Context modelling with transformers: Food recognition
By: Mohammadreza Dorkhah, Sayema Mashhadi and Shannon Lo, Statistics Canada
Introduction
Our team of researchers from Statistic Canada's Data Science Division and Centre for Population Health Data (CPHD) conducted a proof-of-concept project that identifies foods within images and explores an alternative way of collecting nutrition data.
Given that this project was the first of its kind at Statistics Canada, the teams involved in creating this proof-of-concept were required to work exclusively with publicly available food image datasets. As a result, we curated a final dataset with images and labels that matched food and drinks consumed by Canadians based off three other datasets. This resulting dataset was used to develop a deep learning model for food recognition that can predict 187 different types of food or beverage categories and identify multiple products within a single image.
The food recognition deep learning model uses a state-of-the-art vision transformer as an encoder, called a segmentation transformer (SETR), and a multimodal image-text model for context modelling called the Recipe Learning Module (ReLeM). As part of this project, the CPHD team members tested and manually verified the SETR and ReLeM models' performance which we will explain later in this article.
Datasets
The three public datasets that we used to develop our final dataset suited our goal of ingredient level semantic segmentation for food images. However, given that each dataset has a different set of food categories, we had to manually map them to categories derived from a nutrition guide (Nutrient Value of Some Common Foods). Figures 1, 2 and 3 show sample images and their labels for each of the three datasets. The labels are image segmentation masks used to annotate every pixel and distinguish between items such as water, bread, and other foods.
FoodSeg103
7,118 images (4,983 training, 2,135 validations)
102 food categories
Figure 1: Sample image and output from the FoodSeg103 dataset.
An image of cake and sliced strawberries on the left. The output on the right depicts the shape of the cake and strawberries with their own colours.
Output from the FoodSeg103 dataset.
Colour
Colour Name
Original Category
Nutrition Guide
The table cell background is coloured "Light Salmon"
Light Salmon
Cake
Cake
The table cell background is coloured "Magenta"
Magenta
Strawberry
Strawberry
UECFoodPIX
10,000 images (9,000 training, 1,000 validations)
102 food categories
Figure 2: Sample image and output from the UECFoodPIX dataset.
A food image consisting of salmon, omelets, rice, soup and other foods on the left. The output image on the right are shapes of the food images in their corresponding colours.
Output from the UECFoodPIX dataset.
Colour
Colour Name
Original Category
Nutrition Guide
The table cell background is coloured "Lime"
Lime
Others
Other
The table cell background is coloured "Royal Blue"
Royal Blue
Mixed rice
Grains, rice
The table cell background is coloured "Slate Blue"
Slate Blue
Miso soup
Soup
The table cell background is coloured "Medium Slate Blue"
Medium Slate Blue
Beverage
Drink
The table cell background is coloured "Fire Brick"
Fire Brick
Grilled salmon
Fish
The table cell background is coloured "Tan (Burly Wood)"
Tan (Burly Wood)
Rolled omelet
Egg
The table cell background is coloured "Lime"
Lime
Ganmodoki
Other
As shown in the table above, some of the original categories are mapped to different categories within the nutrition guide. Items with no matching category are mapped to "Other".
We used refinement techniques to handle the coarse masks problem.
Figure 3: Sample images from the MyFoodRepo dataset.
A food image consisting of pasta with a cream sauce, garnished with parsley and tomato on the left. Two output images on the right are shapes of the food images in their corresponding colours, one with the original mask and one with a refined mask.
MyFoodRepo dataset.
Colour
Colour Name
Original Category
Nutrition Guide
The table cell background is coloured "Light Steel Blue"
Light Steel Blue
Sauce cream
Sauce
The table cell background is coloured "Purple"
Purple
Parsley
Parsley
The table cell background is coloured "Dark Salmon"
Dark Salmon
Tomato
Tomato
There are some overlapping categories in each labelled dataset which were combined as one in our final dataset. After dropping a few labels due to insufficient image samples, and by combining others to make coherent groupings of similar food types, a total of 187 different types of food and drinks were finalized.
Image segmentation
Image segmentation forms the basis of many downstream computer vision tasks such as object detection and image classification. Image segmentation is a method of dividing an image into subgroups. This division is usually done based on visible boundaries or edges of objects in an image and helps to reduce complexities. Segmentation can also mean label assignment to each pixel in the image to identify important elements. It has many applications in the field of autonomous vehicles, medical image analysis, satellite image analysis, video surveillance and other recognition and detection tasks. Image segmentation is also used in medical imaging, as covered in a recent DSN article, Image Segmentation in Medical Imaging. Neural network-based image segmentation models almost always contain an encoder and decoder. The encoder is for feature representation learning and the decoder is for pixel-wise classification of the feature representations.
Three major types of image segmentation techniques are commonly used on the field of computer vision:
Semantic segmentation: Associates every pixel of an image with a class label such as car, tree, fruit, person, etc. It treats multiple objects of the same class as a single entity.
Instance segmentation: Does not associate every pixel of an image with a class label. It treats multiple objects of the same class as distinct individual instances, without necessarily recognizing individual instances. For example, car 1 and car 2 are identified with different colours in an image.
