Style mixing houses: The role of high- and low-level visual features in human house facade evaluation

The influence of architectural design on well-being is increasingly recognised, underscoring the need to understand how humans evaluate their surroundings. This study examines the impact of low-level visual features and high-level architectural elements in images of house facades generated by a generative adversarial network (GAN). We introduce a novel method to create controlled image datasets using the style mixing component of StyleGAN2-ADA, enabling the combination of specific architectural features with styles representing low-level image properties, such as brightness or colour. Our dataset consists of 900 images encompassing five high-level features (house size, garage door visibility, number of windows, entrance door visibility, and roof shape) and 16 low-level visual features. These GAN-generated images were rated in an online experiment by 303 participants on six dimensions: facelikeness, hominess, relaxation, invitingness, safety, and price. Linear mixed-effects models identified significant predictors, including the number of floors as a high-level feature and green pixel percentage, saturation (SD), brightness (SD), and contrast as low-level features. This indicated that houses with two floors and images with an overall higher amount of green pixel, as well as diversity of saturation and brightness and a lower contrast were rated as, e.g., more inviting and safer. The analysis showed that high- and low-level features explained up to 54 % of the variance in house price ratings, with high-level features accounting for the larger share and highlighting their relevance for house facade evaluations. This work advances the use of GANs and offers a method to modify visual elements that shape architectural evaluations.