In general, those of us in the apartment industry haven't yet jumped into the deep end of the Big Data pool. But our toes are in the water, and we're likely to be swimming laps at a fast and furious pace soon.
The most common example of predictive-analytics usage in the apartment sector right now is background screening of prospects looking to rent housing. You currently can check a would-be resident's credit score, criminal history, and rental payment patterns in a matter of seconds. A few years ago, compilation of this information took days or wasn't possible at all. Furthermore, once the information is in place, you're no longer forced to rely on gut instinct to determine what the data suggest about a prospect's likely behavior. A statistical model predicts whether he or she will pay the rent on time and be a generally responsible resident.
RevMan Takes Center Stage
Perhaps the most-talked-about statistical model today is the revenue management system. This popular business intelligence tool, which provides guidelines on setting rents on a day-to-day basis, is today's hot-button Big Data story for the apartment industry. These stat models take pricing methodologies way beyond considerations such as product characteristics and rental rates at competitive communities and place significant weight on factors like trending product availability or near-term vacancy exposure as well as historical and expected market demand patterns given prevailing economic conditions. RevMan systems nearly instantaneously run calculations that calibrate rents over large portfolios down to the individual unit level. And, importantly, they benchmark performances over time, allowing progress to be measured on an empirical basis.
The most experienced users of apartment revenue management systems are already taking the next steps and using the vast array of information contained in their databases to make operational decisions that extend past simply setting rents. Business intelligence tools either built into the systems or added on top of these platforms now are providing guidance on functions such as general budgeting, marketing campaign planning, and allocation of staffing assignments.
In fact, operations and property management personnel likely will continue to drive adoption of—and innovation in—Big Data usage for the apartment industry. That's logical, since these are the folks who actually have huge volumes of info to study. Every lease signed (or not signed, come renewal time) represents a separate transaction that can be combined with other data points to build hundreds, thousands, and millions of pictures that will tell us lots about renter characteristics, preferences, and behaviors.
Once databases that track property performance are combined with other sorts of information, what we can predict about apartment resident actions will grow exponentially. What sort of segmentation can we do based on renter income, age, gender, ethnicity, or other characteristics? Does any of that really matter relative to the impact that can come from the way we handle service requests, or generally maintain a property, or enable residents to form some sort of relationship with others in the community? What will information that can be gleaned from residents' use of social media tell us about resident behaviors? And how will all of those results vary by property location and product niche?
Examination of our customer bases in more detail will reveal patterns in consumers' housing selection criteria that we simply aren't aware of right now. Furthermore, we'll be able to identify that vast differences in those decision triggers likely exist among those who are opting for urban core versus suburban communities, those who select luxury developments versus more basic properties, and those who live in areas with limited options in a specific price range versus those residing where the selection in a general price category is wider in scope.
From the apartment operations perspective, one of the key goals of all this data analysis will be to facilitate customization of the leasing and apartment living experience. Renters come in many, many shapes and forms, so apartments obviously aren't a one-size-fits-all product. The more we understand the customer base of a given portfolio or individual community, the better we'll be able to adapt our products and services to fit the needs and preferences of various groups. This knowledge will help especially when planning product upgrades. We'll have a grasp on which product features hold the greatest appeal and whether the likely return for those features justifies their costs. Or, viewed a little differently, we'll be able to target and reach the households who are the best fit for the products and services we do offer, fine-tuning our promotional efforts and advertising spends to be more effective.
As we get a better handle on exactly who apartment renters are and what drives their behavior, Big Data won't be a topic for just the operations side of the business. When making investments, for instance, it will become possible to make much better informed decisions about how well a specific project meets the criteria that translate to business success in a narrowly defined market, or whether property adjustments can be made to hit a customer niche where success will likely be greater.