Rohan Weigel

Rohan Weigel, Vice President of Data and Operations at Realm, embraces the process of creating through innovation and digital transformation.

When it comes to the homeowner experience, problems often arise—from renovation issues to zoning questions. Join Rohan Weigel, Vice President of Data and Operations at Realm, to explore how Realm is utilizing machine learning, deep learning, and computer vision to offer homeowners more control over their investment than ever. Plus, discover some of the past innovations Rohan’s been part of and where he sees the future of real-estate heading.

About the Guest

Rohan is passionate about solving logistical problems with technology. He has worked across many industries including real estate, energy, automotive and the transportation of people and things. Whether it is designing launch plans for Uber's alternative transportation networks, or devising a data strategy for Realm to help homeowners to think about their biggest home decisions, he believes technology, data and a strong interdisciplinary team can completely transform any industry.

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Alison Dean (00:09):

TheoremOne Is the leading innovation and engineering firm for the fortune 1000. We design, build, and deliver enterprise-scale technology solutions and are very excited to present the Breakthrough podcast, an ongoing series where we interview technology leaders to share their experiences and perspectives on what's next in tech. Welcome To the breakthrough. I'm Alison Dean, VP of operations at TheoremOne. Today we are talking with Rohan Weigel, currently Vice President of Data and Operations at Realm. Previously, he held roles at Beepi, Uber, and SkyRise. He is passionate about solving logistical problems with technology. Rohan sent me this quote, "Forgetting is not caring enough to remember." Hi Rohan.

Rohan Weigel (01:00):


Alison Dean (01:02):

I want to know what the story is behind that quote.

Rohan Weigel (01:05):

Yeah. It can come out a little abrasive, I think at first, but it was a quote that my dad actually used to say to us and it was abrasive at the time because we were kids. I definitely took it to heart I guess but in a bad way. But over time, I used it to think about ways that I can make sure that the most important things either that I have to do in my day or that I need to remember about certain technology maybe that I don't fully understand at the time, that I really get to know those things. When you write something down or you really care that you want to remember that thing you will because you'll make sure that that happens. I really think that it's a great thing for everyone to live by. You can think about it as I'm just going to make sure that I absolutely remember this and it's been great for me. I think it's just great for the tech industry as well.

Alison Dean (01:52):

Right. I like that story bringing in the dads. All right. I want you to walk us through what a typical day looks like for you at Realm and share a bit about what Realm is doing for the world of real estate

Rohan Weigel (02:05):

Yeah. Great. It's kind of funny. When we first started, it was mid-COVID. We were all waking up rolling out of bed, straight to our computers, which was tough to begin with and actually got more tough after that. We decided, okay, well we're all at home so we might as well hire people wherever they are that are just the best at what they do. That ended up being having a team of people in five different time zones across the world. It's definitely tough. What I found is that everyone adapts and we've really become this strong team of people across data ops, data science, engineering, and data engineering. That team all works together for this common goal. For me, that's been really exciting because we're not all together in an office every day, but we've found a way to communicate and figure out how to build this technology from all different parts of the world, which is really exciting for me. That means I'm responding to things that have come in overnight and then also working with the team during the day to build out a product.

Alison Dean (03:02):

Now, tell everyone what Realm is all about.

Rohan Weigel (03:05):

Yeah. Of course. We're building the first platform for homeowners to really understand all of the big decisions that surround their homes. For 62% of homeowners, it's their biggest asset. There's a lot of things they can do with it. They can take money out of it to help their kid go to college. They can make renovations. They can decide to sell it next year or they can decide to live in it for the rest of their life. There's no one really who can help them with those decisions. Maybe their crazy uncle tells them, oh, you should renovate this one wall of your house. And then there's a broker telling them something and a real estate professional telling them something else. There's no one really to turn to that doesn't have a financial incentive. What we're trying to do is take all this complex data and really distill it into a form that customers can understand and then action on.

