Apartment Owner and Manager UDR Allegedly Used RealPage Algorithms to Set San Diego Rents in Keller v. UDR, Inc.

In Keller v. UDR, Inc., plaintiff Jacob Keller alleged that UDR used RealPage pricing tools to set rents or occupancy levels at its San Diego properties after a city prohibition took effect on June 21, 2025. The complaint claims the tools relied on nonpublic competitor data and contributed to inflated rents for UDR tenants. UDR, a large publicly traded apartment owner and operator, had previously acknowledged using YieldStar in decisions concerning certain multifamily units.

Risk classification

• Primary risk domain: 6 Socioeconomic & Environmental
• Primary risk subdomain: 6.1 Power centralization and unfair distribution of benefits

The incident involves corporate landlords using a shared algorithmic pricing tool to centralize pricing power, leading to inflated rents and an unfair distribution of economic benefits at the expense of tenants.

Additional risk subdomains

• 1.1 Unfair discrimination and misrepresentation: The lawsuit highlights concerns from economists and regulators that these algorithmic pricing models can perpetuate social harms and produce biased or discriminatory outcomes in the housing market.

Causal factors

• Entity: AI
• Intent: Intentional
• Timing: Post-deployment

The financial harm was caused by the deployment of the YieldStar pricing algorithm, which was intentionally used by UDR to set rental rates post-deployment.

EU AI Act risk tier

• Risk tier: 2 High Risk

Risk Level 2: High Risk. The system is used in housing administration, which has significant implications for public interest, safety, and fundamental rights regarding housing access and affordability.

AI system and alleged parties

• AI system: YieldStar (RealPage)
• AI purpose: Personalized Pricing; Value Estimation
• Behaviour type: Assistant
• Alleged developer: Rental pricing algorithm developers, RealPage, Property management software developers
• Alleged deployer: UDR, Residential property management companies, Landlords
• Alleged harmed parties: UDR tenants in San Diego, Renters, Jacob Keller

Harm severity

Severity ratings are based on this scale.

Highest direct severity in any category: Negligible. Severity is scored from Negligible to Catastrophic in each harm category, for harm the reports describe as caused directly or indirectly by the AI system.

• Physical: direct Negligible, indirect Negligible
• Infrastructure: direct Negligible, indirect Negligible
• Property: direct Negligible, indirect Negligible
• Financial: direct Negligible, indirect Negligible
• Environmental: direct Negligible, indirect Negligible
• Malicious content: direct Negligible, indirect Negligible
• Differential treatment: direct Negligible, indirect Minor
• Civil rights: direct Negligible, indirect Negligible
• Democracy: direct Negligible, indirect Negligible
• Privacy: direct Negligible, indirect Negligible
• Psychological: direct Negligible, indirect Negligible
• Epistemic: direct Negligible, indirect Negligible
• Child sexual exploitation and abuse: direct Negligible, indirect Negligible

Differential treatment

Reported: Yes, the report notes that economists and regulators agree that algorithmic pricing can perpetuate social harms and produce biased or discriminatory outcomes.

Indirectly caused: The use of the algorithm may indirectly lead to biased housing outcomes or differential treatment, though specific individual cases are not detailed.

People affected

• Occurrences reported: 1
• People reportedly harmed: 5000
• People reportedly exposed: 5000

Potential causes

Management

• Decentralized Price Setting: Management adopted YieldStar as a tool for decision-making.
• Failure to Halt Use: Management failed to stop using the software after June 2025.

Technology

• Algorithmic Pricing Software: YieldStar automated real-time rental pricing suggestions.
• Risk of Biased AI Outcomes: AI models can produce biased or discriminatory rental outcomes.

Data Inputs

• Nonpublic Competitor Data: Software used nonpublic competitor pricing and lease details.
• Granular Lease Insights: System captured niche, granular insights to set rental rates.

Human Factors

• Reliance on Algorithmic Tool: UDR human operators relied on YieldStar suggestions for pricing.

Process and Methods

• Algorithmic Rate Calculation: Process of using algorithms to set rents and occupancy levels.

Regulatory Environment

• Ordinance Non-Compliance: UDR continued using the software after San Diego banned it.
• New Local Legal Restrictions: San Diego passed Code 98.1103 outlawing algorithmic pricing.

Information quality

• Classification confidence: High
• Reason for confidence: The report clearly details the legal allegations, the specific software used (YieldStar), the user (UDR), and provides a concrete estimate of the financial impact ($99/month rent increase).
• Ambiguities identified: The exact number of affected UDR tenants in San Diego is not specified, and the degree of human override in UDR's pricing decisions is not fully detailed.
• Alternative interpretations: The pricing tool could be viewed as a standard database or statistical tool rather than an AI system, but the report explicitly refers to it as an 'algorithmic device' and 'AI model'.

A class-action lawsuit in San Diego alleges UDR, Inc. used RealPage's YieldStar algorithmic pricing software to inflate rents using nonpublic competitor data. While raising significant concerns about algorithmic collusion, antitrust violations, and local housing affordability, the incident has minor national security implications, primarily affecting local economic conditions rather than national security.

• Overall national security impact: Minor
• Scope: Single nation
• Primary target: United States
• Alleged perpetrator: UDR, Inc.

Threat characteristics

• Imminence: Long-term. This is a civil class-action lawsuit and regulatory compliance issue rather than an active, imminent national security crisis.
• Autonomy: Human-supervised. The algorithm provided real-time pricing suggestions, but UDR admitted to using the software as 'one tool among others' for its final pricing decisions.
• Novelty: Evolved capability. Represents an advanced application of algorithmic pricing and collusion using nonpublic competitor data, though automated pricing itself is an established concept.

Impact by dimension

• Physical security: Negligible. The incident involves software-based rental pricing and has no impact on physical systems, weapons, or critical infrastructure.
• Information security: Negligible. No intelligence compromise, classified data theft, or state-sponsored disinformation campaigns are associated with this commercial pricing software.
• Sovereignty: Negligible. The incident is a local municipal code violation and civil lawsuit, presenting no threat to state authority, borders, or federal government operations.
• Economic security: Minor. While the algorithmic collusion inflated rental prices locally, it does not threaten national financial systems, strategic industries, or overall US economic stability.
• Societal stability: Minor. The software contributed to housing unaffordability and potential economic bias in San Diego, representing localized societal harm but not large-scale civil unrest.