Post-Pandemic Trends in Residential Space Design: An Analysis Using Deep Learning and Expert Evaluation

The COVID-19 pandemic has fundamentally transformed residential spaces, yet traditional survey-based approaches face limitations in objectively capturing these changes. This study investigates residential design trends in the Post-pandemic era, defined as the period in which pandemic-induced lifestyle changes have become institutionalized in everyday living environments. Residential interior images were collected from Pinterest and Instagram and analyzed using an image-based deep learning approach combined with expert evaluation. A pretrained convolutional neural network (ResNet50) was employed as a visual feature extractor to quantify three spatial attributes—openness and comfort, flexibility and diversity, and nature-friendliness—across four residential space types: balconies, living rooms, entrances, and bedrooms. The model-generated proportional scores were validated by experts and compared between pre-pandemic and post-pandemic periods. The results reveal dual transformation patterns of functional specialization and increased multifunctionality. Balconies evolved into well-being-oriented spaces with enhanced nature-related features, while living rooms emerged as multifunctional hubs with a substantial increase in spatial flexibility. In contrast, entrances exhibited reduced openness, functioning as hygienic buffer zones. These findings indicate a reconfiguration of spatial hierarchy in post-pandemic housing, where auxiliary spaces gain prominence and traditional primary spaces adopt flexible roles. This study demonstrates the value of image-based deep learning for objectively identifying residential design trends and provides practical implications for resilient housing design in the post-pandemic era.