Real Estate Taxation: Macroeconomic Challenges, Infrastructure Potential and Tax Base’s Optimization

This article examines the regional characteristics and problematic aspects of the transition to property taxation based on cadastral value, in accordance with Article 378.2 of the Tax Code of the Russian Federation. Based on a comprehensive analysis of the regulatory framework of 89 constituent entities of the Russian Federation, significant differences in approaches to compiling lists of taxable properties have been identified. The authors introduce the concept of ‘area threshold’ into academic discourse, which is understood as the minimum area threshold established by regional legislation, serving as a criterion for excluding certain categories of properties from the list of those subject to taxation based on cadastral value. It has been established that the variability of area thresholds, alongside regional mechanisms for verifying (confirming) the actual use of buildings, creates data sets hidden from accounting that are not reflected in aggregated tax statistics (Form No. 5-NIO). The study pays particular attention to the systemic problem of the ‘grey area’ — the absence in the Unified State Register of Real Estate of information on a significant volume of actually occupied properties (up to 35%), which is due to the historical ‘archival legacy’ of the Bureau of Technical Inventory (BTI) and the declaratory nature of rights registration. The paper analyses the factors determining the effectiveness of the property taxation system: macroeconomic (the impact of inflation), infrastructural (capitalisation of transport links) and institutional (political risks associated with revaluation). Based on a study of innovative regional practices (in particular, the presumption of commercial use in the Voronezh Region and incentive schemes in the Republic of Bashkortostan) and global trends, the authors propose ways to optimise the system. These include: the introduction of ‘rebuttal declaration’ mechanisms, the integration of data from the Bureau of Technical Inventory (BTI) and utility providers, as well as the use of deep learning neural networks (DLNN) to improve the accuracy of mass cadastral valuation. The practical significance of the study lies in the formulation of recommendations for harmonising regional standards and automating tax administration to minimise budgetary losses.