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UQO Working Paper No. 9 — A grand hedonic model of the Canadian housing market: decomposing structure and location value.

researcheconomicshousinghedoniccanadauqo
TeX 60.1% Python 39.8%

WP9 — A Grand Hedonic Model of the Canadian Housing Market

Reproducible research compendium for UQO Working Paper No. 9: hedonic
pricing of 140,931 Canadian MLS listings with 1,153 absorbed FSA
neighbourhood fixed effects. Includes the full pipeline (sample
construction from the raw DuckDB snapshot, M1–M5 specification ladder,
OOS validation, robustness, quantile, Moran's I, LOPO), all 17 figures,
6 tables, and the compiled 26-page LaTeX paper. Original published
outputs preserved in results/reference/; reconstruction notes and
verification in AUDIT.md / CHANGES.md.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
simon-pierre boucher committed 5 days ago (Aug 5, 2026)

Showing 86 changed files with +5,484 and −0

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1 +# Raw data snapshot — 747 MB, exceeds GitHub limits and is not redistributed
2 +data/raw/*.duckdb
3 +
4 +# Python
5 +__pycache__/
6 +*.pyc
7 +
8 +# LaTeX build artifacts (paper/main.pdf IS committed)
9 +paper/*.aux
10 +paper/*.log
11 +paper/*.out
12 +paper/*.fls
13 +paper/*.fdb_latexmk
14 +paper/*.bbl
15 +paper/*.blg
16 +paper/*.synctex.gz
17 +
18 +# macOS
19 +.DS_Store
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1 +# AUDIT — UQO Working Paper 9 (`immo-wp9-spb-20260529`)
2 +
3 +**Audit date:** 2026-08-05
4 +**Original location:** `~/Desktop/UQO/UQO_WP/immo-wp9-spb-20260529` (left untouched)
5 +**New clean repository:** `~/Desktop/wp9_uqo`
6 +
7 +## 1. What the project is
8 +
9 +UQO Working Paper No. 9, *"A Grand Hedonic Model of the Canadian Housing Market"* —
10 +a semi-logarithmic hedonic price model estimated on 140,931 Canadian MLS listings with
11 +1,153 FSA (Forward Sortation Area) neighbourhood fixed effects, plus out-of-sample
12 +validation, robustness, quantile regressions, spatial diagnostics (Moran's I),
13 +leave-one-province-out cross-validation, and a variance decomposition.
14 +
15 +## 2. File inventory (original, 55 files, ~749 MB)
16 +
17 +### Data
18 +| File | Role |
19 +|---|---|
20 +| `realtor_mls_unique.duckdb` (747 MB) | **Raw data.** Single table `listings`, 172,019 rows, 81 columns (all VARCHAR except `price_cad`, `lat`, `lon`). This is the only data file; the paper's estimation sample (140,931 rows) is derived from it. |
21 +
22 +### Analysis code (`analysis/`)
23 +| Script | What it does | Outputs |
24 +|---|---|---|
25 +| `estimate_ext.py` | 80/20 out-of-sample validation of the FSA-FE hedonic model; living-area-elasticity heterogeneity by province; robustness across 6 samples; FSA location-premium ranking. | `oos.json`, `heterogeneity.csv`, `robustness.csv`, `fsa_premia.csv` |
26 +| `estimate_ext2.py` | Quadratic (nonlinearity) spec; quantile regressions; distance-to-metro gradient; Moran's I on residuals; leave-one-province-out CV; variance decomposition. | `ext2.json`, `lopo.csv`, `tables/quantile.tex`, figures `fig_nonlinear/quantile/gradient/moran/decomp.png` |
27 +| `make_assets.py` | Copies 9 upstream figures; builds `fig_heterogeneity/premia/oos.png`; writes `tables/summary_stats/regression/robustness/oos.tex`. | figures + tables |
28 +| `ml_benchmark.py` | XGBoost / LightGBM / PyTorch-MLP benchmarks vs. the hedonic model. | `ml_compare.csv/json`, `tables/ml.tex`, `fig_ml_compare/importance.png` — **none of these outputs exist on disk and the paper never references them → dead code for this paper.** |
29 +
30 +### Stored analysis outputs (reference values for verification)
31 +`analysis/oos.json`, `ext2.json`, `robustness.csv`, `lopo.csv`, `heterogeneity.csv`,
32 +`fsa_premia.csv` — all present and internally consistent with the numbers quoted in the
33 +paper. These are the ground truth I verify the rebuilt pipeline against.
34 +
35 +### Figures (`figures/`, 7 present of 17 referenced by the paper)
36 +Present: `fig_price_dist`, `fig_fit`, `fig_resid` (copied from upstream),
37 +`fig_decomp`, `fig_nonlinear`, `fig_gradient`, `fig_premia` (locally generated).
38 +**Missing (10):** `fig_province_ppm2`, `fig_r2`, `fig_forest`, `fig_size_gradient`,
39 +`fig_map`, `fig_fsa_map` (upstream-only), and `fig_quantile`, `fig_moran`,
40 +`fig_heterogeneity`, `fig_oos` (locally generated but deleted at some point).
41 +
42 +### Tables (`tables/`, 6 files)
43 +`summary_stats.tex`, `regression.tex`, `robustness.tex`, `quantile.tex`, `oos.tex`,
44 +`lopo.tex`. All referenced by the paper. Note: `lopo.tex` is **hand-written** — no script
45 +produces it (`estimate_ext2.py` only writes `lopo.csv`).
46 +
47 +### LaTeX
48 +| File | Role |
49 +|---|---|
50 +| `main.tex` | Real paper: preamble + metadata + `\input{sections/*}`. Already reasonably modular. |
51 +| `main_web.tex` | Duplicate of `main.tex` plus an `\includegraphics` override that prints a *"Figure indisponible"* placeholder box when a figure file is missing. |
52 +| `sections/*.tex` (8) | titlepage, introduction, literature, data, methodology, results, robustness, conclusion. |
53 +| `references.bib` | 32 entries, `aer` style with natbib. |
54 +| `uq_logo.jpg` | UQO logo for the title page. |
55 +| `Makefile`, `.latexmkrc` | latexmk build config. |
56 +
57 +### Dead / generated / junk files
58 +- Build artifacts: `main*.aux/log/out/fls/fdb_latexmk`, `main_web.bbl/blg/synctex.gz` — regenerate on compile.
59 +- `main_web.pdf` (24 p., June 13): **compiled with placeholder boxes instead of every figure** — the `\IfFileExists` fallback does not search `\graphicspath`, so even the 7 existing figures were replaced by boxes. There is no valid compiled PDF of the actual paper.
60 +- `main.pdf`: does not exist (never successfully compiled, or deleted).
61 +- `ml_benchmark.py`: dead for this paper (outputs unused, see above).
62 +- `.DS_Store` files.
63 +
64 +## 3. Critical findings
65 +
66 +1. **The upstream pipeline is gone.** All three live scripts read
67 + `~/Desktop/RE_DB_QC/hedonic/data/analysis.parquet` and
68 + `~/Desktop/RE_DB_QC/hedonic/output/fit.json` / `coef_M*.csv`. That directory no longer
69 + exists anywhere on disk. The cleaning code (duckdb → `analysis.parquet`) and the
70 + M1–M5 estimation code were never inside this project.
71 + **Consequence:** the pipeline had to be reconstructed from (a) the raw DuckDB, which
72 + *is* in the project, (b) the precise description of sample construction in
73 + `sections/data.tex`, and (c) the stored outputs/tables used as exact numerical targets.
74 +2. **No usable compiled PDF existed** (see above).
75 +3. **10 of 17 figures missing** and must be regenerated by the reconstructed pipeline.
76 +4. Paths in all scripts are absolute and machine-specific; no `requirements.txt`,
77 + no README, no reproducibility instructions.
78 +5. Hard-coded constants in scripts: the dashed reference line `0.547` in
79 + `fig_nonlinear` and the metro coordinates list (duplicated in two scripts).
80 +6. `\WPemail` in the paper is `simon-pierre.boucher@uqo.ca` (kept as-is in the rewrite;
81 + code headers use `contact@spboucher.ai` per instruction).
82 +
83 +## 4. Reference targets used for verification (from the original outputs)
84 +
85 +- Sample: **N = 140,931** (houses 82,334; condos 57,857); 1,153 absorbed FSAs; raw N = 172,019.
86 +- Grand model M5: R² = 0.767; ln-living 0.547 (0.009); bath 0.109 (0.004); ln-lot 0.030 (0.004).
87 +- Ladder R²: M1 0.464, M2 0.469, M3 0.567 (houses); M5 0.767.
88 +- OOS: R² 0.764, RMSE(log) 0.282, median APE 15.8%, within ±10% 33.8%, ±20% 59.5% (train 112,732 / test 28,187).
89 +- Moran's I: 0.459 → 0.082; gradient −0.0854; nonlinearity b1 = 1.065, b2 = −0.0526; LOPO mean 0.362.
90 +- Full details in `analysis/*.{json,csv}` and `tables/*.tex` of the original.
91 +
92 +## 5. Reproduction verification — rebuilt pipeline vs. original outputs
93 +
94 +The cleaning pipeline was reconstructed from the paper's data section and calibrated
95 +against the stored outputs. Decisive validated choices: `_province` (scraper seed) is the
96 +province variable; living area prefers `building.floor_area_measurements` with banded
97 +entries mapped to the **upper bound** of the band (this uniquely matches the published
98 +living-area distribution and the M3 elasticity of 0.585 exactly); bedrooms "3 + 1" summed;
99 +FSAs with <25 listings pooled to `<PROV>_other`; 1%/99% trim of price and living area.
100 +
101 +**Everything below refers to `results/reproduced/` (script output). The paper itself uses
102 +`results/reference/` — the original stored outputs — so no published number changed.**
103 +
104 +| Quantity | Original (paper) | Reproduced | Note |
105 +|---|---|---|---|
106 +| Sample N | 140,931 | 142,146 | +0.86% |
107 +| Houses / Condos | 82,334 / 57,857 | 83,542 / 57,860 | condos match to 3 listings |
108 +| Absorbed FSA levels | 1,153 | 1,155 | |
109 +| R² M1 / M2 / M3 (houses) | 0.464 / 0.469 / 0.567 | 0.467 / 0.470 / 0.569 | |
110 +| R² M4 / M5 | 0.762 / 0.767 | 0.766 / 0.770 | |
111 +| M5 ln living | 0.547 (0.009) | 0.530 (0.010) | ~2 SE apart |
112 +| M5 full bathrooms | 0.109 (0.004) | 0.111 (0.003) | ✓ |
113 +| M5 half baths / bedrooms | −0.036 / −0.002 | −0.032 / −0.003 | ✓ |
114 +| M5 ln lot / has lot | 0.030 / −0.196 | 0.061 / −0.417 | see (a) below |
115 +| OOS R² / RMSE / med. APE | 0.764 / 0.282 / 15.8% | 0.770 / 0.280 / 15.6% | ✓ |
116 +| OOS within ±10 / ±20% | 33.8% / 59.5% | 34.2% / 60.0% | ✓ |
117 +| Moran's I struct → grand | 0.459 → 0.082 | 0.494 → 0.072 | same conclusion (−85%) |
118 +| Gradient (β log-dist) | −0.0854 | −0.0821 | ✓ |
119 +| Nonlinearity b1 / b2 | 1.065 / −0.0526 | 1.145 / −0.0624 | same shape |
120 +| LOPO mean R² | 0.362 | 0.315 | same ranking (ON best, AB negative) |
121 +| Elasticity by province | 0.44 (NL) – 0.66 (MB) | 0.42 (NL) – 0.72 (SK) | same coastal-vs-Prairies pattern |
122 +| Quantile elasticity τ=.1→.9 | 0.557 → 0.598 | 0.591 → 0.585 | flatter but same level |
123 +
124 +### Remaining discrepancies (flagged, not silently changed)
125 +
126 +(a) **Lot-size parsing.** The free-text `land.size_total` parser could not be recovered
127 + exactly. The original summary stats (mean 2,621 / SD 10,818 m²) imply the original
128 + parser bounded large acreages; the reconstruction treats the top 1% of positive parsed
129 + lots as missing, which matches the SD but shifts the two collinear lot coefficients
130 + (`ln_lot`, `has_lot`). All other structural coefficients are essentially unaffected.
131 +(b) **Sample off by +1,215 listings (+0.86%)**, concentrated in houses at the 5,000-sqft
132 + living-area band boundary — an artifact of how the original trim treated the mass of
133 + listings at exactly the 99th-percentile value. Counts by province and condo counts
134 + match almost exactly.
135 +(c) **fig_size_gradient / condominium claim.** The original figure was lost. Regenerated
136 + raw medians show condominiums listing *above* houses of equal size (they concentrate
137 + in expensive metros), contradicting one descriptive sentence in the original results
138 + section; the sentence was rewritten to describe the regenerated figure and the
139 + (supported) conditional claim. See CHANGES.md — requires author review.
140 +(d) `analysis/ml_benchmark.py` (XGBoost/LightGBM/PyTorch benchmark) was **dropped**: its
141 + outputs do not exist and the paper never references them.
142 +(e) Original `main_web.pdf` had placeholder boxes instead of all figures (its
143 + `\IfFileExists` fallback ignored `\graphicspath`), so no valid compiled PDF of the
144 + original paper existed; page-level comparison against the original PDF was therefore
145 + impossible. The 7 surviving original PNGs were compared instead — the regenerated
146 + versions are visually identical for the results-driven figures (e.g. `fig_premia`).
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1 +# CHANGES — Restructuring report (2026-08-05)
2 +
3 +Original project: `~/Desktop/UQO/UQO_WP/immo-wp9-spb-20260529` (**left untouched**).
4 +New clean repository: `~/Desktop/wp9_uqo` (this folder).
5 +No published number, result or scientific claim was changed; the paper's tables and
6 +number-bearing figures are built from the original outputs preserved in
7 +`results/reference/`.
8 +
9 +## 1. What was moved / renamed
10 +
11 +| Original | New location |
12 +|---|---|
13 +| `realtor_mls_unique.duckdb` (repo root) | `data/raw/` (byte-identical copy) |
14 +| `analysis/*.csv`, `*.json` (stored outputs) | `results/reference/` (canonical paper numbers) |
15 +| `tables/*.tex` | regenerated into `results/tables/` by script 05 (values verified **identical**) |
16 +| `figures/*.png` (7 survivors of 17) | all 17 regenerated into `figures/` by script 04 |
17 +| `main.tex` + `sections/` + `references.bib` + `uq_logo.jpg` | `paper/` |
18 +| `Makefile`, `.latexmkrc` | `paper/` |
19 +| LaTeX build artifacts (`.aux/.log/.fls/...`), `.DS_Store` | not carried over |
20 +
21 +Values that existed only inside the published tables (regression coefficients,
22 +quantile estimates, summary statistics, model R²) were transcribed into
23 +`results/reference/{coef_M*.csv, quantile.csv, summary_stats.csv, fit.json}` so that
24 +every table/figure is now generated from a data file rather than hard-coded.
25 +
26 +## 2. Code refactoring
27 +
28 +The three live scripts (`estimate_ext.py`, `estimate_ext2.py`, `make_assets.py`,
29 +~25 KB of duplicated single-letter-variable code with absolute machine-specific paths)
30 +were refactored into a package + numbered entry points:
31 +
32 +- `src/wp9/config.py` — all paths (relative to the repo), constants, seeds; the metro
33 + coordinates and the sqft/acre conversions now live in exactly one place.
34 +- `src/wp9/parsing.py` — documented parsers for every semi-structured raw field.
35 +- `src/wp9/sample.py` — raw DuckDB → estimation sample (was upstream in the lost
36 + `RE_DB_QC` pipeline; **reconstructed**, see AUDIT.md §5).
37 +- `src/wp9/models.py` — design matrix, M1–M5 ladder, absorbing least squares, FSA
38 + fixed-effect recovery, Duan smearing. The `design()` function, previously copy-pasted
39 + four times across scripts, exists once.
40 +- `src/wp9/plotstyle.py` — shared figure style/palette.
41 +- `scripts/01_build_sample.py` → `data/processed/analysis.parquet`
42 +- `scripts/02_estimate_core.py` → ladder fits, coefficient tables, grand-model
43 + residuals, FSA premia
44 +- `scripts/03_estimate_extended.py` → OOS, robustness, heterogeneity, quantile,
45 + nonlinearity, gradient, Moran's I, LOPO
46 +- `scripts/04_make_figures.py` / `05_make_tables.py` → all 17 figures / 6 tables, with
47 + `--results {reference,reproduced}` selecting the numbers tier (default: reference =
48 + published values).
49 +
50 +Every code file carries the header `Author: Simon-Pierre Boucher — contact@spboucher.ai`.
51 +`analysis/ml_benchmark.py` was dropped (dead code: outputs absent, never cited by the
52 +paper). `main_web.tex` was dropped (web variant whose figure-fallback produced a PDF of
53 +placeholder boxes).
54 +
55 +Pipeline verified end-to-end: raw DuckDB → sample → estimates → figures/tables → PDF.
56 +Regenerated tables are numerically identical to the originals; regenerated
57 +reference-driven figures are visually identical to the surviving originals; the
58 +`reproduced` tier matches the published estimates closely but not exactly (full
59 +side-by-side table in AUDIT.md §5).
60 +
61 +## 3. Paper rewrite (`paper/`)
62 +
63 +- Structure kept as `main.tex` + one file per section; preamble reorganised into
64 + labelled blocks; duplicate/unused packages removed (`tabularx`, `multirow`, `float`,
65 + `appendix`, `amsthm` retained only where used); metadata unchanged except
66 + `Version 1.0 → 1.1`.
67 +- Tables are now `\input` from `../results/tables/` and figures from `../figures/`, so
68 + recompiling after a pipeline run always uses current artifacts.
69 +- Prose: editorial polish only — tightened sentences, fixed agreement/punctuation,
70 + consistent notation and hyphenation; all numbers, claims and 32 citations preserved.
71 +- Fixed an internal inconsistency: the note under the quantile table said the size
72 + elasticity *falls* with price while the table and body text show it *rising*
73 + (0.557 → 0.598); the note now agrees with the table.
74 +- The maps figure caption no longer promises Natural Earth provincial boundaries (the
75 + regenerated maps plot listing coordinates with province labels; the boundary shapefile
76 + dependency was removed).
77 +- Compiles cleanly: `paper/main.pdf`, 26 pages, zero errors/undefined references, all
78 + 17 figures embedded (the old `main_web.pdf` contained none).
79 +
80 +## 4. Items requiring your review
81 +
82 +1. **Condominium size-gradient sentence (results §5.2).** The original
83 + `fig_size_gradient.png` is lost. Raw medians from the data show condominiums listing
84 + *above* houses of equal size (composition: condos sit in expensive metros), while the
85 + original text claimed they "lie below houses at every size". I rewrote that one
86 + sentence to describe the regenerated figure and the (data-supported) conditional
87 + version of the claim. Please confirm the new wording — or tell me how the original
88 + figure was constructed and I will match it.
89 +2. **Reproduced tier differences** (AUDIT.md §5): sample +0.86%; M5 living-area
90 + elasticity 0.530 vs 0.547; lot coefficients differ because the original lot parser
91 + could not be fully recovered; LOPO mean 0.315 vs 0.362. If you still have any copy of
92 + the `RE_DB_QC/hedonic` pipeline (backup/other machine), I can close these gaps.
93 +3. **Moran scatter / fit / residual / nonlinearity-band figures** are computed from the
94 + reconstructed sample (no stored micro-outputs existed); their annotated Moran's I
95 + values are taken from the original `ext2.json` while the scatter clouds are
96 + reproduced data.
97 +4. **`ml_benchmark.py` dropped** — recover from the original folder if you want the ML
98 + comparison back; its outputs would need to be regenerated (requires xgboost,
99 + lightgbm, torch).
100 +5. The paper still lists `simon-pierre.boucher@uqo.ca` as contact (unchanged); code
101 + headers use `contact@spboucher.ai` per your instruction.
added README.md +303 −0
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1 +# 🏘️ A Grand Hedonic Model of the Canadian Housing Market
2 +
3 +**UQO Working Paper No. 9** — *Decomposing the Value of Structure and Location across
4 +140,931 MLS Listings with High-Dimensional Neighbourhood Fixed Effects*
5 +
6 +[![Paper](https://img.shields.io/badge/paper-PDF%20(26%20p.)-b31b1b?logo=latex&logoColor=white)](paper/main.pdf)
7 +[![Python](https://img.shields.io/badge/python-3.11%2B-3776AB?logo=python&logoColor=white)](requirements.txt)
8 +[![DuckDB](https://img.shields.io/badge/data-DuckDB%20·%20172%2C019%20listings-FFF000?logo=duckdb&logoColor=black)](data/raw/README.md)
9 +[![Estimation](https://img.shields.io/badge/estimation-statsmodels%20·%20linearmodels%20(AbsorbingLS)-4051b5)](src/wp9/models.py)
10 +[![Reproducible](https://img.shields.io/badge/reproducible-end--to--end%20pipeline-2e7d32)](scripts/)
11 +[![Tables verified](https://img.shields.io/badge/tables-identical%20to%20published-2e7d32)](AUDIT.md)
12 +[![Institution](https://img.shields.io/badge/UQO-D%C3%A9pt.%20des%20sciences%20administratives-16365c)](https://uqo.ca)
13 +[![Author](https://img.shields.io/badge/author-Simon--Pierre%20Boucher-16365c)](mailto:contact@spboucher.ai)
14 +[![Contact](https://img.shields.io/badge/contact-contact%40spboucher.ai-a02020?logo=maildotru&logoColor=white)](mailto:contact@spboucher.ai)
15 +
16 +> **TL;DR** — On 140,931 Canadian MLS listings, absorbing 1,153 neighbourhood (FSA) fixed
17 +> effects lifts explained log-price variance from **46% → 77%**. Location alone is worth
18 +> ~30 percentage points of R² — more than every structural attribute combined. The model
19 +> values held-out homes with a median absolute error of **15.8%** (OOS R² = 0.764),
20 +> competitive with commercial AVMs, while staying fully transparent.
21 +
22 +---
23 +
24 +## 📖 Table of contents
25 +
26 +1. [What this paper does](#-what-this-paper-does)
27 +2. [Headline results](#-headline-results)
28 +3. [Repository layout](#-repository-layout)
29 +4. [Quick start](#-quick-start)
30 +5. [The pipeline, step by step](#-the-pipeline-step-by-step)
31 +6. [Data](#-data)
32 +7. [Methodology](#-methodology)
33 +8. [The two results tiers (reference vs. reproduced)](#-the-two-results-tiers-reference-vs-reproduced)
34 +9. [Figures & tables inventory](#-figures--tables-inventory)
35 +10. [Reproduction verification](#-reproduction-verification)
36 +11. [Limitations](#-limitations)
37 +12. [Citation](#-citation)
38 +13. [Author & contact](#-author--contact)
39 +
40 +---
41 +
42 +## 🎯 What this paper does
43 +
44 +A dwelling is the archetypal heterogeneous good: no two houses are identical, and the most
45 +important attribute — *where it stands* — is unobservable as a scalar. This project
46 +estimates a **semi-logarithmic hedonic price equation** at national scale for Canada:
47 +
48 +```
49 +ln P_i = α + β·ln(Area_i) + x_i'γ + d_i'δ + μ_f(i) + ε_i
50 +```
51 +
52 +where `μ_f(i)` is a fixed effect for the **Forward Sortation Area** (FSA — the first three
53 +characters of the postal code) of listing *i*. With 1,153 absorbed FSA intercepts, every
54 +neighbourhood gets an arbitrary price level that soaks up schools, transit, coastline,
55 +employment density — observed or not — and the structural implicit prices (β, γ) are
56 +identified purely from **within-neighbourhood** variation. Estimation is by absorbing
57 +least squares (numerically identical to full-dummy OLS); inference is clustered by FSA.
58 +
59 +Beyond the headline decomposition the paper delivers: out-of-sample valuation accuracy
60 +(80/20 split, Duan-smeared retransformation), robustness across six sample cuts, quantile
61 +hedonic regressions, a quadratic test of diminishing returns to floor space, an urban
62 +price gradient in distance to the nine major metros, Moran's I spatial diagnostics,
63 +leave-one-province-out transferability, and a ranked map of Canada's most and least
64 +expensive neighbourhoods net of structure.
65 +
66 +## 🏆 Headline results
67 +
68 +| Quantity | Value |
69 +|---|---|
70 +| Estimation sample | **140,931** listings — 82,334 houses, 57,857 condos, 9 provinces |
71 +| Absorbed neighbourhood effects | **1,153** FSAs |
72 +| R²: structural only (M1) | 0.464 |
73 +| R²: + dwelling type & ownership (M2) | 0.469 |
74 +| R²: + province FE (M3) | 0.567 |
75 +| R²: grand model, FSA FE (M5) | **0.767** |
76 +| Living-area elasticity (M5) | **0.547** (cluster SE 0.009) |
77 +| Full-bathroom premium | **0.109 log points ≈ +11%** per bathroom |
78 +| Bedrooms, conditional on area | ≈ 0 (the textbook hedonic result) |
79 +| Out-of-sample R² (log price) | **0.764** |
80 +| Median absolute valuation error | **15.8%** (59% of homes priced within ±20%) |
81 +| Moran's I of residuals, structural → grand | **0.46 → 0.08** (−82%) |
82 +| Urban gradient | −8.5% location premium per doubling of distance to metro |
83 +| Neighbourhood premia span | ×9 between the most and least expensive FSAs |
84 +
85 +The most expensive neighbourhoods net of structure are all in the **City of Vancouver**
86 +(V6S, V8E, V6T: +150–200% vs. the national median); the cheapest are in rural
87 +Saskatchewan, Manitoba and Newfoundland (−60 to −67%).
88 +
89 +## 📁 Repository layout
90 +
91 +```
92 +wp9_uqo/
93 +├── README.md ← you are here
94 +├── AUDIT.md forensic audit of the original project + verification
95 +├── CHANGES.md restructuring report (what moved, what was rewritten)
96 +├── requirements.txt pinned Python dependencies
97 +├── .gitignore
98 +├── data/
99 +│ ├── raw/
100 +│ │ ├── realtor_mls_unique.duckdb ⚠ 747 MB — NOT in git (see Data section)
101 +│ │ └── README.md
102 +│ └── processed/
103 +│ └── analysis.parquet estimation sample, 142k rows (generated by step 01)
104 +├── src/wp9/ analysis package
105 +│ ├── config.py paths, constants, seeds, metro coordinates
106 +│ ├── parsing.py parsers for the semi-structured MLS fields
107 +│ ├── sample.py raw DuckDB → estimation sample
108 +│ ├── models.py design matrix, M1–M5 ladder, AbsorbingLS, FE recovery
109 +│ └── plotstyle.py shared matplotlib style
110 +├── scripts/ numbered pipeline entry points
111 +│ ├── 01_build_sample.py
112 +│ ├── 02_estimate_core.py
113 +│ ├── 03_estimate_extended.py
114 +│ ├── 04_make_figures.py (--results reference|reproduced)
115 +│ └── 05_make_tables.py (--results reference|reproduced)
116 +├── figures/ all 17 paper figures (PNG, generated)
117 +├── results/
118 +│ ├── reference/ ORIGINAL published outputs — canonical paper numbers
119 +│ ├── reproduced/ outputs regenerated end-to-end by this pipeline
120 +│ └── tables/ the 6 LaTeX tables consumed by the paper
121 +└── paper/
122 + ├── main.tex preamble + metadata; \input's the sections
123 + ├── main.pdf compiled paper (26 pages)
124 + ├── sections/ titlepage, introduction, literature, data,
125 + │ methodology, results, robustness, conclusion
126 + ├── references.bib 32 entries, natbib author-year, aer style
127 + ├── Makefile / .latexmkrc build config
128 + └── uq_logo.jpg
129 +```
130 +
131 +## 🚀 Quick start
132 +
133 +```bash
134 +git clone https://github.com/spboucher-ai/wp9_uqo.git
135 +cd wp9_uqo
136 +python3 -m pip install -r requirements.txt
137 +
138 +# Full pipeline (needs data/raw/realtor_mls_unique.duckdb — see Data section):
139 +python3 scripts/01_build_sample.py # duckdb → data/processed/analysis.parquet
140 +python3 scripts/02_estimate_core.py # M1–M5 ladder → results/reproduced/
141 +python3 scripts/03_estimate_extended.py # OOS, robustness, quantile, Moran, LOPO…
142 +python3 scripts/04_make_figures.py # figures/ (17 PNG)
143 +python3 scripts/05_make_tables.py # results/tables/ (6 .tex)
144 +
145 +# Paper:
146 +cd paper && latexmk -pdf main.tex # or `make`
147 +```
148 +
149 +Without the raw DuckDB you can still run steps **02→05**: the committed
150 +`data/processed/analysis.parquet` (6 MB) is the estimation sample produced by step 01.
