The　New　Zealand　housing　sector　is　experiencing　rapid　growth　that　has　a　significant　impact　on　society,　the　economy,　and　the　environment.　In　line　with　the　growth,　the　housing　market　for　both　residential　and　business　purposes　has　been　booming,　as　have　house　prices.　To　sustain　the　housing　development,　it　is　critical　to　accurately　monitor　and　predict　housing　prices　so　as　to　support　the　decision-making　process　in　the　housing　sector.　This　study　is　devoted　to　applying　a　mathematical　method　to　predict　housing　prices.　The　forecasting　performance　of　two　types　of　models:　autoregressive　integrated　moving　average　(ARIMA)　and　multiple　linear　regression　(MLR)　analysis　are　compared.　The　ARIMA　and　regression　models　are　developed　based　on　a　training-validation　sample　method.　The　results　show　that　the　ARIMA　model　generally　performs　better　than　the　regression　model.　However,　the　regression　model　explores,　to　some　extent,　the　significant　correlations　between　house　prices　in　New　Zealand　and　the　macro-economic　conditions.