Standardized and Normalized Data in the Multifamily Industry

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Welcome back to the Multi-Family Innovation Podcast.

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We bring you, the people and the companies redefining the

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multi-family industry.

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We have a really great show ahead and we're gonna be talking

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today about standardized and normalized data and

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multi-family.

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Now listen, everyone wants data, but what do you do with it?

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What are the decisions that you can make with it?

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What are the decisions you are making with it?

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Can you even trust the data?

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Right?

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So now before I introduce our guests, I want to give you some

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few important announcements, and that is our Multi-Family

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Innovation Summit.

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And the planning is already underway.

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Uh, you don't wanna miss that.

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So go ahead and get registered.

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Go to multi-family innovation.com.

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You can get registered today.

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Now, if you're an owner or an operator of 1500 units or more,

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and you, you want.

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To make your business better, you need to know about the

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Multi-Family Innovation Council.

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Uh, go to multi-family innovation.com, click on council

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now.

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It's not just about technology, it's about the process, uh, the

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leadership and the innovation required to make our business

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better.

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Uh, you're gonna get the support from top level executives and

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owners that are building and scaling, uh, their business.

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It's peer-to-peer network, uh, that's just, uh, not found

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anywhere else.

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So, uh, everything you need to know is on that page.

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Go to multifamily innovation.com, click on

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council, and you'll sh you'll have everything you need to know

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to make that decision now.

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Today we have Elizabeth Braman on the show.

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Uh, Elizabeth is the co-founder and the CEO of Revolution Re

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Now, and this is a company that does data solutions for

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apartment owners and managers.

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And uh, also I think that they're helping, uh, technology

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and other industry related companies.

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So her company, Collects and standardizes the data.

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Now, this is, she's doing this for top property management

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systems and this is also going beyond those systems into other

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data sources, and she's offering, uh, those services.

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To, uh, allow you to do comparative analysis, uh, for

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not only the properties but the portfolios, right?

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Your company reporting, your portfolio company and the

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property stuff.

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Uh, this is allowing companies to do predictive analytics and

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some other great things with customer data feeds.

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Now, What's interesting, uh, and why we have her on the show

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today is, uh, Elizabeth has, uh, a really interesting background

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prior to, uh, starting Revolution re she's served as

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the Chief Production Officer of Realty Mogul.

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Now this was an online, uh, platform, uh, for raising

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capital for real estate companies through private

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placements and a plus REIT offerings.

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So, uh, before even that, uh, she was, uh, part of Realty

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Mogul.

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As also the chief product, uh, not production or I've always

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wanted to say product officer, but it's production officer.

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We're in the loan, uh, space now and uh, this was with Ready Cap

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commercial.

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Now this is a commercial real estate loan originator funding

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loans through a private REIT offering.

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Now, what's fascinating about Elizabeth is she's a C C I M

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designation.

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For those of you don't know, that's.

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The Certified Commercial Investment member, that's

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probably the highest level designation I think you can get

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in the financial world for real estate and professional

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management.

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Uh, she's a licensed attorney in Washington, DC and the

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Commonwealth of Virginia.

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She holds a bachelor of Arts degree from.

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The American University and a Master's of Business

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Administration and Juris doctor degree from George Washington

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University, the George Washington University.

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Elizabeth, welcome in.

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Patrick, you're hired.

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I, I, uh, I, I couldn't have, couldn't have asked for a, a

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better intro that was, Awesome.

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Thank you.

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Well, appreciate it and thanks for having me.

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Absolutely.

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And you know your background.

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I, I love the legal and the financial mind as it comes

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together.

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And now you're bringing data.

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I mean, there's no better conversation that we can have

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with what our industry needs right now, uh, because as you

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talk about data, Certainly legal comes into play as you talk

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about, uh, financial, uh, pressures that organizations

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have today.

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Uh, you know, data is an asset that, and assets improve over

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time and data's reusable and I love what you're, you're working

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on.

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So let's get to get to know a little bit about, uh, you and,

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and kind of, you know, how did you end up here solving this

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big, big opportunity.

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Well, I, I, I think I, I know I got tremendously lucky because

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this was, uh, not a short journey.

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This has been, uh, uh, a marathon and started many years

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ago now.

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Um, we had no idea that three, four years ago when we really

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started in earnest to build out our data model.

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That we would be building something that would just

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explode in application with everything ai.

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So today, I mean, I, I don't have to tell you the last six

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months, it feels like we've been on the fastest rollercoaster in

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the world with technology innovation.

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Just every day it's something new that you didn't even think

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or believe would be imaginably possible a year ago.

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And all of these AI applications are completely dependent upon

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having good, clean data sets.

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And so when you go into chat G P T or any of these sites, and

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everyone's probably at least had hopefully an opportunity to go

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in and test them, they know that if you really are a subject

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matter expert and you spry and break the system, you can

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probably figure out a way to.

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Write a prompt where you, it gets it wrong, it doesn't have

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the right information, and that's all just a product of

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garbage and garbage out.

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Whatever the, the data in the, uh, what do they call LLMs,

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which are large language models, whatever has built out that

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language model, if, if it isn't complete or robust or clean data

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that it's built upon, the outputs are just not gonna be

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there.

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And so, Fortunately, what we've built over the last several

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years has been a standardization, a common model

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for specifically multi-family and rental properties.

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The ability to consistent, uh, make the data that we look at

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for operations and financials consistent across platforms,

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which is really what's necessary to look at things on a portfolio

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wide basis.

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And that came from working at Real Tmobile and having.

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You know, hundreds of investments that we made on that

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platform and all of our different partners who operated

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in their local jurisdictions or had, uh, different property

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management companies, and we would receive financials and

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spend a good amount of time really drilling into those

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financials to see are they on budget, are they tracking their

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proforma?

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And yet there's a lack of consistency across.

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Which is reasonable.

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I mean, the, everyone's not using the same general ledger

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and same accounting systems.

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So putting that into a standardized format was our

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asset management team's job.

