By Chase Harrington
Apartment operators have pricing structures for conventional homes down to a fine-tuned science.
For the student-housing sector, well … not so much.
On the conventional side, virtually every price-related contingency plan has been created and successfully implemented. The industry has a deft understanding of when to raise and reduce rental rates, adjust for seasonal factors and modify strategies in order to reach occupancy goals.
Student housing is an entirely different entity, however. Most revenue management software was built for the conventional sector, meaning student-housing operators often utilize a system not tailored to their needs. It can be like attempting to put together pieces from separate puzzles.
While recent tech innovations are steering toward solutions for student housing pricing, here’s a look at the challenging factors that surround the issue—and ways they can be conquered.
Lack of data
The student sector is ultra-competitive. Because of that, student-housing operators are reluctant to share data. They have the mentality that sharing concession ideas or data in any form would equate to providing free information for the competition. While it might take a prominent student-housing operator to serve as the pacesetter, a universal sharing of data—including pricing and concession metrics—would be a win for everyone in the student sector.
Pigeonholed leasing/renewal dates
Student housing would seem to be simpler than the conventional sector because nearly all residents move-in and renew at the same time – right before school starts. But that’s also what makes it a challenge. On the conventional side, an apartment community can lower its rental rates if leasing season is slower than expected. They can raise rates if homes are filling up at meteoric pace. On the student side, you have one fire drill of a leasing season and can’t adjust on the fly based on how the market is trending. To address this, student-specific pricing innovationsare being developed that utilize proprietary algorithms that factor multiple datasets of user-driven strategy to optimize rents.
Rental rate factors
Student housing operators have to consider factors that would be irrelevant if they were leasing conventional homes. Proximity to campus, having to rent by bed rather than the entire unit, and the aforementioned timeline-specific leasing season are a few of those unique dynamics. Couple that with the idea that the leasing season can shape a community’s entire year—whether good or bad. If your occupancy goals were reached at a solid revenue-producing rate, you’re set for the school year. If they were not, it could be a lengthy nine months. Being agile with pricing won’t matter much after the school year starts. Continued thought leadership designed to erase the old-school mentality of safeguarding information will help fuel the shift.
The multifamily industry has worked tirelessly to bridge the technology gap in many key areas and has made tremendous headway. The student-housing pricing enigma remains one of the industry’s unconquered hurdles, but solutions are on the way—with regard to both technology and a slowly shifting operator mindset.