Byline: David Moin

NEW YORK — At The Rubin Organization, executives are busy gazing at maps. Not those big, wall-sized war-room maps, perforated with pins to target sites. They’re viewing colorful “hot-spot maps” on computer screens.
In today’s tough retail environment, where many malls suffer from sluggish consumer traffic and retail bankruptcies, hot-spot maps provide Rubin, a Philadelphia-based real estate developer and manager, with a visual tool to readily evaluate the performance of retail real estate, manage tenants and speed the leasing process.
Last January, Rubin purchased Sam Zell’s portfolio of 26 retail property management contracts, thereby doubling the amount of retail space it managed to nearly 25 million square feet, making its task of managing malls and strip centers even more challenging.
“One of the things hot-spot mapping allows us to do is risk management,” said Alan F. Feldman, chief operating officer of Rubin. “Certain areas in malls are prone to more turnovers, and, basically, our goal is to use this tool and technology to help us run our business better. With it, we can do a lot of things beyond just looking for the best tenants.”
It is used as part of the regular course of business, and in conjunction with market research, including demographic and disposable income studies, Feldman said.
Hot-spot maps use GIS (geographic information system) software, not unlike that used in forestry to get the lay of the land, but instead of geographic information, it contains financial data tied to a mall.
“It’s a time saver, like an Excel spreadsheet — a spatial, analytical way to evaluate data usually stored in hard copy or inside a computer,” Feldman said. “A picture tells a lot. And now you’re not looking at 15 pages of financial statements.”
Qualitative and quantitative information is expressed through these maps, which show simple, colored polygons, each representing a store site in a mall and information pertaining to the site. It’s a marriage of data with graphics on a map of a mall.
One site, for example, could be colored red to represent high sales per square foot; another site with low sales would be colored differently. Rubin updates its information monthly.
In addition to sales productivity, Rubin has developed maps to show where merchandise categories are concentrated in the malls. There is also a leasing-status map, a “subjective marketing rating” map that rates the quality of goods sold in different sections of a mall, a map showing different rent levels and an “action plan” map that flags potential bankrupt stores, problem areas and upcoming vacancies.
“We’re looking to incorporate other key information to better understand our properties,” Feldman said, noting that an “occupancy cost” map was recently developed to help determine locations in malls where it is harder for retailers to make money. Occupancy costs are the ratio of sales to rents.
Graduate students from the University of Pennsylvania are working with Rubin to expand the use of GIS to study trading areas around malls. Students have been gathering census data, disposable income data and other information and putting it all into the database. These are linked with the mall information gathered by Rubin from its tenants and used to assess regional supply and demand for certain products and stores, and to make leasing and real estate investment decisions.
Feldman stressed that hot-spot map technology plays a big role in relocating mall tenants.
In one case, Ann Taylor at the Willow Grove Park mall in Pennsylvania moved from the first floor to the second floor after examining the maps and seeing how other stores were performing. The store moved away from Victoria’s Secret and Bebe’s and closer to merchants with more compatible images and merchandise, including Gap, Bloomingdale’s, Eddie Bauer and Warner Bros.
At the North Dartmouth Mall in Massachusetts, hot-spot maps revealed that while some retailers’ sales were weak, jewelry stores were hot, posting $1,000 sales per square foot. The town of North Dartmouth is inhabited by many fishermen who, after long fishing trips, buy jewelry as gifts for their wives, Feldman said. When a vacancy arose, Rubin decided to add another jewelry store.
Computer mapping also helped convince a Friendly’s restaurant to relocate to a food court in one mall; originally, it preferred to be on the opposite side of the mall, where there was less food competition, but also less business.
The bottom line, according to Feldman: “When we’ve moved tenants based on this technology, we have achieved favorable results.”

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