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Market research · October 29, 2026

Algorithmic Pricing Lawsuit Statistics: 2026 Report

By Darin ten Bruggencate, Founder, AI Industry Reviews

Our analysts built a case-level dataset of 24 US algorithmic pricing and pricing-data litigations filed between 2016 and October 2026, drawing on federal and state court dockets, Judicial Panel on Multidistrict Litigation (JPML) transfer orders, court opinions, US Department of Justice (DOJ) Antitrust Division filings and releases, state attorney general releases, and law-firm litigation trackers. For each case we recorded the year first filed, industry, software or data vendor, type of plaintiff, the first ruling on a motion to dismiss, and the status as of October 23, 2026. We then compiled settlement amounts from court orders and official releases, and dated every state and local law restricting algorithmic rent-setting that we could confirm from government records or law-firm summaries.

Many landlords, hotels, insurers, and food processors now set prices with help from software sold by an outside company. The software gathers data from many competing customers, runs it through a model and recommends a rent, room rate, or reimbursement level to each of them. US antitrust law bars competitors from agreeing on prices, and the lawsuits in this report argue that sharing private data with a common vendor and following its recommendations can amount to that kind of agreement, even when the competitors never speak to each other. Defendants answer that buying the same software is lawful, that each company keeps the final say over its own prices, and that the data involved is often old or anonymized.

Courts, prosecutors, and lawmakers have spent the last four years testing those arguments, and the results can now be counted.

In the following sections, we count the cases by year, industry, and vendor, lay out the multidistrict consolidations and government enforcement actions, total the settlements, compare how cases fared at the motion to dismiss stage, and track the state and city laws that now ban algorithmic rent pricing.

Algorithmic Pricing Lawsuits by Year Filed

This section counts litigations rather than individual complaints. Dozens of separate complaints were filed against RealPage alone before they were combined, so a consolidated case or multidistrict litigation (MDL) counts once here, and each government action counts once.

Algorithmic pricing litigations by year first filed and industry

Source: AI Industry Reviews case dataset (Source 1), compiled from court dockets, JPML orders, and DOJ and state attorney general releases. No qualifying litigation was found for 2017 or 2019 to 2021. Other industries are equipment rental (2025), mortgage lending (2025) and gasoline retail (2026).

Key Findings

• The two earliest cases, Broiler Chicken (2016) and Pork (2018), were not originally about algorithms. They challenged price-fixing in which Agri Stats, a data company, circulated detailed production and pricing reports among competing meat processors, and they belong in this count because the same theory of shared nonpublic data now drives the software cases.
• Filings peaked in 2024, with 8 new litigations, including the DOJ case against RealPage and three hotel suits filed in a single year. The 2025 filings shifted toward state attorneys general, who brought four of that year's seven cases.
• For companies that buy pricing software, the spread across seven industries matters more than the yearly total. Mortgage lending, construction equipment rental, and gasoline retail all drew their first suits in 2025 or 2026, so the legal risk is no longer confined to apartments and hotels.

Lawsuits by Software Vendor and Defendant

This section groups the same 24 litigations by the vendor whose software or data sits at the center of each case. In every case the plaintiffs also sued the vendor's customers, such as landlords, hotel operators, insurers, or processors, whose alleged agreement is the core of the claim.

Algorithmic pricing litigations by software or data vendor

Source: AI Industry Reviews case dataset (Source 1); court opinions and complaints in Sources 5, 25 to 33, 35, and 40; DOJ and state releases (Sources 7, 18, 19, and 56); Milbank algorithmic tools updates (Sources 21 to 24).

