# 🧭 Navigation

# 📜 The Paper

# 🔍 Abstract

This paper investigates the relationship between Airbnb short-term rental activity and residential rents in Quebec, Canada. Using cross-sectional microdata comprising ~5,000 Airbnb listings and ~8,300 rental listings, we employ a hedonic pricing framework augmented with spatial econometric techniques to quantify the conditional association between nearby Airbnb presence and monthly rents. For each rental listing we construct Airbnb exposure measures within 250 m, 500 m, 1 km, and 2 km buffers using Haversine distances. Baseline estimates indicate that an additional Airbnb listing within 500 m is associated with a statistically significant increase in monthly rent of approximately 0.3–0.5%, controlling for dwelling characteristics, building type, and city fixed effects. Quantile regressions reveal that the association is stronger at the upper tail of the rent distribution. A complementary hedonic model of Airbnb nightly prices indicates that short-term rental pricing is driven primarily by listing characteristics. Robustness checks — alternative buffer radii, subsample analyses, and machine-learning benchmarks — confirm the stability of the rent–exposure association. We discuss policy implications for housing affordability and short-term rental regulation, while cautioning that cross-sectional associations should not be interpreted as causal effects.

Keywords: Airbnb · short-term rentals · housing rents · hedonic pricing · spatial econometrics · housing affordability · Quebec JEL: R21 · R31 · L83 · C21

# 📖 Inside the Paper — Section by Section

# 🏆 Headline Results

# 📁 Repository Map

wp5_uqo/
├── 📄 README.md                    ← you are here
├── 🔎 AUDIT.md                     ← forensic audit of the original project
├── 📝 CHANGES.md                   ← restructuring & rewrite log
├── 🧪 PAPER_REVIEW.md              ← pre-upgrade critical assessment
├── 📚 UPGRADE_REPORT.md            ← literature expansion report (per-ref justification)
├── 📦 requirements.txt             ← pinned dependencies
│
├── data/
│   ├── raw/                        ← airbnb.csv + rent.json (NOT distributed → README)
│   └── processed/                  ← 5 verified parquet datasets (committed)
│
├── src/                            ← shared modules
│   ├── config.py                   ← all paths & constants
│   ├── geo.py                      ← vectorised Haversine matrix
│   ├── latex_tables.py             ← stargazer-style table builder
│   └── plotting.py                 ← publication matplotlib styles
│
├── scripts/                        ← 01 → 11 numbered pipeline
├── figures/                        ← 16 publication PDF figures
├── results/tables/                 ← 13 LaTeX fragments + 4 CSVs
├── results/logs/                   ← data-inspection log
│
└── paper/                          ← LaTeX → 📄 main.pdf (51 pages)
    ├── main.tex · references.bib (56 entries) · Makefile
    ├── sections/  (titlepage + 8 sections)
    └── appendix/  (data · methods · robustness)

# 💾 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: count · density · mean_price · share_entire_home · mean_rating · superhost_share, plus city-level aggregates.

🔒 Raw files are not distributed (data/raw/README.md). The committed parquets make steps 04–11 and the paper fully reproducible without them.

# ⚙️ Analysis Pipeline

_{⚡ = under 15 s on Apple Silicon. Everything reads/writes through src/config.py; fully deterministic (seed 42).}

# 📐 Econometric Models

# 🛡️ Robustness Programme

The Airbnb coefficient stays positive and significant at 1% in every single check; the leave-one-city-out range is 0.0038–0.0040 — excluding Montreal (half the sample) barely moves it.

# 🚀 Reproducing Everything

git clone https://github.com/spboucher-ai/wp5_uqo.git && cd wp5_uqo
python3 -m pip install -r requirements.txt

# full pipeline (steps 01–03 optional: need data/raw/)
for s in scripts/0{4..9}_*.py scripts/1{0,1}_*.py; do python3 "$s"; done

cd paper && latexmk        # → paper/main.pdf  (51 pages)

# 🧰 Environment & Dependencies

Verified on macOS (Apple Silicon) · Python 3.14.4 · TeX Live 2025.

# ✅ Verification & Provenance

Rebuilt on 2026-08-05 from the original project (kept untouched as backup) under a strict no-result-changes policy:

• ✅ 3/3 merged parquets value-identical to the originals
• ✅ 14/16 tables byte-identical (2 residual float-level diffs documented in AUDIT.md §8)
• ✅ All figures regenerated, including 5 missing from the original (which broke its LaTeX build)
• ✅ Every text↔table inconsistency corrected in the prose only — itemised in CHANGES.md
• ✅ All 32 references added in the scholarly upgrade verified via Crossref/JMLR (DOIs) — per-reference justification in UPGRADE_REPORT.md

# 📖 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       = {May},
  address     = {Gatineau, QC, Canada}
}

# 📬 Contact