Visualizing the Importance of Floor-Plan Image Features in Rent-Prediction Models

This study proposes a rent-prediction model that allows users to understand which parts of a floor-plan image (FPI) significantly affect a rent prediction. Principal Component Analysis (PCA) and Bag of Features (BoF) are used to extract feature vectors from an FPI. The importance of each feature vector, derived from the FPI, is measured and visualized based on 100,000 rental properties sampled from the LIFULL HOME'S dataset. The experimental result suggests that the wall and orientation symbol drawn on an FPI contribute to the rent prediction for both PCA and BoF, although BoF provides a better understanding of which parts of an FPI significantly affect the rent prediction.