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

Artificial intelligence (AI) is transforming industries by changing how people live, work and interact in the rapidly changing world (Seagraves, 2024). The developments in AI introduced agentic AI, a system capable of making autonomous decisions, adapting to changing environments and performing tasks with limited human supervision (Feuerriegel et al., 2024). Unlike traditional AI that responds only to predefined instructions, agentic AI can identify goals, process information, coordinate actions and improve performance in real time (Qin et al., 2024). According to Seagraves et al. (2026), agentic AI is described as the next stage of AI in comercial real estate|commercial real estate (CRE), where autonomous systems integrate large language models (LLMs), natural language processing (NLP), computer vision and machine learning (ML) to improve market efficiency. The Anthropic Economic Index and Occupational Information Network (O*NET) occupational data used as evidence; Seagraves et al. (2026) express that agentic AI adoption in CRE is concentrated in knowledge-intensive tasks, with the exposure of (20%) nearly three times the economy-wide average (7%).

Agentic AI is thereby transforming multiple sectors by improving decision-making, automating complex workflows, enhancing predictive analytics and optimising resource allocation in the built environment. For instance, AI supports clinical diagnosis and personalised treatment in healthcare, fraud detection and risk assessment in finance, predictive maintenance and quality control in manufacturing, and building design, construction management and facility operations (Pan and Zhang, 2021). Among these, the real estate industry is adopting AI to improve its activities, with the growing adoption facilitated by advances in the Architecture, Engineering and Construction industry to enhance project planning, design, construction and asset management (Emaminejad et al., 2021).

Traditionally, the real estate sector has relied on manual processes and slow adoption of technology (Starr et al., 2021). However, agentic AI is seen to have improved investment decisions and urban planning. This calls for a domain-specific AI that supports transparent, evidence-based decision-making in CRE, anchored by Corbis and EQUIRE. AI platform focuses on creating reliable results that help reduce mistakes and manage every part of CRE transactions and investment (Agentic Assets, 2026a, b). The initiative founded by finance professors Cayman Seagraves and Stace Sirmans for CRE practice that lies not in chatbots but in domain-specific agentic AI capable of delivering trustworthy, transparent and auditable investment decisions. Again, Geoffrey Dohrmann, the Founder and Chief Executive Officer (CEO) of Institutional Real Estates, Inc (IREI), also believes that AI needs to be managed with human supervision in institutions to avoid unverified analyses for better and risk-free investment decisions. A notable example is the strategic partnership between IREI and Agentic Assets, established to accelerate the adoption of academically grounded AI tools in institutional investment and private wealth advisory markets (IREI, 2026; Agentic Assets, 2026a, b).

Moreover, these agents can analyse customer preferences, budgets, lifestyle patterns and location characteristics to improve property transactions and also to support smart buildings through energy management and security monitoring (Kabaivanov and Markovska, 2021; Seagraves, 2024). These developments indicate that real estate is gradually transitioning from traditional operations to a connected smart ecosystem. Despite these opportunities, several implementation challenges continue to affect the adoption of agentic AI in the real estate sector, such as weak digital infrastructure, fragmented property databases, limited technological expertise, Internet constraints and governance issues (Torres, 2026). Concerns with data privacy, cybersecurity, algorithmic bias, transparency and workforce displacement further complicate adoption (Coval, 2018). This study, therefore, provides an industry perspective on how agentic AI could disrupt the real estate industry, exploring the opportunities, challenges, strategic implications and considerations associated with its adoption and future study direction. Guiding the study, the central question is: How could agentic AI disrupt the real estate industry across the property lifecycle?

