AI and Data: Better Apartment Investing

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Speaker 1: Welcome back.

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Today's guest is Elizabeth Brayman.

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She is the founder and CEO of Revolution RE.

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That's a SaaS company providing data solutions to apartment

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owners.

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They do this for managers and really any industry-related

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company as more and more data is coming together.

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Prior to forming Revolution RE, elizabeth served as the chief

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production officer at Realty Mogul.

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Forming Revolution RE, elizabeth served as the chief production

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officer at Realty Mogul.

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This was an online platform raising capital for real estate

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companies through private placements and A-plus REIT

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offerings.

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Prior to that, she was a chief production officer at ReadyCap

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Commercial, a small balance sheet commercial real estate

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loan originator funding loans from private REIT offerings that

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has since gone public.

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Now Elizabeth is a certified commercial investment member

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this is the CCIM designation and a licensed attorney in

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Washington DC, in the Commonwealth of Virginia.

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She holds a Bachelor of Arts degree from the American

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University and a Master of Business Administration and JD

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from the George Washington University.

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Today, what I'm doing is I'm bringing in Elizabeth to share

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her knowledge on how AI and data standardization are key to

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unlocking new efficiencies and opportunities in apartment

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investing.

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Elizabeth, welcome to the Multifamily AI Podcast.

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Speaker 2: Awesome.

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Thank you, patrick, it's great to be here.

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Speaker 1: Yeah, so much background and expertise in what

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you do from a legal aspect.

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I love that when we talk data, you're not here to give public

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and legal advice, and nor am I, but it's interesting.

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That type of background, serving at the leadership role

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of a company solving data, is compelling, obviously One of the

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questions I'm thinking about for leaders, business leaders.

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So on this podcast I'm trying to help owners and operators

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that may be from sort of non-technical roles understand

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the value that can be unlocked leveraging data in their

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business, like they leverage debt as a business tool, and

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would love to know what you think those questions should be

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as people think about their data strategy.

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Speaker 2: Great question and I think it really obviously

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depends on the organization.

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I think the first thing I recommend is really taking a

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look in the mirror and saying all right, what is a data

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strategy?

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What does that mean to our organization?

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Because it can mean a lot of different things.

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And what really drives the data strategy should be your

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business goals and objectives.

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What are you looking to accomplish with data?

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What do you think you can accomplish with data?

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And then look at things like how does it impact governance

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and data security and all of those things.

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But really starting with your goals, your objectives defining.

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I'm a big fan of smart framework .

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Is it specific, measurable, achievable, relevant and

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time-bound?

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And I go through this exercise with our clients as well,

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because when they're working with us on developing their data

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strategy, it's really important that it's defined, that they

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know who their key stakeholders are, that they get buy-in from

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their key stakeholders and that they're measuring what success

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means to them.

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Because data strategy is one of those things where, if it's too

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amorphous, it's too vague, there's really no success metric

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that an organization has.

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That's when a lot of time, effort and energy is put into

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things that eventually get abandoned because there's no end

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point to it and people can't look and see why are we doing

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this, which is important because you need your team to really

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participate in the process of implementing a strategy.

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It can't be done by a vendor or in a silo.

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One person can't take it on all by themselves if it's an

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organizational data strategy.

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Speaker 1: And getting into how to actually define this.

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Many business leaders you mentioned we think about

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dashboards, we think about the data that we've always had to

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make P&L decisions, knowing what's going to move the

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investment needle in the business.

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So a lot of the business leaders are more aware of their

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data than they probably want to admit, because it's really it's

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decision making right.

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But when we introduce AI and different, we're talking about

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standardization challenges and things like that.

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When it comes to solving those problems, oftentimes a leader

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will pass it off Okay, that's IT or that's somebody else's job

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internally.

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I'm curious, with the acceleration of technology and

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talent, are these things companies should be doing inside

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their organization or relying on outside parties to guide them

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through right?

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And then what I'm curious about is what's the leader's role in

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understanding that data strategy ?

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Is it something they just hand off to IT or their technology

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team?

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Maybe they don't have one?

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Speaker 2: Yeah, I was going to say goodness, no, I would hope

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not.

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You don't want to give the poor IT a data strategy rollout

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without a lot of input from the business stakeholders.

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Those two things can't be done in a vacuum.

