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UQO Working Paper No. 5 — Airbnb, residential rents and housing market pressure.

researcheconomicshousingairbnbuqo
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Scholarly upgrade: literature expansion (24 -> 56 refs) and framing rewrite

- PAPER_REVIEW.md: pre-upgrade critical assessment
- references.bib: +32 entries, all verified via Crossref/JMLR (DOIs included);
  fixed Glaeser-Gyourko-Saks year (2005 -> 2006)
- Literature review rebuilt into 10 thematic subsections (quasi-experimental
  wave, Canadian evidence, P2P economics, STR pricing, methods strands, gap)
- Introduction: evidence-base framing, Canadian gap, magnitude preview vs
  Berlin/LA quasi-experiments
- Methodology: every estimator grounded (Kelejian-Prucha, Zou-Hastie, etc.)
- Discussion: new 'Findings in the Context of the Literature' + expanded
  limitations (measurement error, MAUP) + Future Work subsection
- UPGRADE_REPORT.md: per-reference justification, flagged claims,
  contradicting literature
- No result, table, or figure changed; PDF 51 pages, 0 unresolved refs

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Simon-Pierre Boucher committed 5 days ago (Aug 5, 2026) parent 8a97ae0

Showing 10 changed files with +706 and −40

added PAPER_REVIEW.md +140 −0
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1 +<!-- Author: Simon-Pierre Boucher — contact@spboucher.ai -->
2 +
3 +# PAPER_REVIEW — Critical assessment before the scholarly upgrade
4 +
5 +Assessment of `paper/` (UQO WP5, 43-page build of 2026-08-05) and of the analysis
6 +code in `scripts/`/`src/`, written **before** the literature expansion. Results,
7 +data, and figures are out of scope: only framing, positioning, and scholarship
8 +are assessed.
9 +
10 +## 1. Core contribution — stated, but under-positioned
11 +
12 +The paper claims three contributions (intro, ¶6): (i) first granular
13 +listing-level analysis of the Airbnb–rent nexus in Quebec; (ii) a multi-method
14 +design (hedonic OLS, SAR/SEM, quantile, ML) on a single dataset; (iii) policy
15 +relevance for Canadian short-term-rental (STR) regulation. These are credible
16 +but under-argued:
17 +
18 +- The **"first for Quebec"** claim is asserted without engaging the Canadian STR
19 + literature at all — notably the McGill group's work on STRs in Canadian cities
20 + (Wachsmuth and coauthors), which quantified Montreal's STR market and
21 + housing-loss estimates. Without citing it, contribution (i) is exposed.
22 +- The **multi-method** point is defensible but currently reads as a list; it
23 + should be framed against the identification hierarchy of the field (IV,
24 + quasi-experiments, panels) — i.e., what a careful cross-sectional
25 + multi-method study adds *given* that stronger designs exist elsewhere.
26 +- The buffer-based exposure construction (exact Haversine counts at 250 m–2 km
27 + around each rental listing) is arguably the paper's most distinctive
28 + methodological feature and deserves to be foregrounded as such.
29 +
30 +## 2. Literature review — thin and dated for a 2026 paper
31 +
32 +The current §2 has ~24 references and six one-paragraph subsections. Major gaps:
33 +
34 +1. **Quasi-experimental Airbnb–housing studies are absent.** The review stops at
35 + Barron et al., Horn & Merante, Garcia-López et al., Sheppard & Udell. Missing:
36 + Los Angeles ordinance evidence (Koster–van Ommeren–Volkhausen), New Orleans
37 + regulation (Valentin), Berlin (Duso et al.), Portugal (Franco & Santos),
38 + France (Ayouba et al.), London (Shabrina et al.), Amsterdam/hotel margins,
39 + and the NYC distributional analysis (Calder-Wang). These are the papers a
40 + referee will name first.
41 +2. **No Canadian evidence.** Combs–Kerrigan–Wachsmuth (national STR analysis),
42 + Wachsmuth–Weisler is cited but only for the rent-gap concept, and the
43 + Toronto displacement work (Grisdale) is absent. For a Quebec paper this is
44 + the single most damaging gap.
45 +3. **Sharing-economy economics** (peer-to-peer market design and welfare):
46 + Zervas et al. (hotel displacement), Farronato & Fradkin (welfare effects),
47 + Einav–Farronato–Levin (P2P markets), Sundararajan (book) — none cited. These
48 + ground the supply-diversion mechanism the paper leans on.
49 +4. **Airbnb pricing hedonics** (Model 2 has no dedicated literature): Wang &
50 + Nicolau, Gibbs et al., Ert et al. on trust/reputation — the superhost
51 + discount found in Table 4 cannot be discussed against prior findings without
52 + them.
53 +5. **Hedonic methodology**: only Rosen + two survey chapters. Missing Lancaster
54 + (characteristics demand), Sirmans et al. (empirical composition survey),
55 + Kuminoff–Parmeter–Pope (specification reliability), Malpezzi (applied
56 + review).
57 +6. **Spatial econometrics**: Anselin and LeSage & Pace are cited, but the
58 + actual estimators used (GM_Lag / GM_Error) descend from **Kelejian & Prucha
59 + (1998, 1999)** — currently uncited, which is a methods-citation hole. Housing
60 + applications (Can; Dubin) also absent.
61 +7. **Quantile regression**: only Koenker–Bassett + Zietz et al. Missing the
62 + canonical applied reference (Koenker & Hallock) and housing-distribution
63 + work (McMillen).
64 +8. **ML methods**: Zou & Hastie (elastic net) uncited although the method is
65 + used; scikit-learn (Pedregosa et al.) uncited although it produced every ML
66 + number.
67 +9. **Tourism gentrification**: Wachsmuth–Weisler is there, but the strand's
68 + antecedents (Gotham's "tourism gentrification"; Cocola-Gant; Gurran & Phibbs
69 + on planning responses) are absent, leaving §7's policy discussion
70 + free-floating.
71 +
72 +## 3. Weak or unsupported argumentation
73 +
74 +- **Intro ¶3 (Quebec context)**: "over 11 million visitors annually", "vacancy
75 + rates … historically low levels" — plausible but uncited; either cite a
76 + source that can be verified or soften to qualitative statements. *Flagged for
77 + the author rather than silently sourced.*
78 +- **§5.2 superhost discount**: the composition-effect interpretation is offered
79 + without any literature on reputation pricing (Ert et al.; Gibbs et al. find
80 + superhost *premia* — a disagreement worth stating).
81 +- **§6.5 ring decomposition** is honest but isolated; connecting it to the
82 + spatial-decay findings in Garcia-López et al. and Koster et al. would turn a
83 + caveat into a comparison.
84 +- **Discussion** interprets magnitudes only against the paper's own numbers;
85 + no comparison of the 0.4%-per-listing semi-elasticity with Barron et al.'s
86 + elasticity, Horn & Merante's 0.4% per one-SD, Koster et al.'s ~2% price/rent
87 + drops from bans, Franco & Santos, or Ayouba et al. The single most useful
88 + addition a reader could ask for.
89 +- **Limitations** are listed (6 items) but not developed: no discussion of
90 + measurement error in scraped exposure (platform coverage), MAUP/buffer-choice
91 + issues, or asking-vs-contract rent literature.
92 +
93 +## 4. Underdeveloped sections (length audit)
94 +
95 +| Section | Current state | Verdict |
96 +|---|---|---|
97 +| 1 Introduction | 7 ¶, good flow | Needs: Canadian/Quebec literature hook, contribution sharpening vs. quasi-experimental strand, magnitude preview |
98 +| 2 Literature | ~2.5 pp, 6 mini-sections | **Weakest section** — needs thematic restructuring + ~25 new refs |
99 +| 3 Data | Solid | Minor: platform-coverage citation |
100 +| 4 Methodology | Solid post-restructuring | Needs method citations (K&P 1998/1999; Zou–Hastie; Koenker–Hallock; Pedregosa) |
101 +| 5 Results | Accurate post-correction | Add brief literature anchors when interpreting (superhost, quantile pattern) |
102 +| 6 Robustness | Strong after Table 12 | Fine |
103 +| 7 Discussion | 5 subsections, ~3 pp | Needs: magnitude comparison table vs. prior studies, expanded limitations, future work |
104 +| 8 Conclusion | Fine | Light touch |
105 +
106 +## 5. Positioning: current vs. target
107 +
108 +**Currently reads as:** a competent regional case study with a methods buffet.
109 +**Should read as:** the first listing-level, multi-method quantification of the
110 +Airbnb–rent association for Quebec — a market with distinctive regulation (CITQ
111 +registration, Bill 25) and Canada's second-largest STR market — explicitly
112 +placed *between* the correlational early literature and the quasi-experimental
113 +strand, with transparent identification limits and magnitudes benchmarked
114 +against both.
115 +
116 +## 6. Upgrade plan (executed in Phases 2–3)
117 +
118 +1. Verify and add ~25–30 references (OpenAlex-verified metadata, DOIs) →
119 + target ≈ 50–55 total.
120 +2. Restructure §2 into thematic subsections: (a) STR platforms & housing costs
121 + — correlational to quasi-experimental; (b) Canadian & Quebec evidence;
122 + (c) sharing-economy market effects; (d) STR pricing hedonics; (e) hedonic
123 + theory & practice; (f) spatial econometrics; (g) distributional/quantile;
124 + (h) ML in housing; (i) tourism gentrification & regulation.
125 +3. Introduction: +3 paragraphs (positioning, Canadian gap, magnitude preview).
126 +4. Methods: ground every estimator with its source.
127 +5. Discussion: add "Findings in context of the literature" subsection with a
128 + magnitude-comparison narrative; expand limitations (measurement error, MAUP,
129 + asking rents) and future work.
130 +6. Length target: ≈ +30 % prose (43 → ~55 pages).
131 +
132 +## 7. Items the author must verify (not resolved by me)
133 +
134 +- The uncited factual claims in intro ¶3 (visitor counts, vacancy rates).
135 +- Whether "first granular listing-level analysis for Quebec" survives the
136 + McGill-group literature now cited (I have worded the claim more precisely:
137 + first *listing-level hedonic/econometric* study for Quebec, distinguishing it
138 + from the McGill descriptive/market-measurement work).
