The　real　estate　industry　places　key　influence　on　almost　every　aspect　of　social　economy　given　its　great　financing　capacity　and　prolonged　upstream　and　downstream　industry　chain.　Therefore,　predicting　housing　prices　is　regarded　as　an　emerging　topic　in　the　recent　decades.　Hedonic　Regression　and　Machine　Learning　Algorithms　are　two　main　methods　in　this　field.　This　study　aims　to　explore　the　important　explanatory　features　and　determine　an　accurate　mechanism　to　implement　spatial　prediction　of　housing　prices　in　Beijing　by　incorporating　a　list　of　machine　learning　techniques,　including　XGBoost,　linear　regression,　Random　Forest　Regression,　Ridge　and　Lasso　Model,　bagging　and　boosting,　based　on　the　housing　price　and　features　data　in　Beijing,　China.　Our　result　shows　that　compared　to　traditional　hedonic　method,　machine　learning　methods　demonstrate　significant　improvements　on　the　accuracy　of　estimation　despite　that　they　are　more　time-costly.　Moreover,　it　is　found　that　XGBoost　is　the　most　accurate　model　in　explaining　and　prediciting　the　spatial　dynamics　of　housing　prices　in　Beijing.　©　2020　ACM.