Panoptic segmentation: Combines concepts of both semantic and instance segmentation and assigns two labels to each pixel of an image–semantic label and instance ID.
Figure 4: An example of semantic segmentation, instance segmentation and panoptic segmentation from a single input image.
Four images depicting an input image and three types of segmentation used on the image—semantic segmentation, instance segmentation, and panoptic segmentation.
Food image segmentation pipeline
Semantic segmentation models were deemed appropriate for our food recognition model. This is mainly due to its ability to recognize the food or drink type, as this was the primary goal of the exercise. The fully convolutional network (FCN) has been a popular choice for semantic segmentation, however, the encoder models based on the FCN down-sample spatial resolution of the input leads to developing lower resolution feature mappings. In the paper Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers, the authors proposed a new segmentation model based on pure transformer architecture termed, SEgmentation TRansformer (SETR). A SETR encoder treats an input image as a sequence of image patches represented by learned patch embedding and transforms the sequence with global self-attention modeling for discriminative feature representation learning. This model further provided more context for the food recognition task using the ReLeM as proposed by the authors in A Large-Scale Benchmark for Food Image Segmentation. Both SETR and ReLeM are further explained below.
Figure 5: Food image segmentation pipeline diagram. Sourced from A Large-Scale Benchmark for Food Image Segmentation.
Text in image: Vision encoder, fc cosine loss/semantic loss tc, Text Encoder, Ingredients: ½, cup A1 Classic Marinade, 1 boneless beef sirloin steak. Instructions: Pour marinade over steak in resealable plastic bag. Seal bag: turn to evenly coat steak with dressing. Vision encoder (weights sharing), Vision decoder
Recipe Learning Module
The ReLeM provides models with contextual information of the ingredients from food recipes. In the paper, A Large-Scale Benchmark for Food Image Segmentation, the authors describe the ReLeM as a "multi-modality pre-training approach... that explicitly equips a segmentation model with rich and semantic food knowledge".
The module was trained using a Recipe1M dataset (see: Learning Cross-Modal Embeddings for Cooking Recipes and Food Images). This dataset contains over one million recipes and 800,000 food images. Through exposure to recipes and food images, ReLeM forms associations between ingredients, similar to the way humans understand what foods are typically found together.
When training a model for food image classification, it's important to use recipes as training data. This allows the module to create associations between ingredients that may vary visually when prepared differently. ReLeM also learns from the food preparation instructions in the recipe. For example, pureed eggplant differs visually from fried eggplant. On the other hand, there may be different ingredients that look similar, such as milk and yogurt. ReLeM has established associations between ingredients and which foods commonly appear together, which is beneficial in these scenarios. For example, if the image contains a glass with a white substance and a plate of chocolate chip cookies, ReLeM could infer that the white substance is more likely milk as opposed to yogurt since there is a known association between milk and cookies. ReLeM uses cosine and semantic loss to determine the similarity between food items.
Segmentation transformer model
Transformers and self-attention models have improved natural language understanding and processing (NLU/NLP) performance. Widely popular GPT-3 (generative pre-trained transformer 3) and BERT (bidirectional encoder representations from transformer) models in NLP domain are based on Transformer architecture. The same architecture can be used for images, but this sequence-to-sequence learning expects 1D sequences at input. The state-of-the-art SETR encoder model pre-processes 2D images before feeding it to the Transformer architecture. The 2D image is decomposed to smaller fixed-size patches and then each patch is converted to a 1D sequence. This sequence of image patches is represented by learned patch embedding discussed in the paper we mentioned above about semantic segmentation. Once this sequence of feature embedding vectors are provided at input, the transformer learns discriminative feature representation which are returned at the output of the SETR encoder. The encoder model is more complex than the decoder model since it needs to learn and produce intricate feature representation for discriminating each class accurately.
Figure 6: SETR encoder sourced from Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.
The diagram illustrates the design of the Segmentation Transformer (SETR).
Text in image: Patch Embedding and Position Embedding, Layer Norm, Multi-Head Attention, Layer Norm, MLP (multi-layer perceptron). Linear Projection, 24x, Transformer Layer, Decoder
A decoder is then used to recover the original image resolution with pixel-level classification. In our case, we used the multi-level feature aggregation (MLA) decoder. The MLA decoder accepts feature representations from every SETR layer. All these feature representations share the same resolution (no loss of resolution like with FCN) and go through a series of reshaping and up-sampling to get the pixel labels.
Figure 7: MLA decoder sourced from Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers.
The diagram depicts multi-level feature aggregation. Specifically, a variant called SETR-MLA.
Text in image: Z24, Z18, Z12, Z6, reshape-conv, conv-conv-4x, conv-4x
Results
Here are the validation results based on the mean intersection over union (mIoU), mean accuracy (mAcc) and overall accuracy (aAcc) metrics:
Metric
Value
mIoU
40.74 %
mAcc
51.98 %
aAcc
83.21 %
Testing results based on the precision, recall and F1-Score metrics:
Metric
Value
Precision
81.43 %
Recall
80.16 %
F1-Score
80.79 %
Without initializing the vision encoder by ReLeM trained weights:
Figure 8: Example of a predicted mask without initializing the vision encoder by ReLeM trained weights.
Image of muffins on the left and an example of predicted masks on the right without initializing the vision encoder by ReLeM trained weights.