139 +- The regulatory description of CITQ registration and Montreal borough rules
140 + (§7.2) is uncited; verify against current provincial law (Bill 25, 2023).
added UPGRADE_REPORT.md +158 −0
@@ -0,0 +1,158 @@
1 +<!-- Author: Simon-Pierre Boucher — contact@spboucher.ai -->
2 +
3 +# UPGRADE_REPORT — Scholarly deepening of the paper (2026-08-05)
4 +
5 +Scope: literature, framing, and prose only. **No result, number, table, or figure
6 +was changed.** The paper grew from 43 to **51 pages**; references from 24 to
7 +**56** (all cited); the PDF builds cleanly (0 unresolved references/citations,
8 +0 BibTeX warnings). The pre-upgrade critical assessment is in `PAPER_REVIEW.md`.
9 +
10 +## 1. New references added (32) — with verification and one-line justification
11 +
12 +Every entry was verified against **Crossref** (title, authors, year, volume,
13 +issue, pages, DOI) — the JMLR paper against jmlr.org — before being added.
14 +No citation was written from memory alone.
15 +
16 +### STR platforms and housing costs (8)
17 +| Key | Reference | Why |
18 +|---|---|---|
19 +| `koster2021short` | Koster, van Ommeren & Volkhausen (2021), *J. Urban Econ.* 124 | The benchmark quasi-experiment (LA ordinances); anchors §2.1 and the magnitude comparison in §7.1 |
20 +| `valentin2021regulating` | Valentin (2021), *Real Estate Economics* 49(1) | New Orleans regulation capitalisation; second quasi-experimental anchor |
21 +| `duso2024airbnb` | Duso, Michelsen, Schäfer & Tran (2024), *Reg. Sci. Urban Econ.* 106 | Berlin per-listing rent effect (7–13 €/month) — the closest comparable magnitude to ours |
22 +| `franco2021impact` | Franco & Santos (2021), *Reg. Sci. Urban Econ.* 88 | Portugal-wide price/rent effects; European breadth |
23 +| `ayouba2020airbnb` | Ayouba, Breuillé, Grivault & Le Gallo (2020), *Int. Reg. Sci. Rev.* 43(1–2) | Spatial panel evidence for French cities; documents cross-market heterogeneity |
24 +| `shabrina2022airbnb` | Shabrina, Arcaute & Batty (2022), *Urban Studies* 59(1) | London neighbourhood evidence; ties commercial "misuse" to rents |
25 +| `li2022market` | Li, Kim & Srinivasan (2022), *Management Science* 68(11) | Structural landlord-choice evidence on the STR/long-term margin |
26 +| `guttentag2015airbnb` | Guttentag (2015), *Current Issues in Tourism* 18(12) | Foundational framing of Airbnb as disruptive tourism innovation |
27 +
28 +### Canadian evidence (2)
29 +| Key | Reference | Why |
30 +|---|---|---|
31 +| `combs2020short` | Combs, Kerrigan & Wachsmuth (2020), *Can. J. Urban Research* 29(1) | The national Canadian STR analysis (~31k units removed); indispensable for the "first for Quebec" claim |
32 +| `grisdale2021displacement` | Grisdale (2021), *Urban Geography* 42(5) | Toronto STR displacement; completes the Canadian evidence base |
33 +
34 +### Sharing-economy economics (3)
35 +| Key | Reference | Why |
36 +|---|---|---|
37 +| `zervas2017rise` | Zervas, Proserpio & Byers (2017), *J. Marketing Research* 54(5) | Canonical hotel-displacement estimate; grounds the supply-diversion incentive |
38 +| `farronato2022welfare` | Farronato & Fradkin (2022), *American Economic Review* 112(6) | Welfare effects of peer entry; the flexible-supply mechanism |
39 +| `einav2016peer` | Einav, Farronato & Levin (2016), *Annual Review of Economics* 8 | P2P market theory framing |
40 +
41 +### STR pricing hedonics (3)
42 +| Key | Reference | Why |
43 +|---|---|---|
44 +| `wang2017price` | Wang & Nicolau (2017), *Int. J. Hospitality Mgmt.* 62 | 33-city Airbnb price determinants; benchmark for Model 2 |
45 +| `gibbs2018pricing` | Gibbs et al. (2018), *J. Travel & Tourism Marketing* 35(1) | Canadian Airbnb hedonics — geographically closest antecedent to Model 2 |
46 +| `ert2016trust` | Ert, Fleischer & Magen (2016), *Tourism Management* 55 | Reputation/trust capitalisation; frames our contrarian superhost discount |
47 +
48 +### Hedonic theory & practice (4)
49 +`lancaster1966new` (*JPE* 74(2) — characteristics demand), `sirmans2005composition`
50 +(*J. Real Estate Lit.* 13(1) — survey of model composition), `malpezzi2003hedonic`
51 +(Blackwell chapter — applied review), `kuminoff2010which` (*JEEM* 60(3) —
52 +specification guidance that our FE + functional-form strategy follows).
53 +
54 +### Spatial econometrics (4)
55 +`kelejian1998generalized` (*JREFE* 17(1)) and `kelejian1999generalized` (*IER*
56 +40(2)) — the actual sources of the GM_Lag/GM_Error estimators used in Model 4
57 +(previously uncited: a genuine methods-citation hole); `can1992specification`
58 +(*RSUE* 22(3)) and `dubin1988estimation` (*REStat* 70(3)) — spatial dependence
59 +in hedonic housing models.
60 +
61 +### Quantile & distribution (2)
62 +`koenker2001quantile` (*JEP* 15(4) — canonical applied reference),
63 +`mcmillen2008changes` (*JUE* 64(3) — house-price distribution decomposition).
64 +
65 +### ML & software (2)
66 +`zou2005regularization` (*JRSS-B* 67(2) — elastic net, used but uncited),
67 +`pedregosa2011scikit` (*JMLR* 12 — the software that produced every ML number).
68 +
69 +### Tourism gentrification & regulation (3)
70 +`gotham2005tourism` (*Urban Studies* 42(7) — coined "tourism gentrification"),
71 +`cocola2016holiday` (*Sociological Research Online* 21(3)), `gurran2017when`
72 +(*JAPA* 83(1) — planners' response to Airbnb).
73 +
74 +### Book (1)
75 +`sundararajan2016sharing` (MIT Press, 2016 — crowd-based capitalism; verified
76 +via publisher metadata/reviews, no DOI as is normal for books).
77 +
78 +### Bibliographic fix to an existing entry
79 +`glaeser2005urban` (Glaeser, Gyourko & Saks, "Urban Growth and Housing Supply",
80 +*J. Econ. Geography* 6(1): 71–89) had year 2005; the article is **2006**.
81 +Corrected. Note the key name still says 2005 (citations render correctly as 2006).
82 +
83 +## 2. Sections expanded and how
84 +
85 +- **§1 Introduction** (+~40%): P2P framing with citations (¶1); the
86 + correlational/quasi-experimental evidence base (¶2); the Canadian gap made
87 + explicit with Combs et al. and Grisdale (¶3); a **new magnitude-preview
88 + paragraph** comparing our $6–10/listing to Berlin's 7–13 €/listing; the three
89 + contributions rewritten with citations and a sharper novelty claim
90 + ("first listing-level econometric analysis … in any Canadian market").
91 +- **§2 Literature Review** (fully rebuilt, ~3× longer): now 10 thematic
92 + subsections with signposted structure — STR & housing costs (correlational
93 + vs. quasi-experimental waves), Canadian/Quebec evidence, P2P economics, STR
94 + pricing, hedonic theory/practice, spatial econometrics, quantile methods, ML
95 + in housing, tourism gentrification & regulation, and an explicit
96 + **Positioning and Contribution** subsection stating the three gaps the paper
97 + fills. Every strand ends by connecting to this paper rather than summarising
98 + in isolation.
99 +- **§4 Methodology**: every methodological choice now grounded — Rosen/Malpezzi
100 + at the hedonic equation, Kuminoff et al. for the FE strategy, Kelejian &
101 + Prucha for GM_Lag/GM_Error, Koenker & Hallock for quantiles, Tibshirani /
102 + Zou–Hastie / Breiman / Friedman / Pedregosa for the ML stack.
103 +- **§5 Results**: two literature anchors added — the superhost discount is now
104 + explicitly contrasted with the premia in Wang & Nicolau / Ert et al., and the
105 + quantile pattern tied to Zietz et al. / McMillen.
106 +- **§7 Discussion** (+~50%): **new subsection 7.1 "Findings in the Context of
107 + the Literature"** with three quantitative benchmarks (Berlin per-listing,
108 + LA ordinance, Boston standardised) and two candid disagreements (superhost
109 + discount; null entire-home share); **Limitations** expanded from 6 to 7 items
110 + with citations, adding exposure measurement error (attenuation argument) and
111 + buffer/MAUP sensitivity; **new "Future Work" subsection** with four
112 + extensions ordered by data requirements (repeated scrapes → panel; Quebec's
113 + 2023 enforcement change as a natural experiment; host-resolved data; lease
114 + registry rents).
115 +- **§8 Conclusion**: strengthened contribution sentence (Canadian evidence
116 + base), cross-design corroboration sentence; no overselling added.
117 +
118 +## 3. Claims flagged for the author's verification (not resolved by me)
119 +
120 +1. **Intro ¶3 factual claims** — "over 11 million visitors annually",
121 + "vacancy rates … historically low": still uncited (kept verbatim from the
122 + original). Cite Tourisme Montréal / CMHC Rental Market Survey, or soften.
123 +2. **Regulatory description** (§7.3 and §2.10): CITQ registration and Montreal
124 + zoning are described from the original text; verify against the current
125 + state of Quebec's Bill 25 (2023) and Montreal by-laws before submission.
126 +3. **"First listing-level econometric analysis in any Canadian market"** —
127 + worded to be defensible against Combs et al. (descriptive) and Grisdale
128 + (qualitative), but a final search for very recent Canadian working papers
129 + is advisable before submission.
130 +4. **Berlin magnitude comparison** (§7.1): 7–13 €/month per listing is taken
131 + from Duso et al. (2024)'s headline range; double-check the exact figure in
132 + the published version when quoting it in referee responses.
133 +
134 +## 4. Literature that potentially contradicts or complicates the findings
135 +
136 +Reported in the paper itself (§7.1), not hidden:
137 +
138 +- **Superhost premium literature** (Wang & Nicolau 2017; Ert et al. 2016;
139 + Gibbs et al. 2018) finds *positive* reputation premia; our Model 2 finds a
140 + significant superhost **discount**. The paper now states the disagreement and
141 + offers a composition interpretation.
142 +- **Entire-home composition**: Horn & Merante (2017) find effects concentrated
143 + in entire-home density, and Combs et al. (2020) build the Canadian
144 + housing-loss argument on frequently-rented entire homes; our Model 1e finds
145 + **no** significant entire-home-share association. Stated as a non-supported
146 + prediction with two candidate explanations.
