Marc is the Co-founder and CEO of a company that created a software to help automate the underwriting process. Enodo will save you time and money in your underwriting process by taking out a lot of the small, tedious tasks that right now, a human does. Hear exactly how his company may be able to help you. If you enjoyed today’s episode remember to subscribe in iTunes and leave us a review!
Best Ever Tweet:
Marc Rutzen Real Estate Background:
- CoFounder and CEO of Enodo, an automated underwriting platform for multifamily real estate
- Enodo helps users analyze more deals in less time and make better investment decisions backed by data science
- Based in Chicago, IL
- Say hi to him at enodoinc
- Best Ever Book: The Lean Startup
Get more real estate investing tips every week by subscribing for our newsletter at BestEver NewsLetter
Made Possible Because of Our Best Ever Sponsor:
List and manage your property all from one platform with Rentler. Once listed you can: accept applications, screen tenants, accept payments and receive maintenance tickets all in one place – and all free for landlords. Go to tryrentler bestever to get started today!
TRANSCRIPTION
Joe Fairless: Best Ever listeners, how are you doing? Welcome to the best real estate investing advice ever show. I’m Joe Fairless, and this is the world’s longest-running daily real estate investing podcast. We only talk about the best advice ever, we don’t get into any of that fluffy stuff.
With us today, Marc Rutzen. How are you doing, Marc?
Marc Rutzen: Hey, I’m doing great.
Joe Fairless: I’m glad to hear that, and welcome to the show. Marc is the co-founder and CEO of Enodo, which is an automated underwriting platform for multifamily real estate. It helps users analyze more deals in less time and make better investment decisions, backed by data science.
Based in Chicago, Illinois. You can learn more about his company after this show at his website, which is EnodoINC.com. With that being said, Marc, do you wanna give the Best Ever listeners a little bit more about your background and your current focus?
Marc Rutzen: Sure. My background is actually in real estate. I got my MSRED in 2011 from Columbia. I went into a really bad market for development at the time. In 2011 there wasn’t much going on, but I did get to work on a few projects to masterplan community developments, federally qualified health centers… I worked in brokerage, in real estate consulting for five years and saw how inefficient the real estate industry is, and during that time, I learned some tech, learned frontend development and learned how to manage a technology team.
I did a few consulting projects on the tech side, a few mobile apps we developed, and ultimately came to the process of creating Enodo, because the way we analyze deals today is completely ineffective, and it requires a lot of money on analysts who then pull data from that software and then analyze it themselves, and then you still pay an appraiser to try to make sense out of it completely objectively on top of that analysis you do. It’s very costly, very labor-intensive, and it produces poor results at the end of the day. We saw that in the results of Enodo that it solved those problems.
Joe Fairless: Where does the labor go in a typical underwriting process, that disappears in what you provide?
Marc Rutzen: If you’ve ever dealt with a Yardi rent roll or RealPage, with all the cost codes broken out, or say you receive a T12 rent roll for a property that you’re considering investing in, and you need to map their line items to your chart of accounts – well, there’s one time savings right there. You just upload your PDF, rent roll and T12 rent roll, and we instantly parse it and map it to your chart of accounts, so it’s underwritten the say way you would underwrite a deal, except you don’t have to do any of the PDF to Excel converter, and copying and pasting, and parsing, and adding and averaging and all that. So there’s one.
Two is we tell you what your statistically most relevant comps are, and do the comp survey in the platform almost instantly. So we pull in their rents from their website, we tell you when we got the data, where we got the data, and help you compare on an apples to apples basis your property to your competitor market, very quickly in the platform… It’s almost instantaneous.
Then finally we actually benchmark operating expenses as well and rent as well, so we tell you what you should be able to generate in terms of rent based on everything that’s going on in the market, your competition, your demographic, demand drivers – we take that into account for your rents. Then for your op-ex, we actually benchmark you among similar properties in terms of your total number of units and all that, and tell you if you’re under or over-performing in each individual line item.
Joe Fairless: Wow, that is impressive. I’m looking forward to digging into the assumptions in each of these, because I think that’s probably gonna be the key to just learn more about this. The first thing you mentioned – I don’t know if there are any assumptions, because it’s just a helpful tool where converting PDF into Excel, and then copying/pasting and making sure the line items line up… That’s a process that my team does, and it sucks, so that certainly would be helpful there, so there’s really nothing else to say about that, I don’t think.
