{"id":"W7130710000","doi":"10.1109/swc65939.2025.00055","title":"Advanced Machine Learning for House Price Prediction","year":2025,"lang":"","type":"article","venue":"","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University of Edmonton","funders":"Concordia University","keywords":"Feature engineering; Gradient boosting; Hyperparameter; Random forest; Real estate; Predictive modelling; Support vector machine; Regression; Key (lock); Boosting (machine learning)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001753707,0.0009102126,0.0007801218,0.001422843,0.0002971701,0.0009240329,0.001096284,0.0008207451,0.002465908],"category_scores_gemma":[0.007096415,0.0003155446,0.0007006166,0.002651193,0.0003143546,0.001395208,0.0006756558,0.001942379,0.001518951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006638267,"about_ca_system_score_gemma":0.0005959211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006941827,"about_ca_topic_score_gemma":0.006658149,"domain_scores_codex":[0.9991417,0.0003146129,0.00005264935,0.0001964862,0.0002387749,0.00005567359],"domain_scores_gemma":[0.9976391,0.001421346,0.0001702504,0.00029751,0.0004228791,0.00004888032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000115464,0.0002936265,0.01174845,0.0001683732,0.0002174885,0.0001022818,0.00005151752,0.5747157,0.001208996,0.01070606,0.01509754,0.3855744],"study_design_scores_gemma":[0.000005077987,0.00001397264,0.0007442123,0.000008899931,0.000006080221,0.00001003639,0.000005401969,0.9906598,0.0002974629,0.007156553,0.001087575,0.000004949185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0734088,0.00512852,0.9076492,0.00185692,0.0004375613,0.000100711,0.002021587,0.002767823,0.006628844],"genre_scores_gemma":[0.7697113,0.002734353,0.217505,0.0003705453,0.0005251801,0.0001370699,0.004594225,0.0001405138,0.004281772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006941827,"threshold_uncertainty_score":0.01380283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01535170770478432,"score_gpt":0.2133206597316888,"score_spread":0.1979689520269045,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}