Canadian Housing Survey

Detailed information for 2018




Every 2 years

Record number:


This survey collects information about housing needs and experiences from a sample of Canadian households. Information is collected on core housing need, dwelling and neighbourhood satisfaction, housing moves or intentions to move, and other aspects of well-being related to housing.

Data release - November 22, 2019 (First results); January 15, 2020 (A profile of first time homebuyers); July 27, 2020 (microdata file); October 2, 2020 (Core housing need of canadian households); February 24, 2021 (Public use microdata file)


The Canadian Housing Survey (CHS) provides information on how Canadians feel about their housing and how housing affects them. Information is collected on core housing need, dwelling characteristics and housing tenure, perceptions on economic hardship from housing costs, dwelling and neighbourhood satisfaction, perceptions on neighbourhood issues and safety, housing moves and intentions to move, volunteering, community engagement, life satisfaction, community satisfaction, dwelling adaptations to improve accessibility, self-assessed health, experience with homelessness, and socio-demographic characteristics.

Social and Affordable Housing (SAH) is an important part of the housing stock that provides housing to people in need. The CHS aims to provide detailed and precise statistics on households in SAH by oversampling this subpopulation.

The 2018 CHS was carried out in all 10 provinces and the 3 territories.

Collection period: The data were collected from the beginning of November of the reference year until the end of March of the following year.


  • Families, households and housing
  • Household characteristics
  • Housing and living arrangements

Data sources and methodology

Target population

The target population includes private households across the provinces and territories of Canada.

Excluded from the survey are:
- people living on reserves and in other Aboriginal settlements
- official representatives of foreign countries living in Canada and their families
- members of religious and other communal colonies
- members of the Canadian Armed Forces living in military bases
- people living in residences for senior citizens
- people living full time in institutions (e.g., inmates of penal institutions, and chronic care patients living in hospitals and nursing homes)
- people living in other types of collective dwellings (e.g., shelters, campgrounds and hotels).

For operational reasons, people living in some small remote areas in the territories where collection costs would be exorbitant are excluded from the survey.

Instrument design

Questionnaire content was determined in consultation with the survey sponsor (Canada Mortgage and Housing Corporation). Standardized content was used when available. Harmonized content was also re-used from past or existing surveys. A questionnaire design specialist was consulted during the design phase. The survey also underwent qualitative testing prior to collection, and all recommendations were incorporated.

The questionnaire follows standard practices and wording used in electronic questionnaires, such as the automatic control of flows that depend on answers to earlier questions, and the use of edits to check for logical inconsistencies and input errors. The computer application for data collection was tested extensively.

The CHS data for the Northwest Territories are obtained through a partnership with NWT Bureau of Statistics. Instead of conducting the CHS in the Northwest Territories, data were obtained from the 2019 NWT Community Survey (NCS), which collects housing information similar to that collected by the CHS.


Sampling unit:

The sampling unit for the CHS is the dwelling. One questionnaire is completed per dwelling by a person who is responsible for the housing decisions.

Stratification method:
The 2018 CHS was designed using the Dwelling Universe File (DUF) as a frame. Administrative data on SAH was used to classify dwellings into strata in the frame. The frame was stratified into geographic areas of interest based on census subdivision (CSD) boundaries.

There are 45 different geographic strata that represent the largest census metropolitan area (CMA) in each province; CMAs with a population of more than 500,000 according to the last census; the census agglomerations (CAs) of Charlottetown, Yellowknife and Whitehorse; combined CMAs and combined CAs in each province; and the regions outside CMAs and CAs in each province and territory.

Each geographic stratum was divided into two groups: SAH dwellings and all other (non-SAH) dwellings. Sub-strata were used to more efficiently sample rented and owned dwellings within the non-SAH strata, which are equally represented in the sample but not in the population.

Sampling and sub-sampling:
The sample size for the CHS was determined by calculating the number of respondents necessary to create estimates with the desired level of precision in each stratum. The precision was measured by the coefficient of variation (CV). The target CV was 7% in CMAs, 10% in CAs, 15% outside CMAs and CAs and in the territories, and 15% in the SAH strata.

For the provinces and Whitehorse, a systematic random sample was selected independently within each stratum after sorting by household income, which was obtained from the socioeconomic indicators file.

To reduce collection costs and the travel time for interviewers, the CHS sample in the territories (excluding Whitehorse) used a two-stage design to allow for data collection by in-person interviews. First, towns (defined by CSD boundaries) were selected. Second, dwellings were selected within each of the selected towns to create the sample. Dwelling selection within towns is similar to dwelling selection in provinces; it uses a stratified, systematic random design.

The CHS sample consisted of 126,465 dwellings. A total of 105,072 dwellings were selected from the non-SAH strata, and 24,393 were selected from the SAH strata.

In the 2019 NWT Community Survey, 3,199 dwellings were sampled in six communities (Hay River, Inuvik, Fort Smith, Yellowknife, Behchoko and Fort Simpson). The remaining communities in the Northwest Territories were surveyed through censuses.

Data sources

Data collection for this reference period: 2018-11-01 to 2019-03-31

Responding to this survey is voluntary.

Data are collected directly from survey respondents and extracted from administrative files.

