Marc Rutzen  20:07  

The best investment I've made has got to be a note. Oh, I mean, man, we it's been a really fun ride. I've learned so much in the process but so many really great people that are doing great things in the industry. And the sky's the limit. I mean, there's no no telling what's going to happen next, but it's been a really fun ride so

Taylor   20:28  

far. Cool. That's great. On the other side of that, what is the worst investment you've ever made?

Marc Rutzen  20:34  

I'd have to say that the first house I bought well second house I bought technically

Marc Rutzen  20:42  

was not the best investment I probably like slightly less than broke even at the end of the day because we just we love doing the projects and around the house I renovated like everything myself over the whole period. I must have sunk like so many thousands of man hours into like, hand renovating stuff. And then at the end of the day, it was like a small tri level it was never going to sell for that much more with so I ended up making in, you know, 50 grand more than what I paid for it, but I probably ate up most of that and renovation costs, man. Yeah, I wager with my labor I certainly lost money at the end of the day. But it was a great experience in terms of learning construction,

Marc Rutzen  21:28  

that I can say,

Taylor   21:30  

Well, you know, life goes on and you move on with that lesson. And that leads into my favorite question is what is the most important lesson that you've learned in investing?

Marc Rutzen  21:43  

Most important is perseverance, I would say because it's not going to be the first or the second or the third or maybe even the hundredth but if you keep looking, you keep trying, you're going to find something that works and you know, it's true and real estate is true in tech, too. we've, been down so many times when trying to develop features and they just don't resonate with the market trying to figure out what's the best next thing to do. Not always certain not ever easy but definitely worth continuing to move forward.

Taylor   22:17  

I love that lesson. I like that a lot. So if people want to learn more about the platform, where can they go if they want to learn more about you? Where can they go? fill us in?

Marc Rutzen  22:28  

Yeah, so go to enodoinc.com It's enodoinc.com in there, you'll actually be able to sign up for a free trial of the software. So that's, that's a huge benefit right there. run some deals through we're eager to hear your feedback. So let us know what you think. And yeah, you'll see past articles you'll see blogs, you'll see all sorts of content. So definitely check it out

Marc Rutzen  22:55  

in odio I NC calm and keep in touch.

Taylor   23:01  

Awesome. Well, thank you for everything today. I think it's a very interesting concept and opportunity. It sounds like it is there's a lot of value in the platform and the software, the technology for the users. So that's fantastic. I'm glad to see that. The industry, especially more like independent real estate investors are moving on from XL models and are getting

Marc Rutzen  23:30  

Yes,

Taylor   23:30  

yeah. Getting into more and more detailed and sophisticated modeling. So, so that's great to hear as well. And yeah, thank you for joining us today.

Marc Rutzen  23:42  

Absolutely. Thanks for having me.

Taylor   23:44  

Everybody out there. Thank you for tuning in. I hope you enjoyed the discussion. It's It was great for me, I learned a lot here in our in our short time together. If you're enjoying the show, please leave us a rating and review on iTunes is a very big help. You know anyone out there that could use a little bit more powerful. massive wealth in their lives. Please share the show with them and bring them into the fold. Once again, thank you for tuning in. I hope you have a great rest of your day and a great week and we will talk to you on the next one. Bye

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