Best practice, best fit, best model? Strategic configurations of corporate real estate management in Europe

Corporates increasingly realize the impact of real estate management on reaching their company targets. Yet, they face the challenge to set up (ìinstitutionalizeî) their CREM activities effectively and efficiently. Help has been given by several studies that researched on issues such as ìWhich targets are most relevant for CREM?î, ìWhich role should CREM play?î, ìWhich ownership rate is optimal?î, or ìWhich outsourcing degree should be pursued?îù that try to identify and communicate relevant best practices. But isolated best practices neglect that institutionalization should address the relevant set-up parameters holistically. A new study accomplished by the TU Darmstadt Real Estate Business Department in cooperation with Corpus Sireo Asset Management Commercial shows that there are some strategic trends in the institutionalization of CREM that can be seen as best practices. But more importantly it turns out that searching for a single ìbest modelî would be futile. Instead, parameters like regional and industry context, size, internationality, company strategy and control systems, hierarchical integration of CREM, etc. are identified that need to be configured in a way that ensures ìbest fitî. Unfortunately, the sheer number of theoretically possible configuration alternatives points out that comprehensive ìbest fitî models of CREM institutionalization will be company specific. On the other hand, some parameter constellations feature a better fit than others, thus giving first hints for strategically viable configurations of CREM. Summarized, the study identifies the relevant parameters to institutionalize CREM and organizes them in a ìCREM mapî. It results in hypotheses about the interaction between those parameters and their impact on corporate target achievement. Configuration theory (Miller/Friesen 1984) lays the base of the considerations. Empirically, in-depth interviews with large European corporates are analyzed with the methodology of qualitative data analysis (QDA).