Agentic AI is an emerging form of AI capable of pursuing predefined objectives autonomously, with minimal human intervention for decision-making (Srivastava and Fuloria, 2026). Rooted in classical AI theory, the system builds on the concept of a rational agent, which performs actions intended to maximise long-term performance through perception (Russell and Norvig, 2016). Agentic AI can formulate subgoals, execute multi-step tasks, interact with external tools and Application Programming Interfaces and continuously adapt to changing environments, capabilities that extend beyond those of conventional AI (Wang et al., 2025). Unlike conventional AI systems that primarily respond to user prompts, agentic AI can select appropriate tools and execute multi-step workflows to achieve user-defined objectives (Bandi et al., 2025). These systems often comprise multiple specialised agents coordinated through an orchestration layer that aligns their activities with overarching goals (International Business Machines Corporation, IBM, 2025). An AI agent is autonomous software capable of reasoning and taking actions to achieve an intended objective, whereas agentic AI involves multiple collaborative agents that coordinate to accomplish complex tasks (Bandi et al., 2025). In the real estate sector, this represents a new phase in the digital transformation by enabling autonomous, goal-directed, multi-step workflows for verifiable decision-making in investment analysis, underwriting, due diligence and transaction management through coordinated, domain-specific agents and human oversight (Tam, 2026; Seagraves et al., 2025). The growing relevance of agentic AI is reinforced by structural changes in the real estate industry. Giudice et al. (2020) explain that the COVID-19 pandemic significantly disrupted global property markets by altering housing demand, investment priorities and urban living patterns. Against this backdrop, interest in agentic AI has rapidly increased since 2024, reflecting a broader shift towards AI systems capable of planning, acting and adapting.

Within the global real estate industry, agentic AI is transforming traditional digital platforms into proactive systems that support complex workflows (Seagraves et al., 2025). These technologies facilitate property analysis, tenant communication, compliance monitoring, portfolio management, scheduling and service operations through predictive coordination and real-time decision-making (Anand, 2025). As a result, agentic AI has the potential to improve operational efficiency, reduce administrative delays and enhance customer experiences throughout the property lifecycle. The emergence of agentic AI also reflects a broader shift in organisational structures, where operational intelligence is distributed between human professionals and intelligent technologies (Baird and Maruping, 2021). Consequently, real estate organisations are evolving into integrated sociotechnical ecosystems that enable collaboration between professionals and AI-driven systems to improve service delivery (Biz4Group LLC, 2025). Despite these opportunities, the adoption of agentic AI remains constrained by varying levels of technological readiness across markets, inadequate digital infrastructure, poor data quality, fragmented legacy systems, governance challenges and shortages of AI-related skills (PwC, 2025a, b; McKinsey and Company, 2026). Nevertheless, organisations are increasingly investing in initiatives such as cloud computing, AI-powered property management platforms, smart building technologies, digital twins and agentic AI systems to improve operational efficiency (McKinsey and Company, 2026; PwC, 2025a, b). These investments underscore the transformative potential of agentic AI to redesign the future of real estate by redesigning end-to-end workflows and enhancing investment, asset and facilities management across the sector.

Interestingly, agentic AI is rapidly emerging as a major economic force. The global agentic AI market was valued at USD 7.29 billion in 2025 and is projected to reach USD 139.19 billion by 2034, growing at a compound annual growth rate of 40.5%. This growth reflects increasing investment and adoption across industries (Fortune Business Insights, 2026). Similarly, the World Economic Forum estimates that the broader AI agents market could reach USD 236 billion by 2034 and generate up to USD 3 trillion in corporate productivity gains through workflow automation and decision-making (Ayers, 2026). These developments are also extending into CRE, where agentic AI is enhancing operational processes. Nevertheless, realising these benefits also requires robust governance, transparency, identity verification and human oversight to ensure that AI systems remain trustworthy and accountable and aligned with organisational objectives (Ayers, 2026).

Agentic AI is transforming sectors such as healthcare, finance, manufacturing, education, retail, transportation, cybersecurity, tourism and software engineering (Ullah et al., 2018). McKinsey and Company (2026) note that AI is helping organisations address labour shortages, improve productivity and support workforce development in real estate activities. IEEE Future Directions (2024) identifies agentic AI as a major driver of digital transformation across industries. Despite these advancements, Massaro (2023) argues that the real estate sector remains comparatively underexplored in relation to the adoption and application of these technologies.

In healthcare, agentic AI facilitates patient monitoring, disease detection, predictive diagnosis, medical imaging analysis, treatment planning, risk stratification, personalised medicine and clinical decision support (Acharya et al., 2025). By enhancing record management, treatment coordination, real-time monitoring and disease prediction, these systems improve clinical decision-making, healthcare efficiency and service delivery while reducing administrative workloads and enabling more personalised patient care (Powell et al., 2026).

