Understanding Surveillance Pricing In Self-Storage

In recent years, advances in data analytics and artificial intelligence have transformed how prices are set across industries. One emerging and controversial development is “surveillance pricing,” a term used to describe pricing strategies that rely on extensive data collection about individuals to determine what they are willing or able to pay. Although not generally used in self-storage, issues surrounding the use of surveillance pricing are being addressed in our industry.

Surveillance pricing refers to the practice of using detailed personal and behavioral data to tailor prices to individual consumers. Unlike traditional pricing models, which rely on supply-and-demand dynamics or broad market segmentation, surveillance pricing attempts to personalize pricing at a granular level. It draws on a wide array of data points—such as browsing history, location data, income indicators, credit profiles, and even inferred preferences—to estimate a consumer’s price sensitivity.

In the self storage rental market, property managers and landlords could utilize sophisticated software platforms that aggregate market data and tenant-related information to recommend optimal rent levels. These systems would likely incorporate real-time market trends, comparable property rates, occupancy levels, and demand forecasts. Increasingly, they may also incorporate tenant-specific insights, potentially influencing the rent offered to a prospective tenant or the renewal rate proposed to an existing one based on their personal data.

At its core, surveillance pricing relies on algorithms trained to identify patterns in large datasets. In rental markets, these algorithms may analyze: historical leasing data across similar properties; seasonal demand fluctuations; demographic and economic data in a given area and tenant-specific characteristics, such as payment history or length of tenancy. Some systems operate at the portfolio level, helping large property management companies adjust rents dynamically across multiple properties.

Other systems function more narrowly, offering recommendations for individual units based on current market conditions and tenant demand signals. For example, if data suggests that a particular renter has limited storage alternatives or is searching in a high-demand area with low inventory of available units, the algorithm may recommend a higher rent. Conversely, if a unit has been listed for an extended period without interest, the system may suggest lowering the price to attract tenants more quickly.

From a business perspective, surveillance pricing offers clear advantages. It enables property owners to maximize revenue by aligning rent levels closely with market demand. It also reduces reliance on manual pricing decisions, which can be inconsistent or influenced by subjective judgment. Automation can improve operational efficiency, particularly for large-scale landlords managing hundreds or thousands of units. By continuously adjusting prices based on real-time data, these systems can help maintain optimal occupancy rates while capturing incremental revenue gains.

Despite these advantages, surveillance pricing has sparked significant pushback. Unlike traditional rent negotiations, where price differences may be openly discussed, surveillance pricing operates largely behind the scenes. This lack of visibility can erode trust and make it difficult for tenants to understand or challenge pricing decisions. Surveillance pricing also depends on the collection and analysis of personal data, sometimes from sources that tenants may not fully understand or have consented to.

The growing use of surveillance pricing has not gone unnoticed by regulators. Policymakers at the federal, state, and local levels are beginning to examine whether existing laws adequately address these practices. Some jurisdictions have already proposed or enacted measures aimed at curbing perceived abuses, such as requiring greater disclosure of pricing methodologies or restricting the use of certain types of data. In New York, the Governor has required the use of a disclosure in rental agreements to state: "THIS PRICE WAS SET BY AN ALGORITHM USING YOUR PERSONAL DATA." (New York Algorithmic Pricing Disclosure Act). Although not specifically intended to include the self storage market, the recommendation is that if self storage operators are using surveillance pricing, that they include this disclosure in their marketing websites and on their rental agreements.

Surveillance pricing sits at the intersection of innovation and regulation. On one hand, it reflects a broader trend toward data-driven decision-making that can improve efficiency and market responsiveness. On the other hand, it raises fundamental questions about privacy, fairness, and the appropriate limits of technology.

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Scott Zucker is a partner in the law firm of Weissmann Zucker Euster + Katz P.C. in Atlanta, GA. Scott specializes in business litigation with an emphasis on real estate, landlord-tenant and construction law. For more visit www.wzlegal.com.