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Zillow Shut Down Zillow Offers Division Allegedly Due to Predictive Pricing Tool's Insufficient Accuracy

5 news reports Synced from source · record last edited 3 Sep 2026

In brief

An AI system built by Zillow Offers and deployed by Zillow allegedly harmed Zillow Offers staff and Zillow.

Risk domain
AI system safety, failures, and limitations Lack of capability or robustness
Occurred
Coverage
5 reportsNov 2021 - Sep 2024

What happened

Zillow's AI-powered predictive pricing tool Zestimate was allegedly not able to accurately forecast housing prices three to six months in advance due to rapid market changes, prompting division shutdown and layoff of a few thousand employees.

Laws that address this harm

Policy angle: Classified under AI system safety, failures, and limitations (Lack of capability or robustness) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case in the United States.

Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (5)

Titles link to the original publisher; report text is not reproduced here.

  1. Zillow to exit its home buying business, cut 25% of staff
    cnn.com · Anna Bahney
  2. Zillow's home-buying debacle shows how hard it is to use AI to value real estate
    cnn.com · Rachel Metz
  3. Why Zillow Couldn’t Make Algorithmic House Pricing Work
    wired.co.uk · Chris Stokel-Walker
  4. What happened at Zillow? How a prized real estate site lost at iBuying
    cnet.com · Alix Langone
  5. Zillow’s artificial intelligence failure and its impact on perceived trust in information systems
    doi.org · Padma Susarla, Dexter Purnell, Ken Scott

Who was involved

Alleged deployer
Zillow
Alleged developer
Zillow Offers
Alleged harmed party
Zillow Offers staff Zillow

AI systems implicated

AI-enabled decision support systems

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
AI tangible harm event
Sectors
real estate activities
Countries
US

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 7.3.

  • Reliability issues

    "Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...

    International AI Safety Report 2025 (Bengio2025)

  • Type 2: Bigger than expected

    Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Type 3: Worse than expected

    AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Ethics and Morality Issues

    LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • Safe learning

    "AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."

    AGI Safety Literature Review (Everitt2018 )

  • Malign belief distributions

    "Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...

    AGI Safety Literature Review (Everitt2018 )

  • Meta-cognition

    "Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...

    AGI Safety Literature Review (Everitt2018 )

  • Technical and operational risks

    "To date, technical limitations and vulnerabilities are present in most generative AI models in various contexts. Consequently, malicious users find it easier to breach an AI system’s safety and ethical guardrails to e...

    Regulating under Uncertainty: Governance Options for Generative AI (G'sell2024)

Linked by editors or by text similarity in the source dataset.

Incidents in the same risk subdomain

All incidents in this subdomain

Source record: incident #149 on the AI Incident Database · all 5 reports