A Standardization Method for Floor Plan Images of Rental Properties Utilizing Structural Relationships Between Rooms

Abstract

In recent years, machine learning has been increasingly applied to rental price estimation in the real estate domain. However, most existing studies are based on structured tabular data. In practice, floor plan images provide important visual cues often emphasized by prospective tenants. This study proposes a semantic segmentation approach to extract such features from floor plan images for rental price estimation. Specifically, we integrate terms into the loss function of a SegFormer-based model to predict room types that are semantically similar but mutually exclusive (e.g., bath, toilet, unit bath). These terms enforce class exclusivity during prediction and improve segmentation consistency across visually confusing room types.