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The purpose of this paper is to identify the variables that influence corporate real estate (CRE) decision-making and gauge their relative importance to each other, thereby understanding the consequent challenges/implications for CRE managers (CREM’s).

Interviews were undertaken with experienced CREM’s using the causal network elicitation technique to create decision networks for the variables they considered for the specifically defined scenario: dealing with surplus property from a change of business strategy. These networks illustrate the complexity of the mental representations required for the realignment of the CRE portfolio. The key variables are more extensive than alignment theory suggests, namely, financial stakeholders. Additional variables identified include risk, lease accounting, costs, financial analysis, business metrics and motivational drivers. The latter indicates the importance of self-esteem and peer recognition for CREM’s and financial benefits for the C-suite. Accordingly strategy alignment needs to incorporate CRE both in terms of strategy creation and implementation.

These networks illustrate the complexity of the mental representations required for the realignment of the CRE portfolio. The key variables are more extensive than alignment theory suggests, namely, financial stakeholders. Additional variables identified include risk, lease accounting, costs, financial analysis, business metrics and motivational drivers. The latter indicates the importance of self-esteem and peer recognition for CREM’s and financial benefits for the C-suite. Accordingly, strategy alignment needs to incorporate CRE both in terms of strategy creation and implementation.

This research appears to be the first that looks in detail at the mental representations used by decision-makers while making CRE decisions.

This paper aims to identify the importance of individual variables in the corporate real estate (CRE) decision-making process.

Nine experts received a posed scenario of a changed business strategy requiring a CRE reduction in individual interviews. Based on their suggested response, a decision network was modelled for each expert using the causal network elicitation technique, incorporating the utilities for decision variables and importance weights for attributes and benefits. The decision model offers a graphical representation of decision-benefit links for the decisions CRE managers make in such a period of decline.

Perceived facilitators of CRE dynamic alignment were identified by calculating lift ratios on their perceived importance of the attributes they mentioned during the interviews as nodes in the network that link decisions to benefits. Facilitators included CRE metrics and workplace strategy, while capital expenditure and landlords inhibit alignment processes. The research provides more granular insight into the variables used in CRE decision-making and the factors that facilitate or inhibit the dynamic alignment process.

The research set a specific scenario for the experts to consider. That could be regarded as small but there was clear evidence of saturation of expert knowledge. Additional face-to-face interviews with the experts may have generated further details on the thought processes of the experts.

The research provides more granular insight into the variables used in CRE decision-making and the factors that facilitate or inhibit the dynamic alignment process. Thereby providing CRE decision-makers with key elements for a decision model.

The research technique, causal network elicitation technique, uses semi-structured interviews to create decision networks, which is a technique that has not been widely applied to CRE research. The research provides a granular view of what are important inhibitors or facilitators of dynamic alignment of CRE to business strategy.