The revenue management landscape is changing in multifamily. Property managers are tired of black box algorithms they can't explain to owners, but they don't have the time to be market experts, salespeople, customer success reps, and every other job expected of them, which is leading to a 50% or higher churn rate in the industry.
But with major anti-trust lawsuits pending against some of the largest revenue management providers and their clients, operators have no choice but to reconsider how they conduct rent surveys and set market rents.
In this article, I discuss how revenue management systems work, why the status quo is a problem, and where the industry is heading.
Revenue management has traditionally relied on supply and demand based algorithms to maximize revenue. These algorithms take into account a property's historical performance in terms of interest from prospects, lease applications, and upcoming lease expirations to help set the highest possible rent for a given occupancy target.
As new prices are set and pushed to their property website and listing sites, the algorithms observe what happens and learn from it. Did more prospects come in at the new price point? Were more applications submitted? More leases signed? Each data point collected helps optimize pricing.
Revenue management systems use this real-time data to generate daily pricing and lease term recommendations, with the dual goals of maximizing revenue and staggering future lease expirations.
By offering better pricing for lease terms that help smooth lease expirations (for example, by offering better pricing on a nine-month lease so the manager doesn't have to list the unit on Christmas eve next year) these systems help property managers avoid a glut of units hitting the market simultaneously or during off seasons.
If this sounds like a great thing, it's because it is. Revenue management systems have been known to improve profitability and make property managers' lives much easier.
Generally, property managers just accept the pricing recommendations generated by this software, which then automatically updates pricing on property websites/listing sites.
The problem isn't the way these models work… it's data they have access to.
If the largest property managers in downtown Dallas met together every month to exchange data on their pricing, volume of applicants and upcoming lease expirations, they would have a powerful dataset to help set rents, right?
With detailed rent rolls from all of the largest properties in one spreadsheet, it wouldn't even take a sophisticated algorithm. Any analyst could just sort by lease expiration dates and tell each property manager which lease terms to offer, helping them avoid advertising too many units at the same time as their competitors.
That analyst could also see how many applicants come in the door and what tenants are actually paying across an entire market. Data like this would make it pretty easy to set similar rents for similar units when those leases expire.
Naturally, the property managers would want to use that analyst's recommendations to keep their rents high and vacancies low. If they didn't use the recommended pricing and lease terms, they could end up with greater competition right when they have a bunch of units hitting the market.
That would be price fixing though… and that's illegal.
But what if instead of humans meeting every month to exchange this data, they all just used the same AI-driven revenue management system?
The system could access real-time data from every property to recommend pricing and lease terms that keep rents growing and vacancy low for everyone. And since it's automated, it's way cheaper to manage, and of course, 100% legal.
Well, the U.S. government disagreed with the 100% legal part. And now the nation's top revenue management providers and several of their largest clients are involved in class action lawsuits because of it.
We all know change is hard. Over the years these automated pricing tools have been deeply integrated into existing workflows. It's not easy to ask people to change how they work, especially when the alternative is to manually analyze comps and set rents when they're already overworked.
At the same time, with interest rates high and deal volumes low, everyone is looking to cut costs these days… and defending yourself against a class action lawsuit isn't cheap.
Naturally, the largest property managers in the country are looking for alternative ways to price their units without using non-public data.
The good news? New AI-driven revenue management systems driven entirely by public data are making this possible.
The truth is, it's significantly easier to use private data to set rents than to build a revenue management system that only uses public data. If every time I submitted a proposal, I could see the proposals of every other company competing for the same business, I'd win in every negotiation, right? But that approach is what got us into this mess.
To get out of it, outperformance in revenue management will depend on the innovative use of publicly available data.
Just because a platform helps you manage your property shouldn't mean it has the right to use your data to help your competitors, and owners are going to push back on this more and more as they realize how valuable their private data really is.
As firms seek to distance themselves from data sources that can get them sued, they'll rely more and more on AI-driven approaches to create more transparent and fair pricing models. With recent advancements in AI, models can now assess a significantly broader range of data points on a much more frequent basis, achieving even better results without relying on non-public information.
Bolstered by the fears around pending litigation, transparency is already being valued more highly by owners and managers. Solutions that can provide the reasoning behind outputs versus will be heavily favored over the traditional "black box" approach with equally opaque data sources.
I'm paying close attention to how these revenue management lawsuits play out in court. In the meantime, I encourage property managers, owners, and investors to embrace a more modern view of revenue management, and to create their own approaches to achieve optimal pricing. It's not easy, but if done right, it will ultimately lead to higher returns and significantly lower risk.
Competitive advantage always arises from thinking outside the box versus following the herd… and as we've seen in multifamily, following the herd can sometimes run your business off a cliff.
Property managers, investors, brokers and appraisers all use HelloData to analyze multifamily comps, optimize rents, and increase deal flow.
