Image Classification of Real Estate Images with Transfer Learning

Abstract: Each minute, over 2 000 searches are made on Sweden’s largest real estate website. The site has over 20 000 apartments for sale in the Stockholm region alone. This makes the search-function a vital tool for the users to find their dream apartment, and thus the quality of the search-function is of significance. As of today, it’s only possible to filter and sort by meta-data such as number of rooms, living area, price, and location, but not on more complex attributes, such as balcony or fireplace. To prevent the need for manual categorization of objects on the market, one option could be to use images of the apartments as data-points in deep neural networks to automatically add rich attributes. This thesis aims to investigate if a high rate of success when classifying apartment images can be achieved using deep neural networks, specifically looking at the categories and attributes balcony, fireplace, as well as type of room. Different types of architectures was compared amongst each other and feature extraction was compared against fine-tuning, in order to exhaustively investigate the thesis. The investigation showed that the balcony model could determine if a balcony exists in an image, with a certainty of 98.1%. For fireplaces, the maximum certainty reached was 85.5%, which is significantly lower. The type-of-room classification reached a certainty of 97,9%. This all proves the possibility of using deep neural networks in order to classify and attribute real estate images.