Did an Algorithm Raise Your Rent? Antitrust Lessons from RealPage for Louisiana Renters
Written By Gerald “Jess” Waltman III | 5 minute read
Almost anyone who rents, especially from a corporate landlord, has likely noticed that rent keeps going up. One reason may be the growing use of algorithmic pricing by corporate landlords.
What Is Algorithmic Pricing?
Algorithmic pricing systems are automated or semi-automated tools that use computation, data analysis, and increasingly artificial intelligence (“AI”) to influence commercial pricing decisions.[1] A pricing algorithm is any automated, semi-automated, artificial intelligence, machine learning, statistical, rules-based, or software-enabled system that recommends, sets, changes, optimizes, ranks, monitors, or otherwise influences prices, discounts, fees, commissions, margins, bids, rebates, promotions, availability, output, inventory, or other commercial terms.[2]
These systems differ fundamentally from traditional manual price-setting practices in three key respects. First, they can assimilate and process significant amounts of information relating to competitor prices, demand, the price and availability of substitutes, and even customers’ personal data, almost instantaneously. Second, they can respond to changes in market conditions in real time. Third, they can set prices to achieve a business objective consistently across all sales.[3] This capacity to process mass amounts of information and execute price changes allows businesses to compete more effectively by responding quickly to market changes.[4]
Algorithmic pricing tools take several forms, including predictive analytics algorithms, optimization algorithms, and revenue management software and systems. In particular, revenue management software and systems may use nonpublic, competitively sensitive data from multiple market participants to generate pricing recommendations. These systems have prompted price fixing lawsuits and significant scrutiny from consumers and government regulators over concerns that they may facilitate price fixing and other anti-competitive results.
The Sherman Antitrust Act
The Sherman Antitrust Act (the “Sherman Act”)[5] prohibits activities that restrict competition in the marketplace, and Sections 1 and 2 of the Sherman Act are especially relevant to the analysis of algorithmic pricing in the context of price fixing. Section 1, broadly, provides that any contract or conspiracy that would restrain trade in interstate commerce is illegal.[6] Section 2 prohibits monopolies or attempts to monopolize any aspect of interstate commerce.[7]
United States et al. v. RealPage et al.
“Companies cannot share sensitive data and manipulate AI tools or algorithms to produce market aligned pricing. That is not only illegal, but exploitative of Americans’ everyday housing needs. This Department will not stand for it.”[8] To that end, the United States Department of Justice, along with California, Colorado, Connecticut, Illinois, Massachusetts, Minnesota, North Carolina, Oregon, Tennessee, and Washington sued RealPage and several other defendant property management companies in the United States District Court for the Middle District of North Carolina alleging RealPage’s revenue management software “collects nonpublic information from competing landlords and uses that combined information to make pricing recommendations.”[9]
According to the complaint, RealPage operated as an algorithmic intermediary by collecting nonpublic, competitively sensitive data from competing landlords and using that combined information to generate and enforce daily rent recommendations. The plaintiffs alleged that this system aligned rivals’ pricing, suppressed competition, and inflated rents in violation of Sections 1 and 2 of the Sherman Act and analogous state laws.[10]
RealPage’s products used this pool of nonpublic data at three stages: (1) model training on millions of lease transactions and rental applications to derive learned parameters; (2) floor plan recommendations based on peer-set competitor data; and (3) unit-level pricing based on competitor supply, seasonality, and amenity valuations.[11]
The products also used peer identification and market range charts to set a “smoothed” market minimum and maximum based on competitors’ executed leases.[12] RealPage’s products would not recommend prices below that minimum.[13] RealPage’s “hard floor” prohibited recommendations below the market minimum; “revenue protection” reduced targeted lease volume to avoid lowering price when demand was low; “sold-out mode” pushed to competitors’ maximum rent once capacity was met; and the “governor” retained higher recent averages rather than reducing prices to current-day optimum when revenue would otherwise fall. These features allegedly favored price increases and limited price decreases.[14] Further, the products used market seasonality, competitors’ future supply, and lease expirations to vary term premiums and “remain in a position of pricing power,” only raising, not lowering, unit-level prices across lease terms.[15]
