{"id":"W4399653621","doi":"10.54097/00506568","title":"Predictive Analytics and Macroeconomic Influence: A Detailed Exploration of the Toronto Housing Market Dynamics","year":2024,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Autoregressive integrated moving average; Diversification (marketing strategy); Predictive analytics; Economics; Econometrics; Analytics; Financial economics; Business; Time series; Marketing; Computer science; Statistics; Data science; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000576122,0.0003541324,0.000256681,0.001444245,0.000747052,0.002752733,0.0005045547,0.0002859037,0.002430329],"category_scores_gemma":[0.003778521,0.0001889759,0.0004199851,0.002498539,0.001018697,0.001606935,0.000811328,0.0005568311,0.000137915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005467091,"about_ca_system_score_gemma":0.004007613,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6476342,"about_ca_topic_score_gemma":0.699328,"domain_scores_codex":[0.99978,0.00004965574,0.000008259519,0.00003472986,0.00008659885,0.00004076993],"domain_scores_gemma":[0.9988948,0.0005457848,0.0001679679,0.0001061575,0.0002075314,0.00007773599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001393574,0.00006324123,0.4982915,0.0002222269,0.0003058959,0.001299553,0.004819837,0.1552479,0.001890926,0.2340055,0.01086039,0.09285379],"study_design_scores_gemma":[0.000007189723,0.00003667795,0.3953642,0.0001764882,0.00009245836,0.0001457952,0.004501751,0.5367016,0.0006329491,0.04662062,0.01566001,0.00006024079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9336552,0.002752136,0.0236126,0.006105467,0.00004464768,0.00004537522,0.002558081,0.0001714479,0.03105502],"genre_scores_gemma":[0.995012,0.001114485,0.002085383,0.00004986214,0.00002379757,0.000004363734,0.000532574,0.00001282274,0.001164685],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3523658,"threshold_uncertainty_score":0.7088819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007697728336362295,"score_gpt":0.1966098978161416,"score_spread":0.1889121694797793,"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."}}