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UQO Working Paper No. 3 — Hedonic housing price models for the US: parametric, quantile, and machine-learning approaches.

research economics housing hedonic machine-learning uqo

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Tue, Nov 3, 2026 1

R03-DATA-06/EDIT-04/ADVE-05/ADVE-12/ECON-10/THEO-08/EDIT-02/ECON-08/ADVE-17: inference v2 (fast steps) and geo-inference script

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- 06_inference_v2.py: zpid/split assertions pass; v2 OLS cluster SEs: 33 of 63 slopes |t|>1.96 under state
  clustering (t(50): 33; Holm 17; BH 26), CSS effective G (state: median 14.2, min 3.7); '000' cluster = 16
  listings, nearest-ZIP3 reassignment; clustered Kennedy; lot recoding on v2 (0.0182 -> 0.0958, matches
  v1.2 Table 29); paired squared-error differences with ZIP3-cluster SEs (stored predictions)
- 09_geo_inference.py: ZIP3-cluster bootstrap of the OLS-vs-ensemble gap per design, LOSO paired tests,
  SSE decomposition, feasible level correction, 2x2 region x rounds, duplicates re-score, gap range

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

Simon-Pierre Boucher 5 days ago · 1 file +299