# 🏠 Airbnb, Residential Rents, and Housing Market Pressure

UQO Working Paper No. 5 — A Hedonic and Spatial Econometric Analysis

TL;DR — Using a 2026 cross-section of 3,456 Airbnb listings and 8,303 residential rental listings in Quebec, the paper measures the conditional association between nearby short-term-rental activity and monthly asking rents. With city fixed effects, each additional Airbnb listing within 500 m is associated with ≈ +0.4 % rent (0.3–0.5 % across specifications), decaying per listing with distance, larger at the top of the rent distribution, and unchanged in SAR/SEM models (total impact 0.40 %). Two results qualify this: Conley and FSA-clustered standard errors are ≈ 3× the HC1 errors (t from 17 to ≈ 5.5), and with postal-code-neighbourhood (FSA) fixed effects the coefficient falls to 0.08 % and is not distinguishable from zero under spatial inference. The association is predominantly between neighbourhoods — consistent with STR supply sorting into high-rent neighbourhoods, not with a within-neighbourhood causal effect. Spatially blocked cross-validation erases the apparent advantage of tree-based ML models. Cross-sectional associations, not causal effects.

# 📌 Status

# 🎯 What this paper does

Peer-to-peer platforms can divert dwellings from the long-term rental stock and raise rents nearby. Quasi-experimental evidence exists for Berlin, Los Angeles, Barcelona or Boston, but Canadian evidence is descriptive. For every rental listing the paper builds Airbnb exposure measures within 250 m, 500 m, 1 km and 2 km buffers (Haversine distances) — count, density, mean nightly price, entire-home share — and estimates

log(rent_i) = α + β · airbnb_count_500m_i + X_i'γ + building-type + city FE + ε_i      (Model 1c)
log(rent_i) = α + β^F · airbnb_count_500m_i + X_i'γ + building-type + FSA FE + ε_i     (Model F2)

with HC1, city-clustered, FSA-clustered and Conley spatial HAC standard errors (1–10 km cutoffs); SAR/SEM models by Kelejian–Prucha GMM with LeSage–Pace direct/indirect/total impacts; quantile regressions; a hedonic model of Airbnb nightly prices (full sample and plausibly priced subsample); city-level regressions; and an ML benchmark evaluated under random and spatially blocked (FSA) cross-validation. The discussion benchmarks magnitudes against the quasi-experimental literature in standardised terms and draws implications for Quebec's registration regime and Montreal's zoning rules.

# 🏆 Headline results

# 🗂 Repository layout

wp5_uqo/
├── README.md
├── requirements.txt                # pinned dependencies
├── docs/                           # REVIEW-REPORT.md · CHANGELOG-REVISION.md (2026-10-17) · AUDIT.md · CHANGES.md ·
│                                   # PAPER_REVIEW.md · UPGRADE_REPORT.md (2026-08-05) · main_pre-revision-20261017.pdf
├── data/
│   ├── raw/                        # airbnb.csv + rent.json — NOT distributed (see data/raw/README.md)
│   └── processed/                  # 5 verified parquet datasets (committed)
├── src/
│   ├── config.py                   # all paths & constants
│   ├── geo.py                      # vectorised Haversine matrix
│   ├── latex_tables.py             # table builder + readable variable labels
│   └── plotting.py                 # publication matplotlib styles
├── scripts/                        # 01 → 12 numbered pipeline
├── figures/                        # 17 publication PDF figures
├── results/tables/                 # 18 LaTeX fragments + CSV/JSON records
├── results/logs/                   # data-inspection log · pipeline run log
└── paper/                          # main.tex + sections/ + appendix/ + references.bib → main.pdf

# 🚀 Quick start

python3 -m pip install -r requirements.txt

# steps 01–03 need data/raw/ (not distributed); steps 04–12 run from the committed parquets
for s in scripts/0{4..9}_*.py scripts/1{0,1,2}_*.py; do python3 "$s"; done

cd paper && make                    # latexmk → main.pdf (59 pages)

⚡ = under 5 s on Apple Silicon. Fully deterministic (seed 42).

# 🗃 Data

Spatial merge: for every rental listing × radius r ∈ {250 m, 500 m, 1 km, 2 km}, six metrics computed against all Airbnb listings via chunked vectorised Haversine distances, plus city-level aggregates. 333 FSAs (289 non-singleton), 253 municipalities (Montreal 59 %). Raw files are not distributed (data/raw/README.md); the committed parquets make steps 04–12 and the paper reproducible.

