Apartment Rentals and Algorithmic Collusion: Is RealPage a Monster?

1.0 Background

Recently, apartment rents in the U.S. have surged by as much as 15% month over month for new rentals. This dramatic increase poses a significant challenge for everyday hard working individuals, couples, and families who rent apartments because purchasing a house is beyond their financial means.

In the world of leisure travel, an airline ticket or hotel stay is a discretionary expenditure, and the typical traveler makes 1 to 2 trips a year. The cost of these leisure trips can also rise sharply, but they are optional and can be postponed. In contrast, apartment rentals involve month-to-month or longer-term leases that directly affect people’s livelihoods. An apartment rental is the single largest monthly budget item, and rising costs drive inflation and heavily influence the mindset of the working class and retirees who are struggling to make ends meet and often have little to no savings. Besides driving inflation, there are also ethical, moral, and fairness-based aspects that play a role in society.

What is driving these phenomenal price increases in apartment rentals? A surge in demand? A shortage of apartments? Or is the culprit software from a vendor that promotes their property management system to landlords? RealPage, a Texas-based company, offers YieldStar software that assists landlords in determining rental prices for apartments nationwide. As rental prices continue to rise, some critics express worry that the company’s unique algorithm may be undermining competition in the market.

With escalating rents and a lack of available housing emerging as significant topics in the 2024 presidential election, the U.S. Justice Department has taken legal action against the software company RealPage. The lawsuit claims that RealPage's algorithm facilitated coordination among landlords across the country to increase rents for tenants.

2.0 What is Competitive Revenue Management?

The computer generated prices for apartments may have its origins in the competitive revenue management business process adopted by many airlines and hotels in the early 2000s {Vinod, 2021; Vinod, 2022]. YieldStar uses an algorithm to analyze data from clients, including what nearby competitors charge, upending the practice of tenants negotiating with apartment staff and discouraging apartment owners from bargaining with renters.?

What is Competitive Revenue Management in Airlines and Hotels?

At the outset it is worth pointing out that the objective of revenue management, in any form, is to maximize total revenues and not yield, which is the price per unit.?Available capacity (airline seats, hotel rooms) is always factored into the decision making process.

?Traditional revenue management forecasts demand and optimizes inventory controls based on a supplier’s internal data. With schedule and fare transparency, a growing consensus emerged in the early 2000s that competitor-selling fare data could be leveraged to improve inventory controls that reflected current market conditions. An airline always factors unsold seats before overriding the inventory controls.

Airlines publish their tariffs to fare aggregators like ATPCO, who in turn disseminate the fares to intermediaries like Global Distribution Systems (GDS) and airlines [Vinod, 2024 (Chapter 5]. An airline has the option to respond to a new fare filed by a competitor by matching the fare or filing a fare that is above or below the competitor’s fare. This fare management process precedes revenue management. Over the past two decades, Competitive Revenue Management has become an established practice in the travel industry [Page, C?té, Savard, 2005; Ratliff and Vinod, 2005; Zhang and Kallesen, 2008; Hartmans, 2008; Kumar, et. al. 2021; Vinod, 2021 (Chapter 7)].

?In lodging, Competitive Revenue Management starts with revenue managers identifying the competitive set (or "comp set") for their property. This includes properties in the same neighborhood that are considered competitors. Hotel competitive sets are asymmetric (Property A may be in B’s comp set, but Property B is not in A’s comp set) [Vinod, 2022] and can be determined from an OTA's click stream data. Hotel rates that are published to internal systems like the hotel CRS are based on the hotel’s pricing strategy, perceived value of the brand, room types and associated room attributes, and neighborhood. Competitive Revenue Management in lodging involves using automated tools to monitor competitors' websites for information on selling rates. Vendors like QL2, RateGain, and Amadeus Hospitality's Demand360 offer services that allow hotels to track competitors' selling rates for future dates. This data is used in conjunction with unsold rooms to adjust inventory controls within the hotel's CRS to recommend the best available rate (BAR). In addition, hotels can also subscribe to Smith Travel Research (STR) for access to historical selling rates at various properties.

Selling fares and selling hotel rates are publicly available with a robotic or manual search for flights and hotels through an OTA or travel agency. Airline and hotel competitive revenue management software do not have access to any confidential data such as the entire tariff structure which may include public and private content or corporate contracts with suppliers such as airlines and hotels.

