![]() 10.Freund, Y., et al.: Experiments with a new boosting algorithm.9.Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding.8.Coleman, W., Johann, B., Pasternak, N., Vellayan, J., Foutz, N., Shakeri, H.: Using machine learning to evaluate real estate prices using location big data.In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 7.Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system.Chen M Liu Y Arribas-Bel D Singleton A Assessing the value of user-generated images of urban surroundings for house price estimation Landsc. 3.Bird, S., Klein, E., Loper, E.: Natural language processing with python: analyzing text with the natural language toolkit.Altmann A Toloşi L Sander O Lengauer T Permutation importance: a corrected feature importance measure Bioinformatics 2010 26 10 1340 1347 10.1093/bioinformatics/btq134 Google Scholar Digital Library (eds.) Proceedings of the 8th International Joint Conference on Computational Intelligence, IJCCI, vol. 1.Ahmed, E.H., Moustafa, M.: House price estimation from visual and textual features.Overall, we shed some light on how textual features can be leveraged by the models, explaining the paths that lead to predictions that end up resulting in performance gains. Our experiments explore different combinations of learning algorithms and methods to extract relevant information from textual descriptions, with some surprising conclusions regarding the best combination of approaches. In this work, we show that the usage of textual data can significantly increase the performance of house price-prediction models. To support decision making, an AVM needs to “look” for the same type of information a person would when valuating a property, including photos and textual descriptions. ![]() ![]() However, most of those models base their estimates only on geographic location and structural characteristics of the property, disregarding several factors that influence prices, such as the need for repairs and sun exposure. Real estate valuation has been vastly studied by the research community, with several articles proposing Automated Valuation Models (AVM).
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