Abstract
Currently, the state of a house is typically assessed by an expert, which is time and resource intensive. Therefore, an automatic assessment could have economic, social and ecological benefits. Hence, this study presents a binary classification model using transfer learning to classify Google Street View images of houses. For this purpose, a three-by-three analysis is conducted that allows to compare three different network architectures and three differently-sized data sets, using properties located in Leuven, Belgium. A DenseNet201 architecture was found to work best, as illustrated quantitatively as well as by means of state-of-the-art explainability methods.
This work was co-sponsored by a research chair (“Real Estate Analytics”) granted by the Royal Federation of Belgian Notaries (Fednot).
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Geerts, M., Shaikh, K., De Weerdt, J., Broucke, S.V. (2022). Predicting the State of a House Using Google Street View. In: Guizzardi, R., Ralyté, J., Franch, X. (eds) Research Challenges in Information Science. RCIS 2022. Lecture Notes in Business Information Processing, vol 446. Springer, Cham. https://doi.org/10.1007/978-3-031-05760-1_46
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