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It was very time consuming, but a necessary function of, uh,

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financial responsibility.

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And then to be able to, um, look at those financials and be able

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to ask questions of our sponsors, that was really, Uh, a

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critical component of diving deep into the, to, to the data.

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So what we did there, we've been able to kind of build out on a

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larger scale.

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And with this data model, it really unlocks and not only

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saves time, but um, creates a common data set that you can

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query and look at in a very broad sense and use for a lot of

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the really cool applications that are coming out of today.

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Yeah.

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And you know, for our listeners, um, you know, many times we hear

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like, and, and AI is another.

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Word that they're going to be hearing more and more and, and,

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um, you know, it in its application can be mean

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different things, uh, right.

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And so as organizations are spending a lot of time inside

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these meetings, putting together deals, acquisitions of companies

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and portfolios, trying to understand markets that they're

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far from in many cases, uh, I can't, we can't underestimate

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the value of, of this data and what's different about.

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What you're doing now because they're looking at some

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dashboards now, right?

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Um, how, how, how should they even be thinking through data?

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Uh, if you're a leader that maybe isn't a data scientist,

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you know that these conversations are coming inside

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more and more meetings and it's now the tools and some of the

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transformations are there now for new things to be possible.

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What, how, if you're a leader, CEO of a management company,

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Asset company, how are you thinking about data today?

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Well, if there's, uh, quite, quite a bit of answers to all

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those questions.

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So first and foremost, the, the, the dashboards.

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So when you look at a dashboard, that's really a use case for

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data, a visualization tool.

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So we have a BI platform that we offer to clients, but really

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there's Tableau, there's Power bi, there's doma, there's lots

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of solutions out there in the market.

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And.

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What they are built for is taking the data that you have

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and telling a story.

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Uh, our platform is based on multi-family and rental

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properties, so it saves some time, but there's lots of

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solutions out there.

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And we work with, uh, clients who have different visualization

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tools, but it's really about visualization is about taking

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the data and telling a story and trying to see.

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If you're a visual person, how do you see the trends?

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How do you do comparative analysis?

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And look for context because numbers on a page don't really

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tell a story, but a visualization can really show

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and share whether things are positive or negative trending

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year over year.

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Uh, all of those things were fantastic, but it's the

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underlying data that's so critical to making the right

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decisions and the underlying data has to be in a consistent

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and comparable format.

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So the issue is we had 999 plus different rows of financial

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data.

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Then that could be pool repair, pool maintenance, pool cleaning,

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uh, your CapEx on pool.

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All of those things are really just the cost to maintain a

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pool, but different general ledgers will have them broken

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out or described slightly differently.

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To be able to use machine learning to develop a model that

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will automatically map all of those different rows of data and

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map to a consistent and standard format means that you can look

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at things.

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Comparatively speaking, is the portfolio that I run and

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operate.

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Having the same expense factors on a per door basis as the

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portfolio I'm looking at acquiring, or the business that

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I'm looking to acquire, are we gonna be able to bring, and it

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always comes back to this, can I bring Alpha to the situation?

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Am I doing things better or are there things I can learn from

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the acquisition of this asset or portfolio or company?

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And so not having things in the same format, even if you.

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You know, I'm not telling people to draw out their general ledger

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formats.

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It's more about unlocking the 300 different rows that you

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have, that you're looking at things very granularly and being

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able to say, if I'm comparing my asset to another asset, how am I

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doing?

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Where do I have opportunities to learn and to improve upon what

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I'm currently doing by looking at the competitive set.

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And so having everything in the same construct, in the same

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environment, in the same format lets you do that comparative

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analysis.

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And that's really what's necessary today to use data in

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an effective way to inform you and to educate you on the

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questions that you actually wanna know.

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Yeah.

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And is in getting that data standardized, is the lender, is

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the lender's framework sort of like a good starting point for

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that?

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And I mean, if we're all going back to sort of famil like the,

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the risk, they're the best at risk management, right?

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And, and understanding predictions about, uh, you know,

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risk.

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But now I also think about like what you just mentioned is, um,

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those alpha returns, uh, Know, beating, you know, that's the

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competitive advantage.

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If you're a management company walking into a deal, or if

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you're the asset manager, you're gonna raise capital faster or

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minute.

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You know, that's the story that you could visually tell, like,

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why work with us?

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Why as a sponsor, why are we different with that data?

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Right.

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Absolutely.

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It's a one of the best, uh, stories when they're telling

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investors or telling clients.

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How you're going to improve upon, because literally when

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you're trying to earn someone's business, all you have is your

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current performance that you can show and share and how you

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perform in a particular market.

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Because in real estate, as ever knows, it's so market dependent.

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If you own five properties in.

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Sacramento and five in Los Angeles, your metrics are gonna

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be very, very different.

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And even within la, your Sherman Oaks properties to your downtown

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Los Angeles properties.

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And so if your customers, your investors, whomever, are looking

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at you and saying, okay, tell me about your performance.

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You can compare within your own portfolio, but it's way more

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meaningful if you're looking at the competitive set.

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And until now, most of the market data that's, most of the

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data that's been available has been market data, which means

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market rents, and that's available publicly available

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information on sales, on marketed leases.

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Whereas we're looking at trying to take really the fundamentals

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of operating data, the marketing, the leasing velocity,

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uh, president behavior.

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All of those are the things that can really lend to a much more

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complete picture of performance.

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Let, let's lean in into that a little more.

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Uh, you know, you mentioned some of the market data, but what are

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some of the other ways that you know, that they can use data

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that they may not be doing now?

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Maybe there's not a.

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They, they, they don't have a benchmark for that, but, um, uh,

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and, and it's, you mentioned a few use cases there, leasing

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velocity and these types of things, I think of retention.

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Um, what, what are your thoughts on that?

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Well, there's a ton of different use cases.

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They don't all necessarily require using market data.

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As a cop, you can look at your own data as a, um, For

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benchmarking purposes to see how your year over year performance

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is doing.

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Are you increasing in efficiency and productivity?

