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Source: MIL-OSI Submissions

Almost 1 in 9 people live in a crowded house – Media release

22 April 2020

Almost 1 in 9 people (10.8 percent) in New Zealand households were living in a crowded house at the time of the 2018 Census, Stats NZ said today.

“Stats NZ has released 2018 Census household crowding data earlier than planned in order to help inform the fight against COVID-19, as we know that crowding is related to a greater risk of infectious disease transmission. Knowing where crowding is most concentrated can help focus resources for vulnerable communities,” wellbeing and housing statistics manager Dr Claire Bretherton said.

What is crowding?

Crowding is caused when the homes that people live in are too small to accommodate the number of people in a household. There are many different measures of crowding. The capacity of a dwelling can be measured by floor area, or the number of bedrooms or rooms. The measure used by Stats NZ is the Canadian National Occupancy Standard (CNOS). This measure calculates the number of bedrooms needed based on the demographic composition of the household. It presumes that there should be no more than two people to a bedroom, but that couples and children of certain ages can share a bedroom. Previous research has found that this index works fairly well within the New Zealand context. It is also used to measure crowding in Australia.

See Finding the crowding index that works best for New Zealand for more details.

Crowding occurs when homes are too small for the number of people in the household. A home is ‘severely crowded’ if the people living there need at least two more bedrooms.

Crowding rates for Pacific peoples and Māori in New Zealand households were higher nationally than for the total usually resident population. These results are consistent with 2013 and 2006 Censuses.

Almost 4 in 10 Pacific people are living in a crowded house

Nationally, household crowding is highest among Pacific peoples, with 38.5 percent living in a crowded house. Within this group, crowding was highest for young people (around 46 percent for 15–24-year olds), but remained high for all age groups. Over a quarter of Pacific people aged 70 years and over (27.4 percent) were living in a crowded house, compared with just 2.7 percent for people aged 70 and over in the total population.

Around 1 in 5 Māori experience crowding

For Māori, crowding rates were higher in the Auckland and Northland regions, and around the east coast of the North Island. In all these areas, around a quarter experienced crowding. Nationally, around a quarter of tamariki and rangatahi (children and young people) lived in a crowded home. The age distribution of crowding is similar for both Māori and Pacific peoples.

Previous research has shown that larger and more complex households, particularly multi-family households, were the most likely to be crowded. In 2013, around 39 percent of multi-family households were crowded, compared with just over 5 percent of all households.

See Living in a crowded house: exploring the ethnicity and well-being of people in crowded households.

Comparing crowding data over time

In 2018, interim collection coverage results from Stats NZ (July 2019) indicated that the response rates for Māori and Pacific peoples were much lower than in previous censuses. However, with the use of administrative data, the final census dataset for 2018 has an estimated coverage of around 96 percent for Māori and Pacific peoples.

There were over 300,000 people who could not be placed into households in the 2018 data. As a result, the number of people, including Māori and Pacific peoples, who lived in a crowded house may be undercounted. However, the impact on a time series is reduced, as in previous censuses there was no imputation or added sources of data used for ethnicity. For example, in 2013, around 200,000 people did not have an ethnicity record, which also meant that crowding for different ethnic groups would have been undercounted.

Where possible, alternative data sources (administrative data, 2013 Census data, imputation) were used to fill in gaps, as explained in About the 2018 Census dataset below.

In 2013, 5.1 percent of dwellings did not have information about the number of bedrooms and therefore crowding could not be calculated. In 2018, less than 0.1 percent of private occupied dwellings did not have any bedroom information.

See Data quality ratings for 2018 Census variables for more information. Crowding is a derived variable, but the inputs for crowding are of very high, high, or moderate quality.

New data consistent with previous censuses

Despite the changes in methodology for the 2018 Census, and the likely undercount for Māori and Pacific peoples in households, the 2018 Census data is consistent with previous census years. Similar distributions of crowding levels are seen when data is broken down by age group, ethnicity, and number of usual residents.

Crowding distributions are also fairly consistent with similar crowding data from both the General Social Survey and the Household Economic Survey.

Note: when creating the crowding dataset, a small number of households were omitted to maintain consistency with the New Zealand Deprivation Index. Households where number of bedrooms were imputed have also been excluded from this analysis. In 2018, around 2.8 percent of occupied private dwellings had number of bedrooms imputed.

About the 2018 Census dataset

We combined data from the census forms with administrative data to create the 2018 Census dataset, which meets Stats NZ’s quality criteria for population structure information.

We added real data about real people to the dataset where we were confident the people should be counted but hadn’t completed a census form. We also used data from the 2013 Census and administrative sources and statistical imputation methods to fill in some missing characteristics of people and dwellings.

Data quality for 2018 Census provides more information on the quality of the 2018 Census data. An independent panel of experts has assessed the quality of the 2018 Census dataset. The panel has endorsed Stats NZ’s overall methods and concluded that the use of government administrative records has improved the coverage of key variables such as age, sex, ethnicity, and place. The panel’s Initial Report of the 2018 Census External Data Quality Panel (September 2019), assessed the methodologies used by Stats NZ to produce the final dataset, as well as the quality of some of the key variables. Its second report 2018 Census External Data Quality Panel: Assessment of variables (December 2019) assessed an additional 31 variables.

In its third report, Final report of the 2018 Census External Data Quality Panel (February 2020), the panel made 24 recommendations, several relating to preparations for the 2023 Census. Along with this report, the panel, supported by Stats NZ, produced a series of graphs summarising the sources of data for key 2018 Census individual variables, 2018 Census External Data Quality Panel: Data sources for key 2018 Census individual variables.

The Quick guide to the 2018 Census outlines the key changes we introduced as we prepared for the 2018 Census, and the changes we made once collection was complete.

The geographic boundaries are as at 1 January 2018. See Statistical standard for geographic areas 2018.

MIL OSI