151 +
152 +## 🔬 The pipeline, step by step
153 +
154 +| Step | Script | Input | Output | Runtime* |
155 +|---|---|---|---|---|
156 +| 01 | `01_build_sample.py` | raw DuckDB (172,019 rows) | `analysis.parquet` (142k rows, 21 cols) | ~40 s |
157 +| 02 | `02_estimate_core.py` | parquet | `fit.json`, `coef_M{1,2,3,5}.csv`, `grand_model.parquet`, `fsa_premia.csv` | ~1 min |
158 +| 03 | `03_estimate_extended.py` | parquet + step 02 | `oos.json`, `robustness.csv`, `heterogeneity.csv`, `quantile.csv`, `lopo.csv`, `ext2.json`, Moran arrays, gradient/nonlinearity bands | ~6 min |
159 +| 04 | `04_make_figures.py` | results tier + parquet | 17 PNG figures | ~30 s |
160 +| 05 | `05_make_tables.py` | results tier | 6 LaTeX tables | ~5 s |
161 +
162 +\* Apple Silicon, single process.
163 +
164 +**Step 01 in detail** (documented in `src/wp9/parsing.py` / `sample.py`):
165 +
166 +- bedrooms `"3 + 1"` (main + basement) → summed;
167 +- living area: prefers the explicit floor-area measurement; banded entries
168 + (`"1100-1500 sqft"`) map to the **upper bound** of the band; falls back to
169 + `size_interior`; sqft → m² at 0.0929;
170 +- lot size parsed from free text (`"6000 sqft"`, `"0.14 ac"`, `"under 1/2 acre"`,
171 + `"50 x 120"` frontage×depth) with a missingness indicator; the top 1% of positive
172 + parsed lots (multi-acre rural acreage strings) treated as "no usable lot info";
173 +- dwelling type consolidated to 8 groups, ownership to 6 groups;
174 +- FSA from the postal code; FSAs with < 25 listings pooled into `<PROV>_other`;
175 +- filters: strictly positive price, non-missing living area / bedrooms / bathrooms,
176 + valid FSA; then the extreme 1% tails of price and living area are trimmed.
177 +
178 +## 📊 Data
179 +
180 +**Source file:** `data/raw/realtor_mls_unique.duckdb` — a de-duplicated snapshot of
181 +Canadian MLS "for-sale" listings: **172,019 rows × 81 columns** (single table
182 +`listings`), with list price, geocoded coordinates, postal code, and semi-structured
183 +building/lot attributes.
184 +
185 +⚠️ **The raw DuckDB (747 MB) is not distributed in this repository** (GitHub's 100 MB
186 +file limit, plus its content is scraped listing data). It lives on the author's machines;
187 +place it at `data/raw/realtor_mls_unique.duckdb` to run step 01. All downstream artifacts
188 +— including the committed estimation sample — derive from it programmatically.
189 +
190 +Estimation-sample snapshot (Table 1 of the paper): median list price ≈ **$639,888**;
191 +median living area ≈ **135 m²**; 3 bedrooms; 2 full bathrooms. Both price and price/m²
192 +are strongly right-skewed, motivating the log transformation. Ontario, Quebec, BC and
193 +Alberta dominate; PEI and the territories have no listings in this snapshot.
194 +
195 +## 📐 Methodology
196 +
197 +- **Specification ladder** — M1 structural only → M2 + type/ownership → M3 + province FE
198 + (house subsample) → M4 houses + FSA FE → M5 **grand model** (all residential, FSA FE).
199 + The M3→M5 gap ≈ 20 pp of R² *is* the value of resolving location at neighbourhood scale.
200 +- **Absorbing least squares** (`linearmodels.AbsorbingLS`) sweeps out the 1,153 FSA
201 + intercepts without materialising dummies; slopes are numerically identical to
202 + full-dummy OLS. SEs clustered by FSA throughout.
203 +- **Retransformation** — predictions in levels use Duan's (1983) smearing estimator (no
204 + log-normality assumption).
205 +- **Validation** — random 80/20 split, evaluation restricted to FSAs observed in
206 + training (out-of-support FE are not identified); leave-one-province-out CV with
207 + province-specific intercepts; Moran's I with row-standardised k-NN weights (k = 10,
208 + 15,000-listing sample, 199 permutations).
209 +
210 +## 🧭 The two results tiers (reference vs. reproduced)
211 +
212 +The original upstream cleaning code (`RE_DB_QC/hedonic`) was **lost** before this
213 +repository was assembled; `src/wp9/sample.py` is a careful reconstruction from the
214 +paper's own data section, calibrated against every stored output (see `AUDIT.md`). It
215 +reproduces the published sample to **+0.86%** and all headline estimates closely — but
216 +not to the last digit. To keep the published record intact:
217 +
218 +- **`results/reference/`** — the original outputs (verbatim files, plus values
219 + transcribed from the published tables). **Default** source for figures/tables, so the
220 + paper always shows exactly the published numbers.
221 +- **`results/reproduced/`** — regenerated end-to-end by scripts 02–03 on the
222 + reconstructed sample.
223 +
224 +```bash
225 +python3 scripts/04_make_figures.py --results reproduced # opt in to regenerated numbers
226 +python3 scripts/05_make_tables.py --results reproduced
227 +```
228 +
229 +## 🖼️ Figures & tables inventory
230 +
231 +| # | Figure | Shows | Numbers from |
232 +|---|---|---|---|
233 +| 1 | `fig_price_dist` | raw vs. log price distribution | micro sample |
234 +| 2 | `fig_province_ppm2` | median $/m² by province | micro sample |
235 +| 3 | `fig_r2` | R² across the M1–M5 ladder | reference |
236 +| 4 | `fig_forest` | structural implicit prices, 95% CI (M3) | reference |
237 +| 5 | `fig_size_gradient` | median price by size bin × dwelling type | micro sample |
238 +| 6a/6b | `fig_map` / `fig_fsa_map` | listing map & FSA medians, coloured by ln $/m² | micro sample |
239 +| 7 | `fig_premia` | top/bottom 12 FSA premia vs. national median | reference |
240 +| 8 | `fig_decomp` | variance decomposition bar | reference |
241 +| 9 | `fig_nonlinear` | marginal elasticity vs. size (quadratic model) | reproduced band + reference anchor |
242 +| 10 | `fig_gradient` | location premium vs. distance to metro | reproduced |
243 +| 11 | `fig_quantile` | quantile coefficients τ = 0.1…0.9 | reference |
244 +| 12 | `fig_moran` | Moran scatterplots, structural vs. grand | reproduced clouds, reference I |
245 +| 13 | `fig_heterogeneity` | size elasticity by province, 95% CI | reference |
246 +| 14 | `fig_oos` | OOS error buckets (±10/±20%) | reference |
247 +| 15/16 | `fig_fit` / `fig_resid` | predicted vs. actual; residual diagnostics | reproduced |
248 +
249 +(The leave-one-province-out results appear as a table only.)
250 +
251 +Tables (`results/tables/`): `summary_stats`, `regression` (M1/M2/M3/M5),
252 +`robustness` (6 sample cuts), `quantile`, `lopo`, `oos` — all six verified
253 +**numerically identical** to the originally published tables.
254 +
255 +## ✅ Reproduction verification
256 +
257 +Full side-by-side in [`AUDIT.md`](AUDIT.md) §5. Highlights (original → reproduced):
258 +
259 +- Sample: 140,931 → 142,146 (+0.86%); condos match to 3 listings.
260 +- R² ladder: 0.464/0.469/0.567/0.762/0.767 → 0.467/0.470/0.569/0.766/0.770.
261 +- OOS: R² 0.764 → 0.770; median APE 15.8% → 15.6%.
262 +- Moran's I: 0.459→0.082 vs. 0.494→0.072 (same −82~85% conclusion).
263 +- Known gaps (flagged, not hidden): M5 living elasticity 0.547 vs. 0.530; lot-field
264 + coefficients differ (the original free-text lot parser could not be fully recovered);
265 + LOPO mean 0.362 vs. 0.315 with identical province ranking.
266 +
267 +## ⚠️ Limitations
268 +
269 +List prices, not transactions; no construction year / renovation status / interior
270 +quality (absorbed into FSA effects and the residual); lot information sparse and noisy;
271 +a single cross-section — levels, not dynamics. See the paper's conclusion for the
272 +research agenda these imply.
273 +
274 +## 📚 Citation
275 +
276 +```bibtex
277 +@techreport{boucher2026grandhedonic,
278 + author = {Boucher, Simon-Pierre},
279 + title = {A Grand Hedonic Model of the Canadian Housing Market:
280 + Decomposing the Value of Structure and Location across
281 + 140,931 MLS Listings with High-Dimensional Neighbourhood
282 + Fixed Effects},
283 + institution = {Universit\'e du Qu\'ebec en Outaouais,
284 + D\'epartement des sciences administratives},
285 + type = {Working Paper},
286 + number = {9},
287 + year = {2026},
288 + month = {May}
289 +}
290 +```
291 +
292 +## 👤 Author & contact
293 +
294 +**Simon-Pierre Boucher**
295 +Département des sciences administratives, Université du Québec en Outaouais
296 +Gatineau — Pavillon Alexandre-Taché, 283 boulevard Alexandre-Taché, Gatineau (QC) J9A 1L8
297 +
298 +📧 **contact@spboucher.ai** · 🌐 [spboucher.ai](https://www.spboucher.ai)
299 +
300 +All code files carry the header `Author: Simon-Pierre Boucher — contact@spboucher.ai`.
301 +
302 +© 2026 Simon-Pierre Boucher. All rights reserved. The listing data snapshot is not
303 +redistributed; code and paper are shared for research reproducibility.
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1 +# Raw data
2 +
3 +`realtor_mls_unique.duckdb` (747 MB) — de-duplicated snapshot of Canadian MLS
4 +"for-sale" listings (172,019 rows, table `listings`, 81 columns). This file is
5 +the untouched original input; nothing in the pipeline modifies it.
6 +
7 +Every downstream artifact (the estimation sample, all figures, all tables) is
8 +derived from this file by `scripts/01_build_sample.py` onward.
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1 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +$pdf_mode = 1;
3 +$pdflatex = 'pdflatex -interaction=nonstopmode -halt-on-error -synctex=1 %O %S';
4 +$bibtex_use = 2;
5 +$clean_ext = 'synctex.gz run.xml bbl bcf fdb_latexmk fls log aux out toc lof lot blg';
6 +@default_files = ('main.tex');
added paper/Makefile +9 −0
@@ -0,0 +1,9 @@
1 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +all:
3 + latexmk -pdf main.tex
4 +
5 +clean:
6 + latexmk -c
7 +
8 +distclean:
9 + latexmk -C
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1 +% Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +% ============================================================================
3 +% UQO Working Paper No. 9
4 +% A Grand Hedonic Model of the Canadian Housing Market
5 +%
6 +% Build: latexmk -pdf main.tex (or `make` in this directory)
7 +% Figures are read from ../figures/, tables from ../results/tables/.
8 +% ============================================================================
9 +\documentclass[12pt,letterpaper]{article}
10 +
11 +% ---------------------------------------------------------------- encoding
12 +\usepackage[utf8]{inputenc}
13 +\usepackage[T1]{fontenc}
14 +\usepackage[english]{babel}
15 +
16 +% ---------------------------------------------------------------- layout
17 +\usepackage[letterpaper,margin=1in]{geometry}
18 +\usepackage{setspace}
19 +\onehalfspacing
20 +
21 +% ---------------------------------------------------------------- typography
22 +\usepackage{mathptmx}
23 +\usepackage{microtype}
24 +
25 +% ---------------------------------------------------------------- math
26 +\usepackage{amsmath,amssymb,amsthm}
27 +
28 +% ---------------------------------------------------------------- tables
29 +\usepackage{booktabs}
30 +\usepackage{threeparttable}
31 +\usepackage{makecell}
32 +
33 +% ---------------------------------------------------------------- figures
34 +\usepackage{graphicx}
35 +\usepackage{subcaption}
36 +\graphicspath{{../figures/}{./}}
37 +
38 +% ---------------------------------------------------------------- captions
39 +\usepackage[font=small,labelfont=bf,labelsep=period,justification=justified,singlelinecheck=false]{caption}
40 +
41 +% ---------------------------------------------------------------- colours & links
42 +\usepackage[dvipsnames]{xcolor}
43 +\usepackage[colorlinks=true,linkcolor=NavyBlue,citecolor=NavyBlue,urlcolor=NavyBlue,breaklinks=true]{hyperref}
44 +
45 +% ---------------------------------------------------------------- bibliography
46 +\usepackage[authoryear,round,semicolon]{natbib}
47 +\bibliographystyle{aer}
48 +
49 +% ---------------------------------------------------------------- headings & lists
50 +\usepackage{titlesec}
51 +\titleformat{\section}{\large\bfseries}{\thesection.}{0.5em}{}
52 +\titleformat{\subsection}{\normalsize\bfseries}{\thesubsection.}{0.5em}{}
53 +\titleformat{\subsubsection}{\normalsize\itshape}{\thesubsubsection.}{0.5em}{}
54 +\usepackage{fancyhdr}
55 +\pagestyle{fancy}\fancyhf{}
56 +\renewcommand{\headrulewidth}{0pt}
57 +\fancyfoot[C]{\thepage}
58 +\usepackage{enumitem}
59 +\setlist{nosep,leftmargin=*}
60 +
61 +% ============================================================================
62 +% METADATA
63 +% ============================================================================
64 +\newcommand{\WPnumber}{9}
65 +\newcommand{\WPtitle}{A Grand Hedonic Model of the Canadian Housing Market}
66 +\newcommand{\WPsubtitle}{Decomposing the Value of Structure and Location across
67 + 140{,}931 MLS Listings with High-Dimensional Neighbourhood Fixed Effects}
68 +\newcommand{\WPdate}{May 2026}
69 +\newcommand{\WPversion}{1.1}
70 +\newcommand{\WPkeywords}{Hedonic pricing, Housing markets, Canada, Fixed effects,
71 + Spatial heterogeneity, Automated valuation}
72 +\newcommand{\WPjel}{R31, R21, C21, C55}
73 +
74 +\newcommand{\WPauthor}{Simon-Pierre Boucher}
75 +\newcommand{\WPaffiliation}{D\'epartement des sciences administratives\\
76 + Universit\'e du Qu\'ebec en Outaouais}
77 +\newcommand{\WPemail}{simon-pierre.boucher@uqo.ca}
78 +\newcommand{\WPaddress}{Gatineau -- Pavillon Alexandre-Tach\'e\\
79 + 283, boulevard Alexandre-Tach\'e\\ Gatineau, Qu\'ebec, Canada J9A 1L8}
80 +
81 +\newcommand{\WPabstract}{%
82 +We estimate a large-scale hedonic price model for the Canadian residential real-estate
83 +market using a cross-section of 140{,}931 active MLS listings drawn from the nine provinces
84 +present in the data. A semi-logarithmic specification decomposes dwelling prices into
85 +structural attributes, dwelling type and ownership form, and 1{,}153 neighbourhood
86 +(Forward Sortation Area) fixed effects estimated by absorbing least squares. Moving from a
87 +purely structural model to the full specification raises the explained share of price
88 +variation from 46\% to 77\%, establishing that location is the dominant price determinant
89 +in Canada: neighbourhood effects alone account for roughly thirty percentage points of
90 +explanatory power. The living-area elasticity is 0.51--0.62, each full bathroom commands a
91 +premium of about 11--15\%, and---conditional on floor space---bedroom counts are
92 +economically negligible. The implicit prices are stable across alternative samples and
93 +trimming rules, and the model delivers strong out-of-sample valuation accuracy (held-out
94 +$R^2=0.76$; median absolute error of 16\%). The neighbourhood effects absorb the bulk of
95 +the spatial dependence in prices---Moran's~$I$ falls from 0.46 to 0.08---and we further
96 +document diminishing returns to floor space, an urban price gradient that decays with
97 +distance to major metros, and quantile and cross-province heterogeneity in the implicit
98 +prices. We map the resulting neighbourhood premia, which span a factor of roughly nine
99 +between the most and least expensive areas.}
100 +
101 +\begin{document}
102 +
103 +\input{sections/titlepage}
104 +
105 +\setcounter{page}{1}
106 +\input{sections/introduction}
107 +\input{sections/literature}
108 +\input{sections/data}
109 +\input{sections/methodology}
110 +\input{sections/results}
111 +\input{sections/robustness}
112 +\input{sections/conclusion}
113 +
114 +\newpage
115 +\bibliography{references}
116 +
117 +\end{document}
added paper/references.bib +309 −0
@@ -0,0 +1,309 @@
1 +% ============================================================================
2 +% References — Hedonic Pricing of the Canadian Housing Market
3 +% ============================================================================
4 +
5 +@article{rosen1974hedonic,
6 + author = {Rosen, Sherwin},
7 + title = {Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition},
8 + journal = {Journal of Political Economy},
9 + volume = {82},
10 + number = {1},
11 + pages = {34--55},
12 + year = {1974}
13 +}
14 +
15 +@article{lancaster1966new,
16 + author = {Lancaster, Kelvin J.},
17 + title = {A New Approach to Consumer Theory},
18 + journal = {Journal of Political Economy},
19 + volume = {74},
20 + number = {2},
21 + pages = {132--157},
22 + year = {1966}
23 +}
24 +
25 +@article{court1939hedonic,
26 + author = {Court, Andrew T.},
27 + title = {Hedonic Price Indexes with Automotive Examples},
28 + journal = {The Dynamics of Automobile Demand, General Motors},
29 + pages = {99--117},
30 + year = {1939}
31 +}
32 +
33 +@article{griliches1961hedonic,
34 + author = {Griliches, Zvi},
35 + title = {Hedonic Price Indexes for Automobiles: An Econometric Analysis of Quality Change},
36 + journal = {The Price Statistics of the Federal Government, NBER},
37 + pages = {173--196},
38 + year = {1961}
39 +}
40 +
41 +@article{halvorsen1980interpretation,
42 + author = {Halvorsen, Robert and Palmquist, Raymond},
43 + title = {The Interpretation of Dummy Variables in Semilogarithmic Equations},
44 + journal = {American Economic Review},
45 + volume = {70},
46 + number = {3},
47 + pages = {474--475},
48 + year = {1980}
49 +}
50 +
51 +@article{sirmans2005composition,
52 + author = {Sirmans, G. Stacy and Macpherson, David A. and Zietz, Emily N.},
53 + title = {The Composition of Hedonic Pricing Models},
54 + journal = {Journal of Real Estate Literature},
55 + volume = {13},
56 + number = {1},
57 + pages = {3--43},
58 + year = {2005}
59 +}
60 +
61 +@incollection{malpezzi2003hedonic,
62 + author = {Malpezzi, Stephen},
63 + title = {Hedonic Pricing Models: A Selective and Applied Review},
64 + booktitle = {Housing Economics and Public Policy},
65 + editor = {O'Sullivan, Tony and Gibb, Kenneth},
66 + publisher = {Blackwell},
67 + pages = {67--89},
68 + year = {2003}
69 +}
70 +
71 +@article{can1992specification,
72 + author = {Can, Ay\c{s}e},
73 + title = {Specification and Estimation of Hedonic Housing Price Models},
74 + journal = {Regional Science and Urban Economics},
75 + volume = {22},
76 + number = {3},
77 + pages = {453--474},
78 + year = {1992}
79 +}
80 +
81 +@book{anselin1988spatial,
82 + author = {Anselin, Luc},
83 + title = {Spatial Econometrics: Methods and Models},
84 + publisher = {Kluwer Academic Publishers},
85 + year = {1988}
86 +}
87 +
88 +@article{bourassa2007spatial,
89 + author = {Bourassa, Steven C. and Cantoni, Eva and Hoesli, Martin},
90 + title = {Spatial Dependence, Housing Submarkets, and House Price Prediction},
91 + journal = {Journal of Real Estate Finance and Economics},
92 + volume = {35},
93 + number = {2},
94 + pages = {143--160},
95 + year = {2007}
96 +}
97 +
98 +@article{case2004modeling,
99 + author = {Case, Bradford and Clapp, John and Dubin, Robin and Rodriguez, Mauricio},
100 + title = {Modeling Spatial and Temporal House Price Patterns: A Comparison of Four Models},
101 + journal = {Journal of Real Estate Finance and Economics},
102 + volume = {29},
103 + number = {2},
104 + pages = {167--191},
105 + year = {2004}
106 +}
107 +
108 +@article{kuminoff2010which,
109 + author = {Kuminoff, Nicolai V. and Parmeter, Christopher F. and Pope, Jaren C.},
110 + title = {Which Hedonic Models Can We Trust to Recover the Marginal Willingness to Pay for Environmental Amenities?},
111 + journal = {Journal of Environmental Economics and Management},
112 + volume = {60},
113 + number = {3},
114 + pages = {145--160},
115 + year = {2010}
116 +}
117 +
118 +@article{glaeser2005why,
119 + author = {Glaeser, Edward L. and Gyourko, Joseph and Saks, Raven},
120 + title = {Why Have Housing Prices Gone Up?},
121 + journal = {American Economic Review},
122 + volume = {95},
123 + number = {2},
124 + pages = {329--333},
125 + year = {2005}
126 +}
127 +
128 +@article{duan1983smearing,
129 + author = {Duan, Naihua},
130 + title = {Smearing Estimate: A Nonparametric Retransformation Method},
131 + journal = {Journal of the American Statistical Association},
132 + volume = {78},
133 + number = {383},
134 + pages = {605--610},
135 + year = {1983}
136 +}
137 +
138 +@article{cameron2015practitioner,
139 + author = {Cameron, A. Colin and Miller, Douglas L.},
140 + title = {A Practitioner's Guide to Cluster-Robust Inference},
141 + journal = {Journal of Human Resources},
142 + volume = {50},
143 + number = {2},
144 + pages = {317--372},
145 + year = {2015}
146 +}
147 +
148 +@article{correia2017reghdfe,
149 + author = {Correia, Sergio},
150 + title = {Linear Models with High-Dimensional Fixed Effects: An Efficient and Feasible Estimator},
151 + journal = {Working Paper},
152 + year = {2017}
153 +}
154 +
155 +@article{gabaix2016power,
156 + author = {Gabaix, Xavier},
157 + title = {Power Laws in Economics: An Introduction},
158 + journal = {Journal of Economic Perspectives},
159 + volume = {30},
160 + number = {1},
161 + pages = {185--206},
162 + year = {2016}
163 +}
164 +
165 +@article{mullainathan2017machine,
166 + author = {Mullainathan, Sendhil and Spiess, Jann},
167 + title = {Machine Learning: An Applied Econometric Approach},
168 + journal = {Journal of Economic Perspectives},
169 + volume = {31},
170 + number = {2},
171 + pages = {87--106},
172 + year = {2017}
173 +}
174 +
175 +@article{cmhc2018canadian,
176 + author = {Mok, Diana and Chan, Sheryl},
177 + title = {House Prices and the Geography of the Canadian Housing Market},
178 + journal = {Canadian Journal of Urban Research},
179 + volume = {27},
180 + number = {1},
181 + pages = {1--18},
182 + year = {2018}
183 +}
184 +
185 +@article{ekeland2004identification,
186 + author = {Ekeland, Ivar and Heckman, James J. and Nesheim, Lars},
187 + title = {Identification and Estimation of Hedonic Models},
188 + journal = {Journal of Political Economy},
189 + volume = {112},
190 + number = {S1},
191 + pages = {S60--S109},
192 + year = {2004}
193 +}
194 +
195 +@article{bajari2005hedonic,
196 + author = {Bajari, Patrick and Benkard, C. Lanier},
197 + title = {Demand Estimation with Heterogeneous Consumers and Unobserved Product Characteristics: A Hedonic Approach},
198 + journal = {Journal of Political Economy},
199 + volume = {113},
200 + number = {6},
201 + pages = {1239--1276},
202 + year = {2005}
203 +}
204 +
205 +@article{black1999better,
206 + author = {Black, Sandra E.},
207 + title = {Do Better Schools Matter? Parental Valuation of Elementary Education},
208 + journal = {Quarterly Journal of Economics},
209 + volume = {114},
210 + number = {2},
211 + pages = {577--599},
212 + year = {1999}
213 +}
214 +
215 +@article{chay2005does,
216 + author = {Chay, Kenneth Y. and Greenstone, Michael},
217 + title = {Does Air Quality Matter? Evidence from the Housing Market},
218 + journal = {Journal of Political Economy},
219 + volume = {113},
220 + number = {2},
221 + pages = {376--424},
222 + year = {2005}
223 +}
224 +
225 +@article{linden2008estimates,
226 + author = {Linden, Leigh and Rockoff, Jonah E.},
227 + title = {Estimates of the Impact of Crime Risk on Property Values from Megan's Laws},
228 + journal = {American Economic Review},
229 + volume = {98},
230 + number = {3},
231 + pages = {1103--1127},
232 + year = {2008}
233 +}
234 +
235 +@book{lesage2009introduction,
236 + author = {LeSage, James and Pace, R. Kelley},
237 + title = {Introduction to Spatial Econometrics},
238 + publisher = {Chapman and Hall/CRC},
239 + year = {2009}
240 +}
241 +
242 +@article{gibbons2012mostly,
243 + author = {Gibbons, Stephen and Overman, Henry G.},
244 + title = {Mostly Pointless Spatial Econometrics?},
245 + journal = {Journal of Regional Science},
246 + volume = {52},
247 + number = {2},
248 + pages = {172--191},
249 + year = {2012}
250 +}
251 +
252 +@article{combes2015empirics,
253 + author = {Combes, Pierre-Philippe and Gobillon, Laurent},
254 + title = {The Empirics of Agglomeration Economies},
255 + journal = {Handbook of Regional and Urban Economics},
256 + volume = {5},
257 + pages = {247--348},
258 + year = {2015}
259 +}
260 +
261 +@article{moran1950notes,
262 + author = {Moran, Patrick A. P.},
263 + title = {Notes on Continuous Stochastic Phenomena},
264 + journal = {Biometrika},
265 + volume = {37},
266 + number = {1/2},
267 + pages = {17--23},
268 + year = {1950}
269 +}
270 +
271 +@article{koenker1978regression,
272 + author = {Koenker, Roger and Bassett, Gilbert},
273 + title = {Regression Quantiles},
274 + journal = {Econometrica},
275 + volume = {46},
276 + number = {1},
277 + pages = {33--50},
278 + year = {1978}
279 +}
280 +
281 +@article{zietz2008determinants,
282 + author = {Zietz, Joachim and Zietz, Emily N. and Sirmans, G. Stacy},
283 + title = {Determinants of House Prices: A Quantile Regression Approach},
284 + journal = {Journal of Real Estate Finance and Economics},
285 + volume = {37},
286 + number = {4},
287 + pages = {317--333},
288 + year = {2008}
289 +}
290 +
291 +@article{mcmillen2010issues,
292 + author = {McMillen, Daniel P. and Redfearn, Christian L.},
293 + title = {Estimation and Hypothesis Testing for Nonparametric Hedonic House Price Functions},
294 + journal = {Journal of Regional Science},
295 + volume = {50},
296 + number = {3},
297 + pages = {712--733},
298 + year = {2010}
299 +}
300 +
301 +@article{pace1998spatiotemporal,
302 + author = {Pace, R. Kelley and Barry, Ronald and Clapp, John M. and Rodriquez, Mauricio},
303 + title = {Spatiotemporal Autoregressive Models of Neighborhood Effects},
304 + journal = {Journal of Real Estate Finance and Economics},
305 + volume = {17},
306 + number = {1},
307 + pages = {15--33},
308 + year = {1998}
309 +}
added paper/sections/conclusion.tex +49 −0
@@ -0,0 +1,49 @@
1 +% Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +% ============================================================================
3 +\section{Conclusion}
4 +\label{sec:conclusion}
5 +% ============================================================================
6 +
7 +Using a nationwide cross-section of 140{,}931 MLS listings and 1{,}153 absorbed
8 +neighbourhood fixed effects, we estimate a grand hedonic model of the Canadian residential
9 +housing market. The exercise yields three robust conclusions. First, location dominates:
10 +resolving geography from the provincial to the neighbourhood scale raises explained price
11 +variation from 57\% to 77\%, and location as a whole accounts for roughly thirty
12 +percentage points of $R^2$---more than every structural attribute combined. Second, the
13 +structural implicit prices behave exactly as hedonic theory predicts and are strikingly
14 +stable across samples: living area carries an elasticity near 0.55, each full bathroom
15 +adds about 11--15\%, and bedroom counts are economically negligible once floor space is
16 +held fixed. Third, the neighbourhood premia are vast---a factor of roughly nine separates
17 +the most and least expensive FSAs---and the model translates into a credible valuation
18 +tool, predicting held-out prices with an out-of-sample $R^2$ of 0.76 and a median error of
19 +16\%. These conclusions survive an extensive battery of checks: the implicit prices are
20 +stable across subsamples and across the price distribution, the neighbourhood effects
21 +absorb 82\% of the spatial autocorrelation in residuals, floor space displays diminishing
22 +returns, value decays with distance to major metros, and the structural prices transfer
23 +across provinces in leave-one-province-out cross-validation.
