Abstract Machine learning methods are increasingly used in economics, yet most applications rely on single-task approaches that model outcomes, markets, or regions in isolation. In this study, we adopt a multi-task learning (MTL) framework for economic and tabular data to analyze interdependencies across related economic units. Using benchmark housing datasets representing different economic environments, we define each task as a regional or categorical subgroup. The MTL approach exploits shared representations while allowing for task-specific effects, leading to improved predictive performance and revealing latent relationships across subgroups. An empirical evaluation across three datasets demonstrates the suitability of MTL for structured economic data. Overall, the results suggest that MTL provides a transparent and flexible framework for jointly modeling related entities such as regions, industries, or firms.
Shahin et al. (Wed,) studied this question.