Standardized and Normalized Data in the Multifamily Industry

Welcome back to the Multi-Family Innovation Podcast. We bring you, the people and the companies redefining the multi-family industry. We have a really great show ahead and we're gonna be talking today about standardized and normalized data and multi-family. Now listen, everyone wants data, but what do you do with it? What are the decisions that you can make with it? What are the decisions you are making with it? Can you even trust the data? Right? So now before I introduce our guests, I want to give you some few important announcements, and that is our Multi-Family Innovation Summit. And the planning is already underway. Uh, you don't wanna miss that. So go ahead and get registered. Go to multi-family innovation.com. You can get registered today. Now, if you're an owner or an operator of 1500 units or more, and you, you want. To make your business better, you need to know about the Multi-Family Innovation Council. Uh, go to multi-family innovation.com, click on council now. It's not just about technology, it's about the process, uh, the leadership and the innovation required to make our business better. Uh, you're gonna get the support from top level executives and owners that are building and scaling, uh, their business. It's peer-to-peer network, uh, that's just, uh, not found anywhere else. So, uh, everything you need to know is on that page. Go to multifamily innovation.com, click on council, and you'll sh you'll have everything you need to know to make that decision now. Today we have Elizabeth Braman on the show. Uh, Elizabeth is the co-founder and the CEO of Revolution Re Now, and this is a company that does data solutions for apartment owners and managers. And uh, also I think that they're helping, uh, technology and other industry related companies. So her company, Collects and standardizes the data. Now, this is, she's doing this for top property management systems and this is also going beyond those systems into other data sources, and she's offering, uh, those services. To, uh, allow you to do comparative analysis, uh, for not only the properties but the portfolios, right? Your company reporting, your portfolio company and the property stuff. Uh, this is allowing companies to do predictive analytics and some other great things with customer data feeds. Now, What's interesting, uh, and why we have her on the show today is, uh, Elizabeth has, uh, a really interesting background prior to, uh, starting Revolution re she's served as the Chief Production Officer of Realty Mogul. Now this was an online, uh, platform, uh, for raising capital for real estate companies through private placements and a plus REIT offerings. So, uh, before even that, uh, she was, uh, part of Realty Mogul. As also the chief product, uh, not production or I've always wanted to say product officer, but it's production officer. We're in the loan, uh, space now and uh, this was with Ready Cap commercial. Now this is a commercial real estate loan originator funding loans through a private REIT offering. Now, what's fascinating about Elizabeth is she's a C C I M designation. For those of you don't know, that's. The Certified Commercial Investment member, that's probably the highest level designation I think you can get in the financial world for real estate and professional management. Uh, she's a licensed attorney in Washington, DC and the Commonwealth of Virginia. She holds a bachelor of Arts degree from. The American University and a Master's of Business Administration and Juris doctor degree from George Washington University, the George Washington University. Elizabeth, welcome in. Patrick, you're hired. I, I, uh, I, I couldn't have, couldn't have asked for a, a better intro that was, Awesome. Thank you. Well, appreciate it and thanks for having me. Absolutely. And you know your background. I, I love the legal and the financial mind as it comes together. And now you're bringing data. I mean, there's no better conversation that we can have with what our industry needs right now, uh, because as you talk about data, Certainly legal comes into play as you talk about, uh, financial, uh, pressures that organizations have today. Uh, you know, data is an asset that, and assets improve over time and data's reusable and I love what you're, you're working on. So let's get to get to know a little bit about, uh, you and, and kind of, you know, how did you end up here solving this big, big opportunity. Well, I, I, I think I, I know I got tremendously lucky because this was, uh, not a short journey. This has been, uh, uh, a marathon and started many years ago now. Um, we had no idea that three, four years ago when we really started in earnest to build out our data model. That we would be building something that would just explode in application with everything ai. So today, I mean, I, I don't have to tell you the last six months, it feels like we've been on the fastest rollercoaster in the world with technology innovation. Just every day it's something new that you didn't even think or believe would be imaginably possible a year ago. And all of these AI applications are completely dependent upon having good, clean data sets. And so when you go into chat G P T or any of these sites, and everyone's probably at least had hopefully an opportunity to go in and test them, they know that if you really are a subject matter expert and you spry and break the system, you can probably figure out a way to. Write a prompt where you, it gets it wrong, it doesn't have the right information, and that's all just a product of garbage and garbage out. Whatever the, the data in the, uh, what do they call LLMs, which are large language models, whatever has built out that language model, if, if it isn't complete or robust or clean data