Startup aims to help real estate investors
Back in February, J.C. Penney said it planned to close about 140 struggling stores. That might be just the beginning.
Analysts at System2 LLC, a big-data startup, have identified another 197 stores at risk. The outlets — from San Bernardino, Calif., to a suburb of Omaha, Neb. — have a more than 64 percent likelihood of closing. How does System2 know? Its computers say so. Founder Matei Zatreanu threw aside a hedge fund career to test a simple theory: that crunching mobile phone pings, demographic information, credit card bills and other unconventional data yields a better way to invest in real estate and other areas.
Using algorithms and machine learning, the company says it can determine which stores have a future and which ones will die — and do it more accurately than investors who rely on conventional information. So far, it’s studied only J.C. Penney Co., because its plans to shutter stores have been in the news, but it’s ready to look at more retailers, Zatreanu said.
“There’s new data that’s out there,” he said. “But then what we try to focus on is how do we use this data in a smarter way.”
Zatreanu last year left King Street Capital Management, which oversaw $19 billion as of January.
Daphne Avila, a spokeswoman for Plano, Texas-based J.C. Penney, declined to comment, citing a policy against commenting on market or industry speculation.
System2 is trying to sell commercial-mortgage bond investors on a kind of analysis that is foreign to many of them: scrutinizing every single retailer tied to mortgages in their securities and figuring out how each company’s fortunes tie into the health of a mall or mall-backed bonds.
The edge that big data and artificial intelligence have given to money managers in equities may help commercial-mortgage bond investors too, according to Zatreanu.
Looking closely at individual loans backing a bond is more common among residential mortgage-security buyers. For commercial mortgages, less data has traditionally been available, and there can be more variables in any transaction because of the wide range of tenants and customers for the property, making forecasting much more difficult.
Traders have already bet a fortune on whether bricks-and-mortar retailers can survive a stampede out of their stores and toward online shopping. J.C. Penney’s stock is trading near an alltime low and wagers against it climbed to 40 percent of shares outstanding in August, data show.
Zatreanu by his own admission isn’t reinventing the wheel; instead his team looks at the kinds of metrics that owners of J.C. Penney stock or the CBL REIT would consider to compile their list of the walking dead. That includes the number of people who visit each store, what a store’s competition looks like, where it is located and whether there are vacant shops nearby.
But Zatreanu’s edge is what he says are better ways to get data faster and to interpret it.
System2 gathered information on existing and closed J.C. Penney outlets.
Using machine learning, software was trained to determine what it was about the dead stores that doomed them.
When System2 ran this program for a set of open J.C. Penney stores, it showed nearly 200 locations faced an almost two-thirds chance of dying. And the model updates as new data sets become available.