Data-Driven Property Management: Using Insights to Boost Productivity
Last modified on May 12th, 2021
By Megan Eales Monroe
Even at a time when companies generate more data than ever, many property management companies struggle to access the data and insights they need to make better-informed decisions. In this episode of The Top Floor, we’re exploring data-driven decision making within the multifamily property management segment: including why these decisions matter, tips to get good data, and common pitfalls to avoid.
We delve into how technology solutions can create a single source of truth, allowing teams to act quickly and confidently. We speak with AppFolio’s Sr. Product Manager Jake Schlingman, and the Principal of D2 Demand Solutions, Dom Beveridge, to uncover:
- Why timely access to insights is so important
- What KPIs multi-family operators should focus on
- Downsides of analyzing the wrong KPIs
- And so much more.
Megan: Ask any leader in property management for their top goals, and a few priorities make every list. There’s building a strong, highly productive team. Delivering better customer service. And of course, driving profits and NOI. But to do all of those things, there’s a very crucial ingredient — access to accurate data that yields actionable insights.
Today on the Top Floor, we’re investigating data-driven decision making.
Even at a time when companies generate more data than ever, many still struggle to get the insights they need to make better business decisions. Why is that? And what can be done to turn all this raw data into business insights — that is, real actions that can be taken? Helping us answer these questions is Dom Beveridge, a principal at D2 Demand Solutions, a leading industry firm for revenue optimization, with decades of experience in multifamily, single family rentals and senior living. Dom spoke with Jake Schlingman, a senior product manager at AppFolio. Here’s their conversation.
Dom: Hi, I’m Dom Beveridge. I work as a principal for D2 Demand Solutions, we’re a consulting firm. I spend most of my career, so well over 20 years, mostly focused on pricing and revenue management and analytics across a number of industries, the travel sector being a big part of my time. But the last eight or nine years I’ve I’ve worked exclusively in multi-family.
Jake: Hello everyone, my name is Jake Schlingman, I’m a Senior Product Manager here at AppFolio.
Pleasure to be talking with you today, Dom. Kind of wanted to dive in, and I’m curious to get your thoughts on kind of why timely access to insights is more important than ever in the property management industry today?
Dom: There are a few things though that are particularly pertinent right now. Work from home is obviously one of them. It’s amazing the number of companies that despite investments in business intelligence and analytics, were still getting a lot of their information by wandering down the corridor and asking somebody. Well when you can’t do that anymore, you need a way to share stuff amongst a much more distributed organization. So companies that have a really good, really centralized, really accessible ways to get timely insights out to users have had a natural advantage in this work from home environment over the last year.
Jake: In the age of data, there’s so many different KPIs now. There’s so much data available, sometimes it can kind of be overwhelming to even know where to start. So kind of how can multi-family operators determine what KPIs they need to stay focused on and not get overwhelmed by kind of the sea of what’s out there?
Dom: Yeah. Great question. I mean the short answer is you’ve got to figure out what’s important to you. It depends heavily on what kind of company it is, and it depends heavily on what kind of asset it is, and what the objectives the owner and operator have for that asset. A company that has long-term hold on properties is naturally going to be far more focused on things that drive the P&L of the property, right? So those operating metrics really come to the floor if you’re not thinking about disposal of the assets.
Jake: Awesome. So, I’m curious in your experience, Dom, what is the potential downside of looking at the wrong KPIs? What have you seen around having people not take the time to really think through what KPIs they need to be tracking and how that affects them and just chasing ones that maybe don’t make sense for their situation?
Dom: If you’re looking at the wrong metrics you’re not going to make the right decisions. I mean, just staying sort of topical to now, one of the obvious examples is the differences that you’re seeing in performance trends that you’ve been seeing for the last year between suburban and urban markets and coastal and non coastal markets for example. If you’re in the habit of looking at metrics in the aggregate without understanding that there are some sub markets that have seen increased performance and some that have seen really depressed performance and different asset types as well, like different floor plans that have experienced wildly different demand conditions over the last year, if you’re looking at any of those things in the aggregate, it’s really likely that you’re making bad decisions about based on the data that you’re looking at.
Dom: Because the trends that we’ve seen through COVID have been so unique, if you don’t understand what constitutes properties, your KPIs are going to be wrong because you’re mixing together trends that are really different from one another. So yeah, it should be obvious to see how that could lead you astray.
Jake: Definitely. So, thinking about the world we live in and I know you touched on it a little bit earlier with how COVID it’s kind of thrown a bit of a wrench in a lot of people’s plans. What are the top problems you’re seeing that multifamily owners and operators have with their data today, especially when they’re trying to get on top of this metrics game?
Dom: Well, the biggest problems that people always have is that, you’ve got reporting these metrics through systems that were not designed with the flexibility to provide insight into the kind stuff that we’re saying. And you typically find that people have got systems that don’t necessarily speak to each other in quite the way you would need them to. So, let me talk a little bit about both of those things. If you’re a multi-family operator and you’re running one property management system and then you go buy a best of breed CRM from someone else, and then you’ve got a screening app, and then you start to have lots of different applications to do the various functions. You need to run your business.