that it's built upon, the outputs are just not gonna be there. And so, Fortunately, what we've built over the last several years has been a standardization, a common model for specifically multi-family and rental properties. The ability to consistent, uh, make the data that we look at for operations and financials consistent across platforms, which is really what's necessary to look at things on a portfolio wide basis. And that came from working at Real Tmobile and having. You know, hundreds of investments that we made on that platform and all of our different partners who operated in their local jurisdictions or had, uh, different property management companies, and we would receive financials and spend a good amount of time really drilling into those financials to see are they on budget, are they tracking their proforma? And yet there's a lack of consistency across. Which is reasonable. I mean, the, everyone's not using the same general ledger and same accounting systems. So putting that into a standardized format was our asset management team's job. It was very time consuming, but a necessary function of, uh, financial responsibility. And then to be able to, um, look at those financials and be able to ask questions of our sponsors, that was really, Uh, a critical component of diving deep into the, to, to the data. So what we did there, we've been able to kind of build out on a larger scale. And with this data model, it really unlocks and not only saves time, but um, creates a common data set that you can query and look at in a very broad sense and use for a lot of the really cool applications that are coming out of today. Yeah. And you know, for our listeners, um, you know, many times we hear like, and, and AI is another. Word that they're going to be hearing more and more and, and, um, you know, it in its application can be mean different things, uh, right. And so as organizations are spending a lot of time inside these meetings, putting together deals, acquisitions of companies and portfolios, trying to understand markets that they're far from in many cases, uh, I can't, we can't underestimate the value of, of this data and what's different about. What you're doing now because they're looking at some dashboards now, right? Um, how, how, how should they even be thinking through data? Uh, if you're a leader that maybe isn't a data scientist, you know that these conversations are coming inside more and more meetings and it's now the tools and some of the transformations are there now for new things to be possible. What, how, if you're a leader, CEO of a management company, Asset company, how are you thinking about data today? Well, if there's, uh, quite, quite a bit of answers to all those questions. So first and foremost, the, the, the dashboards. So when you look at a dashboard, that's really a use case for data, a visualization tool. So we have a BI platform that we offer to clients, but really there's Tableau, there's Power bi, there's doma, there's lots of solutions out there in the market. And. What they are built for is taking the data that you have and telling a story. Uh, our platform is based on multi-family and rental properties, so it saves some time, but there's lots of solutions out there. And we work with, uh, clients who have different visualization tools, but it's really about visualization is about taking the data and telling a story and trying to see. If you're a visual person, how do you see the trends? How do you do comparative analysis? And look for context because numbers on a page don't really tell a story, but a visualization can really show and share whether things are positive or negative trending year over year. Uh, all of those things were fantastic, but it's the underlying data that's so critical to making the right decisions and the underlying data has to be in a consistent and comparable format. So the issue is we had 999,000 plus different rows of financial data. Then that could be pool repair, pool maintenance, pool cleaning, uh, your CapEx on pool. All of those things are really just the cost to maintain a pool, but different general ledgers will have them broken out or described slightly differently. To be able to use machine learning to develop a model that will automatically map all of those different rows of data and map to a consistent and standard format means that you can look at things. Comparatively speaking, is the portfolio that I run and operate. Having the same expense factors on a per door basis as the portfolio I'm looking at acquiring, or the business that I'm looking to acquire, are we gonna be able to bring, and it always comes back to this, can I bring Alpha to the situation? Am I doing things better or are there things I can learn from the acquisition of this asset or portfolio or company? And so not having things in the same format, even if you. You know, I'm not telling people to draw out their general ledger formats. It's more about unlocking the 300 different rows that you have, that you're looking at things very granularly and being able to say, if I'm comparing my asset to another asset, how am I doing? Where do I have opportunities to learn and to improve upon what I'm currently doing by looking at the competitive set. And so having everything in the same construct, in the same environment, in the same format lets you do that comparative analysis. And that's really what's necessary today to use data in an effective way to inform you and to educate you on the questions that you actually wanna know. Yeah. And is in getting that data standardized, is the lender, is the lender's framework sort of like a good starting point for that? And I mean, if we're all going back to sort of famil like the, the risk, they're the best at risk management, right? And, and understanding predictions