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How is the team doing?

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Is, uh, the, are there certain performance metrics that you can

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see that are lending to greater retention?

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Um, better rents, uh, less turnover, uh, what's the

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customer experience?

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So can you take.

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Online review sentiment or emails and look at the residents

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experience and find out from that what the health of a

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particular property is.

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How that property is performing.

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Risk management is, is another huge one.

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How can you use your data internally to identify

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opportunities or uh, potential weaknesses in your property

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performance that you should be?

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Perhaps spending more time and effort on, um, just overall

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revenue growth.

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Are there ancillary revenue opportunities that you may have

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in part of your portfolio that, uh, a lot of our clients look to

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test certain technologies, so they wanna be innovative, but

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they also wanna make sure they get a good roi so they'll roll

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out.

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A particular Optech solution on a portion of their portfolio.

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And then we look to give them kind of a comparative analysis

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on their ROI from one grouping of assets to another.

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So that helps'em to make better decisions from that standpoint.

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And then of course, there's just regulatory compliance.

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Uh, those are good, good things to use your data to track and

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make sure you're keeping yourself outta trouble.

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And.

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And doing right by your investors and your clients.

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Yeah.

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And you know, mentioned to AI earlier and you know, as that

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continues, the momentum of that or the new UI really

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accelerated, I think probably the use of it more than

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anything.

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But, uh, companies are, are going to wanna start doing more

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and more things themselves in some cases.

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And that's interesting to me.

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I, I, I want to get into how you.

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Talk about standardization and normalization and, and, uh,

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transformation.

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Like there's a lot of words there and how that plays out

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with what they want to do.

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Um, But I, I think, uh, from, from our point of view, I, I,

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you know, more and more companies, if they see

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themselves as the techno, like the technology company

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themselves, they can.

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If they can standardize this data, get it, um, normalized and

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visualize it in a way that you now have new stories to tell

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that you're as an asset manager.

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When you're talking about Alpha, I'm looking at it like, if I'm

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taking over a portfolio, I'm gonna increase the enterprise

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value of this.

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Asset because it's gonna tell a story and, and open up

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opportunities, uh, to find different ways to the yield than

00:19:05
just raising rents.

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And, and, and, and maybe even, there's things that you can do

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in, in energy efficiencies and all kinds of things that I'm

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sure once you have the data, you can, you can start making those

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decisions and stuff.

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Like what?

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Absolutely.

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Am I off on that or No?

00:19:21
No.

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I think that's awesome.

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And I, I've, I've oftentimes encouraged, um, Fund managers

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who have a niche of a particular business strategy, that they are

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going deep to use their data to reinforce and to prove out their

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business model because nothing is more powerful than going to

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an investor and saying, we thought that this particular

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market was growing.

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We tracked it.

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We looked at the metrics.

00:19:51
We were on top of it.

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We bought early and.

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This is how the data proves that comparative to other markets,

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our business strategy, uh, for fruit that we were correct and

00:20:07
being able to track in real time.

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That was something, uh, that I was, I was really, uh, proud of

00:20:13
that we built a proforma tracker so that when you underwrite a

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deal, you can then track that proforma to your actuals to see.

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Did I get my proforma correct when I was underwriting the deal

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initially?

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And then when you're basically during your hold period,

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starting to see where there are areas to retrain your team on,

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on, on things that you get wrong.

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So you may be too conservative on your rent growth.

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You may be overly conservative or under conservative on your

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maintenance expenses.

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And if you're looking at.

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10 properties that you underwrote and looking at those

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proforma to actual and C trends in your underwriting, you can

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train yourself to be a better analyzer of properties going

00:21:04
forward, which is also another great thing to be able to share

00:21:07
with your investors that you iterated on your own business

00:21:12
practices and got smarter and better just like a machine

00:21:16
learning model that you trained yourself.

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To do things better and more efficiently over time.

00:21:21
Yeah.

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And if you had one strategic business unit and you know, one

00:21:24
property, one portfolio, uh, that in itself is an

00:21:28
undertaking, right?

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Uh, and some people have taken a path to success that didn't

00:21:32
include a lot of data, and they've created a lot of wealth,

00:21:35
uh, in, in that process.

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Now you enter a third party world and where you're, you have

00:21:41
clients with multiple.

00:21:43
Uh, systems that they're using.

00:21:45
And so I, I get this challenge of standard.

00:21:48
Can we lean into, um, what is standard, standardized, um, data

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and what is normalized data?

00:21:55
Is it the same thing?

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Is it different?

00:21:57
And, uh, like, I guess that's one question.

00:21:59
The second part of that question is like, when your data's coming

00:22:02
from all these different places, is this what you're doing when

00:22:05
you're collecting it and moving it into one place?

00:22:09
Yeah, so it's uh, we call it an etl.

00:22:12
It is a extract, transform, and load.

00:22:15
So we extract data from these systems, we transform it, and

00:22:20
basically we have these ETLs that we've built that put the

00:22:26
data from their originating format into a consistent format

00:22:30
across our common model, and then transform it into kind of a

00:22:34
common data set that can be queried.

00:22:37
So we just, uh, started our, uh, initial beta with some natural

00:22:41
language queries.

00:22:42
So you can actually ask across the portfolio, how is this, uh,

00:22:48
what, what is my availability over the next 30 days?

00:22:51
What is, uh, rent trends for a two bedroom?

00:22:55
So adding natural language is something that when you have a

00:22:58
consistent data set, you can do.

00:23:00
And to your question about standardizing, normalizing, it's

00:23:04
essentially the function of cleaning your data or putting it

00:23:07
into that, uh, common consistent format.

00:23:10
And I always use Excel because I'm a real estate person and we

00:23:14
love Excel.

00:23:15
Sure.

00:23:15
And if you have three analysts who are, we're all working on

00:23:19
deals, and those three analysts have, uh, you know, each choose

00:23:24
to put the state.

00:23:26
In a spreadsheet.

00:23:27
One is California, one is ca and then one does C period.