ReLeM trained weights
Colour
Colour Name
Predicted Category
The table cell background is coloured "Yellow Green"
Yellow Green
Bread, whole grain (whole wheat)
The table cell background is coloured "Turquoise"
Turquoise
Tea
The table cell background is coloured "Orchid"
Orchid
Apple
The table cell background is coloured "Medium Orchid"
Medium Orchid
Sweet potato
The table cell background is coloured "Magenta"
Magenta
Dumpling
With initializing the vision encoder by ReLeM trained weights:
Figure 9: Example of a predicted mask with initializing the vision encoder by ReLeM trained weights.
Image of muffins on the left and an example of predicted masks on the right with initializing the vision encoder by ReLeM trained weights.
Example of a predicted mask with initializing the vision encoder by ReLeM trained weights.
Colour
Colour Name
Predicted Category
The table cell background is coloured "Turquoise"
Turquoise
Cake
The table cell background is coloured "Dark Green"
Dark Green
Banana
Conclusion
The food recognition model accurately predicts many foods and drinks in an image in just less than a second and does consistently well with certain categories like bread but struggles with categories that are visually similar such as beef and lamb. The performance can be improved by adding more labelled data for minority categories, another round of re-categorization of visually similar foods, and using techniques to combat class imbalance.
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CVs for Total sales by geography
This table displays the results of Retail Trade Survey (monthly): CVs for total sales by geography – October 2022. The information is grouped by Geography (appearing as row headers), Month and Percent (appearing as column headers)
The Canadian Housing Statistics Program (CHSP) was launched as a modernization pathfinder projectFootnote 1 in 2017 to provide comprehensive information on non-resident ownership and the financing of residential properties. Its first release focused on the cities of Toronto and Vancouver and has since expanded to cover other geographies and topical issues through the creation of a micro-level database. This standalone database was one of the CHSP's main deliverables and involved standardizing, cleaning, and integrating various databases (e.g., property assessment rolls, land titles, Census of Population, tax data, the Business Register, and the Longitudinal Immigration Database) from internal and external data providers.
This evaluation was conducted by Statistics Canada in accordance with the Treasury Board's Policy on Results and Statistics Canada's Risk-based Audit and Evaluation Plan (2021/2022 to 2025/2026). The objective of the evaluation was to provide a neutral, evidence-based assessment of the CHSP. The evaluation aimed at providing valuable information about the relevance and usefulness of data produced by the CHSP. It also looked at some of the lessons learned from the CHSP so far to inform future direction.
The evaluation methodology consisted of a document review and interviews. Interviews were done with Statistics Canada staff (i.e., CHSP staff, staff from divisions that partnered with the CHSP, and staff from divisions that used CHSP data) as well as with users and data providers external to Statistics Canada. The findings outlined in this report are based on the triangulation of these data collection methods.
Key findings and recommendations
Relevance
Overall, users reported that the CHSP database and data products were relevant and useful and filled important existing data gaps. CHSP data were used to generally understand the housing market, prepare reports, inform policy, conduct research, and communicate with the public.
Possible improvements in areas such as timeliness, accessibility, and available data were noted. Users also identified several needs including information about the rental market, increased granularity and increased geographic coverage. The CHSP is aware of these needs and is currently exploring options to meet them moving forward.
Lessons learned and impact
Several key lessons can be learned from the CHSP as a modernization pathfinder project. These lessons include the importance of supporting innovation, recruiting and retaining skilled staff, and developing relationships with stakeholders. The CHSP also highlighted the complexity involved and the resources required to work with administrative data.
At the broader agency level, the CHSP has highlighted the need for Statistics Canada to be clear about the complexities of working with administrative data, the opportunity to continue to support partnerships and coordination across housing divisions, and the importance of supporting innovation and expediency while managing risk.
Recommendation 1:
The Assistant Chief Statistician (ACS), Economic Statistics (Field 5), should ensure a comprehensive strategic plan is developed that defines the CHSP's core priorities:
The strategic plan should consider the development of new products that meet users' needs and existing gaps, the CHSP's communications goals, and provide a roadmap on how to efficiently achieve these in a standardized and sustainable way.
The plan should be based on a risk analysis that accounts for the CHSP's evolution from a developing program to a more established one — thus impacting the balance between innovation, expediency, and risk appetite.
The strategic plan, either annual or multi-year, should be reviewed periodically by the ACS or appropriate oversight group.
Recommendation 2:
The ACS, Economic Statistics (Field 5), in consultation with relevant partner ACSs, should ensure that there are processes in place, informed by CHSP's lessons learned, to support the CHSP's continued collaboration with other partners across the agency. This includes:
Developing mechanisms and/or governance structures that support coordination and collaboration across divisions that work on housing as well as clearly defining the housing divisions' roles and responsibilities.
Assessing the CHSP's relationships with internal corporate partners (e.g., Stakeholder Relations and Engagement, the Data Integration Division, and the International Cooperation and Methodology Innovation Centre) given it is nearing the end of the first developmental phase. This assessment should identify opportunities for further collaboration, including sharing innovations the CHSP has developed, identifying opportunities to leverage internal partners' expertise, and defining their roles moving forward.
