AI valuation models are transforming property assessments. But their current metrics don't reflect how they actually work.
The Big Picture
For decades, artificial intelligence has been evaluated through machines outperforming humans on isolated tasks. From chess to essay writing, this comparison generates rankings and headlines. It's easy to standardize, compare, and optimize. But there's a fundamental problem: AI is almost never used the way it's benchmarked.
Although researchers and industry have started improving benchmarks by moving beyond static tests to more dynamic evaluation methods, these innovations resolve only part of the issue. They still evaluate AI's performance outside the human teams and organizational workflows where real-world performance unfolds. While AI is evaluated at the task level in a vacuum, it's used in messy, complex environments where it interacts with multiple people. Its performance emerges only over extended periods of use.
“Current benchmarks measure AI in labs, not in hospitals or real estate offices where it actually operates.”
98% accuracy on technical tests might look impressive on paper. But in practice, this metric doesn't capture how decisions are made in multidisciplinary teams where professionals jointly review cases. Planning rarely hinges on a static decision; it evolves as new information emerges over days or weeks. Decisions often arise through constructive debate and trade-offs between professional standards, client preferences, and shared long-term goals.
