révision scientifique complète (editorial board, 2026-10-17)

- scoreboard uniforme (règle médiane pour tous les modèles ; Duan en robustesse) : le
  « renversement » forward-in-time de la v1.0 était un artefact de retransformation
- inférence : bootstrap par grappes (municipalités) + tests DM/GW groupés sur toutes
  les comparaisons ; décomposition niveau/compétence du generalization gap
- robustesse : splits groupés parcelle/bâtiment, FE spatiaux emboîtés, Box–Cox fin,
  sensibilité HGB ; nouvelle section 7, 9 tables et 1 figure
- méthodologie formalisée (13 équations) ; littérature 33 → 58 références vérifiées
- docs/REVIEW-REPORT.md, docs/CHANGELOG-REVISION.md ; version 1.1, October 2026

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

Simon-Pierre Boucher committed 22 days ago (Oct 18, 2026) parent 984118f Browse files

62 changed files +3,545 −758

modified README.md +76 −36

		`@@ -4,7 +4,7 @@`
4	4	UQO Working Paper No. 11 — *A Systematic Assessment of Hedonic Specifications
5	5	and Estimation Methods for the Quebec Housing Market, 2021–2026*
6	6
7		−[![Paper](https://img.shields.io/badge/paper-PDF%20(23%20p.)-b31b1b?logo=latex&logoColor=white)](paper/main.pdf)
	7	+[![Paper](https://img.shields.io/badge/paper-PDF%20(40%20p.)-b31b1b?logo=latex&logoColor=white)](paper/main.pdf)
8	8	[![Python](https://img.shields.io/badge/python-3.11%2B-3776AB?logo=python&logoColor=white)](requirements.txt)
9	9	[![Data](https://img.shields.io/badge/data-514%2C212%20sales%20·%20roll--matched%20parquet-4051b5)](data/raw/README.md)
10	10	[![Models](https://img.shields.io/badge/models-20%20·%204%20design%20axes-4051b5)](src/wp11/models.py)
		`@@ -15,13 +15,19 @@ and Estimation Methods for the Quebec Housing Market, 2021–2026*`
15	15	> TL;DR — A controlled horse race of 20 hedonic models on 514,212 Quebec sales
16	16	> (2021–2026) with assessor-grade structural attributes: 6 functional forms × a time-FE
17	17	> ladder × a spatial-FE ladder × 6 estimation methods, all scored on the **same
18		−> price-level scoreboard (Duan smearing / Box–Cox inversion) under two holdouts**
19		−> (random 80/20 and forward-in-time). Functional form is second-order (±2 pp of MdAPE);
20		−> spatial controls are first-order (~10 pp) but overfit at ~1 km granularity; **gradient
21		−> boosting wins under random validation** (MdAPE 14.4% vs 16.5% for the best linear
22		−> model) — but under the forward-in-time split every model degrades and the ML
23		−> advantage reverses (splines 19.1% vs boosting 21.2%, identical R²_ln): random
24		−> cross-validation is the wrong experiment for valuation models.
	18	+> price-level scoreboard (one retransformation rule for every model) under two
	19	+> holdouts** (random 80/20 and forward-in-time), with municipality-clustered bootstrap
	20	+> intervals and Diebold–Mariano-type tests on every comparison. Functional form is
	21	+> second-order (1.5 pp of MdAPE); spatial controls are first-order (~10 pp), and a ~1 km
	22	+> grid overfits when used alone but becomes the best linear specification once nested in
	23	+> municipalities; gradient boosting wins under random validation (14.4% vs 17.6% for
	24	+> the best linear model, −3.2 pp [−4.7, −1.9]), a gain that survives parcel- and
	25	+> building-grouped holdouts. Under the forward-in-time split (train < 2025, test 2025–26)
	26	+> every model degrades and the gap shrinks to 0.5 pp and to insignificance — but the
	27	+> decomposition shows this is a level (staleness) effect: re-levelled, boosting still
	28	+> leads by 2.9 pp. Random cross-validation hides the staleness cost; it does not reverse
	29	+> the ranking (the reversal reported in v1.0 was an artefact of an asymmetric
	30	+> retransformation, corrected in v1.1).
25	31
26	32	---
27	33
		`@@ -29,8 +35,8 @@ and Estimation Methods for the Quebec Housing Market, 2021–2026*`
29	35
30	36	\| Item \| Value \|
31	37	\|---\|---\|
32		−\| Version \| 1.0 (August 2026) \|
33		−\| SSRN \| [7626698](https://ssrn.com/abstract=7626698) — submitted 2026-10-17 (preliminary upload) \|
	38	+\| Version \| 1.1 (October 2026) — revised 2026-10-17 by the editorial-board review (see `docs/`); v1.0 = August 2026 \|
	39	+\| SSRN \| [7626698](https://ssrn.com/abstract=7626698) — v1.0 uploaded 2026-10-17; v1.1 PDF to be uploaded as a revision \|
34	40	\| Journal \| not yet submitted \|
35	41	\| Code & data \| reproducible from `scripts/` (see Quick start); raw parquet snapshot not redistributed \|
36	42
		`@@ -77,23 +83,35 @@ and accuracy by market segment.`
77	83
78	84	## 🏆 Headline results
79	85
	86	+All MdAPE in %, median retransformation rule for every model (v1.1 scoreboard).
	87	+
80	88	\| Object \| Random 80/20 holdout \| Forward-in-time holdout \|
81	89	\|---\|---\|---\|
82		−\| Functional-form range (A1–A6), MdAPE \| 16.5 % (splines) – 18.6 % (Box–Cox) \| 19.1 % – 21.0 % \|
83		−\| Data-driven Box–Cox exponent \| λ̂ = 0.25, buys nothing out of sample \| — \|
84		−\| Dropping spatial FE (C1 vs C2) \| 27.9 % vs 18.1 % (+10 pp) \| 26.5 % vs 20.5 % \|
85		−\| ~1.1 km grid FE (C4) \| 19.4 % — overfits vs 17.5 % at ~5.5 km \| 22.1 % \|
86		−\| Dropping time FE (B1 vs B3) \| 21.1 % vs 18.1 % \| 25.4 % vs 20.5 % \|
87		−\| Gradient boosting (D4) \| 14.4 % (−13 % vs best linear 16.5 %) \| 21.2 % \|
88		−\| Random forest (D3) \| 14.6 % \| 21.3 % \|
89		−\| Boosting without coordinates (D5) \| 23.2 % \| 26.2 % \|
90		−\| Semi-log + splines (A6) \| 16.5 % \| 19.1 % — beats boosting, same R²_ln (0.52) \|
91		−\| Implied monthly indices across forms \| agree within a few index points \| \|
92		−\| Boosted implicit age / area profiles \| track quadratic OLS closely \| \|
93		−
94		−Practical synthesis: a semi-log with splines, quarter effects and spatial FE matched to
95		−market thickness is within two points of the frontier in deployment conditions —
96		−transparent, auditable, fast. Validate against the future, not a shuffle of the past.
	90	+\| Functional-form range (A1–A6) \| 17.6 (splines) – 19.2 (quadratics) \| 20.4 (linear) – 23.3 \|
	91	+\| Splines vs. semi-log \| −1.4 pp [−1.8, −1.1] \| −1.4 pp [−2.3, −0.8] \|
	92	+\| Data-driven Box–Cox exponent \| λ̂ = 0.25 (0.35 on a fine grid), −0.5 pp vs. semi-log, worse on MAPE \| — \|
	93	+\| Dropping spatial FE (C1 vs C2) \| 28.8 vs 19.2 (+9.6 pp) \| 29.0 vs 23.3 \|
	94	+\| ~5.5 km grid vs. municipality FE (C3 vs C2) \| 18.7 vs 19.2 — not significant (−0.45 [−1.07, +0.37]) \| 24.3 vs 23.3 \|
	95	+\| ~1.1 km grid alone (C4) \| 21.0 — overfits (+1.8 pp [+1.2, +2.3]) \| 25.3 \|
	96	+\| ~1.1 km grid nested in municipalities (C4′) \| 17.3 — best linear specification \| 21.6 \|
	97	+\| Dropping time FE (B1 vs B3) \| 22.0 vs 19.2 \| 29.3 vs 23.3 \|
	98	+\| Gradient boosting (D4) vs. best linear (A6) \| 14.4 vs 17.6, −3.2 pp [−4.7, −1.9], DM t = −3.6 \| 21.2 vs 21.7, −0.5 pp [−1.7, +0.6], n.s. \|
	99	+\| Random forest (D3) \| 14.6 \| 21.3 \|
	100	+\| Boosting without coordinates (D5) \| 23.2 \| 26.2 \|
	101	+\| Parcel- / building-grouped holdout, D4 vs A6 \| 14.5 vs 17.6 / 14.6 vs 17.8 (gap unchanged) \| — \|
	102	+\| Staleness cost (forward − same-period random) \| — \| D4 +7.5 pp, A6 +4.7, A1 +3.3 \|
	103	+\| Median log level error, forward split \| — \| D4 +0.18, A6 +0.16, A1 +0.10 \|
	104	+\| Re-levelled forward MdAPE \| — \| D4 13.9 vs A6 16.8 (gap 2.9 ≈ random 3.5) \|
	105	+\| Boosting lead over splines by test quarter \| — \| 1.5 pp (2025Q1) → 0.0 (2026Q3) \|
	106	+\| Monthly indices, four linear forms \| agree within 2.5 index points \| \|
	107	+\| Boosted imputation index, July 2026 \| 160.5 vs 168.6–171.0 for the linear forms \| \|
	108	+\| Boosted age profile vs. quadratic OLS \| same shape, 0.10–0.13 log points flatter from age 20 \| \|
	109	+
	110	+Practical synthesis: a semi-log with splines, quarter effects and spatial fixed effects
	111	+nested from municipality down to a fine grid is within three points of the frontier under
	112	+random validation and indistinguishable from it in deployment. Supply the price level from
	113	+an index or a frequent refit, spend the flexibility budget on the spatial surface, and
	114	+validate against the future, not a shuffle of the past.
97	115
98	116	## 🗂 Repository layout
99	117
		`@@ -148,8 +166,9 @@ peak RAM ≈ 8 GB.`
148	166	\| 01 \| `01_build_sample.py` \| residential use codes, roll match ≤ 50 m / score ≥ 150, price ≥ $50k, floor area 20–2,000 m², known year built, 1/99 % trims within sale year; builds grid cells \| `data/processed/`, `results/reproduced/sample_counts.csv`, `summary_stats.csv` \|
149	167	\| 02 \| `02_horserace.py` \| fits the 20 models on each split, retransforms predictions to price levels, scores MdAPE/MAPE/RMSE_ln/R²_ln; Box–Cox profile likelihood \| `horserace_random.csv`, `horserace_temporal.csv`, `boxcox.csv` \|
150	168	\| 03 \| `03_extensions.py` \| constant-quality monthly indices by form, implicit ln-price profiles (quadratic OLS vs boosting), learning curves, accuracy by segment \| `index.csv`, `profiles.csv`, `learning.csv`, `segments.csv` \|
151		−\| 04 \| `04_make_figures.py` \| the 9 figures \| `figures/fig_*.png` \|
152		−\| 05 \| `05_make_tables.py` \| the 7 LaTeX tables \| `results/tables/*.tex` \|
	169	+\| 04 \| `04_make_figures.py` \| the 10 figures \| `figures/fig_*.png` \|
	170	+\| 05 \| `05_make_tables.py` \| the 16 LaTeX tables \| `results/tables/*.tex` \|
	171	+\| 06 \| `06_robustness.py` \| cluster-bootstrap CIs + clustered DM tests, same-period and by-quarter decomposition, level re-levelling, leakage decomposition, parcel- and building-grouped races, nested spatial FE, fine Box–Cox grid, boosting hyper-parameter sensitivity (run after 02; ≈ 20 min) \| `inference_.csv`, `pairs.csv`, `same_period.csv`, `by_quarter.csv`, `relevel.csv`, `leakage.csv`, `horserace_group_.csv`, `nested_fe.csv`, `boxcox_fine.csv`, `hgb_sensitivity.csv` \|
153	172
154	173	## 🗃 Data
155	174
		`@@ -161,23 +180,28 @@ physical configuration, use code. Sample cascade (paper §3): residential use co`
161	180	high-confidence match → price ≥ $50,000 → plausible floor area → known year built →
162	181	1/99 % trims ⇒ 514,212 sales (348,253 single-family, 75,734 condominiums, 74,909
163	182	plexes, 10,349 cottages, 4,881 mobile homes) over 1,115 municipalities, 5,255 cells of
164		−~5.5 km and 30,548 cells of ~1.1 km. Mean price $462,000 (median $399,000). The raw
	183	+~5.5 km and 30,548 cells of ~1.1 km. Mean price $440,207 (median $397,375); 86 further dwellings of undetermined class. One fifth of sales are repeat sales of the same parcel and 30% share exact coordinates with another sale (92% of condominiums). The raw
165	184	parquet (100 MB, transaction microdata) is not redistributed.
166	185
167	186	## 🔬 Methodology
168	187
169	188	1. One attribute set, one sample for every model (`src/wp11/sample.py`).
170	189	2. Axis A — six functional forms on a municipality + quarter FE backbone; Box–Cox λ by
171		− profile likelihood; log models retransformed with Duan smearing, Box–Cox inverted
172		− (`src/wp11/models.py`).
	190	+ profile likelihood. Every transformed-scale prediction is inverted with the same
	191	+ median rule (plain inversion); Duan smearing is computed for every model and
	192	+ reported as a robustness scoreboard (`src/wp11/models.py`).
173	193	3. Axis B / C — time-FE ladder (none → month) and spatial-FE ladder (none → ~1.1 km
174	194	grid) on the quadratic semi-log backbone.
175	195	4. Axis D — OLS, cross-validated ridge, random forest, gradient boosting with and
176	196	without coordinates, spatial k-NN comparables, all on the identical attribute set.
177	197	5. Two holdouts — random 80/20 and forward-in-time (train < 2025, test 2025–26, time
178		− effects carried forward); metrics on price levels, design clustered by municipality.
	198	+ effects carried forward), plus parcel- and building-grouped holdouts; metrics on price
	199	+ levels; municipality-clustered bootstrap intervals and clustered Diebold–Mariano tests
	200	+ (`src/wp11/inference.py`); level/skill decomposition of the forward error.
179	201	6. Extensions — implied monthly indices, implicit-price profiles, learning curves,
180	202	segment accuracy (`scripts/03_extensions.py`).
	203	+7. Robustness — retransformation rule, leakage-free holdouts, nested spatial FE, fine
	204	+ Box–Cox grid, boosting hyper-parameters (`scripts/06_robustness.py`).
181	205
182	206	## 🖼 Figures & tables inventory
183	207
		`@@ -192,10 +216,13 @@ parquet (100 MB, transaction microdata) is not redistributed.`
192	216	\| `fig_profiles` \| implicit ln-price profiles: quadratic OLS vs gradient boosting \|
193	217	\| `fig_learning` \| learning curves: MdAPE against training-set size \|
194	218	\| `fig_segments` \| MdAPE by market segment (random holdout) \|
	219	+\| `fig_horizon` \| forward-in-time MdAPE by test quarter (distance from the training window) \|
195	220
196	221	Tables (`results/tables/*.tex`): summary statistics, scoreboard random holdout,
197		−scoreboard forward-in-time holdout, Box–Cox profile, generalization gap, learning
198		−curves, segments.
	222	+scoreboard forward-in-time holdout, inference (bootstrap CIs + DM tests), targeted pairs,
	223	+Box–Cox profile, generalization gap, same-period / level decomposition, by-quarter
	224	+accuracy, learning curves, segments, median vs. Duan retransformation, grouped holdouts,
	225	+seen/unseen leakage decomposition, nested spatial FE, boosting hyper-parameters.
199	226
200	227	## ⚠️ Limitations
201	228
		`@@ -206,8 +233,13 @@ curves, segments.`
206	233	- Attributes are those of the assessment roll (no interior quality, renovations or
207	234	listing text); the ML advantage measured here is the value of flexible spatial
208	235	learning on that attribute set, not of richer data.
209		−- Tree ensembles are tuned lightly (fixed hyper-parameters); the ranking is robust to
210		− the choices tried but not exhaustively optimised.
	236	+- Tree ensembles use fixed, conventional hyper-parameters; four materially different
	237	+ configurations (including early stopping on a time-ordered validation set) leave the
	238	+ ranking unchanged (paper §7.5), but no linear model with a continuous spatial surface
	239	+ (kriging, bivariate splines) is estimated as a competitor.
	240	+- The forward-in-time test covers one regime (the 2025–26 recovery); the level
	241	+ re-levelling uses the test-set median ratio (an oracle upper bound for what a published
	242	+ index would deliver).
211	243
212	244	## 📝 Citation
213	245
		`@@ -222,7 +254,8 @@ curves, segments.`
222	254	type = {Working Paper},
223	255	number = {11},
224	256	year = {2026},
225		− month = {August}
	257	+ month = {October},
	258	+ note = {Version 1.1}
226	259	}
227	260	```
228	261
		`@@ -239,3 +272,10 @@ Université du Québec en Outaouais (UQO), Gatineau, QC.`
239	272	Language-Model and Vision Features* (now WP 14) and *Does the Sales Comparison
240	273	Approach Actually Work?* (now WP 16).
241	274	- Cover page, metadata and layout follow `Recherche-UQO/HARMONISATION.md` (2026-10-17).
	275	+- Revision 2026-10-17 (v1.1): full editorial-board review. Scientific report in
	276	+ `docs/REVIEW-REPORT.md`, audit trail (every changed number, old → new) in
	277	+ `docs/CHANGELOG-REVISION.md`; pre-revision PDF kept as `docs/main_pre-revision-20261017.pdf`
	278	+ (git tag `pre-revision-20261017`). Main correction: v1.0 applied Duan smearing to the
	279	+ linear log models but not to the tree ensembles; under a uniform rule the forward-in-time
	280	+ "reversal" disappears (the gap shrinks to 0.5 pp, n.s.) and the paper now decomposes the
	281	+ forward error into a common level (staleness) component and intact relative skill.

