Abstract
House price prediction plays an important role in estate marketplace. Prediction future house price accurately can provide decision making support for home buyers. With the development of machine learning and AI technology, different machine learning models are proposed for house price prediction. However, the prediction accuracy is still not very high. Model ensembling is a very powerful technique to increase the accuracy of a variety of machine learning models. To address this issue, we propose a stacked generation model which consists of various regression models to predict house price. The experiment results show that the stacked model performs better than traditional machine learning models.
This research is supported in part by the National Natural Science Foundation of China under Grant No. 61571066, No. 61602054, (NSFC, 61571066, 61602054).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anifowose, F., Labadin, J., Abdulraheem, A.: Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines. Appl. Soft Comput. 26, 483–496 (2015)
Antipov, E.A., Pokryshevskaya, E.B.: Mass appraisal of residential apartments: an application of random forest for valuation and a cart-based approach for model diagnostics. Expert Syst. Appl. 39(2), 1772–1778 (2012)
Doumpos, M., Zopounidis, C.: Model combination for credit risk assessment: a stacked generalization approach. Ann. Oper. Res. 151(1), 289–306 (2007)
Gao, G., et al.: Location-centered house price prediction: a multi-task learning approach. arXiv preprint arXiv:1901.01774 (2019)
Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992)
Worzala, E., Lenk, M., Silva, A.: An exploration of neural networks and its application to real estate valuation. J. Real Estate Res. 10(2), 185–201 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Xiong, S., Sun, Q., Zhou, A. (2020). Improve the House Price Prediction Accuracy with a Stacked Generalization Ensemble Model. In: Hsu, CH., Kallel, S., Lan, KC., Zheng, Z. (eds) Internet of Vehicles. Technologies and Services Toward Smart Cities. IOV 2019. Lecture Notes in Computer Science(), vol 11894. Springer, Cham. https://doi.org/10.1007/978-3-030-38651-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-38651-1_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-38650-4
Online ISBN: 978-3-030-38651-1
eBook Packages: Computer ScienceComputer Science (R0)