Zillow's 'iBuying' Algorithms Overpaid For Homes

Kim Velsey assesses Zillow's failed 'iBuying' experiment, a $1-billion venture that saw the real estate giant buying thousands of homes using algorithm-based decisions in the hopes of flipping them for a profit. In the latest twist in the saga, the company has now suspended the program, which did not accurately predict house value appreciation.

According to Zillow chief executive Rich Barton, "the unpredictability in forecasting home prices far exceeds what [the company] anticipated," making the program a much riskier investment than previously thought. Its aggressive buying algorithms led to the company overpaying for properties, Velsey says, while materials shortages and price spikes made renovations more expensive. Consequently, "flips became flops: Zillow listed 66 percent of homes for prices below what it paid for them, with an average discount of 4.5 percent, according to an analysis of 650 Zillow homes by KeyBanc Capital markets," writes Velsey.

Zillow's biggest mistake, argues Velsey, "was not realizing that as much as people love house hunting on Zillow, when it comes to actually buying and selling properties, real estate remains very much an in-person business." Although housing prices continue to rise and automation is gaining steam in other industries, it is unclear that real estate buyers and sellers are unhappy with the traditional model.