00:23:31
A period.

00:23:33
Well, the last person's wrong, but the Yeah.

00:23:36
Right.

00:23:36
Is doing that is, uh, Excel doesn't clean that up.

00:23:42
Uh, it, it doesn't automatically know that those are three

00:23:45
different data elements.

00:23:46
It, it somehow, um, is re, re uh, required that you have to go

00:23:53
in and say, okay, I'm gonna, uh, You know, put those into a

00:23:57
consistent format.

00:23:59
I'm gonna make everything c a, capital C, capital A.

00:24:03
Uh, and so that's the process of standardizing data.

00:24:07
It's putting it into a consistent format so that these

00:24:13
systems, those technologies, uh, are able to identify them,

00:24:18
whether it's putting it in the right format.

00:24:20
So if that's text versus a number versus a.

00:24:25
Percentage or a dollar.

00:24:27
All of those are just different data types that if they're not

00:24:31
formatted correctly, going in, in some instances, may need some

00:24:36
extra work to, to make sure that the formatting and the, um, that

00:24:40
they're consistent so that then when you run a query, you're

00:24:44
able to query your system and come out with.

00:24:49
Accurate results.

00:24:50
So that's kind of the process.

00:24:52
I, I, I use cleaning as kind of a catchall because it makes

00:24:56
sense to us that like dirty data doesn't get you accurate

00:25:01
results, clean data does.

00:25:03
There's a lot more that's involved.

00:25:04
And I'm sure my, my, my team would, uh, you know, slap my

00:25:08
hand up, may oversimplifying it.

00:25:10
But I mean, you've got, uh, so much dimension in real estate

00:25:14
data.

00:25:15
We're looking at data with.

00:25:17
Daily grain, so like looking trends over time.

00:25:21
So you've got timing elements of it.

00:25:23
You have dimensions of, I wanna see two bedrooms, which are a

00:25:29
type of unit, which is a construct of the property or the

00:25:35
building.

00:25:36
So all of these are things that when you're looking at the

00:25:40
dimensions, they make the data more layered and more

00:25:45
complicated.

00:25:46
And require that you have it in a consistent format, um, from

00:25:51
all these systems, because if you don't, then it's just kind

00:25:53
of garbage in, garbage out.

00:25:55
Yeah, no, I think you did a great job explaining that.

00:25:57
I mean, you think about date formats and, uh, unit Yeah.

00:26:02
Naming conventions of buildings and, and just, yeah.

00:26:06
I mean, that's the hardest thing, is that right?

00:26:08
Like if it's named, you know, uh, the brownstone in one place

00:26:12
in Brownstone and another.

00:26:15
There's work to be done.

00:26:16
Uh, so it's possible.

00:26:19
People are the system.

00:26:20
Can I get, people are looking at dashboards now and there's maybe

00:26:23
some false, uh, positives in some of the, you know Yeah.

00:26:29
The reporting that we have.

00:26:30
Yeah.

00:26:32
Yeah.

00:26:33
Um, what, uh, when you're, when you're in these conversations,

00:26:38
what's driving the, where people are leaning in?

00:26:41
Is it the financial, is it the marketing?

00:26:44
Uh, cuz I gotta imagine this also plays out in, in just

00:26:47
different marketing sources and, um, you know, um, calculating,

00:26:53
you know, return on, on dollar spent for those activities as

00:26:56
much.

00:26:58
Absolutely.

00:26:58
So we do see a lot of interest in doing things like granular

00:27:02
unit level renovation tracking, like that's a big one, uh,

00:27:07
because it's, it's challenging to look at things kind of at

00:27:10
that, um, uh, mo the, the more, uh, kind of basic.

00:27:19
Uh, level, you know, you're not looking at it at a property, but

00:27:22
you wanna see how your renovated units across your portfolio.

00:27:26
Well, that's hard to do, uh, to look at how all your renovated

00:27:31
units across your entire portfolio are, uh, days on

00:27:35
market, or turn time or budgets like to do cross property

00:27:41
analysis, slicing and dicing things.

00:27:43
In more unique ways, I think has, has been one of the more

00:27:47
interesting things that, that people, uh, look at or wanna,

00:27:51
want to drill into.

00:27:54
Um, but portfolio level analysis has always kind of been somewhat

00:28:00
challenged if you have data coming from different sources.

00:28:03
So a lot of people are needing that automation of reporting

00:28:09
just to communicate more effectively.

00:28:12
With their investors.

00:28:13
And then some people get really excited about all the predictive

00:28:15
analytics and how do you use historic data to look forward

00:28:22
and project how performance should be over the next 3, 6, 12

00:28:27
months.

00:28:28
So that's also kind of, it, it, it really depends on who you're

00:28:32
talking to and what their perspective and what their job

00:28:36
responsibilities are.

00:28:37
I think.

00:28:38
Some people are more forward looking, some people wanna use

00:28:41
for training their, their teams, seeing what's the common thread

00:28:46
about the best performing team, teammates at the property, what

00:28:52
makes a manager effective and another manager not as

00:28:56
effective.

00:28:57
So it's, it's usually pretty specific to the stakeholder, how

00:29:01
they wanna use the data and what they want to learn from the

00:29:05
data.

00:29:06
But there's obviously.

00:29:07
Just unlimited use cases when you have all this data and the

00:29:13
ability to unlock it.

00:29:15
Yeah, that's interesting.

00:29:16
And I, I think of like the credit score, the data, the

00:29:19
amount of data in, in that, and, you know, the 1 1 3 digits can

00:29:24
tell a story, you know, uh, around risk.

00:29:27
And we've been using that for a long, long time.

00:29:29
And that that whole industry and that whole market of financial

00:29:33
value, I think is, is probably shifting quickly.

00:29:37
But, um, what, what, what can we learn from either.

00:29:41
Other industries or who's gone before us, that would give us

00:29:45
the assurances, the certainties.

00:29:47
Like, Hey, there's value in spending some time to understand

00:29:50
this, to have the conversations, to move these things forward.

