As increasing numbers of regional and global scale building footprint datasets become available, users are presented with new challenges in source selection. The existing and computationally significant techniques required to morphologically match buildings between diverse methods in building detection becomes daunting when attempting to apply this methodology on the scale of hundreds of millions of buildings in an efficient manner. Rethinking this challenge, we propose a new methodological approach utilizing a global scale grid, from one-to-one or one-to-many challenges to one using statistical methods to compare various building sets over small areas. This new approach allows for choices between various sources based on user defined selection criteria with significantly reduced computational requirements and substantially accelerated decision making. Harnessing the common 30 arc second grid resolution found through numerous products, we propose a decision-making methodology at the grid cell level. This flexible methodology, utilizing statistical metrics of the objects in the cell can be applied to a variety of input data sources that are relevant to building modeling and presents the opportunity for comparisons on a common grid between input data sources with widely varying resolutions. With this framework, we can now leverage statistical comparisons to easily choose between footprint sources using global standards such the Global Human Settlement Layer (GHSL) or population models as a comparison point, make decisions about which building feature source is ideal for the specific problem at hand, or choose between different building height estimates. In this presentation, we will discuss the process of deriving this workflow, how we believe it can be applied, and some applied examples.