Use the Market Rent API

The API is a service that, with relevant technical knowledge, can be used in a self-service manner to generate market rent data for a range of user-specified criteria including time period, area definition type, and geographic location.

Market rent API (external link)

Analytical note: medians and quartiles for rent data

Median and quartile data for rents are often requested in order to understand the distribution of rents in a region. This is especially important for social housing, as social housing is not typically targeted at average households.

However, rents tend to cluster at round numbers – a weekly rent of $300 is much more common than a rent of $297.50. This has an effect on median and quartile measures (which are based on actual values from the data), as they tend to plateau for months at a time, before increasing $10 or $20. This can make analysing time series of medians and quartiles difficult.

For this reason, we have developed alternative measures for use with rent data:

• Geometric mean (replacing median): The geometric mean is calculated by multiplying values together and taking the nth root of the result. When a variable is log-normally distributed (a common distribution for variables that must be greater than 0) the geometric mean will closely approximate the median.
• Synthetic Quartiles (replacing quartiles): The synthetic quartiles are designed to find the 25th percentile (for the lower quartile) and 75th percentile (for the upper quartile) of a set of data, assuming the data is lognormally distributed. The mean and variance of the data are not assumed, but instead are calculated. This approach is consistent with using the geometric mean to approximate the median.

Using data under Creative Commons

We're making this raw data available under a Creative Commons licence. This means you can use the data free of charge to perform your own analysis, as long as you credit us ('The Ministry of Business, Innovation and Employment') as the source of the data.

This work is licensed under a Creative Commons Attribution 3.0 New Zealand License.(external link)