How Investors Use Satellite Imagery and Alternative Data

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How Investors Use Satellite Imagery and Alternative Data


When Tom arrived at Alex’s house, the brothers repaired to the back porch to admire the view of Mount Columbia in the distance. But instead of jamming, they took out their laptops. Alex pulled up more images taken by DigitalGlobe’s WorldView-1 satellite. Tom opened the annual reports of several publicly traded retailers.

There is an old story about Sam Walton: In the early days of Walmart, its founder would monitor how stores were doing by counting the cars in the parking lot. After seeing the power of satellite imagery in his factory deal, Tom had a similar idea, but on a scale Walton could not have imagined. He asked his brother, “What if we could count the cars at every Walmart?”

After a week together in the Rockies, the brothers had a plan. Alex left DigitalGlobe and negotiated with the company to sell him three years’ worth of archival imagery. Tom downloaded a mouse-click counter, which allowed him to count the cars in those photos by clicking on each one. After a few months of scouring parking lots—at Home Depot, Lowe’s, McDonald’s, and, yes, Walmart—the brothers had a data set to back-test. Sure enough, the number of cars in a retailer’s parking lots seemed to accurately predict the company’s revenues.

The Diamond brothers started their own company, called RS Metrics. (RS stands for “remote sensing.”) Their first client was a stock analyst who asked them to count cars at McDonald’s, now using real-time satellite imagery. Lowe’s hired them to keep tabs on its own stores—and on Home Depot’s, too. Their big break came in mid-2010, when Neil Currie, then an analyst at the investment bank UBS, bought parking-lot counts for 100 representative Walmart stores and published the results in a quarterly earnings preview. The number of cars in the parking lots, he wrote, suggested that Walmart stock was undervalued.

Currie’s prediction proved correct. As word spread that satellite images were a reliable predictor of corporate profits, a range of investment funds began buying retail-traffic data from RS Metrics. In the following years, the company expanded, tracking not just parked cars but solar-panel installations, lumber inventory at sawmills, and the mining of metals worldwide.

Today the firm, along with start-ups such as Descartes Labs and Orbital Insight, uses a variety of aerial images and data to help investors pick stocks. When traders wanted to monitor the cars being produced at Tesla’s assembly plant in Fremont, California, RS Metrics flew a plane overhead. One morning last November, a train carrying 268 wagons of iron ore derailed in the Pilbara Desert, in Western Australia. Iron-ore prices soared on the news that the supply of a resource used in everything from furniture to paper clips could be interrupted. But some traders carefully analyzed satellite images of the accident and saw the ore piled in a flat area where it could easily be reloaded. They bet that prices would soon decline. They were right—within a couple of weeks the panic had subsided, and they had made a fortune.



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