What's Happening?
Renters in New Hampshire are increasingly encountering rent prices determined by artificial intelligence (AI) and algorithmic software, leading to significant frustration and concerns about potential price fixing. Jacqueline Plante, a physical therapist
in Portsmouth, experienced this firsthand when her lease renewal offered varying prices based on lease duration, with identical apartments in her complex listed at lower rates for new tenants. Her property management company, Forest Properties Inc., attributed these discrepancies to AI revenue management software. This situation is not isolated, as experts indicate many renters in New Hampshire are affected. Nationally, the Department of Justice (DOJ) has settled with at least four corporate landlords and one software company, including RealPage, Greystar Management Services, and Willow Bridge Property Company, for using algorithms and private data to set rent prices in ways that violate federal antitrust laws. These settlements required companies to cease anti-competitive practices but did not include fines or admissions of guilt. The White House Council of Economic Advisers estimated that algorithm-set rents cost renters $3.8 billion in 2023, increasing monthly rents by an average of $70.
Why It's Important?
The widespread adoption of AI and algorithmic software in rent setting poses a significant challenge to the U.S. rental market, particularly in states like New Hampshire with tight housing inventories. This technology, often described as a 'black box' by experts like Nick Taylor, director of Housing Action NH, lacks transparency, making it difficult for renters to understand how prices are determined. The use of non-public data by these algorithms to coordinate rent prices among competitors can stifle competition, potentially leading to artificially inflated rents and reduced housing affordability. This practice disproportionately affects renters, who often have limited options and feel compelled to accept AI-determined prices. The issue has drawn the attention of federal and state lawmakers, with U.S. Rep. Maggie Goodlander sponsoring the 'End Rent Fixing Act of 2025' to prohibit property owners from coordinating rent prices using AI and non-public data. The New Hampshire Department of Justice is also actively monitoring the situation, recognizing the potential for anticompetitive behavior and price fixing.
What's Next?
The legislative landscape is evolving in response to concerns about AI-driven rent setting. U.S. Rep. Maggie Goodlander's 'End Rent Fixing Act of 2025' aims to explicitly forbid property owners from coordinating rent prices using AI, algorithms, and non-public data, building on her previous work in the DOJ's antitrust division. While a similar bill in New Hampshire, House Bill 1612-FN, which would have made the use of price-fixing software a violation of the Consumer Protection Act, did not pass, its sponsor, Rep. Jonah Wheeler, hopes for future reintroduction. Other states, including New York, California, and New Jersey, have already updated their antitrust laws to ban or restrict algorithm-driven rent-setting, indicating a growing trend towards regulatory action. The New Hampshire Department of Justice is actively monitoring the issue and encourages individuals to report suspected anticompetitive behavior. Beyond legislative efforts, experts like Nick Taylor suggest that increasing the supply of rental units and encouraging local community developers could help mitigate the impact of these practices by providing renters with more options and fostering better landlord-tenant relationships.
Beyond the Headlines
The rise of AI in rent setting highlights broader ethical and legal implications concerning algorithmic transparency and consumer protection. The 'black box' nature of these algorithms raises questions about fairness and accountability, as renters are often left without recourse or understanding of how their housing costs are determined. This technological shift redefines the concept of price fixing, moving it from 'smoke-filled backrooms' to sophisticated data-driven systems, challenging existing antitrust laws designed for traditional market behaviors. The issue also underscores the power imbalance between large corporate landlords utilizing advanced technology and individual renters, who may feel 'devalued and stuck' with limited choices. The potential for AI to exacerbate housing affordability crises and drive out young professionals from certain areas, as suggested by Jacqueline Plante's experience, points to long-term societal and economic consequences. Addressing this requires not only legislative updates but also a re-evaluation of how technology is deployed in essential services to ensure equitable outcomes and prevent algorithmic exploitation.
