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Affordability in NZ

Screenshot showing map of Christchurch with sample values and options.

You and your partner (if you have one) are working in Auckland/Canterbury/Wellington. What are the most affordable suburbs for you, in terms of rent and commute costs?

Affordability in NZ is an open source web application, created by transportation consultancy MRCagney, which uses open government data and a mapping interface to enable people to find out which suburbs will be most affordable for them.

Users enter details for income, rental property size, work location, transport method, and more. Suburbs are then colour coded by their affordability.

Because transport plays a big part in affordability, the app enables comparisons to be made between walking, cycling, driving and using public transport. Users can cost their commute time, too. 

How it works

The app was originally published in late 2013, and relaunched in May 2017 using a different dataset for rental bond data, making it easier to update. (It is now updated every calendar quarter.)


Almost all the datasets used in the app are open and several are open government datasets.

Google Maps matrix API was used to calculate commute distances and times, while car commute and car ownership costs came from MaMo2014.

The Affordability in NZ website is open source: its data and source code can be found on Gitlab.

Data challenges

The government datasets were straightforward to use because they were permissively licensed and cleanly formatted.

However, public transport cost data was problematic: data for each of the three regions (Auckland, Wellington and Canterbury) had to be handled differently. Canterbury fares had to be estimated, because there was no journey-planning API.


The app illustrates the multifactorial nature of affordability and how big a role transport plays in it.

In July 2017, publicity around the relaunch generated 60,000 unique visitors over the month.


MRCagney hopes contributors will expand the tool – for example, by adding other regions, such as Waikato.

This case study was published on 17 August 2017.