Besides airlines and hotels, car rental companies and cruise lines monitor competitor rates to augment their revenue management strategies. For instance, rental car companies closely monitor competitor capacity (available cars in the lot) and rates at airport locations to adjust walk-up rates for walk-up customers.

In summary, competitive revenue management in the airline and hotel industries, while they may rely on competitive selling rates, focuses on maximizing total revenue (airline network revenue or hotel revenue over a period) and not yield. For airlines, yield is the revenue per revenue passenger mile and for hotels it is the revenue per occupied room. If the Justice Department’s claims are true, there must be a shortcoming in the YieldStar software if it does not consider vacancies at a property as input before recommending rates, resulting in maximizing the revenue per occupied rental unit and not the revenue per available rental unit at the property. In this situation the property landlords are losing money by not lowering rates to achieve a better mix of rental rates to maximize total revenues for the apartment complex.

3.0 A RealPage Testimonial

Propublica investigators were the first to raise this concern to the wider public. Kortney Balas, a director of revenue management at JVM Realty, provided a testimonial video for YieldStar on the RealPage website [Vogell, Coryne and Little, 2022], that has since been removed. She was quoted as saying “The beauty of YieldStar is that it pushes you to go places that you wouldn’t have gone if you weren’t using it.”

Four US Senators sent a public letter on November 22, 2022 (Senator Elizabeth Warren, Senator Tina Smith, Senator Bernard Sanders, and Senator Edward J. Markey) that was sent to Dana Jones, the CEO of Real Page [Warren, 2022]. The five-page letter voiced their concerns over YieldStar.? The first paragraph on page 2 is quoted below in italics):

YieldStar’s extensive reach, which only grew after regulators approved a merger with “its only significant competitor” in 2017, has grave implications for renters who end up in the grasp of landlords who use it. Using lease and other private information collected from RealPage customers, which “include some of the largest property managers in the country,” YieldStar’s algorithm calculates and suggests the highest rents landlords should charge, “push[ing] [them] to go places that [they] wouldn’t have gone if [they] weren’t using it.” As one RealPage executive put it, “I think [YieldStar is] driving [rising apartment rents], quite honestly. As a property manager, very few of us would be willing to actually raise rents double digits within a single month by doing it manually.”

This is the link to the open letter. /https://www.warren.senate.gov/imo/media/doc/2022.11.22%20Letter%20to%20RealPage%20re%20YieldStar%20Algorithm.pdf?

4.0 Collusion and Algorithmic Collusion

The standard definition for collusion states that it is illegal for landlords to get together to set rent prices for their properties.

Algorithmic collusion occurs when companies unlawfully collaborate to increase prices by utilizing an algorithm that processes their data. Unlike traditional collusion, there are no direct agreements or signals among the competitors. Each company has its own contract with the algorithm provider. The provider in this case, RealPage’s YieldStar, analyzes the contracts and data from these companies to offer pricing recommendations that enhance their profit per unit, ultimately harming the customers. It should also be illegal for a third party like RealPage to do it for apartment owners using competitive data and automated technologies. This is why the law needs to be updated. U.S. Senator Amy Klobuchar (D - Minnesota) introduced a bill in Congress entitled, Preventing Algorithmic Collusion Act, earlier this year (Klobuchar, 2024). In defense of Klobuchar, automated pricing software has existed for a long time, since airline regulation in 1978. Leveraging AI and machine learning for automated pricing is also not the issue. The Klobuchar bill is specifically targeted toward pricing software that uses confidential non-public data such as terms and conditions in leases and their associated pricing structures from competitors to enable cartelization,

The key question is: What is YieldStar maximizing: total revenue (accounting for vacancies and lowering rents when necessary) or revenue per available rental unit?

On August 23, 2024, the U.S. Department of Justice together with the Attorneys General of North Carolina, California, Colorado, Connecticut, Minnesota, Oregon, Tennessee, and Washington, filed a civil antitrust lawsuit against RealPage Inc. “for its unlawful scheme to decrease competition among landlords in apartment pricing and to monopolize the market for commercial revenue management software that landlords use to price apartments.” At the center of the antitrust lawsuit is RealPage’s YieldStar revenue management software that uses an algorithm powered by Artificial Intelligence to help landlords to set rental rates. AI / Machine learning algorithms are self-learning and collude when making recommendations using competitive data. The Justice Department argues that the software drives every opportunity to increase prices even during periods when demand is soft. The Justice Department alleges that RealPage violated Sections 1 and 2 of the Sherman Act. The Sherman Antitrust Act is an U.S. antitrust law passed in 1890. It was the first federal law to prohibit monopolies and unfair business practices. Its objective was to protect consumers by promoting free competition, keeping prices down, and maintaining quality.

