Incremental informational value of floorplans for rent price prediction

Abstract

This report examines whether a consideration of floorplan images of real-estate apartments can effectively improve real-estate rental price predictions. We use a modern computer vision technique to predict the rental price of apartments using the floorplan of the apartment exclusively. Afterward, we use these predictions combined with a more traditional hedonic pricing method to see whether its predictions improved. We found that by including the predictions, we were able to increase the accuracy of the predictions from an R² of 0.915 to an R² of 0.945. This improvement suggests that floorplans contain considerable information about rent prices, not captured in the other explanatory variables used. Further investigation, including more explanatory variables about the apartment itself, could be used in future research to further examine the price structure of real estate and better understand consumer behavior.