Rent Prediction Models with Floor Plan Images

A rent prediction model with floor plan image (FPI) is proposed in order to evaluate that the image contributes to rent prediction accuracy. The proposed model calculates image feature vectors from FPIs using principal component analysis, combines them and attribute vectors, and applies a regressor. Mean squared errors (MSEs) between predicted and ground truth rents of the proposal are measured using 1,089,090 rental properties of the LIFULL HOME'S dataset. The experimental result suggests that the proposed model with linear regression and FPI slightly improves MSEs compared with the without FPI models; however, the differences are not significant and it is difficult to find merits to use FPIs for rent prediction model.