This morning, I engaged in a discussion with a machine learning system that attempted to justify Zillow's decision to liquidate its portfolio of 3,032 properties. This decision resulted in a $304 million write-down, which I found perplexing given my experience in the property industry. The AI system initially echoed the company's rhetoric, which emphasized immediate financial stability and operational challenges.
Initial AI Justifications:
Immediate Financial Stability: Liquidation provided immediate cash flow and reduced operational costs.
Market Volatility: The unpredictability of the real estate market post-pandemic added to the uncertainty of holding onto the properties.
Operational Complexity: Managing a large portfolio of rental properties was deemed outside Zillow's core competencies.
My Perspective:
I offered an alternative view based on my industry experience. I argued that holding onto the properties could allow Zillow to benefit from market appreciation and rental income, potentially recovering losses within a relatively short period. Larger real estate firms manage much larger portfolios effectively, suggesting that Zillow's decision might have been shortsighted.
Financial Analysis:
I asked the AI to calculate the losses and gains of two scenarios over the two years since the liquidation:
Scenario 1: Liquidation and Reinvestment
Cash Released: $2.736 billion (90% of the original $3.04 billion value).
Financing Costs: Approximately $304 million over two years.
Reinvestment Earnings: Assuming a 7% return, earnings would be about $383.04 million over two years.
Net Gain: $79.04 million.
Scenario 2: Holding the Portfolio
Annual Appreciation: 5%.
Net Rental Yield After Management Fees: 7.2%.
Total Annual Return: 12.2%.
Future Value After Two Years: Approximately $3.83567 billion.
Net Gain: $795.67 million.
Revised AI Understanding:
The AI recalculated based on my input, recognizing that the potential net gain from holding the properties ($795.67 million) was significantly higher than the net gain from liquidation and reinvestment ($79.04 million). This analysis highlighted that the decision to liquidate was financially unsound and driven by short-term pressures.
Key Takeaways:
AI Learning: Initially, the AI returned a summary aligned with the company's position. However, when presented with an alternative view and detailed financial analysis, it adapted its understanding.
Importance of Comprehensive Data: Machine learning systems should incorporate long-term financial projections, market trends, and strategic patience to make sound investment decisions.
Experienced Insights: Integrating industry experience and robust data analytics can help avoid financially unsound decisions, as illustrated by the revised understanding of Zillow's situation.