But as far as the statistically relevant rents, in order to determine what the rent could be, I imagine you’re gonna have to know what we plan on doing to the property, because if I’m doing a $500 upgrade versus a $20,000/unit upgrade, that’s gonna influence what type of comps you provide… So how do you reconcile that?
Marc Rutzen: Absolutely. One of the things we do that nobody else does is we break down the individual contribution of each different amenity to the rent you can generate. So we tell you, if you buy a property and it’s got some old lemonade countertops, it’s got a dilapidated old refrigerator, it has no real common area amenities… We could tell you that if you install granite countertops, you’re gonna get $25/unit a month. If you put the stainless steel appliances in, you’re gonna get $12. If you put in a rooftop deck for the residents to use, you’re gonna get $13. We actually go to that level and quantify it.
So you’re talking about it in terms of dollars that you’re gonna invest, but the residents think about it in terms of the features that they’re gonna use, so there’s not always a direct correlation between spending money and earning returns, and what we try to do is tell you “If you were to do this, this and this”, what should the return be based on everyone else that has those amenities in the market, and their comparability to your property.
Joe Fairless: That’s really interesting. So how do you know what each item will achieve on a premium?
Marc Rutzen: This is the core of our algorithm, but simply stated, what we do is we look at the market that you’re located in down to the census tract level, we look at the types of people and the types of properties… So the median incomes, the population, the percent college educated – all that. All the demographic data that you would collect if you were analyzing a market.
Then we look at the distribution of the different unit types, the rents that they’re generating in that particular market, and we reach a demand and supply equilibrium.
Then we look at each adjacent census tract and we kind of expand out. If it’s similar in terms of the people, the supply of buildings and the demand from the people, we gather that market and we add it to what we call the market cluster. Now, once we’ve built a market that has enough data to predict, it’s basically souped up regression from there. We hold everything else counted but the single amenity that we’re trying to analyze, and if the only thing different between group of properties A and group of properties B is that one has a pool and one has not, we isolate the impact of that amenity, and then we go to the next one, and the next one, and the next one, so we quantify the impact of each different amenity statistically, and we give you variance around that, too… So it’s like $13 plus or minus $3.
Joe Fairless: That’s fascinating. When did you launch?
Marc Rutzen: We’ve launched May of 2016, [unintelligible [00:08:11].06] and we’ve raised 2.1 million during that time.
Joe Fairless: Congrats on that. I understand why you have raised the money that you’ve raised. Help me understand – I know you’re not a lawyer, but web scraping – is that legal?
Marc Rutzen: Some sites allow it, some don’t. We go to the ones that are okay with it, and one of the things that we do that is different from anyone else — because all the companies out there are web scraping, or they’ll call it data mining to some extent… What we do to get around that is when we go to the individual property websites – there’s about 40,000 nationwide – and we sit on the actual property website… So it’s not like there are thousands and thousands of people going to a single property website, so for them it doesn’t matter so much that we’re able to collect the data if it becomes available; it’s straight to the source, and it’s minimally disruptive.
Two is we get the data from the users. When they upload from their property management software, they can upload a whole portfolio in one fell swoop, and integrate with Yardi, RealPage – and we’re building Entrata as well – and then have all their properties on the platform to analyze; that’s a big data source.
Then the other is just from the user when they upload rent rolls and T12’s. [unintelligible [00:09:33].21] nothing that you would not consider fair game goes into the platform for anyone else to see, but we could train on all of the data and we can display the market rent, the advertised rent on their rent rolls. When we do that, we’re able to get the freshest possible data straight from the source. That’s kind of how we can get around the issue of having to get too much of the data from web scraping.
Joe Fairless: How have you evolved the features from when you started in 2016 to today?
Marc Rutzen: The biggest thing is the score… So in the beginning, we started with this idea that there’s a composite score that can tell you the investment potential of a property. And we still do wanna have that, that kind of sums up the potential of the market, the potential of the property based on other properties in the market, the long-term growth potential… So that was Enodo’s score at that point, and what we found was that our users told us “No, I really wanna know how should my rent look compared to the competition” or “How should my operating expenses look? Is this a deal that I can make money on or not?”
When you build that trust in the underwriting, you become a tool that people go to to underwrite deals, and they understand the predictions, they trust the results, and then you can distill that into a composite score, and people will trust the score. Otherwise, [unintelligible [00:11:01].16] to say “Buy this property because it’s a 99”, without any other information on it.