Alison Dean (03:54):

Okay. For the other 38% of people where their home isn't their biggest asset, what are those 38% spending their money on?

Rohan Weigel (04:02):

Well still this might be a big portion of their assets and it might be that they own multiple homes. This is the primary residence is their biggest asset. It still works for all of them. I think Realm is in this unique position to be able to help anyone that owns a home, whether it's someone that is looking even to buy one or looking to sell.

Alison Dean (04:22):

Right, right, right. Okay. In terms of the recommendations that Realm puts forth, I want to talk about that. If someone wants to do a new kitchen or do a new bathroom as relates to estimates being created and all that, is it AI or machine learning that's coming into play as a part of that estimation process?

Rohan Weigel (04:45):

100%. Yeah. We actually use a combination of machine learning, deep learning, and computer vision altogether. What we decided when we first started, and this was a tough decision to make was, are we going to build our own valuation model similar to Zillow and Redfin and all the other players or are we going to use them and focus more on the recommendations? And what it ended up being was, if we don't understand the current value of the house and how that's modeled, we can never tell someone what their future value is going to be. We've built this very complex model that takes hundreds of features from every home and uses those to come up with a really good estimate. We started building it in March and we're catching up to Zillow really quickly. It's really exciting. But what that's allowed us to do is it's allowed us to figure out what are the key features of your home specifically that make it worth something.

Rohan Weigel (05:35):

By understanding that, we can tell you, “Hey, we understand this house has had a kitchen remodel in the last two years and so while a kitchen remodel may be something you're looking to do, the kitchen's already new. Just because it's not your style, the return on investment that you're going to get from it is not quite as good as Anne down the block whose kitchen is from 1955.” It really just depends on what we're trying to do is give each homeowner a customized experience that uses all of the data that we know about their home or even data that they give us to give them that customized experience and return on investment, that sort of thing.

Alison Dean (06:13):

For the estimates that are generated, where does that data feed come from?

Rohan Weigel (06:18):

Yeah. We do a combination of buying and scraping data and really the way that we make that decision is again, we're in the business of helping homeowners understand their biggest asset. We're not as much in the business of creating data sets that already exist. If the data set already exists out there, we'll go out and purchase it from whichever data provider has kind of the best service.

Alison Dean (06:40):

That's cool. Okay. Since you've worked across so many industries, real estate, energy, I would love to understand if there's a common thread between them all for you.

Rohan Weigel (06:53):

The most interesting thing is if it's a good business, you always come down to what problem are we solving for customers? Sometimes that problem is not fully apparent or customers don't know that they have that problem. A great example of this is Bloom Energy, which is more of a B2B business that I was actually an intern at. Customers didn't know that this was a problem. There are so many data warehouses and service centers and things like that operate and take up an extremely high amount of energy. Bloom energy is really solving a problem before customers find out about it. I think that's kind of one side of it. Then there's the other side which is like, there's an obvious problem and customers really want it, which is Uber.

Rohan Weigel (07:34):

It was the best business to work for because it was the easiest to explain to my family when I went home for Christmas. Everyone's like, “Oh, you work for Uber. So cool. Tell me all about it.”  Coming home after working for an energy company, it's less exciting. I think every one of these companies is really solving something cool. All of them are doing it with technology, which I think for me is a must-have. I don't think I could go to a company that isn't working on a problem that they see that they can solve with technology in the next six months or the next five years, whatever it might be.

Alison Dean (08:03):

Do you think there are any problems that shouldn't be solved with technology?

Rohan Weigel (08:07):

That's a good question. It's probably the wrong question for me because I look at every problem and I'm like, this could be solved with technology. I was walking in a huge rainstorm today and I was like if only someone had mapped where all the scaffolding is, I can walk to work without getting wet. I think every problem can be solved with technology. It just depends on how much money it's going to take to actually make that happen.

