How Data Quality Impacts Trust and Performance in Workforce Housing

Introduction to Data Discipline in Workforce Housing

Drew: Welcome back to the Peak Property Performance Podcast. I'm your host, Drew Hall. And on today's show, we're going to talk about why data discipline, not just technology flash, but why data discipline makes workforce housing successful at scale, and especially under third-party management. So first let's welcome our host as well, Bill Douglas. Welcome, Bill.

Bill: Hey everybody. Good to see you again. And today we have Ron. Before I read Ron's introduction, Ron Kudus, say hello.

Ron Kudus: Hey Bill. Hey Drew. Hi everybody. Thank you so much for having me. I'm honored to be on.

Drew: Thanks Ron. Like I said, our guest today is Ron Kudus from OneWall Communities. Ron operates in a workforce housing where margins are tight, as we all know, and execution matters. So data discipline and operating visibility aren't an option. He brings an owner-operator perspective on how digital infrastructure and control of information reduce risk, support scale, and keep portfolios running efficiently without adding unnecessary complexity. Today we're focused on what actually works and what breaks when data matters most. So again, Ron, welcome to the show. Glad to have you.

Ron Kudus: Thank you. Glad to be here.

Challenges of Low Margins and Data Errors

Drew: All right. So Ron, let's start off by thinking of like workforce housing as a sort of stress test for this data discipline that we're going to talk about. I mean, listeners who've been here a few times would know that we focus so heavily on data and digital infrastructure as well. But today in our discussion over and around workforce housing, let's think about that and how it serves as a sort of stress test for that data or the discipline of data. So in your experience, would you say that workforce housing exposes weak systems faster than other asset classes might?

Ron Kudus: I think so. I think ultimately, given the fact that it is like you started in the intro, it is a low margin kind of business and margin for error is very, very slim. It kind of, it just puts a spotlight on anything that's not going well. And for us, at least as an operator and third party manager, the ability to make decisions that impact operations is really, really important. And to make those decisions, we need the data. If you don't have the data, it's impossible to make decisions. And we've seen it time and time again, where you're making decisions based on anecdotal evidence that you're given from a community manager or a leasing consultant, and it ends up really hurting you. And in workforce housing, it takes a really long time to unwind a mistake.

Bill: Yeah, that's good. Yeah, he has exactly the power of data versus something like you mentioning anecdotal evidence. Yeah, no matter. Ron, why did you say it takes longer to undo a mistake? Is that because margins are so tight?

Ron Kudus: Because margins are so tight, because I think that let's just use, you know, putting in an unqualified resident into a unit, right? Ultimately, it may take you two, three, four months to evict the resident once they haven't paid. But besides the four months of lost revenue, you're now spending a lot of money to turn those units. In most situations, these units get beat up a lot more than, you know, class A property might. And so you're spending a lot more money to turn it over. And then the cost to actually get a new lease can get pretty high. And depending on where you are in the calendar year, you might hit a slow leasing season and all of a sudden that, you know, four months turned into eight months and you're talking about a really, really large mistake. Or in a maintenance heavy asset, which most workforce housing is, if you just try to patch a leak that has happened over and over because you want to get to the next work order or, you know, you're trying to speed through something. And that pipe bursts. Now that expense is way, way higher.

Drew: That makes sense. I appreciate that. Sorry for interrupting there.

Ron Kudus: No worries.

Drew: Yeah. Well, I mean, that's perfect. You're, you know, you just gave examples right there, even with a pipe bursting, you know, anecdotal evidence versus actual data. If you have actual data, then you very likely have some preemptive data mixed in there that will give you evidence of something that might be about to happen and result in everything you just said. It's more costly and just much more work. So we hinted at it at the beginning in terms of like third-party management, even making the complexity, you know, increasing the complexity here. But talk a little bit about that. When you're operating other people's assets, how does that complexity grow, you know, from the different ownership structures or reporting expectations, risk tolerances, how does that complexity increase in those scenarios?

