AI Governance: Best Practices for Real Estate Organizations

How can real estate organizations leverage the power of AI while maintaining compliance and ethical usage? This is a question professionals in the industry need to address proactively.

From predictive analytics in property valuation to chatbots enhancing customer service, AI offers opportunities for growth and efficiency. However, the deployment of AI technologies also presents challenges related to data privacy, bias mitigation, and regulatory compliance.

Developing a robust framework for AI governance is an important step for real estate organizations looking to harness the technology’s full potential while also mitigating risks. Best practices in AI governance not only protect businesses from legal pitfalls but also enhance trust among clients and stakeholders.

Key Takeaways

• Real estate organizations can leverage AI for growth and efficiency in areas like property valuation and customer service, but must address challenges related to data privacy, bias, and regulatory compliance through robust AI governance frameworks.
• Key risks in AI deployment include inaccuracies, data privacy violations, and bias, necessitating comprehensive governance that integrates technical controls, legal requirements, and organizational culture to protect sensitive data and maintain trust.
• A structured AI governance framework, modeled on standards such as the NIST AI RMF, includes pillars like governance, technology, financial assessments, model testing, transparency, and monitoring to maintain responsible and ethical AI deployment in real estate.

What Is AI Governance

AI governance is the enterprise-level framework and set of policies designed to manage the risks and opportunities associated with developing, deploying, and using Artificial Intelligence (AI) and Generative AI (GenAI) systems. A robust governance program helps AI to be used responsibly, ethically, and in compliance with an evolving legal landscape.

AI Risks and Common Concerns

While a helpful tool, AI is not immune to issues that require strong governance. Primary risks for AI usage include:

• Accuracy and Quality Control – AI can fabricate confident but inaccurate responses – a phenomenon known as “hallucination.” This risk can lead to financial losses or reputational damage.
• Data Privacy and Security – AI models require vast datasets, and if a system is opaque, it raises the risk of exposing or misusing sensitive information. Employees who use unapproved public AI tools to perform work can expose proprietary data and leave the organization legally vulnerable.
• Bias and Fairness – AI algorithms, trained on historical data, can perpetuate and even amplify existing societal biases, leading to discriminatory outcomes.

Why AI Governance Matters for Real Estate Organizations

The integration of AI in real estate, from automated property valuations to smart building management, introduces unique compliance and liability challenges that necessitate clear governance.

• Handling unique and sensitive data: Real estate organizations handle highly sensitive data and proprietary information, including confidential deal terms, leases, client contact information, and financial data. Without strict policies, employees risk inadvertently pasting this data into public GenAI tools, allowing the platforms to retain the inputs and potentially exposing it to third parties.
• Lack of known acceptable use cases: A lack of governance creates a risk of misuse and a significant gap between what management assumes employees are doing and their actual use of AI tools.
• Liability and bias risks: Real estate is an industry prone to liability. Incorrect outputs, such as fake property comps, invented market stats, or incorrect zoning interpretations, need to be fact-checked and verified by a human, treating the AI output as a draft assistant, not a final authority. Additionally, algorithmic bias in applications like tenant screening or rental pricing can lead to violations of fair housing laws and significant reputational damage.

Aspects of AI governance

Effective AI governance for real estate requires integrating technical controls, legal requirements, and organizational culture.

Vendor Management and Data Security

Carefully vet third-party partners and service providers who have access to things like tenant or property data. All contracts should include clear provisions on warranties, liability limits, and recourse options for AI-related errors.

Oversight and Approval Mechanisms

Maintain human oversight in the AI decision-making process to check for errors and biases. AI use should be integrated into the organization’s existing internal control, risk management, and reporting structures.

Change and Communication Management

Establish and document authorized usage policies (open, limited, or prohibited use) and communicate these terms and conditions clearly to staff. Communicate transparently with clients and stakeholders about how AI is used to build trust.

Model/AI System Validation and Monitoring

Conduct regular audits of AI systems to identify and correct biases. Systems used for high-stakes decisions must be explainable, auditable, and fair. Continuous monitoring is essential for maintaining performance and accuracy over time.

Key Components of AI Governance for Real Estate Organizations

The core components of an AI governance framework can be summarized into “Six Key Pillars for Responsible Innovation:”

1. Governance: Establish clear roles, responsibilities, and accountability for AI systems within the organization.  
2. Technology: Evaluate the systems, tools, and technical environments to support responsible AI deployment.  
3. Financial: Assess the total cost of ownership of AI systems and vendor accountability, including potential liabilities.  
4. Model Testing (Validation): Implement rigorous testing and validation processes to measure model performance, identify biases, and establish accuracy before deployment. 
5. Transparency: Provide documentation and communication about how AI systems function, what data they use, and how they impact business decisions.  
6. Monitoring: Establish continuous surveillance mechanisms to track model performance, detect bias, and manage incident response.  

AI Use Policy Examples for Real Estate Organizations

Get Started with a Risk Assessment

Without a proactive and dedicated AI governance framework, these tools can become significant liabilities. By adopting the Six Key Pillars of Governance —establishing clear policies, securing your technology stack, rigorously testing your models for accuracy, and maintaining continuous monitoring —your organization can protect itself against these risks.

Don't wait for a costly data breach or a regulatory challenge to define your strategy. Start by performing a current-state AI risk assessment today to inventory all AI tools currently in use across your organization and identify your highest-risk gaps.

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