2023 was the year AI went mainstream with ChatGPT. But that chatbot is just the tip of the iceberg. AI and machine learning represent a massive opportunity for commercial real estate investors to gain a competitive edge.
Analysts forecast the real estate industry will see a staggering 11% annual adoption of generative AI between 2024 and 2033. For firms that embrace this technology early, there’s a prime chance to get out in front of competitors. The key advantage AI provides? Transforming data into actionable intelligence at an unprecedented scale.
You’re no longer stuck with one-size-fits-all information from third-party providers. With AI and machine learning, you can build in-house data models precisely tailored to your unique investment strategies and criteria. But how can you actually leverage this technology? How do you find profitable opportunities your competitors are missing?
This three-part series will guide you. In this article, I’ll cover how creating your own real estate datasets allows you to harness advanced AI techniques to elevate your decision-making abilities.
Challenges of third-party data brokers
Data has been called the “new oil” for good reason – it’s the power fueling decisions in the information age. In fact, 91.9% of companies report gaining measurable business value from investments in data. Without comprehensive, reliable data, you’re shooting in the dark. Poor information means poor decisions that can cost you millions in lost deals or money pits. But get the data right, and you gain an incredible strategic advantage, capitalizing on insights your rivals completely miss.
For decades, commercial real estate investors had no alternative but to rely on third-party data brokers, paying steep fees to access datasets needed to make multi-million dollar investment decisions. This dependence on external data providers poses several challenges that limit a firm’s competitive potential:
Lack of localized insights
First up, these third-party datasets only give you the 30,000-foot regional view. Critical decisions require hyper-local details that can vary drastically within neighboring areas. Factors like consumer demographics, business activity, and zoning policies all impact values, yet this granular data is often lacking. Without visibility into these micro-level market details, investors risk overlooking hidden opportunities or misjudging an asset’s potential.
Inability to customize data
Another big problem is the inability to customize data. You’re stuck with whatever metrics they decide to track, even if they aren’t the most relevant or valuable for your unique investment criteria and strategies. This disconnect limits your ability to gain an edge.
Costly subscriptions
Steep data broker subscription fees eat into funds that could be better allocated elsewhere.
Advantages of building your own proprietary datasets
The way to get ahead of the competition is by building data models tailored to how you invest – that’s where the big benefit comes in. Standardized third-party resources can only take you so far. To capitalize on opportunities, you need relevant insights that only customized metrics can provide.
Track the exact data points that matter most
With custom intelligence, you can track the exact metrics that matter most for your unique deals and strategies. No more wasting time sifting through irrelevant noise or missing key local factors. You have full control to customize which metrics are monitored as your investment models and criteria naturally evolve over time.
See what your competitors can’t
Having your own information models allows you to identify profitable opportunities that others using generic datasets cannot see. With granular local market details, you can discover patterns and potential deals that fly under the radar of your competitors.
Stay agile and adaptive
Using your own proprietary information assets gives you full control so that you’re not reliant on any third-party broker. As your real estate strategies evolve, you can easily incorporate new information sources and update which metrics you track without jumping through unnecessary hoops.
Why standardizing your data tracking process is key
Building your own datasets is powerful, but it’s only half the battle. The other essential component is standardizing how you track and organize that information over time. Consistent, uniform processes provide the structured data inputs required for leveraging AI and machine learning models.
Recognizing this, Gartner Group highlights the importance of well-governed, high-quality data as the baseline for successfully implementing advanced technologies like AI. Their research emphasizes that such foundational data is crucial for organizations to truly capitalize on the value of analytics initiatives.
Key takeaways
The writing’s on the wall – AI and machine learning are going to shake up commercial real estate in a big way. But firms that get out ahead by building their own models from the ground up are positioning themselves to be the disruptors.
At the end of the day, your data is what’s going to set you apart and give you that competitive leverage. Having custom intelligence engines is how you’ll see opportunities your rivals can’t. And by laying that standardized data foundation now, you’ll be positioned to take advantage of all the AI-powered capabilities still on the horizon.
In the next article, I’ll walk you through how to build an AI-powered data model that’s customized just for your real estate firm.