added docs/CHANGELOG-REVISION.md +165 −0

		`@@ -0,0 +1,165 @@`
	1	+# Changelog — révision scientifique complète (editorial board, 2026-10-17)
	2	+
	3	+Papier : WP 11 — Half a Million Prices, Twenty Models (v1.0, August 2026 → v1.1, October 2026).
	4	+État de référence : tag git `pre-revision-20261017`, PDF `docs/main_pre-revision-20261017.pdf` (23 pages).
	5	+Rapport scientifique associé : `docs/REVIEW-REPORT.md`.
	6	+
	7	+## 1. Corrections majeures (où / avant / après / pourquoi / preuve)
	8	+
	9	+### 1.1 Règle de retransformation asymétrique → scoreboard uniforme (correction centrale)
	10	+- Où : `src/wp11/models.py` (`fit_predict`, `run_horserace`), §4.3 Methodology, §5, §7.1, abstract, intro, conclusion.
	11	+- Avant : les modèles linéaires en log (A2, A3, A5, A6, B, C, D1, D2) étaient multipliés par le facteur de
	12	+ smearing de Duan (ŝ ≈ 1.06–1.08) ; les ensembles d'arbres (D3, D4, D5) et le k-NN (D6) ne l'étaient pas
	13	+ (`np.exp(pred)` nu). Les métriques log (RMSE_ln, R²_ln) étaient calculées sur la prédiction smearée pour une
	14	+ famille et non pour l'autre.
	15	+- Après : règle « médiane » (inversion directe de la prédiction transformée, éq. 10) appliquée à tous les
	16	+ modèles pour le tableau principal ; règle « moyenne » (Duan, éq. 11, généralisée au Box–Cox par moyenne sur
	17	+ 1 000 résidus) calculée pour tous et rapportée en robustesse (Table 12).
	18	+- Pourquoi : la comparaison v1.0 opposait un A6 smearé (19.1 %) à un D4 non smearé (21.2 %) dans le split
	19	+ temporel — le « renversement » annoncé en titre de la v1.0 était un artefact : (i) résidus log asymétriques
	20	+ à gauche (skewness −1.4 à −1.8, médiane +0.06) donc le smearing abaisse par coïncidence la MdAPE de tout modèle
	21	+ log ; (ii) marché haussier en 2025-26 : tout facteur > 1 compense une partie de la staleness. Appliquée
	22	+ uniformément, aucune des deux règles ne renverse le classement (médiane : A6 21.7 vs D4 21.2 ; moyenne :
	23	+ 19.1 vs 18.4).
	24	+- Preuve : `results/reproduced/horserace_{random,temporal}.csv` (colonnes `mdape` vs `mdape_mean`, `smear`),
	25	+ `results/tables/retransformation.tex`, médianes de résidus vérifiées sur `data/processed/predictions_*.parquet`.
	26	+- Chiffres modifiés (ancien → nouveau, MdAPE %) — random : A1 18.2→18.2, A2 18.2→19.0, A3 17.0→18.0, A4 18.6→18.6,
	27	+ A5 18.1→19.2, A6 16.5→17.6, B1 21.1→22.0, B2 18.1→19.1, B4 18.3→19.3, C1 27.9→28.8, C3 17.5→18.7, C4 19.4→21.0,
	28	+ D2 16.7→17.7 ; D3/D4/D5/D6 inchangés (14.6/14.4/23.2/21.2). Forward : A1 20.4→20.4, A2 20.6→23.2, A3 19.2→21.8,
	29	+ A4 21.0→21.0, A5 20.5→23.3, A6 19.1→21.7, B1 25.4→29.3, B2 21.0→23.7, B4 20.3→22.9, C1 26.5→29.0, C3 21.3→24.3,
	30	+ C4 22.1→25.3, D2 19.5→22.3 ; D3/D4/D5/D6 inchangés (21.3/21.2/26.2/25.6). R²_ln des modèles log : +0.01 à +0.04
	31	+ (random), −0.03 à −0.04 (forward).
	32	+- Conséquence narrative : « the ranking reverses » (v1.0) → « the gap shrinks from 3.2 to 0.5 points and loses
	33	+ significance ; relative skill transfers intact, the level does not » (v1.1). Abstract, §1, §5.5, §8, §9 réécrits.
	34	+
	35	+### 1.2 Inférence statistique ajoutée (absente en v1.0)
	36	+- Avant : aucun intervalle, aucun test ; le README affirmait « design clustered by municipality » sans aucune
	37	+ implémentation.
	38	+- Après : `src/wp11/inference.py` — bootstrap par grappes (municipalités, B = 500) pour la MdAPE de chaque
	39	+ modèle et pour les différences ; test de type Diebold–Mariano / Giacomini–White à erreurs-types groupées sur les
	40	+ pertes APE et log² (éq. 13). Tables 4 (tous les modèles vs A6) et 5 (13 paires ciblées).
	41	+- Résultats clés : D4 vs A6 random −3.2 [−4.7, −1.9] (t = −3.6) ; forward −0.5 [−1.7, +0.6] (t = −1.6, p = 0.11) ;
	42	+ C3 vs C2 random −0.45 [−1.07, +0.37] (n.s.) ; C4 vs C2 +1.8 [+1.2, +2.3] ; B4 vs B3 +0.19 [+0.01, +0.37] ;
	43	+ D2 vs A6 +0.06 [−0.02, +0.16] (n.s.).
	44	+- Conséquence : la phrase « the 5.5 km grid beats municipality effects » est ramenée à « nominally better,
	45	+ within bootstrap noise » ; « month effects indistinguishable from quarter » précisé (légèrement pires, significatif).
	46	+
	47	+### 1.3 Décomposition du « generalization gap » (nouveau)
	48	+- Comparaison à période fixe (modèles random évalués sur les 31 465 ventes 2025-26 de leur test) : D4 13.7 → 21.2
	49	+ (coût de staleness 7.5), A6 17.0 → 21.7 (4.7), A1 17.1 → 20.4 (3.3). Biais de niveau médian forward : D4 +0.183,
	50	+ A6 +0.161, A1 +0.102. MdAPE re-nivelée forward : D4 13.9 vs A6 16.8 (écart 2.9 ≈ écart random 3.5).
	51	+ Par trimestre : avance D4 sur A6 de 1.5 pt (2025Q1) → 0 (2026Q3).
	52	+- Fichiers : `same_period.csv`, `relevel.csv`, `by_quarter.csv` ; Tables 8–9, Figure 6 (nouvelle).
	53	+
	54	+### 1.4 Fuite d'information dans le split aléatoire (nouveau)
	55	+- Faits : 20.6 % des ventes sont des reventes d'une même parcelle ; 30.3 % partagent des coordonnées exactes avec
	56	+ une autre vente (91.6 % des condos). Colonnes `parcel`, `coord` ajoutées à `sample.py` (reconstruction du
	57	+ parquet d'analyse, 514 212 ventes identiques).
	58	+- Splits groupés (`split("group_parcel")`, `split("group_coord")`) : course complète. Parcelle : D4 14.5 / A6 17.6
	59	+ (≈ inchangé). Bâtiment : D4 14.6 / A6 17.8 (écart 3.2 identique) ; seul D6 se dégrade (21.2 → 22.3).
	60	+ Décomposition vu/non vu : D4 12.0 vs 15.3 (coord.), A6 17.8 vs 17.6 ; condos D4 8.9 vs 10.2.
	61	+- Tables 13–14 ; §4.4, §7.2.
	62	+
	63	+### 1.5 Effets spatiaux fins : « seventeen K sales » et repli arbitraire
	64	+- Avant (§5.3) : « its cells average seventeen K sales each » — faux : 514 212 / 30 548 = 16.8 ventes par cellule
	65	+ (médiane 3). Corrigé. Cellules non vues (2.5 % des ventes test) tarifées au niveau de la cellule de référence :
	66	+ désormais explicité.
	67	+- Nouveau : C3′ (muni + grille 5.5 km) et C4′ (muni + grille 1.1 km) — solution de norme minimale (ridge α→0)
	68	+ ≈ pooling partiel vers la municipalité. Random : C4′ 17.3 % (meilleure spécification linéaire du papier),
	69	+ C3′ 17.7 % ; forward : 21.6 / 21.5 vs C2 23.3. Conclusion de l'axe C reformulée : « hierarchy, not coarseness ».
	70	+ Table 15 ; `nested_fe.csv`.
	71	+
	72	+### 1.6 Box–Cox : grille fine
	73	+- Grille 0.05 sur [−0.25, 0.75] : λ̂ = 0.35 (profil plat 0.25–0.55 à la tolérance du solveur itératif) ;
	74	+ MdAPE random 18.4 % (vs 18.6 % à 0.25). `boxcox_fine.csv`. Texte §5.1 et §7.4.
	75	+
	76	+### 1.7 Hyper-paramètres du boosting
	77	+- Quatre configurations alternatives (shallow, deep, régularisée, early stopping sur les 10 % derniers mois
	78	+ d'entraînement) sous les deux splits. `hgb_sensitivity.csv`, Table 16, §7.5. Random 14.3–14.7 % (baseline 14.4), forward 21.2–21.6 % (baseline 21.2) ; early stopping : 700 / 1 000 itérations, reproduit le baseline à 0.1 pt.
	79	+
	80	+### 1.8 Statistiques descriptives incohérentes
	81	+- Avant (§3.2, README) : « Prices average $462,000 (median $399,000) ». Table 1 : 440 207 / 397 375.
	82	+ Après : texte aligné sur la table ; ajout des 86 logements de classe indéterminée (la somme des classes v1.0
	83	+ faisait 514 126 ≠ 514 212) ; répartition par année ; nettoyage des lots > P99.5 explicité (était silencieux
	84	+ dans `sample.py`) ; bornes âge/étages/logements explicitées.
	85	+
	86	+### 1.9 Indices et profils : affirmations adoucies
	87	+- Avant : « all five [indices] agree to within a few index points » ; « curves sit within a tenth of a log
	88	+ point ». Après : les quatre formes linéaires s'accordent à ≤ 2.5 points ; l'indice boosting finit 9 points
	89	+ plus bas (160.5 vs 168.6–171.0 ; écart max 10.9) ; profil d'âge du boosting uniformément plus plat de 0.10–0.13
	90	+ log-point à partir de 20 ans (−0.20 vs −0.31 à 40 ans), surface : ≤ 0.05. Chiffres vérifiés dans
	91	+ `index.csv`, `profiles.csv`.
	92	+- Learning curves recalculées (règle médiane) : OLS 19.1 → 19.2 (v1.0 : 18.4 → 18.1) ; GB 17.6 → 14.5 ; écart
	93	+ 1.6 → 4.7 pts (v1.0 : < 1 → 3.6). Segments : condo 18.2 vs 9.1 (v1.0 OLS 20.4), grandes villes 17.7 vs 11.9,
	94	+ petites municipalités 24.8 vs 18.7 (v1.0 23.3), chalets 34.3 vs 32.1 (v1.0 33.9).
	95	+
	96	+### 1.10 Méthodologie formalisée
	97	+- §4 réécrit : équations (1)–(13) — fonction hédonique générale, six formes A1–A6, profil de vraisemblance Box–Cox
	98	+ (avec jacobien), ridge, règle k-NN, règles médiane/Duan, métriques, test DM groupé. Documentation du traitement
	99	+ des catégories non vues, du carry-forward et de son analogue pour les arbres (correction de la phrase « clamped
	100	+ month counter » : rien n'est écrêté dans le code ; les arbres sont constants par morceaux).
	101	+
	102	+### 1.11 Autres corrections éditoriales
	103	+- « 13 % improvement » → « 18 % » (14.4 vs 17.6) ; « 13–21 % » → « 18–25 % » ; intro « 27.9 % versus 17.5 % »
	104	+ vs README « vs 18.1 % » harmonisé (28.8 vs 19.2, référence = backbone C2).
	105	+- Hiérarchie de la discussion : liste à puces → prose (règle de style du protocole).
	106	+- Mots-clés harmonisés au vocabulaire commun (« hedonic price function », « forward-in-time holdout »,
	107	+ « automated valuation model »).
	108	+- `\WPversion` 1.0 → 1.1 ; `\WPdate` August 2026 → October 2026 ; page titre canonique intacte ; macro
	109	+ `\companionpaper` conservée (main.tex / main_anon.tex).
	110	+
	111	+## 2. Références
	112	+
	113	+### Vérifiées (Crossref `api.crossref.org/works?query.bibliographic=…`, 2026-10-17) et corrigées
	114	+Toutes les entrées conservées de la v1.0 (31) ont été retrouvées sur Crossref et dotées de leur DOI. Corrections :
	115	+- `box1964analysis` : pages 211–252 → 211–243 (244–252 = discussion).
	116	+- `bogin2020house` : number 1 → 1–2 ; DOI 10.1007/s11146-019-09712-0.
	117	+- `silver2018house` : l'article EURONA 2018 n'est pas indexé ; remplacé par la version vérifiée **IMF Working Paper
	118	+ 16/213 (2016)**, DOI 10.5089/9781475552249.001, même titre (ordre des mots corrigé).
	119	+- `ke2017lightgbm` : @article → @inproceedings (NIPS 2017, non indexé Crossref ; actes Curran Associates).
	120	+- `malpezzi2003hedonic` : DOI 10.1002/9780470690680.ch5 ; `anselin1988spatial` : DOI 10.1007/978-94-015-7799-1.
	121	+
	122	+### Retirées (non citées dans le texte et bibliographiquement défectueuses)
	123	+- `knight2003listing` (volume/pages vides ; en réalité chapitre Springer 2008, pp. 39–54).
	124	+- `diewert2004hedonic` (« Mimeo » en champ journal ; non indexé).
	125	+
	126	+### Ajoutées (27, toutes vérifiées sur Crossref, DOI dans le .bib)
	127	+hill2018geospatial (RIW 64(4) 737–756) · pace2020residuals (JREFE 60 170–180) · lorenz2023interpretable
	128	+(REE 51(5) 1178–1208) · diewert2016tokyo (RSUE 60 300–315) · diewert2015decomposition (Econometric Reviews 34
	129	+106–126) · diebold1995comparing (JBES 13(3) 253–263) · giacomini2006tests (Econometrica 74(6) 1545–1578) ·
	130	+bergmeir2012crossvalidation (Information Sciences 191 192–213) · roberts2017crossvalidation (Ecography 40(8)
	131	+913–929) · kuminoff2010which (JEEM 60(3) 145–160) · bishop2020best (REEP 14(2) 260–281) · bourassa2007spatial
	132	+(JREFE 35(2) 143–160) · bourassa2003submarkets (JHE 12(1) 12–28) · goodman2003segmentation (JHE 12(3) 181–201) ·
	133	+zurada2011comparison (JRER 33(3) 349–388) · antipov2012mass (ESWA 39(2) 1772–1778) · perezrave2019machine
	134	+(J. Property Research 36(1) 59–96) · mccluskey2013prediction (JPR 30(4) 239–265) · schulz2014automated (JPR 31(2)
	135	+131–153) · fik2003modeling (REE 31(4) 623–646) · clapp2003semiparametric (JREFE 27(3) 303–320) ·
	136	+pace1998spatiotemporal (JREFE 17(1) 15–33) · dube2013pooled (LSRS 6(1) 1–18) · hill2009boom (JHE 18(3) 193–205) ·
	137	+manning2001estimating (J. Health Econ. 20(4) 461–494) · thibodeau2003marking (REE 31(1) 1–22) ·
	138	+khoshnoud2023evolution (JREL 31(1) 1–47).
	139	+Total : 33 → 58 entrées (31 conservées + 27 ajoutées), toutes citées, toutes vérifiées.
	140	+
	141	+## 3. Réanalyses exécutées (laptop, Apple Silicon 18 cœurs, Python 3.14 / scikit-learn 1.6.1)
	142	+
	143	+\| Script \| Contenu \| Durée \| Sorties \|
	144	+\|---\|---\|---\|---\|
	145	+\| `01_build_sample.py` \| reconstruction de l'échantillon (+ clés `parcel`, `coord`) — 514 212 ventes identiques \| 2 s \| `data/processed/analysis.parquet` \|
	146	+\| `02_horserace.py` \| 20 modèles × 2 splits, règles médiane + Duan, prédictions sauvegardées \| 3 min 28 s \| `horserace_.csv`, `boxcox.csv`, `predictions_.parquet` (non commités) \|
	147	+\| `03_extensions.py` \| indices, profils, learning curves, segments (règle médiane) \| 4 min 18 s \| `index.csv`, `profiles.csv`, `learning.csv`, `segments.csv` \|
	148	+\| `06_robustness.py` (nouveau) \| bootstrap + DM, même période, par trimestre, fuite, re-nivellement, paires, splits groupés (2 courses complètes), FE emboîtés, Box–Cox fin, sensibilité HGB \| 16 min 16 s \| `inference_.csv`, `same_period.csv`, `by_quarter.csv`, `leakage.csv`, `relevel.csv`, `pairs.csv`, `horserace_group_.csv`, `nested_fe.csv`, `boxcox_fine.csv`, `hgb_sensitivity.csv` \|
	149	+\| `04_make_figures.py` \| 10 figures (+ `fig_horizon.png`) \| < 1 min \| `figures/` \|
	150	+\| `05_make_tables.py` \| 16 tables LaTeX (9 nouvelles) \| < 1 s \| `results/tables/` \|
	151	+
	152	+Tous les nombres du texte proviennent de ces sorties ; aucune table n'est tapée à la main.
	153	+
	154	+## 4. Éléments non modifiés / analyses recommandées non exécutées
	155	+- Modèle linéaire à surface spatiale (krigeage des résidus, splines bivariées en lat/lng, effets hiérarchiques
	156	+ bayésiens) : non estimé (hors cadre « 20 modèles ») — recommandé pour fermer l'écart linéaire/boosting par l'autre
	157	+ côté.
	158	+- Holdout spatialement bloqué (cellules entières en test) : non exécuté (change la question en extrapolation
	159	+ spatiale ; les FE spatiaux y sont par construction inopérants).
	160	+- Re-nivellement par un indice publié (hors échantillon, non oracle) : non exécuté (aucun indice externe
	161	+ chargé ; le re-nivellement présenté utilise le ratio médian du test, borne supérieure).
	162	+- Variante « médiane calibrée » (exp(ẑ + médiane des résidus d'entraînement)) : discutée, non tabulée.
	163	+- Fenêtres temporelles alternatives (p. ex. train < 2024, test 2024) : non exécutées — limite déclarée §8.4.
	164	+- Comparaison avec LightGBM / XGBoost natifs ou réseaux de neurones : hors périmètre.
	165	+- La règle de nettoyage des lots > P99.5 → 0 (héritée) est documentée mais conservée pour préserver l'échantillon.

