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How Brandon Taubman Utilizes “Moneyball” Strategies in Commercial Real Estate Investing

With over 15 years of experience spanning investment banking, executive sports management, and commercial real estate, Brandon Taubman is often tapped into by colleagues and professionals across industries for his expertise and opinions. The popular podcast Adventures in CRE is no exception, recently inviting Taubman onto the show as a spotlight guest to share his thoughts on analytics and how he has applied them to everything from derivative evaluation to fantasy sports and his time with the Houston Astros.

Hosts Sam Carlson and Spencer Burton also directed the conversation to topics in how a money ball mentality can be applied to commercial real estate investment, which Taubman currently incorporates in his work as the CIO for investment firm Stablewood Properties.

Brandon Taubman on Projecting Value Amid Volatility

While the economics of valuation and volatility risk assessments can be complex in their own right, Brandon Taubman demystified the notion that the concepts are markedly different depending on the topic of industry. He noted that regardless of the field, investors and planners can look for projectable value. This can be seen as cashflow in finance as well as in metrics such as WAR (wins above replacement) for baseball players.

Understanding risks can be less straightforward to pinpoint, and Taubman shared that this is the least strong within commercial real estate firms, which often lack the technology or analytics to back their investment decisions. Taubman shared that aggregated data, whether looking at the stock market or professional baseball players, can present historic volatility and allow for more accurate performance projections, safeguarding investments against foreseeable pitfalls.

Commercial real estate firms run into a disadvantage in this regard since few, if any, have compiled or processed data in large enough amounts to identify trends in risk and reward. “In real estate, we’re talking about like broader generalizations of the risk profiles, but we’re often not getting down to the level of standard deviations of risk,” explained Taubman.

Playing Ball with Real Estate: How Brandon Taubman Brings Moneyball to CRE

Brandon Taubman has been a lifelong fan of the sport of baseball, as shown from his early years attending games with his family to his passion for fantasy sports. His unique approach of applying data analytics to fantasy baseball performance caught the eye of more data-focused teams and managers in Major League Baseball, ultimately leading to the opportunity for him to work for the Houston Astros. There, he was part of the team that overhauled the way the Astros cultivated their talent pool by relying more on data and projections than instinct or recent performance.

This mentality of using historical data for performance projections was popularized by the tactics, book, and movie Moneyball, which focused on the success of GM Billy Beane with the Oakland A’s. Baseball, unlike commercial real estate, has the advantage of deep pockets of historic data to pull from, allowing leadership to get precise with their measures, skills, and risk assessment. Taubman explained the basic premise of this Moneyball approach: “Let’s look at how players have performed historically and see if we can understand what that means about how they will perform in the future.”

Of course, data science cannot deliver all of the answers, which is what makes coaches and management teams indispensable at the major league level. The same is true of real estate firms that have begun to rely on technology and data analysis to drive their investment plans. A blend of market understanding, and savvy are needed alongside clear analytical projections to make well-balanced investment decisions. This approach is applied at Stablewood Properties, where Taubman helps lead the charge on leveraging technology and analytics to better direct decision making.

Overcoming Barriers to Data Science with Commercial Real Estate Investment

According to Taubman, the largest barrier to applying data science principles to large scale commercial real estate investment is the sheer lack of data there is to work with in the first place. “In real estate, there’s very little data relatively speaking, and it’s highly fragmented. Even if it’s out there, it’s hard to get your hands on it.” Despite this challenge, Taubman was eager to become part of the growing trend to aggregate and analyze data in the industry.

Traditionally, acquisitions teams and underwriters have made decisions based on budgets, site visits, and minimal data to rely on. Taubman argued that this should, and can, change by implementing systems, SaaS, and available vendor data sets that can help investors understand opportunities with more clarity. “At Stablewood, we’re trying to first apply as much data as we can to understand the investability of the property.”

This aligns with the Moneyball concept in its reliance on collecting and reviewing historic data about similar situations and properties. As a general manager would review a potential pitcher based on similar left handers in a similar climate, age bracket, and background, so can a real estate investor assess a property by comparing it to historic comparisons. Despite his advocacy for data, Taubman does not shy away from the importance of subject matter experts in the decision-making process, “Where is the data not so sharp or not so complete, we would be foolish to rely on it,” said Taubman, also noting that this is where they bring in their people with expertise in the areas needed.

For firms starting to explore how to apply more technical skills to their investment strategies, Taubman recommended exploring different methods and outcomes available through learning Python, a programming language known for its data structures. “There’s so much that you can do in Python,” explained Taubman, including “machine learning, software development, data engineering.” All of these techniques can help an individual, or an investment firm, stand out in the market and make more data-centric decisions to position themselves for greater long-term growth and success. In the end, Taubman explained, “What we need to do is be calculated in the way that we assign premiums and discounts to a given investment opportunity, whether it’s real estate or a baseball player.”


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