about, uh, you know, risk. But now I also think about like what you just mentioned is, um, those alpha returns, uh, Know, beating, you know, that's the competitive advantage. If you're a management company walking into a deal, or if you're the asset manager, you're gonna raise capital faster or minute. You know, that's the story that you could visually tell, like, why work with us? Why as a sponsor, why are we different with that data? Right. Absolutely. It's a one of the best, uh, stories when they're telling investors or telling clients. How you're going to improve upon, because literally when you're trying to earn someone's business, all you have is your current performance that you can show and share and how you perform in a particular market. Because in real estate, as ever knows, it's so market dependent. If you own five properties in. Sacramento and five in Los Angeles, your metrics are gonna be very, very different. And even within la, your Sherman Oaks properties to your downtown Los Angeles properties. And so if your customers, your investors, whomever, are looking at you and saying, okay, tell me about your performance. You can compare within your own portfolio, but it's way more meaningful if you're looking at the competitive set. And until now, most of the market data that's, most of the data that's been available has been market data, which means market rents, and that's available publicly available information on sales, on marketed leases. Whereas we're looking at trying to take really the fundamentals of operating data, the marketing, the leasing velocity, uh, president behavior. All of those are the things that can really lend to a much more complete picture of performance. Let, let's lean in into that a little more. Uh, you know, you mentioned some of the market data, but what are some of the other ways that you know, that they can use data that they may not be doing now? Maybe there's not a. They, they, they don't have a benchmark for that, but, um, uh, and, and it's, you mentioned a few use cases there, leasing velocity and these types of things, I think of retention. Um, what, what are your thoughts on that? Well, there's a ton of different use cases. They don't all necessarily require using market data. As a cop, you can look at your own data as a, um, For benchmarking purposes to see how your year over year performance is doing. Are you increasing in efficiency and productivity? How is the team doing? Is, uh, the, are there certain performance metrics that you can see that are lending to greater retention? Um, better rents, uh, less turnover, uh, what's the customer experience? So can you take. Online review sentiment or emails and look at the residents experience and find out from that what the health of a particular property is. How that property is performing. Risk management is, is another huge one. How can you use your data internally to identify opportunities or uh, potential weaknesses in your property performance that you should be? Perhaps spending more time and effort on, um, just overall revenue growth. Are there ancillary revenue opportunities that you may have in part of your portfolio that, uh, a lot of our clients look to test certain technologies, so they wanna be innovative, but they also wanna make sure they get a good roi so they'll roll out. A particular Optech solution on a portion of their portfolio. And then we look to give them kind of a comparative analysis on their ROI from one grouping of assets to another. So that helps'em to make better decisions from that standpoint. And then of course, there's just regulatory compliance. Uh, those are good, good things to use your data to track and make sure you're keeping yourself outta trouble. And. And doing right by your investors and your clients. Yeah. And you know, mentioned to AI earlier and you know, as that continues, the momentum of that or the new UI really accelerated, I think probably the use of it more than anything. But, uh, companies are, are going to wanna start doing more and more things themselves in some cases. And that's interesting to me. I, I, I want to get into how you. Talk about standardization and normalization and, and, uh, transformation. Like there's a lot of words there and how that plays out with what they want to do. Um, But I, I think, uh, from, from our point of view, I, I, you know, more and more companies, if they see themselves as the techno, like the technology company themselves, they can. If they can standardize this data, get it, um, normalized and visualize it in a way that you now have new stories to tell that you're as an asset manager. When you're talking about Alpha, I'm looking at it like, if I'm taking over a portfolio, I'm gonna increase the enterprise value of this. Asset because it's gonna tell a story and, and open up opportunities, uh, to find different ways to the yield than just raising rents. And, and, and, and maybe even, there's things that you can do in, in energy efficiencies and all kinds of things that I'm sure once you have the data, you can, you can start making those decisions and stuff. Like what? Absolutely. Am I off on that or No? No. I think that's awesome. And I, I've, I've oftentimes encouraged, um, Fund managers who have a niche of a particular business strategy, that they are going deep to use their data to reinforce and to prove out their business model because nothing is more powerful than going to an investor and saying, we thought that this particular market was growing. We tracked it. We looked at the metrics. We were on top of it. We bought early and. This is how the data proves that comparative to other markets, our business strategy, uh, for fruit that we were correct and being able to track in real time. That was something, uh, that I was, I was really, uh, proud