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.
Custom Made
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.