Specifically, the complaint alleged that several features worked together to convert rivals’ independent pricing into interdependent alignment: the pooling of granular nonpublic transactional and forward-looking occupancy data; the use of peer-set floors and ceilings; elasticity drawn from rivals’ data; seasonality tied to competitors’ supply; and standardized advisory and auto-accept processes. Prices set through the software purportedly tracked market leader movements upward and resisted downward adjustments. Minimum pricing, revenue protection, and other elements stabilized prices at higher levels and reduced the competition that otherwise might have led to lower rents. The plaintiffs alleged that these practices amounted to improper coordination and restraints on trade in violation of Section 1 of the Sherman Act.[16]
The complaint also raised monopolization issues. According to the plaintiffs, RealPage’s unique access to pooled nonpublic “lease transaction data” across millions of units created a “data moat” that entrenched dominance, attracted further adoption, and raised rivals’ entry barriers unless they replicated the same unlawful data sharing practices, thereby maintaining monopoly power in revenue management software in violation of Section 2 of the Sherman Act.[17]
While there are still ongoing state court claims in the suit, RealPage and the federal government reached a settlement agreement in November of 2025. Under that agreement, RealPage agreed to stop using competing landlords’ nonpublic information to determine rental prices during runtime operations; stop using active lease data to train the software’s models; stop conducting market surveys to collect competitively sensitive information; accept monitoring from a court-appointed monitor to ensure compliance with the consent judgment; and cooperate with the federal government against property management companies that used RealPage’s software.[18]
How Does Louisiana Address Algorithmic Pricing Issues?
Louisiana has not enacted a statute specifically defining or regulating algorithmic pricing, and Louisiana courts have not directly addressed the subject. Louisiana's antitrust statutes, however, prohibit restraints of trade and monopolization and should apply to algorithmic pricing conduct in Louisiana.
Louisiana enacted its own antitrust legislation in 1890, the same year as the federal Sherman Act, reflecting the same concerns about monopolization and restraints of trade.[19] Section 51:122 provides that “[e]very contract, combination in the form of trust or otherwise, or conspiracy, in restraint of trade or commerce in this state is illegal.”[20]. Section 51:123 provides that “[n]o person shall monopolize, or attempt to monopolize, or combine, or conspire with any other person to monopolize any part of the trade or commerce within this state.”[21] These provisions are virtually identical to Sections 1 and 2 of the Sherman Act, respectively, with the key distinction that Louisiana's statutes apply to intrastate commerce rather than interstate commerce.[22] Louisiana courts have consistently held that because these provisions are virtually identical to the Sherman Act, federal jurisprudence interpreting the Sherman Act is persuasive, though not controlling, in interpreting Louisiana's antitrust statutes.[23]
The Louisiana Supreme Court in Louisiana Power & Light Co. v. United Gas Pipe Line Co., 493 So. 2d 1149 (La. 1986), confirmed this relationship, holding that federal interpretation of the Sherman Act does not control interpretation of state antitrust law, but that federal analysis provides important guidance. Louisiana courts have applied the same elements as federal courts when analyzing restraint of trade and monopolization claims under state law.[24] A Louisiana Attorney General Opinion has further confirmed that acts which violate federal antitrust laws most often also violate Louisiana antitrust laws, and that the causes of action available under federal law are generally available under Louisiana law as well.[25]
One important distinction is that Louisiana's antitrust statutes apply to conduct occurring within Louisiana or having the purpose of affecting trade in Louisiana; the Louisiana First Circuit Court of Appeal has held that the State failed to state a cause of action under the Louisiana Monopolies Act where the alleged wrongful acts occurred outside Louisiana’s geographic boundaries.[26]
Conclusion
While Louisiana has not enacted statutes specifically regulating algorithmic pricing, the jurisprudence surrounding Sections 51:122 and 51:123 suggests that Louisiana courts could apply principles similar to those implicated in the RealPage litigation when evaluating algorithmic pricing conduct under state antitrust law. For Louisiana renters facing rising rents, that means existing state law may provide a way to challenge pricing practices that cross the line from independent decision-making into unlawful coordination.
