Boosting Versus Foundation Models for Ordinal Home-Sharing Engagement Classification in Latin America

Abstract

Review activity is the standard public proxy of guest engagement on peer-to-peer accommodation platforms, yet is modeled almost exclusively as count regression. This study presents a three-level engagement classification of peer-to-peer home-sharing in Latin America: 76,624 listings from Buenos Aires, Santiago, and Mexico City, with tercile labels from training folds only. A pre-specified, amendment-logged evidence ladder of fifteen attempts plus three contingency campaigns measures which interventions affect macro-averaged F1. A tuned LightGBM over engineered features reaches average macro F1 0.8249, 23.0 points above the best cold-start result (0.5945); a feature-level temporal audit lowers the deployable cold-start reference to 0.5301, widening the gap to 29.5 points. Three findings emerge. First, every full-scenario result lands in a narrow band (0.81–0.83) that doubled budgets do not move: an observed performance plateau within the evaluated feature and model space. Second, Spanish text helps in the cold-start scenario (+2.4 points nominally, +5.3 over the audited strict set) but adds little once review-derived features are present, and sparse lexical vectors match dense multilingual E5 embeddings at far lower cost. Third, an untuned tabular foundation model reaches 0.8205 under a pre-specified 10,000-row context subsample. A clearly labeled post-hoc re-evaluation at full context, outside the pre-specified protocol, reaches 0.8266, the best average observed, which an exploratory multi-resampling confirmation upholds in all twelve paired comparisons. Ordinal metrics, per-class breakdowns, and a Frank–Hall baseline locate the residual error in the middle tercile; repeated outer resampling bounds selection optimism at 0.2–0.3 points.