Key Takeaways
- AI in real estate is shifting from simple chatbots to systems that manage complex, multi‑step business processes while keeping humans in control.
- Large language models (LLMs) now sustain context across lengthy documents, enabling “agentic AI” that can act autonomously on tasks such as underwriting, lease review, and market analysis.
- The primary value of AI lies in automating repetitive administrative work, freeing agents to focus on client relationships and strategic decision‑making.
- As AI capabilities grow, so do fraud risks—deepfakes, cloned voices, and sophisticated phishing demand robust verification practices, including direct phone confirmation.
- Adoption hinges on trust; clear demonstration of AI’s benefits and transparent communication are essential to overcome concerns about job displacement, privacy, and infrastructure demands.
- NAR’s REACH program showcases practical AI tools that streamline transaction processing, improve manufactured‑housing data infrastructure, and consolidate brokerage technology stacks.
- Successful AI integration will depend on balancing technological innovation with human judgment, robust security measures, and trust‑building implementation strategies.
Human‑Centered AI in Real Estate
Sharon Love‑Bates, NAR’s director of emerging technology, opened the “Top 5 Things You Need to Know About AI Right Now” session by emphasizing that the future of AI in real estate is not about replacing professionals but about augmenting their capabilities. She stressed that AI should handle the “heavy lifting” of data‑intensive tasks while agents apply their judgment and expertise. This human‑centered framing set the tone for the discussion, highlighting that technology works best when it serves as a supportive tool rather than a substitute for the nuanced insight and interpersonal skills that real‑estate practitioners bring to every transaction.
Evolution from LLMs to Agentic AI
Love‑Bates explained that large language models (LLMs)—the technology behind ChatGPT, Gemini, Claude, and similar platforms—have matured beyond simple text generation. Modern LLMs can now process lengthy, document‑heavy workflows while retaining context across multiple sources, making them ideal for complex real‑estate functions such as underwriting, permitting, lease reviews, zoning research, and asset management. This advancement has given rise to what many technologists call “agentic AI.” As Love‑Bates put it, “If LLMs are the brains, agentic AI is the action.” Unlike passive chatbots that merely respond to prompts, agentic AI systems can execute tasks autonomously, orchestrating a series of specialized actions throughout a transaction lifecycle.
How Agentic AI Transforms Real Estate Workflows
In practice, agentic AI can learn a buyer’s preferences, identify matching properties, evaluate loan options, analyze pricing trends, monitor market movements, coordinate transaction timelines, and review documents for MLS and fair‑housing compliance—all without constant human prompting. The goal, according to Love‑Bates, is not to relinquish control to machines but to automate repetitive administrative duties, thereby allowing agents to devote more time to high‑value activities such as client counseling, negotiation, and strategic planning. By keeping professionals in the decision‑loop, agentic AI enhances efficiency while preserving the essential human element of the business.
Emerging Risks and Security Challenges
With greater capability comes heightened risk. Love‑Bates warned that the same AI tools that boost efficiency are also enabling more sophisticated fraud schemes. Deep‑fake videos, cloned voices, and convincingly fabricated emails or text messages are becoming easier to produce and harder to detect. Because real‑estate transactions often involve large financial transfers and sensitive personal data, these threats pose significant dangers to agents, clients, and brokerages. She urged practitioners to establish verification protocols—such as calling clients, lenders, title companies, or attorneys directly when encountering unexpected requests or instructions—and to “pick up the phone” as a simple yet effective safeguard against AI‑driven deception.
Trust as the Cornerstone of AI Adoption
Love‑Bates noted that technological barriers are unlikely to be the primary obstacle to AI adoption; instead, human factors such as trust, perception, and organizational culture will shape how quickly AI becomes mainstream in real estate. Agents, consumers, and broader communities harbor concerns about job displacement, misinformation, privacy violations, and surveillance. Additionally, questions about the energy consumption and infrastructure needed to support large‑scale AI systems are emerging. For brokers and business leaders, this means that implementation strategy matters as much as the technology itself. Success, she asserted, depends on demonstrating clear value, explaining exactly what the AI is doing, and building confidence that the tools enhance rather than threaten the profession.
Real‑World AI Solutions from NAR’s REACH Partners
The session also featured three companies from NAR’s REACH program, illustrating how AI is already being applied in the field. Divya Aathresh, founder and CEO of MaxHome.ai, showcased an AI‑powered operating system that automates transaction processing, compliance reviews, and document management. Grayson Gibson, CEO of LotRoll, discussed efforts to modernize manufactured housing through improved data infrastructure, valuation tools, and transaction systems designed to increase transparency and liquidity in that niche market. Dan Stewart, chief strategy officer of StackWrap, presented a platform that consolidates a brokerage’s existing software tools, resources, and workflows into a single branded hub, streamlining technology adoption without necessitating replacement of current systems. Collectively, these examples underscored a common theme: AI’s greatest impact lies in reducing administrative burdens so agents can focus more on serving clients and less on paperwork.
The Future Path for AI in Real Estate
Love‑Bates concluded by envisioning a real‑estate landscape where AI handles the data‑intensive, repetitive aspects of the business while professionals leverage their expertise, empathy, and judgment to guide clients through complex decisions. The path forward will require vigilant security practices, transparent communication, and thoughtful implementation that addresses both the opportunities and the risks posed by advancing AI. By fostering trust and demonstrating tangible benefits, the industry can harness AI’s power to work smarter, not to replace the human touch that remains at the heart of real‑estate success.