In work exhibited at the University of Toronto in the context of the 2018 Smart Geometry Conference, three-dimensional architectural massings for single-family homes are generated by a generative adversarial network. This GAN is trained on a small dataset of three-dimensional models of homes falling into seventeen architectural styles, and that are represented as multi-view heightfield images.