Price Units Right in Student Housing with Revenue Management
Every time you buy a plane ticket, rent a car or book a hotel room, an algorithm somewhere calculates exactly what you should pay on that particular day, based on availability, class and amenities, competitor pricing, season, historical data and other criteria. The algorithm balances supply and demand by adjusting prices to help business managers generate the highest possible revenues in a way no human (or even team of humans) could ever do on a daily basis.
Since around 2000, multifamily owners have been implementing commercial revenue management technology for rent pricing. Today it’s credited as a vital ingredient in the recipe for optimizing asset performance.
Student owners and operators, however, are in the early phases of adopting revenue management tools. This is mostly due to the perception that student housing is “different” due to factors such as the strongly seasonal nature of lease expirations and new leases, renting to a waitlist, and the leasing of beds rather than units (not always a particular bed, either).
While student housing is different in these regards, the difference does not diminish the value that a commercial revenue management system can deliver in the student space. It’s expected that more and more student properties will follow the rest of the industry in using the technology to eliminate guesswork and maximize profit.
Leaving money on the table
Some student properties regularly leave money on the table simply because they’re not pricing beds in an optimized manner.
Some properties offer volume or time-based tiered pricing models that increase or decrease as beds are leased. A property may set the rate at $400 by renting early for the fall semester, $420 when half the beds have been booked, and $450 when there aren’t many left. This seems like a logical approach to pricing at first glance. But is it really? Many of the beds renting earlier in the season were arbitrarily set at a lower tier price – and may have been rentable at a higher price. Worse, in fear of empty beds, some properties offer concessions or discounts for early rental decisions when they might have been able to fill all the beds at a top tier price.
But how can an owner or operator feel confident that it won’t end up with empty beds at the time the semester starts? Student operators have only a short period to get the rent right and ensure “heads in beds” when the new school term begins. If they could, they wouldn’t have to depend on blunt instruments such as tiered pricing or concessions.
This is where commercial revenue management algorithms comes in. Despite the volatility of student housing, it’s still entirely predictable. Revenue management solutions look at bed availability by unit type, future expirations, historic rental patterns, competitor pricing, leasing velocity and other criteria to help you arrive at an ideal rent to charge. Rather than lease to a target occupancy, or even to a target occupancy at arbitrary prices, you’re leasing to achieve maximum revenue – the ideal balance of occupancy and rent price to get there.
Revenue management software has an unlimited capacity to crunch data where people don’t. For example, as part of its data mix it might include the fact that 3-bedrooms tend to rent a bit earlier in the season than 2-bedrooms. Subtle data will figure in the calculation to optimize pricing.
But revenue management software is not a “black box” you merely turn on and wait for to spit out a price, as some operators fear. The idea is to combine the art of property management, intuition and market knowledge with the guidance of the software to arrive at ideal pricing. There might be occasions where a factor outside the normal parameters has an effect on pricing decisions: a campus disturbance driving down attendance, for example, or a flashy short-term concession at a nearby property that is influencing either student or guarantors.
Revenue management software trains the market that prices can change regularly, creating a sense of urgency to lock down a lease and perhaps alert friends and roommates that they need to do so as well. Of course, this translates to a direct benefit to the property in the form of increased leasing velocity.
But is the price volatility accepted by the students themselves, or will there be a backlash? The answer can be found in Uber’s surge pricing, something with which students are very familiar. Prices shift constantly with supply and demand, and over time Uber consumers (who tend to skew younger) have come to understand the logic and view it as reasonable. So there’s a precedent for them to consider.
That’s a quick overview of what’s happening with revenue management in the student space. If you haven’t looked into how it can boost NOI at your own student properties, please click here for more information.