In the financial sector, agentic AI enables fraud detection, credit and risk assessment, investment analysis, algorithmic trading, customer profiling, loan forecasting, treasury management, insurance operations and automated compliance monitoring through real-time data analytics and predictive forecasting (Hill et al., 2025). By leveraging predictive analytics and autonomous systems, financial institutions reduce risks, optimise investment performance and deliver more personalised financial services (Cacciamani et al., 2025).

In manufacturing and industrial environments, agentic AI enhances operational efficiency through predictive maintenance, supply chain optimisation, robotic collaboration, resource planning, production scheduling and real-time operational control (Piccialli et al., 2025). By continuously monitoring equipment performance, identifying potential faults and reducing downtime, these technologies improve productivity, reliability and overall process performance (Çınar et al., 2020). In education and research, agentic AI enhances learning, productivity and knowledge creation by personalising educational experiences, automating administrative tasks, supporting literature reviews, generating hypotheses, designing experiments and analysing data (Chu et al., 2025). Consequently, these capabilities improve learning efficiency, streamline research workflows and accelerate innovation across scientific disciplines.

In addition, in the retail and customer service sectors, agentic AI personalises customer interactions, automates communication, improves targeted marketing, analyses consumer behaviour, anticipates customer intentions and supports adaptive engagement systems that enhance customer satisfaction and operational efficiency (Mangiò et al., 2025). In sales and business management, agentic AI facilitates intelligent lead generation, sales forecasting, negotiation support, performance monitoring, behavioural analysis, intent recognition and real-time feedback (Gonzalez et al., 2026). In military and cybersecurity applications, agentic AI enables autonomous agents to monitor threats, detect anomalies, enforce security protocols, adapt defence strategies and respond to cyberattacks with minimal human intervention (Adabara et al., 2025). Within transportation systems, agentic AI supports intelligent traffic management, dynamic routing, autonomous navigation and multi-agent coordination, improving safety and reducing congestion (Yu, 2025). In software engineering, agentic AI autonomously decomposes complex tasks, selects appropriate tools, generates and tests code, manages workflows, coordinates development activities, supports Information Technology (IT) operations and assists with incident management and software testing (Belcak et al., 2025). In tourism and hospitality, agentic AI enhances demand forecasting, dynamic pricing, process automation, personalised service delivery, compliance monitoring, sustainability management and customer engagement, thereby improving profitability and customer satisfaction (Dwivedi et al., 2026). Overall, these applications demonstrate that agentic AI extends beyond conventional automation by understanding intent, formulating plans, coordinating resources and completing tasks with minimal supervision (Joshi, 2025). Consequently, agentic AI is increasingly deployed in domains that require continuous learning, adaptive responses and autonomous operations, including governance, compliance management, scientific research and industrial automation (Boskabadi et al., 2025). These established applications provide a valuable foundation for exploring how agentic AI may transform strategic decision-making within the real estate industry.

From a financial perspective, agentic AI has emerged as a multi-billion-dollar industry, reflecting rapid enterprise adoption across sectors (Fortune Business Insights, 2026). This growth is expected to transform industries by automating complex processes for proper decision-making. McKinsey and Company (2026) estimate that agentic AI could unlock USD 430–550 billion in annual value by redesigning end-to-end workflows across property investment, facilities management and construction. Rather than supporting isolated tasks, agentic AI enables autonomous execution of interconnected business processes, fundamentally reshaping how real estate organisations operate and create value.

