One of the hottest topics at this year’s ABA Antitrust Spring Meeting was algorithmic pricing—specifically, the case against RealPage, a rental software company accused of enabling landlords to coordinate rental prices using nonpublic data.
The DOJ and ten state attorneys general have charged RealPage and its landlord clients with anticompetitive conduct. At issue is the legal boundary between public and private data: algorithms based on public data may be permissible, but when competitors pool private, proprietary information, regulators argue it veers into collusion.
Key Takeaways:
- Algorithms aren’t inherently anticompetitive, but how they’re trained matters.
- Using private data for coordinated pricing is under increased scrutiny and could trigger liability.
- RealPage may set a precedent for algorithmic pricing and data-sharing boundaries.
Industry Impact Example:
A property management company may think it’s gaining efficiency by using shared market insights from RealPage. But if those insights are based on private, competitor-contributed data, that efficiency could quickly become a liability.
Why It Matters:
Industries beyond real estate—such as airlines, hotels, or e-commerce—should take notice. If your pricing tools are built on shared proprietary data, it’s time to reevaluate. Legal and compliance teams should conduct an audit of algorithmic tools and data sources to stay ahead of emerging enforcement trends.
