{"id":"W3022086884","doi":"10.1108/ijhma-02-2020-0018","title":"Drivers of housing purchasing decisions: a data-driven analysis","year":2020,"lang":"en","type":"article","venue":"International Journal of Housing Markets and Analysis","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Real estate; Originality; Purchasing; Value (mathematics); Marketing; Control (management); Business; Actuarial science; Computer science; Operations research; Engineering; Finance; Qualitative research; Sociology","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":[0.006211341,0.000513943,0.0006108281,0.002547902,0.0007692779,0.002232803,0.001362033,0.0007925754,0.005784004],"category_scores_gemma":[0.01824163,0.0005010496,0.002313879,0.003392863,0.0005937981,0.001187198,0.001273041,0.001397539,0.0007805058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002368151,"about_ca_system_score_gemma":0.002854827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04192102,"about_ca_topic_score_gemma":0.03182159,"domain_scores_codex":[0.9960444,0.002017798,0.0002329988,0.0004453088,0.0008794533,0.0003800934],"domain_scores_gemma":[0.9801468,0.01456756,0.001821547,0.0009697601,0.001971246,0.0005230787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000164784,0.000368982,0.978039,0.0001913116,0.0003710526,0.0003155342,0.001330288,0.006261756,0.0002597591,0.002632116,0.001603645,0.00846184],"study_design_scores_gemma":[0.00004649656,0.0006390281,0.8806147,0.000218769,0.0002821172,0.0004698705,0.008345613,0.09719571,0.0007612349,0.004008774,0.00731825,0.00009939595],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828153,0.000134306,0.005405486,0.0005468589,0.00002040894,0.0004374134,0.009293516,0.00004606528,0.001300576],"genre_scores_gemma":[0.985769,0.0001024298,0.006893632,0.00009913745,0.00001363851,0.0003765953,0.005745301,0.00002026291,0.0009800097],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04192102,"threshold_uncertainty_score":0.083354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05591130557583449,"score_gpt":0.2661472073142631,"score_spread":0.2102359017384286,"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."}}