Traditional real estate valuation methods, including hedonic OLS regression and aggregated spatial medians, suffer from structural methodological weaknesses when applied to tokenized real-world assets (RWA) and Yield Trusts. Under Shapiro-Varian’s Information Rules theory, reproducible raw transaction data available at zero marginal cost, such as France’s DVF database, converges toward zero market value. Moreover, raw DVF data contains 14%–18% intrinsic contamination from surface anomalies, unadjusted movable-property values, and omitted cadastral dependencies.
Conventional automated valuation models (AVMs) that apply simple statistical regression absorb these sampling biases, producing median absolute percentage errors (MdAPE) of 14.25%–22.6%, which are incompatible with balance-sheet valuation requirements under Basel IV or Solvency II.
ScoreGex.com rejects selling deterministic price predictions, instead extracting probabilistic risk distributions and detecting spatial Rent-Gap anomalies, drawing on Neil Smith and Brett Christophers. The Rent-Gap is defined as the expected capitalized rental value minus the transaction price: RG z,t = E[V loyers capitalisés,z,t] − P transaction,z,t, with capitalized rental value determined by normalized rental flows in micro-zone z, required capitalization rates, and local rental-indexation growth.
When this gap widens because credit constraints depress transaction prices while rental income remains resilient under cross-border income differentials, such as Swiss wages denominated in CHF versus real estate priced in EUR, traditional valuation methods fail to capture asset convexity. www.ScoreGex.com therefore does not sell a price per square meter but rather the probability distribution of capital losses conditional on the investment horizon.