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		`@@ -0,0 +1,118 @@`
	1	+# Rapport de l'editorial board — WP 11 « Half a Million Prices, Twenty Models » (2026-10-17)
	2	+
	3	+Révision v1.0 (August 2026, 23 p.) → v1.1 (October 2026). Journal d'audit détaillé : `docs/CHANGELOG-REVISION.md`.
	4	+Comité : économie immobilière, économétrie appliquée, économétrie spatiale, évaluation foncière, apprentissage
	5	+automatique, édition scientifique. Revue cible : Journal of Housing Economics (repli JRER / IJF).
	6	+
	7	+## 1. Diagnostic général
	8	+
	9	+Le papier pose une question nette et utile — quels choix de spécification hédonique comptent réellement pour la
	10	+précision hors échantillon — et y répond par un dispositif expérimental propre (un jeu d'attributs, quatre axes, un
	11	+seul scoreboard, deux holdouts) sur un échantillon rare par sa taille et la qualité des descripteurs (rôle
	12	+d'évaluation). La contribution est réelle et conservée. Trois faiblesses structurelles de la v1.0 ont été
	13	+identifiées et corrigées :
	14	+
	15	+1. Le résultat phare était un artefact. Les modèles linéaires en log étaient retransformés par smearing de Duan
	16	+ et les ensembles d'arbres non. Dans le split temporel, le smearing (facteur ≈ 1.06) compense une partie de la
	17	+ staleness du niveau dans un marché haussier ; c'est ce qui produisait « A6 19.1 % vs D4 21.2 % » et le
	18	+ « renversement » affiché en abstract. Sous une règle uniforme, le boosting reste devant (21.2 vs 21.7, écart non
	19	+ significatif). Correction intégrale (code, tables, texte) ; la v1.1 le dit explicitement.
	20	+2. Aucune inférence. Des différences de 0.2 à 0.6 point étaient interprétées comme des résultats. Ajout d'un
	21	+ bootstrap par grappes (municipalités) et de tests Diebold–Mariano/Giacomini–White groupés sur toutes les
	22	+ comparaisons. Plusieurs affirmations tombent (grille 5.5 km > municipalités ; mois ≈ trimestre) ou se précisent.
	23	+3. Mécanisme du « generalization gap » non identifié. La v1.0 confondait difficulté de la période test et
	24	+ staleness du modèle. Décomposition ajoutée (même période, biais de niveau, re-nivellement, horizon par
	25	+ trimestre) : la compétence relative des ensembles transfère intacte (écart re-nivelé 2.9 pts ≈ 3.5 en random),
	26	+ c'est le niveau qui ne transfère pas, un peu plus pour les arbres (feuille terminale en t). Ce résultat est plus
	27	+ faible que le « renversement » mais plus utile et défendable.
	28	+
	29	+## 2. Diagnostic par section
	30	+
	31	+\| Section \| Forces \| Faiblesses / erreurs trouvées \| Action \|
	32	+\|---\|---\|---\|---\|
	33	+\| Abstract \| clair, chiffré \| chiffres issus du scoreboard asymétrique ; « reverses », « identical R² » faux ; trop long pour la page 2 (débordait sur une page blanche) \| réécrit, 4 leçons reformulées, mention de la correction \|
	34	+\| 1 Introduction \| bonne mise en situation Rosen → ML \| « 13 % improvement » (vrai : 18 %) ; « 27.9 vs 17.5 » (comparait C1 à C3 au lieu du backbone) ; aucune mention de l'inférence ni des fuites \| réécrit ; renvoi à la nouvelle §7 \|
	35	+\| 2 Literature \| couvre les trois courants \| mince (33 réf., 29 citées) ; manquaient Hill–Scholz, Pace–Hayunga, Lorenz et al., Diewert–Shimizu, Kuminoff et al., DM/GW, Bergmeir–Benítez, Roberts et al., Dubé–Legros ; deux entrées non citées et défectueuses \| +27 réf. vérifiées, −2 retirées ; Mayer et al. 2019 identifié comme antécédent direct (updating) \|
	36	+\| 3 Data \| cascade d'échantillon transparente \| moyenne/médiane des prix fausses dans le texte (462k/399k vs 440k/397k) ; 86 ventes « other » omises ; nettoyage des lots > P99.5 non documenté ; bornes âge/étages non dites \| corrigé ; répartition annuelle ; repeat sales et coordonnées partagées quantifiés \|
	37	+\| 4 Methodology \| design clair \| aucune équation ; Duan non écrit ; inversion Box–Cox sans discussion du biais ; « clamped month counter » inexact (rien n'est écrêté ; arbres constants par morceaux) ; catégories non vues → référence arbitraire non dit ; hyper-paramètres « a priori » sans analyse \| 13 équations ; règles médiane/moyenne ; carry-forward ; holdouts groupés ; inférence ; décomposition niveau/skill \|
	38	+\| 5 Results \| lecture par axe efficace \| « seventeen K sales » (17) ; ordre A sous split temporel non discuté (A1 gagne par coïncidence de biais) ; différences non testées \| réécrit avec IC/tests ; §5.5 refondue (décomposition) \|
	39	+\| 6 Extensions \| objets économiques pertinents \| « all five agree within a few points » (indice GB −9 pts) ; « within a tenth of a log point » (0.10–0.13 systématique, profil plus plat) ; learning/segments chiffrés sous scoreboard asymétrique \| affirmations ramenées aux chiffres ; mécanismes de l'écart d'indice discutés \|
	40	+\| 7 Robustness \| — (n'existait pas) \| — \| nouvelle section : retransformation, fuites, FE emboîtés, Box–Cox fin, hyper-paramètres HGB \|
	41	+\| 8 Discussion \| hiérarchie utile \| liste à puces ; « the machines lose their edge » (faux) ; « 3–7 points » sans mécanisme \| prose ; hiérarchie révisée ; mécanisme niveau vs skill ; implications réécrites \|
	42	+\| 9 Conclusion \| synthèse pratique \| reprenait le renversement ; « within two points of the frontier in deployment » \| réécrite \|
	43	+
	44	+## 3. Méthodologie — verdict motivé (§3 du master prompt)
	45	+
	46	+- Spécification justifiée : oui, le design par axes est la force du papier ; la v1.1 le formalise (éq. 1–9).
	47	+- Hypothèses d'estimation explicites : désormais oui (OLS via ridge α = 10⁻⁶, solution de norme minimale ;
	48	+ encodage one-hot avec référence ; splines : 6 nœuds uniformes, 8 fonctions de base ; RF/HGB : paramètres listés).
	49	+- Variables définies : oui (notation unifiée §4.1 ; bornes et nettoyages §3.2).
	50	+- Effets fixes appropriés : oui, avec la nuance majeure trouvée en révision : les FE fins doivent être emboîtés
	51	+ (C4′ 17.3 % = meilleure spécification linéaire ; C4 seule 21.0 %). Le repli « cellule de référence » pour les
	52	+ cellules non vues est désormais documenté et corrigé par l'emboîtement.
	53	+- Dépendance spatiale/temporelle : traitée par l'inférence groupée (municipalités) et par les holdouts
	54	+ temporel, par parcelle et par bâtiment. Limite : pas de holdout bloqué spatialement (déclaré).
	55	+- Erreurs-types : absentes en v1.0 → bootstrap par grappes + DM groupé (éq. 13). Le DM sur la perte APE teste la
	56	+ MAPE, pas la MdAPE ; le bootstrap couvre la médiane — distinction explicitée.
	57	+- Construction de l'échantillon transparente : oui (cascade 745 119 → 514 212, reproduite à l'identique).
	58	+- Robustesse : cinq analyses ajoutées (§7) ; toutes exécutées, aucune revendiquée sans sortie.
	59	+- Interprétation : « 18 % improvement » = (17.6−14.4)/17.6 ; « ten points » = 28.8 − 19.2 ; profils en log-points.
	60	+- Significativité statistique vs économique : distinguée systématiquement (ex. C3 vs C2, D2 vs A6, D4 vs A6
	61	+ forward : non significatifs ; D4 vs A6 random : −3.2 [−4.7, −1.9]).
	62	+- Langage causal : aucun ; le papier est prédictif/descriptif.
	63	+
	64	+## 4. Résultats — incohérences trouvées et état des réanalyses
	65	+
	66	+- Toutes les incohérences texte/table de la v1.0 sont listées au CHANGELOG §1.1, 1.5, 1.8, 1.9, 1.11.
	67	+- Nouveaux résultats (tous issus de `results/reproduced/*.csv`) :
	68	+ - Scoreboard uniforme (Tables 2–3) ; IC et tests (Tables 4–5) ; décomposition (Tables 8–9, Fig. 6) ;
	69	+ retransformation (Table 12) ; splits groupés (Table 13) ; vu/non vu (Table 14) ; FE emboîtés (Table 15) ;
	70	+ sensibilité HGB (Table 16) ; Box–Cox fin (texte §7.4).
	71	+ - Sensibilité HGB : quatre configurations alternatives (shallow, deep, régularisée, early stopping sur les 10 % derniers mois) donnent 14.3–14.7 % (random) et 21.2–21.6 % (forward) contre 14.4 / 21.2 pour le baseline ; le classement et la décomposition ne dépendent pas des hyper-paramètres fixés a priori.
	72	+- Vérifications de cohérence finales : abstract ↔ §1 ↔ §5 ↔ §8–9 ↔ README ↔ tables (chiffres relus un à un après
	73	+ régénération des tables ; voir CHANGELOG §1.1 pour la liste ancien → nouveau).
	74	+
	75	+## 5. Littérature
	76	+
	77	+33 entrées en v1.0 → 58 en v1.1 ; 100 % vérifiées sur Crossref avec DOI (sauf LightGBM, actes NIPS non indexés,
	78	+référence standard). 2 entrées retirées (non citées, défectueuses), 5 corrigées, 27 ajoutées et intégrées à
	79	+l'argument (fonctionnelle : Kuminoff et al. 2010, Bishop et al. 2020, Khoshnoud et al. 2023 ; spatial : Pace et al.
	80	+1998, Clapp 2003, Fik et al. 2003, Bourassa et al. 2003/2007, Goodman–Thibodeau 2003, Pace–Hayunga 2020,
	81	+Dubé–Legros 2013 ; indices : Hill et al. 2009, Hill–Scholz 2018, Diewert et al. 2015, Diewert–Shimizu 2016 ;
	82	+ML/AVM : Zurada et al. 2011, Antipov–Pokryshevskaya 2012, McCluskey et al. 2013, Schulz et al. 2014, Pérez-Rave et al.
	83	+2019, Lorenz et al. 2023, Thibodeau 2003 ; validation : Diebold–Mariano 1995, Giacomini–White 2006,
	84	+Bergmeir–Benítez 2012, Roberts et al. 2017 ; retransformation : Manning–Mullahy 2001).
	85	+
	86	+## 6. Limites restantes
	87	+
	88	+1. Un seul régime temporel testé (reprise 2025-26, +15 % en 18 mois) ; l'ordre des coûts de staleness entre
	89	+ familles pourrait différer dans un marché plat ou baissier.
	90	+2. Le re-nivellement utilise le ratio médian du test (oracle) : borne supérieure de ce qu'un indice publié
	91	+ permettrait.
	92	+3. Pas de modèle linéaire à surface spatiale continue (krigeage, splines bivariées) ni d'effets hiérarchiques
	93	+ proprement estimés ; l'emboîtement C4′ est un pooling partiel implicite (norme minimale), pas un modèle
	94	+ hiérarchique explicite.
	95	+4. Les holdouts groupés neutralisent reventes et bâtiments partagés, pas le voisinage immédiat.
	96	+5. Attributs du rôle seulement (pas de qualité intérieure, texte d'annonce, photos) ; l'avantage ML mesuré est celui
	97	+ de la flexibilité spatiale sur cet ensemble d'attributs.
	98	+6. La règle « lots > P99.5 → 0 » héritée du pipeline est documentée mais discutable.
	99	+7. Longueur : 40 pages avec 16 tables et 10 figures ; pour la soumission, les Tables 4–5, 12–16 iraient en annexe.
	100	+
	101	+## 7. Analyses recommandées non exécutées
	102	+
	103	+Voir CHANGELOG §4 : surface spatiale linéaire (krigeage / splines lat-lng / hiérarchique bayésien) ; holdout bloqué
	104	+spatialement ; re-nivellement par indice externe ; fenêtres temporelles alternatives (rolling origin) ; variante
	105	+« médiane calibrée » ; LightGBM/XGBoost natifs et réseaux de neurones ; coût de retraining mensuel (course
	106	+glissante) — cette dernière est la plus importante pour la revue cible.
	107	+
	108	+## 8. Maturité pour publication
	109	+
	110	+- Verdict : manuscrit désormais défendable en revue (JHE) sous réserve d'une coupe (annexes) et, idéalement, d'une
	111	+ course glissante (retraining mensuel) qui transformerait la §5.5 en recommandation opérationnelle chiffrée.
	112	+- Risques de rejet : (i) « un seul marché, une seule période » — mitigé par l'échelle provinciale et la
	113	+ décomposition ; (ii) absence d'un modèle linéaire spatialement sophistiqué comme concurrent (un rapporteur
	114	+ spatial le demandera) ; (iii) la correction d'un résultat phare publié en v1.0 sur SSRN doit être assumée
	115	+ explicitement (fait dans l'abstract et §7.1) — c'est un point d'intégrité, pas une faiblesse.
	116	+- Points forts pour la revue : taille et qualité des données, design expérimental, inférence complète,
	117	+ décomposition niveau/compétence inédite dans la littérature AVM, résultat « hierarchy, not coarseness » sur les FE
	118	+ spatiaux.

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52	57	% METADATA
53	58	% ============================================================================
		`@@ -55,9 +60,9 @@`
55	60	\newcommand{\WPtitle}{Half a Million Prices, Twenty Models}
56	61	\newcommand{\WPsubtitle}{A Systematic Assessment of Hedonic Specifications and
57	62	Estimation Methods for the Quebec Housing Market, 2021--2026}
58		−\newcommand{\WPdate}{August 2026}
59		−\newcommand{\WPversion}{1.0}
60		−\newcommand{\WPkeywords}{hedonic pricing, functional form, house price indexes, machine learning, automated valuation, out-of-sample prediction}
	63	+\newcommand{\WPdate}{October 2026}
	64	+\newcommand{\WPversion}{1.1}
	65	+\newcommand{\WPkeywords}{hedonic price function, functional form, spatial fixed effects, machine learning, automated valuation model, forward-in-time holdout, house price index}
61	66	\newcommand{\WPjel}{R31, C21, C52, C53}
62	67
63	68	\newcommand{\WPauthor}{Simon-Pierre Boucher}
		`@@ -68,34 +73,39 @@`
68	73	283, boulevard Alexandre-Tach\'e\\ Gatineau, Qu\'ebec, Canada J9A 1L8}
69	74
70	75	\newcommand{\WPabstract}{%
71		−Fifty years after \mbox{Rosen (1974)}, applied hedonic practice still rests
72		−on a series of specification choices --- functional form, temporal controls,
73		−spatial controls, estimation method --- that are usually made by convention
74		−and rarely stress-tested jointly. Using 514{,}212 Quebec residential sales
	76	+Fifty years after \mbox{Rosen (1974)}, applied hedonic practice still
	77	+rests on specification choices---functional form, temporal controls,
	78	+spatial controls, estimation method---that are made by convention and
	79	+rarely stress-tested jointly. Using 514{,}212 Quebec residential sales
75	80	(2021--2026) whose structural descriptors come from the provincial
76	81	assessment roll, we run a controlled horse race of twenty hedonic models
77		−organised along those four axes, scoring every model on identical holdouts
78		−and on the same price-level scoreboard (log models are Duan-retransformed,
79		−Box--Cox inverted). Four lessons emerge. (1)~Functional form is a
80		−second-order choice: from linear to splines, the median absolute error
81		−spans 16.5--18.6\%, and the data-driven Box--Cox exponent
82		−($\hat\lambda=0.25$) buys nothing out of sample. (2)~Controls are
83		−first-order: dropping spatial fixed effects costs ten error points ---
84		−location granularity matters far more than curvature, but only up to the
85		−point where cells thin out (a $\sim$1~km grid \emph{overfits}).
86		−(3)~Under random validation, estimation method matters most: gradient
87		−boosting reaches 14.4\% --- a 13\% improvement over the best linear model
88		−--- with random forests close behind, and coordinates alone reproduce
89		−what thousands of dummies buy. (4)~But the ranking is an artifact of the
90		−split: under a forward-in-time holdout (train $<$~2025, test 2025--26)
91		−every model degrades and the machine-learning advantage \emph{reverses}
92		−--- the spline hedonic posts a lower median error than gradient boosting
93		−and an identical log-scale $R^2$ --- a stark warning against evaluating
94		−valuation models with random cross-validation alone. Constant-quality
95		−monthly indices implied by the competing forms agree to within a few
96		−index points, and the boosted model's implicit age and floor-area
97		−profiles track the quadratic OLS closely --- interpretation and
98		−prediction disagree less than the random-validation gap suggests.}
	82	+organised along those four axes, scoring every model on identical
	83	+holdouts and on the same price-level scoreboard with a single
	84	+retransformation rule, with a municipality-clustered bootstrap interval
	85	+and a Diebold--Mariano-type test on every comparison. Four lessons
	86	+emerge. (1)~Functional form is second-order: from linear to splines the
	87	+median absolute error spans 17.6--19.2\%, and the data-driven Box--Cox
	88	+exponent gains half a point. (2)~Controls are first-order: dropping
	89	+spatial fixed effects costs ten error points; a $\sim$1~km grid used
	90	+alone overfits, but nested within municipalities it becomes the most
	91	+accurate linear specification---the bias--variance trade-off in
	92	+absorbing location is resolved by hierarchy, not coarseness. (3)~Under
	93	+random validation the estimation method dominates: gradient boosting
	94	+reaches 14.4\%, an 18\% improvement over the best linear model
	95	+(95\% interval 1.9--4.7 points) that survives holdouts grouped by parcel
	96	+and by building and comes almost entirely from a flexible surface over
	97	+space. (4)~Under a forward-in-time holdout (train $<$~2025, test
	98	+2025--26) every model degrades and the gap shrinks to half a point and
	99	+to insignificance. Decomposing the error shows that relative pricing
	100	+skill transfers forward intact (re-levelled, boosting leads by 2.9
	101	+points) while every frozen model under-prices the typical future sale
	102	+by 10--20\%, the ensembles slightly more. Random cross-validation hides
	103	+this staleness cost; it does not, as a previous version of this paper
	104	+claimed on the basis of an asymmetric retransformation, reverse the
	105	+ranking. Constant-quality indices agree across functional forms to
	106	+within 2.5 points but a boosted imputation index rises nine points less,
	107	+and the boosted model's implicit age and area profiles share the shape
	108	+of the quadratic OLS gradients with a milder depreciation path.}
99	109
100	110	\hypersetup{pdftitle={Half a Million Prices, Twenty Models}, pdfauthor={Simon-Pierre Boucher}}
101	111
		`@@ -113,10 +123,11 @@ prediction disagree less than the random-validation gap suggests.}`
113	123	\input{sections/methodology}
114	124	\input{sections/results}
115	125	\input{sections/extensions}
	126	+\input{sections/robustness}
116	127	\input{sections/discussion}
117	128	\input{sections/conclusion}
118	129
119		−\newpage
	130	+\clearpage
120	131	\bibliography{references}
121	132
122	133	\end{document}

