A new information age is dawning on the horizon.

Over the past 12 months, Earth’s population has produced about 1.8 zettabytes of data. Translation: You’d need more than 57 billion 32-­gigabyte iPads to hold it all.

The multifamily world is only a small slice of that pie. But if you think about it, your business generates and captures a ton of data, from demographics to lifestyle profiles to consumer behaviors.

Yet, the multi­family sector is a bit schizophrenic compared with many other industries, making it difficult to cull meaningful information from all those statistics. The industry is highly fragmented, populated by hundreds of different players. In fact, if you were to add up the portfolios of all the public REITs, you’d have only a single-digit market share of the nation’s apartment stock.

This lay of the land makes it difficult to share and consolidate the data that apartment firms take in on a daily basis. And that’s likely why data mining—used effortlessly in the retail and hospitality sectors to home in on consumer details and inform strategy—has been slow to take hold in the apartment world.

“Divided ownership of product plays a big role in why we’re late to this party,” says Greg Willett, vice president of research and analysis at Carrollton, Texas–based MPF Research.


For apartment owners, the possibilities presented by data mining are nearly endless. The massive amount of renter information captured every day, both online and within a property management system, would be the envy of many marketers.

Once unearthed and organized, that data can answer some of an operator’s most pressing questions: Are we optimizing our leasing-office hours versus doing outreach marketing? Are we reaching our target demographic and attracting the best renters? Are we fulfilling our current residents’ needs?

The questions vary with each owner, but the answers are closer than you might think. But identifying and downloading the mounds of information available just scratches the surface.

“It’s in bringing data together from separate sources, and looking at information across these boundaries, when it ends up being more useful,” says Donald Davidoff, former senior vice president for strategic systems and marketing at Englewood, Colo.–based Archstone, and now a partner with Imagine, LLC, a pricing and revenue management consultancy.

Case in point: When Dublin, Ireland–based global information provider Experian acquired Atlanta-based RentBureau in 2010, it leveraged RentBureau’s method of pulling rental data every 24 hours from property management systems across the nation. Experian then was able to share rental history about tenants in nearly real time with various screening services. And that gave owners access to very current information that they never had before, allowing them to make more-informed decisions about whom to rent to or, more importantly, whom not to rent to.

While most companies have a broad feel for who their customers are, mining data will help them dig down and segment out results on customer profiles to make smarter decisions.

“The industry as a whole is just sort of coming into its own with data mining,” says Kristy Simonette, senior vice president of strategic services at Camden, a Houston-based real estate trust. “But until now, the data warehouse concept was the new frontier.”

Why the delay? Simply put, it takes time and money, and with so many different systems, there’s never been a one-stop shop in which to dump data, Simonette says.

But many large companies, like Camden, are just now figuring out how to mine and analyze information in a cost-effective manner.


At Camden, data mining efforts are informing both back-office functions and front-end marketing campaigns, ranging from the simple to the highly complex.

After centralizing its accounts payable function, Camden analyzed some internal metrics to figure out how long it took to pay invoices, helping the company take advantage of early-payment discounts. That was a relatively easy project.

In contrast, the company is now using data mining techniques to clearly define its advertising bang for the buck, providing metrics for measuring prospect engagement. By studying where leads are coming from, the company can more efficiently and effectively determine where to place its advertisements.

But the analytics also paint a portrait of who Camden’s current residents are—which are most likely to renew at a certain price point, and which are more likely to leave, for example. What emerges is a 360-­degree demographic profile—an intersection of financial capacity, lifestyle choices, and consumer behavior—that ultimately drives better lead-to-lease conversion, as well as retention rates.

“I can say my residents had this characteristic, came from this lead source, can absorb this rent increase, or stay and renew because of X,” Simonette says. “We get a better view of the whole resident cycle, from prospect to current to past.”


There’s a direct correlation between the satisfaction of residents and renewal rates. And one of the biggest factors in resident satisfaction is social in nature: Do I fit into this community? Does it reflect my values and interests?

When prospective renters take to the Internet to search for an apartment, they primarily search based on three main factors: number of bedrooms, location, and price point.

“But the truth is, if you asked them what they ­really want to know, it would be, What are the people like who live there? What are their habits and hobbies?” says Tom Toomey, CEO of Highlands Ranch, Colo.–based REIT UDR. “So the next search engine is going to be centered around the social media aspect, which is, What type of people live here, and what do they do? And the mechanism to collect that data is all on our websites.”