So that’s the biggest thing that I would say — we start now with the underwriting and we work towards the score, versus vice-versa.
Joe Fairless: On the operating expenses benchmarks, how do you know what other properties are operating at?
Marc Rutzen: Our users, as they’re uploading – we’re getting more than 100 T12’s. And for some context, we released the T12 rent roll parsing not even two months ago, and now on a monthly basis we’re getting more than 100 T12 uploaded, so that’s helping us benchmark at a very granular level, because it’s actual deals, actually uploaded by users.
In addition to that though, we’ll look at the NAA and [unintelligible [00:11:49].12] benchmarks by market, and use that to inform our algorithm so that we’re able to adjust for regional differences. We may not have universal coverage from a few hundred uploaded T12’s, but we’ll get very granular in certain markets and then be able to extrapolate that to other markets because of the benchmark data that’s available nationally from [unintelligible [00:12:09].09]
Joe Fairless: What are some regional differences in operating expenses that you’ve seen?
Marc Rutzen: Florida insurance is 3 to 4 times as high as my hometown of Chicago. On the flipside, Chicago taxes are 3 to 4 times higher than everyone else a lot of the time. Chicago is notorious for that.
Joe Fairless: Yes…
Marc Rutzen: We see stuff like R&M, and salaries of the personnel being a lot cheaper in the South and a lot higher in cities like New York… In larger cities, I would say, because the labor costs are higher.
Joe Fairless: And when you say R&M, is that renovations and maintenance?
Marc Rutzen: Repairs and maintenance. One of the things we see that’s really unique is people call everything something slightly different everywhere you go. There’s no cohesive way to underwrite a deal. Everyone’s got their own way of doing it, and a part of what we wanna bring is that standardization, that this is called “Repair and maintenance”, or “Repairs and maintenance”; this is the one we’re gonna use, and everyone is gonna use that. We get some pushbacks on that… So we’re trying to build it in such a way that everyone gets exactly the way they wanna analyze, but also has a standarded benchmark, too.
Joe Fairless: On that note, how do you take a PDF that’s calling it Repairs and Maintenance, but then your spreadsheet or the person’s spreadsheet says “Turnover costs” or “Cap-ex”, or whatever? How do you do that?
Marc Rutzen: The system that we’ve put in place – and this is with great internal debate that we had about this, but I think it’s the right way to do it – is that the user can designate their chart of accounts, their specific way that they wanna analyze line items in our software. They can go into the admin section, they say “I want R&M to be included, I want Salaries and Personnel, I want turnover separated from R&M, I want landscaping, security etc” and you can put all these different line items exactly the way you want them represented.
Then when you upload a T12, what it will ask you to do is categorize the first, second, third times, categorize for that T12 where do you wanna drop those line items. But you do it two or three times, and then the next one you upload it picks up most of the line items and puts them in the bucket you want right away. The power of machine learning. You do it a few more, it catches almost 100% of everything and jumps it into the appropriate bucket.
Now, there will be some instances where gas or utilities or something are called Reimbursements and Expenses, and in those cases it’s gonna be hard for the machine to pick it up, because it’s based on the text; we’re building in more robust infrastructure to do that based on the value AND the text, so some of that will be handled in the software, but for the most part, when you upload a T12, if you have trained it by uploading a few prior T12’s, it’s gonna do all the categorization and mapping for you instantaneously.
Joe Fairless: And how much does it cost to have this program?
Marc Rutzen: Our base subscription price is $500/user/month. That’s for everything on the platform – that’s for the incremental impact of amenities, it’s for the comps and the instant rent surveys from the comps, it’s for the operating expense values, and of course, the T12 and rent roll parsing with that.
If you wanna do slightly less, we’re building tiers to cater to people that want a portion of that functionality, and we’re happy to have the discussion because we can turn off certain aspects of the interface if you don’t use it. As you see from the website though, a lot of our customers have been bigger national companies, and they do want the whole thing.
So we’re just now starting to figure out what those pricing tiers look like for smaller users, but $500/user/month, and then every user after that is only $250/user/month.
Joe Fairless: Who is this ideal for, and who isn’t it ideal for?
Marc Rutzen: Well, it kind of shapes out in our customer distribution. I think it’s ideal for obviously value-add investors; that makes a ton of sense. If you’re gonna get spend some capital, you wanna know what you’re gonna get on it. Developers and lenders have really shaken out to be two of the biggest customer groups though.