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I think if an organization does have an IT group and a lot of

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companies don't big real estate companies that have I shouldn't

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say big the larger real estate companies obviously have

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internal teams, but there are companies that have billions in

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AUM that don't have a large enough team to take this on

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independently.

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So it depends on the size of the organization.

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But it's either done as something in tandem with the IT

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team or with external vendors.

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But picking the right external vendors can be great or it can

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be not great.

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Getting good references, but also who you connect with, how

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do you meld in terms of your work?

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Because it's going to be a process and there is no quick

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fix.

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You can't bring a vendor in and just wave a magic wand and say,

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okay, data, that's not a thing.

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Standardization obviously can help the process, but there's

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still a requirement of every real estate company to

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participate in the process of and I decide whether it's a

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long-term relationship that you want to build from there and

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look at this kind of as a long-term strategy.

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You can't spin up a complete integration with every single

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data source and data plan and have it done in a week.

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That's having reasonable expectations is critical.

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Getting your vendor or your IT team to buy into those goals,

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expectations, timelines, all of that is really critical to

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having a successful data strategy rollout.

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But I think it's really up to the company to work with

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external or internal.

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There's obviously benefits to having someone with completely

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fresh eyes look at your organization and help guide that

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process, and it's also important for internal folks who

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really know all the nitty gritty of how data has been

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captured and stored.

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So a little bit of both, I'd say, or a lot of both, depending

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on how big your data strategy is and your goals are.

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Speaker 1: Yeah, and I love the idea of the expectations and a

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lot of times when we're evaluating even technology, I

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guess you can group data into a almost like a product right

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Productization of round, I think you've called it.

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Is it's standardization as a service right?

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Productization of round, I think you've called it.

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What is it?

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Standardization as a service right?

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But, more importantly, in terms of those expectations,

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sometimes leaders are looking okay.

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So when I have a problem, what's the problem?

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This technology or this strategy is solving and in many

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cases there may, depending on the leader's role and how they

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are up to speed on where things are in the business.

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We may not know what we don't know.

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So how do you come into an organization and manage those

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expectations and demonstrate the value of getting this

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standardization correct?

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Because it seems like a lot of people want to do it, but

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there's some people run into some challenges doing it.

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Speaker 2: Yeah, for sure.

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I think I always laugh when someone asks me is it done?

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It's never done.

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It's a process, having a really well-structured process, and

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that's why I use the SMART framework and kind of work with

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teams when we're engaged in the onboarding process to really

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define everything they want to accomplish and then trying to

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prioritize and work through timelines for having small wins

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so that you're iterating and building it out over time.

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You have to see some measurable impact when you're looking at

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data and so, depending on how complicated your tech stack is,

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how many data sources and data sets you're looking to bring in,

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how different the data structure is to start, and it's

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why people look for scalable solutions to this stuff so that

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they can find a way to make a meaningful impact right out of

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the gate.

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Just even just getting all your data in one place is an

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accomplishment.

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Right formats, having daily access to property data from

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multiple systems that's a big win to be able to get that done.

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Then you look at okay, how do we refine our KPIs?

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What kind of metrics do we want to start tracking?

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What different teams within the organization want to be

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involved?

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What different teams within the organization want to be

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involved?

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Is it all marketing or is there the leasing team, the

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management boots on the ground?

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Who is this really going to impact and how do we get them

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from step one to step two throughout this journey of data,

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but there is no done.

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Of data but there is no done.

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It's always just a continuous, never-ending product process,

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which is why you have to stay on top of it and create some

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business roles internally.

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But it's a commitment.

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There's definitely a return, which is also why you want to

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have measurable.

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See how you're getting a good return on rolling out a strategy

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.

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Yeah, I like the small wins.

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Speaker 1: I like what you said about the small wins and making

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it measurable when data is coming from so many different

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places.

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What are the challenges of getting this?

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If it's an accomplishment to get your data in one place right

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?

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Speaker 2: Yeah.

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Speaker 1: Are there examples that even other industries are

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doing that we could learn from?

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Speaker 2: Yeah, it's.

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The big difference between real estate and other industries is

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it's not the most open framework .

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I think.

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If you look at banking, you can add your credentials into a lot

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of systems and all of a sudden you've got a direct connection

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with your bank.

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Or marketing, if you want to get all of your Google Analytics

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in one place, open APIs.

