Masterclass: Supercharge your 2023 strategy with data-rich listing content

Pierre Calzadilla (EVP of Growth at Local Logic) joined Lisa Larson (Managing Director, North America at Restb.ai) for a masterclass moderated by Amy Gorce (CEO & Managing Partner at PRESTA Team) on driving more qualified leads with enriched listing content that converts.

For 60 minutes, they discussed how to:

  • Drive more qualified leads: How to get more views on your listing content with precise location and visual property insights
  • Increase your conversion rate: Why sharing relevant content will help you accelerate conversions
  • Hit your revenue goals: How to build a healthy pipeline early on to meet your targets for the year.

Transcript

Amy Gorce (AG), PRESTA:

Good morning. I think it’s morning for everyone — maybe not everyone if we have some folks from Spain joining us. But thank you all for joining us. And thank you to Local Logic and Restb.ai for inviting me to moderate this webinar today. I’m super excited because I’ve been working for 25 years in real estate, primarily focused in data. This topic today is more timely than it’s been and we’ve all accepted that it’s all about the data.

I’m excited to talk to these guys about how we can supercharge our 2023 strategy using data from each of these companies. I’ve personally had the opportunity to work with both of these companies in my former role at CoreLogic and continuing on in my role at PRESTA Team Consulting and some of the other projects I’m working on. So, I’m very, very excited to have my guests here today. By way of a brief introduction for folks who don’t know me, I’m Amy Gorce, CEO and Managing Partner at PRESTA Team Consulting.

Again, very excited to be here today with my friends. We are going to use the Q&A box for Q&A today. So, please feel free to type your questions in there. We’ll try to get to them throughout the presentation. But for sure at the end, we’ll leave plenty of time for Q&A. I would like to take the opportunity to let each of my guests introduce themselves.

We’ll start with you, Lisa Larson. Tell us a little bit about you before we dive in.

Restb.ai: Extracting insights from property photos

Lisa Larson (LL), Restb.ai:

Hello. I’d like to thank both Amy and Pierre for inviting us to be a part of this Masterclass webinar. Super excited. Full disclosure: my first webinar. So, be patient with me and I’ll try not to get too nervous. My name’s Lisa Larson. I’m the Managing Director of North America for Restb.ai. A big shout out to my team in Barcelona who are watching from the King’s Landing conference room. House of Stark here.

I’ve been in real estate information and proptech for over 20 years. We’ve seen a lot of technology over the last (me) 20 years, (Amy) 25 years. When I saw what Restb was doing with the AI — it’s just so cool, it’s so impactful. I’m just so happy that I get to be part of the program and part of the team to bring it to North America, to our MLS and proptech companies out here.

Restb is a leading computer vision solution for real estate. We use artificial intelligence to extract insights from property photos. The interior and exterior, all the features, all the tags, all the architectural style. We process about a billion images a month, which is pretty amazing. We’re in 30 + countries. We have one of the fastest, if not the fastest AI API in the world.

That’s super impactful because when you’re talking about the listing input process and being able to upload the listing, the last thing you want to do is slow down the process. What Restb does is speed it up. We can analyze a single image in about 300 milliseconds. That’s about a third of a second. So, we can process 50 to 100 images in about 5 to 7 seconds. We’re wicked fast. Our accuracy is unmatched.

We work with everybody from MLS to brokers, portals. We do a lot of work with AVM, buyer, insurance, mortgage companies, institutional investors — pretty much anybody in proptech or finance. And we have positioned ourselves to be very agnostic, so we can work with anybody on any platform. We believe that our company should be able to deliver AI to anybody worldwide within proptech and have the benefits of our AI.

AG:

Awesome. Lisa, thank you. Welcome. We look forward to diving into a little more detail as we move along, but it’s exciting to have you over there at Restb.ai as well. I’ve worked with that team, like I said before, and it’s very exciting.

Pierre, welcome. You and I have also have a bit of a rich history and I was always excited about what Local Logic brought to the party in terms of data. So maybe in yourself a little bit before we dive in.

Local Logic: Quantifying the value of a location

Pierre Calzadilla (PC), Local Logic:

Hi everyone. I see many friends in the in the waiting room here. Also 20 years in real estate. Hard to believe it’s been that long. Started on the brokerage side. Then, I was at Trulia for eight years and at Real Scout for almost four. And now here at Local Logic for four. It’s been a really fun journey seeing the industry change from both sides of the table over the past 20 years.

Local Logic does is a location intelligence company. You know that feeling when you’re standing on a corner and you’re like, “Oh, what a great neighborhood, what a good vibe, what a good sense of place.” We quantify that mathematically. We ingest thousands of data sets across the U.S. and Canada.

We have full coverage and we created up to 18 scores for any location that lets you understand what it’s like to be on that street corner. We’ll probably talk about that a little bit more later, but we do work with the entire real estatet industry as well. We work with MLS portals, brokerages.

We also have two sides of the business — a consumer facing side, which is mostly used by residential real estate, and analytical financial side, which is mostly leveraged by the commercial real estate. And we work with the AVM as well.

So, Restb and us are both looking at the built world through two different lenses, which is really awesome. And together we give a really complete picture of what a place is like.

AG:

I love how complementary your two solutions are and how you can get a full picture. We talked yesterday in our planning call about how between your two data sets, you can derive a lot of what you need to know about a property to create a listing. And I look forward to chatting more about that.