Key Findings

• RealPage is the vendor in 8 of the 24 litigations in our dataset, and six of its eight cases were brought by state attorneys general acting on their own. The District of Columbia sued RealPage and 14 landlords in November 2023, alleging that RealPage software priced more than 30% of the District's multifamily units and about 60% of units in buildings with 50 or more units.
• The case lists are long on the customer side. The DOJ's amended RealPage complaint added six large landlords in January 2025, New Jersey sued RealPage with 10 landlords and Maryland with 6, and the potato case targets Lamb Weston, McCain, Simplot, and Cavendish alongside Circana.
• For vendors, the pattern in the vendor table means a single product can generate several separate cases at once, from private classes, the DOJ, and individual states. RealPage faced class claims, a federal case, and six state suits over the same software within three years.

MDL Consolidations

This section covers the two algorithmic pricing disputes that the JPML centralized before a single federal judge. An MDL combines similar federal cases from different districts for pretrial work such as discovery and motions to dismiss, which is why one MDL can stand in for dozens of complaints.

JPML consolidations of algorithmic pricing cases

Sources: JPML transfer orders in MDL No. 3071 and MDL No. 3121 (Sources 2 and 31); RealPage final approval order (Source 3); MultiPlan opinion (Source 32).

Key Findings

• RealPage and 34 landlord defendants asked the JPML to centralize the rent cases, and the panel's order notes that a 22nd action, filed in New Mexico, had been voluntarily dismissed in January 2023. Defendants had favored the Northern District of Texas and many plaintiffs the Western District of Washington, but the panel sent the cases to the Middle District of Tennessee.
• The MultiPlan MDL brought together out-of-network health care providers who allege that insurers used MultiPlan's repricing tools to agree on lower payments. Judge Kennelly kept the antitrust claims in June 2025 and dismissed only the unjust enrichment claims of the providers that had sued directly.
• The other hotel, Yardi, and food cases have proceeded in separate courts, which is one reason outcomes differ so much from case to case. A single MDL judge ruled once on RealPage's motion to dismiss, while the five hotel suits were decided by five different district courts and two appeals courts.

DOJ and State Attorney General Actions

This section covers the 10 government actions in our dataset: two federal cases brought by the DOJ with state partners, and eight separate RealPage-related actions or settlements involving state attorneys general. The table includes state settlements reached inside the federal RealPage case because they were filed and announced separately.

Government enforcement actions over algorithmic pricing and shared pricing data

Sources: DOJ case pages and releases (Sources 6, 7, 10, and 36); Arnold & Porter (Source 9); California Attorney General (Source 8); state attorney general releases and complaints (Sources 11 to 20); Milbank (Sources 22 to 24); Vorp Labs state attorney general tracker (Source 54).

Key Findings

• The DOJ filed its RealPage case with eight states: California, Colorado, Connecticut, Minnesota, North Carolina, Oregon, Tennessee, and Washington. Washington withdrew in February 2025 and filed its own state court case in April 2025, and the Greystar settlement lists nine participating states, adding Illinois and Massachusetts.
• The RealPage final judgment does not require a payment or an admission of wrongdoing. According to Arnold & Porter, it bars RealPage from using competitors' nonpublic data in live pricing, limits model training to data at least 12 months old, and installs a court-supervised monitor for three years.
• The federal remedies so far are conduct rules rather than money, while most of the cash has come from private classes and state suits. Landlords and vendors negotiating with regulators should expect monitoring, data restrictions, and cooperation duties, and should treat state attorneys general as a separate source of damages claims.

Settlement Amounts and Counts

This section totals the cash settlements we could confirm from court orders, official releases, or plaintiff firm case pages. Several resolutions, including the DOJ consent decrees, carry no payment and appear only in the notes.

Cash settlements in algorithmic pricing and pricing-data cases

Sources: RealPage MDL final approval order and settlement website (Sources 3 and 4); DC Office of the Attorney General releases (Sources 14 to 16); Colorado and Massachusetts Attorney General releases (Source 11); Arizona Attorney General (Source 17); Hagens Berman case pages (Sources 37 and 38); Meat+Poultry (Source 39); DOJ (Sources 6, 10, and 36).

