The articles’ central themes proved to be broadly accurate. We are creating more data than ever before; by 2020, more data will be created in a single day than in all of 2010. Employers across a variety of industries are hiring data scientists in record numbers to process and make sense of the massive volumes of data being generated by their operations.
But “Data Science” has been around much longer than recent buzz suggests. First with regression analysis and then econometrics—an indispensable and sizeable part of the Machine-Learning (ML) universe—the original data scientists, economists, have been at the table for decades. And at its founding as Torto Wheaton Research back in 1982, CBRE Econometric Advisors (CBRE EA) was almost certainly the first data science shop specific to commercial real estate (CRE).
Today, as the quality of the data we analyze improves and its quantity increases, at CBRE EA, we are expanding the toolkit of ML techniques that we use to drive insight about asset performance and advise our clients.
Our Live Work Play Index (LWP) is a recent example. Using sophisticated ML techniques, CBRE EA is able to spot pockets of potential investment opportunity in markets across the country by getting far more granular and specific than does traditional submarket-level analysis. Below is a link to a presentation given at ULI’s Fall 2017 meeting in Los Angeles. It provides an overview of big data in CRE, and examples of how LWP can be used to find pockets where assets are likely to outperform, as well as how to leverage LWP to spot up-and-coming areas for investment and development.