Week 8: Data and Decision Making - Zillow Offers Case Study
Learning Objectives:
- Understand the challenges and pitfalls in algorithmic decision-making systems
- Analyze the role of data quality in automated valuation models
- Evaluate risk management in data-driven business strategies
- Apply critical thinking to real-world AI implementation cases
Core Materials:
Required Readings:
- WSJ: What Went Wrong With Zillow? A Real-Estate Algorithm Derailed Its Big Bet
- Zillow Research: Understanding Automated Valuation Models
- Case Study Document (Available on Blackboard)
Supplementary Resources:
Key Topics:
- Automated Valuation Models (AVMs)
- Components and architecture
- Data requirements and quality
- Model validation and testing
- Risk Management in Data-Driven Decisions
- Market volatility considerations
- Data lag effects
- Model limitations and assumptions
- Business Strategy and AI Implementation
- Scaling considerations
- Market feedback loops
- Human oversight requirements
Assignment:
Independent Case Study Analysis
Part 1: Zillow Case Study Review
- Read the Zillow Offers case study provided on Blackboard
- Analyze the key factors that led to algorithmic decision-making challenges
Part 2: Your Own Case Study Research
- Select and research a company (other than Zillow) that:
- Uses algorithmic/AI-driven decision making
- Has faced challenges or successes in implementation
- Has publicly available information about their approach
- Analyze the following aspects:
- The company's data-driven decision making approach
- Technical implementation and challenges
- Business impact and outcomes
- Lessons learned and best practices
Deliverables:
- Written Report:
- PDF format
- Include both Zillow analysis and your case study
- Proper citations and references
- Video Presentation:
- Present your independent case study findings
- Compare and contrast with Zillow's experience
- Include recommendations and insights
Submission Guidelines:
- Submit both written report and video presentation through Blackboard
- Due date: See course schedule