About the Author
Gerald “Jess” Waltman III
Gerald “Jess” Waltman III is a Member at Gordon Arata in New Orleans and represents clients in complex commercial litigation matters. A 2016 graduate of the University of Mississippi School of Law, Jess is a past president of the Mississippi Bar Young Lawyers Division and was named its 2026 Outstanding Young Lawyer. He is admitted to practice in Alabama, Arizona, Georgia, Louisiana, Mississippi, and Texas and is active in numerous professional and civic organizations.
[1] See https://www.law.cornell.edu/wex/algorithmic_pricing; https://nrf.com/blog/algorithmic-pricing-innovation-misunderstood; https://www.nber.org/system/files/working_papers/w32540/revisions/w32540.rev1.pdf
[2] https://www.nber.org/system/files/working_papers/w32540/revisions/w32540.rev1.pdf
[3] https://www.nber.org/system/files/working_papers/w32540/revisions/w32540.rev1.pdf
[4] https://www.nber.org/system/files/working_papers/w32540/revisions/w32540.rev1.pdf
[5] 15 U.S.C. §§ 1-7.
[6] 15 U.S.C. § 1
[7] 15 U.S.C. § 2
[8] https://www.justice.gov/opa/pr/justice-department-reaches-proposed-settlement-willow-bridge-one-americas-largest-landlords?_sp=5e81241d-f41c-4746-8c0a-0274571f0a2b.1789070333657
[9] Am. Compl. ¶ 1, United States v. RealPage, Inc., No. 1:24-cv-00710 (M.D.N.C. Jan. 7, 2025).
[10] Am. Compl., supra note 9, ¶¶ 6, 12, 20, and 281.
[11] Am. Compl., supra note 9, ¶¶ 17, 21, 27, 40-42, and 53.
[12] Am. Compl., supra note 9, ¶ 49.
[13] Id.
[14] Am. Compl., supra note 9, ¶¶ 143-145 and 150-151.
[15] Am. Compl., supra note 9, ¶¶ 54-56 and 159-160.
[16] Am. Compl., supra note 9,¶¶ 3, 6, 48-49, 54, 57, 127-129, 142-145, and 150-154.
[17] Am. Compl., supra note 9, ¶¶ 165-167 and 179-182.
[18] https://www.justice.gov/opa/pr/justice-department-requires-realpage-end-sharing-competitively-sensitive-information-and?_sp=5e81241d-f41c-4746-8c0a-0274571f0a2b.1789156057872. The outcome in RealPage should be compared to the recent decision of Cornish-Adebiyi v. Caesars Ent., Inc., 184 F.4th 172, 176 (3d Cir. 2026), in which the Third Circuit overturned the district court’s dismissal of the claims and held that the plaintiffs had sufficiently alleged parallel conduct and other factors in support of an inference of horizontal agreement. Cornish-Adebiyi was addressing algorithmic pricing in hotel reservations, but many of the principles can be applied to algorithmic pricing in property rentals.
[19] S. Tool & Supply, Inc. v. Beerman Precision Inc., 2001-1749 (La. App. 4 Cir. 5/1/02), 818 So. 2d 256.
[20] La. Stat. Ann. § 51:122.
[21] La. Stat. Ann. § 51:123.
[22] Plaquemine Marine, Inc. v. Mercury Marine, 2003-1036 (La. App. 1 Cir. 7/25/03), 859 So. 2d 110.
[23] Louisiana Power & Light Co. v. United Gas Pipe Line Co., 493 So. 2d 1149 (La. 1986); HPC Biologicals, Inc. v. UnitedHealthcare of Louisiana, Inc., 2016-0585 (La. App. 1 Cir. 5/26/16), 194 So. 3d 784; S. Tool & Supply, Inc. v. Beerman Precision, Inc., 2003-0960 (La. App. 4 Cir. 11/26/03), 862 So. 2d 271.
[24] HPC Biologicals, Inc. v. UnitedHealthcare of Louisiana, Inc., 2016-0585 (La. App. 1 Cir. 5/26/16), 194 So. 3d 784; S. Tool & Supply, Inc. v. Beerman Precision, Inc., 2003-0960 (La. App. 4 Cir. 11/26/03), 862 So. 2d 271.
[25] La. Att'y Gen. Op. No. 01-77 (Apr. 10, 2001).
[26] State By & Through Caldwell v. Fournier Industrie et Sante, 2017-1552 (La. App. 1 Cir. 8/3/18), 256 So. 3d 295.
