Data Visualization · Portfolio Project

Toronto Airbnb, mapped by price and rating

10,560 active listings across 140 neighbourhoods, split into four market quadrants by citywide median price and rating. Data: Inside Airbnb, 2019–2024.

How the four quadrants are defined

Each neighbourhood is plotted by its median price and average rating, then split against the citywide medians. Price median: CAD $ · Rating median: .

Rating ≥ median
Rating < median
Price ≥ median
Q1
Premium & Loved
Pricey, high-rated — the aspirational core.
Q2
Overpriced
Pricey but under-loved — satisfaction lags cost.
Price < median
Q3
Hidden Gems
Affordable and highly rated — best value.
Q4
Budget Struggling
Cheap and lower-rated — under-performing supply.
Filter

Neighbourhood map

Hover a neighbourhood to see its metrics.

Price vs. rating — the four quadrants

Each dot is a neighbourhood. Size = listing count. Dashed lines = citywide medians.

Room type by quadrant

Listing traits by quadrant

What property features drive satisfaction & bookings

Top amenities by prevalence. X = avg rating, Y = avg reviews/month (industry proxy for booking rate). Bubble size = listing share. Dashed lines = citywide average.

Room type: price, rating, booking rate

Values normalised 0–100 across room types for comparison; hover for raw numbers.

Property type — top 8 by supply

Median price vs. avg rating per property type. Bubble size = listing count.

Monthly activity, 2019–2024

Reviews proxy for bookings. Estimated occupancy uses the Inside Airbnb "San Francisco model": bookings = reviews ÷ 0.5 review rate, multiplied by 3-night avg stay, over a rolling 12-month supply.

Neighbourhood leaderboards

Hottest = highest booking rate per listing. Most competitive = largest supply. Highest rated = top avg rating (min 30 listings to avoid small-sample noise).