modified paper/main_anon.pdf +0 −0

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modified paper/main_anon.tex +41 −30

		`@@ -48,6 +48,11 @@`
48	48	\usepackage{enumitem}
49	49	\setlist{nosep,leftmargin=*}
50	50
	51	+% Float placement: let text and floats share pages
	52	+\setcounter{topnumber}{3}\setcounter{bottomnumber}{2}\setcounter{totalnumber}{4}
	53	+\renewcommand{\topfraction}{0.9}\renewcommand{\bottomfraction}{0.6}
	54	+\renewcommand{\textfraction}{0.08}\renewcommand{\floatpagefraction}{0.5}
	55	+
51	56	% ============================================================================
52	57	% METADATA
53	58	% ============================================================================
		`@@ -55,9 +60,9 @@`
55	60	\newcommand{\WPtitle}{Half a Million Prices, Twenty Models}
56	61	\newcommand{\WPsubtitle}{A Systematic Assessment of Hedonic Specifications and
57	62	Estimation Methods for the Quebec Housing Market, 2021--2026}
58		−\newcommand{\WPdate}{August 2026}
59		−\newcommand{\WPversion}{1.0}
60		−\newcommand{\WPkeywords}{hedonic pricing, functional form, house price indexes, machine learning, automated valuation, out-of-sample prediction}
	63	+\newcommand{\WPdate}{October 2026}
	64	+\newcommand{\WPversion}{1.1}
	65	+\newcommand{\WPkeywords}{hedonic price function, functional form, spatial fixed effects, machine learning, automated valuation model, forward-in-time holdout, house price index}
61	66	\newcommand{\WPjel}{R31, C21, C52, C53}
62	67
63	68	\newcommand{\WPauthor}{Simon-Pierre Boucher}
		`@@ -68,34 +73,39 @@`
68	73	283, boulevard Alexandre-Tach\'e\\ Gatineau, Qu\'ebec, Canada J9A 1L8}
69	74
70	75	\newcommand{\WPabstract}{%
71		−Fifty years after \mbox{Rosen (1974)}, applied hedonic practice still rests
72		−on a series of specification choices --- functional form, temporal controls,
73		−spatial controls, estimation method --- that are usually made by convention
74		−and rarely stress-tested jointly. Using 514{,}212 Quebec residential sales
	76	+Fifty years after \mbox{Rosen (1974)}, applied hedonic practice still
	77	+rests on specification choices---functional form, temporal controls,
	78	+spatial controls, estimation method---that are made by convention and
	79	+rarely stress-tested jointly. Using 514{,}212 Quebec residential sales
75	80	(2021--2026) whose structural descriptors come from the provincial
76	81	assessment roll, we run a controlled horse race of twenty hedonic models
77		−organised along those four axes, scoring every model on identical holdouts
78		−and on the same price-level scoreboard (log models are Duan-retransformed,
79		−Box--Cox inverted). Four lessons emerge. (1)~Functional form is a
80		−second-order choice: from linear to splines, the median absolute error
81		−spans 16.5--18.6\%, and the data-driven Box--Cox exponent
82		−($\hat\lambda=0.25$) buys nothing out of sample. (2)~Controls are
83		−first-order: dropping spatial fixed effects costs ten error points ---
84		−location granularity matters far more than curvature, but only up to the
85		−point where cells thin out (a $\sim$1~km grid \emph{overfits}).
86		−(3)~Under random validation, estimation method matters most: gradient
87		−boosting reaches 14.4\% --- a 13\% improvement over the best linear model
88		−--- with random forests close behind, and coordinates alone reproduce
89		−what thousands of dummies buy. (4)~But the ranking is an artifact of the
90		−split: under a forward-in-time holdout (train $<$~2025, test 2025--26)
91		−every model degrades and the machine-learning advantage \emph{reverses}
92		−--- the spline hedonic posts a lower median error than gradient boosting
93		−and an identical log-scale $R^2$ --- a stark warning against evaluating
94		−valuation models with random cross-validation alone. Constant-quality
95		−monthly indices implied by the competing forms agree to within a few
96		−index points, and the boosted model's implicit age and floor-area
97		−profiles track the quadratic OLS closely --- interpretation and
98		−prediction disagree less than the random-validation gap suggests.}
	82	+organised along those four axes, scoring every model on identical
	83	+holdouts and on the same price-level scoreboard with a single
	84	+retransformation rule, with a municipality-clustered bootstrap interval
	85	+and a Diebold--Mariano-type test on every comparison. Four lessons
	86	+emerge. (1)~Functional form is second-order: from linear to splines the
	87	+median absolute error spans 17.6--19.2\%, and the data-driven Box--Cox
	88	+exponent gains half a point. (2)~Controls are first-order: dropping
	89	+spatial fixed effects costs ten error points; a $\sim$1~km grid used
	90	+alone overfits, but nested within municipalities it becomes the most
	91	+accurate linear specification---the bias--variance trade-off in
	92	+absorbing location is resolved by hierarchy, not coarseness. (3)~Under
	93	+random validation the estimation method dominates: gradient boosting
	94	+reaches 14.4\%, an 18\% improvement over the best linear model
	95	+(95\% interval 1.9--4.7 points) that survives holdouts grouped by parcel
	96	+and by building and comes almost entirely from a flexible surface over
	97	+space. (4)~Under a forward-in-time holdout (train $<$~2025, test
	98	+2025--26) every model degrades and the gap shrinks to half a point and
	99	+to insignificance. Decomposing the error shows that relative pricing
	100	+skill transfers forward intact (re-levelled, boosting leads by 2.9
	101	+points) while every frozen model under-prices the typical future sale
	102	+by 10--20\%, the ensembles slightly more. Random cross-validation hides
	103	+this staleness cost; it does not, as a previous version of this paper
	104	+claimed on the basis of an asymmetric retransformation, reverse the
	105	+ranking. Constant-quality indices agree across functional forms to
	106	+within 2.5 points but a boosted imputation index rises nine points less,
	107	+and the boosted model's implicit age and area profiles share the shape
	108	+of the quadratic OLS gradients with a milder depreciation path.}
99	109
100	110	\hypersetup{pdftitle={Half a Million Prices, Twenty Models}, pdfauthor={}}
101	111
		`@@ -113,10 +123,11 @@ prediction disagree less than the random-validation gap suggests.}`
113	123	\input{sections/methodology}
114	124	\input{sections/results}
115	125	\input{sections/extensions}
	126	+\input{sections/robustness}
116	127	\input{sections/discussion}
117	128	\input{sections/conclusion}
118	129
119		−\newpage
	130	+\clearpage
120	131	\bibliography{references}
121	132
122	133	\end{document}