147 +- **Cross-market heterogeneity**: Ayouba et al. (2020) find *no* significant
148 + rent effect in several French cities — a caution against generalising our
149 + positive association across all Quebec markets, reflected in the external-
150 + validity limitation.
151 +- **Model 2 null** (no city-rent premium in nightly prices) runs against the
152 + opportunity-cost intuition articulated in, e.g., Barron et al.; the paper
153 + frames this as an informative null driven by resort/urban composition.
154 +
155 +## 5. Build status
156 +
157 +`latexmk` clean build: **51 pages**, 0 unresolved references, 0 unresolved
158 +citations, 0 BibTeX warnings, all 56 bibliography entries cited in the text.
modified paper/main.pdf +0 −0

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modified paper/references.bib +345 −1
@@ -243,7 +243,7 @@
243 243 volume = {6},
244 244 number = {1},
245 245 pages = {71--89},
246 − year = {2005},
246 + year = {2006},
247 247 publisher = {Oxford University Press},
248 248 }
249 249
@@ -268,3 +268,347 @@
268 268 year = {2013},
269 269 publisher = {American Economic Association},
270 270 }
271 +
272 +% ── Added 2026-08-05 (scholarly upgrade) — every entry verified via Crossref/JMLR ──
273 +
274 +@article{zervas2017rise,
275 + author = {Zervas, Georgios and Proserpio, Davide and Byers, John W.},
276 + title = {The Rise of the Sharing Economy: Estimating the Impact of {Airbnb} on the Hotel Industry},
277 + journal = {Journal of Marketing Research},
278 + year = {2017},
279 + volume = {54},
280 + number = {5},
281 + pages = {687--705},
282 + doi = {10.1509/jmr.15.0204}
283 +}
284 +
285 +@article{farronato2022welfare,
286 + author = {Farronato, Chiara and Fradkin, Andrey},
287 + title = {The Welfare Effects of Peer Entry: The Case of {Airbnb} and the Accommodation Industry},
288 + journal = {American Economic Review},
289 + year = {2022},
290 + volume = {112},
291 + number = {6},
292 + pages = {1782--1817},
293 + doi = {10.1257/aer.20180260}
294 +}
295 +
296 +@article{koster2021short,
297 + author = {Koster, Hans R. A. and van Ommeren, Jos and Volkhausen, Nicolas},
298 + title = {Short-term rentals and the housing market: Quasi-experimental evidence from {Airbnb} in {Los Angeles}},
299 + journal = {Journal of Urban Economics},
300 + year = {2021},
301 + volume = {124},
302 + pages = {103356},
303 + doi = {10.1016/j.jue.2021.103356}
304 +}
305 +
306 +@article{valentin2021regulating,
307 + author = {Valentin, Maxence},
308 + title = {Regulating short-term rental housing: Evidence from {New Orleans}},
309 + journal = {Real Estate Economics},
310 + year = {2021},
311 + volume = {49},
312 + number = {1},
313 + pages = {152--186},
314 + doi = {10.1111/1540-6229.12330}
315 +}
316 +
317 +@article{duso2024airbnb,
318 + author = {Duso, Tomaso and Michelsen, Claus and Sch{\"a}fer, Maximilian and Tran, Kevin Ducbao},
319 + title = {Airbnb and rental markets: Evidence from {Berlin}},
320 + journal = {Regional Science and Urban Economics},
321 + year = {2024},
322 + volume = {106},
323 + pages = {104007},
324 + doi = {10.1016/j.regsciurbeco.2024.104007}
325 +}
326 +
327 +@article{franco2021impact,
328 + author = {Franco, Sofia F. and Santos, Carlos Daniel},
329 + title = {The impact of {Airbnb} on residential property values and rents: Evidence from {Portugal}},
330 + journal = {Regional Science and Urban Economics},
331 + year = {2021},
332 + volume = {88},
333 + pages = {103667},
334 + doi = {10.1016/j.regsciurbeco.2021.103667}
335 +}
336 +
337 +@article{ayouba2020airbnb,
338 + author = {Ayouba, Kassoum and Breuill{\'e}, Marie-Laure and Grivault, Camille and Le Gallo, Julie},
339 + title = {Does {Airbnb} Disrupt the Private Rental Market? {An} Empirical Analysis for {French} Cities},
340 + journal = {International Regional Science Review},
341 + year = {2020},
342 + volume = {43},
343 + number = {1--2},
344 + pages = {76--104},
345 + doi = {10.1177/0160017618821428}
346 +}
347 +
348 +@article{li2022market,
349 + author = {Li, Hui and Kim, Yijin and Srinivasan, Kannan},
350 + title = {Market Shifts in the Sharing Economy: The Impact of {Airbnb} on Housing Rentals},
351 + journal = {Management Science},
352 + year = {2022},
353 + volume = {68},
354 + number = {11},
355 + pages = {8015--8044},
356 + doi = {10.1287/mnsc.2021.4288}
357 +}
358 +
359 +@article{combs2020short,
360 + author = {Combs, Jennifer and Kerrigan, Danielle and Wachsmuth, David},
361 + title = {Short-term rentals in {Canada}: Uneven growth, uneven impacts},
362 + journal = {Canadian Journal of Urban Research},
363 + year = {2020},
364 + volume = {29},
365 + number = {1},
366 + pages = {119--135},
367 + doi = {10.36939/cjur/vol29no1/art274}
368 +}
369 +
370 +@article{grisdale2021displacement,
371 + author = {Grisdale, Sean},
372 + title = {Displacement by disruption: short-term rentals and the political economy of ``belonging anywhere'' in {Toronto}},
373 + journal = {Urban Geography},
374 + year = {2021},
375 + volume = {42},
376 + number = {5},
377 + pages = {654--680},
378 + doi = {10.1080/02723638.2019.1642714}
379 +}
380 +
381 +@article{shabrina2022airbnb,
382 + author = {Shabrina, Zahratu and Arcaute, Elsa and Batty, Michael},
383 + title = {Airbnb and its potential impact on the {London} housing market},
384 + journal = {Urban Studies},
385 + year = {2022},
386 + volume = {59},
387 + number = {1},
388 + pages = {197--221},
389 + doi = {10.1177/0042098020970865}
390 +}
391 +
392 +@article{gotham2005tourism,
393 + author = {Gotham, Kevin Fox},
394 + title = {Tourism Gentrification: The Case of {New Orleans}' {Vieux Carr\'e} ({French Quarter})},
395 + journal = {Urban Studies},
396 + year = {2005},
397 + volume = {42},
398 + number = {7},
399 + pages = {1099--1121},
400 + doi = {10.1080/00420980500120881}
401 +}
402 +
403 +@article{cocola2016holiday,
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modified paper/sections/01_introduction.tex +6 −4
@@ -4,16 +4,18 @@
4 4 % =============================================================================
5 5 \section{Introduction}\label{sec:introduction}
6 6
7 −The rapid expansion of short-term rental platforms has fundamentally transformed urban housing markets around the world. Since its founding in 2008, Airbnb has grown from a modest home-sharing service into a global hospitality platform with millions of listings across virtually every major city. By enabling property owners to rent their dwellings---or portions thereof---to short-term visitors at nightly rates that often exceed what a long-term tenant would pay on a monthly basis, the platform has created powerful economic incentives to divert housing units from the residential rental market.
7 +The rapid expansion of short-term rental platforms has fundamentally transformed urban housing markets around the world. Since its founding in 2008, Airbnb has grown from a modest home-sharing service into a global hospitality platform with millions of listings across virtually every major city, an archetypal case of disruptive innovation in tourism accommodation \citep{guttentag2015airbnb} and of the broader rise of peer-to-peer markets \citep{einav2016peer, sundararajan2016sharing}. By enabling property owners to rent their dwellings---or portions thereof---to short-term visitors at nightly rates that often exceed what a long-term tenant would pay on a monthly basis, the platform has created powerful economic incentives to divert housing units from the residential rental market. The same flexible peer supply that generates large welfare gains in the accommodation market \citep{zervas2017rise, farronato2022welfare} is, from the housing market's perspective, a competing use for the residential stock.
8 8
9 −This diversion mechanism sits at the heart of a growing policy concern: that the proliferation of Airbnb listings may contribute to rising residential rents and reduced housing affordability, particularly in cities with significant tourist appeal. The economic logic is straightforward. When landlords can earn higher returns by listing a unit on Airbnb than by renting it to a long-term tenant, the effective supply of long-term rental housing contracts. On the demand side, Airbnb may attract additional visitors to a neighbourhood, increasing foot traffic, consumption amenities, and---through general equilibrium effects---the desirability of the area for both tourists and residents. The net effect on rents is theoretically ambiguous: supply withdrawal pushes rents upward, while the sign and magnitude of demand-side amenity effects depend on the local context, the composition of Airbnb listings, and the degree of market segmentation between short-term and long-term rentals.
9 +This diversion mechanism sits at the heart of a growing policy concern: that the proliferation of Airbnb listings may contribute to rising residential rents and reduced housing affordability, particularly in cities with significant tourist appeal. A now-substantial empirical literature supports the concern: studies exploiting cross-market variation \citep{barron2021effect, horn2017airbnb}, regulatory discontinuities \citep{koster2021short, valentin2021regulating, duso2024airbnb}, and within-city diffusion \citep{garcia2020airbnb, franco2021impact} consistently find that STR activity raises rents and house prices, with effects that are modest per listing but spatially concentrated. The economic logic is straightforward. When landlords can earn higher returns by listing a unit on Airbnb than by renting it to a long-term tenant, the effective supply of long-term rental housing contracts. On the demand side, Airbnb may attract additional visitors to a neighbourhood, increasing foot traffic, consumption amenities, and---through general equilibrium effects---the desirability of the area for both tourists and residents. The net effect on rents is theoretically ambiguous: supply withdrawal pushes rents upward, while the sign and magnitude of demand-side amenity effects depend on the local context, the composition of Airbnb listings, and the degree of market segmentation between short-term and long-term rentals.
10 10
11 −The province of Quebec, and the city of Montreal in particular, provides a compelling setting in which to study these dynamics. Montreal is Canada's second-largest city and one of North America's foremost tourist destinations, attracting over 11 million visitors annually prior to the COVID-19 pandemic. Its dense, walkable neighbourhoods, vibrant cultural scene, and historic architecture make it especially attractive for short-term rental guests. At the same time, Quebec has experienced significant rental market pressures in recent years. Vacancy rates in the Montreal census metropolitan area have fallen to historically low levels, and median rents have risen substantially. Policymakers at the provincial and municipal levels have responded with a mix of regulatory interventions, including registration requirements for short-term rental operators and zoning restrictions, but the empirical evidence on the housing-market effects of Airbnb in the Quebec context remains limited.