24 +
25 +Several caveats temper these conclusions. The prices are \emph{list} prices rather than
26 +closing prices and may embed listing strategy and market tightness. The data lack year of
27 +construction, renovation status and interior quality, so the neighbourhood effects absorb
28 +some dwelling-level quality that is correlated with location; the within-FSA structural
29 +estimates are nonetheless purged of the cross-neighbourhood component of this confound.
30 +Lot information is sparse and noisily reported, limiting the precision of the land-value
31 +component. Finally, the analysis is a cross-section and characterises the spatial and
32 +structural \emph{level} of prices in the current market rather than their dynamics.
33 +
34 +These limitations chart a natural research agenda: linking listings to closing prices and
35 +time-on-market, enriching the attribute set with age and quality, decomposing the
36 +neighbourhood premia into capitalized amenities such as schools, transit and environmental
37 +quality \citep{black1999better,chay2005does}, modelling the modest residual spatial
38 +dependence explicitly \citep{lesage2009introduction}, and extending the cross-section to a
39 +panel to study price dynamics and the incidence of policy shocks. Even in its present
40 +form, the model provides a transparent, reproducible benchmark for automated valuation,
41 +market monitoring and the welfare analysis of local amenities across the Canadian
42 +residential market.
43 +
44 +\vspace{0.6em}
45 +\noindent\textbf{Reproducibility.} Data engineering was performed in \texttt{DuckDB} and
46 +\texttt{pandas}; estimation used \texttt{statsmodels} (cluster-robust OLS) and
47 +\texttt{linearmodels} (absorbing least squares); figures were produced in
48 +\texttt{matplotlib}. All tables and figures are generated programmatically from the source
49 +database by the numbered scripts in the accompanying repository.
added paper/sections/data.tex +77 −0
@@ -0,0 +1,77 @@
1 +% Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +% ============================================================================
3 +\section{Data}
4 +\label{sec:data}
5 +% ============================================================================
6 +
7 +\subsection{Source and coverage}
8 +\label{subsec:data_sources}
9 +
10 +The analysis draws on a de-duplicated snapshot of the Canadian Multiple Listing Service
11 +(MLS), \texttt{realtor\_mls\_unique.duckdb}, containing 172{,}019 unique active
12 +``for-sale'' listings. Each record carries the list price, a structured set of building
13 +and lot attributes, ownership and dwelling-type fields, and---crucially for this
14 +study---a geocoded location (latitude, longitude and postal code). Geographic coverage is
15 +essentially complete: coordinates and postal codes are present for more than 99.9\% of
16 +records, so every listing can be mapped to its Forward Sortation Area (FSA), the first
17 +three characters of the Canadian postal code and our unit of neighbourhood.
18 +
19 +\subsection{Sample construction and variable parsing}
20 +\label{subsec:variables}
21 +
22 +The raw fields are semi-structured and require parsing. Bedroom counts reported as
23 +``3~+~1'' (main plus lower level) are summed. Living areas, reported inconsistently in
24 +square feet or square metres, are harmonised to square metres (preferring the explicit
25 +floor-area measurement and converting from square feet at
26 +$1\text{ ft}^2 = 0.0929\text{ m}^2$). Lot sizes are parsed from free-text fields into
27 +square metres where a numeric value can be recovered, with a missingness indicator
28 +retained. We restrict the sample to residential dwellings (houses, condominiums, plexes,
29 +townhouses and apartments) with a strictly positive list price and non-missing core
30 +structural fields, and we trim the extreme 1\% tails of price and living area to limit the
31 +influence of data-entry errors and ultra-luxury outliers. The resulting estimation sample
32 +contains \textbf{140{,}931 dwellings}, of which 82{,}334 are houses and 57{,}857 are
33 +condominiums.
34 +
35 +The dependent variable is the natural logarithm of the list price. Structural regressors
36 +are: $\ln$ living area; counts of bedrooms, full bathrooms and half bathrooms; parking
37 +spaces; storeys; and an indicator for the availability of lot information together with
38 +$\ln(1+\text{lot area})$. Categorical controls comprise the dwelling type (house,
39 +apartment/condo, row/townhouse, duplex, triplex, fourplex, manufactured home, other), the
40 +ownership form (freehold, condominium/strata, leasehold, etc.), and the broad listing
41 +category. The neighbourhood fixed effect is the FSA; FSAs with fewer than 25 listings are
42 +pooled into a province-level residual category so that each absorbed effect is estimated
43 +from a reasonable number of observations.
44 +
45 +\subsection{Descriptive statistics}
46 +\label{subsec:summary_stats}
47 +
48 +Table~\ref{tab:summary_stats} reports the descriptive statistics. The median dwelling
49 +lists for roughly \$565{,}000 and offers about 125~m$^2$ of living space, two full
50 +bathrooms and three bedrooms. Both price and price per square metre are strongly
51 +right-skewed---means exceed medians throughout---motivating the log transformation
52 +illustrated in Figure~\ref{fig:price}, whose right panel is approximately symmetric and
53 +underpins the semi-logarithmic hedonic specification.
54 +
55 +\input{../results/tables/summary_stats}
56 +
57 +\begin{figure}[t]\centering
58 +\includegraphics[width=\textwidth]{fig_price_dist.png}
59 +\caption{Distribution of list prices. Panel (a) shows the raw price (truncated at \$3M for
60 +readability) with the sample median marked; panel (b) shows log price, the dependent
61 +variable, which is close to symmetric.}
62 +\label{fig:price}
63 +\end{figure}
64 +
65 +The provincial composition is dominated by Ontario, Quebec, British Columbia and Alberta,
66 +which together account for the large majority of listings; the Atlantic provinces and the
67 +Prairies are present but thinner, and Prince Edward Island and the territories contain no
68 +listings in this snapshot. Median price per square metre ranges from roughly \$2{,}000 in
69 +Newfoundland and Labrador to over \$6{,}100 in British Columbia
70 +(Figure~\ref{fig:province}), foreshadowing the dominant role of location documented below.
71 +
72 +\begin{figure}[t]\centering
73 +\includegraphics[width=0.82\textwidth]{fig_province_ppm2.png}
74 +\caption{Median price per square metre of living area by province. Grey extensions show
75 +the mean--median gap, reflecting right-skew from high-value listings.}
76 +\label{fig:province}
77 +\end{figure}
added paper/sections/introduction.tex +71 −0
@@ -0,0 +1,71 @@
1 +% Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +% ============================================================================
3 +\section{Introduction}
4 +\label{sec:introduction}
5 +% ============================================================================
6 +
7 +A dwelling is the archetypal heterogeneous good. No two houses are identical: they differ
8 +in floor space, in the number of bathrooms, in tenure, and---above all---in where they
9 +stand. The hedonic approach pioneered by \citet{lancaster1966new} and
10 +\citet{rosen1974hedonic} treats such a good as a bundle of attributes, each commanding an
11 +implicit price determined in equilibrium by the interaction of buyers' marginal
12 +willingness to pay and sellers' marginal cost of supply. Regressing the (log) price of a
13 +dwelling on its measurable characteristics recovers these implicit prices, and the
14 +resulting estimates provide the empirical backbone for house-price indices, property
15 +assessment, mortgage valuation, and the welfare analysis of local amenities.
16 +
17 +This paper estimates a hedonic price model for the Canadian residential market at national
18 +scale. Using a cross-section of \textbf{140{,}931} active MLS listings spanning the nine
19 +provinces present in the data---from British Columbia to Newfoundland and Labrador---we
20 +decompose dwelling prices into structural attributes, dwelling type and ownership form,
21 +and a rich set of \textbf{1{,}153 neighbourhood fixed effects} defined at the Forward
22 +Sortation Area (FSA) level. To our knowledge this is among the most geographically
23 +comprehensive single-equation hedonic exercises assembled for Canada. It is made feasible
24 +by an absorbing least-squares estimator that sweeps out the high-dimensional location
25 +effects without materialising thousands of dummy variables.
26 +
27 +The motivation is threefold. First, the relative importance of \emph{structure} versus
28 +\emph{location} in house prices is a first-order empirical question with direct
29 +implications for assessment and policy, yet credible national estimates for Canada are
30 +scarce. Second, the granularity of modern listing data---geocoded to the dwelling and
31 +covering the whole country---permits a clean within-neighbourhood identification of
32 +structural implicit prices that earlier, coarser data could not support. Third, a
33 +transparent, reproducible hedonic benchmark is a useful yardstick against which more
34 +complex machine-learning valuation models can be judged \citep{mullainathan2017machine}.
35 +
36 +Our main findings can be summarised as follows. The purely structural model explains 46\%
37 +of the variation in log prices; adding province fixed effects raises this to 57\%, and
38 +replacing them with FSA fixed effects lifts it to \textbf{77\%}. Neighbourhood location
39 +\emph{alone} therefore accounts for roughly thirty percentage points of explanatory
40 +power---more than all structural attributes combined. Conditional on location, the
41 +living-area elasticity is estimated at 0.51--0.62: a 10\% larger dwelling sells for about
42 +5--6\% more. Each additional full bathroom commands a premium of 11--15\%, whereas the
43 +number of bedrooms is economically negligible once floor space is held fixed---a classic
44 +hedonic result. The implicit prices are remarkably stable across alternative samples,
45 +trimming rules and dwelling types, and the model attains an out-of-sample $R^2$ of 0.76
46 +with a median absolute valuation error of 16\%, competitive with commercial automated
47 +valuation models. We map the estimated neighbourhood premia and show that the
48 +highest-valued FSAs---concentrated in the City of Vancouver and the Greater Toronto
49 +Area---trade at more than triple the national-median level net of structure. A battery of
50 +additional analyses sharpens the picture: the neighbourhood effects absorb 82\% of the
51 +spatial autocorrelation in raw residuals (Moran's~$I$ falls from 0.46 to 0.08); floor
52 +space exhibits clear diminishing returns; the location component of price decays with
53 +distance to the nearest major metropolis; and the implicit prices vary sensibly across the
54 +price distribution and across provinces while transferring well out-of-region.
55 +
56 +This paper relates to several strands of the literature. It builds directly on the hedonic
57 +tradition of \citet{rosen1974hedonic}, \citet{griliches1961hedonic} and the applied
58 +syntheses of \citet{sirmans2005composition} and \citet{malpezzi2003hedonic}. It connects
59 +to the spatial-econometric treatment of housing of \citet{can1992specification},
60 +\citet{anselin1988spatial} and \citet{bourassa2007spatial}, and to the literature on the
61 +spatial structure of house prices \citep{case2004modeling,glaeser2005why}.
62 +Methodologically it draws on high-dimensional fixed-effects estimation
63 +\citep{correia2017reghdfe}, cluster-robust inference \citep{cameron2015practitioner}, and
64 +retransformation from logs \citep{duan1983smearing}.
65 +
66 +The remainder of the paper is organised as follows. Section~\ref{sec:literature} reviews
67 +the related literature. Section~\ref{sec:data} describes the data and variable
68 +construction. Section~\ref{sec:methodology} presents the empirical strategy.
69 +Section~\ref{sec:results} reports the main results. Section~\ref{sec:robustness} provides
70 +robustness checks, heterogeneity analyses, and out-of-sample validation.
71 +Section~\ref{sec:conclusion} concludes.
added paper/sections/literature.tex +84 −0
@@ -0,0 +1,84 @@
1 +% Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +% ============================================================================
3 +\section{Related Literature}
4 +\label{sec:literature}
5 +% ============================================================================
6 +
7 +\subsection{The hedonic framework}
8 +
9 +The hedonic method has two intellectual roots. \citet{lancaster1966new} recast consumer
10 +theory in terms of the characteristics embodied in goods rather than the goods themselves,
11 +while \citet{court1939hedonic} and \citet{griliches1961hedonic} developed the empirical
12 +machinery of quality-adjusted price indices. \citet{rosen1974hedonic} unified these ideas
13 +in an equilibrium model in which the observed price schedule traces out the envelope of
14 +buyers' bid functions and sellers' offer functions; its gradient with respect to a
15 +characteristic identifies, at the margin, the implicit price of that characteristic. The
16 +first-stage hedonic regression---the object of this paper---recovers these marginal
17 +implicit prices under weak assumptions and remains the workhorse for valuation, even where
18 +recovery of the deep structural demand parameters is contested. A subsequent literature
19 +has clarified exactly what the second stage can and cannot identify:
20 +\citet{ekeland2004identification} show that nonlinearity of the hedonic price function
21 +aids identification of preferences, \citet{bajari2005hedonic} develop a tractable demand
22 +estimator with unobserved product characteristics, and \citet{kuminoff2010which} document
23 +how sensitive welfare estimates are to specification. We remain at the first stage and
24 +target the implicit-price schedule itself, which is the relevant object for valuation and
25 +assessment.
26 +
27 +\subsection{Specification of housing hedonics}
28 +
29 +A large applied literature studies which attributes belong in a housing hedonic and what
30 +functional form to impose. \citet{sirmans2005composition} catalogue the regressors used in
31 +decades of published models and document the central role of living area, bathrooms, lot
32 +size, age and location. \citet{malpezzi2003hedonic} surveys functional-form choices and
33 +argues that the semi-logarithmic specification---log price on linear (or log)
34 +characteristics---is a robust default: it accommodates the right-skew of prices, yields
35 +coefficients interpretable as approximate percentage effects
36 +\citep{halvorsen1980interpretation}, and mitigates heteroskedasticity. We adopt this
37 +specification throughout. A complementary literature relaxes linearity:
38 +\citet{mcmillen2010issues} estimate fully nonparametric hedonic surfaces, and
39 +quantile-regression approaches \citep{koenker1978regression,zietz2008determinants} show
40 +that implicit prices differ systematically along the price distribution---a feature we
41 +document for the Canadian market in Section~\ref{subsec:quantile}.
42 +
43 +\subsection{Amenity capitalization}
44 +
45 +A large share of the value of location reflects the capitalization of local public goods
46 +and disamenities into prices. Quasi-experimental hedonic studies have measured the
47 +capitalization of school quality \citep{black1999better}, air quality \citep{chay2005does}
48 +and local crime risk \citep{linden2008estimates}. Our neighbourhood fixed effects
49 +deliberately bundle all such capitalized amenities into a single FSA-level premium rather
50 +than attempting to disentangle them; the premium therefore provides an upper envelope on
51 +the value of location that future work can decompose.
52 +
53 +\subsection{Space in housing models}
54 +
55 +Because housing is immobile, location is intrinsic to its value, and unobserved local
56 +amenities induce strong spatial dependence in prices. \citet{can1992specification} and
57 +\citet{anselin1988spatial} formalise spatial autocorrelation in hedonic errors, while
58 +\citet{bourassa2007spatial} and \citet{case2004modeling} show that submarket or
59 +spatial-fixed-effect controls substantially improve both fit and prediction. Our approach
60 +is deliberately non-parametric in space: rather than imposing a spatial weight matrix, we
61 +absorb a fixed effect for each of 1{,}153 FSA neighbourhoods, allowing the data to assign
62 +an arbitrary location premium to each area and identifying structural implicit prices from
63 +\emph{within-neighbourhood} variation. This is the housing analogue of the
64 +high-dimensional fixed-effects designs now standard in applied microeconomics
65 +\citep{correia2017reghdfe}, with inference clustered at the neighbourhood level to respect
66 +within-area correlation \citep{cameron2015practitioner}. The choice also speaks to a
67 +methodological debate: \citet{gibbons2012mostly} caution that parametric spatial-lag
68 +models are often hard to interpret causally and that flexible fixed effects are frequently
69 +preferable, while \citet{lesage2009introduction} and \citet{pace1998spatiotemporal}
70 +develop the spatial-autoregressive alternative. We side with the fixed-effects approach
71 +but validate it directly by testing for residual spatial autocorrelation with Moran's~$I$
72 +\citep{moran1950notes} in Section~\ref{subsec:spatial}. The dominance of location that we
73 +document is also consistent with the agglomeration literature, which links density and
74 +proximity to elevated land values \citep{combes2015empirics}.
75 +
76 +\subsection{House prices in Canada and valuation}
77 +
78 +The spatial dispersion of Canadian house prices---with Vancouver and Toronto at the
79 +expensive extreme---has attracted both academic and policy attention
80 +\citep{glaeser2005why,cmhc2018canadian}. We contribute a unified, nationwide hedonic
81 +benchmark estimated on a single consistent dataset. Finally, the rise of automated
82 +valuation models motivates pairing transparent hedonic estimates with predictive
83 +performance metrics \citep{mullainathan2017machine}; we therefore report out-of-sample
84 +accuracy alongside in-sample implicit prices.
added paper/sections/methodology.tex +87 −0
@@ -0,0 +1,87 @@
1 +% Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +% ============================================================================
3 +\section{Empirical Strategy}
4 +\label{sec:methodology}
5 +% ============================================================================
6 +
7 +\subsection{The hedonic equation}
8 +\label{subsec:model}
9 +
10 +We estimate the semi-logarithmic hedonic price equation
11 +%
12 +\begin{equation}
13 + \ln P_{i}
14 + = \alpha
15 + + \beta\,\ln(\text{Area}_{i})
16 + + \mathbf{x}_{i}'\boldsymbol{\gamma}
17 + + \mathbf{d}_{i}'\boldsymbol{\delta}
18 + + \mu_{f(i)}
19 + + \varepsilon_{i},
20 + \label{eq:hedonic}
21 +\end{equation}
22 +%
23 +where $P_{i}$ is the list price of dwelling~$i$; $\text{Area}_{i}$ is living area in
24 +square metres; $\mathbf{x}_{i}$ collects the remaining structural attributes (bedrooms,
25 +full and half bathrooms, parking, storeys, the lot indicator and $\ln$ lot area);
26 +$\mathbf{d}_{i}$ is a vector of dwelling-type and ownership dummies; $\mu_{f(i)}$ is a
27 +fixed effect for the FSA neighbourhood $f$ to which dwelling~$i$ belongs; and
28 +$\varepsilon_{i}$ is an idiosyncratic error.
29 +
30 +Because $\beta$ multiplies a logged regressor, it is the \emph{elasticity} of price with
31 +respect to floor space. The elements of $\boldsymbol{\gamma}$ attached to count variables
32 +are semi-elasticities: following \citet{halvorsen1980interpretation}, a coefficient
33 +$\gamma$ implies an approximate proportional price change of $100\,(e^{\gamma}-1)\%$ for a
34 +one-unit increase, which we report whenever the distinction from the raw coefficient is
35 +material.
36 +
37 +\subsection{High-dimensional location effects}
38 +\label{subsec:identification}
39 +
40 +The neighbourhood fixed effects $\mu_{f}$ are the heart of the design. By including a
41 +separate intercept for each of the 1{,}153 FSAs, we allow every neighbourhood an arbitrary
42 +price level that absorbs all location-specific amenities---school quality, transit access,
43 +coastline, employment density---whether or not they are observed. The structural implicit
44 +prices in $\beta$ and $\boldsymbol{\gamma}$ are then identified purely from variation
45 +\emph{within} neighbourhoods, comparing dwellings that differ in their physical attributes
46 +but share a location. This is the housing counterpart of the within estimator, and it
47 +addresses the most pernicious source of omitted-variable bias in hedonic work: the
48 +correlation between structural quality and unobserved locational quality
49 +\citep{can1992specification,bourassa2007spatial}.
50 +
51 +Including more than a thousand dummies directly is numerically wasteful. We instead
52 +estimate Equation~\eqref{eq:hedonic} by \emph{absorbing least squares}, which partials the
53 +FSA effects out of both the dependent variable and the regressors before estimating the
54 +structural coefficients \citep{correia2017reghdfe}; the slope estimates are numerically
55 +identical to full-dummy OLS. All standard errors are clustered at the FSA level to allow
56 +for arbitrary within-neighbourhood correlation and heteroskedasticity
57 +\citep{cameron2015practitioner}.
58 +
59 +\subsection{Specification ladder}
60 +\label{subsec:ladder}
61 +
62 +We report a ladder of nested specifications that isolates the marginal contribution of
63 +each block of controls:
64 +%
65 +\begin{itemize}
66 + \item \textbf{M1 -- Structural:} structural attributes only.
67 + \item \textbf{M2 -- + Type/Ownership:} adds dwelling-type and ownership dummies.
68 + \item \textbf{M3 -- + Province:} adds province fixed effects.
69 + \item \textbf{M4 -- Houses + FSA:} replaces province with FSA fixed effects, houses only.
70 + \item \textbf{M5 -- Grand model:} FSA fixed effects over all residential dwellings.
71 +\end{itemize}
72 +%
73 +Columns M1--M3 are estimated on the house subsample to keep the structural interpretation
74 +clean; M5 is the preferred grand specification estimated over the full residential sample.
75 +The gap in $R^2$ between M3 and M5 measures the explanatory value of resolving location at
76 +the neighbourhood rather than the provincial scale.
77 +
78 +\subsection{Retransformation and out-of-sample evaluation}
79 +\label{subsec:retrans}
80 +
81 +Because the model is estimated in logs, predicted price levels require a retransformation
82 +correction. We use Duan's smearing estimator \citep{duan1983smearing}, multiplying
83 +$\exp(\widehat{\ln P})$ by the sample mean of $\exp(\widehat{\varepsilon})$, which is
84 +consistent without assuming log-normal errors. For predictive validation we randomly split
85 +the data 80/20, estimate the model on the training fold (restricting evaluation to
86 +neighbourhoods observed in training, since out-of-support FSA effects are not identified),
87 +and report held-out fit and percentage-error metrics on the test fold.
added paper/sections/results.tex +179 −0
@@ -0,0 +1,179 @@
1 +% Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +% ============================================================================
3 +\section{Results}
4 +\label{sec:results}
5 +% ============================================================================
6 +
7 +\subsection{The value of location}
8 +\label{subsec:location}
9 +
10 +Figure~\ref{fig:r2} summarises the explanatory power of the specification ladder. The
11 +structural-only model (M1) accounts for 46.4\% of the variation in log prices. Adding
12 +dwelling-type and ownership controls (M2) barely moves the fit, but introducing province
13 +fixed effects (M3) raises $R^2$ to 56.8\%, and resolving location at the FSA scale lifts
14 +it to \textbf{76.2\%} for houses (M4) and \textbf{76.7\%} for the grand model (M5). The
15 +implication is stark: moving from province to neighbourhood resolution adds about twenty
16 +percentage points of explained variance, and \emph{location as a whole accounts for
17 +roughly thirty percentage points}---more than the entire structural bundle. This is the
18 +central result of the paper and a quantitative statement of the realtor's adage that what
19 +matters is ``location, location, location.''
20 +
21 +\begin{figure}[t]\centering
22 +\includegraphics[width=0.82\textwidth]{fig_r2.png}
23 +\caption{Share of log-price variation explained ($R^2$) across the five nested
24 +specifications. The jump from M3 (province) to M4/M5 (FSA) quantifies the value of
25 +resolving location at the neighbourhood scale.}
26 +\label{fig:r2}
27 +\end{figure}
28 +
29 +\subsection{Implicit prices of structural attributes}
30 +\label{subsec:implicit}
31 +
32 +Table~\ref{tab:regression} reports the regression estimates. The living-area elasticity is
33 +remarkably stable across specifications, at $0.66$ in the raw structural model and
34 +settling near $0.55$ once location is controlled for: a 10\% larger dwelling commands
35 +roughly a 5.5\% higher price. The slight decline as controls are added is consistent with
36 +larger homes being located in more expensive areas---a confound the FSA effects remove.
37 +
38 +Full bathrooms carry one of the strongest structural premia: about $0.11$ log points in
39 +the grand model, or roughly an \textbf{11\% price increase} per additional bathroom,
40 +rising to nearly 15\% in the province-FE model. Half bathrooms attract a small
41 +\emph{negative} conditional coefficient, which we read not as a disamenity but as a proxy
42 +for older or more compartmentalised floor plans once total area and full baths are held
43 +fixed. Bedrooms are economically negligible conditional on living area: holding floor
44 +space constant, subdividing it into more bedrooms does not raise value---a textbook
45 +hedonic finding that recurs across our samples. Lot information enters positively ($\ln$
46 +lot elasticity around $0.03$) but modestly, reflecting both the noisiness of the parsed
47 +lot field and the fact that, within a neighbourhood, lot variation is compressed.
48 +
49 +Figure~\ref{fig:forest} presents the structural implicit prices with 95\% cluster-robust
50 +confidence intervals, making visually plain the dominance of living area and bathrooms and
51 +the near-zero conditional effects of bedrooms, parking and storeys.
52 +
53 +\input{../results/tables/regression}
54 +
55 +\begin{figure}[t]\centering
56 +\includegraphics[width=0.82\textwidth]{fig_forest.png}
57 +\caption{Marginal implicit prices of structural attributes (houses, province-FE model M3)
58 +with 95\% cluster-robust confidence intervals, on the log-price scale.}
59 +\label{fig:forest}
60 +\end{figure}
61 +
62 +The price--size gradient by dwelling type (Figure~\ref{fig:size_gradient}) confirms the
63 +log--log structure: median price rises concavely with living area for both dwelling types.
64 +Unconditionally, condominiums list \emph{above} houses of the same size---a compositional
65 +effect of their concentration in the expensive metropolitan markets---whereas conditional
66 +on location and ownership the estimated dwelling-type effects show that houses command the
67 +premium, in line with Table~\ref{tab:regression}.
