The Agentic Frontier: Artificial Intelligence in Commercial Real Estate
Stace Sirmans, Associate Professor of Finance, Harbert College of Business, Auburn University
This article reviews the artificial intelligence (AI) literature in commercial real estate through a mechanism-based framework organized around three steps: Step 1 identifies the economic friction; Step 2 evaluates whether a specific AI method reduces that friction, primarily through improved prediction or information extraction; and Step 3 tests whether friction reduction produces measurable market outcomes such as declining price dispersion, shorter time on market, compressed cap rate spreads, or reduced forecast errors.
It also explores the AI literature in commercial real estate through a framework linking four market frictions (opacity, search costs, illiquidity, and heterogeneity) to AI methods and testable market outcomes. The literature shows strong evidence that AI improves prediction but almost none that better prediction reduces frictions or changes market outcomes.
Adoption data from the Anthropic Economic Index (AEI), merged with occupational task profiles for 16 commercial real estate (CRE) occupations (324 tasks), show that AI usage divides along task type rather than task difficulty: information-processing tasks show high engagement regardless of importance, while tasks requiring physical presence or professional judgment show none. AI engages the highest-importance tasks in each occupation, not the most routine. A friction-by-method classification of 66 articles shows that half the cells are empty. The high-importance tasks at zero AI penetration define the “Agentic Frontier.”
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