Tenure of household (information about this variable and its quality)

Priority level

Priority level 2

We assign a priority level to all census variables: Priority 1, 2, or 3 (with 1 being highest and 3 being the lowest priority).

Tenure of household is a priority 2 variable. Priority 2 variables cover key subject populations that are important for policy development, evaluation, or monitoring. These variables are given second priority in terms of quality, time, and resources across all phases of a census.

The census priority level for tenure of household remains the same as 2013.

Quality Management Strategy and the Information by variable for tenure of household (2013) have more information on the priority rating.

Overall quality rating for 2018 Census

Moderate quality

Data quality processes section below has more detail on the rating for this variable.

The External Data Quality Panel has provided an independent assessment of the quality of this variable and has rated it as moderate quality. 2018 Census External Data Quality Panel: Assessment of Variables has more information.

Subject population

Households in occupied private dwellings

‘Subject population’ means the people, families, households, or dwellings to whom the variable applies.

How this data is classified

Census tenure of household V1.0.0

Tenure of household is a hierarchical classification with three levels. Level 1 contains 4 categories (code length of 3), level 2 contains 5 categories (code length of 1), and level 3 contains 11 categories (code length of 2). Levels 1 and 3 are shown below:

• 001 Dwelling owned or partly owned
• 10 Dwelling owned or partly owned, mortgage arrangements not further defined
• 11 Dwelling owned or partly owned, mortgage payments made
• 12 Dwelling owned or partly owned, mortgage payments not made
• 002 Dwelling not owned and not held in a family trust
• 20 Dwelling not owned and not held in a family trust, rental arrangements not further defined
• 21 Dwelling not owned and not held in a family trust, rent payments made
• 22 Dwelling not owned and not held in a family trust, rent payments not made
• 003 Dwelling held in a family trust
• 30 Dwelling held in a family trust, mortgage arrangements not further defined
• 31 Dwelling held in a family trust, mortgage payments made
• 32 Dwelling held in a family trust, mortgage payments not made
• 999 Not elsewhere included
• 77 Response unidentifiable
• 99 Not stated

Census tenure of household data can be output at the lowest level of the classification, subject to meeting confidentiality requirements.

Alternative labels are also available:Tenure of household – short labels V1.0.0

Home ownership figures given in census publications are often presented as the percentage of households who owned their home or held it in a family trust. Combining these categories provides a summary indication of total households in these situations (which are similar and distinct from not owning) and the overall trend for home ownership.

The classification of tenure of household in the 2018 Census is consistent with the classification used in the 2013 and 2006 Censuses.

‘Dwelling owned or partly owned’ includes households who purchased a dwelling under unit title, stratum title, composite leasehold, or licence to occupy for example households in self-care units in retirement complexes.

‘Mortgage payments made’ includes households on short-term mortgage holidays.

‘Mortgage payments not further defined’ means that information on whether the household was making mortgage payments or not is not available.

Renting is defined as those households who did not own their home or have it in a family trust and were paying rent. This is households in the ‘dwelling not owned and not held in a family trust, rent payments made’ category (category 21). It includes households who were occupying a dwelling under a rent-to-buy agreement.

The broader ‘dwelling not owned and not held in a family trust’ category (category 002) includes households who were living in their home rent-free (category 22) and households for which information on whether they were paying rent or not was not available (category 20), as well as households who were renting their home.

‘Dwelling not owned and not held in a family trust, rent payments not made’ (category 22) includes situations where people were provided with rent-free housing as part of their employment (for example farm workers or managers, motel or hotel workers) and situations where people were living rent-free in housing provided by family or friends.

‘Dwelling held in a family trust, mortgage payments made’ includes situations in which mortgage payments were made by the trust and situations in which mortgage payments were made directly by the household.

The Standards and Classifications page provides background information on classifications and standards.

Question format

Tenure of household data is derived from the following questions on the dwelling form:

• dwelling owned or in family trust
• sector of landlord
• rent indicator
• rent amount, from which weekly rent paid by household is derived
• mortgage payments.

Or questions 5, 6, 7, 8, and 9 on the paper form.