As we dive in, I always like to start with this quote in the famous words of William Edwards Deming, “In God we trust. But all others must bring data.” Pierre, I’m going to stay with you here for a moment here. Let’s talk a little more in depth about the kind of data and insights that Local Logic provides and how those get derived.

And while we’re doing that, I’m going to bring up a few slides that I thought were super impactful when you sent them over to me while we were doing prep. So, go ahead and dive in on what kind of data you’re bringing to the party. And I’ll bring up a couple of slides here that might share what we’re talking about.

Local Logic uses thousands of data points to provide a detailed understanding of a location

PC:

What’s special about Local Logic, one of the special things, is that we were founded by urban planners. They have a different way of looking at the world. They’re really trying to understand how to make and build better cities for people.

Most companies, even in residential real estate, look at the world like this: You look at the world as zip codes and neighborhoods and you’re trying to market to a zip code or market to a neighborhood. But that’s not how Local Logic sees the world. We see the world a little differently. We really look at the streets, the actual streets. So, we understand what’s happening between any of those two white dots that you see in that little corner.

We have thousands of data points that create those 18 scores. An example is that we have a Quiet score for any location. We have a Grocery store. We have a Park score. We have a School score. We have three main buckets, which is the character of the area, the transportation (transit over the area), and the services that are there. Those categories let somebody who’s looking at a home online, looking to invest in a piece of real estate, or looking to redevelop a piece of real estate understand what is the value driver in that location.

Or, why should I care about this place? For example, “Oh, I want to have a five minute drive to the grocery store. I want to have an easy way to drop off my kids. And I also want to be able to go out at night and have a nice dinner.” How can you, in a split second, identify those features without having to scroll through Google Maps or scroll through 20 websites to get it. Local Logic really makes you understand that location in a split second.

AG:

I love that. This really resonated with me. We’re in a brand new home. I’m actually in a suburb of Phoenix in Gilbert. So, we live close to a lot of services, which is really unusual in the suburbs, and that was a priority for us. We live in a rural area in the summertime where there’s one restaurant in the town we live in. So, when I’m here in Phoenix, in the winter, I want to be close to restaurants and things I like. I love the idea of the lifestyle support that gets created through, your data.

So, Lisa, I’m going to ask you the same question and I’m going to bring up a few slides while you start talking. Your intro was pretty detailed, but go ahead and give it some more detail about the kind of data that Restb provides and how that’s derived. And while you’re doing that, again, I’m going to bring up a couple of slides to support you.

Restb uses AI detection to enhance property listings and imagery

LL:

At the core of what we do is taking something that’s very valuable within the real estate transaction process, which is the imagery, and understanding what can we learn from the imagery. So, everything that we approach within our vertical revolves around how do we solve a problem.

When we look at a listing photo, for example, we look at what this picture can tell us. A picture tells us a thousand words, but can we use that data to create better, more meaningful solutions and products for MLS, both real estate buyers and sellers, and for the consumers?

We’re able to go in and use detection in our AI to identify, “What is the room type? Are we looking at a living room, a bathroom, a kitchen, and so forth?” And then we take it a step further and say, “What are the features within this property? What are the floors, what are the countertops? What do the ceilings look like? What are the lighting look like?” And so forth.

Then we’re able to layer that data into the MLS database. I’ll talk more on the MLS side a bit to allow us to see how we fully leverage this. One of the things that we’ve always seen is what additional data can we layer into the MLS to complete that data set. We also look at whether there is anything else that we could tell you about this property that a buyer would be interested in wanting to know — whether it’s the architectural style, whether it’s the entryway, and so forth.

So, when an agent goes in and uploads a listing in the MLS database (we work with everybody from CoreLogic to Rapattoni to FBS to dynaConnections to Black Knight, and so forth), the agent is able to then, in a very short time, upload the listings. We can extract all of the tags. We can immediately fill in those incomplete data fields for the MLS. In addition to that, not only are we enriching the database and using all resold data dictionary tags, we allow the MLS to also push that downstream.

So, when you think about the ADA side of things and being able to be compliant for somebody that may have a seeing disability and need screen readers, the MLS are taking a bold stand. They want to make sure that when data is being published for consumers, it also has an ADA component to protect the consumers and to protect their membership.

It also drives tremendous traffic to those consumers’ web sites. We’re cleaning up the data downstream. It’s so impactful — everything that our data can do and the problems that we’re able to solve. This illustration that you’re showing here shows that we’re also able to explore additional data sets that are all based on realtor permission.

So, the realtor can say, “Yes, I see that you’ve brought up all these additional features that otherwise wouldn’t have been described or tagged,” and then they can push that downstream. The idea is improve the database, create more data for consumers to be able to search, for example, by looking for an outdoor kitchen or an extra large kitchen island. So, it’s not only cleansing the data, it’s creating better search experiences for consumers, too.

Bringing innovation to the listing creation process

AG:

I love that. And I love this idea that your innovation can make agents’ lives easier while enriching the consumer experience. We’ve talked quite a bit with our MLS clients lately about how do we go from what felt really innovative 20 years ago, which was autofill the listing with a public record, to create a listing from a photo and a set of rich data like Pierre is bringing to the party as well. So that the agent can really focus on what they’re good at, which is marketing and finding a buyer for property.