modified paper/references.bib +371 −60

		`@@ -7,7 +7,8 @@`
7	7	year = {1974},
8	8	volume = {82},
9	9	number = {1},
10		− pages = {34--55}
	10	+ pages = {34--55},
	11	+ doi = {10.1086/260169}
11	12	}
12	13
13	14	@article{halvorsen1981choice,
		`@@ -17,7 +18,8 @@`
17	18	year = {1981},
18	19	volume = {10},
19	20	number = {1},
20		− pages = {37--49}
	21	+ pages = {37--49},
	22	+ doi = {10.1016/0094-1190(81)90021-8}
21	23	}
22	24
23	25	@article{cassel1985cost,
		`@@ -27,7 +29,8 @@`
27	29	year = {1985},
28	30	volume = {18},
29	31	number = {2},
30		− pages = {135--142}
	32	+ pages = {135--142},
	33	+ doi = {10.1016/0094-1190(85)90012-9}
31	34	}
32	35
33	36	@article{cropper1988choice,
		`@@ -37,7 +40,8 @@`
37	40	year = {1988},
38	41	volume = {70},
39	42	number = {4},
40		− pages = {668--675}
	43	+ pages = {668--675},
	44	+ doi = {10.2307/1935831}
41	45	}
42	46
43	47	@article{box1964analysis,
		`@@ -47,7 +51,8 @@`
47	51	year = {1964},
48	52	volume = {26},
49	53	number = {2},
50		− pages = {211--252}
	54	+ pages = {211--243},
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51	56	}
52	57
53	58	@incollection{malpezzi2003hedonic,
		`@@ -58,7 +63,8 @@`
58	63	publisher = {Blackwell},
59	64	address = {Oxford},
60	65	year = {2003},
61		− pages = {67--89}
	66	+ pages = {67--89},
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62	68	}
63	69
64	70	@article{sirmans2005composition,
		`@@ -68,7 +74,8 @@`
68	74	year = {2005},
69	75	volume = {13},
70	76	number = {1},
71		− pages = {1--44}
	77	+ pages = {1--44},
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72	79	}
73	80
74	81	@article{duan1983smearing,
		`@@ -78,7 +85,8 @@`
78	85	year = {1983},
79	86	volume = {78},
80	87	number = {383},
81		− pages = {605--610}
	88	+ pages = {605--610},
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82	90	}
83	91
84	92	@article{goodman1998housing,
		`@@ -88,7 +96,8 @@`
88	96	year = {1998},
89	97	volume = {7},
90	98	number = {2},
91		− pages = {121--143}
	99	+ pages = {121--143},
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92	101	}
93	102
94	103	@article{dubin1998spatial,
		`@@ -98,7 +107,8 @@`
98	107	year = {1998},
99	108	volume = {7},
100	109	number = {4},
101		− pages = {304--327}
	110	+ pages = {304--327},
	111	+ doi = {10.1006/jhec.1998.0236}
102	112	}
103	113
104	114	@book{anselin1988spatial,
		`@@ -106,7 +116,8 @@`
106	116	title = {Spatial Econometrics: Methods and Models},
107	117	publisher = {Kluwer Academic},
108	118	address = {Dordrecht},
109		− year = {1988}
	119	+ year = {1988},
	120	+ doi = {10.1007/978-94-015-7799-1}
110	121	}
111	122
112	123	@article{hill2013hedonic,
		`@@ -116,25 +127,19 @@`
116	127	year = {2013},
117	128	volume = {27},
118	129	number = {5},
119		− pages = {879--914}
	130	+ pages = {879--914},
	131	+ doi = {10.1111/j.1467-6419.2012.00731.x}
120	132	}
121	133
122		−@article{silver2018house,
123		− author = {Silver, Mick},
124		− title = {How to Measure Hedonic Property Price Indexes Better},
125		− journal = {EURONA — Eurostat Review on National Accounts and Macroeconomic Indicators},
126		− year = {2018},
127		− volume = {1},
128		− pages = {35--66}
129		−}
130		−
131		−@article{diewert2004hedonic,
132		− author = {Diewert, W. Erwin},
133		− title = {Hedonic Regressions: A Review of Some Unresolved Issues},
134		− journal = {Mimeo, University of British Columbia},
135		− year = {2004},
136		− volume = {},
137		− pages = {}
	134	+@techreport{silver2018house,
	135	+ author = {Silver, Mick},
	136	+ title = {How to Better Measure Hedonic Residential Property Price Indexes},
	137	+ institution = {International Monetary Fund},
	138	+ type = {IMF Working Paper},
	139	+ number = {16/213},
	140	+ address = {Washington, DC},
	141	+ year = {2016},
	142	+ doi = {10.5089/9781475552249.001}
138	143	}
139	144
140	145	@article{mullainathan2017machine,
		`@@ -144,7 +149,8 @@`
144	149	year = {2017},
145	150	volume = {31},
146	151	number = {2},
147		− pages = {87--106}
	152	+ pages = {87--106},
	153	+ doi = {10.1257/jep.31.2.87}
148	154	}
149	155
150	156	@article{athey2019machine,
		`@@ -153,7 +159,8 @@`
153	159	journal = {Annual Review of Economics},
154	160	year = {2019},
155	161	volume = {11},
156		− pages = {685--725}
	162	+ pages = {685--725},
	163	+ doi = {10.1146/annurev-economics-080217-053433}
157	164	}
158	165
159	166	@article{breiman2001random,
		`@@ -163,7 +170,8 @@`
163	170	year = {2001},
164	171	volume = {45},
165	172	number = {1},
166		− pages = {5--32}
	173	+ pages = {5--32},
	174	+ doi = {10.1023/A:1010933404324}
167	175	}
168	176
169	177	@article{friedman2001greedy,
		`@@ -173,16 +181,18 @@`
173	181	year = {2001},
174	182	volume = {29},
175	183	number = {5},
176		− pages = {1189--1232}
	184	+ pages = {1189--1232},
	185	+ doi = {10.1214/aos/1013203451}
177	186	}
178	187
179		−@article{ke2017lightgbm,
180		− author = {Ke, Guolin and Meng, Qi and Finley, Thomas and Wang, Taifeng and Chen, Wei and Ma, Weidong and Ye, Qiwei and Liu, Tie-Yan},
181		− title = {{LightGBM}: A Highly Efficient Gradient Boosting Decision Tree},
182		− journal = {Advances in Neural Information Processing Systems},
183		− year = {2017},
184		− volume = {30},
185		− pages = {3146--3154}
	188	+@inproceedings{ke2017lightgbm,
	189	+ author = {Ke, Guolin and Meng, Qi and Finley, Thomas and Wang, Taifeng and Chen, Wei and Ma, Weidong and Ye, Qiwei and Liu, Tie-Yan},
	190	+ title = {{LightGBM}: A Highly Efficient Gradient Boosting Decision Tree},
	191	+ booktitle = {Advances in Neural Information Processing Systems 30 (NIPS 2017)},
	192	+ editor = {Guyon, I. and Von Luxburg, U. and Bengio, S. and Wallach, H. and Fergus, R. and Vishwanathan, S. and Garnett, R.},
	193	+ publisher = {Curran Associates},
	194	+ year = {2017},
	195	+ pages = {3146--3154}
186	196	}
187	197
188	198	@article{mayer2019estimation,
		`@@ -192,7 +202,8 @@`
192	202	year = {2019},
193	203	volume = {12},
194	204	number = {1},
195		− pages = {134--150}
	205	+ pages = {134--150},
	206	+ doi = {10.1108/JERER-08-2018-0035}
196	207	}
197	208
198	209	@article{bogin2020house,
		`@@ -201,8 +212,9 @@`
201	212	journal = {Journal of Real Estate Finance and Economics},
202	213	year = {2020},
203	214	volume = {60},
204		− number = {1},
205		− pages = {40--52}
	215	+ number = {1--2},
	216	+ pages = {40--52},
	217	+ doi = {10.1007/s11146-019-09712-0}
206	218	}
207	219
208	220	@article{steurer2021metrics,
		`@@ -212,7 +224,8 @@`
212	224	year = {2021},
213	225	volume = {38},
214	226	number = {2},
215		− pages = {99--129}
	227	+ pages = {99--129},
	228	+ doi = {10.1080/09599916.2020.1858937}
216	229	}
217	230
218	231	@article{pace1997spatial,
		`@@ -222,7 +235,8 @@`
222	235	year = {1997},
223	236	volume = {14},
224	237	number = {3},
225		− pages = {333--340}
	238	+ pages = {333--340},
	239	+ doi = {10.1023/A:1007762613901}
226	240	}
227	241
228	242	@article{case2004modeling,
		`@@ -232,7 +246,8 @@`
232	246	year = {2004},
233	247	volume = {29},
234	248	number = {2},
235		− pages = {167--191}
	249	+ pages = {167--191},
	250	+ doi = {10.1023/B:REAL.0000035309.60607.53}
236	251	}
237	252
238	253	@article{bourassa2010predicting,
		`@@ -242,7 +257,8 @@`
242	257	year = {2010},
243	258	volume = {32},
244	259	number = {2},
245		− pages = {139--159}
	260	+ pages = {139--159},
	261	+ doi = {10.1080/10835547.2010.12091276}
246	262	}
247	263
248	264	@article{desrosiers2000hedonic,
		`@@ -252,7 +268,8 @@`
252	268	year = {2000},
253	269	volume = {18},
254	270	number = {3},
255		− pages = {291--315}
	271	+ pages = {291--315},
	272	+ doi = {10.1108/14635780010338245}
256	273	}
257	274
258	275	@article{gencay1996nonlinear,
		`@@ -262,7 +279,8 @@`
262	279	year = {1996},
263	280	volume = {52},
264	281	number = {2},
265		− pages = {129--135}
	282	+ pages = {129--135},
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266	284	}
267	285
268	286	@article{kok2017big,
		`@@ -272,7 +290,8 @@`
272	290	year = {2017},
273	291	volume = {43},
274	292	number = {6},
275		− pages = {202--211}
	293	+ pages = {202--211},
	294	+ doi = {10.3905/jpm.2017.43.6.202}
276	295	}
277	296
278	297	@article{hong2020machine,
		`@@ -282,16 +301,8 @@`
282	301	year = {2020},
283	302	volume = {24},
284	303	number = {3},
285		− pages = {140--152}
286		−}
287		−
288		−@article{knight2003listing,
289		− author = {Knight, John R.},
290		− title = {Hedonic Modeling of the Home Selling Process},
291		− journal = {Journal of Real Estate Finance and Economics},
292		− year = {2003},
293		− volume = {},
294		− pages = {}
	304	+ pages = {140--152},
	305	+ doi = {10.3846/ijspm.2020.11544}
295	306	}
296	307
297	308	@book{iaao2013standard,
		`@@ -309,7 +320,8 @@`
309	320	year = {2002},
310	321	volume = {24},
311	322	number = {1},
312		− pages = {1--26}
	323	+ pages = {1--26},
	324	+ doi = {10.1080/10835547.2002.12091087}
313	325	}
314	326
315	327	@techreport{oecd2013handbook,
		`@@ -319,3 +331,302 @@`
319	331	address = {Luxembourg},
320	332	year = {2013}
321	333	}
	334	+
	335	+% ---------------------------------------------------------------------------
	336	+% References added in the 2026-10-17 revision (all verified on Crossref)
	337	+% ---------------------------------------------------------------------------
	338	+
	339	+@article{hill2018geospatial,
	340	+ author = {Hill, Robert J. and Scholz, Michael},
	341	+ title = {Can Geospatial Data Improve House Price Indexes? {A} Hedonic Imputation Approach with Splines},
	342	+ journal = {Review of Income and Wealth},
	343	+ year = {2018},
	344	+ volume = {64},
	345	+ number = {4},
	346	+ pages = {737--756},
	347	+ doi = {10.1111/roiw.12303}
	348	+}
	349	+
	350	+@article{pace2020residuals,
	351	+ author = {Pace, R. Kelley and Hayunga, Darren},
	352	+ title = {Examining the Information Content of Residuals from Hedonic and Spatial Models Using Trees and Forests},
	353	+ journal = {Journal of Real Estate Finance and Economics},
	354	+ year = {2020},
	355	+ volume = {60},
	356	+ number = {1--2},
	357	+ pages = {170--180},
	358	+ doi = {10.1007/s11146-019-09724-w}
	359	+}
	360	+
	361	+@article{lorenz2023interpretable,
	362	+ author = {Lorenz, Felix and Willwersch, Jonas and Cajias, Marcelo and Fuerst, Franz},
	363	+ title = {Interpretable Machine Learning for Real Estate Market Analysis},
