Enhancing Explainable AI Land Valuations Reporting for Consistency, Objectivity, and Transparency
At the crossroads of technological innovation and established practice, property valuation is experiencing a significant shift with the introduction of artificial intelligence (AI) and machine learning (ML). While these technologies offer new efficiencies and predictive capabilities, their integrati...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/14/5/927 |
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| Summary: | At the crossroads of technological innovation and established practice, property valuation is experiencing a significant shift with the introduction of artificial intelligence (AI) and machine learning (ML). While these technologies offer new efficiencies and predictive capabilities, their integration raises important legal, ethical, and professional questions. This paper addresses these challenges by proposing a structured framework for incorporating Explainable Artificial Intelligence (XAI) techniques into valuation practices. The primary aim is to improve their consistency, objectivity, and transparency to ensure the internal accountability of AI-driven methodologies. Drawing from the international valuation standards, the discussion centres on the essential balance between automated precision and the professional duty of care—a balance that is crucial for maintaining trust in and upholding the integrity of property valuations. By examining the role of AI within the property market and the consequent legal debates about and requirements of transparency, this article underscores the importance of developing AI-enabled valuation models that professionals and consumers alike can trust and understand. The proposed framework calls for a concerted cross-disciplinary effort to establish industry standards that support the responsible and effective integration of AI into property valuation, ensuring that these new tools meet the same high standards of reliability and clarity expected by the industry and its clients. |
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| ISSN: | 2073-445X |