Data were collected using three collection modes: self-response Electronic Questionnaire (rEQ), Computer Assisted Telephone Interviewing (CATI) and Computer-Assisted Personal Interviewing (CAPI). rEQ and CATI were used exclusively in the 10 Provinces and in the city of Whitehorse. CAPI was used in Nunavut and Yukon (excluding Whitehorse), as well as in Yellowknife, which CHS surveyed as a control sample.

In the Northwest Territories, data from the CHS were collected through the 2019 NWT Community Survey using CAPI and self-response. This collaboration between the CHS and the 2019 NWT Community Survey allowed for data to be collected on a wider range of topics and for a larger sample size in the Northwest Territories.

Invitations to participate in the CHS were mailed to selected households. In areas where data were collected via rEQ and CATI, the letters provided a link to the survey and a unique secure access code. Households that did not participate via rEQ received CATI follow-up. In areas where data were collected via CAPI, a letter advising the household of the in-person interview was sent in advance.

In the Northwest Territories, households from 6 large communities (Hay River, Inuvik, Fort Smith, Yellowknife, Behchoko, and Fort Simpson) were sampled to complete the 2019 NWT Community Survey. In 4 of those communities (Yellowknife, Inuvik, Fort Smith and Hay River), self-response was the primary collection mode. CAPI was used to follow up with non-respondents. In smaller communities, the survey was administered via CAPI to all households.

Whenever possible, the survey was completed by the household member with the most knowledge of the household's housing situation. In all cases, this person was aged 15 years or older. Proxy response was accepted for questions about other household members. This allowed one household member to answer questions on behalf of any or all other household members, provided they were willing and able to do so.

View the Questionnaire(s) and reporting guide(s) .

Error detection

The computerized questionnaire contains many features designed to maximize the quality of the data collected. Many edits are built into the questionnaire to compare the reported data with unusual values, and to detect logical inconsistencies. When an edit fails, the interviewer is prompted to correct the information (with the respondent's help, if necessary). Once the data are transmitted to Head Office, a comprehensive series of processing steps is undertaken for the detailed verification of each questionnaire. Invalid responses are corrected or flagged for imputation.


Individuals from a household were considered respondents if they met certain criteria after providing sufficient demographic information about themselves. A household was considered a respondent if at least one person in the household met the criteria. Any missing key data—either person-level data for individuals within the responding household, or data for household-level variables—were imputed.

Missing responses were imputed for key questionnaire fields in two stages. First, imputation was completed for demographic variables at the person level (e.g., age, sex and relationships). Second, household-level variables were imputed (e.g., ownership, need of repairs and rent subsidy).

The CHS uses the nearest-neighbour imputation method, which involves selecting a donor record based on matching variables. First, a set of matching variables is defined, where each variable is correlated with the variables to be imputed. Second, through the combined use of a score function (for categorical matching variables) and a distance function (for numeric matching variables), the most similar consistent donor record is identified and used to impute data for the record. For the mortgage questions, some deterministic imputation was done based on respondent comments provided in the questionnaire.

Not all variables in the CHS database were imputed for non-response.


The survey design weights were first adjusted to compensate for non-response. Weights were adjusted separately within groups of similar respondents so that the respondents within the group also represented non-respondents. The adjustment groups were formed using auxiliary variables that explained the non-response pattern and were related to the survey's key variables of interest. After the non-response adjustment, influential observations were identified and weights were reduced for a small number of extreme observations.

Next, the weights underwent an initial calibration to ensure that certain weighted estimates respected relevant population totals from reliable sources other than the survey.

For each province, population counts by age and sex groups and household size were used. The CHS also used population counts for the largest CMAs targeted by the survey. The second set of totals was the number of wage and salary earners by province. To estimate sampling variance, the bootstrap approach was used. A set of 1,000 bootstrap weights was produced.

The population totals used for the CHS included demographic projections produced by Statistics Canada's Demography Division (based on the 2011 Census), and the number of wage and salary earners by province (based on the Canada Revenue Agency's T4 file).

To complete the weighting process, the influential observation and calibration steps were conducted a second time. The resulting weighted units represented the entire CHS target population.

Quality evaluation

When all processing, weighting and estimation steps were complete, CHS estimates for key housing and related variables were compared with other data sources such as the Census, administrative sources and other Statistics Canada surveys.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects that could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.

In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada Generalized Disclosure Control System (G-Confid). G-Confid is used for primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.

Revisions and seasonal adjustment

Not applicable.

Data accuracy

Response rates:
The overall response rate for the 2018 CHS was 50%.

Non-sampling error:
Non-sampling errors occur because certain factors make it difficult to obtain accurate responses or ensure that responses retain their accuracy throughout processing. Unlike sampling errors, non-sampling errors are not easily quantified. Four types of non-sampling error can be identified: coverage errors, response errors, non-response errors and processing errors.

Non-response bias:
Non-response errors occur when potential respondents do not provide the required information, or when the information they provide is unusable. The main impact of non-response on data quality is that it can cause bias in the estimates if the characteristics of non-respondents differ from those of respondents in a way that affects the housing studied. Although non-response rates can be calculated, they only provide an indication of data quality since they do not measure the degree of bias present in the estimates. The magnitude of non-response can be considered a simple indicator of the risks of bias in the estimates.

While the weights of respondent households are adjusted to compensate for non-respondent households, partial non-response (e.g., as failure to answer some questions) is handled through imputation.


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