Construction　cost　index　has　been　widely　used　to　prepare　cost　estimates,　budgets,　and　bids　for　construction　projects.　It　can　also　be　regarded　as　an　indicator　of　cost　level,　which　makes　it　valuable　to　public　authorities　for　understanding　the　conditions　in　the　construction　industry.　Accurate　forecasting　of　future　construction　cost　index　is　essential　for　construction　industry　at　both　micro-　and　macro-level.　To　improve　the　accuracy　of　the　cost　forecasting,　time　series　modeling　techniques　are　adopted　in　this　study.　The　performance　of　the　exponential　smoothing　models　and　seasonal　autoregressive　integrated　moving　average　(ARIMA)　models　for　forecasting　the　building　cost　of　five　categories　of　residential　building　(one-story　house,　two-story　house,　town　house,　apartment,　and　retirement　village　building)　in　New　Zealand　is　compared.　Exponential　smoothing　models　can　produce　more　accurate　forecasts　for　cost　series　of　the　one-story　house　and　two-story　house　in　New　Zealand,　while　seasonal　ARIMA　models　outperform　exponential　smoothing　models　across　the　cost　series　for　town　house,　apartment,　and　retirement　village　building.　This　study　contributes　toward　the　development　of　the　current　state　of　knowledge　in　the　area　of　cost　index　forecasting　for　New　Zealand　and　provides　insights　that　should　be　valuable　from　the　practitioner　perspectives.　©　The　Author(s)　2019.