Identifying Predictively Essential Information in Machine Learning: An Econometric-Guided Framework with Cross-Dataset Evidence from Housing Markets

This study develops an econometric-guided framework for assessing the predictive consequences of valuation-information removal while distinguishing econometric significance and predictor importance from predictive necessity. Three predefined information environments are examined: a full-information benchmark (Model A), the ex ante removal of an economically meaningful valuation-information channel (Model B), and an independently econometrically screened specification (Model C). Random Forest, XGBoost, and artificial neural networks are evaluated under common within-dataset validation conditions across four public housing datasets. The controlled-removal experiments identify accessibility and local amenities in UCI Real Estate Valuation, geographical location in California Housing, and property quality and living space in Ames Housing and King County as consequential valuation-information channels. Mean hold-out R2 across the four datasets was 0.8313 for Model A, 0.7726 for Model B, and 0.8225 for Model C, corresponding to descriptive predictive-retention ratios of 92.74% and 98.93% for Models B and C, respectively. The findings indicate that observed predictive deterioration depends on the information removed rather than solely on the number of excluded predictors. The proposed framework offers a replicable approach to evaluating information-removal effects across heterogeneous datasets without implying statistical equivalence or cross-market model transferability.