If we are to learn from the experience of major league sports, first, we have to develop the information systems to find out what it tells us about where to invest our time and efforts.
In an article in The Wall Street Journal on December 20, it featured the use of data and analytics, and when it first hit big-league sports, with the Oakland Athletics baseball team in the early 2000s, it took a long time for it to catch on. Now it’s everywhere and has completely changed how games are played.
The article entitled, ‘The Decade When Numbers Broke Sports,’ lays out just how much numbers now guide the actual playing of the game, whether baseball, football or basketball, and the recruitment, retention, and development of the players of the games. How, for instance, the percentage of offensive snaps taken behind the center versus in shotgun formation has shifted to more shotgun and less direct snaps, because passing turns out to be more effective than running. Or, how three-point shots, which now dominate the NBA, are more effective than two-point shots, so more players are taking three-point shots. Or, how in baseball, you see less bunting and base stealing, because home runs prove to be more effective at scoring runs.
Billy Beane, the general manager of Moneyball fame, said in the article, “the fact that this happened is not a surprise at all. Initially, it took longer than I expected. But once it gained momentum, it went faster than I would have ever expected.” The article went on to opine that “the 2000s were a time for the most popular American sports leagues to recognize the power of data. The 2010s were about implementing those analytics and letting numbers dictate strategy. It was an inevitable progression–for better and worse.”
What About Brokerage?
We’ve seen AVM increase over the past 10 to 15 years. The Zestimate wasn’t the first, but it became the most widely known. There are numerous others. The iBuying companies are using this data to purchase more efficiently and sell more effectively, which is an essential part of their value proposition.
Recently, we see Keller Williams, RE/MAX, and others building out massive data platforms that will capture and analyze large databases of consumer information to provide the opportunity for agents to market their services more efficiently. Companies like Redfin, Zillow, and Realtor.com have been doing this for years, and presumably, they are making good use of the data they have to target consumers more efficiently.
The next area is data about agent behavior so that brokerage firms can develop profiles of them in an attempt to discern the likelihood that they will succeed in real estate brokerage or whether they are prone to move from one brokerage to another. Companies, such as Terradatum, Trendgraphix, and Real Data Strategies, offer tools to give some insights already. They are now joined by firms like Relitix, which is going even deeper in the use of intelligent systems to provide even more granular advice and targeting than ever before in these two areas. We also know that some of the national brands are looking at this kind of data closely.
If we are to learn from the experience of major league sports, first, we have to develop the information systems to find out what it tells us about where to invest our time and efforts. Then, those who use this learning will use it to gain an advantage over those who don’t. Then, and only then, will the new practices and tactics become embedded in the industry’s fabric.
Where Are We in This Evolution?
It seems to us that we are at the very front end of this development, and that assumes that leading brokerage firms embrace data as a way to recruit, develop, and retain their agents. While we don’t see a market for trading agents and teams as in professional sports, we do understand that leading firms will begin to use data to know better when to do so through specific commission policies and other related practices.
Further, we know from work with a client that the use of data to identify the most likely potential recruiting opportunities and specific data on retention does work. Once these systems become more useful, we expect the outcome to be similar to that of major league sports—widely used. And, they’ll have a significant impact on how the business is managed.
Final Thought About Big Data
Among all the changes and challenges to brokerage these days, there are two that are profoundly meaningful.
• Agent turnover: The increasing churn of agents and their movement from one firm to another.
• Bifurcation of the agent ranks:
The increased concentration of production from top producers and teams and the increased ranks of lower-producing agents.
What happens when more of the former, realizing they may not become large producers, turn towards the
lowest-cost brokerage options, of which there are more today than ever before?
What happens when the top producers retain most of the profit in their relationship with their brokerage firm?
What happens when the industry becomes an unbundled industry where increasing numbers of agents want to pay a base fee and then ala carte the rest?
Lastly, what do we do with data that suggests that lower-producing agents may see a lowering of their costs but also a lowering of their sales when they move to a lower-cost brokerage option and thereby increase overall industry turnover? Will we see the departure of relatively new agents when they become discouraged about their real estate careers?
We think these are some of the most significant issues for brokerage heading into 2020.
