Real Estate Insights Unleashing the potential of ChatGPT in property valuation reports: the “Red Book” compliance Chain-of-thought (CoT) prompt engineering

Author:

Cheung Ka ShingORCID

Abstract

PurposeThis viewpoint article explores the transformative capabilities of large language models (LLMs) like the Chat Generative Pre-training Transformer (ChatGPT) within the property valuation industry. It particularly accentuates the pivotal role of prompt engineering in facilitating valuation reporting and advocates for adopting the “Red Book” compliance Chain-of-thought (COT) prompt engineering as a gold standard for generating AI-facilitated valuation reports.Design/methodology/approachThe article offers a high-level examination of the application of LLMs in real estate research, highlighting the essential role of prompt engineering for future advancements in generative AI. It explores the collaborative dynamic between valuers and AI advancements, emphasising the importance of precise instructions and contextual cues in directing LLMs to generate accurate and reproducible valuation outcomes.FindingsIntegrating LLMs into property valuation processes paves the way for efficiency improvements and task automation, such as generating reports and drafting contracts. AI-facilitated reports offer unprecedented transparency and elevate client experiences. The fusion of valuer expertise with prompt engineering ensures the reliability and interpretability of valuation reports.Practical implicationsDelineating the types and versions of LLMs used in AI-generated valuation reports encourage the adoption of transparency best practices within the industry. Valuers, as expert prompt engineers, can harness the potential of AI to enhance efficiency, accuracy and transparency in the valuation process, delivering significant benefits to a broad array of stakeholders.Originality/valueThe article elucidates the substantial impact of prompt engineering in leveraging LLMs within the property industry. It underscores the importance of valuers training their unique GPT models, enabling customisation and reproducibility of valuation outputs. The symbiotic relationship between valuers and LLMs is identified as a key driver shaping the future of property valuations.

Publisher

Emerald

Subject

General Economics, Econometrics and Finance,Finance,General Business, Management and Accounting,General Economics, Econometrics and Finance,Finance,General Business, Management and Accounting

Reference2 articles.

1. Anchoring and asymmetric information in the real estate market: a machine learning approach;Journal of Risk and Financial Management,2021

2. RICS (2023), “RICS valuation global standards effective 2022”, available at: https://www.rics.org/content/dam/ricsglobal/documents/standards/2021_11_25_rics_valuation_global_standards_effective_2022.pdf

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