How Zoneomics Is Changing Zoning For Real Estate Optimization

Zoneomics is a real estate intelligence platform utilizing detailed land-use zoning data for investment analysis and business process optimization.

Zoneomics is dedicated to capturing planning and land-use zoning data from fragmented sources and making these important decision-making datasets available in one place and in multiple formats including a map-enabled Web Platform, quick Zoning Reports, Zoning Data API, and Bulk Data.

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How Zoneomics Is Changing Zoning For Real Estate Optimization

Think about it, information is key. How else are you supposed to get this information out there? Are you supposed to have a Craigslist? Long sections, and you've got state by state, and then you're clicking. That's not efficient. The best thing to do is to zoom in and get it right there. GIS is a transformative technology. It's one of these technologies that's enduring and isn't thought of by most people. It's something no one hears about because it’s the back-end command just like Intel, or AMD is powering the chips in your computer.

You've heard of those guys but you haven't even heard of GIS. I wish you could download GIS. It's one of those technologies that is transformational and still hold so much promise because data is sitting in silos everywhere. It's a beautiful thing. What we need are lat-long coordinates, X and Y coordinates, and you can overlay them on a map. It's beautiful because you have a new way of presenting something.

You're going to see it come into more of the forefront with the brim of the Internet of Things. We're going to have a lat-long on everything as you're seeing with transactions. I was driving and you can see that a lot of that data set, that location-based data where you can see your Amazon packages because that Amazon vehicle has the coordinates on that. The lat and long can tell you where that package is. It boils down to that level of usefulness.

As we're speaking about technology, tell us a bit about how does a company like yours get access to this data if it’s sitting in spreadsheets or PDFs? I know the answer here, but let's play with the idea. You saw a problem and it's daunting to realize you're going to have to go through all these PDFs, so you use technology. How do use technology to capture this information?

What I've previously done in Australia, a lot of that data, we were able to go to the state and then do the display, the visual, and deliver it, and make it visually appealing in the UI and focus on that. Also, the analysis of that data and give tools to our users to do that. When we looked at the US problem, we were going to have to start from square one. There is no place to be able to get this data in one place and also in the format that we need it. There are two levels of that. One is how we collect the map-based data. That is utilizing various OCR technology and AI to be able to do that. It's a little bit further.

The OCR, Optical Character Recognition.

There's not a great word for it. We're in the new territory because there's not so much character recognition. A lot of it is vector, raster and lines on a map, but that's the closest thing that we had to go off. That's one part of the data set, which is the map-based data set. The second part of the data set, which is zoning, Municipal Code Ordinance, which is these wordy legal documents that are a thousand pages long. Half of it means nothing. If you're looking at your property, which is a particular zone, the majority of the text in there is of no interest to you. Even the text that might relate to your zone or half of it is not of interest to you. It's tackling that with AI as a problem. That's how we went about it.

It's a big daunting challenge that you're taking on because there is a lot of integration required. To rewind for a second, what we're talking about here is companies like Zoneomics. How do they get all this data? It's pretty much what Google does. Google has these robots that go around the web and they get all this information and then make it relevant to you. Companies like Zoneomics are using similar technologies where they will create a way to automatically create. You could think of it as a robot. There are actual technologies involved here called scraping technologies.

One thing is they can scrape websites. They can go in and they can automatically make sure they download documents. It's a daunting task because there isn't a global language being used like Google. If you want to rank well in Google and you want to play, and you have to play because if you don't play, then you're not going to rank on Google, so you have to play by the rules of Google. People will redesign their website and make it very SEO-friendly. That doesn't exist in the world that companies like Zoneomics are pioneering. There are data sitting in PDFs and disparate places.

When something is updated, that's another headache. Matt, please tell us about the scale you guys are at, but I imagine it's very challenging when information is constantly being updated. No one is calling you and saying, “Matthew, just to let you know, I work at the county's office. We updated a slight modification in this little ZIP code.” You'll first be off the hook, right?

