Real Estate Investing & Analytics
Why are we so far behind other asset classes?
by: John D’Angelo, Managing Director and US Real Estate Consulting Leader, Deloitte Consulting LLP

Section: Spring 2019 Summit - Digital Edition





WHAT’S THE PROBLEM?

Real estate, as an institutional asset class, is significantly behind other asset classes with respect to leveraging data and analytics as a means to help drive alpha. Not only have public asset classes been putting data to work for decades, most public managers would agree (if they’re being honest) that it’s becoming very difficult to generate alpha in these asset classes because large managers have been putting big data, data science, and advanced analytics to work for years to identify opportunities and risks, and incorporate these tools in their investment processes and decision making.

Arguably, if the data exists and the analysis can be modeled, the large managers have likely done it. They’ve plumbed the depths of available data sets and technologies, have exhausted the scenarios that could be defined,  and now have to find other ways of differentiating and generating alpha.

Which begs the question…what’s happened…why is real estate so far behind other asset classes? There are multiple reasons, but the main reasons center on the difficulty of getting good data and dearth of tools, skills, and expertise to put it to work.

As opposed to stocks and bonds, the financial and operational information required to perform analytics is not readily available (real estate doesn’t have a terminal to access data like that which exists for stocks and bonds), and there is not an accepted industry-wide standard for data. Furthermore, the state of the art for gathering, assembling, and trusting an aggregated data set is laborious and involves a tremendous amount of repeated human effort to perform basic analysis and reporting. In a typical manager, the data that does exist sits in individual, disconnect silos throughout the enterprise. In all but a handful of managers, best case is a handful of such data silos and worst case is dozens of individual data silos with primary data stored in a spreadsheet named after a person who has long since departed.

Since so much time is spent on basic analysis and reporting, little or no time is left to perform advanced analytics or modeling; it’s not as though investment professionals aren’t capable of asking more and  better questions to drive value from the data, it’s that they typically don’t have the time or technology to do so. Finally, the skills and tools required to pull together an aggregated data set and put it to work are generally not well understood or resident inside
of today’s managers.


WHAT SHOULD HAPPEN TO HELP OVERCOME THESE ISSUES / CHALLENGES?

Unfortunately, as indicated above, there is no terminal for real estate data (or other illiquid asset classes, for that matter). Since the essential financial and operational information needed to understand  the performance of an individual asset is created by a property manager or JV operating partner, and there are no accepted industry standards that govern the creation of this information, managers need to be deliberate about understanding the information they expect, articulating those expectations to the people responsible for creating it, and determining the best way to get it.

So yes, there’s a practical challenge to advanced analytics in institutional real estate; just getting good data is often a problem. But maybe the bigger challenge is one of mind-set or belief in the fundamental value of data and analytics as tools. In an industry that has long prized intuition, instincts, and gut-level decision making, frankly, even if data were readily available and perfect, there remains a fundamental skepticism on the part of real estate investment managers that there is value in putting that data to work.

As an example, data governance, as a discipline and function, has only recently gained (relatively) widespread acceptance and a place in the organization of managers. This acceptance and adoption has come years after data governance was a common function or discipline in other industries. Typically, those in leadership positions at real estate investment managers weren’t familiar with the function or how and why it could be valuable, save time, and reduce risk. It has taken years of explaining the role and benefit for data governance to become accepted. And even as it has become accepted, I’d argue that it’s not fully understood or appreciated.

To answer the question posed at the top of the section, what should change is for managers and institutional investors to better understand and appreciate what is possible if they put data to  work. If managers really believed that they could generate alpha by better using data and tools on  top of that data, the industry would likely change very quickly. Not by replacing human judgment and instincts with machines, but by using machines to point out potential performance problems,
risks, and opportunities to humans, who then can determine what action (if any) to take based on the information presented. In other words, the goal isn’t to replace people, the goal is to leverage systems and data to help people make better investment decisions and, in so doing, generate alpha.
 

WHAT’S POSSIBLE IF MANAGERS HAVE COMPLETE DATA AND BETTER LEVERAGED IT?

Let’s first address the section heading. If it’s not clear by now, it absolutely is the case that managers, at an enterprise level, typically struggle to maintain a set of complete, current, and trustworthy data. Because real estate operational data is messy and doesn’t benefit from commonly accepted standards, the seemingly simple and critical task of assembling baseline financial and operational data can consume significant time and effort. From direct experience working with several firms, asset managers commonly self-report that 20% to 40% of their time is spent either gathering data or manipulating it (at one firm, the number was 70%!!). Just to make that clear, in the asset management function alone, people spend one to two days per week (sometimes more) hunting, gathering, and manipulating data before they are able to spend a minute thinking about what that data means.

Now imagine a world in which asset managers don’t have to spend any time gathering data or thinking about whether it’s right or not. It just exists. And not only does it just exist, analysis is being performed on that data around the clock, seven days a week. That analysis is “looking” for potential signs of performance issues, for outliers with respect to individual expense categories, and leading performance indicators for a given market and property type. And when something looks interesting (as defined in the analysis), the results are presented to a person or a group of people for further evaluation. At first there will probably be annoying false alarms but sticking with it and incorporating feedback will ensure the results get better over time.

Again, the goal isn’t to replace human judgment and instincts, it’s to focus them. It’s to spend more time thinking, asking more and better questions, and ultimately making better decisions that drive greater returns.
 

GREAT, SO WHAT DO WE DO?

First (and this is going to be self-serving coming from a management consultant), don’t try to do it all yourself. There is plenty of expertise to engage in the marketplace. You don’t need to become an expert in data aggregation, advanced analytics, data visualization, data science, AI, and machine learning, you just need to have a point of view about what sorts of analysis would be valuable if it were available. In other words, you need to have an idea about what analytics would be valuable and actionable if it were available, and how those actions could drive alpha.

Next, all roads lead to Rome in the sense that any analysis being performed today and any advanced analysis you’d want to perform tomorrow needs to start with a good, complete set of consistent and trusted data. If you’re not clear on what that data is and where it comes from, a great exercise is to think about it and make sure, as an enterprise, that you are in agreement about what data you need and how it should be defined. Then write it down. And then communicate it to all of those responsible for creating that data  and providing it to you.
 
Finally, spend a little time dreaming. Too often I talk to managers who are focused on simply getting the basics done and performing reporting and analytics “the way it’s always been done.” They’re mired in limitations rather than on dreaming about how it should be. The managers who will likely first gain an advantage are those who no longer accept the way it’s always been done, but those who have a vision for how it should be.

At some point, track record and size is no longer going to carry the day for capital allocations. Managers who can’t efficiently gather data and who don’t know how to effectively put it to work will struggle to capture investment allocations. There will be a point at which investors will be asking more sophisticated questions about how managers use data to identify opportunities and risks, make better investment decisions and, ultimately, be good stewards of someone else’s investment dollars. My best advice is to be one of the firms driving change, rather than one trying to catch up.
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