Ron Kudus: It's a great question. So I think it comes from every different direction, right? With different owners, you have first and foremost, different reporting requirements, right? Different owners care about different metrics. They, some may be sophisticated. Some may be institutional. Some are trying to tie your reporting to other asset managers that they have or other property managers that they have, and they're reporting to look at their portfolio across the board. And then you have like decision-making, right? We don't actually control the money. And so, especially when it comes to CapEx spending or, you know, different marketing strategies or, you know, what type of service level you want to provide to a resident. So as a third-party management company, we have a standard of the service that we provide at every asset, no matter what location it is, but ownership may not value that service. I'll give you a stupid example, and I don't think anyone in the industry is doing it today. But when I started, we were buying a property from somebody and they did not replace toilet seats when they did a uniter. And it was shocking to most of my staff because that's like, for us, it was somebody moves out, toilet seats get replaced, they get delivered, you know, with the plastic on it, so the new resident knows that you've cleaned the apartment, and this person was like, it's not worth spending the money on it. Like something like that could, luckily this hasn't happened to us as a third party manager, which is something that we've seen, could be a real difference maker between ownership. So there are a lot of different variables that come into play. I would say the biggest issue is that we don't control the spending dollars at the end of the day.

Bill: Wow. That's a hurdle. I had a visual about the toilet seat, the example certainly landed well. It made me really think about what needs to turn when you turn an apartment or unit, I should say. Back to the conversation of digital discipline versus digital noise and controlling the two. How does digital noise show up as integration pain, false confidence, or even missed risk? How do you see that out in your world?

PropTech Overload and Custom Solutions

Ron Kudus: Well, I think that most operators or management companies will tell you that they are overloaded with prop tech today, right? There's a point solution for every little aspect of the business. The problem is that most of them don't talk to each other and most of them have a hard time even giving you data output in a way that's digestible by another, either another software or by your asset management team. And so I can tell you that we've had situations where asset managers or community managers are logging into four or five, six different point solutions, downloading reports, trying to map it all together to match, to get to one specific trend or solution, or even identifying a problem. I think that we as an industry are struggling with like figuring out what an agreed data structure looks like for multifamily in general. And every, you know, every PMS has its own way of categorizing the data. They don't track things the same way. They don't like to play with each other or many of the other point solutions. So what we've actually done is we've taken that upon ourselves to create really our single source of truth. We've taken, we partnered up with another company to extract our data from every technology solution that we have into one place, where has it in a structure that is digestible and easy for anyone in the organization to pull and interact with, and that allows us to then start modeling, understanding, spend more time solving problems than trying to analyze to get to what the problem was. So I think that like, if as an industry, if we can, if we can get the data structure, the same across the board, it would, it would go a long way to bring the industry forward. Um, but that's been a real struggle.

Bill: Ron, we are not just at OpticWise, but Drew and I and everybody we interact with are seeking that. There are some standards out there that are old that have not renovated. And then there are some new ones and we're, we're eager to see some of the more robust ones, whether they're new or old takeover, but I have been watching. I like to study the industry a lot. I've been watching PropTech consolidate for the past three or four years. Some of that is driven by the fact that their second, third, fourth round of VC funding for a point solution is not coming through and they're not able to stand up on their own as a viable business. That's normal consolidation. You can look at almost every technology wave we've had in the United States for the past 30 years and see that happening. And some of it is just because the point solutions that work are being swallowed up by companies that need that solution in their portfolio. And I think everybody listening knows exactly which ones I'm talking about because they've been high profile. But that is welcomed in my opinion, because PropTech was oversold. There was way too many point solutions. Property managers say to us all the time, don't give me another app to log into. Or we say, no, no, no, we're not doing that at all. Like if anything, we're going to just give you something to do to meet this goal. Like if it's operational or something that is going to benefit your tenants or both. But I love the way you said PropTech, there's PropTech noise, noise, noise, noise, and I was going to ask you how point solutions create more friction, but you already answered that question. Wanted to elaborate on what I think the industry is going to and the consolidation is welcomed. But we are still, if there's somebody out there listening, we are still looking for a guest that could tell us they have the data standard. Now, it'd be even better if it was open source for commercial real estate. And we have had some on the show and we've talked to some in and out of the context of this show, but definitely in business. And I don't think we've found it yet, but when we do, we will be the biggest broadcaster of it.

Ron Kudus: Yeah, I hear you. I mean, we, again, we took it upon ourselves. I don't know that it will work for every other company, right? It works for the way that we do things. I've actually taken it a step.

