Ryan Masiello, Chief Strategy Officer, VTS.
The commercial real estate industry is entering a new wave of innovation amid the rise of AI and data science. According to a recent JLL report, AI and generative AI are among the top three technologies expected to have the biggest impact on the industry, and in 2023, there was an impressive $630 million invested in AI-powered proptech.
With these numbers only expected to grow, and the industry continues to face economic headwinds, AI-powered real estate solutions will become increasingly important to property owners and landlords, especially regarding how they better understand the market, take proactive approaches to leasing and tenant engagement and operate more cost-effectively.
Under the umbrella of AI, predictive analytics has emerged as a critical tool that is revolutionizing how commercial real estate owners and operators leverage technology to get ahead of the market and more aggressively prospect tenants.
Understanding Predictive Analytics
Predictive analytics provide technology-generated insights pulled from millions of data points across a given platform or database for a holistic view of a given entity.
In the case of commercial real estate, these data points are pulled to give insights into market activity and real estate portfolios. Having data that can anticipate movements in the market, specifically those of tenants concerning demand, is a capability that had never been available to the industry before, mainly given the slower rate of technological advancement in real estate.
Predictive analytics have now empowered commercial real estate landlords to take a proactive approach to leasing and tenant engagement and have provided them with the ability to anticipate and take action on future tenant behavior to make the best decisions for their portfolio today.
Predictive Analytics And Real-Time Data In Action
Landlords can apply and leverage predictive analytics in a multitude of ways to make more informed decisions and create operational efficiencies.
One particular use case is using this data to forecast market activity, which can enable landlords to anticipate and prepare for market fluctuations. For example, as shown through my organization’s Leasing Prediction Outlook, we can say that leasing in both New York City and San Francisco is seeing positive growth signals year-over-year.
That said, having data aggregated in real-time at a regular cadence is instrumental as a predictive set is generated using historical data, underscoring the importance of utilizing data-oriented solutions in everyday operations.
Before committing to a predictive analytics-focused approach, landlords should ask themselves if there are gaps in their current data collection process and if their current systems provide the real-time data needed to make informed decisions.
This introspection can also help highlight the challenges predictive analytics can solve, such as identifying new opportunities, managing portfolio risks and having a deeper level of competitive insights to help them stay ahead of the market.
Challenges And Considerations
With predictive analytics comes the aggregation of real-time data, which provides the historical account of market activity needed to create predictive insights.
As a first step, landlords should assess what data sources they currently have to best determine the specific solutions they need to provide the right insight into their portfolio and market activity, whether it be at a weekly, monthly or quarterly cadence.
Once landlords have a clearer view of what’s needed, they can invest in predictive analytics tools and platforms to generate real-time data sets. Having the most accurate real-time view of the market and your portfolio, as well as possessing the ability to predict future activity, provides an unparalleled level of insight for CRE owners and operators.
With these new tools implemented, working with a dedicated data analyst would be a wise next step, especially when aggregating these insights. This will ensure that analysis is done thoroughly and accurately and can account for the expected learning curves those using these new insights will have once integrated.
Also, AI-powered solutions are still very new to the real estate industry and are, at times, still treated with skepticism. Therefore, offering the space to explore and train with this technology will be beneficial to teams as they build confidence with these types of tools and learn how to best apply them in their respective roles.
PropTech will only continue to evolve, and predictive analytics are a prime example of how technology for the real estate industry has matured. Tools that provide a 360-degree view of the market have never been more important as the commercial real estate landscape continues to face economic headwinds as a result of shifts to hybrid working models and high interest rates.
Landlords now have another tool that can help build and execute forward-looking strategies that will serve them in the long term and set them up for success.
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