The purpose of this project is to predict the housing price with the data of nearby houses. The housing data consists of latitude, longitude, closing date as well as closing price. A Gaussian-kernel based KNN algorithm was implemented. Additionally, an inverse kernel and average kernel were also integrated to compare to the performance of the Gaussian-kernel. The performance was gauged with root median square error(RMSE). The effects of different types of kernels as well as the value of k have been evaluated based on the code.