Key Findings

• The RealPage class settlements are the largest confirmed recovery from algorithmic pricing software so far. The plan of allocation pays renters in proportion to the rent they paid at qualifying properties, using an expert-calculated overcharge for each market or a 2% baseline overcharge where none was calculated.
• Claims in the RealPage class are due January 29, 2027, and the class covers renters at properties using RealPage revenue management software from October 18, 2018 to November 21, 2025. RealPage itself is not among the 41 settling defendants, and the order preserves the class claims against the defendants that have not settled.
• In the DC case, two of the five landlord settlements, JBG Smith at $8.1 million and MAA at $1.2 million, make up most of the $11.75 million total. For landlords still in litigation, these figures show that state cases can produce payments on top of class settlements over the same conduct.

Motion to Dismiss Outcomes: Survivals and Dismissals

This section compares the first ruling on a motion to dismiss in each of the 14 litigations that have one. A case is counted as surviving if its central antitrust claims went forward, even when some defendants or claims were dropped.

First motion to dismiss rulings in algorithmic pricing cases

Sources: Court opinions and orders (Sources 5, 25 to 30, 32, 33, and 35); Milbank (Sources 21 to 24), including the New Jersey ruling (Source 23); Arnold & Porter (Source 9); California Attorney General (Source 8). Excludes Broiler Chicken and Pork, which predate the software cases, and the Optimal Blue and Kalibrate cases, which had no ruling.

Key Findings

• All five hotel cases lost their first motion to dismiss, and all nine non-hotel cases with a ruling survived in whole or large part. The difference tracks what plaintiffs could allege about data: Gibson and the IDeaS and CoStar courts found no plausible claim that hotels' confidential data was pooled to set each other's prices, while the RealPage, MultiPlan, and potato complaints alleged that the vendor did exactly that.
• The Third Circuit's July 2026 decision in Cornish-Adebiyi reversed the hotel trend in part. It held that allegations that Atlantic City casino hotels shared nonpublic data with Cendyn's Rainmaker system and accepted its recommendations about 90% of the time plausibly alleged a hub-and-spoke conspiracy, which leaves the Ninth and Third Circuits applying the law to similar facts with different results.
• Surviving a motion to dismiss does not decide a case. Mach v. Yardi survived dismissal in August 2024 and then lost on summary judgment in October 2025, after source code evidence showed Yardi's software did not use one customer's confidential data to price another's units, so defendants with clean data practices can still win on the evidence.

Federal Statements of Interest and Remedies

This section covers how federal agencies have shaped the private cases without being parties. The DOJ, sometimes with the Federal Trade Commission, files statements of interest to tell a court how it reads the antitrust laws.

Key Findings

• According to Milbank, the federal agencies filed statements of interest in Cornish-Adebiyi in March 2024, in the Pork case in October 2024, in MultiPlan in March 2025, and in Frozen Potato Products on February 27, 2026. The potato filing argued that information exchanges are not presumed lawful even when the data is aggregated, anonymized, or historical.
• The Agri Stats consent decree of May 7, 2026 requires the company to stop sales and nonpublic pricing reports, restrict facility-level data, and make most reports available to purchasers, under a monitor whose term runs seven years unless the DOJ shortens it. The case reached settlement after the court denied summary judgment in February 2026, according to Milbank.
• The DOJ's Pinnacle settlement of September 4, 2026 follows the same template as the earlier landlord decrees: no use of algorithms trained on competitors' sensitive data, no sharing of that data, and cooperation against the remaining defendants. Every DOJ resolution in our dataset is a conduct decree rather than a cash payment.

State Laws Banning Algorithmic Rent Pricing

This section lists the state laws that restrict algorithmic rent-setting. California's law is broader than rent, since it applies the state antitrust statute to any "common pricing algorithm," but it is included because it has already been used in an algorithmic pricing suit.