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That's not quite the case in multifamily, and so we're

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extracting data from core systems.

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Sometimes we have one, two, three different methods of doing

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it.

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It's challenging because you've got many stakeholders involved,

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whether it's the management company that's generating and

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storing the data, ownership groups who own some of the data

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to different property management systems that all have different

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data structures.

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So extracting the data is the step.

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One is getting it into a common and consistent format.

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That's the transform.

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So ETL is the extract.

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The transform and then loading it into a place where you can

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now use it for BI is just a front-end visualization of data

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that's in a structure that you can use.

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But if it's coming from multiple systems and it's in

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multiple structures and it's in multiple structures, having a BI

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front end and trying to create multiple reports from those

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source systems is a lot of work and it's why a lot of

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organizations are challenged with their reporting if they are

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trying to pull it all together and generate portfolio-wide

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analysis.

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Speaker 1: And obviously having a standardization of data plays

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a huge role in that process, right?

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Are people skipping steps in this?

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Do you think right now?

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Speaker 2: Yeah, I think if you're doing a bespoke approach

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and it's reasonable, it makes sense.

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If you have one system of record and you set it up to feed data

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into a front-end visualization and then you add a second one,

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now you're doing the same process twice side by side.

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But it doesn't really mesh unless you take a step back and

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get all that data in one format.

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Because these systems are not consistent, there isn't an

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industry-wide this is our standard for how data is

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structured and because there isn't one way of doing it, you

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are limited to either having to standardize it internally and

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create a data model, and sure not a lot of real estate

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companies want to create a data model internally.

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It's expensive, time-consuming, requires specialized knowledge

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and it's just messy.

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So that's why we built what we built specifically for the

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industry, because it didn't seem like there was a solution that

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was out there that would provide that same aggregation of data

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for people to be able to then use it for AI applications, for

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BI applications, for all the cool technological use cases,

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because the data just wasn't in a format that made it easy to

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use the data just wasn't in a format that made it easy to use.

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Speaker 1: And when it's in this format, when you mentioned the

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ETL extract, transform and load, are there things that or

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questions we should be asking around?

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Making sure that the quality of that process gets the like, how

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do you ensure that process?

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How do you make sure that ETL process gets the right data in

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the right places?

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Speaker 2: Yeah, that's a good question.

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What do they say?

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Garbage in, garbage out, so we can clean up some of the data

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that comes into our system.

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You can't fill in empty holes.

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If there's big blanks in data, there's no way to create it.

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Ai is also helping to clean and create methods of cleaning data

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, and a lot of these models don't need perfect data.

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They need a lot of directionally correct data to

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generate really interesting results and to be impactful.

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Speaker 1: This could just also be like just as simple as a

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state record right, A field where it's maybe open text in

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one database and the other it's abbreviation.

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Speaker 2: Absolutely.

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Our resident name is last name, comma, first first name and

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somewhere else it's a first name field and a last name field.

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It's all across the board, getting the data in just a

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format that you can query, and everyone who's used Excel with a

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sort function knows that like it needs to be a little bit

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clean to be able to use effectively.

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Some of the AI solutions are now starting to fill in the

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blanks of helping to clean things up.

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So we in our tool if it's Google Analytics, google with

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three O's, we can combine all those and essentially for ad

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sources, we're doing some cleanup on data like that, where

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we're just making it more consistent when at the property

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level it's being input slightly differently.

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Our financial model, which takes millions of rows of data,

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different categories every company has what?

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Four, five, 600 rows of financial data and mapping it to

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a more tight, cleaner set of data points so that you can do

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benchmarking.

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We call it our common model.

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There's huge value in having even a little bit of cleanup of

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the data so that you can do more comparative analysis and market

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analysis.

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Speaker 1: What can go wrong in that process, or what should we

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avoid doing?

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Maybe someone's trying to do this in-house, or oh, it's a

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good question.

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Speaker 2: There's tons of things to avoid, but I'd say you

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want to.

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There's a lot of monitoring and tracking that your data feeds

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are actually active and they aren't stopping for whatever

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reason.

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You might have a night where a system just can't handle.

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It times out.

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It's too much data coming in.

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Speaker 1: In those types of timeouts.

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Does that stuff get queued or is it a restart?

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Speaker 2: Yeah, that's a great question.

