Competing on (Real Estate) Analytics: Some Ideas for Anti-Geeks

Have you read Moneyball by Michael Lewis? It’s one of my favorite books because it tells an entertaining story about how an underdog, underfunded team like Oakland turned the baseball game around by using analytics to challenge conventional wisdom. They hired a fancy Harvard economist to figure out what REALLY mattered when it came to winning games. Based on rigorous statistical analysis they were able to hire unlikely players for less money who actually won more games. Their approach has since been copied by most major teams with great success, most notably Boston (who has the smarts AND the money to hire conventional stars, too.)

Why don’t we do a better job of analyzing our data in real estate? The dealmakers do a great job of understanding market trends but you’d be hard pressed to find many people who are doing deep, rigorous statistical analysis. That’s unfortunate but it also opens up a real opportunity for the early companies who will figure it out. They’ll have the competitive advantage over the dinosaurs who are still struggling to explain budget variances.

Budget variance analysis might tell you WHAT happened but you have to go deeper to find out WHY. You have to go even deeper to figure out what’s GOING to happen in the future. Here’s a simple graphic from the book Competing on Analytics, a terrific book by Thomas Davenport.


I’d venture a guess and say most of us are pretty comfortable in the bottom couple of categories and we all get less comfortable as we move towards the top. If you stop and think about it, though, there are some really compelling questions that can only be answered in those top four categories:

• How can we optimize our portfolio of buildings to drive down the total life cycle cost of real estate?
• How can we do that portfolio optimization but factor in changing labor market dynamics to understand the total cost for people and place?
• How can we understand the potential sales revenue in the market to understand the total VALUE of an optimized portfolio (lower costs, higher revenue at the optimal investment level)?
• How can we increase the efficiency of work order dispatch to factor in varying skill sets, pay rates, self-perform vs. outsource decisions, etc?
• How do we do a better job forecasting headcount?
• How can we model energy consumption to reduce our costs and carbon footprint?
There are lots of smart people and smart companies out there who can help us all answer those types of questions but first we need to be asking them. The companies that ask and answer those questions first will have a competitive advantage over the ones who don’t.