Evaluation of Rent Prediction Models using Floor Plan Images

Abstract

This study constructs rent prediction models with/without floor plan images in order to validate whether such images contribute the prediction accuracy. In addition, applications of PCA (principal component analysis) and convolutional neural network are considered as a feature extractor from floor plan images. The prediction accuracy is measured using properties of 90,000 rental housings in Tokyo. In the experimental results, the root mean squared error values of the prediction model with floor plan images and PCA tend to be higher than without floor plan images. This suggests that the use of floor plan images contributes to accuracy of rent prediction.