11 +The province of Quebec, and the city of Montreal in particular, provides a compelling setting in which to study these dynamics. Montreal is Canada's second-largest city and one of North America's foremost tourist destinations, attracting over 11 million visitors annually prior to the COVID-19 pandemic. Its dense, walkable neighbourhoods, vibrant cultural scene, and historic architecture make it especially attractive for short-term rental guests, and national analyses identify it as one of Canada's largest and most commercialised STR markets \citep{combs2020short}. At the same time, Quebec has experienced significant rental market pressures in recent years. Vacancy rates in the Montreal census metropolitan area have fallen to historically low levels, and median rents have risen substantially. Policymakers at the provincial and municipal levels have responded with a mix of regulatory interventions, including CITQ registration requirements for short-term rental operators and zoning restrictions in Montreal \citep{nieuwland2020regulating}. Yet the econometric evidence on the housing-market effects of Airbnb in the Quebec context remains remarkably thin: existing Canadian work is either national and descriptive \citep{combs2020short} or focused on Toronto's political economy of displacement \citep{grisdale2021displacement}. No study, to our knowledge, has estimated the Airbnb--rent relationship at the individual listing level for Quebec.
12 12
13 13 Estimating the effect of short-term rental platforms on residential rents poses significant empirical challenges. The most fundamental is endogeneity: Airbnb listings are not randomly assigned to locations. Hosts choose to list in neighbourhoods where rents---and tourist demand---are already high, creating a positive correlation between Airbnb density and rent levels that may reflect reverse causality or omitted neighbourhood characteristics rather than a genuine causal effect. In the ideal research design, one would exploit plausible exogenous variation in Airbnb supply---for example, a regulatory shock that differentially affected neighbourhoods---combined with panel data to control for time-invariant unobservables. Our data, however, are cross-sectional: they comprise a single snapshot of Airbnb and rental listings in Quebec, scraped in 2026. We therefore adopt a transparent hedonic pricing framework that quantifies the conditional association between Airbnb presence and rents, controlling for a rich set of dwelling characteristics and location fixed effects, while being explicit about the limitations of causal interpretation.
14 14
15 15 Our empirical strategy proceeds in several stages. First, we estimate a hedonic rent model in which the logarithm of monthly rent is regressed on the count of Airbnb listings within a spatial buffer (our preferred radius is 500 metres), controlling for the number of bedrooms, bathrooms, building type, and city fixed effects. This specification yields a semi-elasticity interpretation: the coefficient on Airbnb count measures the percentage change in rent associated with one additional nearby Airbnb listing, holding observable dwelling characteristics constant. Second, we estimate a complementary hedonic model of Airbnb nightly prices, in which city-level mean rents serve as a control, allowing us to examine the bidirectional pricing relationship between the two market segments. Third, we aggregate the data to the city level to examine cross-city variation in Airbnb penetration and mean rents. Fourth, we incorporate spatial lags and vary the buffer radius (250\,m, 500\,m, 1\,km, 2\,km) to assess the spatial decay of the association. Fifth, quantile regressions at the 10th, 25th, 50th, 75th, and 90th percentiles reveal how the Airbnb--rent association varies across the conditional rent distribution. Sixth, we benchmark our parametric estimates against machine-learning methods---LASSO, elastic net, random forest, and gradient boosting---to assess predictive importance and model robustness.
16 16
17 −This paper makes three contributions. First, it provides the first granular, listing-level analysis of the Airbnb--rent nexus in Quebec, drawing on microdata with precise geographic coordinates that allow exact distance-based matching between Airbnb and rental listings. Second, it applies a multi-method approach---combining hedonic regressions, spatial analysis, quantile regressions, and machine learning---to a single dataset, enabling direct comparison of results across methodological frameworks. Third, it contributes to the ongoing policy debate about short-term rental regulation in Canadian cities by providing empirically grounded estimates of the magnitude of the Airbnb--rent association, even as it highlights the limitations inherent in cross-sectional identification.
17 +To preview the central result: each additional Airbnb listing within 500 metres of a rental unit is associated with approximately 0.4\% higher monthly rent, conditional on dwelling characteristics, building type, and city fixed effects. The association decays monotonically with distance, is present throughout the conditional rent distribution but strongest at its upper tail, and survives spatial-econometric treatment, city-clustered inference, functional-form changes, sample trims, and leave-one-city-out exclusions. At the median rent of roughly \$1{,}950 per month, the point estimate corresponds to \$6--10 per additional nearby listing---strikingly close, in order of magnitude, to the 7--13 euros per listing that \citet{duso2024airbnb} recover from Berlin's regulatory shocks, and consistent with the per-listing effects implied by the Los Angeles ordinance evidence of \citet{koster2021short}. That a transparent cross-sectional design recovers magnitudes in line with the quasi-experimental literature is, we argue, informative in both directions: it lends external credibility to our estimates and out-of-sample support to the causal designs.
18 +
19 +This paper makes three contributions. First, it provides the first granular, listing-level econometric analysis of the Airbnb--rent nexus in Quebec---and, to our knowledge, in any Canadian market---drawing on microdata with precise geographic coordinates that allow exact distance-based matching between Airbnb and rental listings. Prior Canadian evidence is descriptive \citep{combs2020short} or qualitative \citep{grisdale2021displacement}; we complement it with formal estimation. Second, it applies a multi-method approach---hedonic regressions \citep{rosen1974hedonic}, spatial econometrics estimated by generalised moments \citep{kelejian1998generalized, kelejian1999generalized}, quantile regressions \citep{koenker1978regression}, and machine-learning benchmarks \citep{mullainathan2017machine}---to a single dataset, enabling direct comparison of results across methodological frameworks and, in particular, delivering the first distributional (quantile-level) characterisation of the STR--rent association for a Canadian market. Third, it contributes to the ongoing policy debate about short-term rental regulation in Canadian cities by providing empirically grounded estimates of the magnitude and spatial reach of the Airbnb--rent association---quantities that bear directly on the geographic targeting of Quebec's registration-based regime---even as it highlights the limitations inherent in cross-sectional identification.
18 20
19 21 The remainder of the paper is organised as follows. Section~\ref{sec:literature} reviews the related literature on short-term rentals and housing markets. Section~\ref{sec:data} describes the data sources, cleaning procedures, and spatial merge strategy. Section~\ref{sec:methodology} presents the econometric framework. Section~\ref{sec:results} reports the main empirical results. Section~\ref{sec:robustness} provides robustness checks and sensitivity analyses. Section~\ref{sec:discussion} discusses policy implications and limitations. Section~\ref{sec:conclusion} concludes.
modified paper/sections/02_literature.tex +29 −19
@@ -4,40 +4,50 @@
4 4 % =============================================================================
5 5 \section{Literature Review}\label{sec:literature}
6 6
7 −This section reviews the growing body of empirical research on the relationship between short-term rental platforms and housing markets, situating our analysis within the broader literatures on hedonic pricing, spatial econometrics, and the economics of the sharing economy.
7 +This section reviews the empirical and methodological literatures on which the paper builds. We organise the review thematically rather than chronologically: we first survey the evidence on short-term rental (STR) platforms and housing costs, moving from the early correlational studies to the more recent quasi-experimental designs (Section~\ref{sec:lit_str}); we then turn to the Canadian and Quebec evidence (Section~\ref{sec:lit_canada}), the broader economics of peer-to-peer accommodation markets (Section~\ref{sec:lit_p2p}), and the pricing of STR listings themselves (Section~\ref{sec:lit_strpricing}). The remaining subsections cover the methodological foundations of the paper: hedonic pricing theory and practice (Section~\ref{sec:lit_hedonic}), spatial econometrics (Section~\ref{sec:lit_spatial}), distributional analysis via quantile regression (Section~\ref{sec:lit_quantile}), and machine learning in housing economics (Section~\ref{sec:lit_ml}). Section~\ref{sec:lit_regulation} reviews the tourism-gentrification and regulation literature that frames the policy discussion, and Section~\ref{sec:lit_gap} states the gap this paper fills.
8 8
9 −\subsection{Short-Term Rentals and Housing Markets}
9 +\subsection{Short-Term Rentals and Housing Costs}\label{sec:lit_str}
10 10
11 −The seminal empirical study of Airbnb's impact on housing costs is \citet{barron2021effect}, who exploit zipcode-level variation in Airbnb penetration across the United States to estimate the platform's effect on both rents and house prices. Using an instrumental-variables strategy based on Google Trends data for Airbnb-related searches, they find that a 1\% increase in Airbnb listings is associated with a 0.018\% increase in rents and a 0.026\% increase in house prices. The authors attribute the effect primarily to the reduction in rental supply, as landlords convert long-term rental units into short-term listings. Our study differs in geographic scope (Quebec rather than the entire US), unit of analysis (individual listings rather than zipcodes), and identification strategy (hedonic controls rather than instrumental variables), but we share the same underlying economic hypothesis.
11 +Housing costs respond to shifts in effective supply, and markets with inelastic supply capitalise demand shocks into prices and rents \citep{glaeser2005urban, saiz2010geographic, gyourko2005superstar}. STR platforms create precisely such a shift at the margin: by raising the return to allocating a dwelling to visitors rather than tenants, they can withdraw units from the long-term rental stock. The empirical literature testing this mechanism has developed in two waves.
12 12
13 −\citet{sheppard2016airbnb} study the impact of Airbnb on property values in New York City using a hedonic framework. Exploiting variation in Airbnb listing density across census tracts, they estimate that Airbnb activity increased property values by approximately 6--11\% in high-penetration neighbourhoods. Their analysis highlights the capitalisation of short-term rental income potential into property prices, a channel that is distinct from the rental-supply-reduction mechanism but operates through similar spatial proximity channels.
13 +\paragraph{Correlational and panel evidence.} The seminal large-scale study is \citet{barron2021effect}, who exploit zipcode-level variation in Airbnb penetration across the United States, instrumenting Airbnb growth with the interaction of Google Trends search interest and a measure of touristiness. They estimate that a 1\% increase in Airbnb listings raises rents by 0.018\% and house prices by 0.026\%, and attribute the effect to the reallocation of units from long-term to short-term supply. \citet{horn2017airbnb} use listing-level asking rents in Boston and find that a one-standard-deviation increase in Airbnb density is associated with rents approximately 0.4\% higher, an effect concentrated where Airbnb listings are entire homes. \citet{sheppard2016airbnb} document property-value capitalisation in New York City, and \citet{wachsmuth2018airbnb} show that the ``rent gap'' opened by STR revenue potential is systematically related to gentrification pressure. In a similar spirit, \citet{ayouba2020airbnb} apply spatial panel methods to eight French cities and find significant rent effects of Airbnb density in Lyon, Montpellier, and Paris but not elsewhere---early evidence that the association is heterogeneous across markets. Using proprietary platform data and a structural model of landlord choice, \citet{li2022market} show that Airbnb entry shifts landlords at the margin between long-term and short-term supply, with the incidence falling on long-term tenants in high-demand markets.