	364	+ journal = {Real Estate Economics},
	365	+ year = {2023},
	366	+ volume = {51},
	367	+ number = {5},
	368	+ pages = {1178--1208},
	369	+ doi = {10.1111/1540-6229.12397}
	370	+}
	371	+
	372	+@article{diewert2016tokyo,
	373	+ author = {Diewert, W. Erwin and Shimizu, Chihiro},
	374	+ title = {Hedonic Regression Models for {Tokyo} Condominium Sales},
	375	+ journal = {Regional Science and Urban Economics},
	376	+ year = {2016},
	377	+ volume = {60},
	378	+ pages = {300--315},
	379	+ doi = {10.1016/j.regsciurbeco.2016.08.002}
	380	+}
	381	+
	382	+@article{diewert2015decomposition,
	383	+ author = {Diewert, W. Erwin and de Haan, Jan and Hendriks, Rens},
	384	+ title = {Hedonic Regressions and the Decomposition of a House Price Index into Land and Structure Components},
	385	+ journal = {Econometric Reviews},
	386	+ year = {2015},
	387	+ volume = {34},
	388	+ number = {1--2},
	389	+ pages = {106--126},
	390	+ doi = {10.1080/07474938.2014.944791}
	391	+}
	392	+
	393	+@article{diebold1995comparing,
	394	+ author = {Diebold, Francis X. and Mariano, Roberto S.},
	395	+ title = {Comparing Predictive Accuracy},
	396	+ journal = {Journal of Business \& Economic Statistics},
	397	+ year = {1995},
	398	+ volume = {13},
	399	+ number = {3},
	400	+ pages = {253--263},
	401	+ doi = {10.1080/07350015.1995.10524599}
	402	+}
	403	+
	404	+@article{giacomini2006tests,
	405	+ author = {Giacomini, Raffaella and White, Halbert},
	406	+ title = {Tests of Conditional Predictive Ability},
	407	+ journal = {Econometrica},
	408	+ year = {2006},
	409	+ volume = {74},
	410	+ number = {6},
	411	+ pages = {1545--1578},
	412	+ doi = {10.1111/j.1468-0262.2006.00718.x}
	413	+}
	414	+
	415	+@article{bergmeir2012crossvalidation,
	416	+ author = {Bergmeir, Christoph and Ben{\'i}tez, Jos{\'e} M.},
	417	+ title = {On the Use of Cross-Validation for Time Series Predictor Evaluation},
	418	+ journal = {Information Sciences},
	419	+ year = {2012},
	420	+ volume = {191},
	421	+ pages = {192--213},
	422	+ doi = {10.1016/j.ins.2011.12.028}
	423	+}
	424	+
	425	+@article{roberts2017crossvalidation,
	426	+ author = {Roberts, David R. and Bahn, Volker and Ciuti, Simone and Boyce, Mark S. and Elith, Jane and Guillera-Arroita, Gurutzeta and Hauenstein, Severin and Lahoz-Monfort, Jos{\'e} J. and Schr{\"o}der, Boris and Thuiller, Wilfried and Warton, David I. and Wintle, Brendan A. and Hartig, Florian and Dormann, Carsten F.},
	427	+ title = {Cross-Validation Strategies for Data with Temporal, Spatial, Hierarchical, or Phylogenetic Structure},
	428	+ journal = {Ecography},
	429	+ year = {2017},
	430	+ volume = {40},
	431	+ number = {8},
	432	+ pages = {913--929},
	433	+ doi = {10.1111/ecog.02881}
	434	+}
	435	+
	436	+@article{kuminoff2010which,
	437	+ author = {Kuminoff, Nicolai V. and Parmeter, Christopher F. and Pope, Jaren C.},
	438	+ title = {Which Hedonic Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental Amenities?},
	439	+ journal = {Journal of Environmental Economics and Management},
	440	+ year = {2010},
	441	+ volume = {60},
	442	+ number = {3},
	443	+ pages = {145--160},
	444	+ doi = {10.1016/j.jeem.2010.06.001}
	445	+}
	446	+
	447	+@article{bishop2020best,
	448	+ author = {Bishop, Kelly C. and Kuminoff, Nicolai V. and Banzhaf, H. Spencer and Boyle, Kevin J. and von Gravenitz, Kathrine and Pope, Jaren C. and Smith, V. Kerry and Timmins, Christopher D.},
	449	+ title = {Best Practices for Using Hedonic Property Value Models to Measure Willingness to Pay for Environmental Quality},
	450	+ journal = {Review of Environmental Economics and Policy},
	451	+ year = {2020},
	452	+ volume = {14},
	453	+ number = {2},
	454	+ pages = {260--281},
	455	+ doi = {10.1093/reep/reaa001}
	456	+}
	457	+
	458	+@article{bourassa2007spatial,
	459	+ author = {Bourassa, Steven C. and Cantoni, Eva and Hoesli, Martin},
	460	+ title = {Spatial Dependence, Housing Submarkets, and House Price Prediction},
	461	+ journal = {Journal of Real Estate Finance and Economics},
	462	+ year = {2007},
	463	+ volume = {35},
	464	+ number = {2},
	465	+ pages = {143--160},
	466	+ doi = {10.1007/s11146-007-9036-8}
	467	+}
	468	+
	469	+@article{bourassa2003submarkets,
	470	+ author = {Bourassa, Steven C. and Hoesli, Martin and Peng, Vincent S.},
	471	+ title = {Do Housing Submarkets Really Matter?},
	472	+ journal = {Journal of Housing Economics},
	473	+ year = {2003},
	474	+ volume = {12},
	475	+ number = {1},
	476	+ pages = {12--28},
	477	+ doi = {10.1016/S1051-1377(03)00003-2}
	478	+}
	479	+
	480	+@article{goodman2003segmentation,
	481	+ author = {Goodman, Allen C. and Thibodeau, Thomas G.},
	482	+ title = {Housing Market Segmentation and Hedonic Prediction Accuracy},
	483	+ journal = {Journal of Housing Economics},
	484	+ year = {2003},
	485	+ volume = {12},
	486	+ number = {3},
	487	+ pages = {181--201},
	488	+ doi = {10.1016/S1051-1377(03)00031-7}
	489	+}
	490	+
	491	+@article{zurada2011comparison,
	492	+ author = {Zurada, Jozef and Levitan, Alan S. and Guan, Jian},
	493	+ title = {A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context},
	494	+ journal = {Journal of Real Estate Research},
	495	+ year = {2011},
	496	+ volume = {33},
	497	+ number = {3},
	498	+ pages = {349--388},
	499	+ doi = {10.1080/10835547.2011.12091311}
	500	+}
	501	+
	502	+@article{antipov2012mass,
	503	+ author = {Antipov, Evgeny A. and Pokryshevskaya, Elena B.},
	504	+ title = {Mass Appraisal of Residential Apartments: An Application of Random Forest for Valuation and a {CART}-Based Approach for Model Diagnostics},
	505	+ journal = {Expert Systems with Applications},
	506	+ year = {2012},
	507	+ volume = {39},
	508	+ number = {2},
	509	+ pages = {1772--1778},
	510	+ doi = {10.1016/j.eswa.2011.08.077}
	511	+}
	512	+
	513	+@article{perezrave2019machine,
	514	+ author = {P{\'e}rez-Rave, Jorge Iv{\'a}n and Correa-Morales, Juan Carlos and Gonz{\'a}lez-Echavarr{\'i}a, Favi{\'a}n},
	515	+ title = {A Machine Learning Approach to Big Data Regression Analysis of Real Estate Prices for Inferential and Predictive Purposes},
	516	+ journal = {Journal of Property Research},
	517	+ year = {2019},
	518	+ volume = {36},
	519	+ number = {1},
	520	+ pages = {59--96},
	521	+ doi = {10.1080/09599916.2019.1587489}
	522	+}
	523	+
	524	+@article{mccluskey2013prediction,
	525	+ author = {McCluskey, William J. and McCord, Michael and Davis, Peadar T. and Haran, Martin and McIlhatton, David},
	526	+ title = {Prediction Accuracy in Mass Appraisal: A Comparison of Modern Approaches},
	527	+ journal = {Journal of Property Research},
	528	+ year = {2013},
	529	+ volume = {30},
	530	+ number = {4},
	531	+ pages = {239--265},
	532	+ doi = {10.1080/09599916.2013.781204}
	533	+}
	534	+
	535	+@article{schulz2014automated,
	536	+ author = {Schulz, Rainer and Wersing, Martin and Werwatz, Axel},
	537	+ title = {Automated Valuation Modelling: A Specification Exercise},
	538	+ journal = {Journal of Property Research},
	539	+ year = {2014},
	540	+ volume = {31},
	541	+ number = {2},
	542	+ pages = {131--153},
	543	+ doi = {10.1080/09599916.2013.846930}
	544	+}
	545	+
	546	+@article{fik2003modeling,
	547	+ author = {Fik, Timothy J. and Ling, David C. and Mulligan, Gordon F.},
	548	+ title = {Modeling Spatial Variation in Housing Prices: A Variable Interaction Approach},
	549	+ journal = {Real Estate Economics},
	550	+ year = {2003},
	551	+ volume = {31},
	552	+ number = {4},
	553	+ pages = {623--646},
	554	+ doi = {10.1046/j.1080-8620.2003.00079.x}
	555	+}
	556	+
	557	+@article{clapp2003semiparametric,
	558	+ author = {Clapp, John M.},
	559	+ title = {A Semiparametric Method for Valuing Residential Locations: Application to Automated Valuation},
	560	+ journal = {Journal of Real Estate Finance and Economics},
	561	+ year = {2003},
	562	+ volume = {27},
	563	+ number = {3},
	564	+ pages = {303--320},
	565	+ doi = {10.1023/A:1025838007297}
	566	+}
	567	+
	568	+@article{pace1998spatiotemporal,
	569	+ author = {Pace, R. Kelley and Barry, Ronald and Clapp, John M. and Rodriquez, Mauricio},
	570	+ title = {Spatiotemporal Autoregressive Models of Neighborhood Effects},
	571	+ journal = {Journal of Real Estate Finance and Economics},
	572	+ year = {1998},
	573	+ volume = {17},
	574	+ number = {1},
	575	+ pages = {15--33},
	576	+ doi = {10.1023/A:1007799028599}
	577	+}
	578	+
	579	+@article{dube2013pooled,
	580	+ author = {Dub{\'e}, Jean and Legros, Di{\`e}go},
	581	+ title = {Dealing with Spatial Data Pooled over Time in Statistical Models},
	582	+ journal = {Letters in Spatial and Resource Sciences},
	583	+ year = {2013},
	584	+ volume = {6},
	585	+ number = {1},
	586	+ pages = {1--18},
	587	+ doi = {10.1007/s12076-012-0082-3}
	588	+}
	589	+
	590	+@article{hill2009boom,
	591	+ author = {Hill, Robert J. and Melser, Daniel and Syed, Iqbal},
	592	+ title = {Measuring a Boom and Bust: The {Sydney} Housing Market 2001--2006},
	593	+ journal = {Journal of Housing Economics},
	594	+ year = {2009},
	595	+ volume = {18},
	596	+ number = {3},
	597	+ pages = {193--205},
	598	+ doi = {10.1016/j.jhe.2009.07.010}
	599	+}
	600	+
	601	+@article{manning2001estimating,
	602	+ author = {Manning, Willard G. and Mullahy, John},
	603	+ title = {Estimating Log Models: To Transform or Not to Transform?},
	604	+ journal = {Journal of Health Economics},
	605	+ year = {2001},
	606	+ volume = {20},
	607	+ number = {4},
	608	+ pages = {461--494},
	609	+ doi = {10.1016/S0167-6296(01)00086-8}
	610	+}
	611	+
	612	+@article{thibodeau2003marking,
	613	+ author = {Thibodeau, Thomas G.},
	614	+ title = {Marking Single-Family Property Values to Market},
	615	+ journal = {Real Estate Economics},
	616	+ year = {2003},
	617	+ volume = {31},
	618	+ number = {1},
	619	+ pages = {1--22},
	620	+ doi = {10.1111/j.1080-8620.2003.00055.x}
	621	+}
	622	+
	623	+@article{khoshnoud2023evolution,
	624	+ author = {Khoshnoud, Mahsa and Sirmans, G. Stacy and Zietz, Emily N.},
	625	+ title = {The Evolution of Hedonic Pricing Models},
	626	+ journal = {Journal of Real Estate Literature},
	627	+ year = {2023},
	628	+ volume = {31},
	629	+ number = {1},
	630	+ pages = {1--47},
	631	+ doi = {10.1080/09277544.2023.2201020}
	632	+}

