Dynamic Pricing

Photo by Steve Johnson on Unsplash

I think we’ve always thrived on human-centric leadership in multifamily. It’s the backbone of our systems, processes, strategies, and marketing campaigns. However, we’re entering an era where artificial intelligence (AI) is poised to transform our understanding of leadership roles, particularly in complex fields in multifamily.

In the early innings, AI serves as a copilot or digital assistant, handling data analytics, automating repetitive tasks, and streamlining workflow. But as machine learning algorithms grow smarter and more capable, it’s worth asking: What happens when AI takes the steering wheel?

Consider this: We already use advanced AI platforms to identify and predict market trends. Machine learning models analyze data from multiple sources—rental rates, occupancy levels, local regulations—and provide actionable insights beyond human intuition or traditional market analysis methods. The deeper the data, the better the predictive power of the AI. AI is not new – think about the calculator. The wave is cresting, and it’s making landfall. 

But here’s where it gets interesting—what if we let AI manage all the variables that influence rent pricing? High-level, dynamic pricing models governed by machine learning could consider hundreds of factors, from local events and governmental actions to seasonal patterns, essentially making every pricing decision optimized and justified.

In customer relations and community engagement, AI chatbots are no longer limited to answering frequently asked questions. Natural language processing allows them to interpret intent, mood, and context, enabling them to handle complex interactions. These aren’t your run-of-the-mill chatbots; these systems can interpret emotions, de-escalate situations, and offer tailored solutions, essentially serving as virtual community managers. And companies can bolt together the technology in-house. You don’t need to rely on third parties for the platform. Think chatbot platforms. 

Now, let’s discuss decision-making. AI can also be involved in shaping strategies. Picture an AI system that analyzes human behavior in communal spaces to optimize the use and maintenance of these areas. Instead of simple analytics, the AI could proactively suggest changes in communal space designs or services offered, directly affecting the quality of life for the community. Essentially, the AI becomes a proactive strategist rather than a reactive analyst.

It’s tempting to resist these changes, viewing AI as a potential threat to the essence of human-centric leadership. However, in the correct architecture, AI enhances our capabilities. It doesn’t replace human decision-making but refines it. The result is a hybrid leadership model where humans and AI work harmoniously, amplifying the other’s strengths. This is not a zero-sum game; it’s an evolutionary step in multifamily leadership.

Of course, safety and ethics are non-negotiable. As AI systems get more powerful, it’s crucial to have robust ethical frameworks, but that’s a topic for another discussion.

In a continuously evolving time, the multifamily industry has to adapt or risk becoming obsolete. It has been said, “If you don’t like change, you will like irrelevance even less.” Embracing AI in leadership roles is not an option; it’s a necessity. It promises to elevate our strategies, systems, and services to the minimum desired levels, redefining what leadership can achieve in the multifamily space.