of that we built a proforma tracker so that when you underwrite a deal, you can then track that proforma to your actuals to see. Did I get my proforma correct when I was underwriting the deal initially? And then when you're basically during your hold period, starting to see where there are areas to retrain your team on, on, on things that you get wrong. So you may be too conservative on your rent growth. You may be overly conservative or under conservative on your maintenance expenses. And if you're looking at. 10 properties that you underwrote and looking at those proforma to actual and C trends in your underwriting, you can train yourself to be a better analyzer of properties going forward, which is also another great thing to be able to share with your investors that you iterated on your own business practices and got smarter and better just like a machine learning model that you trained yourself. To do things better and more efficiently over time. Yeah. And if you had one strategic business unit and you know, one property, one portfolio, uh, that in itself is an undertaking, right? Uh, and some people have taken a path to success that didn't include a lot of data, and they've created a lot of wealth, uh, in, in that process. Now you enter a third party world and where you're, you have clients with multiple. Uh, systems that they're using. And so I, I get this challenge of standard. Can we lean into, um, what is standard, standardized, um, data and what is normalized data? Is it the same thing? Is it different? And, uh, like, I guess that's one question. The second part of that question is like, when your data's coming from all these different places, is this what you're doing when you're collecting it and moving it into one place? Yeah, so it's uh, we call it an etl. It is a extract, transform, and load. So we extract data from these systems, we transform it, and basically we have these ETLs that we've built that put the data from their originating format into a consistent format across our common model, and then transform it into kind of a common data set that can be queried. So we just, uh, started our, uh, initial beta with some natural language queries. So you can actually ask across the portfolio, how is this, uh, what, what is my availability over the next 30 days? What is, uh, rent trends for a two bedroom? So adding natural language is something that when you have a consistent data set, you can do. And to your question about standardizing, normalizing, it's essentially the function of cleaning your data or putting it into that, uh, common consistent format. And I always use Excel because I'm a real estate person and we love Excel. Sure. And if you have three analysts who are, we're all working on deals, and those three analysts have, uh, you know, each choose to put the state. In a spreadsheet. One is California, one is ca and then one does C period. A period. Well, the last person's wrong, but the Yeah. Right. Is doing that is, uh, Excel doesn't clean that up. Uh, it, it doesn't automatically know that those are three different data elements. It, it somehow, um, is re, re uh, required that you have to go in and say, okay, I'm gonna, uh, You know, put those into a consistent format. I'm gonna make everything c a, capital C, capital A. Uh, and so that's the process of standardizing data. It's putting it into a consistent format so that these systems, those technologies, uh, are able to identify them, whether it's putting it in the right format. So if that's text versus a number versus a. Percentage or a dollar. All of those are just different data types that if they're not formatted correctly, going in, in some instances, may need some extra work to, to make sure that the formatting and the, um, that they're consistent so that then when you run a query, you're able to query your system and come out with. Accurate results. So that's kind of the process. I, I, I use cleaning as kind of a catchall because it makes sense to us that like dirty data doesn't get you accurate results, clean data does. There's a lot more that's involved. And I'm sure my, my, my team would, uh, you know, slap my hand up, may oversimplifying it. But I mean, you've got, uh, so much dimension in real estate data. We're looking at data with. Daily grain, so like looking trends over time. So you've got timing elements of it. You have dimensions of, I wanna see two bedrooms, which are a type of unit, which is a construct of the property or the building. So all of these are things that when you're looking at the dimensions, they make the data more layered and more complicated. And require that you have it in a consistent format, um, from all these systems, because if you don't, then it's just kind of garbage in, garbage out. Yeah, no, I think you did a great job explaining that. I mean, you think about date formats and, uh, unit Yeah. Naming conventions of buildings and, and just, yeah. I mean, that's the hardest thing, is that right? Like if it's named, you know, uh, the brownstone in one place in Brownstone and another. There's work to be done. Uh, so it's possible. People are the system. Can I get, people are looking at dashboards now and there's maybe some false, uh, positives in some of the, you know Yeah. The reporting that we have. Yeah. Yeah. Um, what, uh, when you're, when you're in these conversations, what's driving the, where people are leaning in? Is it the financial, is it the marketing? Uh, cuz I gotta imagine this also plays out in, in just different marketing sources and, um, you know, um, calculating, you know, return on, on dollar spent for those activities as much. Absolutely. So we do see a lot of interest in doing things like granular unit level renovation tracking, like that's a big one, uh, because it's, it's challenging to look at