I love where the innovation is taking us. I’m excited for the future of listing input. It always felt like such a chore before, but I think the kind of innovation that you all are bringing is starting to make it exciting to think about what the future of creating a listing and market harvesting would be like.

LL:

We agree 100%. We actually see us pivoting away from this idea that the agent uploads our input data and a listing into their MLS database. We look at it more from the angle that the agent will just edit and refine it. Because when you put up the address, you have the parcel, you have the public records, you have all the imagery. The agent is just going in and editing it. Something that usually takes 20, 30, 40 minutes should only take 5 to 6 minutes, including being able to put captions and property descriptions.

It’s really cool because we’re seeing a lot of the MLS has entered more of this modern landscape. We just signed a big deal with Toronto — it was really cool because John DiMichele said, “The way that we’ve done business before with interacting with data is dead. This is the new norm.” It’s so cool seeing the MLS executives get behind that type of initiative and that type of perspective on the listing.

Using location data to generate revenue and save money

AG:

Such good stuff. So, Pierre, tell me about some of the ways that your clients are leveraging your data and insights. Are they using it to make money or are they using it to save money? Is it both? What’s the key interesting thing that the clients are doing with your data.

PC:

There are multiple ways to leverage location data. We have clients who are using our data and analytical models to make a lot of money by purchasing assets and reducing their risk by using our data to analyze those purchases. So, that’s a very CRE, investor-level use case.

If you think about it, we’re like a B2B2C company. So, on the consumer side, we work with brokerages and portals who are really their clients (the consumer). So, in that world, what we’re doing is helping the portal or the brokerage engage that consumer in a new way. Some of things that we do are very similar to Restb. We have search filters where a consumer can look at a map and create their own custom heat map on the fly on your brokerage or company site.

They can say, “Hey, I’m moving from New York to Arizona and I want to be near restaurants. I want to be 5 minutes from a grocery store.” In a second, they can check two boxes in all of Arizona (frankly, they can zoom in as the deep as they want), then the map is highlighted for the areas that match their needs.

One of the cool things there is that the consumer will expand their starting search by 20%. They might have said, “Oh, I want to live in Phoenix.” And all of a sudden they’re looking at Scottsdale and at Tempe. They start to expand to areas that they didn’t know really had the lifestyle that they wanted. And similarly, you can also see that use case on the investment side.

To answer the question about make money or save money, there’s also just practical things that we do, like better use of funds. If you can consolidate your spend on data and also increase that experience for the consumer, we can help you save money on the cost of integrating all those different services.

And also save on the cost of paying for the map. I have a client who’s spending 50 grand a month on map costs alone. This is also data that is not even us. This is just pure Google map calls — and we can impact a significant part of that when you consider listing pages and the cost of maps.

At times we can be cheaper than other alternatives and also deliver a better experience. That’s why I think there is a win-win here for increasing the consumer value prop, which obviously makes more leads, makes your agents happy, makes your brand work.

AG:

It’s always good when you can land on both sides of that revenue and expense ledger. I can make you money and I can save you money. Especially today without question.

Leveraging data in a down market

AG:

And that leads us into my next question. Lisa, maybe we’ll start with you, but I’d love for both of you to answer this. It is not a secret that the real estate industry, in the last couple of months, is undergoing some adjustments.

Even though the market’s probably getting closer to just normal than actually down, I think the perception is that there’s definitely a downturn. We’re facing a downward market, if we’re not already in one.

So in a down market, Lisa, does this data even matter? When everything’s good, everybody wants to spend money and they’re more than happy to try something new. But then in a market like this, what does that look like from your perspective? How much does it matter? Does it matter more?

LL:

That’s a great question. It’s interesting to tie into what Pierre was saying too. It’s like you’re finding by features, right? I want to find the features in a neighborhood or that street corner. I want to find the property based on these features and attributes. That’s really what we’re doing here. We’re giving way more transparency into the type of data that a real life consumer wants.

As far as whether this data is important in a down market — I would say even more so. The great reset is happening. No more are we going to see properties sit on the market for six days. We’re going to go back into those longer days on market — 60, 90, 120 days. There’s going to be more inventory.

So, buyers are going to be more selective on what they want. How are you going to reset and position yourself to be able to offer buyers exactly what they want? Are you going to get in front of this idea of providing more data, but not too much data? But what is the right data?

I’ll give you a really good example. Our Chief Product Officer, Nathan, just did a really great use case study with MRED in Chicago. We went toe-to-toe: our AI database versus their AI database. So, Nathan went back and looked at the last 90 days of data that was in MRED’s MLS and then we compared it to what datasets we would have put in there.

The findings were profound. Sometimes it was 1 in 200 times where we were able to provide more data. The type of data that a buyer wants. The number one thing that we hear a lot from agents say is that their buyers don’t know what they want.

What if there was a product that would allow your buyer to be able to find exactly what they want? What if there was a product that you can upload a photo and say, “This is exactly what I’m looking for?” For example, an extra large kitchen island or a specific feature.  And then, how cool would it be to not only identify that property and show you all the active listings, now you can layer on the Local Logic data and say, “Is it in the neighborhood that I want too?”