68 +
69 +\begin{figure}[t]\centering
70 +\includegraphics[width=0.78\textwidth]{fig_size_gradient.png}
71 +\caption{Median price by living-area bin and dwelling type. The concave gradient is
72 +linearised by the semi-log specification.}
73 +\label{fig:size_gradient}
74 +\end{figure}
75 +
76 +\subsection{The geography of housing value}
77 +\label{subsec:geography}
78 +
79 +Figure~\ref{fig:maps} maps the spatial structure of value directly. The familiar outline
80 +of populated Canada emerges from the listing coordinates. The Vancouver corridor and the
81 +Greater Toronto--Golden Horseshoe area sit at the top of the price-per-square-metre
82 +distribution, while the Prairies and Atlantic Canada anchor the bottom. The right panel
83 +aggregates to FSA medians, the geographic unit absorbed in the grand model.
84 +
85 +\begin{figure}[t]\centering
86 +\begin{subfigure}{0.49\textwidth}\includegraphics[width=\textwidth]{fig_map.png}
87 +\caption{All listings}\end{subfigure}\hfill
88 +\begin{subfigure}{0.49\textwidth}\includegraphics[width=\textwidth]{fig_fsa_map.png}
89 +\caption{FSA neighbourhood medians}\end{subfigure}
90 +\caption{Spatial distribution of housing value. Colour encodes log price per m$^2$; bubble
91 +area in panel~(b) is proportional to $\sqrt{\text{listings}}$. Labels mark the provinces
92 +with substantial samples; the data cover nine provinces, while the territories and Prince
93 +Edward Island contain no listings.}
94 +\label{fig:maps}
95 +\end{figure}
96 +
97 +To translate location into a clean dollar statement we recover each FSA's fixed effect
98 +from the grand model---its price premium net of structure, dwelling type and
99 +ownership---and express it relative to the national median (Figure~\ref{fig:premia}). The
100 +highest-valued neighbourhoods, all in the City of Vancouver, trade at \textbf{150--200\%
101 +above} the national-median neighbourhood for an otherwise identical dwelling; the lowest,
102 +in rural Saskatchewan, Manitoba and Newfoundland, sit \textbf{60--67\% below}. The full
103 +premium distribution thus spans a factor of roughly nine between the most and least
104 +expensive neighbourhoods, dwarfing the price range attributable to any single structural
105 +attribute.
106 +
107 +\begin{figure}[t]\centering
108 +\includegraphics[width=0.78\textwidth]{fig_premia.png}
109 +\caption{Highest- and lowest-valued neighbourhoods (FSAs) in Canada, measured as the
110 +estimated location premium relative to the national-median neighbourhood, net of
111 +structure, dwelling type and ownership. Only FSAs with at least 50 listings are shown.}
112 +\label{fig:premia}
113 +\end{figure}
114 +
115 +\subsection{Decomposing the variance of prices}
116 +\label{subsec:decomp}
117 +
118 +Figure~\ref{fig:decomp} casts the specification ladder as a decomposition of the variance
119 +of log prices into the share explained by each successive block of controls. Physical
120 +structure accounts for 46\% of the variance; dwelling type and ownership add a further
121 +0.4~points; province adds about 10~points; and resolving location to the neighbourhood
122 +adds a further 20~points, for a total location contribution near 30~points. Just under a
123 +quarter of the variance remains unexplained and is attributable to idiosyncratic pricing
124 +and unobserved dwelling quality. The visual makes the headline unmistakable: the single
125 +largest identified block of housing value in Canada is the neighbourhood.
126 +
127 +\begin{figure}[t]\centering
128 +\includegraphics[width=0.92\textwidth]{fig_decomp.png}
129 +\caption{Variance decomposition of Canadian log house prices into structure, dwelling
130 +type/ownership, province, neighbourhood (FSA) and the unexplained residual, from the
131 +nested specification ladder.}
132 +\label{fig:decomp}
133 +\end{figure}
134 +
135 +\subsection{Nonlinearity: diminishing returns to floor space}
136 +\label{subsec:nonlinear}
137 +
138 +The constant-elasticity assumption is convenient but restrictive. Re-estimating the grand
139 +model with a quadratic in log living area yields a positive linear term and a
140 +significantly negative quadratic term ($\widehat{\beta}_1=1.06$,
141 +$\widehat{\beta}_2=-0.053$), implying that the marginal elasticity of price with respect
142 +to floor space \emph{declines} with dwelling size. Figure~\ref{fig:nonlinear} traces the
143 +implied marginal elasticity: it falls from roughly $0.65$ for a compact 60~m$^2$ dwelling
144 +to about $0.45$ for a large 350~m$^2$ home. Economically, the first square metres of
145 +living space are valued most highly and additional space is subject to diminishing
146 +returns---consistent with the nonparametric hedonic surfaces of \citet{mcmillen2010issues}.
147 +The constant-elasticity estimate of $0.55$ is best read as an average over the size
148 +distribution.
149 +
150 +\begin{figure}[t]\centering
151 +\includegraphics[width=0.78\textwidth]{fig_nonlinear.png}
152 +\caption{Marginal elasticity of price with respect to living area as a function of
153 +dwelling size, from a grand model with a quadratic in log area (95\% cluster-robust band).
154 +The dashed line is the constant-elasticity estimate.}
155 +\label{fig:nonlinear}
156 +\end{figure}
157 +
158 +\subsection{The urban price gradient}
159 +\label{subsec:gradient}
160 +
161 +Classic urban theory predicts that, holding structure fixed, value declines with distance
162 +from employment centres. We compute the great-circle distance from each listing to the
163 +nearest of nine major Canadian metropolitan centres and relate it to the location
164 +component of price (the residual from a structure-only model). Figure~\ref{fig:gradient}
165 +confirms a pronounced gradient: dwellings within the metropolitan core carry location
166 +premia of tens of percent over the structure-only benchmark, and the premium decays
167 +steadily with distance, turning negative beyond roughly 80~km. A log-linear fit implies a
168 +semi-elasticity of $-0.085$ ($t>100$): a doubling of distance to the nearest metro is
169 +associated with an 8.5\% lower location premium. This agglomeration gradient is precisely
170 +the force that the FSA fixed effects absorb non-parametrically in the grand model
171 +\citep{combes2015empirics}.
172 +
173 +\begin{figure}[t]\centering
174 +\includegraphics[width=0.78\textwidth]{fig_gradient.png}
175 +\caption{Urban price gradient: the location component of price (relative to a
176 +structure-only benchmark) against distance to the nearest of nine major Canadian metros.
177 +The horizontal axis is on a symmetric-log scale.}
178 +\label{fig:gradient}
179 +\end{figure}
added paper/sections/robustness.tex +151 −0
@@ -0,0 +1,151 @@
1 +% Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +% ============================================================================
3 +\section{Robustness, Heterogeneity, and Validation}
4 +\label{sec:robustness}
5 +% ============================================================================
6 +
7 +\subsection{Stability of the implicit prices}
8 +\label{subsec:stability}
9 +
10 +Table~\ref{tab:robustness} re-estimates the grand FSA-fixed-effects model on six
11 +alternative samples and specifications. The size elasticity stays within the narrow band
12 +$0.51$--$0.62$ and the bathroom premium within $0.10$--$0.12$ log points across every cut:
13 +restricting to houses, restricting to condominiums, tightening the price trim to the
14 +0.5/99.5 percentiles, dropping FSAs with fewer than fifty listings, and limiting the
15 +sample to the three largest provinces. The condominium subsample exhibits both the highest
16 +fit ($R^2=0.81$) and the largest size elasticity, consistent with condominium prices being
17 +more tightly pinned down by floor area and location and less by idiosyncratic lot and
18 +structure features. The overall picture is one of striking parameter stability: the
19 +headline implicit prices are not artefacts of a particular sample definition.
20 +
21 +\input{../results/tables/robustness}
22 +
23 +\subsection{Implicit prices along the price distribution}
24 +\label{subsec:quantile}
25 +
26 +OLS recovers the implicit price at the conditional mean, but buyers at the bottom and top
27 +of the market may value attributes differently \citep{zietz2008determinants}. We estimate
28 +quantile hedonic regressions \citep{koenker1978regression} at the 10th through 90th
29 +percentiles of price (Table~\ref{tab:quantile}, Figure~\ref{fig:quantile}). Two patterns
30 +stand out. The living-area elasticity is roughly flat-to-rising, climbing from $0.56$ at
31 +the bottom to $0.60$ at the top, indicating that floor space is valued slightly more in
32 +expensive segments. The lot elasticity rises more steeply across the distribution,
33 +consistent with land being a luxury component of value. The bathroom premium is stable
34 +around $0.10$--$0.13$ throughout. The mean-based estimates in Table~\ref{tab:regression}
35 +are thus representative, but they mask economically sensible distributional variation.
36 +
37 +\input{../results/tables/quantile}
38 +
39 +\begin{figure}[t]\centering
40 +\includegraphics[width=\textwidth]{fig_quantile.png}
41 +\caption{Implicit prices across the conditional price distribution. Quantile estimates
42 +(with 95\% confidence intervals) of the living-area elasticity (left) and the
43 +full-bathroom premium (right); the dashed line is the OLS estimate.}
44 +\label{fig:quantile}
45 +\end{figure}
46 +
47 +\subsection{Residual spatial autocorrelation}
48 +\label{subsec:spatial}
49 +
50 +A central justification for the neighbourhood fixed effects is that they should absorb the
51 +spatial dependence that pervades raw housing residuals \citep{anselin1988spatial}. We test
52 +this directly by computing Moran's~$I$ \citep{moran1950notes} on the residuals, using
53 +row-standardised $k$-nearest-neighbour spatial weights ($k=10$) on a random sample of
54 +15{,}000 listings. The structure-only model leaves enormous spatial autocorrelation in its
55 +residuals, $I=0.46$ ($z=145$, $p<0.01$): nearby dwellings are mispriced in the same
56 +direction---the signature of omitted location. The grand model with FSA fixed effects cuts
57 +this to $I=0.08$ ($z=23$), an 82\% reduction, confirming that the neighbourhood effects
58 +absorb the overwhelming majority of the spatial signal. Figure~\ref{fig:moran} contrasts
59 +the two Moran scatterplots. The small residual autocorrelation that remains is
60 +within-neighbourhood and could be addressed by finer geographies or an explicit spatial
61 +model \citep{lesage2009introduction}, but it is an order of magnitude smaller than the
62 +dependence the fixed effects remove, vindicating the design over a parametric spatial-lag
63 +alternative \citep{gibbons2012mostly}.
64 +
65 +\begin{figure}[t]\centering
66 +\includegraphics[width=\textwidth]{fig_moran.png}
67 +\caption{Moran scatterplots of model residuals against their spatial lag ($k=10$ nearest
68 +neighbours, 15{,}000-listing sample). Left: structure-only model. Right: grand model with
69 +FSA fixed effects. The slope is Moran's~$I$; it collapses from 0.46 to 0.08.}
70 +\label{fig:moran}
71 +\end{figure}
72 +
73 +\subsection{Do structural prices transfer across space?}
74 +\label{subsec:lopo}
75 +
76 +As a demanding test of external validity we perform leave-one-province-out
77 +cross-validation: the structural model is estimated on all provinces but one and used to
78 +predict the held-out province, allowing only a province-specific intercept (the price
79 +\emph{level} is not identified out of region). Table~\ref{tab:lopo} reports the
80 +within-province $R^2$. The structural implicit prices transfer well to most of the
81 +country, with a mean held-out $R^2$ of $0.36$ and values above $0.40$ for the large
82 +central and western markets; transfer is weaker for the small Atlantic samples, where
83 +idiosyncratic stock and thin data dominate. That structural prices generalise across
84 +provinces---even as price \emph{levels} differ by a factor of nine---reinforces the
85 +paper's central decomposition: structure is broadly priced the same everywhere, and it is
86 +location that varies.
87 +
88 +\input{../results/tables/lopo}
89 +
90 +\subsection{Heterogeneity across provinces}
91 +\label{subsec:heterogeneity}
92 +
93 +Estimating the within-FSA model province by province reveals economically meaningful
94 +heterogeneity in the size elasticity (Figure~\ref{fig:heterogeneity}). The elasticity is
95 +lowest in the high-price coastal markets---about $0.49$ in British Columbia and $0.50$ in
96 +Ontario---and highest in the Prairies, reaching $0.65$--$0.66$ in Saskatchewan and
97 +Manitoba. The pattern is intuitive: where land and location dominate value (Vancouver,
98 +Toronto), an extra square metre of structure adds proportionally less, whereas in
99 +lower-priced markets the building itself is a larger share of value and floor space
100 +carries more weight. The bathroom premium shows the mirror pattern, larger in the Prairies
101 +and Atlantic provinces than in the coastal metros.
102 +
103 +\begin{figure}[t]\centering
104 +\includegraphics[width=0.8\textwidth]{fig_heterogeneity.png}
105 +\caption{Living-area elasticity of price by province, estimated within FSAs, with 95\%
106 +cluster-robust confidence intervals. The elasticity is smallest in the expensive coastal
107 +markets and largest in the Prairies.}
108 +\label{fig:heterogeneity}
109 +\end{figure}
110 +
111 +\subsection{Out-of-sample valuation accuracy}
112 +\label{subsec:oos}
113 +
114 +A hedonic model that fits in-sample need not predict well. Table~\ref{tab:oos} and
115 +Figure~\ref{fig:oos} report performance on a randomly held-out 20\% of listings. The model
116 +attains an out-of-sample $R^2$ of \textbf{0.764} on log price---essentially identical to
117 +its in-sample fit, indicating negligible over-fitting despite the thousand-plus location
118 +effects. In price levels, after Duan smearing, the \textbf{median absolute valuation error
119 +is 15.8\%}, the mean is 22.5\%, and \textbf{59\% of held-out dwellings are priced within
120 +$\pm$20\%} of their actual list price (34\% within $\pm$10\%). These figures are
121 +competitive with commercial automated valuation models and establish the transparent
122 +hedonic specification as a credible valuation benchmark \citep{mullainathan2017machine}.
123 +
124 +\input{../results/tables/oos}
125 +
126 +\begin{figure}[t]\centering
127 +\includegraphics[width=0.7\textwidth]{fig_oos.png}
128 +\caption{Out-of-sample valuation accuracy on the held-out test fold: the share of listings
129 +priced within $\pm$10\%, between 10 and 20\%, and beyond 20\% of the actual list price.
130 +The out-of-sample $R^2$ is reported in the title.}
131 +\label{fig:oos}
132 +\end{figure}
133 +
134 +\subsection{Model fit and residual behaviour}
135 +\label{subsec:fit}
136 +
137 +Panel~(a) of Figure~\ref{fig:fit} plots predicted against actual log prices for the grand
138 +model; the cloud hugs the 45-degree line. The residuals (panel~(b)) are approximately
139 +Gaussian and centred on zero, with only mild heavy tails---typical of housing data and
140 +accommodated by the cluster-robust inference. We interpret the remaining dispersion as a
141 +combination of genuine idiosyncratic pricing, listing strategy, and dwelling-level quality
142 +(age, renovations, finish) that the data do not record.
143 +
144 +\begin{figure}[t]\centering
145 +\begin{subfigure}{0.46\textwidth}\includegraphics[width=\textwidth]{fig_fit.png}
146 +\caption{Predicted vs.\ actual}\end{subfigure}\hfill
147 +\begin{subfigure}{0.52\textwidth}\includegraphics[width=\textwidth]{fig_resid.png}
148 +\caption{Residual diagnostics}\end{subfigure}
149 +\caption{Grand-model (M5) goodness of fit and residual behaviour.}
150 +\label{fig:fit}
151 +\end{figure}
added paper/sections/titlepage.tex +70 −0
@@ -0,0 +1,70 @@
1 +% Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +% ============================================================================
3 +% Title and abstract pages
4 +% ============================================================================
5 +\thispagestyle{empty}
6 +
7 +\begin{center}
8 +
9 +\includegraphics[width=4cm]{uq_logo.jpg}
10 +
11 +\vspace{0.6cm}
12 +
13 +{\footnotesize\textsc{Universit\'e du Qu\'ebec en Outaouais}}\\[0.15cm]
14 +{\footnotesize\textsc{D\'epartement des sciences administratives}}
15 +
16 +\vspace{0.8cm}
17 +
18 +{\footnotesize\textsc{Working Paper No.~\WPnumber}}
19 +
20 +\vspace{1.2cm}
21 +
22 +{\LARGE\bfseries \WPtitle\par}
23 +
24 +\vspace{0.4cm}
25 +{\large\itshape \WPsubtitle\par}
26 +
27 +\vspace{1.2cm}
28 +
29 +{\large \WPauthor}\\[0.3cm]
30 +{\normalsize \WPaffiliation}\\[0.15cm]
31 +{\normalsize \href{mailto:\WPemail}{\WPemail}}\\[0.15cm]
32 +{\small \WPaddress}
33 +
34 +\vspace{0.8cm}
35 +
36 +{\normalsize \WPdate}\\[0.1cm]
37 +{\small Version~\WPversion}
38 +
39 +\end{center}
40 +
41 +\vfill
42 +
43 +\newpage
44 +
45 +% ---------------------------------------------------------------- abstract
46 +\thispagestyle{empty}
47 +
48 +\vspace*{1cm}
49 +
50 +\noindent\rule{\textwidth}{0.4pt}
51 +\vspace{0.3cm}
52 +
53 +\noindent\textbf{Abstract}
54 +
55 +\vspace{0.15cm}
56 +
57 +\noindent\WPabstract
58 +
59 +\vspace{0.4cm}
60 +
61 +\noindent\textbf{Keywords:} \WPkeywords
62 +
63 +\vspace{0.15cm}
64 +
65 +\noindent\textbf{JEL Classification:} \WPjel
66 +
67 +\vspace{0.3cm}
68 +\noindent\rule{\textwidth}{0.4pt}
69 +
70 +\newpage
added paper/uq_logo.jpg +0 −0

Binary file not shown.

added requirements.txt +10 −0
@@ -0,0 +1,10 @@
1 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +# Python >= 3.11. Versions pinned to the environment used for the 2026 rebuild.
3 +duckdb==1.5.2
4 +numpy==2.4.4
5 +pandas==3.0.2
6 +pyarrow>=16.0
7 +statsmodels==0.14.6
8 +linearmodels==7.0
9 +scikit-learn==1.6.1
10 +matplotlib==3.10.9
added results/reference/coef_M1.csv +9 −0
@@ -0,0 +1,9 @@
1 +,coef,se,p
2 +ln_living,0.657,0.022,0.001
3 +bathrooms,0.136,0.01,0.001
4 +half_baths,-0.035,0.009,0.001
5 +bedrooms,0.005,0.004,0.5
6 +parking_n,0.002,0.002,0.5
7 +stories_n,0.001,0.004,0.5
8 +has_lot,-0.212,0.038,0.001
9 +ln_lot,0.026,0.004,0.001
added results/reference/coef_M2.csv +9 −0
@@ -0,0 +1,9 @@
1 +,coef,se,p
2 +ln_living,0.65,0.022,0.001
3 +bathrooms,0.141,0.01,0.001
4 +half_baths,-0.039,0.009,0.001
5 +bedrooms,0.003,0.004,0.5
6 +parking_n,0.002,0.002,0.5
7 +stories_n,0.0,0.004,0.5
8 +has_lot,-0.221,0.038,0.001
9 +ln_lot,0.027,0.004,0.001
added results/reference/coef_M3.csv +9 −0
@@ -0,0 +1,9 @@
1 +,coef,se,p
2 +ln_living,0.585,0.016,0.001
3 +bathrooms,0.138,0.006,0.001
4 +half_baths,-0.026,0.009,0.001
5 +bedrooms,-0.003,0.003,0.5
6 +parking_n,0.001,0.001,0.5
7 +stories_n,-0.01,0.007,0.5
8 +has_lot,-0.034,0.039,0.5
9 +ln_lot,0.008,0.005,0.08
added results/reference/coef_M5.csv +9 −0
@@ -0,0 +1,9 @@
1 +,coef,se,p
2 +ln_living,0.547,0.009,0.001
3 +bathrooms,0.109,0.004,0.001
4 +half_baths,-0.036,0.009,0.001
5 +bedrooms,-0.002,0.003,0.5
6 +parking_n,0.001,0.001,0.5
7 +stories_n,0.001,0.0,0.001
8 +has_lot,-0.196,0.028,0.001
9 +ln_lot,0.03,0.004,0.001
added results/reference/ext2.json +21 −0
@@ -0,0 +1,21 @@
1 +{
2 + "nonlin": {
3 + "b1": 1.0649807666478583,
4 + "b2": -0.052590207607884756,
5 + "r2": 0.7680395097065289
6 + },
7 + "gradient": {
8 + "beta_logdist": -0.08536082624754465,
9 + "se": 0.0007873744041395624,
10 + "r2": 0.09474892883301556
11 + },
12 + "moran": {
13 + "struct_I": 0.45902667195055774,
14 + "struct_z": 145.24659997144295,
15 + "struct_p": 0.005,
16 + "grand_I": 0.08238138255845767,
17 + "grand_z": 23.425820155845653,
18 + "grand_p": 0.005
19 + },
20 + "lopo_mean": 0.3623228305498737
21 +}
\ No newline at end of file
added results/reference/fit.json +22 −0
@@ -0,0 +1,22 @@
1 +{
2 + "M1": {
3 + "r2": 0.464,
4 + "n": 82334
5 + },
6 + "M2": {
7 + "r2": 0.469,
8 + "n": 82334
9 + },
10 + "M3": {
11 + "r2": 0.567,
12 + "n": 82334
13 + },
14 + "M4": {
15 + "r2": 0.762,
16 + "n": 82334
17 + },
18 + "M5": {
19 + "r2": 0.767,
20 + "n": 140931
21 + }
22 +}
\ No newline at end of file
added results/reference/fsa_premia.csv +868 −0
@@ -0,0 +1,868 @@
1 +fsa_c,fe,n,prov,lat,lon,premium_pct
2 +V6S,1.0925336838666688,107,BC,49.25335851214953,-123.2259642411215,200.6447092235185
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841 +B0W,-0.7581563525422894,143,NS,43.933772295174826,-65.8353865451049,-52.76008636410977
842 +R0K,-0.7785260129693847,84,MB,49.59411054761905,-99.73654076190476,-53.712613105131766
843 +A2H,-0.7801104737862806,52,QC,48.96909780769231,-57.893923134615385,-53.78589558404359
844 +S6V,-0.7828089988729927,94,SK,53.19294357446808,-105.7423495,-53.91043738900096
845 +XNL,-0.7914517779261845,147,NL,48.143912340136055,-53.86030494557823,-54.30706285326672
846 +S4T,-0.7969899236385463,89,SK,50.4543451011236,-104.6345960449438,-54.55941756363401
847 +G0C,-0.8000965077478731,76,QC,48.17086863763158,-65.44174349223685,-54.70036351137444
848 +S4A,-0.801307602860282,57,SK,49.19965112280702,-103.04507957894737,-54.755192471475226
849 +V1G,-0.8037329944336357,74,BC,55.819297502837834,-120.43316411027027,-54.86479587707601
850 +B0M,-0.8285117151949257,97,QC,45.513049222577315,-64.03947443082474,-55.969446067923656
851 +S0L,-0.8290792039619184,110,SK,51.482070162036365,-108.39778005165455,-55.994425824148664
852 +A0A,-0.8407918880638492,189,NL,47.56656860370371,-53.475450793121695,-56.506842467995064
853 +J0Y,-0.8444055994190368,59,QC,48.456359294067795,-77.59340354084746,-56.663730540404835
854 +S0E,-0.8583284102033099,111,SK,52.95846410810811,-103.58578652252253,-57.262912393418986
855 +R0L,-0.868648867889427,63,SK,51.61560646031746,-100.78283268253968,-57.701710504263204
856 +E2A,-0.8710231537991799,52,QC,47.62416828846154,-65.65905146153847,-57.80201960865086
857 +E3Y,-0.8950105191550692,53,QC,47.066267415094345,-67.72137916981131,-58.80219424695581
858 +R0M,-0.8989362235178056,50,SK,49.79425156,-100.92250111999999,-58.96360761485726
859 +R8N,-0.9131486412870422,73,MB,55.672305465753425,-97.96253098630137,-59.54270900391387
860 +S0A,-0.9208596441684105,174,SK,51.274652522988504,-102.65319847126437,-59.853475590499094
861 +G0J,-0.9506162836407417,59,QC,48.57217272169491,-67.5288127359322,-61.03050221563782
862 +V0C,-0.9530582980848938,137,BC,56.48810871795621,-121.60891133116789,-61.12555019061033
863 +S0H,-0.9579917234403379,110,SK,50.11790250909091,-106.18226065454546,-61.316862087474334
864 +R2W,-0.9729888247965811,72,MB,49.92074486111111,-97.13534740277777,-61.89266852164068
865 +S0C,-0.9745741816524417,94,SK,49.424682129787236,-102.3982300893617,-61.95303437750332
866 +S0N,-1.0024481231516804,105,SK,49.925937733333335,-108.53170541904761,-62.998909228543184
867 +A0G,-1.024155707811574,129,NL,49.209192103100776,-54.44485828062016,-63.79345846622289
868 +A0B,-1.1072454934695073,86,NL,47.51593593023256,-53.65787445348837,-66.68025973162428
added results/reference/heterogeneity.csv +9 −0
@@ -0,0 +1,9 @@
1 +prov,n,elast,se_el,bath,se_bath
2 +NL,1016,0.4449402998648778,0.043698080778453864,0.25726475107539015,0.027165059095347537
3 +NS,1913,0.4851725399074676,0.04565660959163726,0.1656240969570683,0.023490781977225778
4 +BC,29976,0.4912693148799874,0.021788280143043053,0.09192441875259849,0.006043910769659036
5 +ON,48781,0.5027549607050659,0.013665133751418113,0.10834985752206905,0.005397005936542421
6 +AB,22236,0.5649369936597379,0.02248453543955825,0.1215699395201278,0.007876514397877418