Well, each of those systems is going to define data in a different way. Like a revenue management system might characterize occupancy in a different way from a property management system, for example, the definitions might be slightly, but it means the number is different than you get from each of them. On top of that, you then get problems like, they don’t necessarily characterize periods in the same way. Where you have things like batch processing of data extracts, they don’t all come at the same time. Like if I’m getting data from this system today and this other system next week, and another system for some other period, you very rapidly have these kinds of apples and oranges comparisons of your different slices of data have measured in slightly different ways which means you can’t really make sense of any of this.
And we talked a bit about some of the KPIs that have changed a lot in the last year. Well, the standard reporting that you’re getting out of your applications is not going to necessarily reflect the specific things that you’re trying to find out about your portfolio, if I’m trying to separate unit types and property types and sub markets, and I’m trying to measure different things from what I normally try to measure, that means you really need to keep your data at this very granular level that allows me to build up the picture that I’m looking to see, and that there were really very few companies that have really had access to the capabilities to do that, especially if they’re using a lot of different systems.
Jake: So, with that in mind, what are some examples you can think of where you’ve seen property management companies struggle to take action based on the metrics that they’re seeing?
Dom: What you often find is that you’ll get conflicting pieces of information that make it hard to understand what to do. So, if you have a revenue management system or a really good way of reporting market data you might come to the conclusion that you should keep your prices high.
So you frequently get a sort of conflict like that, where people don’t quite know what to do. I mean, the more capable organizations figure out what forward-looking indicators they need to help them make the right decisions. But if you don’t have that forward-looking information available to you, or at least the lesson that you have, the less able you are to make … that decision. So that would be one example.
Another one that springs to mind is sales. Where some of the metrics that we use to measure sales in multi-family lend themselves naturally to conflict. So, the classic example is conversion ratios. When people try to understand leasing, they’re trying to understand the rate at which leads convert. I have all the people that show up or inquire about a property. What’s the percentage of them that ends up signing a lease? That tends to be a touchpoint, but everybody looks to. The trouble is its very limited in what it tells you about the market and can frequently tell you the wrong thing.
Jake: Yes. That makes a lot of sense and I’ve definitely had pains around that before around this idea of specifically in the context of revenue management. These leasing agents feeling like they’re getting penalized because they’re judged based on that conversion ratio, but that’s not really indicative of the job that they’re doing. I imagine that becomes even more problematic if you have a situation where your data points are not necessarily defined the same way, and everybody’s kind of looking at slightly different data and interpreting that data in a slightly different way. What advice would you give to companies who are in that state that you mentioned? They’re looking at data, they’re looking at things, but they don’t necessarily have that forward-looking, they haven’t settled on what those forward looking KPIs are. Is there any advice you can give them on where they can start, how they can dive in to get that insight so that they’re not stuck in that kind of decision paralysis?
Dom: Well, the real problem lies in the technology. Most people don’t have adequate technology to support the kinds of analysis that they want to do. If you’re really curious about finding out what things drive your business and what things you can do to improve, you really need technology that supports that and the reality is most companies don’t have it. What you tend to find, let’s say, companies will persevere for as long as they can with the reporting that they get out of different systems, or they might go buy off the shelf BI which, again, is most of the BI that you get off the shelf is really sort of reporting based. It’s very sort of predefined the types of analysis that you can do.
And if you’re trying to analyze, for example, something new which people have been doing a lot over the last year, the reality is, what ends up happening is, you have to generate multiple different reports, and then you have worked at each reconcile them your export reports into Excel, and you have to do a bunch of analysis on data that doesn’t quite match to try and get as close as you can to the thing that you’re trying to analyze.
The other thing is, to get really good at doing analysis, you need to store this atomic level, this very low level of detail, which allows you to build whatever analysis you want to do with that data. And again, if you’re trying to pull reports from these disparate systems, you can’t do that. You have to sit with an Excel sheet, try and put the picture together yourself from incomplete data.
Jake: Do you have any advice for people in that state – maybe who don’t have the technology that they’d like to have, or don’t have it in their budget even to have that technology? So it’s kind of almost not even an option for them at this point. Do you have any advice for them obviously, besides going out and acquiring that technology? Is there any tips or tricks you can give them to make that process maybe slightly more efficient?
Dom: The step people often don’t take is, you have to get control of your data. So, if you’re completely beholden to whatever reporting your systems give you, then you’ve only got a really tiny slice of the insight that you could possibly guess. Having some kind of data strategy where you’re going to figure out the lowest level of data you can get out of your systems, you’re going to figure out someplace to put that data so that you’re at least collecting as much stuff as you can. You’re applying sufficient governance to that data you can accumulate is going to be useful to you and separating the task of data collection from data analysis is a pretty key step to this.
