{"id":"W3197255841","doi":"10.3390/jrfm14090423","title":"Anchoring and Asymmetric Information in the Real Estate Market: A Machine Learning Approach","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Counterfactual conditional; Real estate; Price premium; Anchoring; Transaction cost; Economics; Microeconomics; Database transaction; Counterfactual thinking; Computer science; Willingness to pay; Finance","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001718643,0.00008293361,0.0002302586,0.0003623254,0.0001097112,0.0001505415,0.00008451266,0.0000388406,0.000007122263],"category_scores_gemma":[0.0001503164,0.00007550447,0.00004712348,0.0003049403,0.00001891201,0.0003799797,0.00006385325,0.0002533412,0.00000276024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004007243,"about_ca_system_score_gemma":0.000008992532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001257347,"about_ca_topic_score_gemma":0.00003215118,"domain_scores_codex":[0.9991695,0.00003741023,0.0005135721,0.000101447,0.00003566872,0.0001423585],"domain_scores_gemma":[0.999416,0.00005260764,0.0004016015,0.00007818473,0.0000191153,0.00003247915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00006343746,0.00005082353,0.2228606,0.00008106811,0.00001597119,0.00003034105,0.002584667,0.0005571377,8.659393e-8,0.01025165,0.0001597609,0.7633445],"study_design_scores_gemma":[0.001446347,0.00008652208,0.6793616,0.00003091317,0.00002709356,0.00004249719,0.001489964,0.01338552,8.468829e-7,0.006353735,0.2975782,0.0001968394],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7444138,0.001387204,0.01645737,0.0001368323,0.0002948897,0.0001328617,0.00001235934,0.00000596674,0.2371587],"genre_scores_gemma":[0.900313,0.09708271,0.00238674,0.00006147763,0.00008954681,0.000002633002,0.00000299611,0.000005548919,0.00005531318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7631476,"threshold_uncertainty_score":0.3078983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009990915120976048,"score_gpt":0.1828560558885347,"score_spread":0.1728651407675586,"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."}}