Reviewing and documenting lessons learned from the CHSP, and sharing these lessons, including innovative in-house solutions, with key partners to promote innovation and expediency.
Acronyms and abbreviations
ACS
Assistant Chief Statistician
CHSP
Canadian Housing Statistics Program
CMHC
Canadian Mortgage Housing Corporation
CODR
Common Output Data Repository
CREA
Canadian Real Estate Association
PUMF
Public Use Microdata File
What is covered
The evaluation was conducted in accordance with the Treasury Board Policy on Results and Statistics Canada's Integrated Risk-based Audit and Evaluation Plan (2021/2022 to 2025/2026). In support of decision making, accountability and improvement, the objective of the evaluation was to provide a neutral, evidence-based assessment of Statistics Canada's Canadian Housing Statistics Program (CHSP). As a modernization pathfinder project, the CHSP pursued new and innovative approaches, which presents the opportunity to gain useful lessons and insights.
The evaluation aimed at providing valuable information about the relevance and usefulness of data produced by the CHSP. It also looked at some of the lessons learned from the CHSP so far to inform the future direction of the program as well as considerations for the broader agency.
The CHSP database and data products
The CHSP was launched in 2017 to provide comprehensive information on non-resident ownership and the financing of residential properties. As part of its work, the CHSP developed a database by standardizing, cleaning, and integrating data from multiple internal and external sources (e.g., property assessment rolls, land titles, Census of the Population, tax data, the Business Register, and the Longitudinal Immigration Database). The database contains information about residential properties and residential property owners (excluding Indian reserves and collective dwellings).
The initial rationale for the database was to provide data about non-resident property owners in Vancouver and Toronto. The scope has since been expanded to continue to address users' needs by releasing new indicators and more jurisdictions. Some examples of variables within the database include the assessed value of the property, property type, square feet of living area, age of the owner, first-time home buyer status, and residency status of the ownerFootnote 2. For a complete list of variables released by the CHSP at the time of the evaluation, refer to Appendix A. The CHSP intends to continue to evolve to meet user needs as it completes its initial development stage.
Using an asymmetric approach, data and data products were released as soon as development and analysis were completed. As of January 2022, the released information covers residential properties and property owners for British Columbia, Ontario, New Brunswick, Nova Scotia, Newfoundland and Labrador, Yukon, Northwest Territories and Nunavut. Refer to Figure 1 for a timeline of the CHSP's data releases. Work is underway to add the remaining jurisdictions.
Figure 1. Timeline of the CHSP's data releases from the program's start to January 2022Description - Figure 1. Timeline of the CHSP's data releases from the program's start to January 2022
The figure 1 depicts the timeline of the CHSP's data releases from the program's start to January 2022. The following key dates are depicted in the figure:
October 2017:
Program launch
December 2017:
Preliminary Toronto and Vancouver data first published
June 2018:
Ontario and British-Columbia data first published
New ownership variables introduced
December 2018:
Nova Scotia data first published
New Ontario and British-Columbia data published
March 2019:
New residency status classification added
May 2020:
New Brunswick data first published
October 2020:
2018 and 2019 Nova Scotia, New Brunswick, Ontario, and British-Columbia property data published
March 2021:
2018 and 2019 Nova Scotia, New Brunswick, Ontario, and British-Columbia owner data published
September 2021:
2020 Nova Scotia, New Brunswick, Ontario, and British-Columbia property data published
Property use data added
Quality indicators introduced
January 2022:
Newfoundland and Labrador, Nunavut, Northwest Territories, and Yukon data first published
The evaluation
The scope of the evaluation encompassed data products and the underlying database that were produced between the program launch to January 2022. The scope was established in consultation with CHSP leadership.
The evaluation was conducted from January to May 2022 and covered products listed in Appendix A.
Two evaluation issues and four evaluation questions were identified for review (Table 1).
Table 1. Evaluation issues and questions
Evaluation issues
Evaluation questions
1. Relevance
1.1 To what extent are the data products and underlying database produced by the CHSP relevant and useful to users?
1.2 What should be considered to improve the future relevance and usefulness of CHSP data products and the underlying database for users?
2. Lessons learned and impact
2.1 What lessons can be learned from the CHSP as a pathfinder project?
2.2 How can these lessons learned be used to improve current agency practices?
Guided by a utilization-focused evaluation approach, the data collection methods outlined in Figure 2 were used.
Figure 2. Data collection methodsDescription - Figure 2. Data collection methods
The figure 2 depicts the three collection methods used for the evaluation: external interviews, internal interviews, and document review.
The external interviews included semi-structured interviews or questionnaires with federal government departments and organizations, provincial and municipal governments, private, media, and academic sectors as well as with data providers. There were 24 external interviews conducted with 29 people.
The internal interviews included semi-structured interviews with program representatives as well as internal partners and/or users who were identified by CHSP leadership as those who had worked closely with the CHSP and/or had used data from the CHSP. They included representatives from the Centre for Income and Socio-Economic Well-being Statistics, Data Integration Infrastructure Division, the International Cooperation and Methodology Innovation Centre, National Economic Accounts Division, Social Analysis and Modelling Division, Stakeholder Relations and Engagement, and Strategic Analysis, Publications and Training. There were 14 internal interviews conducted with 21 people.
