Using real estate occupancy data to reduce costs, mitigate risks and meet business needs

Jess Joaquin Johnson, Ann Duncan and Steven Adams

Corporate Real Estate Journal, 2011, vol. 2, issue 1, 28-42

Abstract: Confronting a dynamic business environment in which all expense-related decisions are under increased scrutiny, innovative CRE managers are collecting data on the actual use of real estate to improve decision-making, measure the performance of real estate assets and identify opportunities to reduce costs. However, translating this data into actionable knowledge requires sound methods of collection and analysis in order to build credibility with stakeholders throughout the organisation and in the C-suite.Through the collaboration between Vertical Integration, Inc, and PricewaterhouseCoopers LLP (PwC), this paper illustrates how big data can be effectively leveraged to reduce real estate costs, ensure real estate resources meet business needs, and mitigate operational risks. Utilising methods such as regression analysis, seasonality adjustments, and Monte Carlo modelling, a data-driven, fact-based approach is applied to corporate real estate's most common challenges, including supporting alternative work strategies, accommodating employees acquired through corporate acquisitions, and supporting the dynamic needs of an increasingly mobile workforce.

Keywords: occupancy; demand; occupancy data; portfolio optimisation; actual use data; Monte Carlo (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2011
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