Ron Kudus: And we partnered with another tech company to bring the entire user experience, not from the resident side, which everybody's focused on. Everyone wants to talk about what the experience for the resident is from the moment they look at your complex to the moment they move out. That entire resident life cycle, there's plenty of companies that are dealing with that. I kind of flipped it on its head and I said, I had to find a company that would work with us that would look at it from a community manager standpoint. Because the community manager, just like the resident, has to go into the 18 different softwares or apps or websites to pull all this information. So what if we could bring it all together and create the seamless workflows that the one wall way, the way that we do things. And regardless of what technology is being used in the background, to build out the actual user interface for my community manager at the end of the day is exactly the same. And my ideal scenario is to get to a place where one day I could say, Hey, partner, we're going to switch PMS. And in six months from now, we need you to have this user interface and all these workflows work with the new PMS. And they can work on that in the background. And none of my staff has to know. And one day we just turn on the switch and everything stays the same for them. But in the background, it's all different. That's kind of what we're really working towards because the community manager needs to get away from the desk in our world, stop pulling reports, stop logging into all of these different port solutions. And at the end of the day, just go talk to our residents, go be with them, foster a sense of community. That's all we want. So if I can make that happen, I'm happy.

Drew: Yeah. That's the perfect transition for you. That's what I was going to say. This next section, we were going to talk about transitions and the nonboarding. So what you just started to allude to is absolutely brilliant. That's perfect, a perfect lead in. Because my question here was going to be when management transitions occur, what kinds of valuable data gets exposed as missing in those moments?

Data Quality and Its Impact on Trust

Ron Kudus: A lot. So first off, you'd be surprised how much demographic data is missing, just like income, things that we're collecting all the time that either management companies have chosen not to save because they're concerned about regulatory risk, or they've chosen not to share on a transition because of whatever reason that they have, or simply it's just the PMS or the point solution that they're using are not interacting with each other to actually capture all of this. So if you're looking at it as a silo, for example, during the application process, you're collecting a ton of information on your customer. But if after the application gets approved, it doesn't actually transition to whether it's your PMS or whatever third-party databases, then you're not pulling it back from your screening app when you're doing a transition. So I think that that's a real missed opportunity. But then we get surprised at just the amount of inaccurate data. There's no data validation. You have a community manager that's inputting data. If they accidentally just type 1150 for rent instead of 1050, happens all the time. There are plenty of property management companies that we've seen where there is no lease audit that's getting done. Now, in today's world, that can be automated. It's very simple. It's an easy task. So we see a lot of just poor data or missing data. And then the last piece is that a lot of these management companies on their way out, like if we're transitioning, we're coming in as the new management company, or even if we've purchased the property, we're getting it from the owner. If they didn't necessarily appreciate or use the data point in their day-to-day business, they may not even know how to get it. So they say, we don't have it, even though it's actually stored somewhere within their data. So we run into a lot of those kinds of things.

Bill: Yeah, that's good. I mean, I know that I would say in terms of our experience from where we sit, often inaccurate data is even more damaging and harmful than just incomplete data because you think you can lean on something that's there versus if it's not there, you know, you got to go and find it. So that inaccuracy can even be a bigger problem. When we built our database with our partner that I was referring to earlier, it took six months to validate all of the data that we had. And there was a lot of automation involved, but then it required somebody in our asset management team to go through and look at every data point that seemed off. And it just took a really long time, but it was well worth it because now we have extremely good data to make decisions off of and understand trends that are happening.

Drew: Yeah. Well, let's explore some of those. In the case of inaccurate data or incomplete data, let's think through what are some of the impacts on residents or owners, operators, like what are some of those impacts that you actually see?

Ron Kudus: I don't know if this is the right example, but I'll go with it and we'll see if it kind of covers this. So we operate in the state of Maryland and we had a year of real high fraud in the application and income verification process. And we noticed that collections, you know, were trending down, evictions were going up. And so it was right around the same time that the industry started talking about it. It was happening a lot in the South and all these different foreign solutions popped up that would solve this issue, right? They would detect fraud and different solutions had a different way of doing it. We tried a few of them. We ended up implementing one that we thought we liked. First month, you know, after we implemented it, we saw application drops significantly. And I looked at it and I said, all right, great. It's doing its job. It's pushing away all the people who would have otherwise committed fraud. They're not completing the application process. They get to that step and they're saying, nevermind. Okay. Month two, the trend continued to go down. By month three, it was like a standstill. We got like all applications stopped. We're like, what's going on? We're trying to figure it out. You know, we debug the software. We're working with the company trying to figure out where the problems are. And what we ended up figuring out, and it only happened by chance because I was looking at something else. We realized that this marketplace, unlike the rest of our portfolio, has a really, really low technology adoption rate, A, and B, an extremely low percentage of residents who pay on one. And what that told me was that, and it's, you know, we should have thought about that on day one and we just weren't thinking in that way. So we didn't pick up on it. But most of these applicants did not have bank accounts. So when it got to the point where they needed to input their bank information so their income could be verified, they just stopped because they didn't have one. They just cashed their checks, right, at the end of the week or the end of every other week. Or they just weren't tech savvy, and they weren't going to spend 45 minutes trying to figure out how to use this app. And so they just went to the next apartment complex that didn't make them do that, right? And so I actually ended up driving away everybody. Now, if we had been tracking that data in words, really, like we have it, but we weren't tracking it, of how many of our residents are paying by money order versus check, right? Like many of us in the industry are tracking online payments because we all want to move everything online. But are we really tracking what the paper method of payment is and whether or not our residents have bank accounts? That wasn't really being tracked. So that's a gap where it really hurt us. And it took us a while to recover from that mistake.