14 14
15 −\citet{garcia2020airbnb} provide detailed evidence from Barcelona, one of the world's most Airbnb-affected cities. Using transaction-level data on housing prices and spatially disaggregated Airbnb data, they find that Airbnb activity led to a 1.9\% increase in transaction prices and a 4.6\% increase in posted rents in areas of high Airbnb concentration. Their identification strategy relies on the sharp spatial variation in tourist attractiveness within Barcelona, combined with pre-/post-Airbnb comparisons. The Barcelona context shares important features with Montreal: both are major tourist destinations with dense urban cores and significant heritage architecture.
15 +\paragraph{Quasi-experimental evidence.} A second wave exploits policy discontinuities and regulatory shocks. \citet{garcia2020airbnb} use the staggered, spatially uneven diffusion of Airbnb across Barcelona neighbourhoods and estimate that Airbnb activity raised rents by 1.9\% and transaction prices by 4.6--7\% in high-activity areas. \citet{koster2021short} exploit Home-Sharing Ordinances adopted by some Los Angeles-area cities but not others in a spatial regression-discontinuity design: ordinances reduced Airbnb listings by roughly 50\% and lowered house prices and rents by around 2\% in treated areas, implying a substantial positive effect of STR activity in the absence of regulation. \citet{valentin2021regulating} finds that New Orleans' licensing reform capitalised into property values where STR use was legalised. \citet{duso2024airbnb} use two Berlin regulatory shocks and estimate that each nearby Airbnb listing raises asking rents by roughly 7--13 euros per month, while \citet{franco2021impact} document positive effects of Airbnb density on both prices and rents across Portuguese municipalities, strongest in tourist-intensive locations. For London, \citet{shabrina2022airbnb} link neighbourhood-level Airbnb ``misuse'' (de facto commercial operation) to higher local rents.
16 16
17 −\citet{horn2017airbnb} focus specifically on the rental market in Boston, estimating the effect of Airbnb listings on asking rents at the census-tract level. They find that a one-standard-deviation increase in Airbnb listings is associated with a 0.4\% increase in asking rents, an effect concentrated in neighbourhoods where a larger share of Airbnb listings are entire-home units rather than shared rooms. This finding motivates our construction of the \texttt{share\_entire\_home} variable and our attention to the composition of Airbnb listings within each spatial buffer.
17 +Taken together, this literature finds positive, modest-per-listing, spatially concentrated associations between STR activity and residential housing costs, with quasi-experimental designs generally confirming---and sometimes exceeding---the magnitudes suggested by earlier correlational work. Our estimates are best read against this benchmark: a cross-sectional design cannot adjudicate causality, but it can establish whether the conditional correlations in a previously unstudied market are consistent with the causal magnitudes established elsewhere.
18 18
19 −\subsection{Hedonic Pricing Theory}
19 +\subsection{Canadian and Quebec Evidence}\label{sec:lit_canada}
20 20
21 −The hedonic pricing framework, formalised by \citet{rosen1974hedonic}, provides the theoretical foundation for our empirical approach. Rosen's model characterises housing as a differentiated good whose price is determined by an implicit market in which consumers bid for bundles of characteristics---including structural attributes (bedrooms, bathrooms, floor area), locational attributes (neighbourhood quality, accessibility, amenities), and environmental attributes. In this framework, the Airbnb count within a spatial buffer can be interpreted as a locational characteristic that captures the degree of short-term rental activity in the neighbourhood, and its hedonic coefficient reveals the marginal implicit price of exposure to that activity.
21 +Evidence for Canada is comparatively scarce and largely descriptive. \citet{combs2020short} provide the first national analysis of Canadian STR markets, documenting rapid but spatially uneven growth, strong revenue concentration among commercial multi-listing operators, and an estimate of roughly 31{,}000 housing units removed from Canadian long-term rental markets by frequently rented entire-home listings in 2018---with Montreal among the three dominant markets. \citet{grisdale2021displacement} traces the political economy of STR-driven displacement in Toronto, linking commercial Airbnb operation to rental-supply loss in central neighbourhoods. Beyond descriptive market measurement and critical urban geography, however, there is little listing-level econometric evidence for Canadian markets, and none, to our knowledge, for Quebec---a striking gap given that the province combines one of Canada's largest STR markets, distinctive provincial registration requirements, and acute rental-market tightness. This paper addresses that gap.
22 22
23 −The hedonic approach has been widely applied in the housing economics literature \citep{palmquist2005property, parmeter2010applied}, and it is well suited to our cross-sectional setting. Its principal limitation is the requirement that all relevant quality differences be observed and included as controls; omitted characteristics that are correlated with both Airbnb density and rents will bias the estimated hedonic coefficients. We address this concern by including city fixed effects, which absorb all unobserved city-level heterogeneity, and by reporting results for multiple specifications with progressively richer control sets.
23 +\subsection{The Economics of Peer-to-Peer Accommodation}\label{sec:lit_p2p}
24 24
25 −\subsection{Spatial Econometrics in Housing Research}
25 +A complementary literature studies STR platforms as peer-to-peer markets. \citet{einav2016peer} analyse how platforms reduce transaction costs and enable trade in underutilised assets, and \citet{sundararajan2016sharing} surveys the rise of ``crowd-based capitalism.'' \citet{guttentag2015airbnb} frames Airbnb as a disruptive innovation in tourism accommodation. On market impacts, \citet{zervas2017rise} estimate that Airbnb entry reduced hotel revenues in the most affected Texas segments by 8--10\%, and \citet{farronato2022welfare} quantify the welfare effects of peer entry in the accommodation industry, showing that flexible peer supply expands capacity precisely when demand peaks, generating consumer surplus while eroding hotel profits. This literature matters for our purposes because it establishes the economic incentive at the heart of the housing channel: peer hosts respond elastically to short-term rental returns, and the same responsiveness that creates accommodation-market surplus is what reallocates dwellings away from long-term tenants when STR returns exceed residential rents.
26 26
27 −The recognition that housing prices exhibit strong spatial dependence has motivated a large literature on spatial econometric methods for housing markets. \citet{anselin1988spatial} developed the foundational spatial lag and spatial error models that account for spillovers and spatial autocorrelation in cross-sectional regression. \citet{lesage2009introduction} provide a comprehensive treatment of spatial econometric techniques, including the construction of spatial weights matrices and the interpretation of direct and indirect (spillover) effects.
27 +\subsection{Pricing of Short-Term Rental Listings}\label{sec:lit_strpricing}
28 28
29 −In the context of Airbnb and housing markets, spatial dependence arises naturally: the rent of a dwelling is influenced not only by its own characteristics but also by the rents and Airbnb activity in nearby locations. Our buffer-based measure of Airbnb exposure is, in effect, a spatially weighted variable that aggregates short-term rental activity within a defined neighbourhood. We complement this approach by including spatially lagged rent variables (the mean rent of nearby listings) to capture peer effects in pricing.
29 +A distinct strand estimates hedonic models of Airbnb nightly prices, which informs our Model~2. \citet{wang2017price} study some 180{,}000 listings in 33 cities and find that entire-home status, capacity, and host attributes (notably superhost status) are dominant price determinants. \citet{gibbs2018pricing} estimate hedonic price equations for five Canadian Airbnb markets---the study closest to our Model~2 in geographic scope---and document significant premia for entire homes and location. \citet{ert2016trust} show that host reputation and trust signals are capitalised into Airbnb prices. Notably, these studies generally find positive superhost premia, whereas our within-city estimates yield a superhost \textit{discount}; we return to this disagreement in Sections~\ref{sec:results} and~\ref{sec:discussion} and interpret it as a composition effect specific to Quebec's mix of urban and resort listings.
30 30
31 −\subsection{Tourism, Commercialisation, and Regulation}
31 +\subsection{Hedonic Pricing: Theory and Practice}\label{sec:lit_hedonic}
32 32
33 −\citet{wachsmuth2018airbnb} examine the ``rent gap'' created by Airbnb, arguing that the platform enables a process of ``tourism gentrification'' in which entire neighbourhoods are transformed from residential to quasi-commercial use. Their analysis of New York City demonstrates that professional, multi-listing hosts account for a disproportionate share of Airbnb revenue, suggesting that the platform has moved well beyond its original peer-to-peer home-sharing model. \citet{ke2017sharing} documents the rise of professional hosts and the commercialisation of Airbnb listings, finding that multi-listing operators are associated with higher prices and greater market concentration.
33 +The hedonic framework descends from \citet{lancaster1966new}, who recast consumer demand as demand for characteristics, and \citet{rosen1974hedonic}, who formalised the market equilibrium in which the prices of differentiated products reveal implicit characteristic prices. In housing applications, the hedonic price of a locational attribute---here, exposure to nearby STR activity---is identified from cross-sectional price variation conditional on structural characteristics \citep{palmquist2005property}. Surveys of five decades of empirical practice document both the workhorse status and the fragility of hedonic estimates: \citet{sirmans2005composition} catalogue the composition of hundreds of published models; \citet{malpezzi2003hedonic} reviews functional-form and specification choices; and \citet{kuminoff2010which} show in large-scale simulations that spatial fixed effects and flexible functional forms are the most effective safeguards against omitted-variable bias in cross-sectional hedonics---a prescription our specification follows (city fixed effects; count, density, and logarithmic exposure forms; extensive robustness analysis). \citet{parmeter2010applied} discuss the identification threats that remain.
34 34
35 −The commercialisation of short-term rental platforms has prompted regulatory responses across many jurisdictions. \citet{nieuwland2020regulating} provide a comparative analysis of regulatory approaches in major cities, ranging from outright bans on short-term rentals to registration requirements, occupancy limits, and zoning restrictions. In Quebec, provincial legislation requires short-term rental operators to register with the Corporation de l'industrie touristique du Qu\'{e}bec (CITQ), and the City of Montreal has implemented additional restrictions in certain boroughs. The effectiveness of these regulations remains an active area of research \citep{agyeman2020airbnb}.
