How NestScore works.

How well-connected the listing is, scored two ways: how easy it is to walk to a stop, and how fast you can actually get places from there.

Walk-to-stop (60% of the score). Every TTC and GO stop within a real 1.2 km walking distance (along the street network, not as the crow flies) contributes to a pool, weighted by service quality — a subway station counts most, then streetcars, GO rail, frequent buses (10-min peak headway), and regular buses last. Closer wins; each extra ~350 m of walking roughly halves a stop's contribution.

Destination reachability (40% of the score). From each address we precompute the morning-peak transit time to every TTC subway station and every in-Toronto GO rail station — 91 places in total — using a real trip planner over TTC and GO schedules. The score uses your five fastest destinations. So a downtown listing isn't punished for being far from Pioneer Village or Pearson, and a North York listing still gets credit for the destinations it can reach quickly.

Whether you can shop your whole list without driving.

We sum up nearby supermarkets, weighted by both how close they are and what kind of store they are. A full-service supermarket (e.g., Loblaws, Metro, Sobeys) counts the most; a discount grocer (No Frills, FreshCo, Food Basics) is most of the way there; an independent grocer contributes less. Multiple stores stack but with diminishing returns, so your second nearby Loblaws boosts your NestScore less than your first.

Pharmacies and walk-in clinics within walking range.

Same mathematical shape as Grocery, but for pharmacies and walk-in clinics. Pharmacies count more than clinics, because most people use them more often (prescriptions, OTC meds, allergy season). Multiple options stack with diminishing returns; anything past 1.5 km doesn't count.

Whether the streets feel alive.

This one's about density — how many places to eat, drink, and grab a coffee are within walking distance, because that's what makes a neighbourhood feel like one. We pool every restaurant, café, bar, and fast-food spot within 1 km, weighted by what kind of place it is: a sit-down restaurant counts the most, a café somewhere in between, a McDonald's the least. Closer wins — each extra ~230 m of distance roughly halves a place's contribution. The total feeds into a saturating curve that approaches 100, so going from 0 to 10 nearby places matters way more than going from 40 to 50.

Cycling infrastructure: stations + lanes.

Two halves, each worth up to 50 points. The first half rewards Bike Share Toronto stations nearby — close ones (≤500 m) count three times as much as further ones (500 m – 1 km). The second half rewards protected lane density, or how many sample points along Toronto's official cycling network are within 500 m of your new home. We sample lanes every ~200 m, so a long protected stretch nearby will accumulate a lot of points.

Green space within a 10-minute walk. The big ones count more!

We measure the distance to each park's edge, not its centre. That matters: standing across the street from High Park, you're roughly 600 metres from its centroid but effectively at the front gate. Bigger parks also get a bonus — a 50 hectare ravine system contributes 3× more than a small playground at the same distance, because we know the experience is meaningfully different. Multiple parks stack with diminishing returns; anything past an 800-metre walk to the edge stops counting.

Nearest elementary plus nearest secondary: best-of-each, no stacking.

For schools we use a best-of-each rule: only the single nearest elementary school (worth up to 60 points) and the single nearest secondary school (worth up to 40 points) contribute. We don't reward the listing for being near five elementary schools since you only need one. The closer the school, the more points; anything past 1.5 kilometres doesn't count.

Spaciousness compared to other Toronto units of the same bedroom count.

We compare the unit's reported sqft to a typical citywide range for that bedroom count. A 1-bed at the top of the typical range scores around 90; a 1-bed near the bottom scores around 20. If the listing doesn't report sqft, we estimate it from the median sqft of comparable listings in our database, so the score is still meaningful if not precise.

Building has a pool: yes or no?

Binary. If we know the building has a pool, the score is 100; if we know it doesn't, the score is 0. If we genuinely can't tell, the pool is left out of the composite rather than counted as zero. We prefer the structured amenity flag when pulling listings and only fall back to scanning the listing description when we have to. Drop this from your own NestScore if it's not important to you.

Unit has a balcony: yes or no?

Same shape as Pool. If we know there's a balcony, 100; if we know there isn't, 0; if we can't tell, it's left out. Structured flag first, description text second. The negation guard catches things like "no balcony".

An inverse methodology means higher score equals quieter neighbourhood.

This is the only factor where we work backwards. We start at 100 and subtract a noise-exposure penalty based on nearby highway corridors (worst), GO and other rail lines, and above-ground TTC subway stops. Noise drops off very quickly, so we use a steep distance decay. A busy road 500 metres away barely registers, but a building right on the Gardiner takes a serious hit. Anything past 800 metres doesn't contribute.

Rent compared to nearby units of the same size.

Price-value answers a single question: am I overpaying compared to similar nearby units? We find every other listing within 2 kilometres that has the same bedroom count, take the median rent, and compare. 30% below median tops out at 100; at the median scores 50; 30% above the median drops to 0. We need at least 3 nearby comparables, so if we don't have them in the listings database, this score is excluded rather than guessed. As more listings join the database, more areas become scoreable.

Whether the unit falls under Ontario's rent-increase cap.

Ontario exempts buildings first occupied on or after 15 November 2018 from rent control, so whether a unit is rent-controlled depends almost entirely on when its building was finished. If the listing tells us the build year, we use it directly. If it doesn't, we match the listing to the nearest building in our database of Toronto building records and read its rent-control flag from there. If we can't get either, the score is excluded rather than being guessed at.

Vehicle volume, speed at the curb, and a toggle for which way you want it.

Most renters want calm streets. Some actively want a busy arterial for quick access in their cars. This factor has a direction toggle next to its slider: Less traffic (the default — calm = good) or More traffic (busy = good). The underlying number doesn't change; we just flip the sign at the point your composite is calculated. We use Toronto's midblock vehicle counts, which are survey points where the city has measured average daily volume (AADT) and the 85th-percentile speed of vehicles passing by. For a listing, we look at every survey point within 200 metres and combine them with a steep distance decay (a count 50 metres away counts way more than one 180 metres away). Volume dominates the severity blend; speed nudges it (a fast residential street feels worse than a slow one with the same volume). Coverage isn't perfect since the city only counts streets it has surveyed, and small residential laneways often aren't in the dataset. If there's no survey point within 200 metres, we leave the score blank rather than guess, and the factor drops out of that listing's composite.