Racing to Buy Homes Sight Unseen

ATLANTA—It took Akuansa Graham seven minutes on a recent morning to craft a $124,000 bid for a three-bedroom Buford, Ga., home he had never seen.

The Starwood Waypoint Residential Trust executive went to public auctions in the years after the financial crisis looking to buy homes lost to foreclosure. Now the 38-year-old crouches over a computer and relies on algorithms that evaluate home values, proximity to schools and crime rates to outrace rivals for any remaining bargains offered by real-estate agents.

“You can’t see your competitors now, so it’s important we move before everyone else does,” said Mr. Graham, a regional director based in Atlanta.

With the low-hanging fruit from the housing bust mostly picked, Wall Street-backed buyers of real estate are increasingly turning to quantitative data analysis as a way of accelerating their search for a dwindling supply of available homes that can be transformed into rental properties. Math-driven models powered by historical patterns can size up homes sight unseen and calculate future income in minutes, allowing private-equity giant Blackstone Group LP, the Alaska Permanent Fund Corp. and other bulk purchasers to skirt neighborhoods with softer rental demand or properties that need costly repairs.

Advances in how companies use technology to evaluate mountains of data has quickened everything from stock trading to student test-performance evaluations to patient care. Behind the new speed in real estate is a change in how big buyers find most of their properties.

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