Finding the Key Influences on the House Price by Finite Mixture Model based on the Real Estate Data in Changchun


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.

Meeting Name

24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 (2019: Apr. 22-25, Chiang Mai, Thailand)


Computer Science

Keywords and Phrases

Expectation Maximization Algorithm; Finite mixture model; Information gain ratio; Real estate

International Standard Book Number (ISBN)


International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version


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© 2019 Springer Verlag, All rights reserved.

Publication Date

01 Apr 2019