35 +\subsection{Spatial Econometrics in Housing Research}\label{sec:lit_spatial}
36 36
37 −\subsection{Quantile Regression and Distributional Effects}
37 +Housing prices are spatially dependent: nearby dwellings share unobserved amenities, and pricing spillovers propagate through comparable-based valuation. Ignoring this dependence invalidates inference and can bias coefficients when spatially correlated unobservables are also correlated with regressors \citep{dubin1988estimation, can1992specification}. The canonical modelling responses are the spatial autoregressive (SAR) and spatial error (SEM) models \citep{anselin1988spatial}, with feasible estimation by spatial two-stage least squares and generalised moments developed by \citet{kelejian1998generalized} and \citet{kelejian1999generalized}---the estimators we employ---and comprehensive treatment in \citet{lesage2009introduction}. Our buffer-count exposure variable is itself a spatially weighted aggregate, so the spatial models play a dual role in this paper: they absorb residual spatial dependence, and they provide a conservative check on whether the Airbnb--rent association merely proxies for spatially correlated unobservables.
38 38
39 −A growing strand of the housing literature recognises that the relationship between housing characteristics and prices may vary across the conditional price distribution. \citet{koenker1978regression} introduced quantile regression as a method for estimating conditional quantile functions, and \citet{zietz2008determinants} applied the technique to hedonic housing models, finding that the implicit prices of many dwelling characteristics differ significantly between the lower and upper tails of the price distribution. In the Airbnb context, distributional heterogeneity is economically plausible: short-term rental activity may have different effects on low-rent versus high-rent dwellings, reflecting differences in market segmentation, neighbourhood desirability, and the types of units most likely to be converted to short-term rentals.
39 +\subsection{Distributional Effects and Quantile Regression}\label{sec:lit_quantile}
40 40
41 −\subsection{Machine Learning in Housing Economics}
41 +Mean regression can mask heterogeneity across the conditional price distribution. Quantile regression \citep{koenker1978regression, koenker2001quantile} recovers characteristic prices at arbitrary quantiles, and housing applications consistently find that implicit prices differ across the distribution: \citet{zietz2008determinants} show that buyers of higher-priced homes value certain attributes differently from buyers of lower-priced homes, and \citet{mcmillen2008changes} decomposes changes in the house-price distribution into characteristics and coefficient effects. In the STR context, distributional heterogeneity is economically meaningful: if STR pressure is concentrated where rents are already high, the affordability consequences differ sharply from those of a uniform shift. Our Model~5 provides, to our knowledge, the first quantile-level evidence on the Airbnb--rent association for a Canadian market.
42 42
43 −Recent advances in machine learning have been increasingly adopted in housing economics for prediction and variable selection. \citet{mullainathan2017machine} discuss the role of machine learning in econometric analysis, distinguishing between prediction tasks (where flexible models excel) and causal inference tasks (where traditional econometric methods remain essential). \citet{athey2019machine} provide a broader survey of machine-learning methods for causal inference. In our robustness analysis, we employ LASSO, elastic net, random forest, and gradient boosting models not to make causal claims but to assess the predictive importance of Airbnb exposure variables relative to other determinants of rent and to verify that our hedonic estimates are not artefacts of linear functional-form assumptions.
43 +\subsection{Machine Learning in Housing Economics}\label{sec:lit_ml}
44 +
45 +Machine-learning methods enter modern hedonic practice in two roles: as prediction benchmarks and as data-driven specification checks \citep{mullainathan2017machine, athey2019machine}. We use the LASSO \citep{tibshirani1996regression} and the elastic net \citep{zou2005regularization} for regularised variable selection, and random forests \citep{breiman2001random} and gradient boosting \citep{friedman2001greedy} as flexible nonlinear benchmarks, with SHAP values \citep{lundberg2017unified} for feature attribution; all models are implemented in scikit-learn \citep{pedregosa2011scikit}. The comparison between OLS and the flexible learners serves an econometric purpose articulated by \citet{mullainathan2017machine}: if a linear hedonic attains predictive accuracy close to the flexible frontier, linearity is an adequate approximation for inference; where it falls short, the gap signals nonlinearities---in our data, primarily geographic---that fixed effects and buffer construction must absorb.
46 +
47 +\subsection{Tourism Gentrification and Short-Term Rental Regulation}\label{sec:lit_regulation}
48 +
49 +Finally, a critical urban literature situates STRs within longer-running processes of tourism-driven neighbourhood change. \citet{gotham2005tourism} coined ``tourism gentrification'' to describe the transformation of New Orleans' Vieux Carr\'e; \citet{cocola2016holiday} identifies holiday rentals as a distinct gentrification battlefront in which transient visitors replace residents; and \citet{wachsmuth2018airbnb} connect this process explicitly to platform-enabled rent gaps. The commercialisation of nominally peer-to-peer platforms is central to this critique: \citet{ke2017sharing} documents that professional multi-listing hosts account for a disproportionate share of Airbnb supply and revenue. On the policy side, \citet{gurran2017when} ask how urban planners should respond to Airbnb; \citet{nieuwland2020regulating} compare regulatory regimes across cities, from outright prohibition to registration and cap systems; and \citet{agyeman2020airbnb} examine the equity dimensions of STR regulation. In Quebec, provincial law requires STR operators to register with the Corporation de l'industrie touristique du Qu\'ebec (CITQ), and Montreal restricts commercial STRs to designated zones---a regime whose spatial targeting our decay results speak to directly.
50 +
51 +\subsection{Positioning and Contribution}\label{sec:lit_gap}
52 +
53 +Three gaps emerge from this review. First, despite a mature international literature spanning correlational and quasi-experimental designs, there is no listing-level econometric evidence on the STR--rent relationship for Quebec, and Canadian evidence more broadly stops at descriptive market measurement \citep{combs2020short} and critical urban geography \citep{grisdale2021displacement}. Second, few studies examine the \textit{distributional} incidence of the association---most report mean effects---although the affordability debate turns on who bears the pressure. Third, the pricing side and the rent side of the market are rarely analysed jointly in the same data, despite the opportunity-cost logic that connects them. This paper addresses all three gaps: it provides the first listing-level, multi-method quantification of the Airbnb--rent association in Quebec, characterises the association across the full conditional rent distribution, and estimates both sides of the market with mutually consistent variable constructions. We are equally deliberate about what the paper does not do: with a single cross-section we do not identify causal effects, and we position our estimates as conditional correlations to be read against---and, as it turns out, found consistent with---the quasi-experimental magnitudes surveyed above.
modified paper/sections/04_methodology.tex +5 −5
@@ -8,7 +8,7 @@ This section presents the econometric models employed to quantify the associatio
8 8
9 9 \subsection{Model 1: Hedonic Rent Model with Airbnb Exposure}\label{sec:model1}
10 10
11 −Our baseline specification is a hedonic rent equation in which the log of monthly rent is regressed on a measure of nearby Airbnb activity and a vector of dwelling-level controls:
11 +Our baseline specification is a hedonic rent equation \citep{rosen1974hedonic, malpezzi2003hedonic} in which the log of monthly rent is regressed on a measure of nearby Airbnb activity and a vector of dwelling-level controls:
12 12
13 13 \begin{equation}\label{eq:hedonic_rent}
14 14 \ln(\text{rent}_i) = \alpha + \beta \cdot \texttt{airbnb\_count}_{i,r} + \mathbf{X}_i' \boldsymbol{\gamma} + \sum_{c} \delta_c \cdot \mathbf{1}[\text{city}_i = c] + \varepsilon_i,
@@ -16,7 +16,7 @@ Our baseline specification is a hedonic rent equation in which the log of monthl
16 16
17 17 \noindent where $\text{rent}_i$ is the monthly rent of listing $i$; $\texttt{airbnb\_count}_{i,r}$ is the number of Airbnb listings within buffer radius $r$ of listing $i$ (our preferred specification uses $r = 500$\,m); $\mathbf{X}_i$ is a vector of dwelling characteristics including the number of bedrooms, number of bathrooms, building type (Apartment, House, Row/Townhouse), and interior size (where available); $\delta_c$ denotes city fixed effects that absorb all time-invariant, city-level unobservables (including average neighbourhood quality, local labour-market conditions, and municipal regulations); and $\varepsilon_i$ is a mean-zero error term.
18 18
19 −The coefficient of interest, $\beta$, has a semi-elasticity interpretation: it measures the approximate percentage change in monthly rent associated with one additional Airbnb listing within the buffer, conditional on observed dwelling characteristics and city. A positive and statistically significant $\hat{\beta}$ is consistent with the hypothesis that Airbnb activity is associated with higher residential rents, though it does not, by itself, establish causality.
19 +The coefficient of interest, $\beta$, has a semi-elasticity interpretation: it measures the approximate percentage change in monthly rent associated with one additional Airbnb listing within the buffer, conditional on observed dwelling characteristics and city. A positive and statistically significant $\hat{\beta}$ is consistent with the hypothesis that Airbnb activity is associated with higher residential rents, though it does not, by itself, establish causality. The reliance on spatial fixed effects and multiple functional forms of the exposure variable follows the specification guidance of \citet{kuminoff2010which}, whose simulation evidence identifies these as the most effective safeguards against omitted-variable bias in cross-sectional hedonic models.
20 20
21 21 We estimate Equation~\eqref{eq:hedonic_rent} by ordinary least squares (OLS) with heteroskedasticity-robust standard errors (HC1).
22 22
@@ -51,7 +51,7 @@ To account for spatial dependence in rents, we estimate a spatial autoregressive
51 51 \ln(\text{rent}_i) = \alpha'' + \rho \cdot \sum_{k} w_{ik} \ln(\text{rent}_k) + \beta' \cdot \texttt{airbnb\_count}_{i,r} + \mathbf{X}_i' \boldsymbol{\gamma}' + \sum_{c} \delta''_c \cdot \mathbf{1}[\text{city}_i = c] + \eta_i,
52 52 \end{equation}
53 53
54 −\noindent where $\mathbf{W} = [w_{ik}]$ is a row-standardised $k$-nearest-neighbour spatial weights matrix with $k = 5$, so that $\sum_k w_{ik} \ln(\text{rent}_k)$ is the mean log rent of the five nearest rental listings. The spatial autoregressive parameter $\rho$ captures the degree to which rents co-move within a spatial neighbourhood, after controlling for observed characteristics. The SEM instead places the spatial process in the disturbance, $\eta_i = \lambda \sum_k w_{ik} \eta_k + \nu_i$, capturing spatially correlated unobservables. Both models are estimated by generalised method of moments \citep[GM\_Lag and GM\_Error;][]{anselin1988spatial, lesage2009introduction}, which avoids the simultaneity bias that OLS estimation of Equation~\eqref{eq:spatial_lag} would entail.