things kind of at that, um, uh, mo the, the more, uh, kind of basic. Uh, level, you know, you're not looking at it at a property, but you wanna see how your renovated units across your portfolio. Well, that's hard to do, uh, to look at how all your renovated units across your entire portfolio are, uh, days on market, or turn time or budgets like to do cross property analysis, slicing and dicing things. In more unique ways, I think has, has been one of the more interesting things that, that people, uh, look at or wanna, want to drill into. Um, but portfolio level analysis has always kind of been somewhat challenged if you have data coming from different sources. So a lot of people are needing that automation of reporting just to communicate more effectively. With their investors. And then some people get really excited about all the predictive analytics and how do you use historic data to look forward and project how performance should be over the next 3, 6, 12 months. So that's also kind of, it, it, it really depends on who you're talking to and what their perspective and what their job responsibilities are. I think. Some people are more forward looking, some people wanna use for training their, their teams, seeing what's the common thread about the best performing team, teammates at the property, what makes a manager effective and another manager not as effective. So it's, it's usually pretty specific to the stakeholder, how they wanna use the data and what they want to learn from the data. But there's obviously. Just unlimited use cases when you have all this data and the ability to unlock it. Yeah, that's interesting. And I, I think of like the credit score, the data, the amount of data in, in that, and, you know, the 1 1 3 digits can tell a story, you know, uh, around risk. And we've been using that for a long, long time. And that that whole industry and that whole market of financial value, I think is, is probably shifting quickly. But, um, what, what, what can we learn from either. Other industries or who's gone before us, that would give us the assurances, the certainties. Like, Hey, there's value in spending some time to understand this, to have the conversations, to move these things forward. And then like, what would a leader do first if, if you were running a company? Oh, it's a, it's a good question. I think you would, a, as a leader of these companies, you have to look around and say, uh, do I have a team that would be committed to. Having a data strategy and u using data most effectively because it is a commitment to training your team to ex exploration, seeing, um, because if you do go about it with either, uh, lack of structure or lack of focus and direction. Not likely much is gonna happen. If you try and go too broad like you wanted to do everything, you'll likely be disappointed. So setting your expectations reasonably as to what your team is most interested and passionate about and where your skillsets lie. Where do you really wanna use the data to support your current business practices and expand upon them. And do you have the, the team that's committed to making that a reality and then bite size chunks? Because it depends, you know, you may have already a clean database of 10 years worth of data and no problems. Uh, some companies, uh, find that that's not the case. Um, there's or data input or there's change in ownership. Which means people are changing how they track their data. Uh, so starting with making sure that your data sets and sources are accurate or as clean as could be, what data sources you wanna work with. And I would even work backwards with what are we hoping to accomplish? What are our measurements of success? Where do we think that we as a team can do best? By setting goals for all of us that are achievable, measurable, and worthwhile. And then getting everyone involved in participating in execution of that plan. And that's, you know, basic business practices for any company. Most companies. Use data for business intelligence. Large companies use all of their data to drive decision making. Real estate is really hard to do that. It's been a traditionally, uh, fragmented industry. It's been a, uh, you know, the, I was looking at the numbers, like the top 50 owners of apartments. Or less than 5%. 5% of the pot of, of, of total ownership. If you look at hotels, branded hotels, it's like a quarter. I mean, like it's a big difference. There is very small numbers of people that own large portfolios of commercial real estate. So property management companies have kind of been filling in the bill. But when you look at 44 million units, And the top property management companies in the us, if you add up all their units, it's, you know, less than 2 million. Like that tells you something. The data is really, really hard to get. Professional ownership has been around for what, 20 or so years. Um, there's a reason why it is so challenging to use rental property data in order to do a lot of these really cool applications. So that, that kind of informs start with something that's really targeted, focused and, um, leading. Yeah. And if you're not building this technology, then it's, it's hard to evaluate it. Right. Uh, in terms of how do you even, what are the questions if I was meeting with you right. What, what, what should I be asking you, you know, uh, about how these things work, how it will work, how much time it would allow my team to make. It happened and, and uh, you mentioned the 44 million and, and I agree, I mean a small amount of the market share is the large companies, but we tend to tend to look at the large companies, which is great cuz they're setting a enterprise value standard. Uh, but it sounds like, are you giving the small, I don't wanna say small owner, I mean someone has a thousand units. That's