The AI Allocability Discount Measuring Computational Liquidity in Italian Real Estate and Hospitality Assets

This working paper introduces the AI Allocability Discount (AAD), a theoretical framework for measuring how poor computational representation may reduce valuation, liquidity, and demand access for real assets in AI-mediated markets.

The paper applies the framework to Italian real estate and hospitality assets, using Italy as a high-value, high-friction case study. It develops a measurement architecture built around AI Allocability, Inference Burden Score (IBS), Computational Liquidity (CL), Global AI Readiness / Allocability Risk Index (GARI), Representation Capital, VPR Readiness, Semantic Portability, and Agent Actionability.

The central argument is that economically valuable assets may become computationally disadvantaged if they remain fragmented, non-canonical, semantically non-portable, or expensive for AI systems to verify. The paper proposes Verified Property Records (VPR), AnswerPacks, and computational asset readiness infrastructure as possible mitigation layers, while explicitly treating the HomeSelf Protocol as an illustrative implementation rather than a commercial recommendation.

This is a theoretical working paper. The proposed metrics and scenario bands are illustrative and require empirical calibration. Nothing in this paper should be interpreted as legal, financial, investment, regulatory, or notarial advice.