Real Estate Insights: How agentic artificial intelligence could disrupt real estate?

Purpose The real estate industry has traditionally relied on conventional operating models that limit efficiency, transparency and strategic decision-making. This study explores how agentic Artificial intelligence (AI) could disrupt the real estate industry, its current and future applications, associated challenges, strategic implications and future research directions. Design/methodology/approach This study adopts an exploratory qualitative approach based on a comprehensive review and synthesis of literature, industry reports and theoretical studies. It explores how agentic AI and related digital technologies are transforming industries and evaluates their potential implications for the real estate sector. Findings The study identifies agentic AI as transforming the real estate value chain by automating knowledge-intensive tasks such as property valuation, investment analysis, underwriting, facilities management, leasing, customer engagement, sustainability, decision-making, forecasting, brokerage, document processing and environmental, social and governance reporting. However, physical inspections and complex professional judgement remain largely human-led. The study also identifies fragmented data, implementation costs, interoperability, cybersecurity risks, algorithmic bias, regulatory uncertainty and ethical concerns as the main barriers to adoption. Practical implications The real estate industry is evolving at a fast pace across all real estate operations, including real estate brokerage, real estate management, real estate finance and investments, and real estate development. This study examines one such change in agentic AI and how it would disrupt what we know to be conventional real estate. The adoption of agentic AI in real estate is of particular importance to real estate stakeholders, as this introduces the possibility of increased efficiency resulting in the possibility of less errors as well as reduced time in performing tasks, therefore the potential of cost-cutting. Originality/value This study offers a timely perspective on the disruptive potential of agentic AI in the real estate industry. It offers practical insights for real estate professionals, facilities managers, investors, developers, policymakers and researchers navigating the transition towards smart and integrated real estate ecosystems.

Authors

Institutions

Publication Details

Journal
Journal of Property Investment and Finance
Published
2026-09-11
DOI
https://doi.org/10.1108/jpif-08-2026-0156
Primary Topic
Facilities and Workplace Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Real Estate Insights: How agentic artificial intelligence could disrupt real estate?

Omokolade Akinsomi, Abubakar Sadiq Mohammed
Journal of Property Investment and Finance
Facilities and Workplace Management
article

Real Estate Insights: How agentic artificial intelligence could disrupt real estate?

Omokolade Akinsomi, Abubakar Sadiq Mohammed
article en

Abstract

Purpose The real estate industry has traditionally relied on conventional operating models that limit efficiency, transparency and strategic decision-making. This study explores how agentic Artificial intelligence (AI) could disrupt the real estate industry, its current and future applications, associated challenges, strategic implications and future research directions. Design/methodology/approach This study adopts an exploratory qualitative approach based on a comprehensive review and synthesis of literature, industry reports and theoretical studies. It explores how agentic AI and related digital technologies are transforming industries and evaluates their potential implications for the real estate sector. Findings The study identifies agentic AI as transforming the real estate value chain by automating knowledge-intensive tasks such as property valuation, investment analysis, underwriting, facilities management, leasing, customer engagement, sustainability, decision-making, forecasting, brokerage, document processing and environmental, social and governance reporting. However, physical inspections and complex professional judgement remain largely human-led. The study also identifies fragmented data, implementation costs, interoperability, cybersecurity risks, algorithmic bias, regulatory uncertainty and ethical concerns as the main barriers to adoption. Practical implications The real estate industry is evolving at a fast pace across all real estate operations, including real estate brokerage, real estate management, real estate finance and investments, and real estate development. This study examines one such change in agentic AI and how it would disrupt what we know to be conventional real estate. The adoption of agentic AI in real estate is of particular importance to real estate stakeholders, as this introduces the possibility of increased efficiency resulting in the possibility of less errors as well as reduced time in performing tasks, therefore the potential of cost-cutting. Originality/value This study offers a timely perspective on the disruptive potential of agentic AI in the real estate industry. It offers practical insights for real estate professionals, facilities managers, investors, developers, policymakers and researchers navigating the transition towards smart and integrated real estate ecosystems.

Journal of Property Investment and Finance
University of the Witwatersrand (ZA), Accra Technical University (GH)
Openalex Percentile: Top 7%
Facilities and Workplace Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.