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MHAM – Maccloud Hybrid Appraisal Method: A Hybrid Methodology for Asset Valuation

MHAM – Maccloud Hybrid Appraisal Method: Metodología Híbrida para Valoración de Activos
Authors: Salaberry-Rivera, Sebastian;

MHAM – Maccloud Hybrid Appraisal Method: A Hybrid Methodology for Asset Valuation

Abstract

Maccloud Hybrid Appraisal Method (MHAM) es una metodología híbrida para la valoración técnica y estratégica de activos físicos e intangibles, desarrollada por Sebastián Salaberry-Rivera para Maccloud. Nace de la necesidad fundamental de crear un estándar objetivo en un mercado dominado por valoraciones subjetivas y modelos de obsolescencia rígidos e irreales. Aunque toma inspiración conceptual de métodos como JANS, MHAM reconstruye el proceso de valoración desde la base. Para lograrlo, integra inteligencia artificial, fuentes de mercado en tiempo real y principios de economía circular, con el objetivo de entregar una valoración precisa, transparente, auditable y, sobre todo, objetiva. La naturaleza híbrida de MHAM radica en su capacidad para sintetizar tres componentes de valor: el técnico, el de mercado y el operativo. De esta síntesis, el método entrega una visión completa e integral, calculando tanto su Valor Operativo (OV), que representa su importancia para la continuidad del negocio, como su Valor Comercial Estratégico (SCV), que estima un precio de venta justo. La metodología es, además, flexible en su aplicación, funcionando con o sin IA para asegurar su universalidad. Finalmente, se establece la contribución de MHAM como un nuevo estándar que cierra la brecha entre el valor contable y el valor operativo real de un activo.

Maccloud Hybrid Appraisal Method (MHAM) is a hybrid methodology for the technical and strategic valuation of physical and intangible assets, developed by Sebastián Salaberry-Rivera for Maccloud. It arises from the fundamental need to create an objective standard in a market dominated by subjective valuations and rigid, unrealistic obsolescence models. Although it draws conceptual inspiration from methods like JANS, MHAM reconstructs the valuation process from the ground up. To achieve this, it integrates artificial intelligence, real-time market data, and circular economy principles, with the objective of delivering a valuation that is precise, transparent, auditable, and, above all, objective. The hybrid nature of MHAM lies in its ability to synthesize three components of value: the technical, the market, and the operational. From this synthesis, the method provides a complete and integral view, calculating both its Operational Value (OV), which represents its importance for business continuity, and its Strategic Commercial Value (SCV), which estimates a fair sale price. The methodology is also flexible in its application, functioning with or without AI to ensure its universality. Finally, MHAM’s contribution is established as a new standard that closes the gap between the book value and the real operational value of an asset.

Keywords

Asset valuation, Risk management, IFRS, Circular economy, Economía circular, Tasación híbrida, Auditable methodology, Gestión de activos, Basel III, Technical valuation, Financial appraisal, ISO 55001

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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Average