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AI-supported Local Tax Inspection for Fair and Sustainable Municipal Governance (Subnational: Alicante)


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Added by:   OECD analyst
Added on:   29 Oct 2026
Updated by:   OECD analyst
Updated on:   29 Oct 2026

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This initiative uses artificial intelligence to support local governments in detecting undeclared constructions and changes of property use. By analysing administrative and geospatial data, AI helps improve fairness in property taxation, reduce legal uncertainty and strengthen trust in local public administration. The system supports public officials’ decisions while maintaining full human oversight.

Initiative overview

Local governments rely on property taxation to finance essential public services. However, undeclared constructions and outdated cadastral information undermine fiscal equity, generate legal disputes and weaken citizen trust. In territories with high urban dynamics, such as the Province of Alicante, these discrepancies accumulate over time due to fragmented administrative systems and limited inspection capacity.

The initiative was developed to address this structural governance challenge. Its objective is to support municipalities in applying property taxation more fairly and consistently, in line with the principle of equal treatment of taxpayers. Artificial intelligence is used as a decision-support tool to help identify potential inconsistencies between declared property data and physical reality. Machine learning models analyse cadastral records, building permits and geospatial information to detect patterns that may indicate undeclared constructions or changes of use.

The system does not automate tax decisions. Instead, it generates explainable alerts that are reviewed and validated by trained public officials before any administrative action is taken. This supports due process, accountability and full human oversight. The initiative is embedded within a shared public data infrastructure that allows municipalities — particularly smaller ones — to access advanced analytical capabilities that would otherwise be unavailable.

Over time, the model is being institutionalised as part of routine inspection workflows and can be extended to additional municipalities and territorial contexts. By strengthening evidence-based decision-making, the initiative aims to contribute to fairer taxation, improved legal certainty and more sustainable local governance.

Results, outcomes and impacts

The initiative has improved municipalities’ capacity to detect undeclared constructions and inconsistencies in property records. It has contributed to fairer taxation, reduced information asymmetries and strengthened legal certainty. Tangible outcomes include the identification of hundreds of previously undeclared or incorrectly registered properties. The initiative supports sustainable municipal revenues and increases transparency and trust in local tax administration.

Other relevant details

Challenges and lessons learned: Key challenges included data fragmentation across administrative systems, differences in municipal data quality, and the need to ensure legal certainty and public trust. These risks were addressed through strong data governance, explainable AI outputs and mandatory human validation. A key lesson learned is that AI delivers public value only when embedded within clear institutional responsibility. Technology alone is insufficient without governance frameworks, trained staff and political ownership. Successful implementation requires shared data standards, inter-municipal co-operation and transparency towards citizens.

About the policy initiative


Category:

  • AI policy initiatives, programmes and projects

Initiative type:

  • AI use cases/projects in the public sector

Status:

  • Active

Start Year:

  • 2022

Target Sectors:


OECD AI Principles:

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Images: