2021 Volume 33 Issue 2 Pages 640-650
In order to evaluate effects of usage of floor-plan images (FPIs) in a rent prediction, we construct rent-prediction models with and without FPIs and compare them in terms of prediction error. Principal component analysis, Bag of Features (BoF), and Fisher Vector (FV) are used as feature extractors, and linear regression and LightGBM are used as a regressor. A rent prediction experiment with the LIFULL HOME’S dataset is performed. For the linear regression models, the experimental results suggest that usage of FPIs improves the means of prediction errors in some categories (combination of a prefecture and a floor-plan standard) and reduces the 95% confidence intervals of prediction errors for all categories. In addition, BoF achieves the best prediction accuracy in the three feature extractors for FPIs.