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It depends.

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So sometimes it just like stops and you have to go and you get

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notification that you have to start over.

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Then there's sometimes you can just pick up where you left off.

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And if it's a timeout, there's also time zone issues, which are

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really strange.

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So if you're doing a nightly and you have some data that's

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coming in, but for the change in time zones things will happen

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before 12 o'clock and then sometimes after, which makes it

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a different day.

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So you have to make sure that you time all of your data feeds

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so that they're not impacted by daylight savings or, if you're

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in a different country, different things like that.

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So there's definitely ways that the extract process can fail.

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Then there's ways that the data can be off.

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A lot of people like to do.

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Timing issues can happen.

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So managers will come in on Monday after a long weekend and

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they input their move outs from over the weekend and then you've

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got different timing issues that directionally, can make

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data not match from one system to the next.

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So that goes back to expectations.

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Speaker 1: Yeah, exactly, and I'm also like from all of this

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work, it's the business decisions, almost like the

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construction process is very it can be messy, yeah, and it is.

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Speaker 2: You're building a house a lot of dust, a lot of

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unknown things, which is why having a good blueprint ahead of

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time keeps you from having structural problems.

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If you set it up wrong or build a house wrong and then have to

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go back and try and figure out like oops, we didn't put the

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plumbing in, or the electricity is in the wrong place, Like

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those are the things that you wish you had done.

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They say derf to do it right the first time.

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Sometimes time makes it hard, because if you've been working

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on this for a very long time, things change and you have to go

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back and revisit some of those structural things.

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But that's technology.

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It's always an iterative process.

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Speaker 1: When you're in these conversations with executives

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today and you're working through this, what are they leaning

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into and what are they pushing?

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Not only say pushing back, but more cautious on?

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Or if we don't understand something, it's hard to make a

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decision about it right, and so there's going to be a lot more

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education, I think, around this and unlocking the value.

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Because you mentioned the different stakeholders,

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collaboration between teams and I always think about the

00:21:08
dashboard is like a lot of the technology that we're using is

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making the assumption that we're going to be in an office

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looking at a computer, using it.

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So if you have this centralized data standardized and it's gone

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through the ETL process, then that can be unlocked in mobile

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like all over right.

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You can take that wherever it needs to be.

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Speaker 2: Yeah.

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Speaker 1: Yeah.

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Speaker 2: That's the load portion.

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You can load it into a number of different applications or

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uses.

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But I think a lot of people are rightfully so leaning in on

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corporate data governance, wanting more visibility into

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tracking their data feeds and making sure that they're

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reporting on the right information, especially if

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they're a public company or have REITs with reporting

00:22:04
requirements.

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All of those things are critical to have a handle on how

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the data is coming in.

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Speaker 1: Tell me more about that data governance.

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Speaker 2: Explain that, what you mean by that in, so explain

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that what you mean by that.

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A lot of companies, at least large corporations, are looking

00:22:21
at how they secure and store their data, how they're

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maintaining a handle on the accuracy and also the security

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issues related to data.

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The multifamily industry we're dealing with residents and

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residents' housing, and housing is obviously a highly regulated

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industry.

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The payments and activities of residents, their data is PII, so

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the storage of their data can be highly regulated.

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There's a lot of different.

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So when we look at corporate governance, it's really how does

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a company maintain the highest integrity and policies and

00:23:10
documentation and legal structure around how they are

00:23:14
storing, using, maintaining data ?

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So it's some effort to be done internally for big companies to

00:23:22
make sure that they're putting a plan in place and documenting

00:23:26
it appropriately.

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Speaker 1: And do you offer?

00:23:28
Is your program a data store or is it wherever they want?

00:23:33
Is it going to other places, or how does that work?

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Speaker 2: It can be yeah, so some companies look to have

00:23:42
their data fed into a data warehouse.

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Some companies want us to maintain and store their data

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for them.

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We have SOC 2 compliance, so all of our data is encrypted in

00:23:56
motion at rest.

00:23:58
Very few people have access to the data and all of our policies

00:24:03
and procedures are available in our SOC 2 reports, which you

00:24:07
can get on our website.

00:24:08
But you have to sign an NDA because our auditors require

00:24:12
that.