Agentic AI and other innovative technologies are being integrated into the global real estate industry. Modern real estate platforms integrate customer management systems, communication technologies, property databases and data analytics to improve organisational performance (Biz4Group LLC, 2025). Stryker (2026) clarifies that agentic AI collects real-time information, identifies patterns, coordinates activities and supports operations. Similarly, Starr et al. (2021) note that agentic AI is transforming real estate through automation and data-driven operations. Within real estate workflows, agentic AI processes large volumes of structured and unstructured information, including leases, rent rolls, offering memoranda, zoning regulations and financial records (Seagraves et al., 2025). These systems extract critical information, evaluate comparable properties, perform cash-flow analyses and generate underwriting reports. Property valuation is one of the most rapidly evolving application areas, with machine-learning models used to estimate property values, assess investment opportunities and analyse market trends (Acharya et al., 2024). Automated valuation models, mass appraisal systems and computer-assisted valuation technologies further improve valuation accuracy and reduce processing time (Walacik and Chmielewska, 2024). Through predictive analytics and automation, gradient boosting algorithms such as XGBoost and LightGBM are particularly well-suited for real estate predictions because they handle non-linear relationships and high-dimensional data. AI also improves property assessments, portfolio management, risk analysis, financial planning and investment decision-making (Cook and Rotenberg, 2026). Agentic AI is also changing Facilities Management (FM) and smart-building operations. It contributes to sustainability through energy optimisation, smart building management, predictive maintenance, environmental monitoring and asset management (Bajwa et al., 2024; Jafary et al., 2022; Mohammed and Amoah, 2025). Caprotti et al. (2024) explain that initiatives such as Alibaba City Brain, CityMind and Masdar City demonstrate the transition from basic automation toward more autonomous urban management systems. According to Yip et al. (2025), cities such as Singapore, Hong Kong and Riyadh are already testing these technologies for traffic management, energy optimisation and public safety. Digital FM platforms monitor building performance, optimise energy consumption, automate maintenance scheduling and support predictive maintenance strategies (Abdelalim et al., 2025). These technologies contribute to improved operational performance, reduced maintenance costs and enhanced sustainability outcomes throughout the building lifecycle (Mohammed et al., 2025). In addition, agentic AI enhances tenant experiences through personalised services, automated communication, virtual assistants, intelligent complaint handling and streamlined leasing processes, thereby increasing tenant satisfaction and retention (Yang, 2024). Platforms such as EliseAI support leasing activities and customer communication through automated interactions and smart responses. Furthermore, AI-driven systems improve security, compliance monitoring and overall performance through real-time data analysis and automated decision-making (Cook and Rotenberg, 2026). Despite these benefits, effective implementation requires human oversight, ethical governance, data interoperability and transparency to ensure responsible and trustworthy adoption in real estate management. Beyond individual buildings, smart technologies are contributing to the emergence of real estate ecosystems characterised by automation, data intelligence and connected infrastructure (Treleaven et al., 2021). Blockchain technologies improve transparency through secure digital transactions and records (Wanve et al., 2022), while digital monitoring systems support environmental, social and governance (ESG) reporting within commercial property portfolios (Kempeneer et al., 2021). Recent evidence suggests that the real estate industry is moving beyond pilot projects toward enterprise-scale AI adoption. Increased investment in PropTech and AI-enabled platforms is transforming real estate operations, indicating that agentic AI is becoming an integral component of CRE rather than an experimental technology (JLL, 2026; Blott Studio, 2026). Current developments further suggest that the adoption of agentic AI in real estate has progressed towards a broader implementation phase. Similarly, Agentic AI is rapidly transforming real estate from isolated automation to autonomous property investment, asset management and capital projects through human–AI collaboration (Wolkomir et al., 2026). Also, Seagraves et al. (2026) further report that agentic AI is transforming CRE functions such as brokerage, lease abstraction, document processing and ESG reporting, while tasks requiring physical inspections and complex professional judgement remain largely human-led. The shift is already evident among leading PropTech firms. Zillow uses AI to support property search, valuation, customer engagement and agent productivity (Zillow, 2026), while Redfin and Trulia employ AI to enhance home search, pricing insights and personalised recommendations. Cotality (formerly CoreLogic) applies AI for property intelligence, mortgage analytics and risk assessment; ZestyAI uses AI-powered property and climate risk models for insurance; and cognition AI develops autonomous software agents that can automate complex analytical workflows adaptable to real estate (Zillow, 2026; McKinsey and Company, 2026)