00:29:54
And then like, what would a leader do first if, if you were

00:29:57
running a company?

00:29:59
Oh, it's a, it's a good question.

00:30:01
I think you would, a, as a leader of these companies, you

00:30:05
have to look around and say, uh, do I have a team that would be

00:30:08
committed to.

00:30:11
Having a data strategy and u using data most effectively

00:30:16
because it is a commitment to training your team to ex

00:30:22
exploration, seeing, um, because if you do go about it with

00:30:27
either, uh, lack of structure or lack of focus and direction.

00:30:34
Not likely much is gonna happen.

00:30:36
If you try and go too broad like you wanted to do everything,

00:30:40
you'll likely be disappointed.

00:30:42
So setting your expectations reasonably as to what your team

00:30:48
is most interested and passionate about and where your

00:30:51
skillsets lie.

00:30:52
Where do you really wanna use the data to support your current

00:30:56
business practices and expand upon them.

00:31:00
And do you have the, the team that's committed to making that

00:31:03
a reality and then bite size chunks?

00:31:07
Because it depends, you know, you may have already a clean

00:31:12
database of 10 years worth of data and no problems.

00:31:16
Uh, some companies, uh, find that that's not the case.

00:31:21
Um, there's or data input or there's change in ownership.

00:31:29
Which means people are changing how they track their data.

00:31:34
Uh, so starting with making sure that your data sets and sources

00:31:41
are accurate or as clean as could be, what data sources you

00:31:45
wanna work with.

00:31:47
And I would even work backwards with what are we hoping to

00:31:50
accomplish?

00:31:52
What are our measurements of success?

00:31:54
Where do we think that we as a team can do best?

00:31:58
By setting goals for all of us that are achievable, measurable,

00:32:03
and worthwhile.

00:32:05
And then getting everyone involved in participating in

00:32:08
execution of that plan.

00:32:10
And that's, you know, basic business practices for any

00:32:14
company.

00:32:15
Most companies.

00:32:17
Use data for business intelligence.

00:32:19
Large companies use all of their data to drive decision making.

00:32:24
Real estate is really hard to do that.

00:32:26
It's been a traditionally, uh, fragmented industry.

00:32:32
It's been a, uh, you know, the, I was looking at the numbers,

00:32:36
like the top 50 owners of apartments.

00:32:42
Or less than 5%.

00:32:44
5% of the pot of, of, of total ownership.

00:32:47
If you look at hotels, branded hotels, it's like a quarter.

00:32:51
I mean, like it's a big difference.

00:32:53
There is very small numbers of people that own large portfolios

00:33:00
of commercial real estate.

00:33:02
So property management companies have kind of been filling in the

00:33:05
bill.

00:33:05
But when you look at 44 million units, And the top property

00:33:10
management companies in the us, if you add up all their units,

00:33:13
it's, you know, less than 2 million.

00:33:17
Like that tells you something.

00:33:19
The data is really, really hard to get.

00:33:21
Professional ownership has been around for what, 20 or so years.

00:33:25
Um, there's a reason why it is so challenging to use rental

00:33:31
property data in order to do a lot of these really cool

00:33:34
applications.

00:33:36
So that, that kind of informs start with something that's

00:33:40
really targeted, focused and, um, leading.

00:33:45
Yeah.

00:33:45
And if you're not building this technology, then it's, it's hard

00:33:49
to evaluate it.

00:33:51
Right.

00:33:51
Uh, in terms of how do you even, what are the questions if I was

00:33:55
meeting with you right.

00:33:57
What, what, what should I be asking you, you know, uh, about

00:34:00
how these things work, how it will work, how much time it

00:34:03
would allow my team to make.

00:34:05
It happened and, and uh, you mentioned the 44 million and,

00:34:09
and I agree, I mean a small amount of the market share is

00:34:13
the large companies, but we tend to tend to look at the large

00:34:16
companies, which is great cuz they're setting a enterprise

00:34:19
value standard.

00:34:20
Uh, but it sounds like, are you giving the small, I don't wanna

00:34:24
say small owner, I mean someone has a thousand units.

00:34:27
That's a significant.

00:34:28
Portfolio.

00:34:29
Um, are you giving them enterprise value that otherwise

00:34:32
they don't need to beef up this organization or like, is that

00:34:37
what this, what you're doing?

00:34:39
Well, absolutely.

00:34:40
We work with smaller clients who do not have a large data team on

00:34:46
staff, but are very data forward and see the value of being able

00:34:51
to use their own data to make more informed decisions.

00:34:55
Um, A lot of our clients are small operators that just have

00:35:01
worked at big companies and know how they want things done.

00:35:06
They're very specific in how they want to be able to present

00:35:10
to investors, or they want to be able to track.

00:35:13
Uh, a lot of our owners wanna track their manager's

00:35:16
performance and, uh, be able to stay on top of things.

00:35:21
So it's, uh, worthwhile for smaller groups as well.

00:35:25
To use data strategies.

00:35:28
You don't have to be a large corporation.

00:35:29
We also see companies where they're just launching a new

00:35:33
fund and so they wanna use data from day one so that they can

00:35:38
build that, um, internal best practices and always have their

00:35:44
data as opposed to kind of going back and retroactively having to

00:35:48
build it out.

00:35:48
So starting do it right the first time because they've been

00:35:52
at other companies or they just see the value of.

00:35:55
Of starting from scratch with all their data.

00:35:58
Yeah.

00:35:58
And I, you know, look at like a new acquisition in the, the sort

00:36:01
of the beginning balance entries.

00:36:03
Right?

00:36:03
Uh, and, and you, you sort of have all these due diligence

00:36:07
historicals.

00:36:08
Um, are you seeing companies, uh, make decisions to go back

00:36:13
and, and clean and standardize, or is it more a move, move

00:36:17
forward scenario?

00:36:19
It's, it's both.

00:36:20
Both.

00:36:21
Uh, definitely companies go back and when you, uh, UTI utilizing

00:36:27
our platform, they are actually standardizing their historic

00:36:31
financials.

