In the evolving world of multifamily revenue management, revenue managers are seeking alternatives to complex algorithms that are difficult to justify to owners, especially given the high turnover rates of 50% or more within the industry. These challenges come when significant legal actions are pending against some of the biggest names in revenue management and their clients, prompting a reevaluation of traditional rent survey methods and market rent determinations.
This piece explores the functionality of revenue management systems, highlights the issues with the current approach, and outlines the industry's future direction.
Understanding Revenue Management Systems
Historically, revenue management has utilized algorithms based on supply and demand to optimize income. These algorithms consider various factors, including a property's past interest, lease applications, and imminent lease renewals, to suggest the most profitable rent for a specified occupancy goal. By adjusting prices on websites and listing platforms and monitoring the responses, these systems refine their pricing strategies based on real-world outcomes, aiming to maximize revenue while balancing future lease renewals.
These platforms offer daily updates on pricing and lease terms, encouraging property managers to distribute lease expiration throughout the year, thereby avoiding market saturation at inopportune times.
While revenue management systems have substantially benefited profitability and operational efficiency, concerns arise regarding the source and application of their data.
The Issue of Algorithmic Collusion
Imagine the major property managers in a large city regularly sharing detailed pricing and occupancy data. Such a dataset could, without sophisticated algorithms, provide actionable insights to set rents competitively. This scenario raises questions about fairness and legality, particularly if the process results in uniform high rents and low vacancies, potentially bordering on price fixing.
The pivot to AI-driven systems, which can analyze data from numerous properties to suggest optimal pricing and lease terms, introduces a modern twist to this dilemma. Although automated and efficient, this approach has attracted legal scrutiny, challenging its legality.
Navigating Change
Adapting to new methodologies in the face of integrated automated pricing tools and legal challenges is daunting. The industry is searching for cost-effective, legally compliant alternatives to traditional pricing strategies, especially as interest rates rise and transaction volumes decrease.
The Path Forward for Revenue Management
The crux of the issue lies in the reliance on private versus public data for rent setting. The shift towards AI-driven systems that utilize publicly available data offers a way forward, promising more transparent and equitable pricing strategies. These advanced AI models, capable of analyzing a wider array of data points more frequently, promise improved outcomes without needing sensitive information. As the industry grapples with legal concerns, the value placed on transparency and the rationale behind pricing decisions is increasingly becoming a priority for owners and managers.
As the legal landscape surrounding revenue management unfolds, stakeholders must adopt a forward-thinking approach to revenue management, leveraging public data and innovative technologies to achieve competitive pricing while minimizing risk.
Innovation and a willingness to diverge from the norm can provide a competitive edge in multifamily revenue management, steering clear of the pitfalls associated with conventional strategies.
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