Alison Dean (08:28):

I think the person executing probably has a big part of it too.

Rohan Weigel (08:32):

Of course.

Alison Dean (08:33):

Things can go in so many different directions. Right?

Rohan Weigel (08:35):


Alison Dean (08:36):

Okay. So Kristi Mansfield, who is the co-founder and CEO of Seer data and analytics, was recently on the podcast and has this question for you. “How is data helping your customers understand housing affordability and access to the housing market and what is the value of data to help new home buyers get into the market?”

Rohan Weigel (08:55):

Great question. I'll start with the first one, which is the affordability question. The most interesting parts are coming around the new legislation. For us, obviously, we're solving homeowner problems the most. Homebuyers and sellers are not as much in our core demographic. But what really makes us unique is the ability for a homeowner to understand their property a lot better. And that means understanding what you can do with your property as well. When you buy a property, you're not thinking like, “Oh, there's going to be a whole bunch of restrictions when I own.” You think of that when you rent, but not when you own. But the reality of it is, there is. one of the hardest data sets out there to both collect and then make usable for customers is zoning. What a lot of cities are doing right now is, is they're making zoning more accessible to put more units on your lot, to expand your home in a more cost-effective way, allowing for new building techniques and customers really don't know about all of these things that are happening.

Rohan Weigel (09:55):

What we're trying to do is figure out how to surface those to customers in a way that they understand it. And then also take that information and actually give actionable insights that have a monetary value. That has in the past been kind of reserved for builders and architects and home industry professionals that want to make money off the customer. People end up getting a bad deal, or if you're not savvy enough, you just kind of get screwed.

Rohan Weigel (10:22):

That's kind of something we're trying to end. For the home buying piece, we're definitely looking at that as a market, but for now, the real big piece of it is, if you're looking to buy a home, you want to know what it could be worth in the future. The nice example that I give about zoning specifically is, imagine two lots, one is on a corner and one is on the interior. They have different restrictions. If you think about a corner lot, you're like, okay, it's kind of the same distance away from the street all the way around, which means you have less space to build on your property. If you're buying a property and you're thinking of expanding your family and you want a bigger house, well, that corner lot might not work out for you. Understanding what that property has potential-wise is really important.

Alison Dean (11:05):

I think that's really interesting, the zoning element. I also think another aspect to home buying that I wonder if Realm is thinking about is the world of HOAs and the data around all those restrictions. Because I think for so many people that buy homes and could be entering into an HOA, it's kind of over overwhelming looking through all the different neighborhood documents that are associated with what are the restrictions and it would be great if there was a platform like Realm to synthesize some of that so that it makes it very clear like, oh, you want to build this, but here's how it would have to be on to meet the restrictions of this. That would be so interesting to really get a handle on that.

Rohan Weigel (11:44):

Definitely. It's definitely something that we have our eyes on. I think right now what we've been focused on is public data sets first, then user-generated data is our second piece. And then obviously the zoning data and data that customers don't really know about and nobody else has built. But when you focus on that user-generated data, I see the HOA data actually being fitting there. Where we say, “Hey homeowner, tell us about your HOA and we'll help you understand the next things that you can do with your house. And then once we do that, then we know all the rules for that HOA now for that whole neighborhood.”  Really we only need a couple of people across the country to give us that information and we'll be able to hopefully crowdsource it and help other homeowners in the neighborhood.

Alison Dean (12:26):

It'd be interesting for the different homeowners association bodies to actually be the ones sending you the data directly, just because it also benefits them to ensure that all their neighborhoods are staying compliant with what's going on. It's sort of like a win-win. Okay. I want to know, what does digital transformation mean to you?

