Realpage's Lance French on Building Trust in AI for Rental Housing

Article originally posted on September 18, 2026 by Christine Serlin, MFE.

As Realpage expands its artificial intelligence (AI) capabilities across leasing, operations, analytics, and investment management, the company is making a significant bet on AI's future role in rental housing. The recent launch of its Lumina AI Suite at the company’s flagship conference marks its most significant platform expansion to date, connecting property operations, portfolio analytics, institutional intelligence, and open model access on a single governed intelligence platform.  

Alongside that technology push, Realpage is also focused on building trust in how AI is developed and deployed. To support that effort, it recently introduced its Five Principles of Trusted AI for Rental Housing, the framework that guides the design and development of the Lumina AI Suite.

The principles include: 

• AI is designed to reason from industry-specific data and workflows rather than the open internet;
• Every metric carries one governed definition;
• Every recommendation is designed to show its work, including the inputs and guardrails; 
• AI is built to operate within the compliance frameworks operators already follow with logged and auditable actions; and
• A person is always accountable for the decisions AI supports.

Chief digital and technology officer Lance French discusses with Multifamily Executive the company's AI vision, the thinking behind the principles, and what responsible AI adoption should look like as the multifamily industry embraces the next generation of technology.

Why did Realpage establish formal AI principles now?

AI in rental housing is moving from isolated assistance into systems that interpret information, make recommendations, and increasingly coordinate or execute work. These capabilities can create meaningful value for operators, owners, property teams, and residents. They also increase the importance of the data, definitions, controls, and human decisions surrounding the AI.

The industry has recognized this need through the Real Estate Technology & Transformation Center’s (RETTC’s) AI Governance Framework, which Realpage contributed to alongside housing providers and other technology partners. Our five principles carry that direction into the design and development of the Lumina AI Suite. They establish concrete expectations for industry-grounded reasoning, governed definitions, traceable recommendations, applicable compliance obligations, and named human accountability. Formalizing them gives our teams and customers a consistent basis for evaluating and scaling AI as its reach and authority grow.

Which principle will have the greatest impact on industry adoption?

If I had to choose one, “Every recommendation has to show its work” is the principle most likely to support adoption. Operators gain confidence when they can understand what informed a recommendation, which definitions were used, what guardrails applied, and how much confidence to place in it. That visibility makes an output easier to test, challenge, explain, and improve.

It also works directly with the principle that a person is always accountable. Evidence becomes meaningful when someone is responsible for interpreting it and acting when the system is wrong. Transparency creates the conditions for effective oversight and accountability. Accuracy, fairness, security, and safety still require their own controls and evidence.

How do the principles address operators’ concerns about AI risks?

Each principle addresses a practical source of concern. Industry grounding gives the AI relevant context. Governed definitions preserve consistent meaning across operations and reporting. Showing the work makes recommendations reviewable. Compliance and auditability keep existing obligations visible. Human accountability preserves clear ownership.

The principles are designed to work as one system. Their value comes from implementation through architecture review, use-case risk assessment, testing, continuous evaluation, red teaming, audit trails, monitoring, and escalation. The level of evidence and oversight should rise with the potential consequence of the use case.

Where will the greatest compliance challenges emerge?

The greatest challenge will be keeping governance synchronized with the full system after deployment. A foundation model may remain unchanged, while data sources, prompts, guardrails, property policies, local requirements, integrations, permissions, or an agent’s action authority change. Any of those changes can affect how the system performs and which obligations apply.

Fairness testing must also fit the capability. Evaluating a conversational leasing agent requires looking at tone, guidance, routing, and escalation across varied interactions. A model producing a score or recommendation requires different statistical and outcome testing. Providers and operators will need clear, shared responsibilities for intended use, data quality, testing, configuration, human review, audit trails, monitoring, correction, and escalation.

Will regulators eventually require AI governance standards for housing technology?

Governance expectations are already becoming more explicit. Existing fair housing, fair credit reporting, consumer protection, privacy, accessibility, and other laws continue to apply when AI is used in those activities. States are also introducing requirements for higher-impact automated decisions.

The United States will likely continue with layered federal, state, and sector-specific expectations. The most effective standards will be risk-based, outcome-focused, and clear about the respective responsibilities of technology providers and housing operators. RETTC demonstrates that the industry can establish useful common language now. Our five principles contribute a product-level standard for how those expectations can inform the design and operation of AI.

How are customers responding?

Yes, customers are asking increasingly specific questions. Our conversations have moved from whether an AI governance policy exists toward the evidence behind it. Operators want to understand success criteria, performance benchmarks, testing protocols, risk classification, oversight, the role of governance in product decisions, and the handoff points between AI and people.

That is a healthy development. These questions give customers a better basis for understanding whether a capability fits their operating and compliance environment. They also improve product design by making boundaries, controls, evidence, and accountability part of the adoption conversation.

What is the biggest misconception housing operators have about AI?

The biggest misconception is that the model is the AI system. The outcome a customer experiences is shaped by the complete workflow, including source data, governed definitions, retrieval, configuration, identity and permissions, integrations, guardrails, monitoring, fallback paths, and human responsibilities.

AI also performs different kinds of work. Prediction, generation, recommendation, and action carry different consequences. The same foundation model can support a low-risk internal task and a higher-impact housing workflow with very different governance requirements. The material questions concern the job the AI performs, the data and people it touches, the authority it has, and the consequence if it is wrong.

What advice would you give multifamily executives about balancing innovation with trust?

My advice is to begin with the actual workflow and a named accountable owner. Define the intended outcome, the data and permissions the AI needs, the actions it may take, its limits, the human handoffs, the success measures, and the evidence required before scaling. Match oversight to the consequence of the decision, monitor the system in operation, and expand its authority as performance evidence supports it.

Ask every technology partner to show their work, and make sure your own organization can show its work as well. That discipline gives teams the clarity and confidence to move faster responsibly.