Jake: Definitely. You want to make sure that you’re really utilizing your people in the most efficient way possible and their skills in the most efficient way possible … So, how can some of these consistency issues that people are running into and that we’ve spoken about a few times now be addressed? What can people do to help narrow down on definitions and narrow down and make sure that their data is as consistent as possible?
Dom: I mean, the key thing is it’s the old saying, you can have your own opinions, but you can’t have your own facts. If multiple different reports support different views, characterize things as simple as like rent or occupancy in different ways, it’s easy to see how you would never agree on anything. So, it’s a really important problem to solve. And again, the solution is to establish a single source of truth. You’ve got to decide what system of record is producing what KPI or other what metric and to get everybody to stick to that.
Jake: Looking at the industry overall, where would you rank those making analytics more predictive as a priority for the industry? I know you said that’s kind of where the money is on a broader sense. Where do you see that and kind of why?
Dom: So, in terms of what people should aspire to in terms of analytics and analytical initiatives, they should really put the highest priority on predictive analytics, but at the same time, there are so many companies that are basically hacking through the jungle with reporting and just with getting data available that we have to be realistic in terms of everybody’s priority, being slightly different based on what opportunities and what resources they have available to them.
Jake: Yeah. That totally makes sense. I’m curious if you have any thoughts around for those companies that are stuck at the beginning stages more about what we talked about earlier, they’re trying to consolidate what their source of data is. They’re trying to define a source of truth. Is there anything they can do to accelerate to getting to that predictive analytics or anything they can start with that’s a little more simplistic on the predictive side or are they really do they really need to get that primary data in first before they can really jump to that predictive level?
Dom: I mean, it’s helpful to think about some of the types of predictive analytics that you could do that would make a material difference to your business. I mean, one example I always come back to is marketing spend. If I can figure out at a more precise level, what my future demand looks like. So over a longer period for different floor plans, what are my exposure levels and where do I need to spend my marketing dollars? And if I know more about what marketing tactics yield, the best outcomes for those sub-markets for those floor plans, et cetera, then I’ve got a much better opportunity to drive a better outcome at a lower costs.
Jake: I’m curious, when it comes to data and analytics capability in the industry overall, what blind spots do you see that people are just missing that you feel like are really important?
Dom: Well, people are inclined to solve the problems that are in front of them. So again, going back to time and resources, you’re going to try and deploy your attention, your resources as best you can to solve the problems that you think are solvable. For lots of company, simply delivering service, collecting rent, and keeping investors happy, is really a full-time job. I need to know whatever things I need to know to just do that. They’re not turning their attention necessarily to, if I leveraged data, I can make those tasks easier and I can improve my ability to deliver them. And I can go find other opportunities I’m not currently capitalizing on at the moment.
Jake: In general, and we’ve touched on this a little bit, but what best practices would you recommend multifamily operators follow in order to ensure access to reliable and accurate data. And then with that as a foundation once you have that, how can leaders then build that strong culture of accountability and analytical decision-making moving forward?
Dom: That’s where you want your decision makers focusing most of the time. You want them understanding trends. You want them understanding cause effect relationships. You ultimately want them making decisions that should be completely separate from the enterprise of organizing and curating the data so that it’s available for that other process. So separating those two things whether that’s done by having somebody build your database and then building analytics on top of it, or whether that’s buying a system that is architected to give you immediate access to all of the data in your systems. That’s another question, but one way or another that’s probably the single most important thing that you have to get.
You need to internalize this idea of critical success factors because you’re a critical success factor, as big as drive rent growth or maximize or optimize rent or something like that. The way that you perceive that goal dictates the types of reports, you create the types of dashboards that you create, the metrics that you’re going to run reports, and it ultimately dictates the way that you want to structure the database that provides all of that information.
Jake: Do you have any recommendations on some strategies you’ve seen work around change management for getting people on board with this more business-focused approach. [Saying,] “Hey, these are the metrics we need to track because these really drive our business” as opposed to maybe the metrics you’ve been tracking for the last five, 10 years.
Dom: I just saw a really good example of this the other day from a fast growing portfolio that’s decided to build a culture of analytics know, data driven culture, highly accountable culture, through what they have designed as a one-page strategy for every property. So, because part of the problem people get when they’re trying to build that culture and build the metrics are going to drive their businesses. There are so many nuances asset by assets that the sort of broad brush approach it doesn’t really work all that well.
Jake: Every time I’m talking to clients, non-customers I always think these questions, this question gives the most interesting responses and I think it can provide a little insight. So I’m curious if you had to describe a perfect world for data and insights and multifamily, what would that look like to you? What would the perfect analytical process and system, how would that be structured? That kind of stuff?
Dom: We want all data from all systems or all processes going to one place where that data is modeled at the most granular level it possibly can be and stored so that it’s available for the maximum flexibility, for whatever analysis you want to do.
Megan: A huge thanks to Dom Beveridge and Jake Schlingman for sharing their expertise on the subject of data-driven decision making. And thanks to our listeners. Stay tuned — we look forward to seeing you on the Top Floor.