Yes. That sounds like what we tackle every day. It depends on how active the city is. Whether you’ve got cities like Los Angeles or New York, where they're updating their zoning map on a monthly or weekly basis, they're the easier ones to look after. It's even more the small ones that are more challenging. You're right, it's updating it and categorizing it. We also have a unique problem from both a data point of view and a real estate point of view.

One, we're creating this standardized or this one place to get this data. There's only so far we can standardize and categorize. We can't go too far without standardization and categorization, or even when we do, we have to be able to make sure it connects back to the source information. To give you an example to the point where a dwelling isn't defined the same, an R1 zone or what we would call a single-family zone in Los Angeles or San Diego is not the same as a single-family zone in Portland. If you try to standardize and categorize it too much, it becomes useless to an investor or developer because if we go to you, it's useful looking at things from a macro and being able to see it life for life.

When you're going and making a decision about that specific property, and we're telling you that it's a single-family, the same as how you should categorize single-family anywhere in America, we're doing you a disservice. We're not providing you with the information that you need. For example, for a single-family in Portland, short-term rental may be permitted. In single-family in San Diego, short-term rental may be prohibited. As you can see, you have to be able to keep making sure that the data connects back to what it is at the original source with that standardization.

The challenge of classification is puzzling because in some ways it looks like a duck, it walks like a duck, and it quacks like a duck, it's probably a duck, but that doesn't apply here because buildings have a cut-off at some arbitrary point. How many square feet is a 1-bedroom, 2-bedroom or 3-bedroom potential? How much of the size of a lot does something need to be? There are many areas that are open to interpretation. If you're leaving that to a machine or AI, it's very difficult because then you're going to try to classify. It’s certainly a meaty challenge.

You've hit something now that we've had to work hard with our AIs to get it up and help it through this process. Some codes will say minimum lot area and some will put min lot area, or they might say minimum lot. For a human, if I’m an urban planner and I go and look up that code, I can instantly make that decision in my head like, “In San Diego they say minimum lot area and in Portland, they say minimum lot, and then they use the little symbol there, I can make that decision.” Teaching the computer that is and was a challenge.

Even if you want to try to determine value, one metric we use in real estate to the horror of agents is price per square feet. I wasn't in real estate at this point, I was running my startup, but when I was looking at rentals and buying places, I would often be looking at, “How many square feet?” “This one is set to X dollars per square feet.” I look at the agent’s eyes, and she or he would have horror like, “That's not the way to do this. You've got to buy something that you have an emotional connection to and is right for you.” I'm like, “Whatever, they just want the sale.” I wanted to go pragmatic.

Zoneomics is an example of a startup that addresses a critical problem and paves the way forward.

The fact is, you can't look at square feet alone as a metric. I'll give you an example, you go to a 2,000 square foot home and another 2,000 square foot home. If I’ve been in some homes and the agent said to me, “It says 2,000 square feet but there's this extra 600 square feet that are not on the record. There's this extra room that was done without code, but you're getting a bigger place, you're getting a bigger garden. That's not factored in the square feet, but also usability.” How much of the square feet is usable and how much of the square foot is occupied by unusable space because you've got stairways and you've got a funky layout? I live in San Francisco, by the way, so here you've got very funky layouts.

You've got old Victorian homes. I don't live in a Victorian home. I'm in the financial area where there's more modern contemporary which is my style, but San Francisco has a charm and it has these Victorian homes. It’s multi-level and condensed. In a square feet, this way is very different to a square feet the other way where it’s horizontal versus vertical rather. Even land like, “Look at this, there are acres of land here.” “This land isn't flat?”

Some of this land is on an unusable hill and you'll never bring any pipes through there. The other thing is, this is a protected area possibly. This might be a protected area and you can't do anything with it. This is why simple metric square feet is so difficult. It's a challenge when you're in PropTech, as a founder yourself. It’s a small problem. How do I teach this?

You've hit on a huge problem there. If you can find a PropTech, we'd like to talk to him as to help us solve some of our clients’ problems. As you pointed out that illustration of resigning a new lot’s land release or subdivision, there are certain jurisdictions where it will change, whether you have on-sites delivery of sewerage, energy or some services is delivered or it's not there, then you can do more dense development depending on the delivery there.

