Cantonal taxes
How to compare Swiss tax burden by postcode
A reliable comparison keeps the ESTV scenario fixed and checks which municipality supplies the tax result shown for each postcode.
Municipality hub
Rheinau
Rheinau connects 2 postcode pages in Zurich. This hub shows membership, address shares, and demographic context.
- Postcodes
- 2
- Population
- 1'319
- Streets
- 110
- Effective tax rate
- 9.92%
- ESTV: single, CHF 80,000, no church tax
Postcodes in Rheinau
The municipality includes 1 split-postcode cases and 2 locality names. Use the links for address, demographic, and map context.
How this municipality cluster behaves
Rheinau connects these postcodes through one administrative structure.
For ESTV tax year 2024, the standard scenario produces CHF 7'932 total tax, or 9.92% effective.
Related municipality hubs
Blog
Guides for comparing Rheinau
These articles explain how to read postcode, municipality, and demographic data for relocation or comparison work.
Moving and taxes
Moving from Zurich to Zug: when the tax switch pays off
Compare 8001 Zurich and 6300 Zug under three identical official ESTV household models, then test whether housing and commuting consume the tax difference.
Moving and taxes
How to compare postcodes before you move (and when to stop)
How to read a postcode without giving it more weight than it deserves. We show which signals help and when you should move on to an official check.
Postcode and municipality
Swiss postcodes vs municipalities: which unit you should trust
Postcodes and municipalities overlap in Switzerland, but they are not the same thing. Keeping them separate makes tax data and location data easier to read.
Postcode and municipality
A municipality with several postcodes: what that means for comparison and moving
The piece explains why several postcodes inside one municipality are normal and how to compare them without confusing neighbourhood differences with municipal ones.
Household fit
Spot a younger population mix: where the data points to more dynamic postcodes
We look at which postcodes skew younger in the dataset, why you should always read 20-39 and 65+ together, and where age structure alone will mislead you.