We’re introducing these really cool, interactive, robust user experiences with this type of data. It’s a great time for MLS to reset and say, “How do I reposition and get out in front of this?” Add more data, add more value. And it’s not rocket science — what we’re doing and what Local Logic is doing. We’re just making the data available to your end users to enhance the buy/sell process.

AG:

Same question for you, Pierre. Lisa brings up a good point. We’ve been talking about search 2.0 for way too long. And I think you all are finally bringing the data that makes those kind of things possible, but same question for you. How does the down market impact what you’re bringing,  positively or negatively?

PC:

I think every company has their own balance sheet that they have to look at. And there’s this kind of market, this is my second major downturn. I was at Trulia from 2007-2008 and experienced that period.

Every company has to look at their balance sheet and decide what’s the core business that they’re building. This is why you’re seeing companies like Redfin, Zillow, and others cut off the iBuying program because that’s not their core business. This is not the time to take risks. And so the core business ultimately for many people is going to be either investing and buying real estate.

So therefore, those folks are very happy right now. They’re able to acquire and scale assets that are underpriced right now. And what they need is data to help them make better decisions. We’re seeing — and I’m just talking from my customer side — tremendous demand on our insights, because if you can increase your return by 5-10% because you have better data, that is a no brainer.

When you go to the consumer side and you think about the consumer problems, they’re in a different boat. They’re now having to trade off a lot of things in their experiences. Maybe to maintain their home, keep their home, to move for a job, etc. So, what are they trading off?

Let’s say there are these two homes that are both available for 480K. Both of them are in the same town, maybe even the same neighborhood. But one of them might make more sense if you have a job and you have to get there at 9 AM and it’s 5 minutes to school and 5 minutes to groceries and 10 minutes to this. Especially in suburbs where the homes are pretty much the same. Now it’s like, “do I live in the cul de sac or do I live at the end of the street and what does that mean for me?”

Data can help that consumer. Ultimately, it’s self-serving to say, yeah, in a down market use our data. I think whether the market is up or down our data makes sense because we’re going to impact a decision and there’s always decisions made in a down or up market.

It’s really about whether your data help move that faster and help increase the confidence. I think if you can bring speed and confidence, like Restb and we do, that’s the no-brainer question to me that always makes sense.

AG:

Yeah, I think the speed and assurance is something that’s really emerging. As someone who bought a home in the craziness of early 2022, just all those extra things I wished for while I was going through this process. We lived in Oregon and I was buying a home in Phoenix.

So, I think it’s really interesting to put yourself in the seat of the consumer these days, and sometimes they don’t know what they don’t know. And so we’re having to bring them along on the journey as well.

LL:

To add to that, for the second part of your question, in speaking to the MLS folks that are participating on this webinar — which thank you for being here — how are you going to leverage this data? Pierre brought up something great which is: What dataset do you have now and how can you increase the value of that data by adding these layers?

What additional data can Restb put on top of your data to increase the value on the B2B side to 10% or 20%. Whether it’s all of our interior/exterior tags, property condition scores — what are you doing with data and distributing it and selling it. A lot of the conversations that I’ve been having over the last six months is for the executives who have been in the downturn market before. We use this time to reset and re-evaluate how can we improve our data and generate more revenue to offset some of that membership loss of dues we’re going to see.

Amy, you’re with REdistribute . Whether you’re going to participate in a program like that or you use Trestle, Bridge, AMP, or your own homegrown, look at the data that we can add on top of what you have and what Local Logic has. Then get your data valued and decide how much can we truly value this data.

There are several MLS, like TRREB, across the country that have been hiring outside companies to come in and value their data, like PWC and big accredited firms, to say, “We know we’re sitting on a gold mine. What is it valued now? If we add these extra datasets on top of it, what’s the value then? And then how do we get out and distribute it? How do we say to the insurance companies, title companies, and who do partner with what to do with that?” That’s this wave that’s happening now. I’m just so excited to see that MLS has become more involved in the data and tech side of it.

Sharing client success stories

AG:

That’s a great segue into my next question. I always love hearing the stories about clients that have used data successfully to create some sort of new KPI or metric.

Lisa, I’ll start with you. Tell us about an implementation that you’re particularly proud of and what it accomplished for the client. I’d love for this audience to hear more about real-life details. If you can’t mention the client name, that’s okay. I like real life stories that tell the story for the data.

LL:

I’d love to. There are a lot of clients we’re not allowed to disclose. However, we have partners of ours that we can share and have used our property condition and property score because they want to enhance their AVM. AVM have been blind to the conditioning quality when it comes to imagery.

So, we work with a lot of those big companies, appraisal companies, and valuation companies that have a portfolio that may be worth $300,000 or $300 million. They will ask us to analyze the imagery and tell them what the true value of it is. And we’ve moved the needle. We’ve been able to take a valuation and move it by 1-2%. That’s significant when you’re looking at these big portfolios. This is one of the things that is very near and dear to my heart, because I was a Vice President of an MLS and ran Product, so I’m always really excited to elevate the MLS vertical.

We just recently partnered up with Toronto Real Estate Board with John DiMichele. I’m so excited about the initiative because not only is this an MLS asset within their platform that we are integrating with all of our AI solutions — whether it’s MLS match, doing a Pinterest search or the tagging, photo compliance. Hus members get the benefit of data distribution downstream.

