For Better or Worse? Revenue Forecasting with Machine Learning Approaches

Abstract

The recent rapid development of artificial intelligence (AI) is expected to transform how governments work by enhancing the quality of decision-making. Despite rising expectations and the growing use of AI by governments, scholarly research on AI applications in public administration has lagged. In this study, we fill gaps in the current literature on the application of machine learning (ML) algorithms with a focus on revenue forecasting by local governments. Specifically, we explore how different ML models perform on predicting revenue for local governments and compare the relative performance of revenue forecasting by traditional forecasters and several ML algorithms. Our findings reveal that traditional statistical forecasting methods outperform ML algorithms overall, while one of ML algorithms, KNN, is more effective in predicting property tax revenue. This result is particularly salient for public managers in local governments to handle foreseeable fiscal challenges through more accurate predictions of revenue.