An integrated, data-driven Smart Real Estate life cycle benchmark as a foundation for innovative delivery models in the real estate sector

The project delivers direct benefits for research, practice, and education in the field of life cycle-oriented real estate and facility management. By systematically integrating and structuring existing datasets, it establishes a solid foundation for developing a Real Estate Life Cycle Benchmark that enables transparency and comparability across different properties, uses, and delivery models. In doing so, the project supports data-driven and holistic decision-making processes throughout the entire life cycle of real estate.

From a scientific perspective, the project contributes to the advancement of methodological approaches to life cycle-oriented evaluation and strengthens the positioning of IFM in the fields of life cycle management, benchmarking, and data-driven real estate analysis. At the same time, it creates a robust basis for further research and development projects as well as for competitive third-party funding proposals.

For practice, the project provides clear added value by offering owners, operators, and other stakeholders a structured decision-making foundation for the planning, operation, and further development of real estate. The results can also serve as an enabler for integrated delivery and evaluation models, thereby contributing to the professionalization and further development of existing processes.

Decisions across the real estate life cycle (from planning and construction to operation and further development) are often made based on fragmented data and isolated evaluation models. Different stakeholders, systems, and delivery logics lead to limited visibility of the relationships between quality, costs, sustainability, and operations (Bauen Digital Schweiz, 2022). Studies and practical observations show that it is not individual indicators or phases that determine real estate performance, but rather their integrated consideration across the entire life cycle (Hale et al., 2009). In particular, the lack of comparability and transparency between properties, uses, and delivery models makes well-informed decisions and the development of innovative delivery approaches more difficult (Pichler, 2023).

At IFM, extensive data from SGNI/IM, WPM, HM, and SBM provide valuable information across different life cycle phases and evaluation dimensions. However, these data are currently analyzed mostly in isolation and are not yet systematically used within an overarching life cycle context. The focus is therefore on examining how existing data can be integrated, harmonized, and structured into a robust Real Estate Life Cycle Benchmark. Evidence from research and practice shows that data-driven benchmarks can enhance transparency, improve comparability, and act as enablers for new, integrated delivery models (Apanaviciene et al., 2020). The aim is to make a measurable contribution to life cycle–oriented real estate evaluation and development through the structured use of existing data.

The objective of this seed project is the conceptual, methodological, and content-related preparation of a larger research and development project focused on life cycle–oriented real estate evaluation. The core task is to assess how existing datasets from SGNI/IM, WPM, HM, and SBM can be combined into a reliable Real Estate Life Cycle Benchmark through integration, harmonization, and structuring. The project aims to increase transparency and comparability across different properties, uses, and delivery models, thereby creating a solid foundation for life cycle–oriented decision-making. In this context, SGNI emerges as a potential practice partner, contributing expertise, access to relevant data, and a practical perspective to the development and validation of the benchmark.

Building on the results, a competitive research proposal (e.g., Innosuisse / Innocheque, SNSF, or foundations) will be prepared, addressing both scientific and practical questions. In the long term, the project aims to support integrated delivery and evaluation models in the real estate sector and to provide data-driven decision-making foundations for planning, operation, and further development of real estate.