The other part of the partnership is reselling our data. So he us taking all that data now through our partnership with him. He is going to represent us in Canada by selling to all of the banks, the appraisal companies, the title companies.

All of the data that he knows that they want from our insights, he is getting into that platform. He’s going to benefit tremendously as are his members because the MLS is a fresh source of the data. We’ve always known that. But now the MLS have finally been given permission by their boards, their committees, and their membership to go out and do what they do best — which is get into this world of technology and data and partner up with whoever they need to bring more resources, technology, and tools and do their job to the best of their ability too.

I’m just so excited to see MLS is getting into this data distribution world and using that to their benefit to help them grow and scale. It’s going to look a lot different in the next 2-to-4 years.

AG:

I feel like we’ve been talking about it for a while, but it takes a while for us to refactor our systems to support all the new stuff. I’m super excited for what you all are bringing. Same question. I love hearing stories about client successes. So, Pierre, tell us about an implementation that your team is particularly proud of and know how it help the client.

PC:

I’ll try to go over a couple different versions and keep it efficient. One of our oldest clients is Centris, and we’re fully integrated into their consumer front end. So, we’re in the consumer facing front-end and we’re in their back-end which is CoreLogic. We are in Matrix. We also work in the U.S. and we just launched Black Knight there as well. We’re fully available in Black Knight and in CoreLogic.

It’s been a huge value to them because now the consumers are able to use those tools, but also the agents are also able to use those tools on the agent side. So, they can be as informed as a consumer and use the same tools that the consumer uses to help them find the location that best suits their their needs.

On the portal side, Redfin uses our data for SEO content. We work with Zumper for location data on their listings. We work with several other portals like Rental Beast and even other integrations where we’re able to provide that value to the consumers. And for them, they get huge value on the conversion, on the traffic, etc.

On the brokerage side, we work with CB Bain in Seattle and they’ve loved it. It’s been fantastic for them. That’s the partnership with Delta Media Group. We also work with pretty much everybody inside real estate. If you’re an inside real estate agent, you can just go to the marketplace, check a box, and Local Logic is live on your on your site.

We’ve done a lot in the background because we integrate seamlessly with all these providers. So, their brokerages, their clients, their consumers are able to leverage all this data and impact everything from traffic, top of line traffic. We’ve seen increases of 10 to 15% in new traffic coming in. We’ve seen conversion rates as high as 36%.

Royal LePage, one of our oldest clients, was one of the first big brokerages to go with us live over six years ago. The impact on their conversion rate alone was just tremendous for them. And that’s actually how we got into inside real estate eventually. So, all of these relationships we’ve had for years, we’ve kept — I think that’s also a big testament — and we grow with them.

I’m thrilled when our customers win because we win. The last one I’ll share is the investment one. I can’t say names also, but we have an investor who buys all across the U.S. They don’t purchase a property without running an algorithm across it because they’ve determined that certain scores that we have, that they have put into their model, are directly related to the outcomes that they want to pursue. So, it’s great to hear that kind of stuff. We’re excited because next year we’ll have some public information to share about this. We’ll do some white papers and case studies and share this with the world. We’re really looking forward to that.

AG:

That’s awesome. I love it. Congratulations to both of you. Both had recent announcements about strategic partnerships that are really significant. Pierre, you and Local Logic with the Black Knight announcement. Lisa, you’ve talked a little bit about the Toronto, the TRREB announcement as well, so congratulations to both of you on that. We did get a question about what platforms you’re integrated with.

Building flexible platform integrations

AG:

I think you’ve both sort of touched on that a little bit, but it sounds like you all are pretty platform agnostic. So Lisa, maybe just talk a little bit about your partnerships and how someone would integrate with you.

LL:

We want to be able to put our technology and integrate it into any platform, whether it’s on the back-end or the front-end. This year, one of my main goals was to put those partnerships together with all the big MLS vendors. CoreLogic was the first one, and that was because of you, Amy, thank you.

PC:

Ditto.

AG:

It was my pleasure. These two deals I did for CoreLogic were two that I was most proud of. So, it’s exciting for me to have this thing come full circle.

LL:

Well, thank you. It was super impactful for us. Once we integrate into a MLS platform, we keep coming out with new products. When we started with you, for example, we started with the photo compliance, which makes sure that there’s no violations, there’s no watermarks, and so forth, inside those photos.

Since then, we’ve added tags, we’ve added our new product MLS match. We continue to add more products. So, once we get into a platform, the idea is to continue to grow. We have agreements now in place with Rapattoni. We’ve been working with FBS for a very long time and continuing to grow that platform. With dynaConnections, with AMP, and Stratus, which we’re very excited about.

We are in the final stages of some contracts with some of the other big MLS vendors. I’m sure you guys can do the math on it, but I’m hoping by the end of this year we will have everybody wrapped up. Including homegrown. So, not only do we work with MLS through their vendors, we can also work with you if you have a homegrown system.

And nowadays it’s like the MLS platform of choice. So, we can go into an MLS we launched in Miami. Miami has Matrix and Rapattoni. If you have multiple platforms, that’s fine. Wherever the point of entry comes in, we can distribute those tags to each one of those platforms and make it very easy for the MLS to want to work with us and be very cost effective as well.

It’s been fun working with our partners because we just work alongside their sales reps. They bring us in as the AI product expert. Sometimes the MLS will come to us and ask us to do the demos too. We are super flexible on how we can work.

