The CDO Stack: Advanced and Predictive Analytics in CRE

Let’s talk about AI. As you’ll recall from a few months ago, my favorite definition of AI is a system that replicates human interactions and decision-making.

John D’Angelo

I think that definition is useful as it sets the bar reasonably high for the sorts of examples that are best described as artificial intelligence. While real estate may not be the first area that comes to mind for applied AI, there are actually several valuable applications of advanced and predictive analytics in the space, and I think it’s important to understand them and how to make them a reality.  I previously promised to dive into how paring analytics with human experience and expertise, can be used to make a better investor, owner and operator—so let’s do that now.

First of all, the promise isn’t for analytics in real estate to replace people, judgement and instincts. Rather, it is for analytics to enhance human decision-making. Putting predictive and advanced analytics to work is not a substitute for people exercising judgement and making decisions (at least not today), but it can be used to help us make better or more informed decisions. 

After spending a significant amount of time in real estate, I’m familiar with the data challenges that asset and portfolio managers face on a regular basis as they work to optimize the performance, returns from—and value of—both individual assets and collections of those assets. 

The challenge—and the opportunity—is to have sufficient information that’s accurate and trustworthy, and sufficient time to understand and act on this information. Analytics can’t help (as much) with the former, but it can certainly help with the latter. What if an asset could ‘tell’ the asset manager that it is likely to need a major investment in building systems or the odds of each tenant either renewing or vacating at the end of their current lease?

Time to Think

What if the asset manager could easily understand the contribution of the property manager or operating partner to asset performance—distinct from what would have happened given market conditions?  How much could an asset manager improve asset performance if she were able to spend more time on the assets that most need that time? Analytics can help with these “what if” scenarios.

Portfolio managers arguably have an even more challenging situation because they are tasked with maximizing returns from collections of individual assets. How do they best understand which asset managers are adding the most value, which assets have maximized the value they can contributed and should be recycled, and which portfolio moves should be considered and when? Again, these are all potential areas in which analytics can be applied to help inform decision-making. Granted, the precise analytics will typically take iteration, patience and persistence to get right, but the prize of leveraging big data sets with analytics to help inform and improve decision-making and, ultimately, the performance of assets is substantial.

Seeing this area mature will require a different mindset and new skill sets. Getting there will also require the fundamental belief that there is value to be found in greater leverage from data and analytics.  I’m optimistic that this belief is building and that leaders in real estate can and will make investments and changes required to get from the nascent stages to a time in which advanced and predictive analytics are simply part of the basic toolkit of the industry.

John D’Angelo is a managing director with Deloitte and is the firm’s real estate solutions leader, designing solutions to address client challenges and push the industry forward. With over 30 years of experience as a management consultant to the global real estate industry, D’Angelo has helped some of the biggest names in real estate leverage technology and use data to optimize and transform their operations.

Read the March 2022 issue of CPE.