Abstract
Nowadays it’s difficult for us to analyze the development law of real estate. What’s more, predictable house price and understandable key influences can also build a healthier real estate market. Therefore, we propose a model which can predict the house price, while it can find key influences which are important influences on the house price. Our method is inspired by the finite mixture model (FMM) and information gain ratio (IGO). Specifically, we collect data that includes detail information about houses and communities from Anjuke Inc. which is an online platform for house sales. Then, according to the data, we find the scope of latent groups number by cluster methods to avoid blind searching the number of latent groups. Next, we use IGO to rank the features and weight them and we build a regression model based on the finite mixture model. Finally, the experimental results demonstrate our method performance on predicting house price, and we find key influences on house price.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Besley, T., Mueller, H.: Estimating the peace dividend: the impact of violence on house prices in Northern Ireland. Am. Econ. Rev. 102(2), 810–33 (2012)
Chen, W., Wang, S., Long, G., Yao, L., Sheng, Q.Z., Li, X.: Dynamic illness severity prediction via multi-task RNNs for intensive care unit. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 917–922. IEEE (2018)
Chen, W., et al.: EEG-based motion intention recognition via multi-task RNNs. In: Proceedings of the 2018 SIAM International Conference on Data Mining, pp. 279–287. SIAM (2018)
Fu, Y., Xiong, H., Ge, Y., Yao, Z., Zheng, Y., Zhou, Z.-H.: Exploiting geographic dependencies for real estate appraisal: a mutual perspective of ranking and clustering. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1047–1056. ACM (2014)
Jang, H., Ahn, K., Kim, D., Song, Y.: Detection and prediction of house price bubbles: evidence from a new city. In: Shi, Y., et al. (eds.) ICCS 2018. LNCS, vol. 10862, pp. 782–795. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93713-7_76
Xu, X., et al.: Dr. right!: embedding-based adaptively-weighted mixture multi-classification model for finding right doctors with healthcare experience data. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 647–656. IEEE (2018)
Zhu, H., et al.: Days on market: measuring liquidity in real estate markets. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 393–402. ACM (2016)
Zhu, J., Brown, S., Pryce, G.B.: Immigration and house prices under various regional economic structures in england and wales. Urban Studies (Sage) (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, X. et al. (2019). Finding the Key Influences on the House Price by Finite Mixture Model Based on the Real Estate Data in Changchun. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_49
Download citation
DOI: https://doi.org/10.1007/978-3-030-18590-9_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18589-3
Online ISBN: 978-3-030-18590-9
eBook Packages: Computer ScienceComputer Science (R0)