The Policy Moment Is Now
Thirty-five years of technology policy experience is a long baseline. Which is why it matters when Adam Thierer, resident senior fellow at the R Street Institute, describes the current level of legislative and regulatory activism around AI as unlike anything he has previously witnessed.
The volume of proposed frameworks, state-level bills, and federal discussions is not background noise. It is the signal. Real estate professionals who treat AI governance as a distant policy concern are misreading the timeline.
Michael Kratsios, director of the White House Office of Science and Technology Policy, framed the moment with notable optimism: AI represents the most powerful tool ever made available to small business owners. For real estate practitioners — many of whom operate as independent professionals or small brokerages — that framing is worth internalizing. The technology is not reserved for enterprise players. The access is already here.
What Is Actually at Stake for Real Estate
AI is already embedded in the day-to-day operations of real estate businesses. Marketing automation, market trend analysis, administrative task management, customer service interfaces — these are not hypothetical applications. They are in active use across the industry.
The risk is not that AI fails to deliver value. The risk is that it delivers value in ways that are difficult to audit, explain, or defend when something goes wrong.
Travis Hall, director for state engagement at the Center for Democracy and Technology, put the structural problem precisely: AI obfuscates. It obscures how decisions are made, which data inputs drove which outputs, and — critically — who bears responsibility when outcomes cause harm. In a sector where decisions carry significant financial and legal weight, that opacity is not a minor inconvenience. It is a compliance liability.
1. Accountability Gaps
When an AI-assisted recommendation leads to a discriminatory outcome — in tenant screening, property valuation, or lending referrals — the question of liability becomes genuinely complex. Is the responsible party the brokerage that deployed the tool, the vendor that built it, or the model that generated the output? Current frameworks do not answer this cleanly.
Real estate professionals need to understand that deploying AI tools does not transfer accountability. It layers it. Practitioners remain responsible for the outcomes their tools produce, regardless of how those outcomes were generated.
2. Transparency Requirements
Regulatory pressure is moving toward explainability. Consumers and regulators increasingly expect to understand, at least in principle, why an AI system produced a particular result. For real estate applications — especially those touching credit, housing access, or pricing — the ability to explain a decision is becoming a baseline expectation, not a premium feature.
Selecting AI tools that offer interpretable outputs and documented decision logic is no longer just good practice. It is becoming a compliance prerequisite.
3. Fragmented Jurisdiction
One of the most operationally challenging aspects of the current environment is inconsistency. State-level AI legislation is advancing at different speeds and in different directions. A compliance posture that satisfies requirements in one jurisdiction may fall short in another.
Panelists at the forum were candid: meaningful regulatory clarity is unlikely to arrive within the next 12 months. Firms operating across multiple markets need to build compliance frameworks flexible enough to adapt as the patchwork evolves.
The Opportunity Side of the Equation
Regulatory pressure, handled correctly, is also a competitive differentiator.
Firms that invest early in responsible AI practices — documented vendor assessments, clear data governance policies, consumer-facing transparency disclosures — will be better positioned when compliance requirements harden. They will also be better positioned in the market. Consumer trust in AI-assisted services is not guaranteed. It is earned through demonstrated accountability.
NAR’s Chief Advocacy Officer Shannon McGahn articulated the dual mandate well: encourage innovation while ensuring consumers remain protected and the marketplace remains fair, transparent, and competitive. That is not a contradiction. It is a design requirement. The firms that treat it as such will have a structural advantage over those that treat AI adoption as purely a speed-to-deployment problem.
What Compliance Preparation Looks Like in Practice
The forum reinforced that ongoing engagement — between industry, policymakers, and technology providers — is not optional. It is the mechanism through which workable standards get built. Real estate professionals have both standing and incentive to participate in that process.
In practical terms, preparation for 2026 and beyond involves several concrete steps.
Audit the AI tools currently in use. Understand what data they consume, what decisions they influence, and what documentation the vendor provides. Establish internal accountability structures that clarify who owns AI-related decisions within the organization. Monitor state-level legislative developments in every market where the business operates. And engage with industry associations that are actively shaping the policy conversation — because the standards being written now will govern the tools deployed tomorrow.
The Regulatory Window Is Open — But Not Indefinitely
The current moment is one of genuine policy formation. Frameworks are being debated, not yet finalized. That creates both risk and opportunity: risk for those who assume the status quo will persist, and opportunity for those who engage proactively.
Real estate is, as Kratsios noted, a compelling sector for AI application. It is also a sector with deep consumer trust implications, significant financial stakes, and a long history of regulatory scrutiny. The combination means that AI governance in real estate will be watched closely — and that getting it right matters beyond any single firm or transaction.
The professionals and organizations that treat responsible AI not as a constraint but as a foundation will be the ones best positioned when the regulatory landscape finally settles. That settlement may be further away than anyone would prefer. The preparation cannot wait for it.
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