An　accurate　cost　estimate　not　only　plays　a　key　role　in　project　feasibility　studies　but　also　in　achieving　a　final　successful　outcome.　Conventionally,　estimating　cost　typically　relies　on　the　experience　of　professionals　and　cost　data　from　previous　projects.　However,　this　process　is　complex　and　time-consuming,　and　it　is　challenging　to　ensure　the　accuracy　of　the　estimates.　In　this　study,　the　bivariate　and　multivariate　transfer　function　models　were　adopted　to　estimate　and　forecast　the　building　costs　of　two　types　of　residential　buildings　in　New　Zealand:　Low-rise　buildings　and　high-rise　buildings.　The　transfer　function　method　takes　advantage　of　the　merits　of　univariate　time　series　analysis　and　the　power　of　explanatory　variables.　In　the　dynamic　project　conduction　environment,　simply　including　building　cost　data　in　the　cost　forecasting　models　is　not　valid　for　making　predictions,　because　the　change　in　demand　must　be　considered.　Thus,　the　time　series　of　house　prices　and　work　volume　were　used　to　explain　exogenous　effects　in　the　transfer　function　model.　To　demonstrate　the　effectiveness　of　transfer　function　models,　this　study　compared　the　results　generated　by　the　transfer　function　models　with　autoregressive　integrated　moving　average　models.　According　to　the　forecasting　performance　of　the　models,　the　proposed　approach　achieved　better　results　than　autoregressive　integrated　moving　average　models.　The　proposed　method　can　provide　accurate　cost　estimates　that　can　help　stakeholders　in　project　budget　planning　and　management　strategy　making　at　the　early　stage　of　a　project.