00:24:12
But the whole process is not an insignificant amount of effort,

00:24:16
but it's really important and something that I always

00:24:19
encourage people to talk to their vendors about, because it

00:24:24
is incumbent upon the real estate companies to make sure

00:24:27
that they're the ones that could be liable for any data breaches

00:24:32
if they're not maintaining good practices, if they're being not

00:24:39
careful with how they store and maintain their data.

00:24:44
Speaker 1: And leaning back into AI, how is AI impacted?

00:24:47
It seems like a lot of these transformations and

00:24:51
standardizations are getting easier.

00:24:53
There still needs to be the governance side of things.

00:24:57
It's one thing to turn something over to AI, but I'm

00:25:00
under the belief that people begin and end the process, so

00:25:04
maybe it's taking out some level of review.

00:25:07
But how is AI impacting the standardization process for your

00:25:11
perspective?

00:25:12
Speaker 2: Well, it's a great question and there's obviously

00:25:15
lots of different applications for AI.

00:25:18
The AI is thriving.

00:25:21
The need for standardization because AI needs like data in a

00:25:26
structure that it doesn't have in its knowledge base.

00:25:35
It kind of lies and gives you answers that you're a little

00:25:39
confused with because it's like what is it talking about?

00:25:42
It makes stuff up and that's because it doesn't have the

00:25:46
right data to answer the questions.

00:25:50
It tries to fill in the blanks.

00:25:52
It's being helpful, but not really the blanks.

00:25:58
It's being helpful, but not really.

00:25:59
So there's certain things that AI is driving in terms of the

00:26:00
need for standardization, and then there's, equally, ai is

00:26:04
providing standardization with more ways to make data clean,

00:26:12
usable, accessible, so they feed each other in certain ways, and

00:26:17
how much faster and better the technology is at letting us make

00:26:24
informed models using available data.

00:26:29
So it's requiring less and less as long as the models are

00:26:35
trained and maintained.

00:26:37
But yeah, I know it's interesting.

00:26:39
They feed each other in some ways back and forth.

00:26:42
But there's so many AI applications and real estate has

00:26:46
been pretty lagging in how these applications are being

00:26:53
utilized.

00:26:54
I think there was a good study McKinsey put out and if anyone's

00:26:57
interested, feel free to email me.

00:26:59
I'll share it, but it was talking about how the data is

00:27:02
really the shape of data.

00:27:04
The lack of a foundational data model can really hinder

00:27:08
people's ability to use data in a lot of these AI applications.

00:27:14
Speaker 1: And these large language models are moving so

00:27:17
fast and it's so expensive to do that.

00:27:19
It's interesting, like when you compare it with your private

00:27:22
company data in an environment where it's not training the

00:27:26
large language model.

00:27:27
But it goes back to what we're talking about here, which is, if

00:27:30
you don't have the standardization of the data or

00:27:33
in the data in the right place, you can't retrieve it and then

00:27:37
get better responses when you're using AI applications inside

00:27:42
workflows.

00:27:42
And so I always wonder, as you're moving companies' data

00:27:46
into a single source of truth and also standardizing it, what

00:27:52
are you seeing the challenges there?

00:27:55
Is it they have different systems?

00:27:58
Maybe they're a third party operator and they don't just

00:28:01
have one system?

00:28:03
They're trying to manage multiple systems and I imagine

00:28:06
training people and all of that comes into play.

00:28:08
But in terms of the technical, what are those challenges in

00:28:12
bringing all these things together?

00:28:15
Speaker 2: Yeah, that's exactly right.

00:28:16
It's ownership groups who work with multiple property

00:28:19
management companies.

00:28:20
It's property management companies that have multiple

00:28:24
ownership groups that require them to use different core

00:28:28
systems and or are buying property management companies.

00:28:33
So sometimes it's just a company buys another company,

00:28:37
and it's just the nature of real estate.

00:28:39
It's transactional in nature.

00:28:41
So when you have a sale of one property to another buyer, are

00:28:48
they going to use the same system, are they going to use a

00:28:53
new system, and so you've got a lack of kind of historic data

00:28:59
and then you're onboarding it into a new system.

00:29:01
So anytime you have people manually adding data into

00:29:05
systems, that also add some room for error, we might say and

00:29:11
there's just, they're inconsistent.

00:29:12
They're not.

00:29:13
These systems weren't made to be exactly the same and have the

00:29:19
same definitions and have the same data structure.