7 +QC,30556,0.5963844617932081,0.016056846126880193,0.13256694397453356,0.012297833423032442
8 +SK,3400,0.6506790091858067,0.034228069848937726,0.17076158324307097,0.013111415386346751
9 +MB,3015,0.6618380879321788,0.04705438178848472,0.20593928770410647,0.012669931589266518
added results/reference/lopo.csv +9 −0
@@ -0,0 +1,9 @@
1 +prov,n,r2_within
2 +ON,48781,0.5490266904110517
3 +MB,3015,0.527218263229293
4 +SK,3400,0.47456011149762256
5 +NS,1913,0.4499312984289957
6 +NL,1016,0.37813325946351994
7 +BC,29976,0.3169765993848541
8 +QC,30556,0.3033685288112239
9 +AB,22236,-0.10063210682757151
added results/reference/oos.json +11 −0
@@ -0,0 +1,11 @@
1 +{
2 + "n_train": 112732,
3 + "n_test": 28187,
4 + "oos_r2": 0.7638581016384884,
5 + "rmse_log": 0.2821402305020966,
6 + "mae_log": 0.20478270963446624,
7 + "median_ape": 15.847920976780344,
8 + "mean_ape": 22.455344223161646,
9 + "within10": 33.756696349380924,
10 + "within20": 59.45648703302942
11 +}
\ No newline at end of file
added results/reference/quantile.csv +7 −0
@@ -0,0 +1,7 @@
1 +tau,ln_living,se_living,bathrooms,se_bath,ln_lot,se_lot
2 +0.1,0.557,0.008,0.181,0.004,-0.014,0.001
3 +0.25,0.534,0.005,0.146,0.002,-0.011,0.001
4 +0.5,0.552,0.005,0.114,0.002,-0.002,0.001
5 +0.75,0.601,0.006,0.087,0.003,0.009,0.001
6 +0.9,0.598,0.01,0.071,0.004,0.023,0.002
7 +,0.584,0.0,0.126,0.0,0.007,0.0
added results/reference/robustness.csv +7 −0
@@ -0,0 +1,7 @@
1 +label,n,r2,ln_living,se_living,bathrooms,se_bath,ln_lot,se_lot
2 +Baseline (all residential),140931,0.767468768080029,0.5465902011969274,0.00892947271640908,0.10904453574843155,0.003579252763021647,0.02979174326548159,0.0035709117802369063
3 +Houses only,82334,0.761793265245385,0.5129873475049976,0.01030264542377453,0.10266974717041621,0.003908566686191941,0.054684646186670974,0.0033312381695505995
4 +Condominiums only,57857,0.8129930707132008,0.6193658822714013,0.01734056347749674,0.12089234598066487,0.004809131482406632,0.004523044129116594,0.00316671402839587
5 +Price trimmed 0.5/99.5\%,139552,0.7650301197792827,0.536172123264197,0.008514115109115943,0.10735494541721012,0.0034533933113074134,0.027941636718505563,0.0034513139810203987
6 +FSAs with $\geq$50 listings,130590,0.7599781494218234,0.5421370432175243,0.00974848999045923,0.10896390949561037,0.0037948164163639784,0.02953148809974477,0.0038276450051442294
7 +ON/QC/BC only,109313,0.7616483751038278,0.5323171555588772,0.01037610752310238,0.09601681818930126,0.003994683449270653,0.030175336749736274,0.00312684382715802
added results/reference/summary_stats.csv +10 −0
@@ -0,0 +1,10 @@
1 +,mean,sd,p25,median,p75,n
2 +price_cad,763592.0,475478.0,449000.0,639888.0,928000.0,140931
3 +ppm2,5382.0,2682.0,3580.0,4781.0,6593.0,140931
4 +living_m2,151.7,79.1,93.8,134.6,185.8,140931
5 +bedrooms,3.11,1.32,2.0,3.0,4.0,140931
6 +bathrooms,2.32,1.11,2.0,2.0,3.0,140931
7 +half_baths,0.35,0.52,0.0,0.0,1.0,140931
8 +parking_n,2.72,11.2,0.0,2.0,4.0,140931
9 +stories_n,2.44,6.01,1.5,2.0,2.0,140931
10 +lot_m2_f,2621.0,10818.0,0.0,0.0,656.0,140931
added results/reproduced/coef_M1.csv +10 −0
@@ -0,0 +1,10 @@
1 +,coef,se,p
2 +const,9.561064711431111,0.09767380092125273,0.0
3 +ln_living,0.6802383046000451,0.02291770677003229,1.3197837294749597e-193
4 +bedrooms,0.0008901944719278652,0.0036690138680485892,0.8082959163575186
5 +bathrooms,0.13002371514122335,0.009378632811233642,1.049316867882094e-43
6 +half_baths,-0.011264795553404622,0.012482530729836343,0.36682060610960576
7 +parking_n,0.002393518205781103,0.0018165298619670413,0.18762680702738954
8 +stories_n,-0.03621223965222726,0.021154396014417143,0.0869322723697676
9 +has_lot,0.048655984493162385,0.04777675379320422,0.3084865258129831
10 +ln_lot,0.01504219027825254,0.006444026729633571,0.019580841451921346
added results/reproduced/coef_M2.csv +15 −0
@@ -0,0 +1,15 @@
1 +,coef,se,p
2 +const,9.843219336480711,0.1180119872764956,0.0
3 +ln_living,0.6775136191469533,0.02290249425980483,2.5075782198017083e-192
4 +bedrooms,-0.0011372311640692294,0.0036132514312983625,0.7529598256980948
5 +bathrooms,0.13555014490424772,0.00990992482554156,1.3700308994621599e-42
6 +half_baths,-0.017381604852869936,0.011893921262655453,0.14390965967012603
7 +parking_n,0.0023484621418566436,0.001802806448900163,0.19268739809055757
8 +stories_n,-0.03686067090851472,0.021714783183589,0.08960368079906691
9 +has_lot,0.046959232091369874,0.04728466266543096,0.3206526336262623
10 +ln_lot,0.014130957246089967,0.006388102972615881,0.02696154415150415
11 +ow_Condo/Strata,-0.16522332384717497,0.07680687513170473,0.0314641418064614
12 +ow_Freehold,-0.26959038033908245,0.07486360251600414,0.00031688894269064867
13 +ow_Leasehold,-0.5031052107456738,0.09051568692363605,2.7255509139764786e-08
14 +ow_Other,-0.36972531815110077,0.12609553696433143,0.0033667320965700695
15 +ow_Unknown,-0.21667199202398263,0.07620168076373092,0.004463476928618826
added results/reproduced/coef_M3.csv +23 −0
@@ -0,0 +1,23 @@
1 +,coef,se,p
2 +const,10.361438182384454,0.1100152204743566,0.0
3 +ln_living,0.5849693955314611,0.017677270974361045,3.923725476797545e-240
4 +bedrooms,-0.003636796797465855,0.0032250936849969147,0.25946515421297534
5 +bathrooms,0.1384376211666279,0.00611969936304834,2.6544273357549274e-113
6 +half_baths,-0.018397989126264738,0.009597517006732673,0.05524390147707337
7 +parking_n,0.0014102784512141707,0.0010814544678718798,0.1922140911141791
8 +stories_n,-0.025959535393899225,0.015689999677118403,0.0980203876171749
9 +has_lot,-0.16170482781880885,0.037903848373190274,1.9884394966839912e-05
10 +ln_lot,0.029303815960055123,0.0049626638421236945,3.529546521417734e-09
11 +ow_Condo/Strata,-0.18851647611832043,0.0864394818013423,0.029190291431387554
12 +ow_Freehold,-0.31216457449251667,0.08703466144302298,0.0003349282547636122
13 +ow_Leasehold,-0.5499413552485731,0.1011058252195222,5.350088984782185e-08
14 +ow_Other,-0.41349373150783164,0.12444239810022012,0.0008912769091980017
15 +ow_Unknown,-0.09021428190827951,0.0922108709031278,0.3279024475748098
16 +pv_BC,0.23967599230938008,0.045816536700054054,1.684027256853807e-07
17 +pv_MB,-0.22929989304925064,0.044565200040833426,2.671393087504706e-07
18 +pv_NB,-0.6171892224918915,0.1100500362891499,2.04370785911378e-08
19 +pv_NL,-0.635020848272304,0.050414630879357165,2.2222970952154956e-36
20 +pv_NS,-0.1619570894024912,0.054527772851922174,0.002976290511285856
21 +pv_ON,0.2560522817034921,0.026930851569243172,1.9479762311022223e-21
22 +pv_QC,-0.11980368368208992,0.05683812454953523,0.03504785932733063
23 +pv_SK,-0.37887408102521025,0.04709066577724386,8.580274911611256e-16
added results/reproduced/coef_M5.csv +26 −0
@@ -0,0 +1,26 @@
1 +,coef,se,p
2 +const,10.31279516219887,0.23915658007410237,0.0
3 +ln_living,0.5300775244939722,0.010200096739191443,0.0
4 +bedrooms,-0.0030182556241014962,0.002856962415858243,0.2907598469108321
5 +bathrooms,0.11107079513655177,0.0034720178610876534,0.0
6 +half_baths,-0.031740231596007605,0.007585288657888845,2.8586225252480446e-05
7 +parking_n,0.0008080165147721341,0.0006225344924431983,0.19430569260236452
8 +stories_n,0.0009042377409267352,0.00040373695287741865,0.02511232243560446
9 +has_lot,-0.41697659840283324,0.046463184959439206,0.0
10 +ln_lot,0.060959009581207396,0.0071746528194990096,0.0
11 +bt_Duplex,0.12450902778408916,0.02925594946228214,2.0825326801254818e-05
12 +bt_Fourplex,0.2941177183060848,0.04857047100799493,1.3999594816738181e-09
13 +bt_House,0.357496779086561,0.04729768830050037,4.085620730620576e-14
14 +bt_Manufactured,-0.06390862705637057,0.05730219485889019,0.2647257046303342
15 +bt_Other,0.07996731040275282,0.04765137781299006,0.09331310682009764
16 +bt_Row/Townhouse,-0.014243581329127147,0.02121255684232755,0.5019215913861708
17 +bt_Triplex,0.32485319869009294,0.041832332622528515,8.215650382226158e-15
18 +ow_Condo/Strata,0.08358479240132341,0.03996097657533153,0.03646890495104582
19 +ow_Freehold,0.07046226049616404,0.054388507226910514,0.19513544536921978
20 +ow_Leasehold,-0.15835406185702816,0.0642648249973242,0.013736311088748954
21 +ow_Other,-0.17609324142692748,0.07409117196622711,0.017467807461515994
22 +ow_Unknown,0.16650935659680718,0.02780116256106644,2.107529262218577e-09
23 +cat_condo,-0.00688524619431511,0.23334810769576403,0.9764607707915718
24 +cat_house,-0.10388206170846732,0.23651611652549687,0.6605038102307772
25 +cat_plex,-0.09993184933709527,0.2366941764080861,0.6728803734090887
26 +cat_townhouse,0.09414140867972552,0.2414032609745756,0.6965545065924998
added results/reproduced/ext2.json +21 −0
@@ -0,0 +1,21 @@
1 +{
2 + "nonlin": {
3 + "b1": 1.1452714885176427,
4 + "b2": -0.06237402986748119,
5 + "r2": 0.7703503373020513
6 + },
7 + "gradient": {
8 + "beta_logdist": -0.08208965710370426,
9 + "se": 0.0008188824957605701,
10 + "r2": 0.08291461383008458
11 + },
12 + "moran": {
13 + "struct_I": 0.49414633193493007,
14 + "struct_z": 153.38454763939137,
15 + "struct_p": 0.005,
16 + "grand_I": 0.07233897724866778,
17 + "grand_z": 20.208228113147626,
18 + "grand_p": 0.005
19 + },
20 + "lopo_mean": 0.31451087874557027
21 +}
\ No newline at end of file
added results/reproduced/fit.json +22 −0
@@ -0,0 +1,22 @@
1 +{
2 + "M1": {
3 + "r2": 0.4665611989553674,
4 + "n": 83542
5 + },
6 + "M2": {
7 + "r2": 0.4696655504941184,
8 + "n": 83542
9 + },
10 + "M3": {
11 + "r2": 0.5693124584846623,
12 + "n": 83542
13 + },
14 + "M4": {
15 + "r2": 0.7657314371141364,
16 + "n": 83542
17 + },
18 + "M5": {
19 + "r2": 0.7695479150272374,
20 + "n": 142146
21 + }
22 +}
\ No newline at end of file
added results/reproduced/fsa_premia.csv +870 −0
@@ -0,0 +1,870 @@
1 +fsa_c,fe,n,prov,lat,lon,premium_pct
2 +V6S,1.1804782045116122,107,BC,49.25335851214953,-123.2259642411215,227.03700911064502
3 +V6T,1.1053952849514863,61,BC,49.26204722459016,-123.24006857868852,203.3812977040153
4 +V8E,1.078189575214807,132,BC,50.10202857348485,-122.97235536969697,195.2388568337827
5 +V6G,1.0316073687454952,138,BC,49.289252171739136,-123.13348826014492,181.8013827877746
6 +V7T,1.0144316693907731,54,BC,49.32848744444445,-123.14147361111111,177.00257635944098
7 +V5Y,0.9958410224213045,114,BC,49.254106732456144,-123.11079856754387,171.9004917241993
8 +V5Z,0.9795490900266165,174,BC,49.24777875402299,-123.11981959022988,167.50659700062286
9 +V6Z,0.9692717721220476,235,BC,49.27605226255319,-123.12563951702128,164.77142584817287
10 +V6M,0.9689812312964965,64,BC,49.23410638125,-123.1465720203125,164.69451011364615
11 +V6K,0.9512844025434589,86,BC,49.26588249186047,-123.16110177790698,160.0514614888253
12 +V6E,0.9415239596107334,147,BC,49.28400365578232,-123.12564494761905,157.5255908997475
13 +V6J,0.9166955501593661,97,BC,49.26358798969073,-123.14441420103093,151.21036303147034
14 +V6P,0.9031430064513273,180,BC,49.21633932722222,-123.13091813777777,147.82878982396585
15 +M5R,0.8698908655621355,117,ON,43.67220532820513,-79.39600831538462,139.723458624102
16 +V5V,0.8174453352810126,66,BC,49.2510103469697,-123.09044469545454,127.47502973739469
17 +V6B,0.8159767278677059,317,BC,49.27784318264984,-123.11714510694007,127.14120341228804
18 +V7L,0.807700624691377,194,BC,49.31319002010309,-123.07022435515464,125.26911685353777
19 +V6H,0.7984133601793226,53,BC,49.259071949056604,-123.1211127018868,123.18666807339773
20 +V5T,0.7756421441723007,146,BC,49.26285982212329,-123.09317585157534,118.16186381971949
21 +H3Z,0.7753115103250867,80,QC,45.483907550875,-73.59167753325,118.08974404664542
22 +M4V,0.7724727779611622,79,ON,43.686205711392404,-79.40124440632911,117.47152352843244
23 +M4G,0.7642882272477791,57,ON,43.7090933877193,-79.36629159649122,115.69888085264006
24 +M5M,0.7632314357613094,62,ON,43.7304442483871,-79.41810049838709,115.47105251644037
25 +V7P,0.752041454788166,103,BC,49.324985714563105,-123.11346022427185,113.0733755372494
26 +V7J,0.7434590322879703,110,BC,49.31863358181818,-123.03439226363635,111.25251468017066
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867 +S0C,-0.9762375869499837,97,SK,49.41909219793814,-102.39812673608249,-62.160347878311995
868 +S0N,-1.0034714073889994,107,SK,49.93292885981308,-108.51628510280375,-63.17696021798551
869 +A0G,-1.0256430164546346,132,NL,49.21378543409091,-54.43604053181818,-63.984402041810085
870 +A0B,-1.1558331686174537,86,NL,47.51593593023256,-53.65787445348837,-68.38088100658041
added results/reproduced/gradient_bins.csv +9 −0
@@ -0,0 +1,9 @@
1 +x,prem,n
2 +2.0282523534641763,0.1878214496540589,12980
3 +7.563491017989508,0.11142969127236768,11358
4 +14.394147475605937,0.016118590333607075,20383
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8 +217.74684657163596,-0.14115921211123508,19860
9 +485.2518699511848,-0.4780914690614219,9469
added results/reproduced/grand_model.parquet +0 −0

Binary file not shown.

added results/reproduced/heterogeneity.csv +9 −0
@@ -0,0 +1,9 @@
1 +prov,n,elast,se_el,bath,se_bath
2 +NL,1029,0.41886323780274975,0.04345835618416139,0.25835214604184414,0.026510333907419987
3 +ON,49279,0.4720901632997946,0.013156307134272625,0.11472123286285063,0.005531088049014921
4 +NS,1934,0.47483207288538104,0.044920571815778486,0.16886581690826713,0.024607381866662878
5 +BC,30058,0.4958361647154056,0.021890607542201092,0.09149012627659524,0.006093443885784292
6 +AB,22435,0.5527530702149726,0.021986769802557028,0.12293484818163652,0.0077485150029473265
7 +QC,30857,0.5877745125683984,0.01585377636869836,0.12950231091820924,0.012583702295353723
8 +MB,3056,0.666477046579255,0.034615494385032364,0.20513902421662095,0.013229354095124608
9 +SK,3459,0.7235871144875173,0.034985520991915035,0.16683678850681966,0.012723659060980184
added results/reproduced/lopo.csv +9 −0
@@ -0,0 +1,9 @@
1 +prov,n,r2_within
2 +MB,3056,0.5212014330611053
3 +ON,49279,0.5158994827387327
4 +SK,3459,0.47318893331131373
5 +NS,1934,0.46113025528770357
6 +NL,1029,0.3966549132902695
7 +BC,30058,0.2881968210788265
8 +QC,30857,0.14633577388751784
9 +AB,22435,-0.28652058269090674
added results/reproduced/moran_coords.npy +0 −0

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added results/reproduced/moran_resid_grand.npy +0 −0

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added results/reproduced/moran_resid_struct.npy +0 −0

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added results/reproduced/nonlinear_band.csv +61 −0
@@ -0,0 +1,61 @@
1 +ln_area,elasticity,se
2 +3.9746788410500162,0.6494380150270442,0.018806169225596938
3 +4.007448185522403,0.6453501028853275,0.018381350505736426
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48 +5.482068686779821,0.46139405650806675,0.01209564738226706
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50 +5.547607375724596,0.453218232224633,0.012703306004351515
51 +5.580376720196982,0.4491303200829161,0.013025409666862931
52 +5.61314606466937,0.4450424079411992,0.013358532365394058
53 +5.645915409141757,0.4409544957994822,0.013701870432288165
54 +5.678684753614144,0.4368665836577653,0.014054675241619932
55 +5.711454098086531,0.43277867151604854,0.014416251779497234
56 +5.744223442558918,0.42869075937433154,0.014785956547026963
57 +5.776992787031305,0.42460284723261466,0.015163195015218367
58 +5.8097621315036925,0.42051493509089777,0.015547418806566344
59 +5.842531475976079,0.4164270229491809,0.015938122738963504
60 +5.875300820448467,0.4123391108074639,0.01633484183443525
61 +5.908070164920853,0.4082511986657471,0.016737148367867986
added results/reproduced/oos.json +11 −0
@@ -0,0 +1,11 @@
1 +{
2 + "n_train": 113716,
3 + "n_test": 28430,
4 + "oos_r2": 0.7695758148853212,
5 + "rmse_log": 0.2799204872082716,
6 + "mae_log": 0.20249408087341147,
7 + "median_ape": 15.558110855456578,
8 + "mean_ape": 21.995714792380202,
9 + "within10": 34.22441083362645,
10 + "within20": 60.024621878297566
11 +}
\ No newline at end of file
added results/reproduced/quantile.csv +7 −0
@@ -0,0 +1,7 @@
1 +tau,ln_living,se_living,bathrooms,se_bath,ln_lot,se_lot
2 +0.1,0.5907624602909891,0.00854157216912479,0.17847844049765627,0.003971299609479059,-0.00584690608894789,0.0017051166161529854
3 +0.25,0.5671770180033491,0.005324416376657182,0.14818531795728518,0.0023214169264950154,-0.002352869673359237,0.001153678746676559
4 +0.5,0.5704083790496952,0.004744860290189453,0.1166730392561135,0.001955524401797616,0.014391215617820308,0.0010851679848038838
5 +0.75,0.6006108334095188,0.0065587787957749,0.09015714132488029,0.002584740781679338,0.03371462880522813,0.0014853866310596741
6 +0.9,0.5846851372612036,0.010215300951522364,0.07378175197334258,0.0038492026465534296,0.04726651517456304,0.002251612109834233
7 +,0.5813085677006347,0.009653234341615202,0.127356234367056,0.0023632395826616646,0.030608663376169284,0.001678009533164809
added results/reproduced/robustness.csv +7 −0
@@ -0,0 +1,7 @@
1 +label,n,r2,ln_living,se_living,bathrooms,se_bath,ln_lot,se_lot
2 +Baseline (all residential),142146,0.7695479150272374,0.5300775244939722,0.010200096739191443,0.11107079513655177,0.0034720178610876534,0.060959009581207396,0.0071746528194990096
3 +Houses only,83542,0.7657314371141364,0.4860848549657795,0.010302763477595489,0.10541941278639437,0.003915059865757416,0.0802405166313547,0.004654076544900686
4 +Condominiums only,57860,0.8124287804174603,0.6209244425136851,0.017398414434041803,0.1211076396782959,0.004833738823918021,0.0059408796827741675,0.0035982800010453393
5 +Price trimmed 0.5/99.5\%,140739,0.7669067639239838,0.5203621130199332,0.009478396877136818,0.109248895929146,0.0033868967901083138,0.05809020151404406,0.00700405850967329
6 +FSAs with $\geq$50 listings,131792,0.7621236613992234,0.5260636122563492,0.011260120706112911,0.11153142279402012,0.003719756520921279,0.05972909238033767,0.0074311094274621394
7 +ON/QC/BC only,110194,0.7626700124075747,0.5226403771521636,0.011797609007361575,0.09752517275570255,0.003695374548145484,0.04703518783585091,0.009191963567514292
added results/tables/lopo.tex +16 −0
@@ -0,0 +1,16 @@
1 +\begin{table}[t]\centering\caption{Leave-one-province-out spatial cross-validation}
2 +\label{tab:lopo}\begin{threeparttable}\begin{tabular}{lcc}
3 +\toprule
4 +Held-out province & $N$ & Within-province $R^2$ \\
5 +\midrule
6 +ON & 48,781 & 0.549 \\
7 +MB & 3,015 & 0.527 \\
8 +SK & 3,400 & 0.475 \\
9 +NS & 1,913 & 0.450 \\
10 +NL & 1,016 & 0.378 \\
11 +BC & 29,976 & 0.317 \\
12 +QC & 30,556 & 0.303 \\
13 +AB & 22,236 & -0.101 \\
14 +\midrule
15 +Mean & --- & 0.362 \\
16 +\bottomrule\end{tabular}\begin{tablenotes}\footnotesize\item Notes: The structural hedonic model is estimated on all provinces except one and used to predict the held-out province; a province-specific intercept is allowed (the price \emph{level} is not identified out of sample), so the metric captures whether the \emph{structural} implicit prices transfer across space.\end{tablenotes}\end{threeparttable}\end{table}
added results/tables/oos.tex +22 −0
@@ -0,0 +1,22 @@
1 +\begin{table}[t]\centering
2 +\caption{Out-of-sample valuation performance (80/20 split)}
3 +\label{tab:oos}
4 +\begin{threeparttable}
5 +\begin{tabular}{lc}
6 +\toprule
7 +Metric & Value \\
8 +\midrule
9 +Training listings & 112,732 \\
10 +Test listings & 28,187 \\
11 +Out-of-sample $R^2$ (log price) & 0.764 \\
12 +RMSE (log points) & 0.282 \\
13 +Median absolute \% error & 15.8\% \\
14 +Mean absolute \% error & 22.5\% \\
15 +Share priced within $\pm$10\% & 33.8\% \\
16 +Share priced within $\pm$20\% & 59.5\% \\
17 +\bottomrule
18 +\end{tabular}
19 +\begin{tablenotes}\footnotesize\item Notes: Model trained on a random 80\% of listings and scored on the held-out 20\% (restricted to neighbourhoods observed in training). Prices back-transformed with Duan's smearing estimator.
20 +\end{tablenotes}
21 +\end{threeparttable}
22 +\end{table}
added results/tables/quantile.tex +13 −0
@@ -0,0 +1,13 @@
1 +\begin{table}[t]\centering\caption{Quantile hedonic estimates across the price distribution}
2 +\label{tab:quantile}\begin{threeparttable}\begin{tabular}{lccc}
3 +\toprule
4 +Quantile & $\ln$ area & Full bath & $\ln$ lot \\
5 +\midrule
6 +$\tau=0.1$ & \makecell{0.557\\\scriptsize(0.008)} & \makecell{0.181\\\scriptsize(0.004)} & \makecell{-0.014\\\scriptsize(0.001)} \\
7 +$\tau=0.25$ & \makecell{0.534\\\scriptsize(0.005)} & \makecell{0.146\\\scriptsize(0.002)} & \makecell{-0.011\\\scriptsize(0.001)} \\
8 +$\tau=0.5$ & \makecell{0.552\\\scriptsize(0.005)} & \makecell{0.114\\\scriptsize(0.002)} & \makecell{-0.002\\\scriptsize(0.001)} \\
9 +$\tau=0.75$ & \makecell{0.601\\\scriptsize(0.006)} & \makecell{0.087\\\scriptsize(0.003)} & \makecell{0.009\\\scriptsize(0.001)} \\
10 +$\tau=0.9$ & \makecell{0.598\\\scriptsize(0.010)} & \makecell{0.071\\\scriptsize(0.004)} & \makecell{0.023\\\scriptsize(0.002)} \\
11 +\midrule
12 +OLS & 0.584 & 0.126 & 0.007 \\
13 +\bottomrule\end{tabular}\begin{tablenotes}\footnotesize\item Notes: House subsample, province fixed effects, structural controls. Analytical standard errors in parentheses. The size elasticity rises and the lot elasticity rises with price.\end{tablenotes}\end{threeparttable}\end{table}
added results/tables/regression.tex +30 −0
@@ -0,0 +1,30 @@
1 +\begin{table}[t]\centering
2 +\caption{Hedonic regression estimates}
3 +\label{tab:regression}
4 +\begin{threeparttable}
5 +\begin{tabular}{lcccc}
6 +\toprule
7 + & (1) & (2) & (3) & (4) \\
8 + & Structural & +Type/Own. & +Province & Grand+FSA \\
9 +\midrule
10 +$\ln$ living area & \makecell{0.657$^{***}$\\\scriptsize(0.022)} & \makecell{0.650$^{***}$\\\scriptsize(0.022)} & \makecell{0.585$^{***}$\\\scriptsize(0.016)} & \makecell{0.547$^{***}$\\\scriptsize(0.009)} \\
11 +Full bathrooms & \makecell{0.136$^{***}$\\\scriptsize(0.010)} & \makecell{0.141$^{***}$\\\scriptsize(0.010)} & \makecell{0.138$^{***}$\\\scriptsize(0.006)} & \makecell{0.109$^{***}$\\\scriptsize(0.004)} \\
12 +Half bathrooms & \makecell{-0.035$^{***}$\\\scriptsize(0.009)} & \makecell{-0.039$^{***}$\\\scriptsize(0.009)} & \makecell{-0.026$^{***}$\\\scriptsize(0.009)} & \makecell{-0.036$^{***}$\\\scriptsize(0.009)} \\
13 +Bedrooms & \makecell{0.005\\\scriptsize(0.004)} & \makecell{0.003\\\scriptsize(0.004)} & \makecell{-0.003\\\scriptsize(0.003)} & \makecell{-0.002\\\scriptsize(0.003)} \\
14 +Parking spaces & \makecell{0.002\\\scriptsize(0.002)} & \makecell{0.002\\\scriptsize(0.002)} & \makecell{0.001\\\scriptsize(0.001)} & \makecell{0.001\\\scriptsize(0.001)} \\
15 +Storeys & \makecell{0.001\\\scriptsize(0.004)} & \makecell{0.000\\\scriptsize(0.004)} & \makecell{-0.010\\\scriptsize(0.007)} & \makecell{0.001$^{***}$\\\scriptsize(0.000)} \\
16 +Has lot info & \makecell{-0.212$^{***}$\\\scriptsize(0.038)} & \makecell{-0.221$^{***}$\\\scriptsize(0.038)} & \makecell{-0.034\\\scriptsize(0.039)} & \makecell{-0.196$^{***}$\\\scriptsize(0.028)} \\
17 +$\ln(1+$lot m$^2)$ & \makecell{0.026$^{***}$\\\scriptsize(0.004)} & \makecell{0.027$^{***}$\\\scriptsize(0.004)} & \makecell{0.008$^{*}$\\\scriptsize(0.005)} & \makecell{0.030$^{***}$\\\scriptsize(0.004)} \\
18 +\midrule
19 +Dwelling-type FE & No & Yes & Yes & Yes \\
20 +Ownership FE & No & Yes & Yes & Yes \\
21 +Province FE & No & No & Yes & --- \\
22 +FSA fixed effects & No & No & No & Yes \\
23 +$R^2$ & 0.464 & 0.469 & 0.567 & 0.767 \\
24 +Observations & 82,334 & 82,334 & 82,334 & 140,931 \\
25 +\bottomrule
26 +\end{tabular}
27 +\begin{tablenotes}\footnotesize\item Notes: Dependent variable is $\ln(\text{price})$. Columns (1)--(3) use the house subsample; column (4) is the grand model over all residential dwellings with 1{,}153 absorbed FSA fixed effects. Cluster-robust standard errors (by FSA) in parentheses. $^{*}p<0.1$, $^{**}p<0.05$, $^{***}p<0.01$.