That kind of social search engine was used with stunning and sophisticated efficiency during the presidential campaign season by President Obama’s re-election team. If you were an Army veteran working a union job, for example, and the campaign targeted you as a likely Obama voter, then a local union member who had served in the military would be asked by the campaign to give you a call, or send you a Facebook message, urging you to vote.

The question is, how can the multifamily industry similarly leverage social media to such great effect? While social media are playing a growing role in multi­family marketing efforts, plenty of industry experts still doubt social media’s use in data mining.

“It’s clearly not something that’s going on very much, because it’s hard to get information from residents on social media right now,” says Steve ­Lefkovits, president of Emeryville, Calif.–based Joshua Tree Conference Group, an executive-education provider dedicated to the apartment industry. “It’s difficult to get actionable ­information.”

While the sites are good for referrals and generating a small percentage of leads, personal information about residents that might be used to benefit the industry is protected by privacy laws.

Even so, data mining is not as simple as learning how to bring in and retain new customers as much as it is determining what price you’re delivering to them, says Lefkovits.


While the terms “big data” and “data mining” are becoming buzzwords, the ideas behind them are certainly nothing new.

Consider revenue management software, which mines a huge ­variety of data to assist the user in determining pricing strategies. When Archstone first introduced the technology to its portfolio in 2001, the company expected it to take off almost immediately. More than 10 years later, the industry is still just getting comfortable with it.

“Back in 2001, I thought that within three years it would be standard,” Davidoff says. “It wasn’t until 2008 or 2009 that it began to be standard.”

Now, revenue management software is a mainstay, and industry veterans don’t even question its usefulness anymore. And it’s had a powerful effect on the industry: Early adopters clearly outperformed their peers, and the software has allowed for much more leasing customization. Instead of implementing a broad brushstroke to determine rent prices, the software allows property managers to be more surgical.

“If you want to reduce price, you do it for a specific unit, for a specific floor plan,” Simonette says. “We weren’t able to go down to a unit or floor-plan level ­before.”

It’s no coincidence, then, that the biggest revenue management company today, born out of Archstone’s initial efforts in 2001, is now mining social media and other sources to bring structure to new pools of ­information.

“Being able to take this unstructured data, define it, and then put models on top of that is what we’re working on,” says Amar ­Duggasani, executive vice president at Atlanta-based Rainmaker LRO. “There are a lot of external factors that have not been [put] into revenue management solutions before, and that’s kind of the next big wave in how we can take all of these data sources and apply these data mining principles.”

Everything from a site’s search engine optimization to local macro­economic conditions can figure into the overall equation, adds Duggasani. And just as revenue management software has allowed small apartment owners to mimic the practices of large firms such as Archstone, so, too, will the next wave of data mining software help to level the playing field.

The data-driven possibilities lie chiefly in customer relationship management (CRM). That’s where we’ll see a boom in CRM system use in the multifamily industry, Duggasani predicts. CRM already ­offers comprehensive leasing data to operators as it synchronizes business processes—­including gathering information about tenants and marketing to new ones.

With a desire to search and utilize more data, ­operators may realize benefits from what CRM systems already offer. There’s a strong interest in better disciplined and more transparent ways of managing leads and doing proper customer conversions, for example, and there just might be room in the marketplace for new software companies to flourish.

“Primarily, the movers in data analytics and multi­family are going to be software providers that see an opportunity to aggregate information and can sell that organized information and interface to owners,” Lefkovits says.


Given the volume of renter information out there, and the ever-­expanding number of data sources, apartment companies have to approach analytics with clearly defined goals in mind.

There’s simply too much data for any one person to plow through to make smarter decisions about his or her properties. And until a surefire system is created to streamline and organize such data, it’s best for operators to search for information in an arena where they’re trying to make immediate changes.

“It takes some science and a lot of art,” Simonette says. “You can’t measure everything. We have to pick certain things we think are important and then draw a benchmark.”

For example, Camden tracks leads from its contact center, which represent only half of the firm’s lead volume but are the most pure and accurate data it receives, Simonette notes.

It’s not enough to have the data in front of you, experts say. Just as important to know is what you intend to do with it. Better yet, how do you stack up against the competition?

Data sources are constantly changing, and the pace of technological evolution makes the process of data mining even more difficult, like trying to hit a target that runs faster and faster. As such, it’s difficult to see when data mining will officially catch on in multifamily.

“It’s not something where we can say that six months down the road, we’ll have everything addressed,” says Duggasani. “By then, there will be new data sources to look at.”

With so many facets of the business, and so many details to uncover, the industry has a long, but promising, future in data mining.

“It’s just going to continue to grow,” Simonette says. “We’re just in our infancy.”