Applying Directed Acyclic Graphs to Assist Specification of a Hedonic Model

Abstract

This research empirically tests the hypothesis that utilizing directed acyclic graphs (DAGs) as an ex-ante process to select variables for a hedonic model improves the model's performance. The results for both new and existing house submarkets indicated that DAG analysis mitigated the multicollinearity issue commonly observed in hedonic models. Using DAG analysis also improved the goodness-of-fit of the hedonic model for the new submarket. However, model specification through DAG analysis does not offer clear implications for improving forecasting accuracy, efficiency, and spatial error autocorrelation. The findings imply that DAG analysis for model specification can be a complementary step in the process of estimating hedonic models, especially when reducing standard error bias by alleviating potential multicollinearity is important in determining the attributes that affect housing prices.