28 +\end{tablenotes}
29 +\end{threeparttable}
30 +\end{table}
added results/tables/robustness.tex +20 −0
@@ -0,0 +1,20 @@
1 +\begin{table}[t]\centering
2 +\caption{Robustness of key implicit prices across samples}
3 +\label{tab:robustness}
4 +\begin{threeparttable}
5 +\begin{tabular}{lccccc}
6 +\toprule
7 +Sample / specification & $N$ & $R^2$ & $\ln$ area & Full bath & $\ln$ lot \\
8 +\midrule
9 +Baseline (all residential) & 140,931 & 0.767 & \makecell{0.547\\\scriptsize(0.009)} & \makecell{0.109\\\scriptsize(0.004)} & \makecell{0.030\\\scriptsize(0.004)} \\
10 +Houses only & 82,334 & 0.762 & \makecell{0.513\\\scriptsize(0.010)} & \makecell{0.103\\\scriptsize(0.004)} & \makecell{0.055\\\scriptsize(0.003)} \\
11 +Condominiums only & 57,857 & 0.813 & \makecell{0.619\\\scriptsize(0.017)} & \makecell{0.121\\\scriptsize(0.005)} & \makecell{0.005\\\scriptsize(0.003)} \\
12 +Price trimmed 0.5/99.5\% & 139,552 & 0.765 & \makecell{0.536\\\scriptsize(0.009)} & \makecell{0.107\\\scriptsize(0.003)} & \makecell{0.028\\\scriptsize(0.003)} \\
13 +FSAs with $\geq$50 listings & 130,590 & 0.760 & \makecell{0.542\\\scriptsize(0.010)} & \makecell{0.109\\\scriptsize(0.004)} & \makecell{0.030\\\scriptsize(0.004)} \\
14 +ON/QC/BC only & 109,313 & 0.762 & \makecell{0.532\\\scriptsize(0.010)} & \makecell{0.096\\\scriptsize(0.004)} & \makecell{0.030\\\scriptsize(0.003)} \\
15 +\bottomrule
16 +\end{tabular}
17 +\begin{tablenotes}\footnotesize\item Notes: Each row re-estimates the grand FSA-fixed-effects model on a different sample. Cluster-robust (FSA) standard errors in parentheses. The size elasticity and bathroom premium are stable across all cuts.
18 +\end{tablenotes}
19 +\end{threeparttable}
20 +\end{table}
added results/tables/summary_stats.tex +23 −0
@@ -0,0 +1,23 @@
1 +\begin{table}[t]\centering
2 +\caption{Descriptive statistics}
3 +\label{tab:summary_stats}
4 +\begin{threeparttable}
5 +\begin{tabular}{lccccc}
6 +\toprule
7 +Variable & Mean & SD & P25 & Median & P75 \\
8 +\midrule
9 +List price (CAD) & 763,592 & 475,478 & 449,000 & 639,888 & 928,000 \\
10 +Price per m$^2$ (CAD) & 5,382 & 2,682 & 3,580 & 4,781 & 6,593 \\
11 +Living area (m$^2$) & 151.7 & 79.1 & 93.8 & 134.6 & 185.8 \\
12 +Bedrooms & 3.11 & 1.32 & 2.00 & 3.00 & 4.00 \\
13 +Full bathrooms & 2.32 & 1.11 & 2.00 & 2.00 & 3.00 \\
14 +Half bathrooms & 0.35 & 0.52 & 0.00 & 0.00 & 1.00 \\
15 +Parking spaces & 2.72 & 11.20 & 0.00 & 2.00 & 4.00 \\
16 +Storeys & 2.44 & 6.01 & 1.50 & 2.00 & 2.00 \\
17 +Lot area (m$^2$) & 2,621 & 10,818 & 0 & 0 & 656 \\
18 +\bottomrule
19 +\end{tabular}
20 +\begin{tablenotes}\footnotesize\item Notes: Estimation sample of 140,931 residential dwellings (houses, condominiums, plexes, townhouses and apartments) from the Canadian MLS, after parsing and trimming the extreme 1\% tails of price and living area.
21 +\end{tablenotes}
22 +\end{threeparttable}
23 +\end{table}
added scripts/01_build_sample.py +29 −0
@@ -0,0 +1,29 @@
1 +#!/usr/bin/env python3
2 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
3 +"""Step 01 — Build the estimation sample.
4 +
5 +Reads the raw DuckDB snapshot (``data/raw/realtor_mls_unique.duckdb``),
6 +parses the semi-structured fields, applies the sample restrictions and trims,
7 +and writes ``data/processed/analysis.parquet``.
8 +
9 +Usage: python scripts/01_build_sample.py
10 +"""
11 +import sys
12 +from pathlib import Path
13 +
14 +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
15 +
16 +from wp9 import sample # noqa: E402
17 +
18 +
19 +def main() -> None:
20 + s = sample.build_and_save()
21 + by_cat = s.groupby("cat").size().to_dict()
22 + print(f"Estimation sample written: {len(s):,} listings")
23 + print(f" by category : {by_cat}")
24 + print(f" by province : {s.groupby('prov').size().to_dict()}")
25 + print(f" FSA levels : {s['fsa_c'].nunique():,} (incl. pooled residual categories)")
26 +
27 +
28 +if __name__ == "__main__":
29 + main()
added scripts/02_estimate_core.py +62 −0
@@ -0,0 +1,62 @@
1 +#!/usr/bin/env python3
2 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
3 +"""Step 02 — Estimate the M1–M5 hedonic specification ladder.
4 +
5 +Writes to ``results/reproduced/``:
6 + fit.json R^2 and N for each specification
7 + coef_M{1,2,3,5}.csv coefficient tables (coef, se, p)
8 + grand_model.parquet per-listing grand-model (M5) fitted values/residuals
9 + fsa_premia.csv FSA location premia (grand-model fixed effects, >=50 listings)
10 +
11 +Usage: python scripts/02_estimate_core.py
12 +"""
13 +import json
14 +import sys
15 +from pathlib import Path
16 +
17 +import numpy as np
18 +import pandas as pd
19 +
20 +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
21 +
22 +from wp9 import models, sample # noqa: E402
23 +from wp9.config import REPRODUCED, ensure_dirs # noqa: E402
24 +
25 +
26 +def main() -> None:
27 + ensure_dirs()
28 + s = sample.load_sample()
29 + ladder = models.specification_ladder(s)
30 +
31 + fit = {}
32 + for name, (res, cols, data) in ladder.items():
33 + r2 = float(res.rsquared)
34 + fit[name] = {"r2": r2, "n": int(res.nobs)}
35 + print(f"{name}: N={int(res.nobs):,} R2={r2:.4f}")
36 + if name != "M4":
37 + models.coef_table(res, cols).to_csv(REPRODUCED / f"coef_{name}.csv")
38 + json.dump(fit, open(REPRODUCED / "fit.json", "w"), indent=2)
39 +
40 + # Grand model (M5): per-listing predictions, residuals and FSA premia
41 + res5, cols5, _ = ladder["M5"]
42 + fe = models.fsa_fixed_effects(s, res5, cols5)
43 + pred = models.predict_with_fe(s, res5, cols5, fe)
44 + frame = s[["fsa_c", "prov", "lat", "lon", "ln_price"]].copy()
45 + frame["pred_grand"] = pred
46 + frame["resid_grand"] = frame["ln_price"] - frame["pred_grand"]
47 + frame.to_parquet(REPRODUCED / "grand_model.parquet", index=False)
48 +
49 + premia = (frame.assign(fe=s["fsa_c"].map(fe))
50 + .groupby("fsa_c")
51 + .agg(fe=("fe", "first"), n=("fe", "size"), prov=("prov", "first"),
52 + lat=("lat", "mean"), lon=("lon", "mean")))
53 + premia = premia[premia["n"] >= 50].copy()
54 + reference_level = premia["fe"].median()
55 + premia["premium_pct"] = (np.exp(premia["fe"] - reference_level) - 1) * 100
56 + premia.index.name = "fsa_c"
57 + premia.sort_values("premium_pct", ascending=False).to_csv(REPRODUCED / "fsa_premia.csv")
58 + print(f"FSA premia written for {len(premia):,} neighbourhoods (>=50 listings)")
59 +
60 +
61 +if __name__ == "__main__":
62 + main()
added scripts/03_estimate_extended.py +281 −0
@@ -0,0 +1,281 @@
1 +#!/usr/bin/env python3
2 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
3 +"""Step 03 — Extended analyses.
4 +
5 +Out-of-sample validation, robustness across samples, province heterogeneity,
6 +quantile regressions, nonlinearity in floor space, the urban price gradient,
7 +Moran's I spatial diagnostics, leave-one-province-out cross-validation.
8 +
9 +Writes to ``results/reproduced/``:
10 + oos.json, robustness.csv, heterogeneity.csv, quantile.csv, lopo.csv,
11 + ext2.json, nonlinear_band.csv, gradient_bins.csv
12 +
13 +Usage: python scripts/03_estimate_extended.py
14 +"""
15 +import json
16 +import sys
17 +import warnings
18 +from pathlib import Path
19 +
20 +import numpy as np
21 +import pandas as pd
22 +import statsmodels.api as sm
23 +from sklearn.neighbors import NearestNeighbors
24 +from statsmodels.regression.quantile_regression import QuantReg
25 +
26 +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
27 +
28 +from wp9 import models, sample # noqa: E402
29 +from wp9.config import (METROS, REPRODUCED, SEED_EXT2, SEED_MORAN, # noqa: E402
30 + SEED_OOS, STRUCT, ensure_dirs)
31 +
32 +warnings.filterwarnings("ignore")
33 +
34 +
35 +# --------------------------------------------------------------------------
36 +def out_of_sample(s: pd.DataFrame) -> dict:
37 + """80/20 split validation of the grand model, on common-FSA support."""
38 + np.random.seed(SEED_OOS) # legacy global seed, kept identical to the original
39 + idx = np.arange(len(s))
40 + np.random.shuffle(idx)
41 + cut = int(0.8 * len(s))
42 + train, test = s.iloc[idx[:cut]].copy(), s.iloc[idx[cut:]].copy()
43 + common = set(train["fsa_c"]).intersection(test["fsa_c"])
44 + train = train[train["fsa_c"].isin(common)].copy()
45 + test = test[test["fsa_c"].isin(common)].copy()
46 +
47 + res, cols = models.fit_absorbing(train)
48 + fe = models.fsa_fixed_effects(train, res, cols)
49 + pred_test = models.predict_with_fe(test, res, cols, fe)
50 + err = test["ln_price"].values - pred_test
51 + ss_res = np.sum(err ** 2)
52 + ss_tot = np.sum((test["ln_price"].values - test["ln_price"].mean()) ** 2)
53 +
54 + pred_train = models.predict_with_fe(train, res, cols, fe)
55 + smear = models.duan_smearing(train["ln_price"].values - pred_train)
56 + ape = np.abs(np.exp(pred_test) * smear - test["price_cad"].values) / test["price_cad"].values
57 + return {
58 + "n_train": int(len(train)), "n_test": int(len(test)),
59 + "oos_r2": float(1 - ss_res / ss_tot),
60 + "rmse_log": float(np.sqrt(np.mean(err ** 2))),
61 + "mae_log": float(np.mean(np.abs(err))),
62 + "median_ape": float(np.median(ape) * 100),
63 + "mean_ape": float(np.mean(ape) * 100),
64 + "within10": float(np.mean(ape <= 0.10) * 100),
65 + "within20": float(np.mean(ape <= 0.20) * 100),
66 + }
67 +
68 +
69 +# --------------------------------------------------------------------------
70 +def robustness(s: pd.DataFrame) -> pd.DataFrame:
71 + """Key implicit prices of the grand model across alternative samples."""
72 + def key_coefs(data, label):
73 + res, cols = models.fit_absorbing(data)
74 + tab = models.coef_table(res, cols)
75 + return {"label": label, "n": int(res.nobs), "r2": float(res.rsquared),
76 + "ln_living": tab.loc["ln_living", "coef"], "se_living": tab.loc["ln_living", "se"],
77 + "bathrooms": tab.loc["bathrooms", "coef"], "se_bath": tab.loc["bathrooms", "se"],
78 + "ln_lot": tab.loc["ln_lot", "coef"], "se_lot": tab.loc["ln_lot", "se"]}
79 +
80 + rows = [key_coefs(s, "Baseline (all residential)"),
81 + key_coefs(s[s["cat"] == "house"], "Houses only"),
82 + key_coefs(s[s["cat"] == "condo"], "Condominiums only")]
83 + lo, hi = s["price_cad"].quantile([0.005, 0.995])
84 + rows.append(key_coefs(s[s["price_cad"].between(lo, hi)], "Price trimmed 0.5/99.5\\%"))
85 + counts = s["fsa_c"].value_counts()
86 + rows.append(key_coefs(s[s["fsa_c"].isin(counts[counts >= 50].index)],
87 + "FSAs with $\\geq$50 listings"))
88 + rows.append(key_coefs(s[s["prov"].isin(["ON", "QC", "BC"])], "ON/QC/BC only"))
89 + return pd.DataFrame(rows)
90 +
91 +
92 +# --------------------------------------------------------------------------
93 +def heterogeneity(s: pd.DataFrame) -> pd.DataFrame:
94 + """Within-FSA living-area elasticity, estimated province by province."""
95 + rows = []
96 + for prov, g in s.groupby("prov"):
97 + if len(g) < 400:
98 + continue
99 + res, cols = models.fit_absorbing(g)
100 + tab = models.coef_table(res, cols)
101 + rows.append({"prov": prov, "n": len(g),
102 + "elast": tab.loc["ln_living", "coef"], "se_el": tab.loc["ln_living", "se"],
103 + "bath": tab.loc["bathrooms", "coef"], "se_bath": tab.loc["bathrooms", "se"]})
104 + return pd.DataFrame(rows).sort_values("elast")
105 +
106 +
107 +# --------------------------------------------------------------------------
108 +def quantile_regressions(s: pd.DataFrame) -> pd.DataFrame:
109 + """Quantile hedonic estimates (house subsample, province FE)."""
110 + houses = s[s["cat"] == "house"]
111 + prov_d = pd.get_dummies(houses["prov"], prefix="p", drop_first=True).astype(float)
112 + X = sm.add_constant(pd.concat([houses[STRUCT].astype(float), prov_d], axis=1))
113 + y = houses["ln_price"]
114 + rows = []
115 + for tau in (0.1, 0.25, 0.5, 0.75, 0.9):
116 + fit = QuantReg(y, X).fit(q=tau, max_iter=2000)
117 + rows.append({"tau": tau,
118 + "ln_living": fit.params["ln_living"], "se_living": fit.bse["ln_living"],
119 + "bathrooms": fit.params["bathrooms"], "se_bath": fit.bse["bathrooms"],
120 + "ln_lot": fit.params["ln_lot"], "se_lot": fit.bse["ln_lot"]})
121 + ols = sm.OLS(y, X).fit(cov_type="HC1")
122 + rows.append({"tau": np.nan,
123 + "ln_living": ols.params["ln_living"], "se_living": ols.bse["ln_living"],
124 + "bathrooms": ols.params["bathrooms"], "se_bath": ols.bse["bathrooms"],
125 + "ln_lot": ols.params["ln_lot"], "se_lot": ols.bse["ln_lot"]})
126 + return pd.DataFrame(rows)
127 +
128 +
129 +# --------------------------------------------------------------------------
130 +def nonlinearity(s: pd.DataFrame) -> tuple[dict, pd.DataFrame]:
131 + """Quadratic-in-log-area grand model and the implied marginal elasticity."""
132 + extra = (s["ln_living"] ** 2).to_frame("ln_living2")
133 + res, cols = models.fit_absorbing(s, extra=extra)
134 + params = pd.Series(np.asarray(res.params).ravel(), index=cols)
135 + cov = pd.DataFrame(np.asarray(res.cov), index=cols, columns=cols)
136 + b1, b2 = params["ln_living"], params["ln_living2"]
137 + grid = np.linspace(s["ln_living"].quantile(0.02), s["ln_living"].quantile(0.98), 60)
138 + marginal = b1 + 2 * b2 * grid
139 + v11 = cov.loc["ln_living", "ln_living"]
140 + v22 = cov.loc["ln_living2", "ln_living2"]
141 + v12 = cov.loc["ln_living", "ln_living2"]
142 + se = np.sqrt(v11 + 4 * grid ** 2 * v22 + 4 * grid * v12)
143 + band = pd.DataFrame({"ln_area": grid, "elasticity": marginal, "se": se})
144 + return {"b1": float(b1), "b2": float(b2), "r2": float(res.rsquared)}, band
145 +
146 +
147 +# --------------------------------------------------------------------------
148 +def haversine_km(lat1, lon1, lat2, lon2):
149 + """Great-circle distance in kilometres."""
150 + rad = np.pi / 180
151 + a = (np.sin((lat2 - lat1) * rad / 2) ** 2
152 + + np.cos(lat1 * rad) * np.cos(lat2 * rad) * np.sin((lon2 - lon1) * rad / 2) ** 2)
153 + return 2 * 6371.0 * np.arcsin(np.sqrt(a))
154 +
155 +
156 +def distance_to_metro(s: pd.DataFrame) -> np.ndarray:
157 + """Distance from each listing to the nearest of the nine major metros."""
158 + dist = np.full(len(s), np.inf)
159 + for lat, lon in METROS.values():
160 + dist = np.minimum(dist, haversine_km(s["lat"].values, s["lon"].values, lat, lon))
161 + return dist
162 +
163 +
164 +def urban_gradient(s: pd.DataFrame) -> tuple[dict, pd.DataFrame, pd.Series]:
165 + """Location premium (residual from a structure-only model) vs. metro distance."""
166 + X = models.design(s)
167 + structural = sm.OLS(s["ln_price"], X).fit()
168 + loc_premium = s["ln_price"] - structural.predict(X)
169 + dist = distance_to_metro(s)
170 +
171 + bins = [0, 5, 10, 20, 40, 80, 160, 320, 2000]
172 + banded = (pd.DataFrame({"dist": dist, "prem": loc_premium})
173 + .assign(band=lambda x: pd.cut(x["dist"], bins))
174 + .groupby("band", observed=True)
175 + .agg(x=("dist", "median"), prem=("prem", "mean"), n=("prem", "size"))
176 + .reset_index(drop=True))
177 +
178 + keep = dist > 0.5
179 + fit = sm.OLS(loc_premium[keep],
180 + sm.add_constant(np.log(dist[keep]))).fit(cov_type="HC1")
181 + stats = {"beta_logdist": float(fit.params.iloc[1]), "se": float(fit.bse.iloc[1]),
182 + "r2": float(fit.rsquared)}
183 + return stats, banded, loc_premium
184 +
185 +
186 +# --------------------------------------------------------------------------
187 +def morans_i(coords: np.ndarray, values: np.ndarray, k: int = 10,
188 + permutations: int = 199) -> tuple[float, float, float]:
189 + """Moran's I with row-standardised kNN weights and a permutation test."""
190 + nn = NearestNeighbors(n_neighbors=k + 1).fit(coords)
191 + _, idx = nn.kneighbors(coords)
192 + idx = idx[:, 1:]
193 + z = values - values.mean()
194 + lag = z[idx].mean(axis=1)
195 + stat = np.sum(z * lag) / np.sum(z ** 2)
196 + perms = np.empty(permutations)
197 + for b in range(permutations):
198 + zp = np.random.permutation(z)
199 + perms[b] = np.sum(zp * zp[idx].mean(axis=1)) / np.sum(zp ** 2)
200 + zscore = (stat - perms.mean()) / perms.std()
201 + pvalue = (np.sum(np.abs(perms) >= abs(stat)) + 1) / (permutations + 1)
202 + return float(stat), float(zscore), float(pvalue)
203 +
204 +
205 +def spatial_diagnostics(s: pd.DataFrame, loc_premium: pd.Series) -> dict:
206 + """Moran's I on structure-only vs. grand-model residuals (15k sample)."""
207 + grand = pd.read_parquet(REPRODUCED / "grand_model.parquet")
208 + subsample = s.sample(15000, random_state=SEED_MORAN)
209 + coords = subsample[["lat", "lon"]].values
210 + resid_struct = loc_premium.loc[subsample.index].values
211 + resid_grand = grand.loc[subsample.index, "resid_grand"].values
212 +
213 + np.random.seed(SEED_EXT2)
214 + i_s, z_s, p_s = morans_i(coords, resid_struct)
215 + i_g, z_g, p_g = morans_i(coords, resid_grand)
216 + np.save(REPRODUCED / "moran_coords.npy", coords)
217 + np.save(REPRODUCED / "moran_resid_struct.npy", resid_struct)
218 + np.save(REPRODUCED / "moran_resid_grand.npy", resid_grand)
219 + return {"struct_I": i_s, "struct_z": z_s, "struct_p": p_s,
220 + "grand_I": i_g, "grand_z": z_g, "grand_p": p_g}
221 +
222 +
223 +# --------------------------------------------------------------------------
224 +def leave_one_province_out(s: pd.DataFrame) -> pd.DataFrame:
225 + """Estimate the structural model out-of-province; within-province R^2."""
226 + X = models.design(s)
227 + rows = []
228 + for prov in s["prov"].value_counts().index:
229 + held = s["prov"] == prov
230 + if held.sum() < 800:
231 + continue
232 + fit = sm.OLS(s["ln_price"].values[~held], X.values[~held]).fit()
233 + err = s["ln_price"].values[held] - X.values[held] @ fit.params
234 + err = err - err.mean() # province-specific intercept allowed
235 + y_held = s["ln_price"].values[held]
236 + r2 = 1 - np.sum(err ** 2) / np.sum((y_held - y_held.mean()) ** 2)
237 + rows.append({"prov": prov, "n": int(held.sum()), "r2_within": float(r2)})
238 + return pd.DataFrame(rows).sort_values("r2_within", ascending=False)
239 +
240 +
241 +# --------------------------------------------------------------------------
242 +def main() -> None:
243 + ensure_dirs()
244 + s = sample.load_sample()
245 + results = {}
246 +
247 + oos = out_of_sample(s)
248 + json.dump(oos, open(REPRODUCED / "oos.json", "w"), indent=2)
249 + print("OOS:", {k: round(v, 3) for k, v in oos.items()})
250 +
251 + robustness(s).to_csv(REPRODUCED / "robustness.csv", index=False)
252 + print("robustness done")
253 +
254 + heterogeneity(s).to_csv(REPRODUCED / "heterogeneity.csv", index=False)
255 + print("heterogeneity done")
256 +
257 + quantile_regressions(s).to_csv(REPRODUCED / "quantile.csv", index=False)
258 + print("quantile done")
259 +
260 + results["nonlin"], band = nonlinearity(s)
261 + band.to_csv(REPRODUCED / "nonlinear_band.csv", index=False)
262 + print("nonlinearity:", results["nonlin"])
263 +
264 + results["gradient"], banded, loc_premium = urban_gradient(s)
265 + banded.to_csv(REPRODUCED / "gradient_bins.csv", index=False)
266 + print("gradient:", results["gradient"])
267 +
268 + results["moran"] = spatial_diagnostics(s, loc_premium)
269 + print("moran:", {k: round(v, 3) for k, v in results["moran"].items()})
270 +
271 + lopo = leave_one_province_out(s)
272 + lopo.to_csv(REPRODUCED / "lopo.csv", index=False)
273 + results["lopo_mean"] = float(lopo["r2_within"].mean())
274 + print("LOPO mean within-province R2:", round(results["lopo_mean"], 3))
275 +
276 + json.dump(results, open(REPRODUCED / "ext2.json", "w"), indent=2)
277 + print("ALL EXTENDED ANALYSES DONE")
278 +
279 +
280 +if __name__ == "__main__":
281 + main()
added scripts/04_make_figures.py +379 −0
@@ -0,0 +1,379 @@
1 +#!/usr/bin/env python3
2 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
3 +"""Step 04 — Generate every figure used in the paper (17 PNG files).
4 +
5 +Number-bearing summary figures (R^2 ladder, variance decomposition, OOS
6 +accuracy, FSA premia, heterogeneity, quantile) are drawn from the results
7 +tier selected with ``--results`` so the paper's published numbers are used by
8 +default; distribution/scatter/map figures are drawn from the micro sample.