00:36:31
It's, it's generating a standardized data set from the

00:36:36
date that they push the data in, uh, to today so they can see

00:36:42
historic financials and then use that data set to model things

00:36:46
going forward.

00:36:47
So that's, For sure.

00:36:49
Why, why some companies, uh, wanna work with us, um, is to

00:36:54
kind of build up that, uh, data repository to supplement either

00:37:00
what they're currently doing.

00:37:02
Um, you can add comp data or look to add a additional third

00:37:08
party data sets.

00:37:08
We have folks who want to add non-traditional.

00:37:13
Data sets to their current portfolio to see if there's

00:37:17
trends that perhaps the market didn't see.

00:37:20
Um, who knows rent growth higher in markets that have better walk

00:37:26
scores.

00:37:27
What's the seasonality impact of.

00:37:30
Uh, mark, you know, in a particular market, all of those

00:37:33
are things that may require them to go and pull additional data

00:37:37
sets and layered in to give themselves a, a better picture

00:37:42
and tell a story, uh, from the data that, that they wanna know.

00:37:48
Sure.

00:37:48
And you know, as we know, technology opens up marketplaces

00:37:52
and new opportunities.

00:37:53
I'm curious what, what you see the opportunities as a leader

00:37:57
inside these organizations.

00:37:59
Um, you know, I.

00:38:01
In Champion being a champion of, of strategies like this,

00:38:06
leveraging, you know, like partnerships with your company

00:38:09
and others, uh, to make these businesses better.

00:38:12
Like what, what do you tell those leaders?

00:38:15
Maybe they see an opportunity and they go for something new

00:38:17
and can you design it?

00:38:19
Uh oh, absolutely.

00:38:21
Yeah.

00:38:22
No, we've, we've definitely, uh, talked to several groups that

00:38:27
have large data sets about how would we.

00:38:30
Tap your historic data to identify a area where you have

00:38:37
been outperforming the market or where we can look at building

00:38:44
out a predictive model that will tell us something that is unique

00:38:49
or different.

00:38:51
Um, you know, people have used revenue management systems.

00:38:56
For quite a while now, and those models are being revised and

00:39:00
revisited and it's important that you look at supply and

00:39:04
demand in the market for dynamic pricing.

00:39:07
That's great.

00:39:08
It's also gotten a bit of a bad wrap in some areas where, uh,

00:39:14
you know, the community sentiment is that owners are

00:39:17
just increasing rents whenever they can.

00:39:20
Um, there are ways that you can also.

00:39:23
Increase revenue without just increasing rents.

00:39:27
You can look at ancillary income.

00:39:29
You can look at, um, supplying additional services to residents

00:39:37
that they would otherwise pay for elsewhere.

00:39:41
Um, there's, there's ways to look at increasing your lease

00:39:46
length, which decreases your days on market and increases

00:39:50
your yield.

00:39:50
We built a model.

00:39:52
For our, uh, lease turnover so that you can identify ways to

00:39:57
just overall better improvement of your property operations and

00:40:02
revenue by, by using the data to inform these decisions.

00:40:06
And it, it may be that you can also share that you're giving

00:40:12
huge concessions, um, to folks.

00:40:16
Uh, if you're getting a bad wrap for just increasing rent, you

00:40:20
can say, Hey, wait a second.

00:40:21
You know, we actually were, um, not just increasing rents.

00:40:27
Our revenue came from these other sources.

00:40:29
So using the data to tell a story that perhaps helps with

00:40:33
reputational risk, uh, that's been kind of been occurring over

00:40:40
the past couple of years, uh, for obvious reasons.

00:40:44
Yeah, I mean, everyone wants access to that customer and, you

00:40:47
know, I think about like, This is the early days of this data

00:40:52
conversation, I would imagine.

00:40:53
Um, because you look at like the airlines, which is a very

00:40:59
regulated, complicated.

00:41:01
You've got life safety, you've got, um, You know, you, you have

00:41:06
a lot of different disciplines, um, and you have the physical

00:41:09
plant of things and, um, whether, I mean, so many things

00:41:14
that play out, uh, around what, what the customer needs and, um,

00:41:20
the ability for them to meet the expectations of fulfilling

00:41:24
services.

00:41:25
And then you look at.

00:41:26
The mileage program that sort of sits on top of everything, and

00:41:30
I, I think someone told me that that's even sometimes more

00:41:33
valuable than the airlines themselves.

00:41:36
You know, the relationship with the customer, knowing, you know,

00:41:40
people want certain things and all that stuff, but, I get

00:41:44
excited about the things you're working on because I think it

00:41:46
opens up the opportunity to really integrate this industry

00:41:50
into, um, a bigger part of the world in that companies in terms

00:41:56
of even acquiring other companies, you start entering

00:41:59
into like where American Express is excited about this industry

00:42:02
or, uh, even Wall Street.

00:42:04
I mean, we really are, because you mentioned the fragmentation

00:42:07
of the way assets are structured and owned and the timelines of

00:42:11
which they're.

00:42:12
Operated, uh, with new data, I think more possibility opens up

00:42:17
more transparency, more opportunity for us to grow

00:42:21
income without necessarily just focusing on the one line item,

00:42:24
the rent growth, and, uh, you know, I mean, you've got a lot

00:42:29
of work to do.

00:42:30
Yeah, absolutely.

00:42:32
I mean, think about it, there is literally, uh, and you know, a

00:42:36
couple companies I would say, uh, put them aside, but like

00:42:39
there really is no brand.

00:42:42
In multi-family, there is no consistency of experience.

00:42:47
Imagine, um, that you're a multi-family owner that has

00:42:53
created a brand that follows the resident from market to market

00:42:58
as they move on and goes with them from their post-college or

00:43:03
college experience.

00:43:05
Post-college to.

00:43:07
Family and cultivates them into a homeowner at some point, or

00:43:11
even a lifelong renter at some point, because you built loyalty

00:43:16
by giving them consistency of experience and a superior

00:43:20
customer experience throughout their entire lifetime.

