Price & yield by ZIP code
Gross yield is only colored on the map for ZIP codes with at least medium data quality (≥5 comparable buy & rent listings in the trailing week). Low-quality ZIP codes are shown in grey. Duplicate listings (same unit posted on more than one portal) are deduplicated before any median is computed — the most complete record is kept, nothing is deleted.
How the gross-yield estimate works: none of these units are actually rented out, so there's no real rent to measure. For each ZIP code we take the median asking price per m² (buy listings) and the median asking rent (converted to a monthly rent for a representative apartment in that ZIP, as described below), then divide annualised rent by price to get an estimated gross yield. If a ZIP has too few rental comps of its own, we fall back to the canton-wide median rent instead — ZIPs below the medium-quality threshold are greyed out rather than shown with a shaky estimate. It's a sound proxy for relative comparison across ZIP codes, not a guarantee of actual achievable rent.
Net rents: all rents on this page are net rents, excluding service charges (Nebenkosten) — the amount an owner actually receives. Where a listing does not state its service charges, we deduct the typical charges per m² of comparable homes in the same area. Gross yield is calculated on this net rent, as is usual in Switzerland. Only comparable listings are used: furnished, temporary or shared rentals, swap offers, auctions and very large homes are set aside.
How the monthly rent figure works: our raw rent data is a CHF/m²/month median, which isn't directly comparable to a monthly rent at a glance. So for each ZIP code we multiply its median CHF/m²/month by that ZIP's own median rental apartment size (falling back to the canton-wide median apartment size, 85 m², when a ZIP has fewer than 5 rental comps) to get the monthly rent for a representative apartment there — not any single unit's actual rent.
Short-term trends by location + room range
7-day average of daily readings. On each day we read the property portals we take the median of every comparable listing on the market that day, for the chosen location and room range. Each point on the chart is the average of those daily medians over the 7 days up to and including that date, which smooths out day-to-day noise and keeps the line continuous. Hover any point for its exact date and values. Dates on the x-axis mark each Monday. Room ranges: 1–2, 2–3, 3–4, 4+ rooms (split at the half-room mark, e.g. a 3.5-room flat falls in 3–4). Price-to-buy and gross yield share the upper chart on their own two axes; monthly rent — a very different CHF scale — gets its own chart below. The history only grows: a day's figures are fixed two days after it ends and never changed again, so you can follow the market from the first day we started tracking (19 September 2026). The sample size of the latest reading is shown below the charts.
Why price per m² steps down in early October: on 8 October 2026 two more property portals joined our data. The listings they added are, on the whole, priced lower per m² than the ones we already tracked (canton-wide median on 9 October: about CHF 11’000 per m² for the new listings against about CHF 11’900 for the rest), so the median moved down as the wider sample came in. It reflects a broader, more complete picture of the market, not a fall in prices. Because each point is a 7-day average, the step is spread over the week after 8 October.
Net rents: the rent line shows net rents, excluding service charges (Nebenkosten), and the gross yield is calculated on them. Only comparable listings enter these daily medians.
How the rent figure is calculated: our raw rent data is a CHF/m²/month median, which isn't directly comparable to price-to-buy at a glance. So for each room range we first computed a median apartment size canton-wide from 24,375 current rental listings with both a room count and a living area (1–2 rooms: 42 m² · 2–3: 67 m² · 3–4: 90 m² · 4+: 125 m² · All rooms (blended): 86 m²). The chart then shows CHF/m²/month × that room range's median size — i.e. the monthly rent for a representative apartment in that room range, not a per-m² rate. It's a representative figure, not any single unit's actual rent.
How the lending-rate chart works: every day we read the published rate of each lender in our Financing comparison and take the middle value (the median) across all of them; the chart shows the 7-day average of those daily medians — for SARON mortgages (lender margin plus SARON), 5-year and 10-year fixed mortgages. Lending rates are the same wherever you buy in the canton, so this chart stays the same when you change the location or room filter. Our daily rate readings start on 25 September 2026; for the days before that no lender rates were recorded, so the lines start there.
How "All rooms" is calculated: this isn't a 5th category in the raw data — it's the 4 room ranges combined. Price and rent are each a listing-count-weighted average of the 4 room ranges' own daily medians (weighted by that range's buy or rent listing count that day, not a plain average of 4 numbers), and gross yield is recomputed from that combined price and rent rather than averaged directly, since rent ÷ price stays a valid ratio at any level of aggregation.
Historical evolution & leading indicator
Correlation & significance vs. home-price growth
Significance tiers (predefined): High |r| ≥ 0.50 · Medium |r| ≥ 0.30 · Low below 0.30. With only 15–18 annual observations per series, even "high" correlations here carry wide uncertainty — read these as exploratory signal strength, not confirmed causal effects.
Forward-looking assessment — Casaris Blended Outlook
By Gemeinde (162 municipalities, Canton Zürich)Casaris Blended Outlook = equal-weighted average of (a) a UBS-derived sub-score and (b) each Gemeinde's own ZKB price-level percentile, both rescaled to 0–1. On granularity: this is now Gemeinde-level (162 municipalities) rather than the earlier 10-region view — ZKB's Preisniveau rating is itself reported at the Gemeinde level, so this is as granular as that source goes; UBS, by contrast, only confirms ONE Zürich-canton data point in its report text ("the city of Zürich shows a high level of imbalance"), so its contribution is a uniform national baseline everywhere except Zürich itself, with ZKB supplying all of the genuine Gemeinde-to-Gemeinde texture you see on the map. Going finer than Gemeinde level (e.g. to ZIP) isn't possible with either public source as published. Wüest Partner remains excluded (no free, structured public feed) per your instruction. Click any Gemeinde for its full breakdown.