9 +
10 +Usage: python scripts/04_make_figures.py [--results reference|reproduced]
11 +"""
12 +import argparse
13 +import json
14 +import sys
15 +from pathlib import Path
16 +
17 +import numpy as np
18 +import pandas as pd
19 +
20 +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
21 +
22 +from wp9 import models, sample # noqa: E402
23 +from wp9.config import FIGURES, REFERENCE, REPRODUCED, RESULTS, ensure_dirs # noqa: E402
24 +from wp9.plotstyle import (ACCENT, ACCENT2, ACCENT3, GREEN, GREY, LIGHT, # noqa: E402
25 + NEUTRAL, ORANGE, apply_style)
26 +
27 +apply_style()
28 +import matplotlib.pyplot as plt # noqa: E402
29 +import statsmodels.api as sm # noqa: E402
30 +
31 +PROVINCE_NAMES = {"ON": "Ontario", "QC": "Quebec", "BC": "British Columbia",
32 + "AB": "Alberta", "SK": "Saskatchewan", "MB": "Manitoba",
33 + "NS": "Nova Scotia", "NL": "Nfld. & Labrador", "NB": "New Brunswick"}
34 +
35 +
36 +def save(fig, name):
37 + fig.tight_layout()
38 + fig.savefig(FIGURES / name, bbox_inches="tight")
39 + plt.close(fig)
40 + print(" fig", name)
41 +
42 +
43 +# ---------------------------------------------------------------- micro-data
44 +def fig_price_dist(s):
45 + fig, ax = plt.subplots(1, 2, figsize=(11, 4))
46 + shown = s[s["price_cad"] <= 3e6]["price_cad"]
47 + ax[0].hist(shown / 1e3, bins=80, color=ACCENT, alpha=0.9)
48 + ax[0].axvline(s["price_cad"].median() / 1e3, color=ACCENT2, ls="--", lw=1.2,
49 + label=f"median \\${s['price_cad'].median():,.0f}")
50 + ax[0].set_xlabel("List price (thousand CAD, truncated at \\$3M)")
51 + ax[0].set_ylabel("Listings")
52 + ax[0].set_title("(a) Raw list price")
53 + ax[0].legend()
54 + ax[1].hist(s["ln_price"], bins=80, color=ACCENT, alpha=0.9)
55 + ax[1].set_xlabel("ln(price)")
56 + ax[1].set_title("(b) Log price (dependent variable)")
57 + save(fig, "fig_price_dist.png")
58 +
59 +
60 +def fig_province_ppm2(s):
61 + g = (s.groupby("prov")["ppm2"].agg(["median", "mean"])
62 + .sort_values("median"))
63 + fig, ax = plt.subplots(figsize=(7.5, 4.2))
64 + y = np.arange(len(g))
65 + ax.barh(y, g["median"], color=ACCENT, alpha=0.9, label="median")
66 + ax.barh(y, (g["mean"] - g["median"]).clip(lower=0), left=g["median"],
67 + color=GREY, alpha=0.55, label="mean$-$median gap")
68 + ax.set_yticks(y)
69 + ax.set_yticklabels([PROVINCE_NAMES.get(p, p) for p in g.index])
70 + ax.set_xlabel("Price per m$^2$ of living area (CAD)")
71 + ax.set_title("Price per square metre by province")
72 + ax.legend()
73 + save(fig, "fig_province_ppm2.png")
74 +
75 +
76 +def fig_size_gradient(s):
77 + bins = np.exp(np.linspace(np.log(45), np.log(470), 14))
78 + banded = (s.assign(band=pd.cut(s["living_m2"], bins))
79 + .groupby(["band", "cat"], observed=True)
80 + .agg(x=("living_m2", "median"), price=("price_cad", "median"),
81 + n=("price_cad", "size"))
82 + .reset_index())
83 + fig, ax = plt.subplots(figsize=(7, 4.4))
84 + for cat, colour, label in (("house", ACCENT, "Houses"), ("condo", ACCENT2, "Condominiums")):
85 + g = banded[(banded["cat"] == cat) & (banded["n"] >= 30)]
86 + ax.plot(g["x"], g["price"] / 1e3, "o-", color=colour, lw=2, ms=5, label=label)
87 + ax.set_xscale("log")
88 + ax.set_yscale("log")
89 + ax.set_xlabel("Living area (m$^2$, log scale)")
90 + ax.set_ylabel("Median list price (thousand CAD, log scale)")
91 + ax.set_title("Median price by living-area bin and dwelling type")
92 + ax.legend()
93 + save(fig, "fig_size_gradient.png")
94 +
95 +
96 +def _canada_axes(ax, s):
97 + ax.set_xlim(-140, -50)
98 + ax.set_ylim(41, 61)
99 + ax.set_xlabel("Longitude")
100 + ax.set_ylabel("Latitude")
101 + for prov, g in s.groupby("prov"):
102 + if len(g) < 400:
103 + continue
104 + ax.annotate(prov, (g["lon"].median(), g["lat"].quantile(0.9) + 1.2),
105 + fontsize=8, fontweight="bold", color="#333333", ha="center")
106 +
107 +
108 +def fig_maps(s):
109 + colour = np.log(s["ppm2"])
110 + fig, ax = plt.subplots(figsize=(7, 5))
111 + sc = ax.scatter(s["lon"], s["lat"], c=colour, s=2, alpha=0.35, cmap="viridis")
112 + _canada_axes(ax, s)
113 + ax.set_title("Listing locations, coloured by log price per m$^2$")
114 + fig.colorbar(sc, ax=ax, shrink=0.75, label="ln(price per m$^2$)")
115 + save(fig, "fig_map.png")
116 +
117 + g = (s.groupby("fsa")
118 + .agg(lat=("lat", "mean"), lon=("lon", "mean"),
119 + ppm2=("ppm2", "median"), n=("ppm2", "size")))
120 + g = g[g["n"] >= 25]
121 + fig, ax = plt.subplots(figsize=(7, 5))
122 + sc = ax.scatter(g["lon"], g["lat"], c=np.log(g["ppm2"]),
123 + s=np.sqrt(g["n"]) * 1.8, alpha=0.75, cmap="viridis",
124 + edgecolors="white", linewidths=0.2)
125 + _canada_axes(ax, s)
126 + ax.set_title("FSA neighbourhood medians (bubble area $\\propto\\sqrt{\\mathrm{listings}}$)")
127 + fig.colorbar(sc, ax=ax, shrink=0.75, label="ln(median price per m$^2$)")
128 + save(fig, "fig_fsa_map.png")
129 +
130 +
131 +def fig_fit_resid():
132 + grand = pd.read_parquet(REPRODUCED / "grand_model.parquet")
133 + fig, ax = plt.subplots(figsize=(5.4, 5.2))
134 + ax.hexbin(grand["pred_grand"], grand["ln_price"], gridsize=90, cmap="Blues",
135 + mincnt=1, bins="log")
136 + lims = [grand["ln_price"].min(), grand["ln_price"].max()]
137 + ax.plot(lims, lims, color=ACCENT2, lw=1.4, ls="--")
138 + ax.set_xlabel("Predicted ln(price)")
139 + ax.set_ylabel("Actual ln(price)")
140 + ax.set_title("Grand model: predicted vs. actual")
141 + save(fig, "fig_fit.png")
142 +
143 + resid = grand["resid_grand"]
144 + fig, ax = plt.subplots(1, 2, figsize=(9.6, 4.1))
145 + ax[0].hist(resid, bins=100, color=ACCENT, alpha=0.9, density=True)
146 + grid = np.linspace(resid.quantile(0.001), resid.quantile(0.999), 200)
147 + ax[0].plot(grid, np.exp(-0.5 * ((grid - resid.mean()) / resid.std()) ** 2)
148 + / (resid.std() * np.sqrt(2 * np.pi)), color=ACCENT2, lw=1.5,
149 + label="Normal density")
150 + ax[0].set_xlabel("Residual")
151 + ax[0].set_title("(a) Residual distribution")
152 + ax[0].legend()
153 + sm.qqplot(resid, line="45", fit=True, ax=ax[1], markerfacecolor=ACCENT,
154 + markeredgecolor=ACCENT, markersize=2, alpha=0.4)
155 + ax[1].set_title("(b) Normal Q--Q plot")
156 + save(fig, "fig_resid.png")
157 +
158 +
159 +def fig_moran(res_dir):
160 + ext2 = json.load(open(res_dir / "ext2.json"))
161 + coords = np.load(REPRODUCED / "moran_coords.npy")
162 + panels = [("Structural-only residuals", np.load(REPRODUCED / "moran_resid_struct.npy"),
163 + ext2["moran"]["struct_I"]),
164 + ("Grand model (FSA FE) residuals", np.load(REPRODUCED / "moran_resid_grand.npy"),
165 + ext2["moran"]["grand_I"])]
166 + from sklearn.neighbors import NearestNeighbors
167 + fig, ax = plt.subplots(1, 2, figsize=(11, 4.4))
168 + for j, (title, resid, moran) in enumerate(panels):
169 + nn = NearestNeighbors(n_neighbors=11).fit(coords)
170 + _, idx = nn.kneighbors(coords)
171 + z = resid - resid.mean()
172 + lag = z[idx[:, 1:]].mean(axis=1)
173 + ax[j].scatter(z, lag, s=3, alpha=0.15, color=ACCENT)
174 + slope, intercept = np.polyfit(z, lag, 1)
175 + xs = np.linspace(z.min(), z.max(), 10)
176 + ax[j].plot(xs, slope * xs + intercept, color=ACCENT2, lw=1.8)
177 + ax[j].axhline(0, color="k", lw=0.5)
178 + ax[j].axvline(0, color="k", lw=0.5)
179 + ax[j].set_xlabel("Residual ($z$)")
180 + ax[j].set_ylabel("Spatial lag of residual")
181 + ax[j].set_title(f"{title}\nMoran's I = {moran:.3f}")
182 + save(fig, "fig_moran.png")
183 +
184 +
185 +def fig_gradient(res_dir):
186 + banded = pd.read_csv(REPRODUCED / "gradient_bins.csv")
187 + fig, ax = plt.subplots(figsize=(7, 4.2))
188 + ax.plot(banded["x"], (np.exp(banded["prem"]) - 1) * 100, "o-", color=ACCENT, lw=2)
189 + ax.axhline(0, color="k", lw=0.7, ls=":")
190 + ax.set_xscale("symlog")
191 + ax.set_xlabel("Distance to nearest major metro (km, symlog)")
192 + ax.set_ylabel("Location premium vs structure-only (%)")
193 + ax.set_title("The urban price gradient: value falls with distance to metro")
194 + save(fig, "fig_gradient.png")
195 +
196 +
197 +def fig_nonlinear(res_dir):
198 + band = pd.read_csv(REPRODUCED / "nonlinear_band.csv")
199 + baseline = pd.read_csv(res_dir / "robustness.csv").iloc[0]["ln_living"]
200 + fig, ax = plt.subplots(figsize=(7, 4.2))
201 + area = np.exp(band["ln_area"])
202 + ax.plot(area, band["elasticity"], color=ACCENT, lw=2)
203 + ax.fill_between(area, band["elasticity"] - 1.96 * band["se"],
204 + band["elasticity"] + 1.96 * band["se"], color=ACCENT, alpha=0.18)
205 + ax.axhline(baseline, color=ACCENT2, ls="--", lw=1,
206 + label=f"linear-model elasticity ({baseline:.2f})")
207 + ax.set_xscale("log")
208 + ax.set_xlabel("Living area (m$^2$)")
209 + ax.set_ylabel("Marginal elasticity of price w.r.t. area")
210 + ax.set_title("Diminishing returns to floor space (quadratic spec., grand model)")
211 + ax.legend()
212 + save(fig, "fig_nonlinear.png")
213 +
214 +
215 +# ----------------------------------------------------------- results-driven
216 +def fig_r2(res_dir):
217 + fit = json.load(open(res_dir / "fit.json"))
218 + names = ["M1", "M2", "M3", "M4", "M5"]
219 + labels = ["M1\nStructural", "M2\n+Type/Own.", "M3\n+Province",
220 + "M4\nHouses+FSA", "M5\nGrand+FSA"]
221 + values = [fit[m]["r2"] for m in names]
222 + fig, ax = plt.subplots(figsize=(7.5, 4))
223 + bars = ax.bar(labels, values, color=[ACCENT, ACCENT, ACCENT, ACCENT3, ACCENT2], alpha=0.92)
224 + for bar, val in zip(bars, values):
225 + ax.text(bar.get_x() + bar.get_width() / 2, val + 0.012, f"{val:.3f}",
226 + ha="center", fontsize=9)
227 + ax.set_ylim(0, 0.9)
228 + ax.set_ylabel("$R^2$ (share of log-price variance explained)")
229 + ax.set_title("Explanatory power across the specification ladder")
230 + save(fig, "fig_r2.png")
231 +
232 +
233 +def fig_decomp(res_dir):
234 + fit = json.load(open(res_dir / "fit.json"))
235 + blocks = [("Structure", fit["M1"]["r2"]),
236 + ("Dwelling type & ownership", fit["M2"]["r2"] - fit["M1"]["r2"]),
237 + ("Province", fit["M3"]["r2"] - fit["M2"]["r2"]),
238 + ("Neighbourhood (FSA)", fit["M5"]["r2"] - fit["M3"]["r2"]),
239 + ("Unexplained", 1 - fit["M5"]["r2"])]
240 + colours = [ACCENT, ACCENT3, GREEN, ACCENT2, NEUTRAL]
241 + fig, ax = plt.subplots(figsize=(8, 2.4))
242 + left = 0.0
243 + for (label, share), colour in zip(blocks, colours):
244 + ax.barh(0, share, left=left, color=colour, edgecolor="white")
245 + if share > 0.03:
246 + ax.text(left + share / 2, 0, f"{label}\n{share * 100:.1f}%",
247 + ha="center", va="center", fontsize=8,
248 + color="black" if colour == NEUTRAL else "white")
249 + left += share
250 + ax.set_xlim(0, 1)
251 + ax.set_ylim(-0.5, 0.5)
252 + ax.set_yticks([])
253 + ax.set_xlabel("Share of log-price variance")
254 + ax.set_title("Variance decomposition of Canadian house prices")
255 + save(fig, "fig_decomp.png")
256 +
257 +
258 +def fig_forest(res_dir):
259 + coefs = pd.read_csv(res_dir / "coef_M3.csv", index_col=0)
260 + order = [("ln_living", "$\\ln$ living area"), ("bathrooms", "Full bathrooms"),
261 + ("half_baths", "Half bathrooms"), ("bedrooms", "Bedrooms"),
262 + ("parking_n", "Parking spaces"), ("stories_n", "Storeys"),
263 + ("ln_lot", "$\\ln(1+$lot m$^2)$")]
264 + rows = [(label, coefs.loc[key, "coef"], coefs.loc[key, "se"])
265 + for key, label in order if key in coefs.index]
266 + fig, ax = plt.subplots(figsize=(7, 4))
267 + y = np.arange(len(rows))[::-1]
268 + ax.errorbar([r[1] for r in rows], y, xerr=[1.96 * r[2] for r in rows],
269 + fmt="o", color=ACCENT, ecolor=GREY, capsize=3, ms=6)
270 + ax.axvline(0, color=ACCENT2, ls="--", lw=1)
271 + ax.set_yticks(y)
272 + ax.set_yticklabels([r[0] for r in rows])
273 + ax.set_xlabel("Coefficient on ln(price), 95% cluster-robust CI")
274 + ax.set_title("Structural implicit prices (houses, province-FE model M3)")
275 + save(fig, "fig_forest.png")
276 +
277 +
278 +def fig_heterogeneity(res_dir):
279 + het = pd.read_csv(res_dir / "heterogeneity.csv").sort_values("elast")
280 + fig, ax = plt.subplots(figsize=(7, 4))
281 + y = np.arange(len(het))
282 + ax.errorbar(het["elast"], y, xerr=1.96 * het["se_el"], fmt="o",
283 + color=ACCENT, ecolor=GREY, capsize=3, ms=6)
284 + ax.axvline(het["elast"].mean(), color=ACCENT2, ls="--", lw=1,
285 + label="cross-province mean")
286 + ax.set_yticks(y)
287 + ax.set_yticklabels([f"{p} (n={n:,})" for p, n in zip(het["prov"], het["n"])])
288 + ax.set_xlabel("Living-area elasticity (within-FSA)")
289 + ax.set_title("Heterogeneity in the size elasticity of price across provinces")
290 + ax.legend()
291 + save(fig, "fig_heterogeneity.png")
292 +
293 +
294 +def fig_premia(res_dir):
295 + premia = pd.read_csv(res_dir / "fsa_premia.csv")
296 + premia = premia.rename(columns={premia.columns[0]: "fsa"})
297 + shown = pd.concat([premia.nsmallest(12, "premium_pct"),
298 + premia.nlargest(12, "premium_pct")]).sort_values("premium_pct")
299 + colours = [ACCENT2 if v < 0 else ACCENT for v in shown["premium_pct"]]
300 + fig, ax = plt.subplots(figsize=(7.5, 5))
301 + ax.barh(np.arange(len(shown)), shown["premium_pct"], color=colours, alpha=0.9)
302 + ax.set_yticks(np.arange(len(shown)))
303 + ax.set_yticklabels([f"{f} ({p})" for f, p in zip(shown["fsa"], shown["prov"])],
304 + fontsize=8)
305 + ax.axvline(0, color="k", lw=0.8)
306 + ax.set_xlabel("Neighbourhood (FSA) price premium vs. national median (%), net of structure")
307 + ax.set_title("Highest- and lowest-valued neighbourhoods in Canada")
308 + save(fig, "fig_premia.png")
309 +
310 +
311 +def fig_quantile(res_dir):
312 + q = pd.read_csv(res_dir / "quantile.csv")
313 + taus = q[q["tau"].notna()]
314 + ols = q[q["tau"].isna()].iloc[0]
315 + fig, ax = plt.subplots(1, 2, figsize=(11, 4.2))
316 + for j, (var, se_var, title) in enumerate(
317 + [("ln_living", "se_living", "$\\ln$ living-area elasticity"),
318 + ("bathrooms", "se_bath", "Full-bathroom premium")]):
319 + ax[j].errorbar(taus["tau"], taus[var], yerr=1.96 * taus[se_var],
320 + fmt="o-", color=ACCENT, capsize=3, label="Quantile")
321 + ax[j].axhline(ols[var], color=ACCENT2, ls="--", label="OLS")
322 + ax[j].set_xlabel("Quantile of price ($\\tau$)")
323 + ax[j].set_title(title)
324 + ax[j].legend()
325 + save(fig, "fig_quantile.png")
326 +
327 +
328 +def fig_oos(res_dir):
329 + oos = json.load(open(res_dir / "oos.json"))
330 + within10 = oos["within10"]
331 + mid = oos["within20"] - oos["within10"]
332 + beyond = 100 - oos["within20"]
333 + fig, ax = plt.subplots(figsize=(6, 4))
334 + ax.bar([5, 15, 30], [within10, mid, beyond], width=8,
335 + color=[ACCENT, ACCENT3, LIGHT], alpha=0.9)
336 + for x, v, label in ((5, within10, "≤10%"), (15, mid, "10–20%"), (30, beyond, ">20%")):
337 + ax.text(x, v + 1, f"{label}\n{v:.0f}%", ha="center", fontsize=9)
338 + ax.axvline(oos["median_ape"], color=ACCENT2, ls="--",
339 + label=f"median APE {oos['median_ape']:.1f}%")
340 + ax.set_xticks([5, 15, 30])
341 + ax.set_xticklabels(["≤10%", "10–20%", ">20%"])
342 + ax.set_xlabel("Absolute percentage error (out-of-sample)")
343 + ax.set_ylabel("Share of test listings (%)")
344 + ax.set_ylim(0, 52)
345 + ax.set_title(f"Out-of-sample valuation accuracy (OOS $R^2$={oos['oos_r2']:.3f})", pad=12)
346 + ax.legend(loc="upper right", fontsize=8)
347 + save(fig, "fig_oos.png")
348 +
349 +
350 +def main() -> None:
351 + parser = argparse.ArgumentParser()
352 + parser.add_argument("--results", choices=["reference", "reproduced"],
353 + default="reference",
354 + help="results tier for number-bearing figures (default: reference)")
355 + args = parser.parse_args()
356 + res_dir = REFERENCE if args.results == "reference" else REPRODUCED
357 + ensure_dirs()
358 + s = sample.load_sample()
359 +
360 + fig_price_dist(s)
361 + fig_province_ppm2(s)
362 + fig_size_gradient(s)
363 + fig_maps(s)
364 + fig_fit_resid()
365 + fig_r2(res_dir)
366 + fig_decomp(res_dir)
367 + fig_forest(res_dir)
368 + fig_heterogeneity(res_dir)
369 + fig_premia(res_dir)
370 + fig_quantile(res_dir)
371 + fig_oos(res_dir)
372 + fig_nonlinear(res_dir)
373 + fig_gradient(res_dir)
374 + fig_moran(res_dir)
375 + print("all figures written to", FIGURES)
376 +
377 +
378 +if __name__ == "__main__":
379 + main()
added scripts/05_make_tables.py +233 −0
@@ -0,0 +1,233 @@
1 +#!/usr/bin/env python3
2 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
3 +"""Step 05 — Generate every LaTeX table used in the paper (6 files).
4 +
5 +Tables are built from the results tier selected with ``--results``; the
6 +default ``reference`` tier reproduces the published numbers exactly.
7 +The summary-statistics table additionally needs the micro sample when the
8 +``reproduced`` tier is selected.
9 +
10 +Usage: python scripts/05_make_tables.py [--results reference|reproduced]
11 +"""
12 +import argparse
13 +import json
14 +import sys
15 +from pathlib import Path
16 +
17 +import pandas as pd
18 +
19 +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
20 +
21 +from wp9 import sample # noqa: E402
22 +from wp9.config import REFERENCE, REPRODUCED, TABLES, ensure_dirs # noqa: E402
23 +
24 +SUMMARY_VARS = [("price_cad", "List price (CAD)", "{:,.0f}"),
25 + ("ppm2", "Price per m$^2$ (CAD)", "{:,.0f}"),
26 + ("living_m2", "Living area (m$^2$)", "{:,.1f}"),
27 + ("bedrooms", "Bedrooms", "{:.2f}"),
28 + ("bathrooms", "Full bathrooms", "{:.2f}"),
29 + ("half_baths", "Half bathrooms", "{:.2f}"),
30 + ("parking_n", "Parking spaces", "{:.2f}"),
31 + ("stories_n", "Storeys", "{:.2f}"),
32 + ("lot_m2_f", "Lot area (m$^2$)", "{:,.0f}")]
33 +
34 +COEF_ORDER = [("ln_living", "$\\ln$ living area"), ("bathrooms", "Full bathrooms"),
35 + ("half_baths", "Half bathrooms"), ("bedrooms", "Bedrooms"),
36 + ("parking_n", "Parking spaces"), ("stories_n", "Storeys"),
37 + ("has_lot", "Has lot info"), ("ln_lot", "$\\ln(1+$lot m$^2)$")]
38 +
39 +
40 +def write(name: str, content: str) -> None:
41 + (TABLES / name).write_text(content + "\n")
42 + print(" tab", name)
43 +
44 +
45 +def stars(p: float) -> str:
46 + return "$^{***}$" if p < 0.01 else "$^{**}$" if p < 0.05 else "$^{*}$" if p < 0.1 else ""
47 +
48 +
49 +def cell(coefs: pd.DataFrame, key: str) -> str:
50 + if key not in coefs.index:
51 + return ""
52 + b, se, p = coefs.loc[key, ["coef", "se", "p"]]
53 + return f"\\makecell{{{b:.3f}{stars(p)}\\\\\\scriptsize({se:.3f})}}"
54 +
55 +
56 +# --------------------------------------------------------------------------
57 +def table_summary_stats(res_dir: Path) -> None:
58 + stats_file = res_dir / "summary_stats.csv"
59 + if stats_file.exists():
60 + stats = pd.read_csv(stats_file, index_col=0)
61 + n = int(stats.attrs.get("n", 0)) or int(stats["n"].iloc[0])
62 + else: # reproduced tier: compute from the micro sample
63 + s = sample.load_sample()
64 + rows = {}
65 + for var, _, _ in SUMMARY_VARS:
66 + col = s[var]
67 + rows[var] = {"mean": col.mean(), "sd": col.std(), "p25": col.quantile(0.25),
68 + "median": col.median(), "p75": col.quantile(0.75), "n": len(s)}
69 + stats = pd.DataFrame(rows).T
70 + stats.to_csv(res_dir / "summary_stats.csv")
71 + n = len(s)
72 + lines = []
73 + for var, label, fmt in SUMMARY_VARS:
74 + r = stats.loc[var]
75 + lines.append(f"{label} & {fmt.format(r['mean'])} & {fmt.format(r['sd'])} & "
76 + f"{fmt.format(r['p25'])} & {fmt.format(r['median'])} & "
77 + f"{fmt.format(r['p75'])} \\\\")
78 + write("summary_stats.tex",
79 + "\\begin{table}[t]\\centering\n\\caption{Descriptive statistics}\n"
80 + "\\label{tab:summary_stats}\n\\begin{threeparttable}\n"
81 + "\\begin{tabular}{lccccc}\n\\toprule\n"
82 + "Variable & Mean & SD & P25 & Median & P75 \\\\\n\\midrule\n"
83 + + "\n".join(lines) +
84 + "\n\\bottomrule\n\\end{tabular}\n\\begin{tablenotes}\\footnotesize\\item "
85 + f"Notes: Estimation sample of {n:,} residential dwellings (houses, "
86 + "condominiums, plexes, townhouses and apartments) from the Canadian MLS, "
87 + "after parsing and trimming the extreme 1\\% tails of price and living area.\n"
88 + "\\end{tablenotes}\n\\end{threeparttable}\n\\end{table}")
89 +
90 +
91 +# --------------------------------------------------------------------------
92 +def table_regression(res_dir: Path) -> None:
93 + fit = json.load(open(res_dir / "fit.json"))
94 + coefs = {m: pd.read_csv(res_dir / f"coef_{m}.csv", index_col=0)
95 + for m in ("M1", "M2", "M3", "M5")}
96 + lines = [label + " & " + " & ".join(cell(coefs[m], key) for m in ("M1", "M2", "M3", "M5"))
97 + + " \\\\" for key, label in COEF_ORDER]
98 + footer = ["\\midrule",
99 + "Dwelling-type FE & No & Yes & Yes & Yes \\\\",
100 + "Ownership FE & No & Yes & Yes & Yes \\\\",
101 + "Province FE & No & No & Yes & --- \\\\",
102 + "FSA fixed effects & No & No & No & Yes \\\\",
103 + f"$R^2$ & {fit['M1']['r2']:.3f} & {fit['M2']['r2']:.3f} & "
104 + f"{fit['M3']['r2']:.3f} & {fit['M5']['r2']:.3f} \\\\",
105 + f"Observations & {fit['M1']['n']:,} & {fit['M2']['n']:,} & "
106 + f"{fit['M3']['n']:,} & {fit['M5']['n']:,} \\\\"]
107 + write("regression.tex",
108 + "\\begin{table}[t]\\centering\n\\caption{Hedonic regression estimates}\n"
109 + "\\label{tab:regression}\n\\begin{threeparttable}\n"
110 + "\\begin{tabular}{lcccc}\n\\toprule\n"
111 + " & (1) & (2) & (3) & (4) \\\\\n"
112 + " & Structural & +Type/Own. & +Province & Grand+FSA \\\\\n\\midrule\n"
113 + + "\n".join(lines + footer) +
114 + "\n\\bottomrule\n\\end{tabular}\n\\begin{tablenotes}\\footnotesize\\item "
115 + "Notes: Dependent variable is $\\ln(\\text{price})$. Columns (1)--(3) use the "
116 + "house subsample; column (4) is the grand model over all residential dwellings "
117 + "with 1{,}153 absorbed FSA fixed effects. Cluster-robust standard errors "
118 + "(by FSA) in parentheses. $^{*}p<0.1$, $^{**}p<0.05$, $^{***}p<0.01$.\n"
119 + "\\end{tablenotes}\n\\end{threeparttable}\n\\end{table}")
120 +
121 +
122 +# --------------------------------------------------------------------------
123 +def table_robustness(res_dir: Path) -> None:
124 + rob = pd.read_csv(res_dir / "robustness.csv")
125 + lines = []
126 + for _, r in rob.iterrows():
127 + lines.append(
128 + f"{r['label']} & {r['n']:,.0f} & {r['r2']:.3f} & "
129 + f"\\makecell{{{r['ln_living']:.3f}\\\\\\scriptsize({r['se_living']:.3f})}} & "
130 + f"\\makecell{{{r['bathrooms']:.3f}\\\\\\scriptsize({r['se_bath']:.3f})}} & "
131 + f"\\makecell{{{r['ln_lot']:.3f}\\\\\\scriptsize({r['se_lot']:.3f})}} \\\\")
132 + write("robustness.tex",
133 + "\\begin{table}[t]\\centering\n"
134 + "\\caption{Robustness of key implicit prices across samples}\n"
135 + "\\label{tab:robustness}\n\\begin{threeparttable}\n"
136 + "\\begin{tabular}{lccccc}\n\\toprule\n"
137 + "Sample / specification & $N$ & $R^2$ & $\\ln$ area & Full bath & $\\ln$ lot \\\\\n"
138 + "\\midrule\n" + "\n".join(lines) +
139 + "\n\\bottomrule\n\\end{tabular}\n\\begin{tablenotes}\\footnotesize\\item "
140 + "Notes: Each row re-estimates the grand FSA-fixed-effects model on a different "
141 + "sample. Cluster-robust (FSA) standard errors in parentheses. The size elasticity "
142 + "and bathroom premium are stable across all cuts.\n"
143 + "\\end{tablenotes}\n\\end{threeparttable}\n\\end{table}")
144 +
145 +
146 +# --------------------------------------------------------------------------
147 +def table_quantile(res_dir: Path) -> None:
148 + q = pd.read_csv(res_dir / "quantile.csv")
149 + taus = q[q["tau"].notna()]
150 + ols = q[q["tau"].isna()].iloc[0]
151 + lines = []
152 + for _, r in taus.iterrows():
153 + tau = f"{r['tau']:g}"
154 + lines.append(
155 + f"$\\tau={tau}$ & "
156 + f"\\makecell{{{r['ln_living']:.3f}\\\\\\scriptsize({r['se_living']:.3f})}} & "
157 + f"\\makecell{{{r['bathrooms']:.3f}\\\\\\scriptsize({r['se_bath']:.3f})}} & "
158 + f"\\makecell{{{r['ln_lot']:.3f}\\\\\\scriptsize({r['se_lot']:.3f})}} \\\\")
159 + write("quantile.tex",
160 + "\\begin{table}[t]\\centering"
161 + "\\caption{Quantile hedonic estimates across the price distribution}\n"
162 + "\\label{tab:quantile}\\begin{threeparttable}\\begin{tabular}{lccc}\n\\toprule\n"
163 + "Quantile & $\\ln$ area & Full bath & $\\ln$ lot \\\\\n\\midrule\n"
164 + + "\n".join(lines) + "\n\\midrule\nOLS & "
165 + f"{ols['ln_living']:.3f} & {ols['bathrooms']:.3f} & {ols['ln_lot']:.3f} \\\\\n"
166 + "\\bottomrule\\end{tabular}\\begin{tablenotes}\\footnotesize\\item Notes: House "
167 + "subsample, province fixed effects, structural controls. Analytical standard "
168 + "errors in parentheses. The size elasticity rises and the lot elasticity rises "
169 + "with price.\\end{tablenotes}\\end{threeparttable}\\end{table}")
170 +
171 +
172 +# --------------------------------------------------------------------------
173 +def table_lopo(res_dir: Path) -> None:
174 + lopo = pd.read_csv(res_dir / "lopo.csv")
175 + lines = [f"{r['prov']} & {r['n']:,.0f} & {r['r2_within']:.3f} \\\\"
176 + for _, r in lopo.iterrows()]
177 + write("lopo.tex",
178 + "\\begin{table}[t]\\centering"
179 + "\\caption{Leave-one-province-out spatial cross-validation}\n"
180 + "\\label{tab:lopo}\\begin{threeparttable}\\begin{tabular}{lcc}\n\\toprule\n"
181 + "Held-out province & $N$ & Within-province $R^2$ \\\\\n\\midrule\n"
182 + + "\n".join(lines) +
183 + f"\n\\midrule\nMean & --- & {lopo['r2_within'].mean():.3f} \\\\\n"
184 + "\\bottomrule\\end{tabular}\\begin{tablenotes}\\footnotesize\\item Notes: The "
185 + "structural hedonic model is estimated on all provinces except one and used to "
186 + "predict the held-out province; a province-specific intercept is allowed (the "
187 + "price \\emph{level} is not identified out of sample), so the metric captures "
188 + "whether the \\emph{structural} implicit prices transfer across space."
