Property managers weigh such factors as occupancy rates and competitors’ pricing when setting rents, but the process often can be informal, sometimes combining market factors with a manager’s gut instinct. Enter revenue management software.

Long a mainstay of the hotel and airline industries, revenue management software, also known as yield management software, is winning a growing number of converts in multifamily property management.

The software analyzes a variety of data—seasonal traffic rates, weighted competitor rents, and recent demand among them—to recommend pricing for a given move-in date, unit type, and lease duration, maximizing rents for each community in a property management firm’s portfolio.

“There are a lot of instances where rents could be set higher, and the customer is still willing to pay,” said Tammy Farley, principal of Rainmaker Group, Inc.

The company develops revolution Lease Rent Optimizer (LRO), a software product that it purchased from property management giant Archstone-Smith in January 2006. “It simplifies the pricing strategy employed by multifamily companies and also adds a level of discipline that they have not had,” Farley said.

Racing to raise rents

Maintaining consistent rent-setting processes portfolio-wide can be difficult, especially when renewals are negotiated. “Emotions sort of take over when pricing comes into play with long-term residents,” said Jamie Teabo, senior vice president at Post Properties, which manages 58 communities and about 20,000 units. “We’ve found that our on-site folks in a renewal situation were negotiating more than we would’ve liked.”

Post Properties decided to try out LRO for a six-month pilot period at six sample properties. All of the LRO-testing properties had a control group—closely comparable, nearby communities in the company’s portfolio—to provide a reality check against the software’s recommendations.

“After about six weeks, we were seeing significant results, and decided to accelerate the rollout,” said Teabo. “We starting raising rents on the control properties, thus eliminating the control properties.” So in the same six-month span targeted for a pilot test, the company ended up rolling out the software portfolio-wide.

One of the software’s key features is its ability to predict market fluctuations. In one pilot community in Tampa Fla., LRO suggested pushing up rents an average 20 percent—a figure the company never would have targeted through its old rent-setting process.

“The Tampa market last year was recovering strongly, and the system pushed rents quicker than we would’ve been comfortable with,” said Teabo. “It’s a great system to use as a market is coming out of a downturn because it accelerates rent growth quicker than human nature would suggest.”

At that Tampa property, the company started hiking rents within four days of rolling out the system, boosting the rent for one unit type, priced at $1,000, to $1,200. “The site staff said, ‘No way, we can’t do it,’ and that afternoon, they had leased a couple of apartments at the new rate,” she said. “It gave our other properties in Tampa confidence that they could get the same results.”

Keeping eyes on the prize

Western National Property Management has been piloting the technology for four months at four properties.

While the company holds frequent pricing meetings to coordinate corporate pricing strategy across its portfolio, automating the process frees up resources and gives key decision-makers another tool to anticipate demand and set a course.

“I think the benefit of the LRO system is that it’s able to respond to market conditions a lot faster than we normally would,” said Ken Hodges, vice president of information technology for Western National. “It allows the pricing revenue manager to focus on what’s important, and that is setting overall strategy for the company and maximizing the revenue.”

With a company the size of Western National, even a 1 percent or 2 percent increase in the bottom line is substantial. The firm manages approximately 160 properties, totaling nearly 26,000 units. “We’re expecting a modest increase—and a modest increase in revenue over what we normally would get is a lot of money,” Hodges said.

Dissecting demand

One of the more than 100 factors that go into LRO’s forecasting and profit optimization models is seasonality, which measures tenant traffic at a given site for the last three years. For instance, renters in Chicago are less likely to move in and out during the winter months, which makes it important to fill up units before the snow begins to fall.

Recent demand, independent of the season, also goes into the mix. Guest cards—the information collected from every prospective tenant who tours a community—play a big part in measuring unconstrained demand, which refers to the total number of prospects who have toured a property compared to eventual renters.

Measuring traffic and comparing it to the capture rate helps optimize rents. “If you’ve got a real high capture rate, then LRO may say you should raise the rents because a lot of people are accepting it,” said Hodges.

Market conditions are another big part of the calculation. The users determine who their chief competitors are, and can then weight each competitor based on their relevance to the community in question. The user then inputs changes in competitor rents, measured against the rents its own properties charge for various unit types, to help set a threshold for the forecast.

A tangential benefit of the software is a loosening of term restrictions. While companies often only lease for a six- or 12-month term, the software quantifies the optimal price for any given rental duration for a particular unit.