Abstract
Abstract
House price prediction is an important problem that could benefit home buyers and sellers. Traditional models for house price prediction use numerical attributes such as the number of rooms but disregard the house description text. The recent developments in text processing suggest these can be valuable attributes, which motivated us to use house descriptions. This paper focuses on the house asking/advertising price and studies the impact of using house description texts to predict the final house price. To achieve this, we collected a large and diverse set of attributes on house postings, including the house advertising price. Then, we compare the performance of three scenarios: using only the house description, only numeric attributes, or both. We processed the description text through three word embedding techniques: TF-IDF, Word2Vec, and BERT. Four regression algorithms are trained using only textual data, non-textual data, or both. Our results show that by using exclusively the description data with Word2Vec and a Deep Learning model, we can achieve good performance. However, the best overall performance is obtained when using both textual and non-textual features. An
$R^2$
of 0.7904 is achieved by the deep learning model using only description data on the testing data. This clearly indicates that using the house description text alone is a strong predictor for the house price. However, when observing the RMSE on the test data, the best model was gradient boosting using both numeric and description data. Overall, we observe that combining the textual and non-textual features improves the learned model and provides performance benefits when compared against using only one of the feature types. We also provide a freely available application for house price prediction, which is solely based on a house text description and uses our final developed model with Word2Vec and Deep Learning to predict the house price.
Publisher
Cambridge University Press (CUP)
Subject
Artificial Intelligence,Linguistics and Language,Language and Linguistics,Software
Reference33 articles.
1. Rehurek, R. and Sojka, P. (2010). Software framework for topic modelling with large corpora. In In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, Citeseer.
2. An Intelligent System for Identifying Influential Words in Real-Estate Classifieds
3. Greedy function approximation: A gradient boosting machine.
4. A suggestion for using powerful and informative tests of normality;D’agostino;The American Statistician,1990
5. Chen, X. , Wei, L. and Xu, J. (2017). House price prediction using LSTM. arXiv preprint arXiv:1709.08432.
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献