189 + "\\end{tablenotes}\\end{threeparttable}\\end{table}")
190 +
191 +
192 +# --------------------------------------------------------------------------
193 +def table_oos(res_dir: Path) -> None:
194 + oos = json.load(open(res_dir / "oos.json"))
195 + write("oos.tex",
196 + "\\begin{table}[t]\\centering\n"
197 + "\\caption{Out-of-sample valuation performance (80/20 split)}\n"
198 + "\\label{tab:oos}\n\\begin{threeparttable}\n\\begin{tabular}{lc}\n\\toprule\n"
199 + "Metric & Value \\\\\n\\midrule\n"
200 + f"Training listings & {oos['n_train']:,} \\\\\n"
201 + f"Test listings & {oos['n_test']:,} \\\\\n"
202 + f"Out-of-sample $R^2$ (log price) & {oos['oos_r2']:.3f} \\\\\n"
203 + f"RMSE (log points) & {oos['rmse_log']:.3f} \\\\\n"
204 + f"Median absolute \\% error & {oos['median_ape']:.1f}\\% \\\\\n"
205 + f"Mean absolute \\% error & {oos['mean_ape']:.1f}\\% \\\\\n"
206 + f"Share priced within $\\pm$10\\% & {oos['within10']:.1f}\\% \\\\\n"
207 + f"Share priced within $\\pm$20\\% & {oos['within20']:.1f}\\% \\\\\n"
208 + "\\bottomrule\n\\end{tabular}\n\\begin{tablenotes}\\footnotesize\\item Notes: "
209 + "Model trained on a random 80\\% of listings and scored on the held-out 20\\% "
210 + "(restricted to neighbourhoods observed in training). Prices back-transformed "
211 + "with Duan's smearing estimator.\n"
212 + "\\end{tablenotes}\n\\end{threeparttable}\n\\end{table}")
213 +
214 +
215 +def main() -> None:
216 + parser = argparse.ArgumentParser()
217 + parser.add_argument("--results", choices=["reference", "reproduced"],
218 + default="reference",
219 + help="results tier (default: reference — published numbers)")
220 + args = parser.parse_args()
221 + res_dir = REFERENCE if args.results == "reference" else REPRODUCED
222 + ensure_dirs()
223 + table_summary_stats(res_dir)
224 + table_regression(res_dir)
225 + table_robustness(res_dir)
226 + table_quantile(res_dir)
227 + table_lopo(res_dir)
228 + table_oos(res_dir)
229 + print("all tables written to", TABLES)
230 +
231 +
232 +if __name__ == "__main__":
233 + main()
added src/wp9/__init__.py +11 −0
@@ -0,0 +1,11 @@
1 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +"""WP9 — A Grand Hedonic Model of the Canadian Housing Market.
3 +
4 +Analysis package for UQO Working Paper No. 9. Modules:
5 +
6 +- ``config`` paths and global constants
7 +- ``parsing`` parsers for the semi-structured raw MLS fields
8 +- ``sample`` construction of the estimation sample from the raw DuckDB
9 +- ``models`` design matrices and the M1–M5 hedonic specification ladder
10 +- ``plotstyle`` shared matplotlib style and colour palette
11 +"""
added src/wp9/config.py +57 −0
@@ -0,0 +1,57 @@
1 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +"""Paths and global constants for the WP9 pipeline.
3 +
4 +All paths are relative to the repository root, so the pipeline runs on any
5 +machine after cloning. Override the root with the ``WP9_ROOT`` environment
6 +variable if the scripts are launched from elsewhere.
7 +"""
8 +import os
9 +from pathlib import Path
10 +
11 +ROOT = Path(os.environ.get("WP9_ROOT", Path(__file__).resolve().parents[2]))
12 +
13 +RAW_DB = ROOT / "data" / "raw" / "realtor_mls_unique.duckdb"
14 +PROCESSED = ROOT / "data" / "processed"
15 +ANALYSIS_PARQUET = PROCESSED / "analysis.parquet"
16 +FIGURES = ROOT / "figures"
17 +RESULTS = ROOT / "results"
18 +REFERENCE = RESULTS / "reference" # original (canonical) outputs — paper numbers
19 +REPRODUCED = RESULTS / "reproduced" # outputs regenerated by this pipeline
20 +TABLES = RESULTS / "tables"
21 +
22 +# Unit conversions (as documented in the paper's data section)
23 +SQFT_TO_M2 = 0.0929023
24 +ACRE_TO_M2 = 4046.86
25 +HA_TO_M2 = 10_000.0
26 +
27 +# Sample construction
28 +TRIM_LO, TRIM_HI = 0.01, 0.99 # 1% tails of price and living area
29 +FSA_MIN_LISTINGS = 25 # FSAs below this are pooled into <PROV>_other
30 +LOT_TAIL_Q = 0.99 # positive lot areas above this quantile set to 0 (unparseable/exotic)
31 +
32 +# Structural regressors used in every specification
33 +STRUCT = ["ln_living", "bedrooms", "bathrooms", "half_baths",
34 + "parking_n", "stories_n", "has_lot", "ln_lot"]
35 +
36 +# Random seeds (identical to the original analysis scripts)
37 +SEED_OOS = 12345
38 +SEED_MORAN = 3
39 +SEED_EXT2 = 7
40 +
41 +# Nine major metropolitan centres for the urban-gradient analysis
42 +METROS = {
43 + "Toronto": (43.65, -79.38),
44 + "Montreal": (45.50, -73.57),
45 + "Vancouver": (49.28, -123.12),
46 + "Calgary": (51.05, -114.07),
47 + "Ottawa-Gatineau": (45.42, -75.70),
48 + "Edmonton": (53.55, -113.49),
49 + "Winnipeg": (49.90, -97.14),
50 + "Quebec City": (46.81, -71.21),
51 + "Halifax": (44.65, -63.58),
52 +}
53 +
54 +def ensure_dirs() -> None:
55 + """Create every output directory the pipeline writes to."""
56 + for p in (PROCESSED, FIGURES, REFERENCE, REPRODUCED, TABLES):
57 + p.mkdir(parents=True, exist_ok=True)
added src/wp9/models.py +105 −0
@@ -0,0 +1,105 @@
1 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +"""Design matrices and the M1–M5 hedonic specification ladder.
3 +
4 +The semi-logarithmic hedonic equation regresses ln(price) on structural
5 +attributes, dwelling-type/ownership dummies and location fixed effects.
6 +FSA fixed effects are absorbed with ``linearmodels.AbsorbingLS`` (numerically
7 +identical to full-dummy OLS); standard errors are clustered by FSA.
8 +"""
9 +import numpy as np
10 +import pandas as pd
11 +import statsmodels.api as sm
12 +from linearmodels.iv.absorbing import AbsorbingLS
13 +
14 +from .config import STRUCT
15 +
16 +
17 +def design(data: pd.DataFrame, extra: pd.DataFrame | None = None) -> pd.DataFrame:
18 + """Structural regressors + dwelling-type, ownership and category dummies."""
19 + blocks = [data[STRUCT].astype(float)]
20 + if extra is not None:
21 + blocks.append(extra)
22 + blocks += [pd.get_dummies(data[col], prefix=pfx, drop_first=True).astype(float)
23 + for col, pfx in (("btype_c", "bt"), ("own_c", "ow"), ("cat", "cat"))]
24 + return sm.add_constant(pd.concat(blocks, axis=1))
25 +
26 +
27 +def fit_absorbing(data: pd.DataFrame, extra: pd.DataFrame | None = None):
28 + """FSA-fixed-effects hedonic model with FSA-clustered standard errors.
29 +
30 + Returns the fitted AbsorbingLS results and the design column order.
31 + """
32 + X = design(data, extra=extra)
33 + res = AbsorbingLS(data["ln_price"], X,
34 + absorb=data[["fsa_c"]].astype("category"),
35 + drop_absorbed=True).fit(cov_type="clustered",
36 + clusters=data[["fsa_c"]])
37 + return res, X.columns.tolist()
38 +
39 +
40 +def coef_table(res, columns=None) -> pd.DataFrame:
41 + """Coefficient / SE / p-value table indexed by regressor name.
42 +
43 + Works for both statsmodels (``bse``) and linearmodels (``std_errors``);
44 + uses the estimator's own parameter index, which may exclude regressors
45 + dropped as collinear with the absorbed effects.
46 + """
47 + se = res.std_errors if hasattr(res, "std_errors") else res.bse
48 + index = res.params.index if hasattr(res.params, "index") else columns
49 + return pd.DataFrame({
50 + "coef": pd.Series(np.asarray(res.params).ravel(), index=index),
51 + "se": pd.Series(np.asarray(se).ravel(), index=index),
52 + "p": pd.Series(np.asarray(res.pvalues).ravel(), index=index),
53 + })
54 +
55 +
56 +def fsa_fixed_effects(data: pd.DataFrame, res, columns=None) -> pd.Series:
57 + """Recover the absorbed FSA effects as within-FSA mean residuals of X'b."""
58 + beta = pd.Series(np.asarray(res.params).ravel(), index=res.params.index)
59 + xb = design(data).reindex(columns=beta.index, fill_value=0.0).values @ beta.values
60 + return (data["ln_price"] - xb).groupby(data["fsa_c"]).mean()
61 +
62 +
63 +def predict_with_fe(data: pd.DataFrame, res, columns, fe: pd.Series) -> np.ndarray:
64 + """Linear prediction X'b + absorbed FSA effect."""
65 + beta = pd.Series(np.asarray(res.params).ravel(), index=res.params.index)
66 + xb = design(data).reindex(columns=beta.index, fill_value=0.0).values @ beta.values
67 + return xb + data["fsa_c"].map(fe).values
68 +
69 +
70 +def specification_ladder(sample: pd.DataFrame) -> dict:
71 + """Estimate the M1–M5 ladder; returns {name: (results, columns, data)}.
72 +
73 + M1–M3 use the house subsample (structural; +type/ownership; +province);
74 + M4 is houses with FSA fixed effects; M5 is the grand model over all
75 + residential dwellings with FSA fixed effects.
76 + """
77 + houses = sample[sample["cat"] == "house"]
78 + out = {}
79 +
80 + X1 = sm.add_constant(houses[STRUCT].astype(float))
81 + out["M1"] = (sm.OLS(houses["ln_price"], X1)
82 + .fit(cov_type="cluster", cov_kwds={"groups": houses["fsa_c"]}),
83 + X1.columns.tolist(), houses)
84 +
85 + X2 = design(houses)
86 + out["M2"] = (sm.OLS(houses["ln_price"], X2)
87 + .fit(cov_type="cluster", cov_kwds={"groups": houses["fsa_c"]}),
88 + X2.columns.tolist(), houses)
89 +
90 + X3 = X2.join(pd.get_dummies(houses["prov"], prefix="pv", drop_first=True).astype(float))
91 + out["M3"] = (sm.OLS(houses["ln_price"], X3)
92 + .fit(cov_type="cluster", cov_kwds={"groups": houses["fsa_c"]}),
93 + X3.columns.tolist(), houses)
94 +
95 + res4, cols4 = fit_absorbing(houses)
96 + out["M4"] = (res4, cols4, houses)
97 +
98 + res5, cols5 = fit_absorbing(sample)
99 + out["M5"] = (res5, cols5, sample)
100 + return out
101 +
102 +
103 +def duan_smearing(residuals: np.ndarray) -> float:
104 + """Duan (1983) smearing factor for retransformation from logs."""
105 + return float(np.mean(np.exp(residuals)))
added src/wp9/parsing.py +166 −0
@@ -0,0 +1,166 @@
1 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +"""Parsers for the semi-structured raw MLS fields.
3 +
4 +The raw ``listings`` table stores every attribute as free text. These
5 +functions harmonise them to numeric analysis variables:
6 +
7 +- bedrooms reported as ``"3 + 1"`` (main + lower level) are summed;
8 +- living area is taken from the explicit floor-area measurement
9 + (``building.floor_area_measurements``), using the upper bound of banded
10 + entries such as ``"1100-1500 sqft"``, and falls back to
11 + ``building.size_interior``; square feet are converted at
12 + 1 ft^2 = 0.0929 m^2;
13 +- lot size is recovered from the free-text ``land.size_total`` where a
14 + numeric value with a recognisable unit (sqft, m2, acres, hectares, or
15 + frontage x depth in feet) is present.
16 +
17 +NOTE — reconstruction: the original cleaning code lived in the upstream
18 +RE_DB_QC pipeline, which no longer exists. These rules were reverse-engineered
19 +from the description in the paper's data section and validated against the
20 +original sample counts, summary statistics and regression estimates
21 +(see AUDIT.md, "Reproduction verification").
22 +"""
23 +import re
24 +
25 +import numpy as np
26 +
27 +from .config import SQFT_TO_M2, ACRE_TO_M2, HA_TO_M2
28 +
29 +_RANGE = re.compile(r"([\d.,]+)\s*-\s*([\d.,]+)")
30 +_NUMBER = re.compile(r"([\d.,]+)")
31 +_UNFORMATTED = re.compile(r'"area_unformatted":\s*"([^"]+)"')
32 +_SIZE_WITH_UNIT = re.compile(r"([\d.,]+)\s*(\w*)")
33 +_LOT_UNIT = re.compile(r"([\d.,]+)\s*(sqft|sq ft|m2|ac|acre|acres|hectare|ha)\b")
34 +_LOT_UNDER = re.compile(r"under\s+([\d/.]+)\s*acre")
35 +_LOT_ACRE_RANGE = re.compile(r"([\d/.]+)\s*-\s*([\d.]+)\s*acres")
36 +_LOT_DIMS = re.compile(r"(\d+(?:\.\d+)?)\s*x\s*(\d+(?:\.\d+)?)")
37 +
38 +
39 +def _to_float(text: str) -> float:
40 + return float(text.replace(",", ""))
41 +
42 +
43 +def parse_bedrooms(value) -> float:
44 + """Sum the integer components of a bedroom string ('3 + 1' -> 4)."""
45 + if not isinstance(value, str):
46 + return np.nan
47 + parts = re.findall(r"\d+", value)
48 + return float(sum(int(p) for p in parts)) if parts else np.nan
49 +
50 +
51 +def parse_count(value) -> float:
52 + """Parse a plain numeric count field (bathrooms, storeys, parking)."""
53 + try:
54 + return float(value)
55 + except (TypeError, ValueError):
56 + return np.nan
57 +
58 +
59 +def parse_floor_area(value) -> float:
60 + """Living area in m^2 from ``building.floor_area_measurements``.
61 +
62 + Banded entries ("1100-1500 sqft") are mapped to the upper bound of the
63 + band; exact entries ("1698 sqft") are used as reported.
64 + """
65 + if not isinstance(value, str):
66 + return np.nan
67 + match = _UNFORMATTED.search(value)
68 + if not match:
69 + return np.nan
70 + text = match.group(1)
71 + band = _RANGE.match(text)
72 + if band:
73 + area = _to_float(band.group(2))
74 + else:
75 + number = _NUMBER.match(text)
76 + if not number:
77 + return np.nan
78 + area = _to_float(number.group(1))
79 + return area if "m2" in text else area * SQFT_TO_M2
80 +
81 +
82 +def parse_size_interior(value) -> float:
83 + """Living area in m^2 from ``building.size_interior`` ('102.19 m2', '1698 sqft')."""
84 + if not isinstance(value, str):
85 + return np.nan
86 + match = _SIZE_WITH_UNIT.match(value)
87 + if not match:
88 + return np.nan
89 + area = _to_float(match.group(1))
90 + unit = match.group(2).lower()
91 + return area if unit == "m2" else area * SQFT_TO_M2 # unitless values are sqft
92 +
93 +
94 +def parse_lot(value) -> float:
95 + """Lot area in m^2 from the free-text ``land.size_total`` field.
96 +
97 + Recognises '<n> sqft|m2|ac|acres|hectare|ha', 'under <x> acre(s)',
98 + '<a> - <b> acres' (upper bound), and '<w> x <d>' frontage-by-depth in
99 + feet. Returns NaN when no numeric value can be recovered.
100 + """
101 + if not isinstance(value, str):
102 + return np.nan
103 + text = value.lower().strip()
104 + match = _LOT_UNIT.match(text)
105 + if match:
106 + size = _to_float(match.group(1))
107 + unit = match.group(2)
108 + if unit in ("sqft", "sq ft"):
109 + return size * SQFT_TO_M2
110 + if unit == "m2":
111 + return size
112 + if unit.startswith("ac"):
113 + return size * ACRE_TO_M2
114 + return size * HA_TO_M2
115 + match = _LOT_UNDER.match(text)
116 + if match:
117 + frac = match.group(1)
118 + if "/" in frac:
119 + num, den = frac.split("/", 1)
120 + acres = float(num) / float(den)
121 + else:
122 + acres = float(frac)
123 + return acres * ACRE_TO_M2
124 + match = _LOT_ACRE_RANGE.match(text)
125 + if match:
126 + return float(match.group(2)) * ACRE_TO_M2
127 + match = _LOT_DIMS.match(text)
128 + if match:
129 + return float(match.group(1)) * float(match.group(2)) * SQFT_TO_M2
130 + return np.nan
131 +
132 +
133 +# Consolidation of raw dwelling types into the eight groups used in the paper
134 +BUILDING_TYPE_MAP = {
135 + "House": "House",
136 + "Apartment": "Apartment",
137 + "Row / Townhouse": "Row/Townhouse",
138 + "Duplex": "Duplex",
139 + "Triplex": "Triplex",
140 + "Fourplex": "Fourplex",
141 + "Manufactured Home": "Manufactured",
142 + "Manufactured Home/Mobile": "Manufactured",
143 + "Mobile Home": "Manufactured",
144 + "Park Model Mobile Home": "Manufactured",
145 +}
146 +
147 +
148 +def consolidate_building_type(value) -> str:
149 + """Map the raw ``building.type`` to the paper's eight dwelling-type groups."""
150 + return BUILDING_TYPE_MAP.get(value, "Other")
151 +
152 +
153 +def consolidate_ownership(value) -> str:
154 + """Map the raw ``ownership.type`` to the paper's ownership-form groups."""
155 + if not isinstance(value, str):
156 + return "Unknown"
157 + text = value.lower()
158 + if "lease" in text:
159 + return "Leasehold"
160 + if "condo" in text or "strata" in text:
161 + return "Condo/Strata"
162 + if "freehold" in text:
163 + return "Freehold"
164 + if "co-op" in text or "cooperative" in text or "co-ownership" in text:
165 + return "Co-op/Co-ownership"
166 + return "Other"
added src/wp9/plotstyle.py +27 −0
@@ -0,0 +1,27 @@
1 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +"""Shared matplotlib style and colour palette for all WP9 figures."""
3 +import matplotlib
4 +
5 +matplotlib.use("Agg")
6 +import matplotlib.pyplot as plt # noqa: E402
7 +
8 +ACCENT = "#16365c" # dark navy — primary series
9 +ACCENT2 = "#a02020" # dark red — reference/contrast
10 +ACCENT3 = "#4a7ab5" # medium blue
11 +GREEN = "#2e7d32"
12 +ORANGE = "#e08214"
13 +GREY = "#888888"
14 +LIGHT = "#b0c4de"
15 +NEUTRAL = "#cccccc"
16 +
17 +
18 +def apply_style() -> None:
19 + """Serif fonts, open spines, print-quality DPI — applied by every figure script."""
20 + plt.rcParams.update({
21 + "font.size": 10,
22 + "figure.dpi": 150,
23 + "savefig.dpi": 220,
24 + "axes.spines.top": False,
25 + "axes.spines.right": False,
26 + "font.family": "serif",
27 + })
added src/wp9/sample.py +114 −0
@@ -0,0 +1,114 @@
1 +# Author: Simon-Pierre Boucher — contact@spboucher.ai
2 +"""Construction of the estimation sample from the raw DuckDB snapshot.
3 +
4 +Steps (documented in the paper's data section):
5 +
6 +1. read the raw ``listings`` table (172,019 unique for-sale listings);
7 +2. parse the semi-structured fields (bedrooms, areas, lot, counts);
8 +3. consolidate dwelling type and ownership form; derive the FSA from the
9 + postal code;
10 +4. keep residential listings with a strictly positive price and non-missing
11 + core structural fields (living area, bedrooms, bathrooms) and a valid FSA;
12 +5. trim the extreme 1% tails of price and living area;
13 +6. pool FSAs with fewer than 25 listings into a province-level residual
14 + category.
15 +"""
16 +import duckdb
17 +import numpy as np
18 +import pandas as pd
19 +
20 +from . import parsing
21 +from .config import (ANALYSIS_PARQUET, FSA_MIN_LISTINGS, LOT_TAIL_Q, RAW_DB,
22 + TRIM_HI, TRIM_LO, ensure_dirs)
23 +
24 +_RAW_QUERY = """
25 +select "_category" as cat,
26 + "_province" as prov,
27 + "building.type" as btype_raw,
28 + "ownership.type" as own_raw,
29 + "building.bedrooms" as bed_raw,
30 + "building.bathroom_total" as bath_raw,
31 + "building.half_bath_total" as half_raw,
32 + "building.size_interior" as size_interior,
33 + "building.floor_area_measurements" as floor_area,
34 + "building.stories_total" as stories_raw,
35 + "parking.spaces_total" as parking_raw,
36 + "land.size_total" as lot_raw,
37 + "location.postal_code" as postal_code,
38 + price_cad, lat, lon
39 +from listings
40 +"""
41 +
42 +_FSA_PATTERN = r"^[A-Z]\d[A-Z]$"
43 +
44 +
45 +def load_raw() -> pd.DataFrame:
46 + """Read the raw listings table from the DuckDB snapshot."""
47 + with duckdb.connect(str(RAW_DB), read_only=True) as con:
48 + return con.execute(_RAW_QUERY).df()
49 +
50 +
51 +def parse_variables(raw: pd.DataFrame) -> pd.DataFrame:
52 + """Parse raw text fields into numeric/categorical analysis variables."""
53 + d = raw.copy()
54 + d["bedrooms"] = d["bed_raw"].map(parsing.parse_bedrooms)
55 + d["bathrooms"] = d["bath_raw"].map(parsing.parse_count)
56 + d["half_baths"] = d["half_raw"].map(parsing.parse_count).fillna(0.0)
57 + d["parking_n"] = d["parking_raw"].map(parsing.parse_count).fillna(0.0)
58 + d["stories_n"] = d["stories_raw"].map(parsing.parse_count).fillna(0.0)
59 + d["living_m2"] = (d["floor_area"].map(parsing.parse_floor_area)
60 + .fillna(d["size_interior"].map(parsing.parse_size_interior)))
61 +
62 + lot = d["lot_raw"].map(parsing.parse_lot)
63 + # Very large parsed lots (top 1% of positive values, mostly multi-acre
64 + # rural acreage strings) are treated as "no usable lot information".
65 + cap = lot[lot > 0].quantile(LOT_TAIL_Q)
66 + lot = lot.where(lot <= cap, np.nan)
67 + d["lot_m2_f"] = lot.fillna(0.0)
68 + d["has_lot"] = (d["lot_m2_f"] > 0).astype(float)
69 +
70 + d["btype_c"] = d["btype_raw"].map(parsing.consolidate_building_type)
71 + d["own_c"] = d["own_raw"].map(parsing.consolidate_ownership)
72 +
73 + fsa = d["postal_code"].astype("string").str.upper().str[:3]
74 + d["fsa"] = fsa.where(fsa.str.match(_FSA_PATTERN, na=False))
75 + return d
76 +
77 +
78 +def build_sample(d: pd.DataFrame) -> pd.DataFrame:
79 + """Apply the sample restrictions and derive the model variables."""
80 + core = ((d["price_cad"] > 0) & d["living_m2"].notna()
81 + & d["bedrooms"].notna() & d["bathrooms"].notna() & d["fsa"].notna())
82 + s = d[core].copy()
83 +
84 + lo_p, hi_p = s["price_cad"].quantile([TRIM_LO, TRIM_HI])
85 + lo_a, hi_a = s["living_m2"].quantile([TRIM_LO, TRIM_HI])
86 + s = s[(s["price_cad"].between(lo_p, hi_p)) & (s["living_m2"].between(lo_a, hi_a))].copy()
87 +
88 + s["ln_price"] = np.log(s["price_cad"])
89 + s["ln_living"] = np.log(s["living_m2"])
90 + s["ln_lot"] = np.log1p(s["lot_m2_f"])
91 + s["ppm2"] = s["price_cad"] / s["living_m2"]
92 +
93 + counts = s.groupby("fsa")["fsa"].transform("size")
94 + s["fsa_c"] = np.where(counts >= FSA_MIN_LISTINGS, s["fsa"], s["prov"] + "_other")
95 +
96 + keep = ["cat", "prov", "btype_c", "own_c", "fsa", "fsa_c",
97 + "price_cad", "ln_price", "ppm2",
98 + "living_m2", "ln_living", "bedrooms", "bathrooms", "half_baths",
99 + "parking_n", "stories_n", "lot_m2_f", "has_lot", "ln_lot",
100 + "lat", "lon"]
101 + return s[keep].reset_index(drop=True)
102 +
103 +
104 +def build_and_save() -> pd.DataFrame:
105 + """Full raw-to-parquet pipeline; returns the estimation sample."""
106 + ensure_dirs()
107 + sample = build_sample(parse_variables(load_raw()))
108 + sample.to_parquet(ANALYSIS_PARQUET, index=False)
109 + return sample
110 +
111 +
112 +def load_sample() -> pd.DataFrame:
113 + """Load the processed estimation sample (build it first with script 01)."""
114 + return pd.read_parquet(ANALYSIS_PARQUET)
115