State laws restricting algorithmic pricing of rent

Sources: California Legislature and Governor (Source 48); New York Governor and Senate (Source 49); CT Mirror and Baker McKenzie (Source 50); New Jersey Governor and Legislature (Source 51); RealPage v. James order (Source 52); Colorado General Assembly (Source 53); Kalibrate complaint (Source 40).

Key Findings

• New York was the first state to pass a ban aimed specifically at rent-setting software, according to Morgan Lewis, and its law reaches both the vendors that pool data from multiple landlords and the landlords that use the results. In September 2026 a federal judge blocked the part of the law that treats a landlord's use of such recommendations as an unlawful agreement, while leaving the ban on facilitating coordination in place.
• The National Apartment Association, the apartment industry's trade group, counts five states and 18 localities with laws on revenue management software as of August 26, 2026, and reports that 9 of the 15 state bills it tracked since September 2025 failed. Its state count includes Colorado; we could not confirm an enacted Colorado rent algorithm law, because HB25-1004 was vetoed in 2025 and the 2026 bill we found, HB26-1210, was a broader surveillance pricing bill that was also vetoed.
• The California law has already produced its first test case. The June 2026 Kalibrate complaint relies on AB 325 and alleges, citing outside research, that gas stations switching to algorithmic pricing raise prices by about 6 cents per gallon on average and by as much as 30 cents where adoption is high. For businesses outside housing, the case shows that AB 325 is being applied to pricing software in industries other than rent.

City and County Bans on Algorithmic Rent Pricing

This section dates the local ordinances. Cities moved first: San Francisco's ban took effect in October 2024, more than a year before any state law, and most local measures followed in 2025.

Local laws restricting algorithmic rent-setting, by date enacted

Sources: Morgan Lewis (Source 41) for enactment dates of San Diego, Berkeley, Providence, Minneapolis, Jersey City, Seattle, Hoboken, Philadelphia, and San Francisco; City of San Francisco (Source 42); Philadelphia City Council (Source 43); King County Council (Source 44); City of Portland (Source 45); City of Spokane (Source 46); City of Rockville (Source 47); National Apartment Association (Source 55) for Minneapolis, Portland, Spokane, Santa Ana, and West Hollywood effective dates.

Key Findings

• Ten of the 15 local measures we could date were enacted in 2025, the same year three states acted. When King County passed its ordinance in September 2025, its council described the county as the 11th jurisdiction in the country to prohibit this kind of algorithmic rent coordination.
• Vendors have challenged local and state bans in court. RealPage sued Berkeley in 2025, and enforcement of Berkeley's ordinance is paused, according to Morgan Lewis, while the New York challenge produced the partial injunction described above.
• For landlords operating in several markets, the local map is uneven. A property manager with buildings in Seattle, Spokane, and unincorporated King County faces three separate local bans in Washington alone, with different penalties, alongside the Washington Attorney General's pending state case.

What These Numbers Mean for Pricing Software

The cases sort less by industry than by what the software did with data. Where plaintiffs could allege that a vendor pooled competitors' confidential information and fed it back as price recommendations, as in the RealPage, MultiPlan, Zelis, potato, and equipment rental cases, the claims survived. Where the allegations rested on competitors simply buying the same product, as in most of the hotel suits, courts dismissed them, and the Third Circuit's Cornish-Adebiyi ruling shows that even that line now depends on the details of each complaint.

The money has followed the rental cases. More than $379 million has been paid or committed by landlords in RealPage-related settlements we could confirm, while the DOJ has taken conduct remedies, monitors, and data restrictions instead of payments. State attorneys general have become the second front, with six RealPage suits of their own outside the federal case and with laws in four states and at least 15 cities and counties that now restrict the software itself.

Several large questions remain open. Yardi's summary judgment motion, the remaining RealPage MDL claims, the states' claims in the federal RealPage case, and the first AB 325 suit over gasoline prices will show whether the pleading-stage results hold up on evidence. So if you sell or use pricing software, map exactly which competitor data flows into its recommendations, since that question was central to every motion to dismiss ruling in the motion to dismiss table.

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