Abstract: Founded in 2006, Zillow established itself as the leading online real estate marketplace. In 2018, Zillow launched Zillow Offers, a new business that purchased and sold homes. Zillow Offers provided home sellers with a faster purchase process than traditional realtors by gathering data from sellers online and making offers immediately, a process known as “iBuying” (i.e., “Instant Buying”). Though new to iBuying, Zillow quickly established a goal of generating $20 billion in annual revenue within three-to-five years. Zillow believed that its artificial intelligence/machine learning (AI/ML) platform for predicting home values, aka “Zestimate,” could be a competitive advantage in the iBuying marketplace. However, after losing $421 million in its iBuying business during the third quarter of 2021, the company closed this once-promising business unit rather than risk further losses. CEO Rich Barton asserted that the AI’s inability to accurately predict home prices caused the failure of its iBuying business. This case study examines the trajectory of Zillow Offers and discusses several factors that contributed to its demise. After exploring the challenges of its home-price prediction algorithms within iBuying, we argue that the failure of Zillow Offers extends beyond the limitations of its initial AI/ML system. Zillow Offers’ focus on hypergrowth over profitability led to operational changes that failed to balance estimated price predictions and operational purchase price decisions. Through this analysis, we raise important questions for students and practitioners about the appropriate and effective use of data-driven AI/ML models for operational decision making.

Keywords: Zillow Offers, Artificial intelligence, Machine learning, iBuying, Zestimate

Download This Article: JISE2024v35n1pp67-72.pdf

Recommended Citation: Gudigantala, N., & Mehrotra, V. (2024). Teaching Case: When Strength Turns Into Weakness: Exploring the Role of AI in the Closure of Zillow Offers. Journal of Information Systems Education, 35(1), 67-72. https://doi.org/10.62273/TRCF3655