Interestingly on Jan 7, 2025, the DOJ and attorney generals from 10 states filed an amended complaint against RealPage that includes 6 landlords for allegedly "participating in algorithmic pricing schemes" that harmed renters.?

5.0 A Comparison of Predatory Pricing and Algorithmic Collusion

Unlike predatory pricing where a business sets prices for a product unrealistically low to eliminate the competition and create a monopoly, algorithmic collusion, automated or otherwise, raises prices to unprecedented levels. While predatory pricing violates antitrust laws, algorithmic collusion needs to be addressed by amending the laws to make it easier for prosecutors.

Predatory pricing does not always work, since the predator is losing revenue as well as the competition. The predator must raise prices eventually. At that point, new competitors will emerge.

Both predatory pricing and algorithmic collusion to maximize the price per unit is a deliberate attempt to undermine the marketplace.

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6.0 Conclusion

If YieldStar is maximizing the price per unit and not the total revenue for the apartment complex, it is an opportunity for new entrants, that are aligned with core revenue management principles, to emerge, factoring in unused capacity and make rational price recommendations for a balanced marketplace for apartment rentals.

Although there is overwhelming evidence, we cannot make a judgement call on whether RealPage is a monster that undermines the middle class or a victim of its own success in the marketplace. Ultimately the courts will have to decide whether RealPage broke the law.

References

1.????????? Department of Justice Press Release (2023) Justice Department Sues RealPage for Algorithmic Pricing Scheme that harms millions of American Renters, Department of Justice, August 23. https://www.justice.gov/opa/pr/justice-department-sues-realpage-algorithmic-pricing-scheme-harms-millions-american-renters

2.????????? Kumar, R, Wang, W., Arunachalam, S.K., Simrin, S.K., Guntreddy B.R., Walczak, D. (2021) Competitive Revenue Management Models with Loyal and Fully Flexible Customers. Journal of Revenue and Pricing Management, https://doi.org/10.1057/s41272-021-00311-4

3.????????? Hartmans, G. (2008) Competitor fare availability monitoring and usage, AGIFORS Revenue Management Study Group, Tahiti, French Polynesia, May.?

4.????????? Klobuchar, Amy (2024) S.3686 - Preventing Algorithmic Collusion Act of 2024, Congress,Gov, January 30, https://www.congress.gov/bill/118th-congress/senate-bill/3686

5.????????? Page, J-F, C?té, J-P, Savard, G. (2005) A new framework for revenue management and pricing optimization based on bilevel programming, AGIFORS Revenue Management Study Group, Capetown, South Africa, May.?

6.????????? Ratliff, R. and Vinod, B. (2005) Airline pricing and revenue management: a future outlook. Journal of Revenue and Pricing Management, 4, 3, 302–307.

7.????????? Vinod, B. (2021); The Evolution of Yield Management in the Airline Industry: Origins to the Last Frontier, ISBN-13:?978-3030704230, ISBN-10:?3030704238, Springer Nature, Switzerland, May.

8.????????? Vinod (2022) Revenue Management in the Lodging Industry: Origins to the Last Frontier, ISBN: 978-3-031-14304-5, ISBN: 978-3-031-14302-1, Springer Nature, Switzerland, November.

9.????????? Vinod, B. (2024) Mastering the Travel Intermediaries: Origins and Future of Global Distribution Systems, Travel Management Companies, and Online Travel Agencies, ISBN 978-3-031-51523-1, Springer Nature, Switzerland, May

10.????? Vogell, H., Coryne, H., Little, R. (2022) Rent Going Up? One Company’s Algorithm Could Be Why? Propublica.org, October 15, https://www.propublica.org/article/yieldstar-rent-increase-realpage-rent

11.????? Warren, Elizabeth (2022) United States Senate letter to RealPage, November 22, 2022, https://www.warren.senate.gov/imo/media/doc/2022.11.22%20Letter%20to%20RealPage%20re%20YieldStar%20Algorithm.pdf

12.????? Zhang, D., Kallesen, R. (2008) Incorporating competitive price information into revenue management, Journal of Revenue and Pricing Management, 7(1), 17-26.

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