Real Estate Insights AI: real estate’s new roommate – the good, the bad and the algorithmic

Abstract

Purpose

This paper aims to provide a comprehensive analysis of the transformative impact of Artificial Intelligence (AI) and Large Language Models (LLMs), such as GPT-4, on the real estate industry. It explores how these technologies are reshaping various aspects of the sector, from market analysis and valuation to customer interactions and evaluates the balance between technological efficiency and the preservation of human elements in business.

Design/methodology/approach

The study is based on an analysis of the strengths and weaknesses of AI as a technology in applications for real estate. It uses this framework to assess the potential of this technology in different use cases. This is supplemented by an emerging literature on the topic, practical insights and industry expert opinions to provide a balanced perspective on the subject.

Findings

The paper reveals that AI and LLMs offer significant benefits in real estate, including enhanced data-driven decision-making, predictive analytics and operational efficiency. However, it also uncovers critical challenges, such as potential biases in AI algorithms and the risk of depersonalising customer interactions.

Practical implications

The paper advocates for a balanced approach to adopting AI, emphasising the importance of understanding its strengths and limitations while ensuring ethical usage in the diverse and complex landscape of real estate.

Originality/value

This work stands out for its balanced examination of both the advantages and limitations of AI in real estate. It introduces the novel concept of the “jagged technological frontier” in real estate, providing a unique framework for understanding the interplay between AI and human expertise in the industry.

Citation

Szumilo, N. and Wiegelmann, T. (2024), "Real Estate Insights AI: real estate’s new roommate – the good, the bad and the algorithmic", Journal of Property Investment & Finance, Vol. 42 No. 2, pp. 211-217. https://doi.org/10.1108/JPIF-01-2024-0001