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Housing price prediction is of great significance in financial real estate investment and urban construction planning. Multiple linear regression models are commonly used for housing price prediction. However, traditional methods are mostly focused on the characteristics of the houses themselves, without or little considering the features of the surroundings. The features of the surroundings are also important for house price prediction. Motivated by these, we propose a geographic feature integrated multivariate linear regression method for house price prediction. Especially, the Zip Code is chosen as the additional geographic feature for its convenience to obtain. Then the integrated features are used to learn the multivariate linear regression model. We conduct an extensive experiment on the real-world case of the King County area and compare our method linear regressions. The results verified the effectiveness and superiority of our model.

A Geographic Feature Integrated Multivariate Linear Regression Method for House Price Prediction