As the real estate industry globally continues to generate large volumes of structured and unstructured data, including text documents, images, floor plans, satellite imagery, LiDAR scans and financial records, future agentic AIs are expected to leverage these data sources more autonomously and intelligently (Seagraves et al., 2025). The integration of ML, NLP, computer vision and LLMs is expected to create more advanced systems capable of automating workflows, enhancing property intelligence and supporting real-time decision-making throughout the property lifecycle. Future applications are likely to extend beyond current valuation and investment tools towards more autonomous investment forecasting, site selection, due diligence and portfolio optimisation systems. By simultaneously analysing financial, environmental, zoning, market and climate-related information, agentic AI could support sophisticated strategic investment decisions (Build Technologies Inc, 2026; Hoang and Wiegratz, 2023). Within property and FM, future agentic AI may integrate building information modelling, internet of things (IoT) devices, digital twins and smart monitoring platforms to coordinate maintenance activities, optimise resource allocation, automate compliance management and enhance asset performance (Salzano et al., 2023). Such capabilities could contribute to lower operational costs, improved sustainability outcomes and more autonomous building operations. Customer engagement and transaction processes are also expected to evolve through automation. Agentic systems may provide highly personalised property recommendations, automate scheduling and documentation processes, support real-time customer interactions and facilitate seamless transaction management (Stryker, 2026). Blockchain technologies and smart contracts could further enhance transparency, security and transaction efficiency throughout the property lifecycle (Shelke et al., 2023). At a broader level, these developments are expected to accelerate the emergence of Real Estate 4.0, where connected infrastructure, cloud computing, automation, digital twins, geospatial analytics and intelligent urban systems shape property operations and urban development (Starr et al., 2021). Real Estate 4.0 represents the latest stage of digital transformation in the property industry, integrating technologies such as AI, big data, cloud computing, the IoT, virtual reality, drones and PropTech to transform property development, marketing, transactions and value creation (McHugh, 2024). Building on the initiatives initiated during Industry 3.0, Real Estate 4.0 extends beyond technology adoption to digital business models, systems integration and dynamic organisational capabilities that enable firms to remain competitive (Harth et al., 2026). This transformation provides the foundation for agentic AI, which represents the next evolution of Real Estate 4.0 by enabling autonomous, goal-oriented agents that enhance investment analysis, FM and strategic decision-making across the real estate value chain. Consequently, continuing advances in agentic AI have the potential to fundamentally reshape future real estate operations; while current applications primarily support decision-making and operational efficiency, future agentic technologies may evolve towards autonomous real estate regulatory compliance and urban infrastructure management. These developments could fundamentally redefine the roles of real estate professionals and reshape industry value chains.

Although agentic AI offers opportunities for the real estate industry, several challenges continue to hinder its adoption. These challenges extend beyond technological implementation and encompass technical, organisational, ethical and governance considerations. One of the primary challenges is data quality and availability. Agentic AI systems depend on large volumes of accurate and reliable data to support effective decision-making and automation. However, fragmented property records, inconsistent datasets and incomplete information can reduce analytical accuracy and compromise system performance (Shi et al., 2024). Given the diverse nature of real estate data, financial records, market information, legal documentation and customer information, maintaining data integrity remains critical. Technical challenges also hinder implementation. Agentic AI systems may face difficulties related to dynamic task decomposition, planning complexity, scalability and integration with external tools and data sources (Jeyakumar et al., 2024). In addition, real estate organisations continue to rely on legacy systems and fragmented operational structures, making the integration of these technologies complex and resource-intensive (Hill et al., 2025).

Cybersecurity and privacy concerns represent another major barrier because agentic AI systems process sensitive customer, operational and financial data (Sartor and Lagioia, 2020). As reliance on interconnected digital platforms increases, vulnerabilities to cyberattacks, unauthorised access and data misuse may create significant reputational risks. Ethical and governance concerns further complicate adoption. Issues relating to privacy, accountability, transparency, security and algorithmic bias require careful consideration (Mukherjee and Chang, 2025). Transparency and explainability are important because many advanced AI systems operate as “black-box” models whose decision-making processes are difficult to interpret (Dimopoulos and Bakas, 2019). Limited explainability may reduce stakeholder trust and create accountability challenges in areas such as property valuation, mortgage assessments and tenant screening. Similarly, biased datasets can reinforce inequalities and produce unfair outcomes within automated systems (Pagano et al., 2023; Acharya et al., 2024). Furthermore, multi-agent environments may experience communication failures, coordination challenges and unintended behaviours, highlighting the need for robust governance frameworks and continued human oversight (Piccialli et al., 2025). Organisational factors such as resistance to change, limited digital competencies, workforce concerns and uncertainty regarding implementation costs may also slow adoption. The successful adoption of agentic AI in real estate will depend on balancing technological innovation with effective governance frameworks, reliable data systems, cybersecurity protection, organisational readiness, ethical safeguards and continued human oversight.