00:29:22
They just they weren't set up that way.

00:29:24
And there's not that many systems out there.

00:29:29
It's not.

00:29:29
The percentage of the real estate market that's

00:29:32
professionally managed is that it's consolidated, but those

00:29:38
companies aren't using a ton of different core systems and those

00:29:42
core systems really are capturing all the financial data

00:29:45
, all the operational data, and then you have all this ecosystem

00:29:49
of prop tech companies that kind of have opened in the last

00:29:55
however many years, really taken off, and so there's lots of

00:29:58
stuff, but that's just adding more data to the problem.

00:30:01
Where is it being stored?

00:30:03
Where the core systems aren't storing that data, and these new

00:30:09
PropTech solutions aren't storing all the underlying

00:30:13
property data.

00:30:13
So you're just creating additional data silos.

00:30:17
You're exacerbating the foundational issues that exist

00:30:21
with new technology.

00:30:23
So it makes your head explode.

00:30:26
Speaker 1: Yeah, it can.

00:30:27
When you think about these companies, companies', entities

00:30:31
stay around longer than properties in the investment

00:30:34
cycle, are shorter term, right, and so we're asking people to

00:30:38
think longer term about things when they're in a short term

00:30:41
investment cycle, right.

00:30:42
So that's always been a challenge.

00:30:45
But if you think about the entities themselves and also the

00:30:48
data, you may have company data and then you have property data

00:30:51
and in the way that all of that is organized, I'm just curious

00:30:58
if and it's probably true, this was my belief in that when a

00:31:02
developer builds an apartment building, the utilities and the

00:31:07
city infrastructure is value that a new buyer doesn't have to

00:31:11
come in and do and do.

00:31:13
In other words, because somebody took the risk and developed the

00:31:17
plumbing, the electrical, the utility, all the things that are

00:31:25
required under the ability to go vertical, the new buyer

00:31:26
obviously gets that right, but nobody really values it on the

00:31:29
sale of an asset, right?

00:31:31
Yes, but when we talk about, if you just think about the

00:31:35
listeners, just think about all the different entities you have

00:31:37
you may have your company entity and then a holding entity and

00:31:40
then each property has an entity .

00:31:41
Is that a way to think through data in terms of if we get

00:31:46
property data at a specific location that later, if there's

00:31:50
a disposition or a consolidation or merger, that there's

00:31:54
enterprise value that can be realized on the exit by making

00:32:02
investments in getting this clean data for the new buyer.

00:32:06
Speaker 2: Yeah, I totally think so, but it's really up to

00:32:10
sellers to view that as an asset or buyers to ask for the

00:32:17
historic data of the seller.

00:32:20
They may or may not feel comfortable doing that.

00:32:22
Speaker 1: You're lucky to get a PDF today, I know right.

00:32:24
Speaker 2: Are they going to?

00:32:25
I guess, if the market gets tight enough, you could put that

00:32:29
into purchase contracts, that you want two years of historic

00:32:34
data or access to historic data files.

00:32:37
I think it'd be really hard to get a seller to agree to it, but

00:32:41
it would be great for a buyer.

00:32:42
Speaker 1: Let me just ask though that's the business kind

00:32:45
to me again what would that mean if you had that on a new

00:32:48
acquisition?

00:32:51
Speaker 2: If you had it on a new acquisition, you could

00:32:53
probably track the trends of your performance a lot better.

00:32:56
So if you're trying to and I think this is one of the best

00:33:00
use cases of data is to show your investors that your

00:33:05
operational strategy brings alpha, that you have somehow

00:33:10
managed to create opportunity in the asset by doing X, y and Z,

00:33:18
that's hard to prove when you're starting with ground zero and

00:33:22
there's nothing to compare it to .

00:33:23
If you had the historic data, you could show what's been doing

00:33:27
this for the past 12, 24, 36 months and even adjusting for

00:33:32
market conditions.

00:33:32
And even adjusting for market conditions.

00:33:35
This is how much value we've been able to add.

00:33:38
This is how much that we're putting into the property.

00:33:47
That's creating a bump in NOI and this is the multiple that we

00:33:50
, as a data-driven company people like to say we're a

00:33:53
data-driven company to prove it, to actually show that what

00:33:56
you've done has created this major impact.

