/>iAntitrust
Last week, in Gibson et al. v. MGM Resorts Int’l et al.,1 the United States District Court for the District of Nevada dismissed a putative antitrust class action without prejudice against Cendyn Group, LLC (“Cendyn”), a private company that provides technology for the hospitality industry, and the operators of several hotels on the Las Vegas strip, including the Bellagio, Wynn, Caesar’s Palace, MGM Grand, and Mandalay Bay (the “Hotel Operators,” and together with Cendyn, “Defendants”).2 Cendyn and two of the Hotel Operators, along with several other hotel operators, are facing three similar putative class actions in New Jersey.3
The Las Vegas court’s opinion potentially provides guidance for both plaintiffs and defendants in antitrust cases relating to pricing algorithms, including the parties in the New Jersey actions. Additionally – and more broadly – the opinion potentially provides guidance to businesses using pricing algorithms so that they can ensure compliance with Section 1 of the Sherman Act.
Background
The putative class plaintiffs (“Plaintiffs”) alleged that the Hotel Operators engaged in a price-fixing conspiracy with the assistance of three of Cendyn’s pricing algorithm products. According to Plaintiffs, the pricing algorithms analyze real-time pricing and occupancy information from other hotels on the Las Vegas strip to generate room-specific pricing recommendations for the Hotel Operators. Plaintiffs point to comments made by a Cendyn executive emphasizing the “ultimate goal” of “maximizing profits” over occupancy growth, and suggest that these comments reveal a motivation to maintain supra-competitive prices, even if accomplishing that goal meant that hotel rooms would be left vacant.4 Plaintiffs claim that 90% of hotels on the strip use Cendyn’s products.5 With such widespread adoption, the Hotel Operators were allegedly able to “keep room rates artificially high and defy fundamental supply and demand dynamics.”6
Plaintiffs claimed that this conduct violates Section 1 of the Sherman Act, which makes it unlawful to enter into an agreement that unreasonably restrains competition. Critical to the analysis of a Section 1 claim is a determination as to whether the members of the alleged conspiracy had a “conscious commitment to a common scheme,”7 even if the alleged agreement is tacit in nature. Plaintiffs sought treble damages and injunctive relief.
The Court’s Opinion
The court held that Plaintiffs failed to allege the “agreement” prong of a Section 1 claim for several reasons.8First, the court held that Plaintiffs failed to adequately allege that the Hotel Operators used the same pricing algorithm.9 Without clarity on that point, the court could not infer that the Hotel Operators had entered into an agreement to act in concert.
Second, the court held that Plaintiffs failed to allege that the Hotel Operators were required to accept the prices recommended by the software.10 Plaintiffs claimed that the recommendations of one of the three Cendyn products were accepted 90% of the time. The court could not infer an agreement among the Hotel Operators from this fact, because the statistic was not specific to the hotels on the strip or to the pricing conduct of the Hotel Operators. Further, the fact that hotels reject the recommendation 10% of the time suggests that there is not an agreement to maintain supra-competitive prices.
Third, the court held that the complaint lacked the required specificity as to who entered into the alleged agreement and when the purported conspiracy began.11 Although the court would not go so far as to require the names of specific employees, the court required specificity beyond “Hotel Operators.”
Additionally, the court held that Plaintiffs’ failure to adequately allege an agreement among the Hotel Operators meant that Plaintiffs failed to adequately allege a hub-and-spoke conspiracy. To prevail on a Section 1 claim based on a hub-and-spoke theory, a plaintiff must show that there was an agreement among the spokes of the wheel (i.e., the Hotel Operators).12 Otherwise, there is no “rim.” Here, Plaintiffs could not prevail on a hub-and-spoke theory without alleging that the Hotel Operators used Cendyn’s software to exchange nonpublic information with each other.13 Although the complaint alleged that the Hotel Operators provided nonpublic information to the pricing algorithm as input, it was unclear whether the Hotel Operators were alleged to have received nonpublic information from each other. The court emphasized that “[c]onsulting public sources to determine how to price a hotel room by viewing your competitor’s rates does not violate the Sherman Act.”14
Based on counsel’s comments at oral argument, it appears that Plaintiffs plan to amend their complaint. The court gave them 30 days to do so.
The New Jersey Litigations
There are three putative class actions pending in the District Court for the District of New Jersey based on similar allegations concerning hotel operators’ use of Cendyn’s pricing algorithm products in Atlantic City. Two of the Hotel Operators in the Gibson case are also defendants in the New Jersey actions.
It is debatable whether the New Jersey plaintiffs have already cleared some or all of the hurdles that got in the way of the Gibson Plaintiffs. By way of example, the New Jersey plaintiffs allege that the defendants’ pricing algorithms analyze nonpublic information provided by competitor hotels to generate pricing recommendations.15 Although the hotel operators may not receive disaggregated nonpublic information belonging to competitors, they may receive recommended prices that are allegedly based on nonpublic information belonging to competitors. It remains to be seen whether the court will view this indirect reliance on nonpublic price and/or occupancy information as evidence of an agreement among the hotel operators.
The Use of Pricing Algorithms & Antitrust Risk
The court’s opinion in Gibson echoes widespread commentary on the use of pricing algorithms in that it reinforces the importance of employing a human being to understand, monitor, and control pricing algorithms. The fact that hotel operators reject pricing recommendations about 10% of the time was vital to the Gibson court’s opinion that Plaintiffs failed to allege an actionable agreement. Indeed, the court called this fact a “fatal deficiency” in the complaint. Employing a human to make decisions about when to accept or reject a pricing algorithm’s recommendations, which requires resisting the temptation of the “set it and forget it” approach, could, therefore, materially reduce antitrust risk. Further, in light of Gibson, ensuring that your business understands what information the pricing algorithm is using to generate recommended prices – and whether nonpublic information that is competitively sensitive could be extracted based on those recommended prices – could be critical to reducing risk.
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1 Gibson et al. v. MGM Resorts Int’l et al., No. 2:23-cv-00140-MMD-DJA (D. Nev.).
2 Order, Gibson, No. 2:23-cv-00140-MMD-DJA (D. Nev. Oct. 24, 2023), ECF No. 141 (“Order”).
3 Cornish-Adebiyi et al. v. Caesars Entertainment, Inc., No. 1:23-cv-02536-KMW-EAP (D.N.J.); Blair-Smith v. Caesars Entertainment, Inc. et al., No. 1:23-cv-06506-KMW-EAP (D.N.J.); Fabel v. Boardwalk 1000, LLC et al., No. 1:23-cv-06576-KMW-EAP (D.N.J.).
4 Class Action Complaint at ¶¶ 9-10, Gibson, No. 2:23-cv-00140-MMD-DJA (D. Nev. Jan. 25, 2023), ECF No. 1 (“Complaint”).
5 Id. at ¶ 7.
6 Id. at ¶ 46.
7 See Monsanto Co. v. Spray-Rite Service Corp., 465 U.S. 752, 768 (1984).
8 Order at 8.
9 Id.at 4-5.
10 Id.at 5.
11 Id. at 6-8.
12 See In re Musical Instruments & Equip. Antitrust Litig., 798 F.3d 1186, 1192 (9th Cir. 2015).
13 Order at 8-9.
14 Id. at 11.
15 Amended Class Action Complaint at ¶ 6, Cornish-Adebiyi, No. 1:23-cv-02536-KMW-EAP (D.N.J. Aug. 21, 2023), ECF No. 53.
