Demand Forecasting

Journal of Corporate Real Estate Volume 2 Number 2

Strategies for demand forecasting in corporate real estate portfolio management

Martha A. O'Mara Received (in revised form): 15th December, 1999

Harvard Design School Office, 12 Maple Avenue, Cambridge, MA 02139, USA: Tel: 1 (617) 868 5221 begin_of_the_skype_highlighting 1 (617) 868 5221 end_of_the_skype_highlighting; Fax: 1 (617) 868 5229; e-mail: maomara@mediaone.net Martha A. O'Mara PhD is the author of 'Strategy and Place: Managing Corporate Real Estate and Facilities for Competitive Advantage'.1 A lecturer in executive education at the Harvard Design School, where she taught real estate for eight years, Dr O'Mara is also a member of the core faculty of the Institute for Corporate Real Estate. She lectures and consults widely on corporate real estate strategy, planning and management development.

requires an equal amount of effort towards managing demand for that inventory. Balancing between the demand for and the supply of corporate space is at the heart of the fundamental corporate real estate planning challenge: 'The need to make real estate commitments which often require physical and financial obligations far into the future, is at odds with the highly turbulent competitive environments many companies operate in today.'3 This 'structuring dilemma' is grounded in the uncertainty of a company's external strategic environment, as well as in the ways the company's corporate real estate decision-making processes respond to uncertainty. Forecasting for facility needs, particularly for administrative and office space, is part of the overall business forecasting process for corporate infrastructure, which also includes human resources and information technology. However, corporate real estate differs in two key ways: personnel can be readily hired or fired and technology may be outsourced or acquired off the shelf, while physical space has the longest lead time for acquisition and for disposal. With the greater use of virtual technologies, increased globalisation and new forms of organising, the planning of the entire corporate infrastructure must be integrated. This paper describes the findings of a

ABSTRACT Planning for future real estate and facility needs in a highly uncertain competitive environment can benefit from a four-stage process of demand forecasting. Based upon research conducted within the Corporate Real Estate Portfolio Alliance2 and a review of general business forecasting techniques, each successive stage relies on more abstract data and increased dialogue about business strategy with the lines of business as uncertainty about the future increases. Each stage requires increasing flexibility in the supply of real estate as the range of probabilities around the forecast widens.

Keywords: portfolio management, strategy, demand forecasting, strategic planning, uncertainty, competitive advantage

INTRODUCTION Managing the supply of corporate real estate inventory, both owned and leased,

Journal of Corporate Real Estate Vol. 2 No. 2, 2000, pp. 123-137. Henry Stewart Publications, 1463-001X

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Strategies for demand forecasting in CRE portfolio management

research effort on demand forecasting that was part of the Corporate Real Estate Portfolio Alliance. (The overall Alliance research effort is described in Thomas Bomba's paper in this issue.) The goals of this portion of the Alliance research were threefold: to understand better how corporate real estate demand forecasting for office space is accomplished at a wide range of organisations; to compare these methods with the current thinking in business forecasting; and to develop a model of the demand forecasting process which has practical application to the Corporate Real Estate profession. At each participating company, the overall demand forecasting process was reviewed and documented. Practices were compared, presented and discussed among the Alliance participants. A preliminary model was then proposed, reviewed and refined, based upon discussions with the Alliance participants. The resulting 'four-stage' approach to corporate real estate demand forecasting described in this paper provides a basis to compare forecasting methods across organisations. The model is linked to strategic uncertainty and identifies those forecasting methods most appropriate for particular business conditions. Companies can compare their current methods to those outlined in this paper, discover opportunities for improvement in their own processes, and prioritise where their additional forecasting efforts should be best directed. To provide an example of the model in action, the forecasting practices of two of the participants, Sun Microsystems and Florida Power and Light, are reviewed and compared to the four-stage model. It should be noted here that a starting point for effective demand forecasting is a thorough knowledge of the current inventory of facilities and real estate. This is often not as easy as it sounds: a company may have acquired its real estate piecemeal within the businesses, or have

legacy space from acquired companies. There may be difficulty in tracking occupancy if the churn rate is high or the portfolio is geographically dispersed. Space should also be tracked by its ownership and leasing structure, especially lease expiration or rollover dates. To model scenarios, the database must be easy to access and manipulate. At many companies, the corporate real estate information system either is out of date or lacks compatibility with other corporate MIS systems.

LESSONS FROM BUSINESS FORECASTING The difficulty of predicting a company's future need for real estate shares many characteristics with the overall challenge of forecasting future business conditions. Forecasting is an increasingly difficult problem for many companies operating in fast-changing industries. The first step in the Alliance effort was to review the current thinking in overall business forecasting and see what lessons might be applied to corporate real estate demand forecasting. An early discussion of classic forecasting methods distinguishes between a deterministic forecasting strategy, where the present has a close relationship with the future; a symptomatic strategy, where there are some future indicators which can be used as a sort of barometer of probable future outcomes; and a systematic approach, which tries to find the underlying regularities of demand even when