The document review included a review of Statistics Canada's files, documents, and web trends information.
Three main limitations were identified, and mitigation strategies were employed (Table 2).
Table 2. Limitations and mitigation strategies
Limitations
Mitigation strategies
The perspectives gathered through external interviews may not be fully representative.
External interviewees were selected using specific criteria to maximize strategic reach for the interviews. Multiple recruitment strategies were used. Evaluators were able to find consistent overall patterns.
Interviews have the possibility of self-reported bias, which occurs when individuals who are reporting on their own activities portray themselves in a more positive light.
By seeking information from a range of stakeholders, evaluators were able to find consistent overall patterns.
Some interviewees had low familiarity with the information produced by the CHSP, limiting their ability to offer a complete response to some of the questions.
During interviews, additional information on the CHSP was provided when required. Furthermore, the data analysis took into consideration both a participant's responses to a given question and the consistency between the response and other information gathered during the participant's interview. Finally, results were presented at an aggregate level.
What we learned
1. Relevance
Evaluation question
1.1 - To what extent are the data products and underlying database produced by the CHSP relevant and useful to users?
Summary
Overall, external and internal users reported that the CHSP database and data products were relevant and useful and filled important existing data gaps. Data products, especially data tables, were used for several purposes including preparing reports, informing policy, conducting research, and communicating with the public. Possible improvements in areas such as timeliness, accessibility, and available data were noted.
The CHSP was viewed as useful and relevant and was expected to continue to be so. Data were used to generally understand the housing market, prepare reports, inform policy, conduct research, and communicate with the public.
Overall, almost all external and internal users reported that the CHSP was useful and relevant and indicated they would be interested in using the data again. Users reported several benefits of the CHSP including filling long-standing data gaps, informing conversations on hot topics, and providing evidence to help inform policy. For example, users reported that without the CHSP data, there would be a lot more guessing and estimating, particularly with topics like non-resident ownership, housing stock, and owner occupancy. As shown in Table 3, CHSP data were used for multiple purposes depending on the type of user. A few users indicated that the analytical products were useful to inform the methodology of their work.
Table 3. Common uses of CHSP data by user group
Federal government
(both internal Statistics Canada users as well as other departments)
Gain a general understanding of the housing market and monitor its current state (e.g., investment in housing, foreign ownership, participants in the housing market)
Add background context or support analyses in reports and briefs
Inform policy (e.g., formulate and evaluate tax policy)
Cost government programs and announcements
Inform conversations with media (e.g., refer journalists to reports with data)
Support work by connecting CHSP data to other databases (e.g., use industry information from the CHSP to help classify housing unit stock)
Provincial and municipal government
Gain a general understanding of the housing market and trends
Add background context or support analyses in reports (e.g., modelling the housing market)
Communicate with the public (e.g., via a provincial daily statistics email service)
Update or brief politicians
Support an audit
Academia and private consultants
Gain a general understanding of the housing market
Communicate with the public and politicians
Use in analyses for reports and peer-reviewed research articles
Private sector
Gain a general understanding of the housing market
Inform forecasts and assessment of market conditions
Media
Inform media articles
Many of the variables in the CHSP were relevant to users. Topics that were of particular interest included understanding participants in the housing market (including non-resident and immigrant owners, investor ownership, and owners' incomes), owner occupancy, and the housing stock (including property characteristics).
Users also appreciated that the CHSP data supported comparisons across jurisdictions. Even if users primarily focused on data from certain jurisdictions, such as Ontario or British Columbia, it was perceived as valuable to have multiple jurisdictions to be able to compare with and to provide additional context. Another benefit was that the CHSP published data products that were publicly available.
The relevance and usefulness were expected to increase for both external and internal users as the program expands its coverage and develops a time series, as housing was predicted to continue to be a topic of importance moving forward.
Some data gaps, including the granularity of available data, geographic coverage, and not including the rental market, influenced its relevance and usefulness.
While users were appreciative of the information available through the CHSP, they identified several gaps that affected its relevance. The most common need for external users was for information about the rental market. Currently, the CHSP covers owners and properties. It does not cover the number of dwellings (i.e., units) in a property, which impacts understanding the rental stock and the housing stock more generally. Many users wanted information about the profile of renters and rental housing (e.g., condition of buildings, vacancy rates, evictions). This was perceived as important to answer key questions about affordability especially given the proportion of the population that rents. Within the rental market, some users also wanted more information about subsidized or affordable housing, including demographics and whether residents are receiving rent supplements versus living in public or non-profit housing. There was also interest in gaining a better understanding of who owns rental structures and rental housing developments. This included specifying whether owners are institutional investors, corporations, pension funds, or individuals. Users recognized the difficulties in acquiring and integrating rental data.
Many external users also wanted more granularity in the data. While some users had a higher-level or more regional lens to their work, others, such as municipalities, needed more detailed data to make the most use of it. They reported having information at the census tract as well as more easily accessible microdata, where possible given privacy considerations, would be helpful.
Another key gap that impacted many external and internal users was that the CHSP does not yet cover all provinces and territories in Canada. For some users, this meant that the CHSP could not be their primary or only source of data or could not be used in reports given they needed a nationwide perspective.
