Predictive models can lose the plot. Here's how to keep them on track

Abstract

Organizations are increasingly turning to sophisticated data analytics algorithms to support real-time decision-making in dynamic environments. However, these organizational efforts often fail—sometimes with spectacular consequences.

In 2018, real-estate marketplace Zillow launched Zillow Offers, an “instant buyer” arm of the business that leveraged a proprietary algorithm called Zestimate, which calculated the estimated sales prices of real estate. Based on these calculations, Zillow Offers planned to purchase, renovate, and resell properties for a profit.1 While it had some success for the first few years, the model failed to adjust to the new dynamics of a more volatile market in 2021. Zillow lost an average of $25,000 on every home they sold in the fourth quarter of 2021—resulting in a write-down of $881 million.2 This is an instance of what we call algorithmic inertia: when organizations use algorithmic models to take environmental changes into account, but fail to keep pace with those changes. Here, we explain algorithmic inertia, identify its sources, and suggest practices organizations can implement to overcome it.