We’re starting to get more into the consumer facing side of MLS too, which is great to see. MLS is now being given permission to be able to have consumer facing sites as well. So, we’re just all over the place here.

AG:

Pierre, you’ve already mentioned a few high profile partners you work with, but I also remember from our days of due diligence with you for a partnership that you have a pretty robust API. So, maybe just talk a little bit about both how you support partnerships, but how you’re flexible in terms of integration.

PC:

Now APIs are table stakes. You can’t just come to a company and say, “Put this NASCAR widget into your experience.” So, we’re very much API forward. We make it really easy to integrate. If I went through 15 customer sites or integrations, they’re all unique and different. And that’s an option.

There are other people who just want to have it easy and just go and run with it. Having APIs, having flexibility, being able to be a good partner and matching the needs of that client is really crucial.

Prioriziting data privacy for users

PC: I think somebody had a question about data, which I’ll just talk about for a second. From our side, we don’t collect any personal identifiable information from anybody. The things that we look at is the community, the outside of the four walls aspect of the asset. We understand what’s happening on the search side of it. But we don’t collect any personally identifiable information or sell it.

AG:

The question, just to clarify for the audience, that came from one of our attendees is, “How does selling or partnering of MLS data maintain and respect the privacy of the users and the data that these systems generate?” I think it’s really important that we’re talking about features and characteristics of a neighborhood or features and characteristics of a property as opposed to personally identifiable information, which is a little bit more of a sticky wicket there.

LL:

We store no data. We used the restful API. So, at the most, the data enters our system for 10 minutes, 15 at the maximum. We’re able to extract and tag the imagery and we push it along. We store no data, we sell no one’s data. We’re very, very strict about that.

Preserving data integrity and longevity

AG:

That’s great. Another thing — speaking of data, working with data, and acquiring data — Pierre, you talked a little bit about focused data strategy and how they might want to consolidate their strategy. Tell me from your perspective, because you’re both industry veterans. I certainly have my opinions on this, too.

What should you the industry, whether it’s an MLS, a broker, or a portal, think about when they’re acquiring data or working with data in terms of integrity and longevity? So, I’ll start with you Pierre. What should we be thinking about or what kind of questions should they be asking that maybe they don’t know to ask?

PC:

The source of data is always good to know. The frequency of updates, ownership rights — all these things should be spelled out black and white in these agreements. Is there any data going two ways? Is is one way?

Like Lisa just said, Restb a great example of that. I think they’re only holding MLS data for 15 minutes. We don’t touch listing data at all. We don’t even touch it. We only receive a lat lng and send back information as an example. It really depends on where we sit talking to general data partners. Yet those are the things that you want to look at.

I think in some cases you want to understand the provenance of the company, where they come from, what their goals and motivations are. And these are all just standard things. But I think understanding the business that they’re trying to build really helps understand where they’re headed.

We clearly are a location intelligence company. To us, location is everything. Everything outside the four walls is what matters. And that’s where we sit. And we are really happy to become the masters of that and really bring that expertise to our clients.

Lastly, I think is really companies that are able to roll with you. I think Lisa gave great examples of having multiple integration partners and being API forward. This industry has been plagued by garden walls. Like we don’t play with that company, they don’t integrate. That is over. Those days are over. It’s really clear. And so making sure that you’re not playing with somebody in that environment. That if you switch vendors, you don’t lose your services.

These are things that are table stakes. And having good contract terms that allow you to be flexible in your business. I think those are the key things that I always looked at.

Maximizing your data solution with a reliable support team

AG:

I would add to something that’s sometimes a little harder to quantify unless you have some experience in the industry — which is the reputation of the teams for supporting the products and sitting down with you and identifying the right solutions. I know in working with each of your teams that we spent a considerable amount of time trying to figure out what a premium experience inside these highly customizable applications is.

Pierre, I spent a lot of time with your team trying to figure out how this would work the best in Matrix or in the client cloud portal. Lisa, with your team, listing data checker was a well used product at CoreLogic, but certainly had some of its own limitations in the enhancements that Restb brought through. A lot of ideas that came from your teams is really something that took one plus one and made it four.

While it’s hard to quantify, I always recommend to folks, when you’re looking for a data provider, the cheapest isn’t always the best way. I want to remind folks, feel free to use the Q&A to submit your questions.

Maximizing your data solution with a reliable support team

AG:

I would add to something that’s sometimes a little harder to quantify unless you have some experience in the industry — which is the reputation of the teams for supporting the products and sitting down with you and identifying the right solutions. I know in working with each of your teams that we spent a considerable amount of time trying to figure out what a premium experience inside these highly customizable applications is.

Pierre, I spent a lot of time with your team trying to figure out how this would work the best in Matrix or in the client cloud portal. Lisa, with your team, listing data checker was a well used product at CoreLogic, but certainly had some of its own limitations in the enhancements that Restb brought through. A lot of ideas that came from your teams is really something that took one plus one and made it four.

While it’s hard to quantify, I always recommend to folks, when you’re looking for a data provider, the cheapest isn’t always the best way. I want to remind folks, feel free to use the Q&A to submit your questions.

What’s coming up next in 2023?