Rohan Weigel (12:46):

I think we kind of touched on it earlier, but really it's taking something that either there's a customer problem or there's something that we're doing manually that we really could be doing better with technology. There's a lot of people that want that as well. You can figure out a way to digitally transform a lot of different things, but each thing is slightly different. I think the way that we've thought about it in the past is okay, let's figure out if this customer need is there first in a cheap way. And then after we do that, let's figure out how to build it. I'll give you an example. When we first started, we were specifically looking at accessory dwelling units in California. We were trying to determine if we could build them there on each person's property. We were figuring out the fastest way to qualify a property for an AU.

Rohan Weigel (13:33):

At first, we were doing it ourselves. We're like, okay, we're using geospatial data and we're figuring out from the aerial view how to do it. And then we were like, okay, let's train a team in India to do it and see if it continues to work. And then if it does, let's build some technology to make it work even better. That's what we did and it worked really well. We don’t use the whole process anymore, but we use a part of it still today. I think, to me is really cool. There's a way of testing, iterating, figuring out better ways to do it, and then slowly building the tech around it.

Alison Dean (14:02):

What project comes to mind that is most representative to you of a digital transformation.

Rohan Weigel (14:08):

There are tons. I think that one is one of my favorite examples, but I think the same goes for all of this advice. When you think about the way that people give advice to their friends for what they should do on their home, I actually think about the way that each of those people gives the advice and I'm like, okay, how do we turn that into technology? How do we turn this bad advice into good advice, but then use technology to power it. That's what our whole product is about. Is figuring out how to turn human advice into good human advice that's powered by data.

Alison Dean (14:41):

I want to know what project comes to mind that you would say was the most memorable for you and why?

Rohan Weigel (14:47):

I think it was not a Realm project. It was actually at Uber. When we first found out that Bird was launching scooters, I was actually working in Santa Monica, out of the Santa Monica office. And at first, it was funny. We were laughing, like what is happening? Why are there scooters on the street? And then we decided, we're like, oh, we should see if this is affecting our trips at all. What we found very quickly was that all of the pool rides from the Santa Monica pier and a ton of the bars and all of these other places where people were trying to save money on rides, were just completely gone. There was a huge drop-off and it was very obvious that those trips were then being taken on scooters because on the days that it rained, that didn't happen.

Rohan Weigel (15:31):

And we’re in Santa Monica so they were few and far between, but we did test it. We decided to launch scooters too and the thing that I loved about Uber was we never hesitated on decisions. Let's launch scooters and the next day we were shipping them from China. I ended up going around and launching them in Madrid and in Paris and all these other places and it was wild. The coolest part was we were using all of the data from our Uber trips. We were cannibalizing our own trips, but it didn't matter. We were getting them, which was really cool and we knew where people were getting picked up and being dropped off. We knew where those short trips were happening. We could figure out using geospatial data again and a process of just understanding where and when to put the scooters, we could pick up those trips and then get them on our own platform, which kind of gave us a tactical advantage over Bird and the other players, because we actually knew where to put them.

Alison Dean (16:23):

Yeah. What comes to mind when I say, what was the most difficult project that you've been part of?

Rohan Weigel (16:29):

Well, I have to go back to companies before that, to Beepi. We were working on this used car delivery and one of the hardest parts was we would sell cars and we just wanted to sell as many as possible. We didn't want them to depreciate. We would sell people cars from New Jersey to people in California. To me, I was like, this is not Amazon. We can't be doing that. But what I quickly figured out was like, okay, I can hire some truck drivers. And I actually did this myself, ran the test, and actually figured out how to do it. If we're going to move the cars anyway, let's move them ourselves. And then let's preempt where they're going to be bought so that we can move them before the buyer buys them and then they still get charged for the shipping, but the car's already there so we can deliver it in two days.

Rohan Weigel (17:12):

It was super difficult because we had to run all these analyses and build the technology to be able to do that. And then I had to go out and hire truck drivers. I was like 22 hiring truck drivers from Florida that are driving these trucks. There's just mayhem every day. It was the most cross-functional team I'd ever worked on. There's a truck driver, there are analysts, there’s a  dispatcher, there are the engineers that are working on this. We're all kind of in the same room together trying to work these things out. It was crazy. It was very, very hard, but really cool.

