By: Miles G. Michaud
Traditionally, large commercial apartment buildings have set their rental prices using notions of supply and demand. Or,perhaps the building’s management would conduct market research of comparable properties in the surrounding area to determine the price of its vacant studio units. However, todays large commercial landlords set their prices rather differently. Many landlords have taken to using real estate technologies like YieldStar, which set prices for them using artificial intelligence.
YieldStar is revenue generating software which aids landlords in setting rental prices for their commercial properties. YieldStar is one of many novel technologies, but it is the first of its kind to use machine learning and artificial intelligence. YieldStar automates pricing for multifamily units by algorithms, which use customer submitted and publicly available data. Essentially, landlords input private information about their property and the artificial intelligence generates a recommended rental price.
Widespread use of YieldStar has spurred a wave of lawsuits. Frustrations with the service has been generated by tenant dissatisfaction with inflated rental prices throughout cities in the United States. For example, over ninety percent of commercial apartment buildings use YieldStar in the broader Washington DC area and the same technology is employed by many of the country’s largest landlords such as Greystar, William C. Smith & Co, and Bozzuto Management. More than twenty lawsuits in the District of Columbia, New York, Seattle, Boston, and elsewhere were consolidated in Nashville Federal Court on multi-district litigation. The complaint chiefly alleges a price fixing scheme to unlawfully coordinate on pricing and vacancy. This claim is brought under the Sherman Act, and charges the defendants with “knowingly combining their sensitive, nonpublic pricing and supply information in an algorithm to make price decisions, with the knowledge and expectation that other competitors would do the same . . . .” The Sherman Act was enacted to preserve business competition.
The hub-and-spoke model is traditionally used to describe a conspiracy in the classic sense. The spokes are the landlords,and the hub is YieldStar. To establish a violation of the Sherman Act the Plaintiff will have to demonstrate that (1) the conspiracy was knowingly formed and was in existence at or about the time alleged; (2) the defendant knowingly joined the conspiracy; (3) and the charged conspiracy either substantially affected interstate commerce or occurred within the flow of interstate commerce.
At the center of RealPage’s lawsuit exists YieldStar, AI software with rent setting capabilities. To use YieldStar, the customer, i.e. landlord, must first input private information regarding theirproperty and rental agreements to the service. This information ranges from actual rental prices and lease terms to vacancy rates. YieldStar also uses public information such as square footages, rental history, bed and bathroom count. The software then complies this information and generates a rental price recommendation to the landlord, which is aimed at maximizing the building’s revenue. If the landlord conforms to the recommendation, the price is typically three to seven percent above competitive levels in the area. In fact, the technology sometimes favors higher rents even if the property has a low occupancy rate. Also, in some circumstances YieldStar has kicked landlord users off the platform for not conforming to its recommended rental prices.
What many landlord’s may not know is that YieldStar compilesprivate information from the community of landlord’s who use YieldStar to set rental prices; not just that individual landlord. Thus YieldStar has become a melting pot of competitor information at the disposal of its artificial intelligence. For example, when the landlord at 123 Main Street inputs his actual rental prices in YieldStar, the software uses that information to produce a rental price for the landlord at 125 Main Street. One can envision a situation where the AI at YieldStar is recommending the same prices to the entire community of landlords at an increasingly higher rate.
Some newspapers have even called the community of landlords who use YieldStar a “rental cartel.” For example, when over ninety percent of landlords use YieldStar, rental inflation becomes readily apparent. However, conviction under the Sherman Act requires at least knowingly participating in a conspiracy and that may be a challenge to prove for the tenants. This begs the question, does the collective use of artificial intelligence amount to illegal collusion under United States anti-trust law? That answer may come down to whether the landlords or AI made a conscious commitment to participate in a rental cartel.
Student Bio: Miles is a second-year law student at Suffolk University Law School. He is a staff writer on the Journal of High Technology Law and member of the Real Estate/Trust & Estates Association. Miles received a Bachelor of Arts from Bates College in Lewiston, Maine.
Disclaimer: The views expressed in this blog are the views of the author alone and do not represent the views of JHTL or Suffolk University Law School.