# 🔬 Methodology

1. Hedonic rent models 1a–1e — OLS, HC1 SE, building-type and city fixed effects; density and entire-home share variants (scripts/06).
2. Neighbourhood (FSA) fixed effects F1–F6 — within transformation, 250 m / 500 m counts, extensive + intensive margin, log(1+count), size (scripts/12).
3. Inference — HC1, cluster-city, cluster-FSA, Conley spatial HAC (Bartlett kernel, 1/2/5/10 km) (scripts/12).
4. Spatial models — SAR (GM_Lag, White SE) and SEM (GM_Error), KNN k = 5 on projected km coordinates; LeSage–Pace direct/indirect/total impacts with simulated SEs (scripts/07).
5. Quantile regressions — τ ∈ {0.10, 0.25, 0.50, 0.75, 0.90} plus a fine grid (scripts/08).
6. Airbnb pricing models 2a–2e — log nightly price on city mean log rent and listing controls; city FE absorb the city rent; restricted sample ≤ $1,000 (scripts/06).
7. Machine-learning benchmark — OLS / LASSO / Elastic Net / RF / GBM, 80/20 split, SHAP, random vs FSA-blocked 5-fold CV (scripts/09).
8. Robustness — buffer radii, alternative exposures, subsamples, trimming, city-clustered SEs, size, log(1+count), ring decomposition, leave-one-city-out (scripts/07, 10, 11).

# 🖼 Figures & tables inventory

Tables (results/tables/*.tex, 18 fragments): exposure summary, summary statistics (2), hedonic rent models, neighbourhood FE, inference comparison, spatial models, spatial impacts, quantile regressions, Airbnb pricing, city-level regressions, ML comparison, random vs spatial CV, buffer robustness, subsample and trimming checks, extended robustness, correlation matrix. JSON records: spatial_models.json, neighbourhood_fe.json.

# ⚠️ Limitations

• Cross-sectional listing data (asking rents and displayed nightly prices), no panel or quasi-experimental variation: estimates are conditional associations; the city-FE estimate is an upper bound on any causal within-neighbourhood effect.
• The Airbnb price field partly records total-stay prices (second mode above $1,000): the full-sample pricing model is unreliable; the count-based rent results are unaffected.
• No room type or host identifiers (entire-home status is a property-type proxy; commercial operation unmeasured).
• Scraped winter samples are not census-complete; Realtor.ca over-represents agent-listed, higher-priced units; coverage concentrated in Montreal.
• Quantile regressions use coarse city groups and non-spatial SEs; quantile and spatial models with FSA FE not estimated.

# 📝 Citation

@techreport{boucher2026airbnb,
  author      = {Boucher, Simon-Pierre},
  title       = {Airbnb, Residential Rents, and Housing Market Pressure:
                 A Hedonic and Spatial Econometric Analysis},
  institution = {Universit\'e du Qu\'ebec en Outaouais, D\'epartement des sciences administratives},
  type        = {Working Paper},
  number      = {5},
  year        = {2026},
  month       = {October},
  note        = {Version 1.1}
}

# 👤 Author & contact

Simon-Pierre Boucher — Département des sciences administratives, Université du Québec en Outaouais (UQO), Gatineau, QC. 📧 simon-pierre.boucher@uqo.ca

# 🗒 Notes

• Revision 2026-10-17 (v1.1): full scientific review by the editorial-board protocol (Recherche-UQO/REVISION-MASTER-PROMPT.md). Report: docs/REVIEW-REPORT.md; audit trail of every change (old → new numbers, reasons, evidence): docs/CHANGELOG-REVISION.md. Pre-revision PDF: docs/main_pre-revision-20261017.pdf (git tag pre-revision-20261017). Headline interpretation changed: the Airbnb–rent association is predominantly between neighbourhoods (FSA FE, Conley SE); Model 2 superhost "discount" and the Berlin/LA magnitude comparisons of v1.0 were corrected.
• Format follows Recherche-UQO/HARMONISATION.md (2026-10-17): template title page, natbib + aer bibliography style, docs/ for reports.
• The original flat May 2026 project (immo-wp5-spb-20260519) is archived in Recherche-UQO/_archives/versions-mai-2026/airbnb-rents-housing-pressure--immo-wp5-20260519/; docs/AUDIT.md §8 and docs/CHANGES.md document the 2026-08-05 rebuild.
• Build verified 2026-10-17: 0 errors, 0 undefined references, 67 references rendered (all DOI/Crossref-verified).