Alison Dean (17:45):

I want you to talk a little bit about SkyRise too. You built a whole database there and it sounded like you did it pretty quickly.

Rohan Weigel (17:52):

Yeah. When I first started, we were trying to build an application that would allow people to go from door to sky to door, which meant there were car rides in between those. The thing that we really had to get right was the timing. Unlike any of their piece of transportation because you have such a short window to make it faster than a car ride. You need to understand exactly what's going on and when.

Rohan Weigel (18:18):

That meant installing trackers in both the cars and the helicopters so that we could understand exactly when they took off, exactly when the customers arrived, exactly when they arrived at their day destination so that we could understand that entire process. That meant storing all of that data every time we did one of these trips so that we could figure out what went wrong every single time. There was definitely some convincing that was needed at first because everyone wanted to perfect the operations, but was less focused on understanding that we need the data in order to get better at the operations. Really focusing on building that infrastructure first is super important because otherwise, you can't make the operations any better.

Alison Dean (19:00):

Given the velocity that technology changes, I'm curious what recent projects that you've been part of have you learned the newest things?

Rohan Weigel (19:09):

Yeah. I'll give an example that relates directly to our evaluation model. We hired a data scientist who was absolutely brilliant. The thing that I really liked about him is that he shares all of his learnings with the team and helps us all understand what he's doing. one of the things that I didn't understand before this business is that you can create models that don't need the same data for every single house in the country, for example. Let's take a house on Long Island where the county doesn't record how many bedrooms or bathrooms the house has, which is very strange. But then another county in California records even if the house has the partial view or a really nice view or whatever. We have this weird disparate data, but we're trying to build the same model that can predict what the house is going to sell for.

Rohan Weigel (19:57):

This new technology around neural networks and boosted tree models has allowed us to get a lot better at understanding that valuation without changing the way that data is structured, which has been really great for us because we can iterate on these models really quickly when we have extra data to inject. That data doesn't have to be perfect across all homes. It just has to be there and that really will affect the model in a different way. This technology has only been around for a couple of years and so that's what's really kind of making this the perfect time for us right now.

Alison Dean (20:30):

Yeah. That's pretty cool. I'm also curious because you're an ops guy too, do you have a favorite off-the-shelf SAS platform?

Rohan Weigel (20:39):

That is a great question. We use so many of them. Zapier has always been one of my favorites. I think it's the coolest tool to iterate a company on. The entire team can use it, marketing, sales, data engineering, literally everyone can use it and you can pipe data and information wherever you want across tons of different platforms. But I've recently had a recent love for Airtable. I love Airtable. It's great. You can use it for anything. It's really changing the way that we're thinking about what we will use for our sales and marketing team and our advisors. It's just so much easier to visualize, understand, report on data and edit it than any other basic data platform that exists out there.

Alison Dean (21:25):

Okay. Shifting gears a little bit, I want to know what are the biggest lessons that you've learned from being a leader within technology?

Rohan Weigel (21:32):

There's a ton. The main thing, especially when you're leading a tech team that's pretty cross-functional as most of them are when you're in technology, is that you have to make the rest of the team understand that everything's going to break. It's going to break every single day. Engineers generally understand this so it's easier with them, but when you have a team that is expecting something to work because either they've come from a big company or it's not their job to make sure that the technology works, it can be frustrating. Understanding that and then having six backup plans for everything is super important because you just need to know that this technology is never going to be perfect. If it was then it's already been built. That's the both exciting thing and challenging thing that I've had to learn over the years and to make sure that the rest of the team also embraces that.

Alison Dean (22:20):

I like that. What do you want your direct reports to remember you for?