Bill: Yeah, that's good. Well, let's talk about data as a trust infrastructure because you're talking about trust with tenants, but let's go further to owners, LPs, and residents. So how do fragmented systems like we've been talking about or one-off applications erode LP trust? I assume the LPs are your clients as the property manager, correct?

System Differences and Transparency Issues

Ron Kudus: Yeah. So we're owner-operators also. So we do have JV partners, institutional partners, syndicated deals, but we also have owners. And in 2022, we made a PMS change and we had just closed the deal with a very large institutional investor. And they had a reporting requirement and we would send it to them straight out of our system. And then we would get on our weekly or monthly calls and they would ask questions. And the regional manager was in front of her computer looking at the business intelligence dashboard that our PMS system had provided us. And she would answer the questions and it was very data-driven and a lot of questions about data. And she would provide answers. And before you know it, you start seeing that what we were providing in real time was different than the reports being pulled because the PMS was using either different calculations in different areas of the software to come up with the same result or the data was a day old. And that very quickly eroded the trust with that JV partner. We didn't know what we were doing. And to be honest, it never recovered. That trust never recovered. And we really learned our lesson from that. That was part of the reason why we were like, we need to have a single source of truth.

Drew: So was the math actually different or was it just doing math on a different data set because the timestamp wasn't the same?

Ron Kudus: Both. They had different definitions for different things. And I think the reason for that is that specific PMS was similar to what you spoke about before was purchasing other point solutions and they were using those native way calculations of doing things and trying to merge different solutions under one version.

Bill: Well, this is a great example of why transparency becomes harder under third-party management. But is that the core of it? Is it systems differences or is it a language difference? Because their definition of what you thought was A is not A, it's 8.1 or it's B. What is the reason that transparency becomes harder?

Ron Kudus: I think it's both, but I think it's mostly the systems piece because the definition piece, if you're a good communicator, you can get ahead of that. Like our reporting today, well, let me tell you a little bit about our reporting today because I think it'll help. So we used to send a monthly package to our investors, to our owners that was about, I think it's 16-page PDF that had financial data, operational KPIs, bank recs, whatever, a bunch of different things.

Ron Kudus: It would take me roughly a half hour to review every single one of these packages. I can only imagine that, and I was going through it pretty quickly, I can imagine that the investors took a little bit more time. And by the end of it, I would have a headache and I'd be like, all right, I need to digest this. And by the time I actually digested it and understood what it all meant, it's already, you know, this report is already three weeks old and it was about last month.

Drew: Or you got it two weeks late, right?

Leveraging AI for Data Automation

Ron Kudus: Yeah. So I challenged my team and we were able to do this to think about it differently. And we really leveraged AI with this. And what we've done is we've automated the pulling of the data as soon as our books are closed. We gave very strict guidelines to AI on how to analyze the data. And what it turned into is a link, a really beautiful web interface that is a dashboard broken into leasing, maintenance, risk management, loan compliance, whatever the different areas are, where everybody in our organization, from a maintenance technician to the owner of the property or the investor, get to see the same thing within six days of the end of the month, and good, bad, and ugly.

Ron Kudus: But they also, with every different metrical calculation, if they just hover over it, it shows them exactly what the source data is, how it calculated it. But the beautiful thing is that it provides insight into what's coming. So if you go back to the maintenance issue that we talked about at the beginning, using AI, we can say, hey, this resident, you've had three work orders for an electrical issue with the HVAC unit that you've fixed. And every time the maintenance technician goes in, he goes in and he says everything is fine. He just triggers something and it all works perfectly fine. Next door, every time you have this work order, there is a trend that the day before the microwave trick, there's something going on in the electric lines between these two units you should look at.