So we’ve got national developers — there’s a lot of uncertainty when you do a new development, because you don’t know how it’s gonna perform. For an existing property you can look at T12’s, but for the development there is not T12, there is no in-place rent to speak of. So developers like it because they can [unintelligible [00:16:44].01] a hypothetical property, put in the unit mix, put in amenities, and instantly see how they would perform in terms of rent, what their comps would be, and what their stabilized op-ex would be.
Lenders like it for the volume. You can do the same analysis that you do on our interface with our API without ever touching the interface; just query our algorithms individually… And lenders like to run many loan applications through it and see how should these be performing in terms of rents, and how should they be performing in terms of occupancy, [unintelligible [00:17:18].01] and then each individual op-ex line item, is it a good deal or not? For a lender, you can do that a lot more quickly with Enodo than manually.
Joe Fairless: And who isn’t it ideal for?
Marc Rutzen: I’d say property managers we have not had the best traction with; asset managers yes, because they can quantify individual pricing of amenities, and it helps them look at managing the portfolio a little bit more efficiently… But property managers – there’s a lot of products out there to help property managers, and sometimes on occasion they can be reluctant to learn a new software product with all the stuff they already have to contend with… So that’s what we’re run into, that’s kind of been the group that is least receptive.
Joe Fairless: What is your best real estate investing advice ever?
Marc Rutzen: Well, obviously, the first one would be to use Enodo, right? But my advice – and I’ve said this before – I think the way you build a product and the way you build a real estate investment company are similar to an extent; you start small, you build something or buy something that has strong revenue potential, and you try to make a good first couple of deals. Then you roll that into the next few – in my case – features for the product, roll that revenue into new features, new team members to build new features, and in the case of an investor, roll it into new properties that are larger and larger in terms of unit count, square footage, and all that… And then diversify into different verticals – in my case, catering to different people, and in the real estate industry case, different markets.
Eventually, you build from that strong base to go into different markets, and if you do it that way instead of trying to acquire way too much up front on the property side, or build way too much up front on the product side, you’ll have the incremental revenue there, the customer base to weather the downturns, and to keep building and to keep advancing… Versus if you did it all upfront, you either succeed big or fail big. A lot of times it ends up being that you fail big, because you didn’t really build up a strong revenue stream first.
Joe Fairless: We’re gonna do a lightning round. Are you ready for the Best Ever Lightning Round?
Marc Rutzen: Sure.
Joe Fairless: Alright, then let’s do it. First, a quick word from our Best Ever partners.
Break: [00:19:36].21] to [00:20:12].25]
Joe Fairless: What’s the best ever book you’ve read?
Marc Rutzen: I would say the lean startup is probably the best for me… It’s a roadmap to exactly how I’m running the company.
Joe Fairless: Best ever deal you’ve done real estate-wise?
Marc Rutzen: Probably a two-flat renovation… I’m not a huge real estate investor by any means, but I got it for very cheap from Fannie Mae in 2010, renovated it, and was able to lease it for pretty good rates, so I was happy with that.
Joe Fairless: What’s a mistake you’ve made on a transaction?
Marc Rutzen: I’m thinking about my development deals… One time we started negotiating with a landowner a little bit too early and revealed a little bit too much before we had the turnkey development that we wanted to build for, before we had the customer fully secured, and we ended up not getting as good of a deal as we thought, because they found out about that customer and then upped the price… So we lost some value there. That’s probably the biggest mistake I’ve made.
Joe Fairless: Best ever way you like to give back?
Marc Rutzen: To give back, I like to do a lot of presentations in universities about my career path, and how I went from a real estate person to a product developer and CEO of a technology company… Because it’s an interesting journey, and I think it helps young people figure out more certainly what they wanna do with their career.
Joe Fairless: And how can the Best Ever listeners learn more about what you’re doing and your company?
Marc Rutzen: I would say just check out this podcast first of all, and then EnodoInc.com. You can watch our explainer video, or schedule a demo and I’d be happy to walk you through the software.
Joe Fairless: Mark, thank you so much for being on the show and congratulations on the launch of this business… It certainly serves a need for groups like ours, and it is interesting to hear your thought process and the data behind each of these. It will be helpful for others as well, I’m sure, so thanks again for being on the show. I hope you have a best ever day, and we’ll talk to you soon.
Marc Rutzen: Absolutely. Thanks for having me.