a significant. Portfolio. Um, are you giving them enterprise value that otherwise they don't need to beef up this organization or like, is that what this, what you're doing? Well, absolutely. We work with smaller clients who do not have a large data team on staff, but are very data forward and see the value of being able to use their own data to make more informed decisions. Um, A lot of our clients are small operators that just have worked at big companies and know how they want things done. They're very specific in how they want to be able to present to investors, or they want to be able to track. Uh, a lot of our owners wanna track their manager's performance and, uh, be able to stay on top of things. So it's, uh, worthwhile for smaller groups as well. To use data strategies. You don't have to be a large corporation. We also see companies where they're just launching a new fund and so they wanna use data from day one so that they can build that, um, internal best practices and always have their data as opposed to kind of going back and retroactively having to build it out. So starting do it right the first time because they've been at other companies or they just see the value of. Of starting from scratch with all their data. Yeah. And I, you know, look at like a new acquisition in the, the sort of the beginning balance entries. Right? Uh, and, and you, you sort of have all these due diligence historicals. Um, are you seeing companies, uh, make decisions to go back and, and clean and standardize, or is it more a move, move forward scenario? It's, it's both. Both. Uh, definitely companies go back and when you, uh, UTI utilizing our platform, they are actually standardizing their historic financials. It's, it's generating a standardized data set from the date that they push the data in, uh, to today so they can see historic financials and then use that data set to model things going forward. So that's, For sure. Why, why some companies, uh, wanna work with us, um, is to kind of build up that, uh, data repository to supplement either what they're currently doing. Um, you can add comp data or look to add a additional third party data sets. We have folks who want to add non-traditional. Data sets to their current portfolio to see if there's trends that perhaps the market didn't see. Um, who knows rent growth higher in markets that have better walk scores. What's the seasonality impact of. Uh, mark, you know, in a particular market, all of those are things that may require them to go and pull additional data sets and layered in to give themselves a, a better picture and tell a story, uh, from the data that, that they wanna know. Sure. And you know, as we know, technology opens up marketplaces and new opportunities. I'm curious what, what you see the opportunities as a leader inside these organizations. Um, you know, I. In Champion being a champion of, of strategies like this, leveraging, you know, like partnerships with your company and others, uh, to make these businesses better. Like what, what do you tell those leaders? Maybe they see an opportunity and they go for something new and can you design it? Uh oh, absolutely. Yeah. No, we've, we've definitely, uh, talked to several groups that have large data sets about how would we. Tap your historic data to identify a area where you have been outperforming the market or where we can look at building out a predictive model that will tell us something that is unique or different. Um, you know, people have used revenue management systems. For quite a while now, and those models are being revised and revisited and it's important that you look at supply and demand in the market for dynamic pricing. That's great. It's also gotten a bit of a bad wrap in some areas where, uh, you know, the community sentiment is that owners are just increasing rents whenever they can. Um, there are ways that you can also. Increase revenue without just increasing rents. You can look at ancillary income. You can look at, um, supplying additional services to residents that they would otherwise pay for elsewhere. Um, there's, there's ways to look at increasing your lease length, which decreases your days on market and increases your yield. We built a model. For our, uh, lease turnover so that you can identify ways to just overall better improvement of your property operations and revenue by, by using the data to inform these decisions. And it, it may be that you can also share that you're giving huge concessions, um, to folks. Uh, if you're getting a bad wrap for just increasing rent, you can say, Hey, wait a second. You know, we actually were, um, not just increasing rents. Our revenue came from these other sources. So using the data to tell a story that perhaps helps with reputational risk, uh, that's been kind of been occurring over the past couple of years, uh, for obvious reasons. Yeah, I mean, everyone wants access to that customer and, you know, I think about like, This is the early days of this data conversation, I would imagine. Um, because you look at like the airlines, which is a very regulated, complicated. You've got life safety, you've got, um, You know, you, you have a lot of different disciplines, um, and you have the physical plant of things and, um, whether, I mean, so many things that play out, uh, around what, what the customer needs and, um, the ability for them to meet the expectations of fulfilling services. And then you look at. The mileage program that sort of sits on top of everything, and I, I think someone told me that that's even sometimes more valuable than the airlines themselves. You know, the relationship with the customer, knowing, you know, people want certain things and all that stuff, but, I get excited about the things you're working on because I think it opens up the opportunity