We’re starting to wind down here, but I want to make sure that I give you both the opportunity to talk about where you’re headed. 2023 is around the corner. What does that road map look like for you? What are you all thinking about in terms of what’s next? Lisa, I’ll start with you.

LL:

If I could just add a little bit to that last question that you had. Whenever we work with a partner, even if we do it in tandem with one of our MLS vendor partners, we still have open communication with that MLS customer. We open Slack channels. When those MLS do get a chance to experience how hands on we are, we’re very engineer and developer heavy.

We have a very talented team that loves what they do. We launched in Miami, for example, Bill Cole said, “I knew you said it was fast and accurate, but this is wicked. And your team responds immediately and that’s a modern tech company for you.”

We’re very responsive. We believe in creating win-wins. To Pierre’s point, if you do go to another platform or you add a new platform or a new product, we want to be a part of that solution. It’s very important that we have that ongoing repertoire and relationship with the MLS, in addition to the other partners involved with whatever platform we use.

As far as where we’re going… MLS is really getting out in front of their data. How are you going to leverage it? How are you going to generate revenue? We’ve always talked about this idea of like nonmember dues. This is really happening.

I encourage anybody on this call, reach out. Amy, I know that you’re doing some really great stuff with REdistribute and Pierre. We are more than happy to help you go down that path of distributing your data and what additional data we can put on it. Not only do we put the tags on it, we also have built our own proprietary property condition, property score model. That’s massive. Nobody has this. For us to deliver this to a MLS, to be able to go out there and add that to your dataset, is a huge win for you.

Please reach out to me or anybody on my team. We would be more than happy to set up a demo and show you what that looks like. And I think you’ll be pleasantly surprised on what the ROI and return is.

AG:

It’s exciting work that you guys are doing. Pierre, we did get a question from the audience and it looks like it’s targeted at you. Do you offer transportation cost, either financial or greenhouse gases, by transportation mode (car bike, transit, etc.) to further assist the different personal and societal impacts of the site?

PC:

In terms of what’s coming next, this question is right in that wheelhouse. We just launched our Wellness score, which is a new way to look at location where we’re understanding healthy food, activities, like gyms and fitness, parks, and the things that contribute to it too, especially post-COVID.

It became very apparent to everybody that your home was really much more than a home now. The area really mattered tremendously more than before, because if you were stuck in your house or you’re working from home, you now needed to get a quick access to things.

Taking a quick walk during a meeting became the thing that people did to stay in shape and to not be stuck in a house. So, if you lived in a neighborhood that wasn’t walkable and didn’t have a good stuff to walk through, you now really had a crappy walk.

On the CRE, high-level investor or even retail investor, this question points towards transportation costs or greenhouse gases. We are absolutely looking at the components that involve the environmental factors for an area. Everything from commute to transportation to the reduction of greenhouse gases. There are different technical things to talk about here, like scope one, scope two, scope three, emissions, and things like that. And there’s a lot of ways to slice that.

I think that as we go forward here, that’s we’re going to be focused on. So no, we don’t have the costs. We don’t have the costs today, but we may have something along the lines of of greenhouse gas emissions or things like that as we go towards the future. But what’s definitely in the near future is looking at quantifying location across the U.S. and Canada. We can now start to build predictive models as to where locations are going and how locations are changing. We can start to do things that bring more value to the space and are more indicative of if you want to find the deal.

If you’re trying to find a spot that maybe is on the upswing, that is changing, or that maybe more restaurants or transit are coming in. How do you spot those things as a consumer or as an investor, so that you can make those those decisions with that information too?

There’s a lot of opportunity here still for location. And we’re really looking at that. And I think understanding we’ve already done this work, but really pushing the envelope on quantifying the value of location. We know — everybody knows — that locations are everything in real estate. Location, location, location.

If you take an Upper East Side condo and put it into the countryside, the price is going to change. We all understand that intuitively. But what is it like? What is the number for the client, for the park, for the sidewalk, for the parking? What is the value, the financial model to that? We are really honing in on this and I think that will be a tremendous new thing that we’ll have to bring out to the space over the next few months.

AG:

Yeah, I think it’s super fascinating to watch kind of how that changes over time. I’m in an older neighborhood, but across the road from where all the growth is happening. And so something that was a farm yesterday is a new outdoor mall the next day. Som I think the kind of data you guys are bringing to help people understand exactly what you’re saying, sort of those emerging markets or new popularity and things like that. It’s super, super, super interesting.

LL:

Something else I can add to it is we’ve recently been asked to analyze a pretty substantial database where we can go in and identify how green is this property. This is really cool because when the current administration passed down a ruling that said if you reach a certain score, you’re going to get a half a point off your interest rate. That’s impactful, especially at this time that we’re seeing interest rates coming through.

This is how can our technology help us become better human beings and how we cab use this technology to measure the type of properties or neighborhoods that we want to move into. And then what are the benefits that we’re getting in addition to that. Whether you have a better neighborhood because it’s green, a better walk scorem or you got a discount on your interest rate because you bought a house that was more energy efficient.

These are all amazing ways that this data is helping to elevate proptech across the board. We were saying if Local Logic and Restb had a baby, it would be the perfect place to be in a neighborhood.

AG:

That’s true. Lisa, while I have you, we did get a question from the audience and it’s directed at you which is, “What is that property condition score?” Help us understand a little bit about that.

