Is multifamily ready for big data?

In the next decade over 35 trillion gigabytes (an average laptop contains 15 gigs of storage) of content will be captured via the Internet, much of it coming from social media, Internet searches, blogs and trolling technology designed to capture profile information of online consumers.

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The amount of information being gathered, stored, and now mined, for commercial use can be confounding to the the human mind. Compound this with the fact that all of this data is unique, though concurrently and thinly connected, similar to the “six degrees of separation” within human existence. The technical term for this mass of un-correlated information is “big data” and it’s the latest buzz for technologists.

The challenge before us is, first, how to harness content of such great magnitude, and then, how to glean financial return from the useful information hidden within. At the forefront of Web-based data mining are Google, Oracle, IBM and Cisco Systems, a few multinational conglomerates holding the processing power required to succeed; along with non-commercial research organizations as NASA and U.C. Berkeley.

What are the insights within the volumes of content flooding in from our Web analytics, call center records, resident loyalty programs, social media referrals, and how are they applicable to our own daily business operations?

Dhrubo Sircar, consultant with Global Planning and Strategy Services, LLC, explains, “Big data is, and will continue to be, a factor in all areas of American business that deals with the retail customer. So obviously banks, hotels, e-commerce (Amazon), and car manufacturers, will be early adopters. But because heavy analysis will impact and modulate customer preferences, we in multifamily will face this challenge sooner, than later.” What solutions lie ahead of our market, and who will bring transparency to the transaction?

Logically, it would seem that large management companies are in the best position, with the required resources, to harness big data gathering and content management, while the core of management operators would find the task daunting and unrealistic.

Tom Figert, CIO of Harbor Group International, LLC, puts the word “big” into perspective: “Presently, I think the property management data being produced is on a scale that can be managed in the classic Relational Database Management System.”

“The average size of a property management system is 30GB. A property management data store is determined by the number of properties, amount of history and added modules; with this many variables, the size of these stores can vary widely. But it’s rare that a property management database, even with hundreds of properties and over a decade of history, exceeds 100GB in size, which is still manageable by any of the major DBMS products on the market,” he concludes.

Multifamily remains an industry that will likely struggle to allocate the appropriate time, talent and platforms necessary to parse through the volume of data.

“Computers have been around for awhile, but up until now, they processed stable, closed and relatively small databases,” says Jim Charles SVP of NWP Services Corp. “What is new is the growing scale, and constant renewal of information. This spills into gigantic flows of data that pour in and out of open databases. Add to that, the growing sophistication of formats and the interwoven nature of databases, and suddenly the complexity becomes a challenge.”

“Aside from hardware issues, it’s the software nature of analysis tools which is most challenged, Charles added. “Traditional decision-making applications are completely overtaken by the mass of data and its fragmentation. Big data information is not wholly contained in databases. It lies, above all, outside.”

Is multifamily really that expansive?

Considering that multifamily is but one business vertical within a larger world of commerce, i.e. real estate, perhaps not.

What our history affords us, in the way of business intelligence and data visualization, and what we are poised to glean as the providers of rental housing for a specific demographic of consumers, living in certain geographic area, under our management and operation, is our consumer’s profile.

Consider this: Technology conglomerates, such as Facebook, operate in widely disparate environments as they attempt to obtain the Web profile of their 500 million subscribers. Even in the worst-case scenario, apartment managers handle but 200,000 consumers, and a commensurate number of leasing prospects.

While this relatively small number of consumers remains staggering to the industry, it’s not for lack of tools. Systems are available that can successfully handle process, tracking and discovery of this content, so it’s not for lack of invention.

Figert, who makes a point of reviewing key elements of data analytics for his organization, suggests that multifamily is ultimately constrained by resources and investment. His primary focus is by departmental function. From that jumping off point, he mines transactions for data, and optimizes it into business reporting. Some of Figert’s points of analysis include:

Marketing vs. operations: Tracking applications, forms, and data exchanges deliver valuable information on both prospect profiles and the leasing process.

Training vs. helpdesk: An analysis of helpdesk transactions can yield training issues.

Market survey vs. market research: One is an application, the other an opportunity in data collection and mining.

Actual vs. budgets: Comparisons of budgets to actual is old news (budget variance reports), today it’s about pro-forma information. Deep data collection delivers an asset’s “cradle to grave” comparison, resulting in overall company performance.

Such examples require financial management systems and networked operations on an internal system.

Most big data proponents negate such a myopic, self-contained view. When an organization is isolated to its own data and enterprise, the nature of the organization is thought to create bias in the results.

Data advocates hold that the ability to deliver accurate and deep analytical conclusions is indicative of the system.

Sircar adds, “Our current technology providers are about 7-10 years behind other industries in terms of business intelligence and predictive analytics. Due to industry consolidation, big data may happen more rapidly, say within 4 to 5 years.”

Companies may still elevate data handling with foresight and vision, even with constraints on their intellectual and physical systems.

Sixty percent of the companies surveyed by ComputerWeekly.com retain at least a year’s worth of online data, such as email, consumer profiles, online form collection, Web traffic patterns, and marketing references from a variety of sources, internal and external. Over half of those companies referenced hold three years of information.

It’s easy to speculate that property management systems, alone, hold an extreme amount of data spanning consumer payment patterns, relocation details, and institutional information. Add to that, the profiles of residents who “friended” or “liked” our property on Facebook over the last few years, and an even greater level of insight can be gleaned with little additional effort.

The cost of building a data warehouse has dropped dramatically. As well, many Internet sites provide complimentary reporting tools and simple data downloading repositories. The only remaining barrier for management teams is the time and talent needed to wade through the vast information.

Figert forewarns, “There are many ‘cross pollination’ opportunities-but at the end of the day, it’s really about providing useful information to decision-makers. I caution my fellow technologists to avoid being caught up in the term of the day, and use the department’s mandate as your compass.”

Maybe there is an even higher value to data presentation, as suggested by Charles, “Staying ahead in business today means capturing, storing, and analyzing more big data than ever before. This trend doesn’t look like it will slow down any time soon.”

Applying this thought to the multifamily vertical as a whole, he adds, “Ultimately, property analytics will evolve from simply providing data to decision-makers, to providing actionable information to automated business processes that tie together the whole of property operations including utility information, repair and maintenance information.”