to really integrate this industry into, um, a bigger part of the world in that companies in terms of even acquiring other companies, you start entering into like where American Express is excited about this industry or, uh, even Wall Street. I mean, we really are, because you mentioned the fragmentation of the way assets are structured and owned and the timelines of which they're. Operated, uh, with new data, I think more possibility opens up more transparency, more opportunity for us to grow income without necessarily just focusing on the one line item, the rent growth, and, uh, you know, I mean, you've got a lot of work to do. Yeah, absolutely. I mean, think about it, there is literally, uh, and you know, a couple companies I would say, uh, put them aside, but like there really is no brand. In multi-family, there is no consistency of experience. Imagine, um, that you're a multi-family owner that has created a brand that follows the resident from market to market as they move on and goes with them from their post-college or college experience. Post-college to. Family and cultivates them into a homeowner at some point, or even a lifelong renter at some point, because you built loyalty by giving them consistency of experience and a superior customer experience throughout their entire lifetime. And that journey is not something that can be done without having a data-focused approach. Knowing that customer, knowing with the lifetime value of that customer, which is not a concept that multi-family is really, uh, drilled into, we actually have a report that does cost to customer acquisition and lifetime value. And let me tell you, it is not a common, uh, theme among multi-family operators. Most of the property management systems, when you renew a lease, It's as if it's a new lease. So they look at lease length as opposed to tenancy. How long has this person lived here? And without looking, when you're looking at their average lease length, that doesn't tell you how long they've lived in unit. That becomes anecdotal. So how do you run a predictive model to know what the likelihood of that person renewing is or not? So all of these things are all tied together. It's a totally different conversation, but it is something that's. Starting to be had. Um, I think multifamily is starting to get there where they're looking and saying, yeah, hospitality has really did a great job of building a brand and a consistent experience and, um, using that to elongate or to improve upon the revenue generated per customer viewing residents as customers and not tenants. Uh, which kind of is the old, right. We're evolving. Right. And you know, I, I'm gonna play around here a little bit because you think about like, every customer in every unit, homeowner, wherever you are, they're brushing their teeth every day. And you think about like a company like Crest toothpaste or something like that coming in and, and, and, and, and playing a part of. The experience of, you know, brands that our residents use every day are still, um, important, right? Like they, they live in these communities and, um, you know, having that data, um, opens up opportunities to understand the things that these customers need and the value that can be created when you collide them with, you know, the auto industry did this really well. Um, You know, with even, uh, they figured out early, like, we're not really good at making radios and stereos and cars, so they bring in bows or, you know, the other ones and, and customer that's a brand and they just immediately ride along with that brand. Uh, but I'm sure there's some data around those conversations before those deals happened, you know? Um, yeah. So, yeah, no, that's a good example. You've got your bo speaker and your, yeah. Car. And you would never think that Toyota or Honda's gonna be supplying your speakers these days cuz you want, you want something better than what they were offering when they were the ones that were in charge of doing Yeah. Your radio and the speakers were, the, the sound system in your car was just bad. Right. Um, so yeah, partnerships is, is clearly, that's, that's what goes back to the ancillary revenue. How do we. Convince owners and managers that there are so many opportunities to partner on things that can actually increase their margins, and that allows them to improve their salaries to the onsite teams so that those teams can be both better trained, have longer retention. So the trickle down effect of you increase margins by. Encouraging them to sell products, um, that perhaps the resident was already getting previously, uh, but now can feel like it's a, it's almost like an amenity that they're getting. Some of these services right within their home. It's real convenient and they would've paid for it anyway. It's the old AAA brand back in the day around the, the discount, right? Uh, if you're a member here, you, you know, you're, you benefit, your life gets better. And, and it, you're, uh, you know, I always say this around Elon Musk. I pick on these two, uh, Jeff Bezos and Elon Musk. They, and, and multifamily is about creating wealth, right? So it's, it's, uh, and protecting it. For generations. And so when we are going out trying to increase revenue, our goal is to meet investor expectations and returns. But at the end of the day, to the wealthiest people in the world, I'll pick on Jeff Bezos first care about, uh, the, the values of how he's publicly stated this around. He knows in 10 years that they're gonna innovate to knowing that the customer in 10 years will probably want lower costs of things, and they will want to get it in a frictionless way. And Elon Musk was asked during, I think it was the sort of, uh, uh, supply chain issues around, is the cost of the Tesla gonna go up? And he kind of paused and he