LL:

Our Chief Product Officer has really been heading this up where we’ve worked with Fannie Mae and Freddie Mac and we’ve used the traditional C1C6, Q1Q6 property condition score. I think I had a couple slides there, if you want to pull those up. We are able to go in and analyze a property and say based on these photos, “What is the condition that is coming in on the output? What is the quality of it? Is it despair? Is it poor? Is it average? Good, excellent?”

In addition to that, we’ve been able to combine both the quality and the condition score. We’ve built our own algorithm, which we call the R1R6. And we’re able to, for the first time, show an AVM valuation based on imagery. That’s never been done before.

It’s interesting because this speaks to whether it’s an appraisal. It prevents an inspector from having to go back again multiple times. It really also evaluates the condition of the property and what the true value should be.

You’re seeing a lot of the investors coming in with a sweet spot. So they’re able to say, “If the property is about a 2.8 to 3.2, that’s the lane where we want to get in and we want to be able to invest into this property. Whether we’re going to turn it into a short-term rental, a long-term rental, how are we going to put this in our portfolio?”

There are companies out here that are consuming this data from us and they are quietly mapping the entire country. They want to know what the condition and quality of every single property is in the U.S. This is so huge to be able to have to this.

In addition, it allows somebody to come in and look at a property — not only just on the image level, but on the property level. I think there might be another slide in there that will show that. We can even tell you if one or two of the rooms have been remodeled, but the other ones have it. So, we can look at it not only from an individual image level, but then we can also give you what the property score is on it too.

Pierre, I love what you guys are doing this, where you’re doing your ratings in the neighborhoods, and where does that fall on the rating spectrum. The same thing is happening with this on the property score. So, we really are in line with how we’re working together and understanding that this is the new norm. This is what the Reeds, the Blackstones of the world want. So anybody that works with AVM that’s on this call, please reach out to us. We would be more than happy to show how this would work with your personal AVM.

AG:

Anything too late to clarift? You all know that I could talk about data all day, but I know we’re coming close to our hour here. So, Pierre, I’ll give you one minute and Lisa, you one more minute to talk about what’s left to clarify that you want this audience to know more of.

PC:

We have a new UI coming out this year for the customer-facing side, which is really bringing this to the next level. We’re bringing heatmaps to the listing page, bringing a lot of information to the page, and most of all, bringing more context.

For example, let’s say that a home scores an 8 for walkability. The neighborhood is a 5 for walkability and the average for this whole city is a 7. So, therefore, this home sits in the most walkable spots in the city and in the neighborhood.

That’s the kind of thing that we’re bringing to the listing page in the next year and that’s part of our new UI. It’s  going to continue to elevate the understanding of location and how people can interact with that.

We’re excited to talk to anybody, any ideas, anything, new stuff to do. We love working with our partners and are excited to enter 2023 with a whole new slew of folks on board.

AG:

That’s exciting. Good stuff. Lisa, how about you? Anything I forgot to ask?

LL:

You’ve done such a great job. This has been really cool. And like you, I can talk data forever. Obviously, everybody that’s on this call understands that we are entering into a reset. What data do you have now? How do you want to enhance it? How do you want to improve it? How can you better leverage it?

We’re here to work with you if you want to reach out to us just to come in and give you an overview of what we can do, look in your systems, and show you how we can elevate your systems or improve upon it and on the value. We would love to work with you and talk with you.

AG:

That’s awesome. I do hope people will reach out to you. I think you both have really fascinating and modern solutions here. And I want to thank you both again for inviting me. We do have one last question, “If there’s a poor home score, can this negatively impact sellers that have not updated their properties?”

PC:

The scores are not really good or bad. They’re the reality. If you put ten photos of a house that needs to be gutted, there’s ten photos of a house that needs to be gutted There’s nothing to hide here. And if a neighborhood is quiet, that’s not bad. If neighborhood is loud, that’s not bad either. It just is. And so either I want to live somewhere loud or I want to live somewhere quiet.

So, no, I don’t think there’s any negative impact by a poor score. Actually, for others, that might be the exact reason why the house is for sale and why they want to buy it. Agents always freak out when there is a score on a home. But I think there’s a difference between a score that’s making a decision for someone and a score that’s informing them of something that’s really there.

AG:

I think this era of consumer transparency sailed on. To your point, Pierre, you can look at a kitchen and see that the cabinets are 1980s. I happen to own some right now that are coming out. I love the idea of consumer transparency above all else too.

LL:

Just to add to that. The whole idea for us and entering into that specific product or vertical, it is to remove the bias. We want to remove the bias from any property. It doesn’t matter what zip code you’re in, what race, what religion.

We’ve been working with Fannie Mae and Freddie Mac. They’re actually going to even rename some of the the terms that they used. They’ve asked us to even come in. And it was actually a ruling that we have to remove religious objects from any photo in the sense that a property is a property. If it meets the needs, for whatever reason you’re buying that property, we want to be able to give that to you and remove any bias from it. We are creating better experiences and removing the bias from that. And that really is wholeheartedly our intention.

AG:

I cannot think of a better way to end it. I super appreciate both of you and wish you much success in 2023, if we don’t see each other before then. Thank you again for inviting me to to moderate this. I think it’s really good stuff.

Local Logic

December 12, 2022 | 53 minutes read

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