AI technologies, including ML, NLP and predictive analytics, are gradually transforming real estate by enabling the analysis of large datasets and the generation of actionable insights (Adediran et al., 2026). Current applications include tenant communication through chatbots and virtual assistants, predictive maintenance, intelligent leasing, market analysis, investment forecasting, work-order automation and smart building management (Kaur and Solomon, 2022). Although adoption varies across regions, countries such as the USA, UK and Australia have embraced AI for customer support, predictive maintenance and leasing management, while Nordic, Baltic and African markets are progressing more gradually (Wang, 2025; Adediran et al., 2026). Nevertheless, Sweden and Denmark are exploring AI-driven solutions for rent prediction and energy optimisation in smart buildings (Adediran et al., 2026). According to Zhang and Maharjan (2026), regulatory frameworks are evolving to address the growing influence of AI. The European Union (EU) has introduced the EU AI Act alongside complementary regulations, including the General Data Protection Regulation, the NIS2 Directive, the Cyber Resilience Act and the Data Act. Miao et al. (2021) further note that organisations such as the Institute of Electrical and Electronics Engineers (IEEE) and UNESCO are developing standards for responsible digital governance. Together, these frameworks establish guidelines for data governance, cybersecurity, transparency and responsible AI deployment, thereby providing an important foundation for the safe and effective integration of autonomous systems across industries. Real estate technologies are evolving from standalone applications into integrated ecosystems that connect, finance, provide tenant services, sustainability monitoring and investment functions (Al-Rimawi and Nadler, 2025). Through intelligent automation, connected infrastructure and data-driven platforms, these ecosystems support workflow coordination, operational monitoring and resource optimisation throughout the property lifecycle. This transition carries important implications for industry stakeholders. Investors may utilise AI-driven portfolio optimisation systems to strengthen asset selection, risk assessment and investment forecasting. Property managers and FM professionals can leverage predictive maintenance and smart asset-management systems to improve building performance and resource utilisation. Developers may integrate digital twins and intelligent design technologies to support project planning, construction optimisation and lifecycle management. At the industry level, policymakers and regulators will play a critical role in ensuring transparency, accountability and privacy protection. Consequently, future competitiveness within the real estate industry will depend on organisations’ ability to integrate smart systems while maintaining effective governance and human-centred oversight.

Although agentic AI demonstrates potential to transform real estate operations, several areas require further exploration to support effective implementation. Future studies should examine how agentic AI can coordinate property valuation, leasing, FM investment analysis, tenant services and compliance monitoring within unified operational platforms. Studies should also investigate its integration with blockchain, digital twins, IoT systems and geospatial analytics in property transactions, land administration, urban planning and asset management. Further research is required to assess the effectiveness of predictive analytics, ML models and intelligent monitoring systems in valuation, market forecasting and portfolio optimisation across different property markets. Within FM, greater attention should be given to applications such as predictive maintenance, energy optimisation, automated fault detection and smart-building coordination, particularly their contributions to building performance, cost reduction and sustainability.

The ethical, governance and regulatory implications of agentic AI also call for greater empirical attention. Future studies should examine areas such as algorithmic transparency, fairness, accountability, privacy protection, cybersecurity and appropriate human oversight. Comparative analyses could further examine how governance frameworks, regulatory environments and market conditions influence the adoption and performance of agentic AI across the global real estate industry.

In view of the growing role of agentic AI as the next generation of AI in CRE for market analysis, financial modelling and investment decision-making while maintaining human oversight, studies should examine its implications for real estate finance and investment and its effects on capital deployment, governance and investment performance within institutional real estate markets. Finally, these priorities underscore the urgent need for robust empirical research on the long-term impacts of agentic AI in the real estate industry.

This study demonstrates that agentic AI possesses significant potential to transform the future of the real estate industry by enhancing operational effectiveness, sustainability and customer experiences. However, its successful implementation depends on reliable data systems, robust governance frameworks, cybersecurity protection, organisational readiness and continued human oversight. To prepare for increasingly autonomous and data-driven environments, real estate firms should invest in digital infrastructure, workforce capabilities and effective governance. As the industry evolves towards smarter and more integrated ecosystems, firms that successfully balance innovation with responsible implementation are likely to achieve sustainable competitive advantages through improved asset performance, operational resilience and enhanced stakeholder value.