AI-powered floor plan analysis for feature extraction in automated valuation models

What the paper says

Purpose The purpose of this article is to test whether floor plan image segmentation can be used to improve automated valuation model (AVM) accuracy and whether image segmentation provides the opportunity to assess single aspects of property floor plans. Design/methodology/approach Using a dataset comprising floor plans of 5,498 apartments sold in Oslo, we estimate balcony sizes by image-segmenting the rooms in floor plans using our machine learning model FloorPlanNet and extracting the size of the balcony from the segmentation. We also extract balcony size using text recognition. Then we utilize two models for AVM estimation – hedonic regression (linear OLS) and the non-linear XGBoost model – before measuring feature importance using SHAP. Findings Our experiments show that including balcony size as a feature in AVMs enhances model performance. We also find that balcony size has a positive but diminishing impact on property price. Research limitations/implications Demonstrating that image segmentation can be used for valuation in AVMs opens up the possibility to value numerous other aspects of dwelling floor plans. Practical implications The use of floor plans in AVMs can provide a more objective valuation of single apartment floor plan aspects, giving architects and developers better insights into how homes should be designed. Originality/value To our knowledge, this is the first attempt to extract features for use in AVMs from floor plans.