Data and the Power of Aggregation

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By Mike Corr
Senior Vice President
Jones Lang LaSalle

Quality data and effective strategic real estate planning are inextricably linked: the success of one relies on the other.

Unfortunately, many organizations collect occupancy data haphazardly if at all. Good data is critical for building and maintaining credibility with the C-suite, enabling strategic decision-making, developing sound business cases for investment in real estate programs and initiatives, and for measuring, managing and reporting performance. The data you collect and maintain should link to your performance objectives and comprehensively cover supply, demand, availability, vacancy and space utilization in addition to suitability, total occupancy costs and pertinent geographical and local market factors.

Data should also adhere to the “3 A’s”:

Accessible: Can you quickly and easily attain complete and accurate data? Do you have ready access to benchmarks for comparing your real estate performance to peers within an industry, or type and size of portfolio? Does your data provide a historical perspective in addition to a future-view?

Accurate: Does your data offer visibility into your true vacancy—even shadow vacancy? Do you have accurate space, headcount and financial data? Is your information real-time and dynamic, or outdated and static?

Aggregated: Can data be easily aggregated to provide a dash board view of portfolio/real estate performance? Can you quickly and effectively model potential scenarios, manipulating individual factors to gauge and report potential impact?

The power of aggregation is the key concept underlying good judgment.  Francis Galton (cousin of Charles Darwin) identified the power of aggregation. At a county fair in England, Galton observed a mixture of “experts” (farmers and butchers) and ordinary people (doctors, shopkeepers, and others) making guesses about the weight of an ox. “Forecasters” bought a sixpenny ticket to record their guesses, and prizes were awarded to those whose guesses were most accurate.

After the guesses were reviewed and the winners announced, Galton reviewed the 787 legible tickets and found that the median (the guess of the “middle” participant, when all guesses were ordered from highest to lowest estimated weight) was only nine pounds (0.8%) off the actual 1,198-pound weight of the ox. In a March 1907 article in Nature, he called the median the “vox populi” estimate, and recommended that a similar approach be used for the decisions of juries. (Galton subsequently calculated the simple average or “mean” and discovered that it was within one pound of the true weight of the ox; nevertheless, he continued to advocate the median as the best estimator.)

What decision making process are you using, and who are your stakeholders creating the “median” for one of your most important business decisions?

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