In 1997, Billy Beane, the former general manager of the Oakland A’s, applied Bill James’ sabermetric analytical concepts to the team’s scouting and player evaluation practices. This exercise, while not particularly complex, was innovative, and the outcome changed the game forever.
The need for this change was twofold. Prior to that time, baseball scouting used a criteria of player standards as old as the Major Leagues themselves, with very little variation from team to team. The criteria was heavily subjective, based on scouts’ visual cues such as body type, size, and “look”.
Additionally, there was an utter lack of curiosity around the best way to use the data and this riddled the evaluation with personal biases. Scouts recruited the same high school “phenom” who possessed “five tools”: strong hitting, hitting for power, running, fielding and throwing. For decades, baseball was built on a process passed down from the “good old boys” doctrine of baseball recruiting.
Today, there are still many industries that suffer from blinders put in place by convention. But a new generation of leaders in these established industries, like commercial real estate, are more comfortable abandoning “the way it has always been done” for a different perspective. Bill James, Billy Beane, and Michael Lewis’ bestselling book Moneyball renewed a curiosity with data that extends beyond the Major Leagues.
What does it mean to take the “Moneyball” approach?
Billy Beane’s “Moneyball” concept was extraordinary in its simple innovation. While working with the same data as his predecessors and his contemporaries, his approach to player evaluation through different metrics allowed Beane to value different player characteristics more highly than others. This allowed Oakland, a small market team with an even smaller budget, to allocate resources much more efficiently and productively than their competitors.
This “Moneyball” approach, which has been standardized in baseball for some time, has proliferated in other professional sports leagues, industries, and asset classes. In what we’ve aptly named the “Moneyball” era in commercial real estate, we believe broad digitization and structuring of building data, the adoption of AI/ML, and cloud-based data management will create the unencumbered way forward to better data, analytics, reporting and insights – resulting in better returns, lower costs to operate, and quicker high confidence decision-making.
Three benefits of adopting AI tech in CRE
While experience, market nuance, and industry knowledge will continue to be important, now is the time for commercial real estate professionals to adopt AI-based best practices and navigate the vertical market. Here are three benefits to adopting the “Moneyball” approach.
Increased availability & access to data
There’s a place and time for procedure, but with nearly 90% of organizations’ data classified as unstructured, according to Forbes, it seems many industries need to move away from conventional in-house data governance. As it applies to commercial real estate, today asset allocators and operators manually compile their own portfolio data or turn to expensive and commoditized “scraped” public market data to make critical decisions. This has led to years of siloed access to important assets as well as lopsided data sets with irrelevant and inaccurate information.
With a comprehensive digitized and structured data approach to portfolio management, commercial real estate professionals can easily abstract, share, and cross-reference trusted and sizable data sets. Professionals can then use that comprehensive data to impact the economic value of portfolio assets and make critical asset allocation and operating decisions.
Faster business intelligence
In addition to data access, AI commercial real estate tools can also help users reach insightful conclusions more quickly, leading to faster decision-making. Advanced analytical tools and techniques, such as machine learning and predictive modeling, expedite the process of gathering large amounts of data and effectively extracting valuable insights, and this can ultimately increase the speed of business-crucial decisions, giving firms that adopt this technology a competitive edge.
For an increasingly institutional-driven and highly competitive industry undergoing massive changes, the “status quo” means slow death with dramatically lower capital returns. Firms that embrace “Moneyball” concepts position themselves to generate above-market returns, leaving competitors who rely on siloed systems, “scraped” data, gut decisions, and slow, manual data synthesis.
This new “Moneyball” world leverages AI to centralize structured building data to speed up data-informed decision-making, lower costs by reducing manual service dependencies, allow critical systems to share data, and brings data transparency to an opaque operating environment.
Finding faster and better ways to manage asset data will only become more critical as commercial real estate continues to change. However, with the help of AI and a more holistic approach to data, the “Moneyball” method will give players of an ages’ old game an entirely new set of rules.