Stats NZ Store House has samples for both the individual and dwelling paper forms.

There have been changes to the method of collection for this variable since the 2013 Census.

In 2018, information from sector of landlord was used in the derivation for tenure of household. The purpose of this was to improve identification of households in the ‘do not own or hold in a family trust’ categories, many of whom rent their home.

There have been changes to the questions used to derive tenure of household for 2018 compared with 2013:

• in 2018 questions on home ownership and family trusts were combined in one question, previously in 2013 they were separate questions
• in 2018 mortgage payments questions for owned dwellings and dwellings in a family trust were combined in one question. In 2013 these were separate question.

There were differences between the wording and question format in the online and paper versions of these questions:

• rent amount on the online form has a text box that only appeared when the respondent selected ‘other’ for the payment frequency
• respondents were asked to enter the ‘period between rent payments’ online, whereas on paper the wording was ‘print period’.

There were also differences in the way a person could respond: On the online dwelling form:

• it was not possible to give a multiple response to these questions
• the sector of landlord and rent indicator questions were only shown if the response to the owned or family trust question was ‘neither of these’
• the rent amount question only appeared if the respondent selected yes for rent indicator (ie pays rent)
• the mortgage payments question was only shown if the respondent said that the dwelling was owned or in a family trust
• for the rent amount question, the write-in box for payment frequency only appeared if the respondent selected ‘other’
• the highest possible rent amount that could be given was $99,999.

On the paper dwelling forms:

• it was possible to answer all, or any combination, of the questions used to derive tenure of household
• it was possible to give a rent amount higher than $99,999, although there was only space for five digits.

These responses were resolved by edits.

How this data is used

Outside Stats NZ

• For monitoring trends and changes in home ownership rates.
• For formulating and monitoring of housing policy by central and local government.
• In constructing the New Zealand Deprivation Index.

Within Stats NZ

• Used together with sector of landlord, rent paid and number of rooms, to establish weights in the Consumers Price Index for the rental of dwellings and home ownership costs.
• Used together with number of rooms and rent paid to compile estimates of the value of imputed rental income from owner-occupied homes, and to establish a reference benchmark for the production account of the residential sector of the rental industry. These in turn provide input to the estimates of the value of household consumption and the contribution to gross domestic product of the rental industry.

2018 data sources

We used alternative data sources for missing census responses and responses that could not be classified or did not provide the type of information asked for. Where possible, we used responses from the 2013 Census, administrative data from the Integrated Data Infrastructure (IDI), or imputation.

The table below shows the breakdown of the various data sources used for this variable.

The ‘no information’ percentage is where we were not able to source tenure of household data for a household in the subject population.

Administrative data sources

Data from the following administrative source was used:

• Housing New Zealand Corporation
• Tenancy Bonds, Ministry of Business, Innovation and Employment.

Please note that when examining tenure of household data for specific population groups within the subject population, the percentage that is from 2013 Census data, administrative data, and statistical imputation may differ from that for the overall subject population.

Missing and residual responses

‘No information’ in the data sources table, is the percentage of the subject population coded to ‘not stated’. In recent previous censuses, non-response was the percentage of the subject population coded to ‘not stated.’

In 2018, the percentage of ‘not stated’ is lower than previous censuses due to the use of the additional data sources described above.

Percentage of ‘not stated’ for households in occupied private dwellings

• 2018: < 0.1 percent
• 2013: 5.1 percent
• 2006: 4.7 percent.

In 2018, admin data and statistical imputation was used to replace responses coded to residual categories such as response unidentifiable. In output for the 2013 and 2006 censuses, responses that could not be classified or did not provide the type of information asked for were grouped with ‘not stated’ and classified as ‘not elsewhere included’.

Percentage of ‘not elsewhere included’ for households in occupied private dwellings:

• 2018: < 0.1 percent
• 2013: 6.3 percent
• 2006: 6.2 percent.

2013 Census data user guide provides more information about non-response and imputation in the 2013 Census.

Data quality processes

Overall quality rating: Moderate quality

Data was evaluated to assess whether it meets quality standards and is suitable for use.

