In this paper a system is designed and implemented to predict the price of second-hand housing. This system based on Lambda architecture can execute prediction in both real-time and batch modes so it can give two kinds of different price predictions that reflect current and historical conditions respectively. The kNN related algorithms are used for price prediction. By comparing the performance of brute kNN, kd tree and ball tree, kd tree is selected as the price prediction model of the system. In system implementation the kd tree model is chosen to predict prices in both real-time and batch services. The kd tree model can also recommend housings to user besides price prediction. The experiment shows the effectiveness of our system. Time and space performance of brute kNN, kd tree and ball tree are compared by experiments. And the evaluation metrics of other available maching learning models are compared. The reason of choosing the kd tree model is also explained by the experimental results.