Article Summary
The use of RealPage, an AI-powered platform for setting rent prices, has become the center of multiple lawsuits alleging algorithmic price-fixing. RealPage collects property data from clients, which it then uses to suggest rent prices. If a critical mass of landlords uses RealPage, it could lead to lockstep price increases and remove incentives for innovation. Plaintiffs argue that this has contributed to the housing affordability crisis in over 40 housing markets in the US. Antitrust laws and precedents, however, might not be equipped to handle the complexities of algorithmic price-fixing.
What This Means for You
- Be aware of the increasing role of AI and algorithms in influencing prices, even if it’s not immediately obvious.
- Understand that the existing legal system might be struggling to address the complexities of algorithmic price-fixing, which could mean higher costs and limited competition for consumers in the long run.
- Urge local and national governments to pass legislation that specifically targets algorithmic price-fixing, providing better protection against this emerging issue.
- Stay vigilant for future technologies and innovations that could affect housing and rental markets or other aspects of daily life.
Algorithmic Rent and the Future of Price-Fixing
The RealPage lawsuits reveal a critical issue: existing antitrust laws and procedures might not be sufficient to deal with the complexities of algorithm-driven price-fixing. RealPage offers “bespoke pricing recommendations” but has been accused of tactics that encourage enforced compliance (a key feature of cartels).
While the courts have allowed class-action cases against RealPage to proceed, other comparable lawsuits against different companies have been dismissed. Existing laws and precedents might not be able to handle the intricacies of algorithmic price-fixing, especially if it doesn’t involve a direct agreement between competitors.
Moreover, antitrust scholar Maurice Stucke and co-author Ariel Ezrachi argue that algorithms could fix prices even without the creators’ or users’ intentions. This phenomenon, along with the potential for algorithms to learn and collude autonomously, has not yet been addressed by existing antitrust laws.
People Also Ask About
- What is algorithmic price-fixing, and how does it differ from traditional price-fixing schemes?
Algorithmic price-fixing uses AI-driven algorithms for collusion, whereas traditional price-fixing requires secret agreements between competitors. Algorithms can learn and adapt on their own, making the practice even more difficult to detect and prosecute.
- Can antitrust laws address the complexities of algorithmic price-fixing, and are existing laws sufficient?
There are concerns that existing antitrust laws may not be sufficient to regulate algorithmic price-fixing, especially if it doesn’t involve direct agreements between competitors. New legislation and precedents need to be developed to keep up with this emerging issue.
What are the potential risks of algorithmic price-fixing for consumers?
If left unregulated, algorithmic price-fixing could result in permanent rent increases and higher costs for consumers, eroding the foundation of free-market capitalism and the incentives for innovation.
Expert Opinion
The Senate Democrats’ bill providing greater protection against algorithmic price-fixing signals a step in the right direction. However, local governments may need to take the lead in this area, as seen with San Francisco’s recently passed ordinance banning software that combines non-public competitor data to set or advise on rents and occupancy levels.
Key Terms
- Algorithmic Price-Fixing
- Artificial Intelligence (AI)
- Antitrust Laws
- RealPage Lawsuits
- Housing Affordability Crisis
- Enforced Compliance
- Autonomous Algorithmic Collusion
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