Cities around the world have found that Airbnb can push rents up. But when I looked for
evidence in Canada, there was very little, and almost nothing measuring what
happened after Toronto's short-term rental by-law took effect. Meanwhile, Toronto had
more than 14,000 entire-home Airbnb listings. A big phenomenon, barely measured.
Question 1 · Impact
Does Airbnb raise long-term rents in Toronto?
And if it does, by how much, and where does it hit hardest?
Question 2 · Policy
Is the city's by-law actually working?
Toronto's rules only cover stays under 28 days. Do listings inside and outside that line affect rents differently?
Literature reviewGap analysisResearch questions that policy can act on
02 Data engineering
Build the dataset that didn't exist
There was no ready-made dataset linking Toronto rents to Airbnb activity, so I built one.
I wrote Python programs to pull data from public APIs, combined scraped listings with
three census years, cleaned the gaps with documented rules, and merged everything into
a single table where every rental listing carries 20+ variables.
From 10 raw sources to one analysis-ready dataset
Every arrow is Python, spreadsheet, or GIS work: collecting, cleaning, geocoding, joining, validating.
One clean dataset6,165 rentals × 20+ engineered variables, each mapped to its neighbourhood
→
Deliver
Statistical modelsWhat actually drives rents
Maps & chartsWhere the impact lands
Policy evaluationIs the by-law working?
10data sources
14,120Airbnb listings processed
6,165rental listings analyzed
3transit systems woven in
Some variables had to be invented, not downloaded. For every single rental I computed a
walkability score, a transit score, a custom
"tourist appeal" score built from Google ratings of nearby attractions,
and the real door-to-door public transit time to the airport, combining street
maps with actual TTC, GO, and UP Express timetables. That last one becomes the hero of chapter 4.
PythonAPIs & web dataData cleaningGeocodingExcel
03 Spatial analysis
Put every listing on the map
Rent isn't just about the unit; it's about the neighbourhood. Using QGIS, I matched
more than 14,000 Airbnb listings and 6,165 rentals to their census tracts (Toronto's
standard neighbourhood unit). Now every rental "knows" its local context: income,
employment, education, population change, and how dense Airbnb is around it.
How a rental learns about its neighbourhood
Illustrative diagram of the spatial join. Darker tracts have more Airbnb listings per 1,000 homes.
Conceptual illustration. In the real analysis, "Airbnb density" = entire-home Airbnb listings per
1,000 private dwellings in each census tract, built from Inside Airbnb and Census 2021 data.
The spatial work also powered the custom accessibility measures: I used street networks and
transit timetables to compute how long each rental's real transit trip to the airport
takes (door to door, with transfers) for thousands of addresses at once. In a separate project,
the same skill set surfaced 20 years of gentrification patterns across Toronto.
Here's the trap: neighbourhoods that attract Airbnbs (central, walkable, lively) are the
same neighbourhoods where rent is high anyway. A naive comparison would blame Airbnb for rent
that downtown living explains. The model has to untangle that.
Step 1: Control for everything else
How much of the variation in rents the model explains, as each layer of controls is added.
Share of rent variation explained (adjusted R²) across model specifications, from the thesis.
Step 2: the clever part. Even with 19 control variables, something invisible could
still drive both Airbnb activity and rents. So I borrowed a technique from economics
(an instrumental-variable design) and built the instrument myself:
public transit time to the airport.
The logic: a quick train to the airport matters a lot to tourists, so Airbnbs cluster
where airport access is good. But long-term renters rarely pay extra rent just to reach
the airport faster, especially once the model already accounts for general transit quality,
walkability, and distance to downtown. That one-sidedness lets the model isolate the part of
Airbnb activity driven by tourism, and measure what that does to rents.
One-way lever: why airport transit time works
And then I checked the machinery: formal tests confirmed the correction was needed
(a Durbin–Wu–Hausman test showed the naive model really was biased), that the lever is
statistically strong (first-stage F-statistic above the conventional threshold of 10),
and that the result survives a stricter confidence test (an Anderson–Rubin interval that is
wider, but still clearly above zero). The estimate:
each additional entire-home Airbnb per 1,000 homes is linked to a 0.42% rent increase,
statistically significant across all 6,165 rental listings (p < 0.05).