In addition to these three gaps, external and internal users also identified the following areas as needs:
Information about financing (e.g., how are people getting in the market and staying in the market, including topics like mortgages, gifts, the size of down payments made, levels of indebtedness)
Building a time series, both moving forward as well as a historical time series to inform unresolved questions from 2016 and earlier
Additional information about the properties (e.g., number of dwellings, information about additional types or uses of properties like laneway houses or vacation properties, what the property is being used for if it is not owner-occupied)
Information about market price instead of only assessed value
Additional information about owners and residents, including equity deserving groups, non-individual owners, inter-jurisdictional or intra-jurisdictional property owners, and who else is living in the household
Information about mobility and migration (e.g., how often and where are people moving from/to)
Bridging information about housing stock with information about the flow
While users accessed other housing data sources, they were viewed as complementary to the CHSP.
External and internal users often used other housing data sources, including:
Other Statistics Canada products (e.g., the Census, Canadian Housing Survey)
Canada Mortgage and Housing Corporation (CMHC) products (e.g., housing market reports, rental market reports, housing starts and completions data tables)
Municipal, provincial, or territorial databases (e.g., BC Assessment, BC Housing, Yukon Housing Corporation)
Teranet-National Bank House Price Index
Canadian Real Estate Association (CREA), MLS, and local real estate boards
Rental websites that they web-scraped
Users perceived minimal duplication between these other data sources and the CHSP (excluding cases where the source was an input into the CHSP like BC Assessment data). They were generally viewed as complementary sources.
Other sources were used because they contained additional variables (e.g., flow information, rental information), were timelier (e.g., sales data), had more granularity, had better geographic coverage, or the user had direct access to them (e.g., a jurisdictional data provider). Because of these reasons, a few users noted that they used the CHSP as a secondary or supplemental source of data. However, not all other data sources were perceived to be of high quality. Users also reported gaining access to other data sources could be costly or subject to non-disclosure agreements, which limited how their analyses could be used.
External users primarily used the data tables on the website to support their needs.
The CHSP developed, contributed to, and produced several data, methodological and communication products based on the underlying database, including:
Common Output Data Repository (CODR) tables available through the website
The Daily releases
Analytical products that are published in the Economics Insights publication or the Housing Statistics in Canada publication (which was developed by CHSP)
Standalone or embedded infographics
Thematic maps
The Canadian Statistical Geospatial Explorer Hub (formerly the Housing Data Viewer)
Customized data products
Microdata files available through Research Data Centres
Metadata
A Quality Assurance Framework which provides quality indicators for administrative data
More information about these products can be found in Appendix A.
According to external users interviewed, the most common method of accessing the CHSP data was through the CODR tables. They reported they liked to access data directly from the tables and then use it for their own purposes. The second most common method was through the analytical products such as publications in Economic Insights or Housing Statistics. According to web metrics acquired by the program, the tables and The Daily articles have the most views per month—comprising 73% of the CHSP's website views.
While experienced external users found that data products were accessible, less experienced users reported some challenges navigating the website or finding relevant tables. Potential opportunities for improvement were noted.
External users who regularly used Statistics Canada's website generally found the data products, especially the tables, easy to access. A few used an R statistical package to pull the data, which facilitated their access.
Some external users though, especially those who did not regularly use Statistics Canada's products, experienced challenges navigating the website or accessing the data. Challenges included using the search engine on the website and understanding which table was relevant for them or the differences between the tables, especially given the diversity of data the CHSP covers. A few users noted it was easier to go through an external search engine like Google instead of searching on Statistic Canada's website. Although they were not asked specifically about the Housing Portal, only one user mentioned the Housing Portal by name as a way of finding data. No other users commented on the Housing Portal, either negatively or positively.
CHSP staff helped to facilitate external users' access. Some users reported they engaged with CHSP staff who directed them to the appropriate table because it was easier than trying to find it themselves. CHSP staff also reached out to several external users to inform them of an upcoming release that may be of interest, which supported their awareness of data products.
Overall, users found the methodology clearly communicated and perceived the CHSP data to be of high quality.
Most external and internal users indicated the methodology was sufficiently clear to them for their purposes and perceived the data to be of high quality. In some cases, users shared that they had not looked for detailed information about the methodology and trusted Statistics Canada to do appropriate checks on the quality. A few external users indicated that the data quality indicators were helpful and informed their use of the data.
When external users reached out to clarify methodology with program staff, they were perceived as very helpful and contributed to reducing misinterpretations. Areas where external users needed more information or had observed others misinterpreting the data included:
Interpreting specific definitions (e.g., non-resident vs. foreign, property vs. dwelling, assessed versus market value, the different categories of corporate investors, affordability)
Understanding transformations performed and/or the linkage process (e.g., which years of data had been linked for tax and assessment value data, what uncertainties there were with the linkage process)
One external user also noted they could not identify which data were used in analytical products when comparing with published data tables.
While the CHSP data were timely for some users, others noted the lag time impacted their ability to use the CHSP data for up-to-date monitoring of the housing market.
