Tokenized real estate has moved beyond the question of digital ownership. The next challenge is far bigger: building the data infrastructure that allows AI to understand, evaluate, and eventually transact real estate autonomously. That was the key takeaway from our panel, Beyond Tokenization: Building the Data Layer for Real Estate 3.0. The conclusion was clear: before AI can transform real estate, the industry must make buildings machine-readable. An AI agent can't accurately value, analyse, or trade an asset it can't read. In this article, we explore what happens when real estate becomes machine-readable—and why structured, trusted data may become the most valuable layer of the next generation of property markets. Written by Julia Buchholz, featuring insights from Denis PΞtrovcic, Matthew A. Schneider, Matthieu Merchadou Melki, and Ludovico Rossi. Read the full article here👇 https://lnkd.in/gj-jSNyg
- Tokenized real estate is evolving beyond digital ownership to focus on building the data infrastructure necessary for AI-driven transactions.
- The key challenge is making real estate 'machine-readable' so that AI agents can accurately value, analyze, and trade properties.
- Structured, trusted data is identified as the most valuable layer for the next generation of property markets.
Topics: Asset types, Infrastructure providers, Ai automation, Real assets, Tokenization platforms, Automated compliance reporting, Process automation, Predictive analytics
Tags: #tokenizedrealestate #datainfrastructure #ai #machinereadablebuildings #propertymarkets #blocksquare #realestate30 #autonomoustransactions
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