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This article examines a methodology for developing and making strategic management decisions in real estate based on cadastral valuation. The relevance of this topic stems from the role of real estate as a key economic asset, influencing regional development, budgets, and investment. Traditional approaches to cadastral valuation focus on technical cost calculations, ignoring the strategic implications for business and society. This leads to planning errors, asset overvaluation, and risk underestimation. The aim of this article is to propose a systematic methodology that integrates cadastral valuation with strategic management tools. The originality of this approach lies in the use of multicriteria analysis and machine learning algorithms to predict real estate value changes, which improves decision accuracy by 15-20% compared to traditional methods. The methodology consists of five stages: data collection and analysis (including cadastral characteristics from the Unified State Register of Real Estate and market indicators), scenario modeling (baseline, optimistic and pessimistic with calculation of NPV, IRR and VaR), risk assessment (risk matrix and Monte Carlo simulations), decision-making (AHP method with criteria of economic efficiency, social benefit and environmental sustainability) and monitoring with adjustments.
cadastral valuation, strategic management, real estate, decision-making, methodology, artificial intelligence
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