Digital Building Lifecycle: The Data-Driven Feasibility Study

Looking ahead, we at the Microsoft real estate group look forward to wider implementation of an emerging new approach to building and construction that uses technology to improve every aspect of the process. As we wrote earlier this year in this space, this Digital Building Lifecycle approach is a method of deploying data to deliver built environments with higher total performance at lower total cost. It is a method which takes the long view of a building’s lifecycle, thus enabling efficiencies, proactive maintenance and predictive operations, and maximized investments—through data. Technologies help to make this possible.

Phase one of the Digital Building Lifecycle approach is the feasibility study, which uses data to drive reliable decision making about investments. Feasibility studies and pro-formas identify the viability of the real estate development project. They take into account the key performance indicators and other factors that inform that investment decision, influencing the potential owner’s decision to build, perform tenant improvements, retrofit, or renovate a building. This is where bottom-line decisions on cost are set, design objectives are defined—and the ultimate “go or no-go” investment determination can be made. 

Traditionally, feasibility studies have been drafted based on estimates and assumptions. As the real estate group for Microsoft, we use Power BI, Microsoft Dynamic, or Digital Twins to enable projections based on much firmer data. The amount of reliable structured visual data that is available to designers is expanding rapidly, which makes the new Digital Building Lifecycle feasibility study a viable option. When a feasibility study is data driven, building owners can manage financial risk, by laying out the total costs during the entire lifecycle of the building, and allowing investors and building operators to make the most informed decision about their investments. This will become even more important as buildings become smarter, with a wide spectrum of building materials and integrated systems.

Let’s say a developer wants to develop a building and hold it for ten years, with a return on investment (ROI) expectation of 10 percent the first year, then 20 percent going forward. To accurately evaluate this expectation, a developer needs an accessible and democratic data environment to understand how future workplace needs are evolving, expected real estate development rates for the region, and so on.

From there the developer can extrapolate whether their investment decision makes financial sense in the current climate. If the city introduces new regulations—mandating universal design for accessibility and inclusivity, for instance, or net zero building standards for sustainability—having that data readily available enables the developer to layer it in and process it into different forms of information, to determine whether the project is still feasible within the budget projection and timeframe. 

And here’s the game-changing part: Precise data allows that developer to not just protect but maximize their investment, by ensuring that the building is precisely tailored to its market and its location. Say a developer usually builds ten-story Class A office towers with retail on the ground floor. For a new building, if the developer is using a cloud technology like Microsoft Azure, the developer can pull the data off Azure for that specific project, plug in the data from the site being proposed, then—using the interactive dashboards enabled through a technology like Microsoft’s Power BI business analytics service—analyze what it will cost to build and operate that building type in that place.

If the developer finds that, say, zoning for the area allows for taller construction, and GIS data reveals that the area is not populated enough to support ground-floor retail, the developer is empowered to make the data-driven decision to change course. Now the developer can analyze the cost of a 12-story building with office amenities on the ground floor instead—a decision that might turn out to be much more cost-effective. And data made the difference. 

What kinds of data are we talking about? The kind many companies are filling data warehouses with. Companies collect all kinds of data on aspects of their built environment, from energy efficiency statistics to usage data on various parts of their building to measurements of employee satisfaction in a space. All of this can be stored and integrated on a cloud platform like Azure. Cities, counties, and government officials often provide open data sets on socio-economic climate, transportation streams, zoning code requirements, and growth projections for the region. Developers can use current and historic data on base rents and turnover vacancy for the area. When the Azure Machine Learning Service is applied, more accurate predictions can be made about future rent and vacancy rates for a given property.

The technology to support data-based feasibility studies is ready, and our real estate group is deploying it toward the design and construction of more efficient built environments. We look forward to a future where more and more owners and developers adopt the Digital Building Lifecycle approach—leveraging the technologies to deploy data toward better decision making, and moving the entire industry toward a more efficient, collaborative, and accurate way of doing business. #

Article #1: The Digital Building Lifecycle: Taking the Long View

Article #2: Digital Building Lifecycle: The Data-Driven Feasibility Study

Article #3: Digital Building Lifecycle: How Technology can Transform Schematic Design