Ron Kudus: It's just stuff like that, that neither a service technician nor a community manager has the time to even cross-reference that, but a computer could do that really quickly.

Bill: Well, we are huge believers in the fact that you described two data silos and nobody's looking between them, probably because it's different vendor data sets. There's no aggregated data lake and there's no analysis for anomalies and correlations between the data sets. So I was going to ask you for an example and you preempted the question and I appreciate that, but you set the expectation, the data was high quality, but you also set the expectation to the viewers of the data, what the translation is.

Bill: So I think that is really at the core of the process, but then you went further to explain how, in that example between the microwave and the HVAC next door unit, that is a perfect example of if you own and control your data, you can start looking at that. You need to own and control your data. End of speech, but thanks for making the point.

Enhancing Tenant Experience with Data

Ron Kudus: Yeah, absolutely. And ultimately, there's so much more, right? Imagine we have our own one wall app that we developed and it's, to be honest, surprisingly used a lot more than I would have thought. I would have thought it was going to be used to pay rent and maintenance tickets, but there's a social component and our residents are really seeking a community and they use the group and marketplace and all that kind of stuff a lot.

Ron Kudus: And there's a lot of data that can be pulled from that in order to service the residents better, right? Somebody may mention that, I don't know, the elevator was down, but they didn't tell maintenance, right? And if the AI can pick up on it, we're not there yet, but if the AI can pick up on it and send an alert to our service techs to go check the elevator before we even get complaints about it, imagine the level of service that we can provide our residents that is reserved right now for high-end luxury hotels. That's kind of where I think things can go.

Personal Insights and Career Advice from Ron Kudus

Drew: Yeah, yeah. Data is a great equalizer in that regard, for sure. Okay, well, Ron, before we wrap up, we always like to, at the very end of our podcast when we have guests with us, go through what we consider the extra floor is what we call it. But it's really just five questions, gut level responses, as brief as you want it to be, just the first thing that comes to your mind. But here's the first of these five questions. What is a book or a podcast that has shaped how you think?

Ron Kudus: I have a podcast, but it has nothing to do with real estate. It's called I'll Be Back, and it's really about geopolitics more than anything, but I like to think of the world from a big picture standpoint, and it provides me a lot of insight into how geopolitics work and people think at a much higher level than what I deal with on a daily basis.

Bill: Well, that's interesting because I would say that's the first time that we've ever had a guest who can legitimately answer that your own podcast is what is shaping how you think. Sounds like that's a decent answer here, your very own podcast.

Ron Kudus: Yeah, it's beautiful. Well, say the podcast. What is it? Come on.

Ron Kudus: Well, sorry, it's called Call Me Back. Yeah.

Bill: Oh, that's right. That's cool. So, we made another point. It's called Call Me Back. Ron, what's the best piece of career or life advice you have ever received?

Ron Kudus: Decisions are not right or wrong when you make them. It's what you do with them that turns them into being the right or wrong decision.

Drew: That's good. All right. Number three, what's one habit or practice that consistently makes you more effective?

Ron Kudus: Getting a good night rest.

Drew: Well, on that light, are you an early bird or a night owl?

Ron Kudus: Neither. I like my sleep. I'll take it on both sides.

Drew: Take both sides?

Ron Kudus: Yeah. I love it.

Drew: So, what's your number? How many hours you need?

Ron Kudus: I try to get eight hours of sleep, sometimes nine.

Drew: Cool. All right. Last one. Ron, when you're not working, what do you love to do that recharges you?

Ron Kudus: Go to football games, Buffalo Bills games with my kids.

Drew: Very cool. Love it. I have one more question, but it's not about Ron. It's personally, anyway. It's how do our listeners contact you? We will put these in the show notes, but for those listening not looking, how could they contact you?

Ron Kudus: You can go to our website, www.onewallcommunities.com, spelled out O-N-E-W-A-L-L, communities.com. There's a form there that if you fill out, it'll get redirected to me. You can find me on, I guess I'm on LinkedIn. Just look me up. I think that's it.

Bill: Yeah. Well, Ron, thank you so much for joining us today. We really appreciate your time. This is valuable information. We know that our viewers will find it to be so as well. And for our listeners and viewers, thanks so much for joining us as always. And if you haven't already, please do follow and like and subscribe to what we're doing here. And until then, we'll see you on the next episode of Peak Property Performance. Thanks, everyone.

Ron Kudus: Thank you.

Drew: Thank you.