00:33:59
I think that's super powerful and it's hard to raise capital

00:34:02
right now.

00:34:03
That's a tremendous use case.

00:34:07
Speaker 1: We're talking about the apartment car facts

00:34:09
basically.

00:34:10
Speaker 2: Yeah, some people have been called it what's like

00:34:13
the for hotels, the report that kind of shows the market value

00:34:18
for that.

00:34:18
Speaker 1: yeah, sure, but it'd be interesting.

00:34:20
You buy a property and this is the data from the time that we

00:34:23
owned it, but imagine, as it transferred ownership over three

00:34:30
decades, that it would be more valuable in terms of

00:34:34
underwriting, repairs, maintenance and remaining life.

00:34:36
There's so many other.

00:34:37
Where are we in all?

00:34:39
Speaker 2: of this.

00:34:39
We can do that with financials.

00:34:40
We can do that with the financials.

00:34:41
So if you have a property that you're looking to acquire and

00:34:45
you have the trailing 12 months and you can get maybe the past

00:34:49
couple of years, we can show from a financial perspective how

00:34:54
you can compare from a financial perspective.

00:35:00
How you can compare because we can convert it into a common,

00:35:02
consistent format with your current performance, your

00:35:03
current tracking.

00:35:04
So being able to take your pro forma track to that track, to

00:35:09
budget, but tracking to historic performance.

00:35:11
It's a really interesting use case and, for sure, something

00:35:15
that I encourage people to do, if not with our platform, but

00:35:19
with their own internally see how a property was performing

00:35:25
and how your efforts have changed the story at that asset.

00:35:32
That asset is really compelling when you're going out to market

00:35:35
and trying to get people to invest in you.

00:35:36
They say past performance is not an indicator of future

00:35:43
results.

00:35:43
But when you can show comparative how this property

00:35:44
was doing compared to the market and then how we're doing

00:35:46
compared to the market, it just tells a really interesting story

00:35:50
of performance.

00:35:54
Speaker 1: Yeah, that's interesting and I know that

00:35:56
there's so much more that we can cover.

00:35:58
On all of this, I go back to where we started, which was

00:36:01
getting those small wins.

00:36:02
I've been calling it crawl, walk, run right.

00:36:05
So it's maybe somebody listening, has access to data,

00:36:08
obviously, but maybe doesn't have a defined strategy in a

00:36:12
market where that alpha beating the returns in other ways than

00:36:18
just passing on rent increases if we can find ways to innovate,

00:36:22
the business having that data is huge in that decision-making

00:36:27
process.

00:36:27
Do you ever find people get overwhelmed with this because

00:36:30
they are not data scientists and there's not an abundance of

00:36:35
people to help with this and relying on the tools you have

00:36:40
available to you?

00:36:41
And there's just gaps, that they don't see, that they're missing

00:36:45
, and we can go down the whole unstructured data conversation

00:36:48
and getting that into a structured Like that's a whole

00:36:50
nother, like phase two probably, of unlocking value.

00:36:54
But are you feeling or do you see that people get overwhelmed

00:36:58
with this conversation and how do they prioritize making the

00:37:02
next move and making sure it's going to drive actual

00:37:05
effectiveness in the measured results?

00:37:08
Speaker 2: That's a really good question, I think.

00:37:10
I say don't try and boil the ocean.

00:37:12
Pick a couple of discrete KPIs that every company has.

00:37:16
The thing that they think is this is the thing that we are

00:37:19
really good at.

00:37:23
Speaker 1: Where would?

00:37:23
Speaker 2: be a good place to start.

00:37:25
Look at reducing days on market or days to if you are turning a

00:37:30
unit.

00:37:30
If you can turn a unit in two days less than you have been

00:37:38
Huge impact on a property.

00:37:39
So what we do is we set up, you track your goal, you put in

00:37:49
alerts, you have a start date and an end date and you look at

00:37:51
what is the overall impact and that's something that's truly

00:37:54
measurable.

00:37:54
If you're dropping two days from the days that it takes to turn

00:37:59
a unit, that reduces your days on market overall, adding two

00:38:05
more days of rent to each unit that you're turning.

00:38:08
That is something that you can show adds to your NOI and adds

00:38:14
to the value of your asset.

00:38:15
Same thing with looking at reducing an expense, your

00:38:19
marketing expense, if you're able to drop it.