While some users indicated the data were timely, especially as housing is a hot topic, others noted that the lag time in data releases impacted how they could use the CHSP data. In particular, not having up-to-date data during the COVID-19 pandemic affected its usefulness. They felt the CHSP was better suited to inform longer-term understanding instead of current or up-to-date monitoring. Users acknowledged the difficulties in having timely data due to the delays that are outside the CHSP's control, such as in processing assessment and tax data.
Some external users expressed that having more frequent releases (e.g., quarterly for some indicators) would be beneficial. However, annual releases, especially once a time series was built up, were still appreciated and were viewed as filling a gap between census years.
Evaluation question
1.2 - What should be considered to improve the future relevance and usefulness of CHSP data products and the underlying database for users?
Summary
Users identified several areas of improvement for the CHSP that would support data products to be more relevant and useful for their needs. These needs include filling data gaps, increasing timeliness, improving accessibility, and improving clarity about the methodology. Due to ongoing engagement with users, the CHSP is aware of these key data gaps and needs and is currently exploring options to meet them.
Engagement with users has supported the CHSP to be aware of key gaps that affect the relevance of its data. The CHSP plans to address these gaps when possible given limitations in data availability.
The CHSP regularly engages with its external users to inform the program's direction. They have a "parking lot" of ideas where they store and prioritize users' requests. Through their user engagement, the CHSP is aware of the gaps identified in this evaluation, such as the granularity of data, geographic coverage, and the rental market.
They have plans to explore opportunities to address users' needs where possible recognizing that there are several challenges including data availability, data quality, and cost as well as privacy considerations. In addition to filling gaps in available data, they also are working on streamlining and standardizing their processes to increase the timeliness of data releases. Given the number of users' needs, staff identified that it will be important for the CHSP to prioritize needs (considering importance as well as feasibility) to allow staff to focus on completing key tasks.
Some ways the program is currently aiming to meet users' needs include expanding geographic coverage. This is a high priority, and they are currently working on adding the remaining jurisdictions. They also are looking into acquiring additional data sources and partnering with new data providers to help address some of the identified gaps. Another way they are trying to fill data gaps is by developing innovative methods, such as using artificial intelligence and Google Maps.
Additionally, the CHSP wants to increase the granularity of data released by developing a synthetic micro-level data set for the general public. This is a similar concept to Public Use Microdata Files (PUMFs) and addresses key privacy and confidentiality concerns because all the data are synthesized.
While the CHSP is aware of data gaps, a few external users identified that it would be beneficial to conduct further engagement with users and partners about which analytical products would be helpful. This is particularly the case if an analytical product focuses on one specific jurisdiction instead of looking across all available jurisdictions.
Users suggested several ways to improve the accessibility of the data and the clarity of the methodology.
In addition to filling data gaps, external users identified a need to improve the accessibility of CHSP data. Suggestions included:
Having videos or webinars to orient users to Statistic Canada's website/tables
Making a specific page for the CHSP that shows the latest releases or data
Clarifying what information is included in each data table
Categorizing tables thematically
Having non-static tables that allowed users to select the data for the subset they were interested in (versus the current tables that are fairly fixed in terms of what can be included/excluded)
Allowing users to calculate percentages within the CODR tables
Making the microdata more accessible (e.g., through a PUMF or improving remote access options)
Providing examples for different audiences on the website (e.g., "if you are municipal staff, you may want to look here")
Providing a publishing schedule so users can prepare for the data
While generally the methodology used was clear, external users identified opportunities to further improve the clarity and reduce misinterpretations. Suggestions included:
Highlighting when terminologies may be different than other Statistics Canada products (e.g., non-resident versus foreign)
Providing definitions in both technical and plain language and ensuring all definitions are included in the glossary
Highlighting any key differences in data that would affect comparability between provinces or overtime
Increasing accessibility of metadata and methodology (e.g., a directory of methodological papers or tab showcasing the structure to different methodology-related pages)
Keeping historical records of data tables that get updated to support reproducibility of previous work
Explaining why some variables are only available at certain levels (e.g., provincial, census metropolitan area, census subdivision)
Providing more support to help users understand what can be done with the data or what can be asked to support use and reduce misinterpretation (e.g., the current data are about stock, not about flow)
2. Lessons learned and impact
Evaluation question
2.1 - What lessons can be learned from the CHSP as a pathfinder project?
Summary
Several key lessons can be learned from the CHSP as a modernization pathfinder project. These include the importance of supporting innovation, recruiting and retaining skilled staff, and developing relationships with stakeholders. The CHSP also highlighted the complexity involved and resources required to work with administrative data and the need to balance innovation and expediency with risk management.
The CHSP has had many successes so far, including creating a comprehensive database of residential properties and owners despite the complexity of this task. Several factors contributed to their successes, including encouraging and implementing processes to increase efficiency as well as having a strong team, intentional stakeholder engagement, and the ability to be flexible, dynamic, and innovative.
The CHSP has had many successes over the last five years, including those that move forward Statistics Canada's modernization program and each of the modernization pillarsFootnote 3. One of the CHSP's most notable successes, creating a comprehensive database of residential properties and owners by linking over 20 different data sources (including unstructured and unformatted data), advanced the Leading-edge Methods and Data Integration pillar. This task was very complex and involved many internal and external partners.