Rohan Weigel (22:24):

I've been decently hands-off in the past and now. I think with the exception of wanting to know how everything works and I think there are two reasons for that. So one is, I just want to know how it works because that's who I am as a person. But I also want the team to understand that I know how it works because you really can't build something as a team if you feel like you're communicating something that you're building and the person on the other end is hearing it and it's going in one ear and out the other. Especially when you're talking to the engineering team and then you're also talking to the CEO and someone on the sales team. They all want to know that when they're talking to you about a decision that needs to be made, that you're giving them a good answer based on the fact that you know what they're talking about and you know what needs to be done for both the business and for them.

Alison Dean (23:12):

Okay. What are the most important lessons that you've learned from your mentors?

Rohan Weigel (23:17):

Probably distilling learnings. I'll kind of explain what I mean by that. It's really important for sure at startups, but it's also important at the late stage. When you're in an early stage or you're with only a small team and you're like, Hey guys, we made this mistake and this is how we're going to fix it and you're only talking to the engineering team. Everyone's like, yep. Get it. Cool. Moving on. But when you have a big team of people that are over sales and marketing and CEO and everyone else, it's really important to communicate what the problem was in a way that everyone understands and then also communicate what the solution was in a way that everyone understands. If you fail at either one of those things, I think you lose people, and then when the mistake happens again, people don't understand why and it creates a lot more confusion. And so this definitely took me some time to get good at, but it's really making sure that this report or postmortem or whatever it is, has something for everyone. That has been really good for me in the last couple of years.

Alison Dean (24:21):

what projects are especially interesting to you as you continue in your career?

Rohan Weigel (24:26):

I'm really into things that are going to be the next big thing. I think building for today is fun, but I like building things that will be the future of something. Thinking about how are Gen Z homeowners going to be different than the current demographic of homeowners? I like to think of all of the digital aspects of their life and how they'll be thinking about the home. I think we're obviously in the middle of that, but we want to be thinking about how that's going to work too. It's something that I think about, not constantly because it would take up a lot of my day, but it's definitely something that excites me.

Alison Dean (25:02):

I like it. What other future innovations are you really excited about? That could be personally or professionally, but where do you see things progressing?

Rohan Weigel (25:12):

I really love cars and transportation and that's Uber and Beepi, et cetera. The innovation in the automotive industry in the last couple of years has been phenomenal. For me, the thing that's really exciting, I always love sci-fi, is seeing the transformation that cars have taken into being able to drive on their own, fly, things like that. I think those are really cool and they make people's lives a lot easier. It will reduce stress. It will make people's overall lives just a lot better. That's what I'm excited for.

Alison Dean (25:46):

What do you think is going to drive most impact on the real estate industry in the next five years?

Rohan Weigel (25:52):

Technology. The biggest comparison that I like to make and that our CEO likes to make as well is, comparing it to all of the other ways that you manage your finances, your assets, stocks, bonds, et cetera. There are a thousand different ways to do it digitally. Stocks you have Robinhood, you have Betterment, you have all of these different companies, but there really isn't one for your home. I think people have been afraid of it just because you can't just do it from technology all from the start, there's a manual aspect. The thing that I think we haven't been afraid of doing is, if there's a piece that we can't do with technology, we will roll up our sleeves and do it manually.

Alison Dean (26:28):

Manual. Yeah. That's so difficult for so many people at this stage in the game. Okay. Is there a question that I haven't asked you that I should have asked you?

Rohan Weigel (26:37):

Figuring out how technology plays a role obviously in real estate is one of our big question marks, but the question mark that I actually have a question on and I'll try to answer it once I ask myself it is, how will homeowners react to this and will they adopt it quickly? The answer is that I think yes. People, when they talk to us about the things that they want to do to their home, they're really excited by someone who can hold their hand through the process and obviously we do that through technology. We do that through our advisory service. But being able to be a trusted source for people to make their homes better, I think it's really exciting. I think we are definitely at this moment in time where we are well-positioned to do it.