00:38:23
Or I look at customer acquisition, your lifetime value

00:38:27
, looking at that as a ratio across your portfolio at each

00:38:31
property and setting a target for I'm going to reduce my cost

00:38:37
to acquire residents but I'm going to increase the lifetime

00:38:41
value of that resident.

00:38:43
Those are things that you can really show to investors who are

00:38:47
being really thoughtful, not just to transactionally turn

00:38:51
these tenants but actually to increase the value of the

00:38:56
housing experience for them.

00:38:58
Renewal rates, increasing those that's a huge way to increase

00:39:06
your overall.

00:39:07
If you're looking at your trade-outs, these types of

00:39:10
things, sitting down and picking , just start with three, start

00:39:14
with five, but looking at, if I did this and then was able to

00:39:19
accomplish it, what does that do to my property and portfolio

00:39:23
and how do I communicate that to my key stakeholders, whether

00:39:27
it's a property management company wanting to tell their

00:39:30
ownership groups we've added $213 to the property value

00:39:36
this quarter by doing X, y and Z and we help to automate the

00:39:41
process of communicating that, of showing that value.

00:39:44
That has tremendous ROI.

00:39:48
Speaker 1: when you look at things that way, yeah, and do

00:39:51
you find that people want to do something like that?

00:39:54
Look at those.

00:39:55
They have tools that aim to deliver some element of that,

00:40:00
but it's in that environment, right?

00:40:01
Especially if you're in multiple systems.

00:40:03
That's where it becomes tricky to understand that stuff.

00:40:06
And do you find that when you get these small wins under their

00:40:11
ability to measure something that truly makes a financial

00:40:15
difference in the business that they lean into and get more

00:40:18
motivated into investing more and more into the data strategy?

00:40:22
Speaker 2: Oh, absolutely, it's almost.

00:40:24
You want to gamify it in a lot of ways Get your managers

00:40:28
involved or your analysts involved in creating these goals

00:40:34
and tracking to them, because it's not one property, it's the

00:40:39
entire portfolio.

00:40:40
So, each person doing their part and making these small

00:40:49
incremental changes, they can have a huge overall impact to

00:40:50
the value of the portfolio just by being more thoughtful about

00:40:55
how you look at your data and how you track to performance in

00:41:01
small ways.

00:41:01
Overall, that adds.

00:41:06
It adds up.

00:41:07
It all adds up.

00:41:08
Speaker 1: Yeah, it sure does I always think about.

00:41:10
When you mentioned the slip days of time to turn, especially

00:41:14
in renting.

00:41:15
It's like empty airline seats that we just can never recapture

00:41:19
.

00:41:20
And that's that alpha, as you mentioned, that you can offer a

00:41:23
definitive or differentiating value in running your business

00:41:26
through this certain management company.

00:41:28
Listen, this has been great, Elizabeth.

00:41:30
I really appreciate you coming on.

00:41:31
I know that I would love to have you back for more on this.

00:41:34
For those of you listening, if you want to know more about AI

00:41:37
and data for better apartment investing, reach out to us and

00:41:41
let us know what are your questions that you have around

00:41:43
this, specifically to your portfolio, to your use case, and

00:41:46
just go to multifamilyaipodcastcom.

00:41:49
You can click there and you can even send us a message, but

00:41:52
reach out to us, let us know and go find that information.

00:41:55
Probably Elizabeth has it top of mind, but would love to visit

00:41:59
back with you, Elizabeth, as we continue down this journey of

00:42:02
extracting the knowledge ETL extracting the knowledge,

00:42:06
transforming it in a way that non-technical business leaders

00:42:10
understand it and then loading it into their business.

00:42:12
That was a pun on the ETL.

00:42:13
Speaker 2: I love it.

00:42:13
No, that's so great.

00:42:15
Speaker 1: Yeah, look, you're doing great.

00:42:17
We love that you're making an impact in this industry and we'd

00:42:21
love to talk to you more about this.

00:42:22
But until then, if you want to learn more about Elizabeth and

00:42:25
some of the projects she's working on, go to Multifamily AI

00:42:28
Podcast.

00:42:29
In the show notes you can click links and get to her LinkedIn

00:42:32
and all the other great places to connect with her.

00:42:34
Until then, wishing you guys the best and we'll see you on

00:42:37
the