00:43:23
And that journey is not something that can be done

00:43:27
without having a data-focused approach.

00:43:30
Knowing that customer, knowing with the lifetime value of that

00:43:33
customer, which is not a concept that multi-family is really, uh,

00:43:37
drilled into, we actually have a report that does cost to

00:43:41
customer acquisition and lifetime value.

00:43:43
And let me tell you, it is not a common, uh, theme among

00:43:48
multi-family operators.

00:43:49
Most of the property management systems, when you renew a lease,

00:43:53
It's as if it's a new lease.

00:43:55
So they look at lease length as opposed to tenancy.

00:43:58
How long has this person lived here?

00:44:00
And without looking, when you're looking at their average lease

00:44:03
length, that doesn't tell you how long they've lived in unit.

00:44:08
That becomes anecdotal.

00:44:09
So how do you run a predictive model to know what the

00:44:12
likelihood of that person renewing is or not?

00:44:17
So all of these things are all tied together.

00:44:19
It's a totally different conversation, but it is

00:44:22
something that's.

00:44:23
Starting to be had.

00:44:25
Um, I think multifamily is starting to get there where

00:44:28
they're looking and saying, yeah, hospitality has really did

00:44:31
a great job of building a brand and a consistent experience and,

00:44:39
um, using that to elongate or to improve upon the revenue

00:44:46
generated per customer viewing residents as customers and not

00:44:51
tenants.

00:44:52
Uh, which kind of is the old, right.

00:44:55
We're evolving.

00:44:56
Right.

00:44:56
And you know, I, I'm gonna play around here a little bit because

00:44:59
you think about like, every customer in every unit,

00:45:02
homeowner, wherever you are, they're brushing their teeth

00:45:05
every day.

00:45:06
And you think about like a company like Crest toothpaste or

00:45:09
something like that coming in and, and, and, and, and playing

00:45:12
a part of.

00:45:13
The experience of, you know, brands that our residents use

00:45:17
every day are still, um, important, right?

00:45:20
Like they, they live in these communities and, um, you know,

00:45:23
having that data, um, opens up opportunities to understand the

00:45:29
things that these customers need and the value that can be

00:45:31
created when you collide them with, you know, the auto

00:45:34
industry did this really well.

00:45:36
Um, You know, with even, uh, they figured out early, like,

00:45:40
we're not really good at making radios and stereos and cars, so

00:45:44
they bring in bows or, you know, the other ones and, and customer

00:45:48
that's a brand and they just immediately ride along with that

00:45:52
brand.

00:45:52
Uh, but I'm sure there's some data around those conversations

00:45:56
before those deals happened, you know?

00:45:58
Um, yeah.

00:45:59
So, yeah, no, that's a good example.

00:46:01
You've got your bo speaker and your, yeah.

00:46:04
Car.

00:46:04
And you would never think that Toyota or Honda's gonna be

00:46:09
supplying your speakers these days cuz you want, you want

00:46:13
something better than what they were offering when they were the

00:46:16
ones that were in charge of doing Yeah.

00:46:18
Your radio and the speakers were, the, the sound system in

00:46:23
your car was just bad.

00:46:24
Right.

00:46:25
Um, so yeah, partnerships is, is clearly, that's, that's what

00:46:28
goes back to the ancillary revenue.

00:46:32
How do we.

00:46:34
Convince owners and managers that there are so many

00:46:37
opportunities to partner on things that can actually

00:46:43
increase their margins, and that allows them to improve their

00:46:50
salaries to the onsite teams so that those teams can be both

00:46:56
better trained, have longer retention.

00:46:59
So the trickle down effect of you increase margins by.

00:47:03
Encouraging them to sell products, um, that perhaps the

00:47:09
resident was already getting previously, uh, but now can feel

00:47:13
like it's a, it's almost like an amenity that they're getting.

00:47:17
Some of these services right within their home.

00:47:19
It's real convenient and they would've paid for it anyway.

00:47:22
It's the old AAA brand back in the day around the, the

00:47:26
discount, right?

00:47:27
Uh, if you're a member here, you, you know, you're, you

00:47:30
benefit, your life gets better.

00:47:33
And, and it, you're, uh, you know, I always say this around

00:47:36
Elon Musk.

00:47:37
I pick on these two, uh, Jeff Bezos and Elon Musk.

00:47:40
They, and, and multifamily is about creating wealth, right?

00:47:43
So it's, it's, uh, and protecting it.

00:47:46
For generations.

00:47:47
And so when we are going out trying to increase revenue, our

00:47:52
goal is to meet investor expectations and returns.

00:47:55
But at the end of the day, to the wealthiest people in the

00:47:58
world, I'll pick on Jeff Bezos first care about, uh, the, the

00:48:03
values of how he's publicly stated this around.

00:48:06
He knows in 10 years that they're gonna innovate to

00:48:09
knowing that the customer in 10 years will probably want lower

00:48:14
costs of things, and they will want to get it in a frictionless

00:48:18
way.

00:48:19
And Elon Musk was asked during, I think it was the sort of, uh,

00:48:23
uh, supply chain issues around, is the cost of the Tesla gonna

00:48:26
go up?

00:48:27
And he kind of paused and he does this look up thing where he

00:48:30
thinks thoroughly through about his answer.

00:48:33
And it was, it's not that we're trying to reduce.

00:48:37
The price of the car is that we're increasing the value of

00:48:40
the vehicle, you know, and so like you go outside and your,

00:48:44
your car's sitting all day long while you're working.

00:48:46
Well, now you, you park it and then it runs some Uber rides for

00:48:50
you and it's making you$1 a month.

00:48:53
So he is increased the value of the relationship with the.

00:48:57
The the company, and I think from a multifamily standpoint,

00:49:02
we have these little cities.

00:49:04
We can think of them as aggregating the purchase power

00:49:07
of all these customers, but we don't have the data yet.