Three quality metrics contributed to the overall quality rating:

• data sources and coverage
• consistency and coherence
• data quality.

The lowest rated metric determines the overall quality rating.

Data quality assurance for 2018 Census provides more information on the quality rating scale.

Data sources and coverage: Very high quality

We have assessed the quality of all the data sources that contribute to the output for the variable. To calculate a data sources and coverage quality score for a variable, each data source is rated and multiplied by the proportion it contributes to the total output.

The rating for a valid census response is defined as 1.00. Ratings for other sources are the best estimates available of their quality relative to a census response. Each source that contributes to the output for that variable is then multiplied by the proportion it contributes to the total output. The total score then determines the metric rating according to the following range:

• 98–100 = very high
• 95–<98 = high
• 90–<95 = moderate
• 75–<90 = poor
• <75 = very poor.

The 2013 Census data was mostly comparable with 2018 Census responses while admin data was highly comparable, and data sourced through statistical imputation was moderately comparable with census forms.

The high proportion of data from received forms in comparison to the low proportion sourced from admin sources and statistical imputation contributed to the score of 0.98, determining the very high quality rating.

Note: using 2013 Census data involved linking to the dwelling rather than to a household. It is possible that the household living in a particular dwelling may have changed since 2013 and that their tenure may differ from that of the household who lived there previously.

Data sources, editing, and imputation in the 2018 Census has more information on the Canadian census edit and imputation system (CANCEIS) that was used to derive donor responses.

Consistency and coherence: Moderate quality

Changes to the questionnaire design, data collection, and the use of imputation, 2013 data, and administrative data to fill the gaps in response for 2018 means that the data for tenure of household in 2018 is not entirely comparable with previous censuses.

• There is an increase in data in the ‘mortgage arrangements not further defined’ categories compared with previous years affecting time series. Whether a household pays a mortgage or not varies over time so it was not possible to use 2013 Census data to obtain this information.
• The data is not totally consistent with previous trends for own or family trust.
• The data is not totally consistent with previous trends for households who do not own their home or have it in a family trust.
• Some changes in the data may be partly due to the lower than expected response to the census overall.
• There is an increase in the number of households who rent which may be partly due to more complete data for these households for 2018 due to use of other data sources and better identification of these households.

Data quality: Moderate quality

The data quality checks for tenure of household included edits for consistency within the dataset and cross-tabulations to the regional council level.

Tenure of household data has various data quality issues involving several categories or aspects of the data, or an entire level of a hierarchical classification. The data quality issues could include problems with the classification or coding of data, such as vague responses resulting in coding issues, or responses that cannot be coded to a specific (non-residual) category, thereby reducing the amount of useful, meaningful data available for analysis. The use of other data sources may be contributing to these issues.

Tenure of household data has various data quality issues including the following:

• some bias toward home owners/family trust households and under-representation of households who do not own their home or have it in a family trust
• data quality may vary at the regional level and be lower for some regions than others due to greater use of alternative data sources in those areas
• missing information on mortgage payments for many households who own their home or have it in a family trust
• incorrect tenure of household for households living in retirement villages due to respondent error.

Recommendations for use and further information

While the final quality rating for the data is moderate due to the low response rate, the data quality and the consistency with expectations and time series is good. This means the 2018 Census data can be compared with 2013 and 2006 data with some caution.

When using this data you should be aware that:

• overall this data may have some bias toward home owners/family trust households and some under-representation of households who do not own their home or have it in a family trust, including those who rent their home. The patterns and trends seen in this data may not always fully represent the real-world situation or real-world changes.
• there is less data in residual categories for 2018 due to use of other data sources. Some caution needs to be applied when interpreting time series data because other data sources have been used for the 2018 data that were not used previously.
• data has been checked to territorial authority and Auckland local board level. Some variation is possible at geographies below this level.
• use of administrative data to identify households who rent their home has improved the quality of the data on renting for 2018, making it more complete than previous data. As administrative data was not used previously, care should be taken when interpreting changes in the data as these may be due to the changes in the data collection method rather than real-world change. Some households included in the renting category for 2018 may have previously been in the not stated category.

Contact our Information Centre for further information about using this variable.