54 +\noindent where $\mathbf{W} = [w_{ik}]$ is a row-standardised $k$-nearest-neighbour spatial weights matrix with $k = 5$, so that $\sum_k w_{ik} \ln(\text{rent}_k)$ is the mean log rent of the five nearest rental listings. The spatial autoregressive parameter $\rho$ captures the degree to which rents co-move within a spatial neighbourhood, after controlling for observed characteristics. The SEM instead places the spatial process in the disturbance, $\eta_i = \lambda \sum_k w_{ik} \eta_k + \nu_i$, capturing spatially correlated unobservables. Both models are estimated by the feasible generalised-moments procedures of \citet{kelejian1998generalized} and \citet{kelejian1999generalized} (\texttt{GM\_Lag} and \texttt{GM\_Error}), which avoid the simultaneity bias that OLS estimation of Equation~\eqref{eq:spatial_lag} would entail; see \citet{anselin1988spatial} and \citet{lesage2009introduction} for the underlying theory.
55 55
56 56 This specification serves two purposes. First, the spatial models absorb variation from spatially correlated unobservables (e.g., neighbourhood amenities that affect both rents and Airbnb desirability). Second, comparing $\hat{\beta}'$ from Equation~\eqref{eq:spatial_lag} with $\hat{\beta}$ from Equation~\eqref{eq:hedonic_rent} provides a diagnostic for the sensitivity of the Airbnb coefficient to spatial confounders.
57 57
@@ -59,7 +59,7 @@ In addition to the spatial lag model, we examine the robustness of the Airbnb ex
59 59
60 60 \subsection{Model 5: Quantile Regression}\label{sec:model5}
61 61
62 −To investigate heterogeneity in the Airbnb--rent association across the conditional rent distribution, we estimate quantile regressions \citep{koenker1978regression}:
62 +To investigate heterogeneity in the Airbnb--rent association across the conditional rent distribution, we estimate quantile regressions \citep{koenker1978regression, koenker2001quantile}:
63 63
64 64 \begin{equation}\label{eq:quantile}
65 65 Q_{\tau}\!\left[\ln(\text{rent}_i) \mid \mathbf{X}_i, \texttt{airbnb\_count}_{i,r}\right] = \alpha_\tau + \beta_\tau \cdot \texttt{airbnb\_count}_{i,r} + \mathbf{X}_i' \boldsymbol{\gamma}_\tau + \sum_{c} \delta_{c,\tau} \cdot \mathbf{1}[\text{city}_i = c],
@@ -69,7 +69,7 @@ To investigate heterogeneity in the Airbnb--rent association across the conditio
69 69
70 70 \subsection{Model 6: Machine-Learning Robustness}\label{sec:model6}
71 71
72 −We complement the parametric analysis with four machine-learning methods: LASSO, elastic net, random forest, and gradient boosting, alongside an OLS benchmark. These models are trained to predict $\ln(\text{rent}_i)$ from the Airbnb exposure measures at the 500\,m radius (count, density, mean price, entire-home share), dwelling characteristics (bedrooms, bathrooms), and geographic coordinates, and are evaluated on a held-out test set (20\% of the sample) using root mean squared error (RMSE), mean absolute error (MAE), and $R^2$.
72 +We complement the parametric analysis with four machine-learning methods: the LASSO \citep{tibshirani1996regression}, the elastic net \citep{zou2005regularization}, random forests \citep{breiman2001random}, and gradient boosting \citep{friedman2001greedy}, alongside an OLS benchmark; all are implemented in scikit-learn \citep{pedregosa2011scikit}. These models are trained to predict $\ln(\text{rent}_i)$ from the Airbnb exposure measures at the 500\,m radius (count, density, mean price, entire-home share), dwelling characteristics (bedrooms, bathrooms), and geographic coordinates, and are evaluated on a held-out test set (20\% of the sample) using root mean squared error (RMSE), mean absolute error (MAE), and $R^2$.
73 73
74 74 The machine-learning models serve three purposes. First, they provide a benchmark for the predictive accuracy of the hedonic model: if the OLS model achieves comparable $R^2$ to the flexible ML models, this suggests that the linear specification is not severely misspecified. Second, LASSO and elastic net coefficients reveal which variables are selected as predictors, providing a data-driven assessment of the importance of Airbnb exposure relative to dwelling characteristics. Third, random forest and gradient boosting models yield variable-importance measures (SHAP values) that quantify the contribution of each feature to predictive accuracy, without imposing functional-form assumptions.
75 75
modified paper/sections/05_results.tex +2 −2
@@ -36,7 +36,7 @@ Table~\ref{tab:hedonic_airbnb} reports the estimates of the Airbnb pricing model
36 36
37 37 Contrary to the opportunity-cost hypothesis, the city-level mean rent does not enter significantly in any specification: the coefficient $\hat{\theta}$ is small and statistically indistinguishable from zero, with a negative point estimate in columns~(2a) and~(2b) and a positive one in column~(2c). Airbnb nightly prices in our sample are therefore not systematically higher in higher-rent cities once listing characteristics are taken into account; the pricing of short-term rentals appears to be driven primarily by the properties of the listing itself rather than by conditions in the local long-term rental market.
38 38
39 −Among the listing-level controls, dwelling size matters most: bedrooms and, especially, bathrooms are associated with significantly higher nightly prices. Superhost status carries a significant \textit{negative} coefficient, which likely reflects composition effects---superhosts in the sample are concentrated in more modest, high-volume urban units rather than in luxury properties---and guest capacity enters negatively once size is controlled for. The entire-home indicator is positive and significant once city fixed effects are included (column~2c), consistent with whole units commanding a premium over rooms in shared dwellings within the same market.
39 +Among the listing-level controls, dwelling size matters most: bedrooms and, especially, bathrooms are associated with significantly higher nightly prices, in line with the STR pricing literature \citep{wang2017price, gibbs2018pricing}. Superhost status carries a significant \textit{negative} coefficient---a notable departure from the positive reputation premia typically reported \citep{wang2017price, ert2016trust}---which likely reflects composition effects: superhosts in our Quebec sample are concentrated in more modest, high-volume urban units rather than in the luxury chalet segment, and guest capacity enters negatively once size is controlled for. The entire-home indicator is positive and significant once city fixed effects are included (column~2c), consistent with whole units commanding a premium over rooms in shared dwellings within the same market \citep{gibbs2018pricing}.
40 40
41 41 When city fixed effects are included, the listing-level coefficients retain their signs and broad magnitudes, suggesting that the within-city pricing structure of Airbnb listings is largely independent of the cross-city rent variation.
42 42
@@ -100,7 +100,7 @@ Table~\ref{tab:quantile} and Figure~\ref{fig:quantile_plot} present the quantile
100 100
101 101 The quantile regression results reveal meaningful heterogeneity in the Airbnb--rent association across the conditional rent distribution. The coefficient is positive and statistically significant at every quantile considered. It is roughly flat over the lower half of the distribution (0.0037 at $\tau = 0.10$, 0.0034--0.0035 at $\tau = 0.25$ and $\tau = 0.50$) and then rises in the upper tail, reaching 0.0041 at $\tau = 0.75$ and its largest value, 0.0047, at $\tau = 0.90$---roughly 25\% above the OLS estimate.
102 102
103 −This pattern is economically interpretable. High-rent listings---typically located in desirable neighbourhoods with strong tourist appeal---are precisely the locations where Airbnb activity is most intense and where the supply-withdrawal mechanism is most plausible. The quantile results thus suggest that the association between Airbnb and rents, while present throughout the distribution, is strongest in the upper segment of the rental market.
103 +This pattern is economically interpretable. High-rent listings---typically located in desirable neighbourhoods with strong tourist appeal---are precisely the locations where Airbnb activity is most intense and where the supply-withdrawal mechanism is most plausible. The finding that implicit prices vary across the conditional distribution echoes the broader hedonic evidence \citep{zietz2008determinants, mcmillen2008changes}, and extends it to STR exposure: the association between Airbnb and rents, while present throughout the distribution, is strongest in the upper segment of the rental market.
104 104
105 105 \subsection{Machine-Learning Robustness}
106 106
modified paper/sections/07_discussion.tex +19 −7
@@ -4,7 +4,13 @@
4 4 % =============================================================================
5 5 \section{Discussion}\label{sec:discussion}
6 6
7 −This section interprets the empirical findings in the context of housing policy, discusses the mechanisms that may underlie the observed associations, and addresses the limitations of the analysis.
7 +This section places the empirical findings in the context of the international literature, interprets them for housing policy, discusses the mechanisms that may underlie the observed associations, and addresses the limitations of the analysis.
8 +
9 +\subsection{Findings in the Context of the Literature}\label{sec:discussion_context}
10 +
11 +How do our magnitudes compare with prior work? Three benchmarks are instructive. First, \citet{duso2024airbnb}, using regulatory shocks in Berlin, estimate that each nearby Airbnb listing raises asking rents by roughly 7--13 euros per month; our preferred estimate implies \$6--10 CAD per listing at the median Quebec rent---the same order of magnitude, obtained from an entirely different market and design. Second, \citet{koster2021short} find that Los Angeles ordinances which halved Airbnb activity lowered local rents by about 2\%; read through our semi-elasticity, removing half of the mean nearby exposure in our sample would predict a rent differential of a comparable magnitude in Airbnb-dense neighbourhoods. Third, \citet{horn2017airbnb} report that a one-standard-deviation increase in Airbnb density in Boston is associated with 0.4\% higher asking rents; our per-listing coefficient of 0.4\%, combined with the dispersion of exposure in our data, implies a somewhat larger standardised association, consistent with Quebec's rental stock being more concentrated in dense, Airbnb-exposed neighbourhoods. Our estimates also sit comfortably within the range documented across European markets \citep{garcia2020airbnb, franco2021impact, ayouba2020airbnb}.
12 +
13 +Two disagreements with the literature deserve emphasis rather than concealment. First, unlike most STR pricing studies \citep{wang2017price, ert2016trust, gibbs2018pricing}, we find a superhost \textit{discount} and no significant association between city-level rents and nightly prices (Model~2); we attribute both to composition---Quebec's STR market mixes urban apartments with resort chalets, and the cross-city dimension is dominated by resort communities where nightly prices are high but long-term rents are not. Second, the compositional prediction of the supply-withdrawal hypothesis---that entire-home listings should matter most \citep{horn2017airbnb, combs2020short}---is not supported in our data: the entire-home share within the buffer is statistically indistinguishable from zero once fixed effects are included (Model~1e). Either our property-type proxy for entire-home status is too coarse, or in Quebec the intensity of nearby STR activity matters more than its composition. Both null results are informative for the Canadian debate precisely because they were not guaranteed by the design.