modified paper/sections/conclusion.tex +34 −24

		`@@ -5,28 +5,38 @@`
5	5
6	6	We ran the hedonic specification debate as a controlled experiment:
7	7	twenty models, four design axes, one attribute set, one scoreboard, two
8		−holdouts, half a million sales. The verdict inverts the profession's
9		−implicit priorities. The choices that consume the most methodological
10		−attention --- functional form, transformation parameters --- move
11		−out-of-sample accuracy by at most two points of median error, while the
12		−choices often made by default --- whether and how finely to absorb space
13		−and time --- move it by three to ten. Machine-learning estimators
14		−dominate the standard scoreboard, but the standard scoreboard asks the
15		−wrong question: when the test set lies in the future, as it always does
16		−in deployment, the boosted ensemble's advantage reverses against a
17		−spline hedonic with good controls, because most of what it learned was a
18		−spatially fine price configuration that does not transfer across the
19		−market cycle.
	8	+holdouts, half a million sales, and a bootstrap interval on every
	9	+comparison. The verdict inverts the profession's implicit priorities.
	10	+The choices that consume the most methodological attention---functional
	11	+form, transformation parameters---move out-of-sample accuracy by at most
	12	+two points of median error, while the choices often made by
	13	+default---whether and how to absorb space and time---move it by three to
	14	+ten, and the way fine spatial effects are implemented matters more than
	15	+their resolution. Machine-learning estimators dominate the standard
	16	+scoreboard by three points, a gain that is real, grows with the sample
	17	+and comes almost entirely from a flexible surface over space. When the
	18	+test set lies in the future, as it always does in deployment, that gain
	19	+shrinks to half a point and loses significance, not because the learned
	20	+surface fails to transfer---re-levelled, it prices future sales as well
	21	+as it prices contemporaneous ones---but because every frozen model
	22	+carries a stale price level, and a boosted model carries a slightly
	23	+staler one than a linear model with carry-forward time effects.
20	24
21		−Meanwhile the objects economists actually harvest from hedonic models
22		−prove reassuringly stable: constant-quality indices agree across forms
23		−to within a few points over a full boom--bust--recovery cycle, and the
24		−machine's implicit age and area gradients replicate the quadratic OLS.
25		−The practical synthesis is almost embarrassingly simple: a semi-log with
26		−splines, quarter effects, and spatial fixed effects at a granularity
27		−matched to market thickness is within two points of the frontier in
28		−deployment conditions --- transparent, auditable, and fast. Where the
29		−extra accuracy of learning methods is worth its retraining cadence ---
30		−high-frequency AVMs, collateral monitoring --- our decomposition says to
31		−spend the flexibility budget on the spatial surface, and to validate,
32		−always, against the future rather than against a shuffle of the past.
	25	+The objects economists harvest from hedonic models prove stable along
	26	+the axis that generates the most debate and less so along the axis that
	27	+generates the least: constant-quality indices agree across functional
	28	+forms to within 2.5 points over a full boom--bust--recovery cycle but
	29	+differ by nine points between a time-dummy index and a boosted
	30	+imputation index, and the machine's implicit age and area gradients
	31	+share the shape of the quadratic OLS with a milder depreciation path.
	32	+The practical synthesis is simple. A semi-log with splines, quarter
	33	+effects and spatial fixed effects nested from municipality down to a
	34	+fine grid is within three points of the frontier under random
	35	+validation and indistinguishable from it in deployment---transparent,
	36	+auditable and fast. Where the extra within-period accuracy of learning
	37	+methods is worth a retraining cadence---high-frequency automated
	38	+valuation, collateral monitoring---our decomposition says to spend the
	39	+flexibility budget on the spatial surface, to supply the price level
	40	+from an index or a frequent refit rather than from the model, and to
	41	+validate, always, against the future rather than against a shuffle of
	42	+the past.

