Real Estate Insights: Establishing transparency – setting AI standards in property valuation

Abstract

Purpose

This real estate insight scrutinises the emerging role of Artificial Intelligence (AI), particularly Large Language Models (LLMs) like ChatGPT, in property valuation, advocating for establishing standardised reporting guidelines in AI-enabled property valuation.

Design/methodology/approach

Through a conceptual exploration, this piece examines the shift towards AI integration in property valuation and the critical role of Explainable Artificial Intelligence (XAI) in this transition. It discusses the CANGARU framework for developing inclusive and universally applicable reporting guidelines and the importance of human oversight in validating AI-enabled valuations.

Findings

Integrating LLMs into property valuation signifies potential efficiency gains and task automation but also introduces risks related to accuracy, bias, and ethical dilemmas. Standardised reporting guidelines are identified as essential for responsibly harnessing AI’s benefits.

Practical implications

The article underscores the need for the real estate industry to adopt transparent reporting practices, with valuers acting as expert interpreters of AI outputs. Emphasising error reporting in XAI not only aids in understanding AI-generated insights but also builds trust among stakeholders, ensuring AI’s ethical and effective application in property valuation.

Originality/value

This commentary contributes to the discourse on AI’s role in property valuation by focusing on the need for standard reporting guidelines that align with professional standards and legal frameworks. It advocates for a balanced approach to AI integration, where technological advancements complement traditional valuation expertise, ensuring accurate, fair, and transparent property valuations.

Citation

Cheung, K.S. (2024), "Real Estate Insights: Establishing transparency – setting AI standards in property valuation", Journal of Property Investment & Finance, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JPIF-04-2024-0050