The Balance-Sheet Economics of AI-Mediated Demand: Customer Acquisition, Distribution Costs, and Margin Pressure in Real Estate and Hospitality

The migration of discovery and comparison from human-mediated search to AI-generated answers and agentic interfaces may alter the economics of acquiring and distributing demand in physical-asset markets. This paper examines how AI-mediated demand formation could affect customer acquisition costs, distribution dependency, contribution margins, and asset productivity in real estate and hospitality. The paper proposes a dual-path transmission mechanism. Representation deficits may transmit through lower AI eligibility, computational demand leakage, greater paid and intermediated dependency, acquisition-cost inflation, and contribution-margin compression. Separately, weaker qualified-demand capture may affect occupancy, time-to-match, time on market, and asset productivity. Contribution margin and asset productivity may subsequently interact through operating, pricing, utilization, and reinvestment feedback effects. The paper introduces a measurement architecture designed for empirical validation, including the VPR Integrity Score (VIS), Global Agent-Readiness Index (GARI), AI Recommendation Share (ARS), Computational Demand Leakage (CDL), Paid Demand Dependency (PDD), Representation-Adjusted Acquisition Cost (RAAC), Computational Margin Pressure (CMP), and Representation Return on Investment (RROI). The Verified Property Representation (VPR) is positioned as a proposed persistent, structured, verifiable, portable, and auditable representation layer and as a testable intervention for empirical research. All constructs and relationships are presented as proposed and require empirical validation before claims about commercial or financial effects can be made.