We've had a lot of requests like, “Is there a national database for where sewerage, water supply, electricity, and telecommunications supply are provided?” The next question is do we do that? We're having a good enough challenge dealing with zoning. Hopefully, you can find a PropTech out there that's looking at that problem.

This is a very exciting time to be in PropTech. Zoneomics is an example of a startup that is addressing a critical problem, and is paving the way forwards for where the industry is going to go. Like building a highway where cars are going to flow, if you get this zoning data and you get it in the hands of consumers and you make this information that was once put all over the place transparent, you're going to change the prices of real estate.

I'm very excited about real estate too, that real estate does go up in value. Inflation is more real than it's ever as of mid-2021. You might read this in 2026 saying, “He was right. Inflation is crazy.” Real estate does go up and we're living in extraordinary times. As information becomes more transparent, this is one of those technologies where I think, fast forward to the future and you believe that there's so much quantitative trading going on in the stock markets.

You want quantitative trading to be going on and real estate. Information like this is going to be critical to allow that to happen because for sure, years or maybe decades from now, everything will be bought on a real-time basis. Fractional shares of real estate will trade hands very quickly and easily. Machines can look at a plot of land and quickly assess potential value. It will sound obvious to how hedge funds are able to make massive moves in stock markets and these institutions are so powerful. This is coming to real estate. It's only a matter of time for this to come. For this future to be inexistent, we need companies that are building the infrastructure layer.

There are many good PropTech out there, they're doing that. We're tackling a zoning issue and there are others that are doing different things with that, particularly from an AEC point of view. Our business model is we look to partner because zoning is universal in the way that it affects many different parts of real estate. We know we can't specialize in everything when it comes to zoning.

We want to be the base provider of zoning information and be able to, through our API, empower a lot of these new PropTechs that are already there and are coming. To be able to build weird and wonderful things depending on the AEC space or whether it be communication space. That’s where we see lots of partners that we're utilizing our dataset to build amazing things from a tech point of view.

The API side is very interesting. What an API allows is other companies to access that data that you control and have. They can access that data writing a few lines of code, and then they get that information that you've worked hard to compile and collect. This is something about having focus as a founder. Doing something that doesn't seem sexy can be extremely rewarding if that's what you do and you do it well. The boring thing, the zoning stuff, who wants to deal with that? If you make that, you’ve got to focus. You don't get distracted by all these bells and whistles, and building other products for the verticals. You just focus on solving this one key problem because this is a big problem.

That was a challenge for me that as a founder, I had to come to grasp with. I couldn't do everything from my point of view. I couldn't build the best 3D building scenario or building an envelope solution. I also couldn't go out and do this because you can't do it all. What we had to learn was, “It's okay. We’re the best at this.”

We understand zoning. We understand the data. We understand the information. We also understand what the use cases are for. We understand that there are a lot of other founders out there and other smart companies that can take our data, and for that particular use case, take it all the way to its fruition. Whatever we can focus on, we continue to also empower those companies.

Matthew, we've got a wide range of readers. Many entrepreneurs as well as people in real estate are interested in moving into PropTech. How can people reach you?

The easiest way to reach me is via email. They can reach me at Matthew.Player@Zoneomics.com. That last name is sometimes a little confusing. If you put Info@Zoneomics.com and CC, they'll make sure it gets through to me. That’s the easiest way to get in touch with me, via my email.

Matthew Player, thank you so much for coming on the show.

Thanks for having me. It was great.

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About Zain Jaffer

Zain Jaffer is an accomplished executive, investor, and entrepreneur. He started his first company at the age of 14 and later moved to the US as an immigrant to found Vungle, after securing $25M from tech giants including Google & AOL in 2011. Vungle recently sold for $780m.

His achievements have garnered international recognition and acclaim; he is the recipient of prestigious awards such as “Forbes 30 Under 30”, “Inc. Magazine’s 35 Under 35” and the “SF Business Times Tech & Innovation Award”. He is regularly featured in major business & tech publications such as The Wall Street Journal, VentureBeat, and TechCrunch.