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An intelligent automatic valuation system for real estate based on machine learning

The demand for intelligent automatic real estate evaluation system is growing rapidly with the growth of the economy and the promotion of real estate marketization. House price is affected by a lot of factors from every aspect, how to deal with the insufficient multi-dimensional data is a great challenge. Besides, with the development of the artificial intelligence, applying machine learning and deep learning technology in the real estate valuation field is becoming more and more a tendency. This paper proposes a high-accuracy real estate valuation system based on machine learning. Concretely, the system is composed of three parts: 1) the repeated record recognition model to solve the problem of repeated house records collected from different web crawlers. 2) feature extraction and quantification module to deal with various types of characteristic factors. 3) automated evaluation module based on machine learning. Specially, we use the ensemble learning to integrate different machine learning models in order to improve the model performance further. And the above three parts are integrated into the intelligent automatic valuation system for real estate. The experiments on our self-constructed real estate dataset of Hangzhou, China has shown the effectiveness and superiority of our model.