does this look up thing where he thinks thoroughly through about his answer. And it was, it's not that we're trying to reduce. The price of the car is that we're increasing the value of the vehicle, you know, and so like you go outside and your, your car's sitting all day long while you're working. Well, now you, you park it and then it runs some Uber rides for you and it's making you$1,500 a month. So he is increased the value of the relationship with the. The the company, and I think from a multifamily standpoint, we have these little cities. We can think of them as aggregating the purchase power of all these customers, but we don't have the data yet. We don't have it standardized. We can't go into those conversations to tell those stories yet. To make those Bose kind of partnerships because we don't have the data. And so I think that there's some real opportunities for those asset managers, owners, and operators to have, um, a way to differentiate and, and also this is an affordable housing story, you know, being able to. Increase the value of the lease by allowing the customer to have access to things that would give them more purchasing power. Uh, you know, when we look at, um, group buying of insurance for employee healthcare, we know that it's good for people to be healthy. They'll show up at work, they'll be more productive, and it's the right thing to do. But for our residents, imagine a situation where we're providing group buying for healthcare and you know, these types of things that, uh, there's wide markets are open for that, but you have to have the data and, you know, certainly, oh yeah, no, we, we, uh, I have, uh, a friend who was offering as an amenity, um, the, uh, medical. Insurance for, uh, doing, what is it called? The in-home healthcare and calling Yeah. Right. Just, uh, to, to do, uh, phone calls for their doctor's visits, which improves the overall experience health of their residents. It improves the experience offering, um, tax preparation services offering. Uh, Language classes. Um, even some of the high, high end, uh, properties have the concierge services, the um, uh, you know, it, it used to be Taco Tuesdays. Now it's, you know, uh, we're gonna bring in a sommelier and have them, uh, teach us all wine tasting. But those experiences, those amenities have a value to them that sometimes is hard to quantify. Um, so without looking at a large swath of data and looking at sentiment analysis, how do you determine what your ROI is on Taco Tuesday? Uh, it's using sensors to see who showed up that's looking at the data over a period of time to say that your retention, uh, increases. After someone engages with three different onsite events, and then looking at your ROI overall for these amenities, these events, and seeing is it worthwhile based on how it impacts my current resident, does it impact my online reviews, which are the number one way that most people find their housing these days? And so am I getting more traffic? From online leads. Whenever someone posts a positive review based on their experience at Taco Tuesday, and they thought the tacos were great, or they thought they were lousy, and they give us two stars, and now we have to respond to and apologize for ordering from the wrong place. But all of this is, it's, it's someone's house, it's their home, it's their community. Um, these are things that in today, it matters. It used to be, That it, it, it didn't matter because we didn't have this online communities that you could share every thought and every bit of feedback that you had. And so it was very hard for people. I remember looking for places 20 years ago and it was really hard to know, is this a good neighborhood? Is this the right apartment? Is this a good idea? And now there's so much information. Online that you can check and one bad review, and you're kind of walking away, it's, it's, um, challenging when the data's out there, uh, that's kind of working perhaps for you or against you. Um, it's incumbent upon us to, to take that data and figure out how to, how to best utilize it. Does it matter or does it not matter? Yeah, that's interesting. And, and you know, look, they're exciting times ahead for all those thinking about how to create value for the customer and investor and the companies and the employees that they serve. So this has been refreshing and I know, um, I'm, I'm sparked to think about all kinds of different ways that we can have follow up conversations. But until then, uh, for the viewers and the listeners are there, um, Are there ways that, uh, you recommend people reaching out and getting in touch? I mean, obviously on, we'll put your information on the show notes. Oh, absolutely. I can be reached liz@revolutionre.com. Would be thrilled to hear from anyone, uh, who has interest in talking about data. Obviously it's something that I'm quite passionate about, happy to discuss, and, uh, Look forward to being a part of the conversation in, in the industry. So thank you so much Patrick. Really appreciate you having me on and look forward to doing. Uh, additional stuff with you guys. Yeah. Awesome. And, you know, listen, let's keep this conversation going on social media. Follow us on Instagram, LinkedIn, anywhere that you follow us online. Go ahead and, uh, give us a shout. You'll get some behind the scenes access of what we're doing to spend time with some of the most amazing forward thinking leaders in the industry. And if you want. Notes or sh uh, you know, links to what we've mentioned in this episode, go to multifamily innovation.com. Now. Listen, please subscribe, rate, and review this show. What it does is it tells us, number one, we're doing a good job secondly, and allows others to discover and listen to these types of episodes. We'll see you on the next one. See you guys.