1{"record_id": "3284b13d3d2589a5886f884f24420e24a883d1c3", "eligible": true, "short_ref": "Geerts et al. (2025)", "country": "multi: USA, Belgium, Spain, China", "city": "King County (USA); Flanders (Belgium); Barcelona (Spain); Beijing (China)", "property_type": "residential (transactions; Barcelona = listings)", "price_type": "sale price (transaction; listing price for Barcelona)", "n_obs": 20832, "sample_period": "King County 2014-05 to 2015-05; Flanders 2015-01 to 2023-05; Barcelona 2018-03 to 2018-12; Beijing 2015-01 to 2017-12", "data_source": "Kaggle kc-house-data (King County); proprietary Flanders transactions; Barcelona listings (Lopez-Vargas); Kaggle Lianjia Beijing", "methods_compared": ["LLM in-context learning (Llama 3.2:3B, Llama 3.1:70B, GPT-4o-mini; zero-shot, few-shot, market-report, hybrid prompts)", "kNN", "LightGBM without coordinates", "LightGBM"], "best_method": "LightGBM (with coordinates)", "metric": "MAPE", "best_value": 0.1378, "hedonic_value": null, "test_set": "holdout", "features_ai": "none", "incremental_ai_gain": null, "incremental_ai_metric": "", "marginal_effects": "SHAP", "notes": "Table 2 (MAPE printed as a fraction, 1000 random test examples per dataset from a 60:20:20 split): LGBM 0.1378 King County / 0.2625 Flanders / 0.1556 Barcelona / 0.1056 Beijing; best LLM (GPT-4o-mini, 10 ex. mixed prompt) 0.1861 / 0.3170 / 0.1842 / 0.4125; kNN 0.2105 / 0.3207 / 0.2638 / 0.3810. n_obs = King County full dataset (12914+4349+3569); other datasets larger (Flanders 288448, Barcelona 61348, Beijing 195695). No OLS/hedonic benchmark (explicitly excluded). SHAP top-5 features of LGBM compared with LLM self-explanations; conformal prediction intervals also reported. best_value is the King County column; multi-market study, metrics not pooled.", "confidence": "medium"}2