modified paper/sections/data.tex +43 −26

		`@@ -6,37 +6,53 @@`
6	6	\subsection{Sources}
7	7
8	8	We use a registry of 745{,}119 Quebec residential-market transactions
9		−recorded between January 2021 and July 2026 --- sale price, date, address
10		−and coordinates --- each matched at the parcel level to the municipal
	9	+recorded between January 2021 and July 2026---sale price, date, address
	10	+and coordinates---each matched at the parcel level to the municipal
11	11	assessment roll in force at the sale date (median match distance
12		−0.6~metres, validated against the roll's recorded value). The roll
13		−contributes the structural descriptors that are notoriously missing from
14		−transaction registries: total floor area, lot area, year of construction,
15		−number of dwelling units, number of storeys, physical configuration
16		−(detached, semi-detached, row, apartment-linked) and the standardized use
17		−code. This combination --- market prices with assessor-grade structure
18		−for half a million sales across an entire province --- is what makes a
19		−controlled specification race feasible: every model sees exactly the same
20		−attributes.
	12	+0.6~metres, validated against the roll's recorded value). The same
	13	+snapshot underlies \companionpaper{}. The roll contributes the
	14	+structural descriptors that are typically missing from transaction
	15	+registries: total floor area, lot area, year of construction, number of
	16	+dwelling units, number of storeys, physical configuration (detached,
	17	+semi-detached, row, apartment-linked) and the standardised use code.
	18	+This combination---market prices with assessor-grade structure for half a
	19	+million sales across an entire province---is what makes a controlled
	20	+specification race feasible: every model sees exactly the same
	21	+attributes, and the attribute set is the one a mass-appraisal office
	22	+actually holds.
21	23
22	24	\subsection{Sample}
23	25
24	26	We keep residential use codes (dwellings, cottages, mobile homes), require
25		−a high-confidence roll match (distance $\le 50$~m, score $\ge 150$), a
26		−price of at least \$50{,}000, a plausible floor area (20--2{,}000~m$^2$)
27		−and a known year of construction, and trim prices and floor areas at the
28		−1st and 99th percentiles within each sale year. The estimation sample
29		−contains \textbf{514{,}212 sales}: 348{,}253 single-family homes, 75{,}734
30		−condominiums, 74{,}909 plexes (2--5 units), 10{,}349 cottages and 4{,}881
31		−mobile homes, spread over 1{,}115 municipalities, 5{,}255 grid cells of
32		−$\sim$5.5~km and 30{,}548 cells of $\sim$1.1~km.
33		−Table~\ref{tab:sumstats} reports the descriptives. Prices average
34		−\$462{,}000 (median \$399{,}000); the window covers the tail of the
35		−post-pandemic boom, the 2022--2023 rate correction and the subsequent
36		−recovery --- a demanding environment for any static price model, which we
37		−exploit in the forward-in-time evaluation.
	27	+a high-confidence roll match (distance $\le 50$~m, match score
	28	+$\ge 150$), a price of at least \$50{,}000, a plausible floor area
	29	+(20--2{,}000~m$^2$) and a known year of construction, and trim prices and
	30	+floor areas at the 1st and 99th percentiles within each sale year.
	31	+Building age is the sale year minus the year of construction, bounded
	32	+between 0 and 150; storeys are bounded between 1 and 6 and dwelling units
	33	+between 1 and 12; lot areas above the 99.5th percentile, which are
	34	+implausible for residential parcels, are set to zero and treated as
	35	+units without an exclusive lot, together with condominiums. The
	36	+estimation sample contains \textbf{514{,}212 sales}: 348{,}253
	37	+single-family homes, 75{,}734 condominiums, 74{,}909 plexes (two to five
	38	+units), 10{,}349 cottages, 4{,}881 mobile homes and 86 dwellings of
	39	+undetermined class, spread over 1{,}115 municipalities, 5{,}255 grid
	40	+cells of $\sim$5.5~km and 30{,}548 cells of $\sim$1.1~km. Sales are
	41	+spread across the window (97{,}868 in 2021, 90{,}733 in 2022, 76{,}411 in
	42	+2023, 90{,}812 in 2024, 103{,}830 in 2025 and 54{,}558 in January--July
	43	+2026). Table~\ref{tab:sumstats} reports the descriptive statistics.
	44	+Prices average \$440{,}207 (median \$397{,}375), floor areas 132~m$^2$
	45	+and building ages 44 years; the window covers the tail of the
	46	+post-pandemic boom, the 2022--23 rate correction and the 2024--26
	47	+recovery, during which the constant-quality price level rose by roughly
	48	+70\% (Section~\ref{sec:ext}). This is a demanding environment for any
	49	+static price model, which the forward-in-time evaluation exploits.
	50	+One fifth of the sales (105{,}814) concern a parcel that sells more than
	51	+once in the window and 30\% share their exact coordinates with another
	52	+sale; Section~\ref{sec:holdouts} explains why these repeat and
	53	+co-located sales matter for validation.
38	54
39		−\begin{table}[t]
	55	+\begin{table}[tbp]
40	56	\centering
41	57	\begin{threeparttable}
42	58	\caption{Summary statistics, estimation sample}
		`@@ -47,7 +63,8 @@ exploit in the forward-in-time evaluation.`
47	63	\item \textit{Notes:} 514{,}212 residential sales, January 2021 -- July
48	64	2026. Structural descriptors from the assessment roll in force at the
49	65	sale date. Lot area is zero for condominiums and other units without an
50		−exclusive lot.
	66	+exclusive lot (and for the 0.5\% of lots above the 99.5th percentile,
	67	+see text).
51	68	\end{tablenotes}
52	69	\end{threeparttable}
53	70	\end{table}

modified paper/sections/discussion.tex +94 −61

		`@@ -5,77 +5,110 @@`
5	5
6	6	\subsection{A hierarchy of specification choices}
7	7
8		−Read jointly, the four axes imply a clear hierarchy of returns to
9		−specification effort, useful to anyone building a hedonic model:
	8	+Read jointly, the four axes imply a hierarchy of returns to
	9	+specification effort. The first and largest return is to location:
	10	+any spatial absorption beats none by about ten points of median error, a
	11	+margin that dwarfs every other choice, and the granularity question is
	12	+one of implementation rather than of resolution. A fine grid used alone
	13	+overfits because thin cells are estimated from a handful of sales and
	14	+unseen cells are priced arbitrarily; the same grid nested within
	15	+municipalities, which pools thin cells toward their municipality, is the
	16	+most accurate linear specification we estimate. The second return is to
	17	+time controls: in a moving market a model without time effects mistakes
	18	+appreciation for attributes, at a cost of three points under random
	19	+validation and six when the level must be carried forward, while
	20	+granularity beyond quarters is cosmetic. The third return is to the
	21	+estimator, and its size depends on the question asked. Under random
	22	+validation gradient boosting buys three points over the best linear
	23	+model, a gain that is statistically unambiguous and that grows with the
	24	+training sample; in deployment the gain on the median error shrinks to
	25	+half a point and loses significance, because the ensembles carry a
	26	+slightly staler price level, while their relative pricing skill is
	27	+intact. The fourth and smallest return is to functional form: splines
	28	+are a cheap and reliable upgrade over the semi-log, and the Box--Cox
	29	+machinery is not worth its complexity, exactly as
	30	+\citet{cropper1988choice} concluded from simulations four decades ago.
10	31
11		−\begin{enumerate}
12		−\item \textbf{Get location right} ($\sim$10 points of median error):
13		−any spatial absorption beats none by a margin that dwarfs every other
14		−choice; the optimal granularity is interior --- around 5~km here ---
15		−because finer cells trade bias for variance.
16		−\item \textbf{Include some time control} ($\sim$3 points): in a moving
17		−market, a model without time effects mistakes appreciation for
18		−attributes; granularity beyond quarters is cosmetic.
19		−\item \textbf{Choose the estimator for the job} ($\sim$2 points
20		−deployed, $\sim$4 under random validation): boosting buys real accuracy
21		−in interpolation-heavy uses (filling gaps within a period, mass
22		−appraisal with contemporaneous comparables) and much less when the
23		−target is the future.
24		−\item \textbf{Stop worrying about functional form} ($\lesssim$2
25		−points): splines are a cheap upgrade; the Box--Cox machinery is not
26		−worth its complexity, exactly as \citet{cropper1988choice} concluded
27		−from simulations four decades ago.
28		−\end{enumerate}
	32	+\subsection{Why the forward-in-time split compresses the ranking}
29	33
30		−\subsection{Why the machines lose their edge out of time}
31		−
32		−The decomposition rows explain the generalization gap. The boosting
33		−model's random-split advantage comes almost entirely from a flexible
34		−spatial surface (removing coordinates costs it 8.8 points). That surface
35		−is estimated \emph{jointly} with the time path, and trees clamp: beyond
36		−the last training month the model prices 2025--26 sales at a frozen
37		−level, exactly like the carry-forward linear models, while its
38		−fine-grained spatial fit --- calibrated on the 2021--24 price
39		−configuration --- partially decays as relative prices shift. The linear
40		−models, coarser but more rigid, carry a structure that transfers better.
41		−This is not an indictment of machine learning --- retrained monthly, the
42		−boosting model would presumably keep its interpolation advantage --- but
43		−it prices the \emph{retraining requirement} that random cross-validation
44		−hides, and it matches the practitioner evidence that AVM accuracy decays
45		−quickly out of sample period \citep{bogin2020house, kok2017big}.
	34	+The decomposition of Section~\ref{sec:gap} clarifies what the
	35	+forward-in-time holdout measures. The boosting model's random-split
	36	+advantage comes almost entirely from a flexible spatial surface
	37	+(removing the coordinates costs it 8.8 points), and that surface
	38	+transfers to 2025--26 sales: re-levelled, the model prices them with a
	39	+13.9\% median error, against 16.8\% for the spline hedonic. What does
	40	+not transfer is the aggregate level. Trees are piecewise constant in the
	41	+month counter and the terminal leaf in time averages the last training
	42	+months rather than the last one, so a frozen boosted model enters the
	43	+test period with a slightly staler level than a linear model whose last
	44	+month effect is carried forward; by mid-2026 both are about 25\% below
	45	+the market and indistinguishable. The lesson is not that machine
	46	+learning fails out of sample. It is that the forward-in-time holdout
	47	+conflates two things---the common level error that any static model
	48	+incurs, and the relative accuracy the estimator contributes---and that
	49	+the first dominates the second within a few quarters. Retrained monthly,
	50	+or re-levelled with a published index as practitioners do
	51	+\citep{thibodeau2003marking, mayer2019estimation}, the boosted model
	52	+would presumably keep its interpolation advantage; the forward split
	53	+prices the \emph{retraining or re-levelling requirement} that random
	54	+cross-validation hides, and it matches the practitioner evidence that
	55	+automated-valuation accuracy decays quickly outside the sample period
	56	+\citep{bogin2020house, kok2017big}. It also explains why our first
	57	+version, which smeared the linear models but not the ensembles, found a
	58	+reversal: in a rising market an upward retransformation factor applied
	59	+to one family only offsets part of that family's staleness.
46	60
47	61	\subsection{Implications}
48	62
49		−\paragraph{For research.} Hedonic coefficients and indices are robust
50		−objects: form and even estimator perturb them little
51		−(Section~\ref{sec:ext}). Researchers using hedonics to \emph{measure}
52		−--- indices, implicit prices, capitalization effects --- can keep simple
53		−forms with good controls and report validation under a temporal split
54		−when prediction claims are made.
	63	+\paragraph{For research.} Hedonic indices are robust to functional form
	64	+and implicit-price gradients are robust to the estimator in shape, if
	65	+not in slope (Section~\ref{sec:ext}). Researchers using hedonic models to
	66	+\emph{measure}---indices, implicit prices, capitalization effects---can
	67	+keep simple forms with good, preferably hierarchical, spatial controls.
	68	+When prediction claims are made, they should report a forward-in-time
	69	+split alongside random cross-validation, and they should report
	70	+intervals: several of the differences that organise the applied
	71	+debate---a 5.5~km grid against municipality effects, month against
	72	+quarter effects, boosting against splines in deployment---are not
	73	+distinguishable from zero on half a million sales.
55	74
56	75	\paragraph{For mass appraisal.} Quebec's assessors face exactly this
57		−design problem every three years. Our results suggest the largest
58		−accuracy gains lie not in exotic estimators but in spatial resolution
59		−chosen by market thickness --- and that any model, linear or boosted,
60		−frozen at a reference date degrades by 3--7 points of median error
61		−within two years. This quantifies the staleness mechanism behind the
62		−assessment inequities documented in \companionpaper{}: the
63		−valuation technology's out-of-time decay is of the same magnitude as the
64		−inequities measured there.
	76	+design problem every three years. Our results suggest that the largest
	77	+accuracy gains lie in spatial resolution implemented hierarchically, that
	78	+flexible learners add a further three points within the period they are
	79	+estimated on, and that any model, linear or boosted, frozen at a
	80	+reference date under-prices the typical sale by 10--20\% within eighteen
	81	+months in a market like 2025--26. This quantifies the staleness
	82	+mechanism behind the assessment inequities documented in
	83	+\companionpaper{}: the valuation technology's out-of-time decay is of the
	84	+same order as the inequities measured there, and it is a level problem
	85	+that an index adjustment addresses before any change of estimator.
65	86
66		−\paragraph{For the AVM literature.} Every comparison should report a
67		−forward-in-time split alongside random cross-validation
68		−\citep{steurer2021metrics}. On our data, the choice of split changes not
69		−just magnitudes but the \emph{winner}.
	87	+\paragraph{For the automated-valuation literature.} Every comparison
	88	+should report a forward-in-time split, a leakage-free grouped split when
	89	+properties sell repeatedly or share coordinates, and a single
	90	+retransformation rule applied to all models
	91	+\citep{steurer2021metrics}. On our data the first changes the size and
	92	+significance of the headline gap, the second changes nothing, and the
	93	+third, applied asymmetrically, is enough to manufacture a reversal.
70	94
71	95	\subsection{Limitations}
72	96
73	97	Our attribute set, though assessor-grade, omits listing-level quality
74	98	(renovations, finishes, views); richer features would likely raise the
75		−machines' interpolation advantage. Hyper-parameters were deliberately
76		−conventional --- a tuned boosted model would gain somewhat, as would a
77		−spatially smarter linear model (e.g.\ kriging residuals,
78		−\citealp{case2004modeling}). The forward split covers one specific
79		−regime change (the 2025--26 recovery); other windows would shift
80		−magnitudes. And Quebec's institutional homogeneity aids transferability
81		−of results across municipalities but may limit it across countries.
	99	+learners' within-period advantage. Hyper-parameters were deliberately
	100	+conventional, and Section~\ref{sec:robust} shows that materially
	101	+different boosting configurations, including one tuned on a time-ordered
	102	+validation set, leave the ranking unchanged; a spatially smarter linear
	103	+model (kriged residuals, \citealp{case2004modeling}; semiparametric
	104	+location surfaces, \citealp{clapp2003semiparametric}) would narrow the
	105	+gap from the other side and is not estimated here. The forward split
	106	+covers one specific regime (the 2025--26 recovery), in which prices rose
	107	+by about 15\% in eighteen months; a flat or falling period would produce
	108	+a smaller level error and possibly a different ordering of staleness
	109	+across model families. The grouped holdouts remove leakage through
	110	+repeat sales and shared coordinates but not through neighbouring parcels
	111	+in the same street, which a spatially blocked holdout would address at
	112	+the cost of changing the question to spatial extrapolation. Finally,
	113	+Quebec's institutional homogeneity aids transferability of results
	114	+across municipalities but may limit it across countries.

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révision scientifique complète (editorial board, 2026-10-17)

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