Urban Second-Hand Housing Price Evaluation System Based on Bayesian Probabilistic Model

Combining data mining technology into housing price evaluation problem has increased great attention in recently years because it improves the prediction accuracy. To facilitate the application, this paper builds an urban secondhand housing price evaluation system based on our Bayesian probabilistic model under location submarket division. Using urban data such as house location, surrounding environment and point of interest (POI) information, a prediction model is constructed based on the second-hand house transaction data crawled from the network. It helps users get the price as well as location, POI information and characteristic attributes of the target house, and query suitable houses meeting some given requirements. The system provides visual display of query results and evolves by using query results.