Incorporating Interior Property Images for Predicting Housing Values

Abstract

The property valuation process for the real estate market is essential for predicting a fair market value. This process is traditionally carried out by brokers, including inspecting and assessing the subject property to find comparable sales for comparative market analysis (CMA). Meanwhile, an automated valuation model (AVM) can help achieve an autonomous version of this process, which speeds up the process but lacks some of the inputs that a manual assessment provides. AVMs have difficulty considering more subjective architectural qualities, such as beauty, stability, and utility, due to the difficulty of quantifying these aspects objectively. New advancements in Visual Transformers (ViT), self-supervised learning and Contrastive Language- Image Pre-training (CLIP) technologies have shown favourable improvements in the field of computer vision. Therefore, this study explores the potential improvements of these new techniques within the visual feature extraction task to enhance the AVMs from interior images. By applying ViTs as binary classifiers, clusters, and textual descriptions matching, we aim to enrich the feature extraction process for a property valuation model in the region of Uppsala County, Sweden. Our findings show modest enhancements in the AVM’s performance, which align with prior studies, but also highlight that these new technologies can extract more detailed features compared to previous methods. Furthermore, they demonstrate the potential for these technologies to capture more comprehensible architectural qualities from images, which could significantly assist brokers in the valuation process.