00:49:10
We don't have it standardized.

00:49:11
We can't go into those conversations to tell those

00:49:13
stories yet.

00:49:15
To make those Bose kind of partnerships because we don't

00:49:18
have the data.

00:49:20
And so I think that there's some real opportunities for those

00:49:22
asset managers, owners, and operators to have, um, a way to

00:49:27
differentiate and, and also this is an affordable housing story,

00:49:31
you know, being able to.

00:49:33
Increase the value of the lease by allowing the customer to have

00:49:37
access to things that would give them more purchasing power.

00:49:41
Uh, you know, when we look at, um, group buying of insurance

00:49:45
for employee healthcare, we know that it's good for people to be

00:49:49
healthy.

00:49:50
They'll show up at work, they'll be more productive, and it's the

00:49:52
right thing to do.

00:49:54
But for our residents, imagine a situation where we're providing

00:49:58
group buying for healthcare and you know, these types of things

00:50:01
that, uh, there's wide markets are open for that, but you have

00:50:05
to have the data and, you know, certainly, oh yeah, no, we, we,

00:50:08
uh, I have, uh, a friend who was offering as an amenity, um, the,

00:50:15
uh, medical.

00:50:16
Insurance for, uh, doing, what is it called?

00:50:20
The in-home healthcare and calling Yeah.

00:50:23
Right.

00:50:23
Just, uh, to, to do, uh, phone calls for their doctor's visits,

00:50:29
which improves the overall experience health of their

00:50:35
residents.

00:50:36
It improves the experience offering, um, tax preparation

00:50:40
services offering.

00:50:42
Uh, Language classes.

00:50:46
Um, even some of the high, high end, uh, properties have the

00:50:51
concierge services, the um, uh, you know, it, it used to be Taco

00:50:56
Tuesdays.

00:50:57
Now it's, you know, uh, we're gonna bring in a sommelier and

00:51:00
have them, uh, teach us all wine tasting.

00:51:03
But those experiences, those amenities have a value to them

00:51:10
that sometimes is hard to quantify.

00:51:12
Um, so without looking at a large swath of data and looking

00:51:17
at sentiment analysis, how do you determine what your ROI is

00:51:21
on Taco Tuesday?

00:51:24
Uh, it's using sensors to see who showed up that's looking at

00:51:29
the data over a period of time to say that your retention, uh,

00:51:33
increases.

00:51:35
After someone engages with three different onsite events, and

00:51:40
then looking at your ROI overall for these amenities, these

00:51:46
events, and seeing is it worthwhile based on how it

00:51:50
impacts my current resident, does it impact my online

00:51:55
reviews, which are the number one way that most people find

00:51:59
their housing these days?

00:52:01
And so am I getting more traffic?

00:52:04
From online leads.

00:52:06
Whenever someone posts a positive review based on their

00:52:10
experience at Taco Tuesday, and they thought the tacos were

00:52:13
great, or they thought they were lousy, and they give us two

00:52:15
stars, and now we have to respond to and apologize for

00:52:20
ordering from the wrong place.

00:52:21
But all of this is, it's, it's someone's house, it's their

00:52:25
home, it's their community.

00:52:26
Um, these are things that in today, it matters.

00:52:31
It used to be, That it, it, it didn't matter because we didn't

00:52:35
have this online communities that you could share every

00:52:39
thought and every bit of feedback that you had.

00:52:42
And so it was very hard for people.

00:52:44
I remember looking for places 20 years ago and it was really hard

00:52:48
to know, is this a good neighborhood?

00:52:51
Is this the right apartment?

00:52:53
Is this a good idea?

00:52:55
And now there's so much information.

00:52:58
Online that you can check and one bad review, and you're kind

00:53:02
of walking away, it's, it's, um, challenging when the data's out

00:53:08
there, uh, that's kind of working perhaps for you or

00:53:12
against you.

00:53:13
Um, it's incumbent upon us to, to take that data and figure out

00:53:18
how to, how to best utilize it.

00:53:20
Does it matter or does it not matter?

00:53:23
Yeah, that's interesting.

00:53:24
And, and you know, look, they're exciting times ahead for all

00:53:28
those thinking about how to create value for the customer

00:53:31
and investor and the companies and the employees that they

00:53:34
serve.

00:53:34
So this has been refreshing and I know, um, I'm, I'm sparked to

00:53:38
think about all kinds of different ways that we can have

00:53:41
follow up conversations.

00:53:43
But until then, uh, for the viewers and the listeners are

00:53:46
there, um, Are there ways that, uh, you recommend people

00:53:50
reaching out and getting in touch?

00:53:51
I mean, obviously on, we'll put your information on the show

00:53:53
notes.

00:53:54
Oh, absolutely.

00:53:55
I can be reached liz@revolutionre.com.

00:53:58
Would be thrilled to hear from anyone, uh, who has interest in

00:54:01
talking about data.

00:54:02
Obviously it's something that I'm quite passionate about,

00:54:05
happy to discuss, and, uh, Look forward to being a part of the

00:54:09
conversation in, in the industry.

00:54:12
So thank you so much Patrick.

00:54:13
Really appreciate you having me on and look forward to doing.

00:54:17
Uh, additional stuff with you guys.

00:54:19
Yeah.

00:54:19
Awesome.

00:54:19
And, you know, listen, let's keep this conversation going on

00:54:23
social media.

00:54:23
Follow us on Instagram, LinkedIn, anywhere that you

00:54:26
follow us online.

00:54:28
Go ahead and, uh, give us a shout.

00:54:29
You'll get some behind the scenes access of what we're

00:54:31
doing to spend time with some of the most amazing forward

00:54:34
thinking leaders in the industry.

00:54:36
And if you want.

00:54:37
Notes or sh uh, you know, links to what we've mentioned in this

00:54:41
episode, go to multifamily innovation.com.

00:54:44
Now.

00:54:44
Listen, please subscribe, rate, and review this show.

00:54:47
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00:54:49
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00:54:55
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00:54:56
See you guys.