58% of surveyed U.S. property-management and real-estate professionals said they use AI in their jobs—but that does not mean 58% of real-estate firms have formally adopted it. The figure comes from an online survey of 3,662 professionals conducted in May and June 2026 through IREM’s membership and AppFolio’s contact database. It measures reported individual use, not a firm-level census or the share of companies with AI integrated into core operations.
What does the 58% figure actually mean?
The Florida Realtors report on the IREM/AppFolio survey says 58% of respondents used AI in their jobs. That can include an employee using a general-purpose assistant to draft text; it does not establish that their employer has approved, integrated, or deployed AI across the business.
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Other real-estate surveys use different populations and questions, so their percentages should not be treated as competing estimates of the same adoption rate. In NAR’s 2025 Technology Survey, 20% of respondents said they used AI daily, 22% weekly, 27% a few times a month, and 32% had not used AI in their business. Separately, 58% named ChatGPT among the AI tools used; that is a tool-specific response, not an adoption rate. The NAR survey release reports those figures.
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What are real-estate professionals using AI for?
Agent writing and communications
For REALTORS® who use AI, the NAR REALTORS® Technology Report identifies listing descriptions as the most common reported task: 75% of AI users said they used it for that purpose. Social-media posts followed at 56%, and emails and follow-ups at 52%. These are task-use figures among AI users, not shares of all agents or firms.
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Property-management support
Examples in the IREM/AppFolio survey coverage include drafting replies to leasing inquiries, preparing presentations, and processing invoices. These examples describe assistance with pieces of work; they do not establish that AI independently handles leasing decisions, verifies invoice accuracy, or replaces a property manager’s responsibility for the result.
Does using AI produce a business benefit?
Reported impact varies by survey and year. In NAR’s 2025 Technology Survey, 17% of respondents described AI’s business impact as significantly positive, 33% as moderately positive, and 46% said it had made no noticeable difference. The separate NAR REALTORS® Technology Report page reports that 55% of its respondents said AI had a positive effect on their real-estate business, and that 23% used AI daily. The reports have different survey snapshots; their results should not be combined into one trend or treated as directly comparable.
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Commercial real estate illustrates why personal use and operational deployment are different measures. Deloitte’s 2027 Commercial Real Estate Outlook, published in 2026, surveyed 950 C-level executives and direct reports at CRE owners and investment companies with at least US$250 million in assets under management across North America, Europe, and Asia Pacific. In that sample, 92% were in AI research or pilot phases and 8% had integrated AI solutions. Deloitte also describes nearly half of surveyed real-estate organizations as having agentic AI in some live production workflows. These are distinct deployment descriptions: a live workflow using an agentic system is not necessarily the same as broad integration of AI solutions across an organization.
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For added context, Keyway’s company-associated 2025 survey article says 58% of respondents expected to purchase new AI software within the next year. That expectation is not proof of subsequent purchasing, and the article does not establish a probability sample representative of the industry.
What are firms missing as AI use grows?
Reliable outputs still need accountable review
Accuracy and reliability were the leading reported barriers in the IREM/AppFolio survey coverage, followed by training. Generated listing facts, financial calculations, tenant communications, and screening-related outputs should be checked against authoritative records and reviewed by someone responsible for the outcome. A fluent answer is not evidence that a fact or calculation is correct.
Policies and training lag behind use
In the same IREM/AppFolio survey, 8% of respondents reported a formal written AI policy and 26% reported formal AI training. These are respondents’ reports, not a firm-by-firm census. A useful policy makes the rules operational: which tools are approved, what data staff may enter, which uses are restricted, who reviews outputs, and where employees escalate uncertain or harmful results.
Data and legacy systems can stall deployment
Deloitte identifies uneven data foundations and legacy processes as obstacles to moving pilots into production. Keyway’s company-associated survey article reports significant data-infrastructure gaps for 76% of its respondents; that finding should be read within its survey context rather than as an industry-wide prevalence estimate. Before automating a workflow, firms need to know whether the relevant records are accurate, accessible to the system, and traceable to their source.
Governance matters most when systems can take action
Deloitte warns that unguided AI agents may act in operationally biased or policy-noncompliant ways, and flags tenant-screening and pricing workflows as areas requiring care. The consequences depend on the system, its use, the facts, and applicable law; no single survey establishes a categorical legal conclusion. Firms should define what systems may be built or used, who can access them, which data they may use, what actions require approval, and how activity is logged, monitored, or stopped.
As David Barrow, managing director of WatchPoint Commercial Real Estate, put it in the Florida Realtors coverage: “Liability is still there for property managers, whether AI does the calculations or a human.”
Usage is not the same as value
A high number of users or AI-assisted tasks does not show that a business is better off. Set a baseline before rollout and measure outcomes such as response time, staff hours, error rates, service quality, customer satisfaction, and financial results. Deloitte recommends recurring return-on-investment monitoring for high-impact use cases; if a workflow does not improve outcomes enough to justify its cost and risks, expanding it is not success.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should a real-estate firm scale AI responsibly?
- Inventory tools and third parties. Record the AI systems in use, what work each supports, what data it can access, and which vendors or processors handle that data.
- Set boundaries before expanding access. Document approved tools, permitted and restricted data, prohibited or higher-risk uses, and the actions that require human authorization.
- Train staff on real tasks. Explain how to protect sensitive information, verify generated content against source records, recognize uncertainty, and escalate questionable results.
- Assign a human owner to each consequential workflow. Define who checks outputs, who can override or stop the system, and how decisions and corrections are recorded.
- Pilot against a measured baseline. Choose a bounded workflow and compare its results with the existing process using service, accuracy, time, and cost measures that matter to the business.
- Review and scale only when controls and value hold up. Monitor errors and outcomes over time, revisit access and data rules as the system changes, and expand only when evidence supports the added deployment.
For agents and property managers, AI can already help with routine drafting and administrative work. The harder organizational task is making sure that useful assistance is reliable, appropriately governed, and measurably worth scaling.
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