Alison Dean (27:22):

How's Realm going to bridge that for a person that is a contractor? Where are they going to play into your target audience of people?

Rohan Weigel (27:33):

That's a great question. The thing that makes us unique for contractors is the ability for us to prevet what the customer wants to do. Both a want perspective and a financial and feasibility perspective. By doing that, we can hand off a lead to a contractor that's really, really good. Their getting this lead who is financially capable of doing the project, knows that the project can be done on their lot because we have all of this zoning information, and they already know what they want because they've designed their project through Realm. Contractors are really excited by that because their job is not going around trying to make sure that projects are feasible. It's to build. By doing that, I think we'll be able to give kind of a big upside to contractors because they'll be able to spend a lot more time doing what they should be doing.

Alison Dean (28:27):

What about a contractor that just bought a house and may have ideas on what they are thinking they want to do to it? How are they going to be pulled into the platform?

Rohan Weigel (28:36):

I think the interesting thing about home value is that there's obviously the structure that makes up the value. But there are so many external factors as well. It's really hard as a human being to understand what those are because you just don't know. The way that someone else decides how much they're willing to spend on your house, you don't know. The best way to do it is to understand many, many millions of houses and then you'll get a better idea. What are the schools like? How far do you have to drive to drop your kid off at school? Where is the closest grocery store? What is being built around you that may be of interest?

Rohan Weigel (29:15):

What is the neighborhood like? What are the things that people who are moving into the neighborhood are getting excited about? Maybe people really want an outdoor kitchen and everyone else has it, but you as a contractor may know that. You know how to build it, but you may not know that everyone wants it. The cool thing about these types of models is that it takes all of that stuff into account. Where one person might be a professional and a couple of pieces of the home industry, it's unlikely that someone is a professional at every single one of them.

Alison Dean (29:45):

I'm so curious to follow this along with you. The final question that I always end with is, can you speak about a breakthrough that you've had recently?

Rohan Weigel (29:54):

Yeah. A lot of the breakthroughs that we've had are obviously things that have to do with our team. Have an amazing team of really cool people. We have breakthroughs on things basically every day. But one of the things that I'm most excited about and I've already talked about this a lot is really figuring out this zoning database. I'll walk you through an example of why this is hard. Zoning is a legal language that is not home-specific. The zoning code doesn't say, “Hey, this is your house. This is exactly how many square feet you can build and this is where you can build it.”  They say, “If your house is this far from the street on the right side, notwithstanding, you are 50 feet away from a railroad, but your house is not higher and 50 feet.” It's really complicated.

Rohan Weigel (30:45):

Figuring out how to turn that into a database that actually works with your home is really important and really hard because every city figures out a different way to do it. The city of Malibu has a logarithmic graph of land size versus buildable space. Okay. How are we going to turn this into code? Every city has kind of come up with some crazy different thing and we've figured out this way that we can make it work over every city. To me that was one of the coolest things that I've done at this company, is build a database that works for tons of different laws. It was just never something I ever thought of doing. For me, that's really exciting.

Alison Dean (31:28):

Well, I thank you so much for all of the insights. I feel like this is one of those conversations, especially because I am a homeowner, I could literally pick your brain for five hours on this.

Rohan Weigel (31:38):

There's so much information swirling there, but our models are definitely better. I still have to admit that.

Alison Dean (31:44):

Yeah. Well, we are going to stay in touch. I appreciate your time. This was fun.

Rohan Weigel (31:50):

This was so fun. Thank you for letting me talk to you about Realm and all of my adventures over the years.

Alison Dean (31:55):

My pleasure. Thank you for tuning into the Breakthrough. Brought to you by TheoremOne. Make sure to hit that subscribe button and leave us a comment. You can find wherever you listen to podcasts and for more great content, follow us on Twitter and Instagram at Breakthrupod that's break T-H-R-U-P-O-D. I'm your host Alison Dean until next week.

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