8 14
9 15 \subsection{Policy Implications for Housing Affordability}
10 16
@@ -37,17 +43,23 @@ Our findings suggest that this tension is empirically present in the Quebec cont
37 43 We reiterate and expand upon the key limitations of our analysis:
38 44
39 45 \begin{enumerate}[label=(\roman*)]
40 − \item \textbf{Cross-sectional identification:} Our data represent a single point in time. We cannot distinguish the causal effect of Airbnb on rents from reverse causality (high rents attracting Airbnb hosts) or confounding by unobserved neighbourhood characteristics. A credible causal estimate would require panel data---ideally combined with a policy shock that exogenously shifted Airbnb supply in some locations but not others---or a valid instrumental variable for Airbnb penetration.
46 + \item \textbf{Cross-sectional identification:} Our data represent a single point in time. We cannot distinguish the causal effect of Airbnb on rents from reverse causality (high rents attracting Airbnb hosts) or confounding by unobserved neighbourhood characteristics. A credible causal estimate would require panel data---ideally combined with a policy shock that exogenously shifted Airbnb supply in some locations but not others, as in \citet{koster2021short}, \citet{valentin2021regulating}, and \citet{duso2024airbnb}---or a valid instrumental variable for Airbnb penetration in the spirit of \citet{barron2021effect}. That said, the consistency of our magnitudes with these designs (Section~\ref{sec:discussion_context}) suggests the cross-sectional bias, whatever its direction, is unlikely to be dramatic.
41 47
42 − \item \textbf{No host-level data:} The absence of host identifiers prevents us from identifying multi-listing operators and from distinguishing professional from casual hosts, a distinction that is central to the commercialisation debate.
48 + \item \textbf{No host-level data:} The absence of host identifiers prevents us from identifying multi-listing operators and from distinguishing professional from casual hosts, a distinction that is central to the commercialisation debate \citep{ke2017sharing, combs2020short} and to well-targeted regulation \citep{gurran2017when}.
43 49
44 − \item \textbf{Asking vs.\ transacted rents:} Our dependent variable measures asking rents on Realtor.ca rather than actual contract rents. If asking rents systematically overstate transacted rents (due to landlord bargaining power or strategic posting), our estimates may be biased, though the direction of this bias is ambiguous.
50 + \item \textbf{Asking vs.\ transacted rents:} Our dependent variable measures asking rents on Realtor.ca rather than actual contract rents. If asking rents systematically overstate transacted rents (due to landlord bargaining power or strategic posting), our estimates may be biased, though the direction of this bias is ambiguous. We note that several benchmark studies share this feature \citep{horn2017airbnb, duso2024airbnb}, which aids comparability even if it does not remove the concern.
45 51
46 − \item \textbf{Platform coverage:} Both datasets are scraped from specific platforms (Airbnb and Realtor.ca) and may not capture the universe of short-term or long-term rental listings. Alternative short-term rental platforms (e.g., VRBO, Booking.com) are not included, and the Realtor.ca data may underrepresent informal or unposted rental units.
52 + \item \textbf{Exposure measurement error and platform coverage:} Both datasets are scraped from specific platforms and may not capture the universe of short-term or long-term rental listings. Alternative STR platforms (e.g., VRBO, Booking.com) are not included, scraped snapshots miss delisted or calendar-blocked units, and the Realtor.ca data may underrepresent informal or unposted rentals. To the extent that measurement error in the buffer counts is classical, it attenuates $\hat{\beta}$ toward zero, making our estimates conservative; non-classical error correlated with neighbourhood type cannot be ruled out.
47 53
48 − \item \textbf{External validity:} Our sample is dominated by Montreal and a handful of other Quebec cities and resort areas. The findings may not generalise to other Canadian provinces or to rural housing markets where short-term rental dynamics differ.
54 + \item \textbf{Buffer choice and aggregation:} Any distance-based exposure measure faces a modifiable-areal-unit-style sensitivity: results could, in principle, depend on the radii chosen. We address this directly by reporting four radii, a ring decomposition, density and logarithmic functional forms, and continuous-decay-consistent patterns (Section~\ref{sec:robustness}), but the discreteness of any buffer set remains a simplification of the underlying spatial process \citep{can1992specification}.
49 55
50 − \item \textbf{Static analysis:} Our cross-sectional framework cannot capture dynamic adjustments in the housing market, such as the construction of new supply in response to rising rents or the exit of Airbnb hosts in response to regulatory pressure.
56 + \item \textbf{External validity:} Our sample is dominated by Montreal and a handful of other Quebec cities and resort areas. The findings may not generalise to other Canadian provinces or to rural housing markets where short-term rental dynamics differ---a caution reinforced by the cross-market heterogeneity documented in France \citep{ayouba2020airbnb} and Canada \citep{combs2020short}.
57 +
58 + \item \textbf{Static analysis:} Our cross-sectional framework cannot capture dynamic adjustments in the housing market, such as the construction of new supply in response to rising rents \citep{glaeser2005urban, saiz2010geographic} or the exit of Airbnb hosts in response to regulatory pressure.
51 59 \end{enumerate}
52 60
53 61 These limitations motivate the cautious interpretive stance adopted throughout the paper and underscore the need for future research with richer data structures.
62 +
63 +\subsection{Future Work}
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65 +Four extensions strike us as most valuable, in ascending order of data requirements. First, replicating the buffer-based design on repeated scrapes of the same platforms would deliver a listing-level panel for Quebec, enabling fixed-effects and event-study designs around the province's evolving registration regime. Second, Quebec's 2023 tightening of STR registration enforcement offers a natural policy discontinuity analogous to those exploited in Los Angeles \citep{koster2021short}, New Orleans \citep{valentin2021regulating}, and Berlin \citep{duso2024airbnb}; combining it with our exposure measures is the most direct route to causal estimates for Canada. Third, host-resolved data would permit separating commercial from casual supply, testing the commercialisation channel that our entire-home-share proxy cannot \citep{ke2017sharing, combs2020short}. Fourth, integrating administrative contract rents (e.g., lease registry data) would resolve the asking-rent measurement concern and allow distributional welfare analysis in the spirit of \citet{li2022market}.
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9 9 Our key findings are as follows. First, each additional Airbnb listing within 500 metres of a rental unit is associated with an approximate 0.4\% increase in monthly rent, conditional on dwelling characteristics and city fixed effects---an estimate that is stable across spatial and trimmed-sample specifications (roughly 0.3--0.5\%). Second, this association exhibits spatial decay, with the per-listing coefficient largest at narrow buffer radii and attenuating at wider distances. Third, quantile regressions reveal that the association, while significant throughout the distribution, is strongest at the upper tail, suggesting that high-rent segments of the market are most strongly linked to Airbnb activity. Fourth, a complementary hedonic model of Airbnb nightly prices indicates that short-term rental pricing is driven primarily by listing characteristics; city-level mean rents carry no significant premium, offering no support for a simple opportunity-cost pricing channel in our cross-section. Fifth, machine-learning models confirm the predictive relevance of Airbnb exposure variables and the adequacy of the linear hedonic specification for inference.
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11 −The principal contribution of this paper is to provide the first granular, listing-level analysis of the Airbnb--rent nexus in Quebec, leveraging precise geographic coordinates for exact distance-based spatial matching. By applying multiple econometric and machine-learning methods to a single dataset, we demonstrate the consistency of the finding across methodological frameworks and provide a rich set of robustness checks that characterise the sensitivity of the estimates to alternative specifications.
11 +The principal contribution of this paper is to provide the first granular, listing-level econometric analysis of the Airbnb--rent nexus in Quebec---extending a Canadian evidence base that was previously descriptive \citep{combs2020short, grisdale2021displacement}---leveraging precise geographic coordinates for exact distance-based spatial matching. By applying multiple econometric and machine-learning methods to a single dataset, we demonstrate the consistency of the finding across methodological frameworks and provide a rich set of robustness checks that characterise the sensitivity of the estimates to alternative specifications. Notably, our per-listing magnitude is of the same order as those recovered from regulatory quasi-experiments in Berlin and Los Angeles \citep{duso2024airbnb, koster2021short}, which we read as mutual corroboration between designs.
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13 13 We are candid about the limitations of our analysis. The cross-sectional nature of the data precludes causal identification: the positive association between Airbnb density and rents may reflect reverse causality, omitted neighbourhood characteristics, or spatial sorting, rather than---or in addition to---a genuine supply-withdrawal effect. Establishing causality in this domain requires panel data combined with plausibly exogenous variation in Airbnb supply, such as a regulatory discontinuity or a natural experiment. Our results should therefore be interpreted as well-controlled conditional correlations that are consistent with the supply-withdrawal hypothesis but do not definitively confirm it.
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15 15 Several directions for future research emerge from this analysis. First, the construction of panel data---tracking the entry and exit of Airbnb listings and the evolution of rents over time at the neighbourhood level---would enable difference-in-differences or event-study designs that can more credibly isolate the causal effect. Second, the exploitation of regulatory shocks---such as the tightening of Montreal's short-term rental regulations or the introduction of provincial registration requirements---would provide natural-experiment variation for causal inference. Third, the integration of host-level data would allow researchers to distinguish the effects of commercial multi-listing operators from those of casual home-sharers, sharpening the policy relevance of the analysis. Fourth, extending the geographic scope to include other Canadian cities would improve external validity and enable cross-city comparisons of regulatory effectiveness.
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17 −In sum, our findings add to a growing body of evidence suggesting that short-term rental platforms are associated with higher residential rents, at least in cross-section and in localities with significant tourist appeal. While the estimated magnitudes are modest at the per-listing level, their cumulative significance in high-tourism neighbourhoods---combined with the documented concentration of effects at the upper end of the rent distribution---underscores the importance of evidence-based regulatory frameworks that balance the economic benefits of the platform economy with the imperative of housing affordability.
17 +In sum, our findings add to a growing body of evidence---now spanning correlational, quasi-experimental, and structural designs \citep{barron2021effect, koster2021short, li2022market}---that short-term rental platforms are associated with higher residential rents, at least in localities with significant tourist appeal. While the estimated magnitudes are modest at the per-listing level, their cumulative significance in high-tourism neighbourhoods---combined with the documented concentration of effects at the upper end of the rent distribution---underscores the importance of evidence-based regulatory frameworks that balance the economic benefits of the platform economy with the imperative of housing affordability.
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