The business of running a property generates a wealth of information, including maintenance history, utilities payments and leasing performance.
On its own, this information can be daunting. However, when paired with a business intelligence framework, data can be analyzed in illuminating ways.
For instance, a property management report may not make trends in lease-up obvious. However, if the data in that report is laid over a map of the property, it may be easier for managers to see if one part of the property is not performing as well as another, in an example given by Houston-based multifamily technology consulting firm 20for20 in its recent white paper.
Dom Beveridge, principal of 20for20, sees lots of opportunities for the use of big data in the multifamily industry.
“I tend to be of the view that everything can be characterized in data,” Beveridge told Multifamily Dive. “And if it can't, at the moment, it's not because it's impossible, it's because we didn't figure out how to do it yet.”
Here, Beveridge talks with Multifamily Dive about how to implement business intelligence systems, findings from his company’s white paper and the potential of artificial intelligence integration.
Note: This interview has been edited for brevity and clarity.
MULTIFAMILY DIVE: How do you define business intelligence?
DOM BEVERIDGE: Business intelligence is a type of application that organizes data and allows you to get value from it. It's different from things like reporting, which just pulls data into a format and produces it for you.
Business intelligence gathers and stores data in a way that allows you to go and retrieve it to perform whatever kind of analysis that you want.
What are some of the best ways to encourage BI adoption among property companies?
The first order of business for any company that wants to be successful at this is, don't view it the same way that you view rolling out other technology. This isn't like rolling out a payment system or a CRM, because it involves data that sort of transcends everything across your business.
You need a pretty far-sighted view of the kinds of things you want people to be able to do in your company. Which means that it's the job of the technology and business leaders to think about, how do we build the foundation? Is there a product that we can go by externally?
The alternative, if the company's doing well, is to go custom build your own thing. Lots of companies have done that, particularly larger companies. But you basically have to think about, from an enterprise perspective, how do I build this foundation that is going to gather every piece of data from every system that I could be interested in analyzing data from? How do I organize that in one place? How do I make it easy for people to access that data? What tools do I need to build?
And that's where you've probably heard people talk about things like dashboards. If on the one hand, I'm building this foundational thing that organizes my data, the other thing that I have to do is say, what things do we want to improve about marketing?
You're sort of marrying this very personalized effort of putting in front of people exactly the data that they need to get things done, which requires that you do this very companywide thing of organizing where all of your data is kept. So that everything has whatever data it needs to support whatever analysis people do.
What are some of the best sorts of applications to start with?
The industry follows this very odd adoption pattern. Companies typically go as far as they can without implementing it. They're just using the reports that you get from their applications. But it's very, very limited in what it can tell you.
That’s because the thing that I want to know about my business is different from the things that other people want to know about their businesses. So when you're using a pre-canned set of reports that somebody else designed, it's frequently not reporting the thing that adds value to you.
So they'll go and buy a business intelligence product off the shelf. That works for a while, but they always outgrow it. If you're trying to be a data-driven organization, the stuff that you buy off the shelf from your PMS vendor is never good enough, ultimately. And what people tend to do is to go and design their own data model, or they do it with things like Microsoft tools.
Designing a data model is a really big undertaking. Then you have to build it, then you have to deal with all the integrations into the platform, and so on. So it's very costly in both time and money, but then you have a really good capability at the end of it.
What are some ways in which AI can be included in these models?
The thing that you always have to be cautious about in multifamily is that because we have 12-month leases, the number of pieces of data that a property generates is really small. And what you find with AI is, it's extremely good at dealing with really big problems.
We don't have particularly big data problems in multifamily. They're mostly quite small. AI is good at looking at a really large set of data and saying, “Well, these things tend to correlate with people that renew and these things tend to correlate with people that don't.”
It has the potential to find things out that you otherwise wouldn't. And so that potentially gives you the opportunity to get better at predicting who is going to renew and who isn't. And that makes your business more predictable, which is potentially a useful thing in managing your business.
The jury is very much out on whether AI is going to give you a better answer to those questions than many of the [tools] that we're already familiar with. Those are some of the things that people are working on, how much better it is and how much value that improvement adds. That's far from settled right now.”