0.42% per listing sounds tiny, until you stack listings the way Toronto's neighbourhoods
actually do. Averaged across the city (weighting neighbourhoods by how many renters live there),
Airbnb activity is associated with rents about 5.62% higher than they would
otherwise be. In the single densest tract, the model puts the premium near 50%.
The premium compounds with density
Estimated rent premium by neighbourhood Airbnb density, derived from the model estimate.
Curve computed from the thesis estimate (0.42% per listing per 1,000 homes, compounding).
Toronto's most Airbnb-dense census tract reaches roughly this level. Tenants there are
estimated to pay about 50% more than if their tract had no Airbnb listings.
What does 5.62% mean for a real tenant?
Drag the slider to your rent. This applies the citywide average estimate. It's an illustration, not a quote for any specific unit.
$133of it each month is the estimated Airbnb premium
$1,596per year, roughly a month's groceries… or more
Math: a rent that is 5.62% higher than its no-Airbnb baseline means about 5.3 cents of every
rent dollar is premium (5.62 ÷ 105.62). Citywide average applied for illustration.
Toronto's short-term rental by-law only covers stays of under 28 days. Set the
minimum stay to 28 days or more, and a listing slips outside the rules: no licence, no
accommodation tax. I split the data along that line and measured each side separately.
Inside the rules
Minimum stay under 28 days · governed by By-law 503-2024
✓ Must register and hold a licence
✓ Pays the Municipal Accommodation Tax
✓ Pays GST/HST
Outside the rules
Minimum stay 28+ days · falls under general tenancy law instead
✕ No licence required
✕ Exempt from the accommodation tax
✕ Exempt from GST/HST
Most of the market moved outside the rules
Where Toronto's 14,120 entire-home Airbnb listings stood in September 2024.
All entire-home listings
66.2% outside the by-law
33.8%
28+ day minimum (outside)Under 28 days (inside)
…and within those outside listings
76.3% unlicensed
23.7%
No licence: committed to staying outsideLicensed anyway
And the outside listings hit rents harder
Estimated rent increase per additional listing per 1,000 homes, by listing type.
Listings outside the by-law are linked to a significantly larger rent impact than
regulated ones, roughly 2.5× that of the average listing.
The verdict: the by-law works, but only inside its own fence. Rather than returning
homes to the long-term market, most hosts stepped just over the 28-day line, kept charging
nightly-style rates, and kept the pressure on rents. For policymakers, the message is concrete:
the biggest lever isn't tightening the rules inside the fence. It's deciding what to do about
the two-thirds of the market standing outside it.
The numbers are local, but the forces aren't. Global investment money and global platforms
now reach into every neighbourhood's housing market, pulling very different cities toward
the same outcome: less affordable rentals. My thesis proposes an updated framework,
the "Converging Housing Ecosystem", to help researchers and policymakers
see local housing decisions inside that global context.
The housing market, before and after platforms
Then: a local balance Social housing vs. for-profit housing, shaped mostly by national policy
Now: a global pull Finance and platforms reach into local markets everywhere at once
Global forces: investment capital + rental platforms
↓ ↓ ↓
Social housing squeezed further
Long-term rentals now compete with…
Short-term rentals a new rival inside the same housing stock
Simplified from the thesis framework. The practical takeaway: a city can't regulate its rental
market as if it were sealed off: platforms connect it to global demand.
Research only matters if people can use it. The full analysis lives in a 135-page thesis;
a 25-minute presentation to my examining committee told the same story; and the whole
project compresses into one sentence anyone can repeat:
"Airbnb is linked to rents about 5.6% higher across Toronto, and the listings that sidestep
the city's by-law are linked to over twice the impact of those that follow it."
That habit (original data work, careful methods, plain-language delivery) is what the
SOAN ENGAGE Conference recognized with its Methodological Excellence Award (April 2026),
citing "original dataset creation, complex data analysis methods, and clear communication of findings."
It's the same approach I bring to briefing notes, dashboards, and stakeholder presentations
in my current research roles, and the approach I'd bring to your team.
I'm open to planning, housing, and data analyst roles. The topic here was Airbnb, but the pipeline transfers to any policy question: vacancy taxes, zoning changes, transit investment. I'm always happy to talk methods in as much (or as little) detail as you like.