Try out the QUEST Predictive Valuation Tool!

The draft version of the QUEST Tool is ready! QUEST has developed a predictive tool to help project developers and investors in buildings to estimate the value-impact of quality management services in building projects. Quality Management Services help you calculate the real energy performance of buildings and support you to assess the impact your investment will have on the energy performance and carbon footprint. QUEST evaluates the financial added value that various Quality Management Services can have on investments in your building projects.

Dr. Stefan Plesser, the project coordinator and Managing Director at synavision GmbH, explains the use and need for the tool in building projects:

"We have to make sure that investments into building projects lead to the sustainable results that the investors are looking for. For this we have developed a tool that helps building owners and asset managers to evaluate the added value of different quality management services, these are Technical Monitoring, Commissiniong Management and Green Building Certification like DGNB, LEED or BREEAM."

 

QUEST's value add calculation is based on data input by users, it does this by looking at the information that the user knows about the building project. The tool looks at the savings in operational expenditure and income that the quality management services can bring over a certain investment period. The current default value for the investments period in this draft version of the tool is 10 years - in the final version the users will be able to choose the investment period. 

Click here to try out the QUEST Predictive Valuation Tool!

 

During a webinar in March 2021 co-developer Cormac Ryan from COPILOT gave a detailed explanation on how the tool works.

 

The tool is currently still in draft version and the QUEST-consortium is further fine-tuning it to make it as user-friendly and practical for you to use. Have any feedback? Contact us at  and share it with us!

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This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Grant Agreement number 846739. The European Union is not liable for any use that may be made of the information contained in this document, which is merely represention the author's view.