{"id":"W3048735171","doi":"10.48550/arxiv.2008.05880","title":"Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Valuation (finance); Real estate; Computer science; Database transaction; Transaction data; House price; Data mining; Econometrics; Economics; Finance; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004933159,0.0007711044,0.0003415989,0.0009590148,0.000491378,0.0009133088,0.001287002,0.0009241068,0.002326333],"category_scores_gemma":[0.002317928,0.0002084396,0.0003540591,0.001812167,0.0005427506,0.001444196,0.0004565867,0.0009166565,0.0005181698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004091494,"about_ca_system_score_gemma":0.001036937,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2940531,"about_ca_topic_score_gemma":0.4223186,"domain_scores_codex":[0.9997789,0.00004350101,0.00001026727,0.00006388194,0.00006783388,0.00003563876],"domain_scores_gemma":[0.9993174,0.0003022247,0.00007175469,0.0001038994,0.000142469,0.00006225232],"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":[0.0004547739,0.000520725,0.09619758,0.0001904665,0.0001828551,0.002036075,0.0003678599,0.7569475,0.002335348,0.006968463,0.03208549,0.1017129],"study_design_scores_gemma":[0.0000100252,0.00002000103,0.0100721,0.000007727392,0.000008765263,0.00004807378,0.00008326065,0.9856635,0.00067945,0.001338385,0.00205801,0.00001066004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.950888,0.001069361,0.03192591,0.001698312,0.00009149911,0.0001169172,0.006410202,0.001041005,0.006758935],"genre_scores_gemma":[0.9797306,0.0002954106,0.01128797,0.00007238837,0.00003515608,0.0000270247,0.005728085,0.00003807106,0.002785245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7059469,"threshold_uncertainty_score":0.5846831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06214013791138944,"score_gpt":0.1907633311891971,"score_spread":0.1286231932778076,"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."}}