Predictive Analysis is Now Leading the Evolution of Apartment Pricing
The modern age of the apartment industry is looking a little like America’s favorite pastime.
New advances in revenue management, driven by data science remind many of the sabermetrics now prevalent in baseball. You may recall the story in 2002 of how Oakland A’s general manager Billy Beane, facing a low budget and the loss of three superstars from a playoff team, signed new players who were rather pedestrian but had high probabilities of reaching base. The A’s maintained their competitive edge and returned to the post-season without breaking the bank.
Revenue Management and Apartment Pricing
Rich Hughes, RealPage, Inc.’s, Head of Data Science, says a similarly systemic approach to revenue management will help the multifamily win the pennant. Instead of targeting a few thoroughbreds who hit for percentage, though, the audience is much broader.
RealPage’s data science group digs into the numbers and analyzes millions and millions of individuals applying for apartments every day to drive YieldStar, its revenue management system. The solution, which utilizes data and trends to formulate effective rents, is the latest in the evolution of apartment pricing.
“It’s probably the frontier for revenue management going forward,” Hughes said.
Predictive analysis leaves in the dust more traditional pricing practices of the multifamily industry over the last three decades. The solution factors in various conditions, occupancy rates, rent rates and other data based on resident behavior to automatically recommend a price.
Evolution of pricing included only periodic or annual analytics
Setting rents have evolved from a similar mold of revenue management that the airline industry discovered in the early 1980s – to use analytics to predict consumer behavior to optimize revenue from inventory. Initially, apartments set prices once a year and offered concessions if units weren’t moving.
Revolutionary amenity-based pricing followed, generating higher rents for more desirable units. For example, an apartment that overlooked the pool or was more conveniently located on the property commanded more rent than others. Next was pricing based on periodic manual analysis, enabling properties to adjust prices throughout the year as market conditions dictated.
About the same time, revenue management started to include comps in its pricing based on market surveys.
The solutions each had merit, but they have been lapped by new technology that now helps build a better apartment team.
Today’s pitch on revenue management is building a better apartment team
Today, effective revenue management digs deeper, making it more of a numbers game that enables property managers to approach rents scientifically.
RealPage has the industry’s largest lease-transaction database, which delivers real-time pricing strategies that balance shifting supply and demand factors. Hughes’s data science team compares data that help forecast changes in new lease trends based on seasonality, which enables properties to calculate precise renewal offers or rents.
David Danish, Director of Advisory Services for YieldStar, says such an approach is important to identifying risks associated with some residents who, for example, have completed their contracted lease term and may be considering vacating.
“When someone’s lease becomes month to month, they are no longer on a long term lease contract like other residents,” he said. “Their lease term has become undefined, therefore it’s hard to measure when that lease is going to end to help you manage expirations, to help you decide how to price a new lease.”
Predictive analysis through the study of resident behavior factors in duration of month-to-month leases and original lease terms to make a price recommendation. The metrics also help to identify if there is any risk in occupancy associated with having residents on month-to-month leases.
“If you have a building with 300 units and one or two residents are on a month-to-month lease, that doesn’t really influence your pricing that much,” Danish said. “But if 10-15 or more are on a month-to-month lease, you need to acknowledge that they could provide notice at any given moment. That rent needs to be accounted for.”
Right revenue management solution as easy as a game of catch
Hughes said the right solution for revenue management will more effectively predict supply and demand. On the supply side, a good solution should predict whether people will actually renew or break the lease. On the demand side, traffic originations, what guests are saying and other information help create inputs for setting more accurate pricing. Kortney Balas, Director of Revenue Management and Business Systems at JVM Realty Corp., says using data science in determining pricing has been a highly effective tool for the multifamily real estate investment and property management company headquartered in Oak Brook, Ill.
“What the model will do is it looks at your demand,” she said. “If you’re in a down market or not doing quite as well, it’s going to reduce that price a little bit when you have demand forecasted, which allows you to get a couple of apartment homes off your shelf, and ride the revenue wave back up. When it’s in a high market and you have demand forecasted, what will happen is the pricing will continue to push, and it will push to limits you would never do manually.”
Almost as easily as a game of catch.