Through their work creating the database, the CHSP also contributed to leading-edge statistical advancements, frameworks, and processes. These advancements include developing data linkage processes, partnering with Statistics Canada's International Cooperation and Methodology Innovation Centre to create quality indicators for administrative data (a first internationally), and exploring the use of tools like artificial intelligence to fill data gaps. This is another example of how the CHSP contributed to the Leading-edge Methods and Data Integration pillar as well as the Sharing and Collaboration Modernization pillar due to its work with internal partners. The CHSP's work also led them to contribute more broadly to Statistics Canada's frameworks and processes. For example, CHSP staff supported the development of Statistic Canada's Necessity and Proportionality Framework and have helped to refine data acquisition processes for the agency.
One factor that supported the CHSP's achievements was staff and management's desire to develop and implement processes to innovate and increase efficiency to meet their project timelines and deliverables. Some examples of processes they developed, in addition to their work standardizing and streaming processes across multiple phases (e.g., processing, production), include:
A web metrics dashboard from web scraping the Statistics Canada's website
A machine learning Python algorithm to review resumes to identify skilled candidates
An online platform to record questions received from users and related documentation
An integrated release workflow to reduce the amount of manual work required for The Daily releases by using LaTeX
Another enabler was their ability in assembling a team of passionate individuals with diverse skills. The CHSP was able to do this in part because candidates were interested in contributing to highly innovative, leading-edge work. They also used various recruitment mechanisms, such as requesting Human Resources provide the entire list of candidates so they could use algorithms to review resumes, recruiting from across the country, and promoting that they were hiring at public events they were at. This supported them to hire many skilled and knowledgeable candidates. Once candidates were hired, the CHSP provided support such as assigning mentors and promoting professional development. They also started having team members work on multiple stages of a process, which has helped staff to stay engaged and develop more knowledge about the program. The work done to hire talented staff and develop a supportive environment supports the Modern Workforce and Flexible Workplace pillar.
The CHSP also actively engaged with and developed relationships with users and data providers to help better understand their needs. This allowed them to be up to date about user needs, support users to access and use the data (including customized requests), and change directions or priorities according to users' needs. They have also reached out to international partners, including the Organisation for Economic Co-operation and Development, and built relationships with academic institutions, such as Ryerson University. This work has helped the CHSP advance the User-centric Delivery Service pillar as well as the Statistical Capacity Building and Leadership pillar as staff supported users to understand how to use the data correctly. CHSP staff reported that they benefited from being a new program as they had the flexibility and opportunity to respond to users' needs in ways that were not possible for more established Statistic Canada programs. Developing relationships with data providers also led to stronger partnerships and a smoother data acquisition process. Staff reported that it was beneficial having a direct link with data providers so that their team could communicate efficiently to troubleshoot technical problems.
The CHSP's experience highlighted the challenges of working with administrative data, developing an adaptable framework that can accommodate future changes, and coordinating housing work done across Statistics Canada.
The CHSP's work highlighted the challenge and complexity involved in working with administrative data. Challenges were encountered across all stages of working with the data, including addressing privacy concerns to acquiring, cleaning, validating, and processing data. In particular, the CHSP highlighted the general challenges associated with the privatization of data as well as the associated costs of acquiring data, which in some cases can be prohibitive. The CHSP also experienced challenges in making sure that the data ultimately are useful and comparable, especially because data providers across jurisdictions collect and store data differently. Addressing these challenges related to working with administrative data required sufficient resources, having time to make sure things were done properly, and working on standardization processes to increase timeliness. It was also beneficial for the CHSP to partner with two internal divisions, Strategic Analysis, Publications and Training and the Social Analysis and Modelling Division, to prepare analytical products early on as this helped to test the data.
Given the dynamic nature of the program and the asymmetrical approach it took to releasing data, the CHSP also demonstrated the need to have an adaptive framework and strategic plan that can accommodate future changes. The CHSP was under pressure to deliver results quickly, which required working with data as it was acquired instead of waiting to acquire and process all data simultaneously. Given challenges in acquiring data from all jurisdictions in a timely manner, this approach meant the CHSP was able to publish data for at least some jurisdictions. However, this approach also meant that the CHSP had to consider how easy it would be to revise previous data. The pressure to deliver also meant that there was a lot of focus on getting things done and evolving to meet users' many and immediate needs. Staff noted though that it could be beneficial to take the time to plan a roadmap and ensure priorities and processes are efficient, standardized, and focused.
In Statistics Canada, several divisions work on or with housing data. The CHSP's experiences highlighted that there are opportunities to continue to promote coordination amongst these divisions. While the CHSP convened a working group for these divisions and participates in joint committees with external partners, it was noted there were varying levels of participation and that more work could be done to have improved coordination and one voice for housing. Collaboration was viewed as more dependent on an individual's actions instead of structures or mechanisms, which could result in duplication or gaps. Additionally, it was reported that the dispersed nature of housing meant external partners did not always know which division to approach with housing-related requests. This meant there were occasions where divisions responded to external requests when another division could have also contributed to the work.
The CHSP has shown potential risks that need to be considered, including the need to support staff retention, the reliance on being able to continue to obtain external data sources, and the balance required to manage innovation and expediency with risk.
There are also lessons to be learned from the potential risks the CHSP faces. One risk is staff retention and the associated risk of loss of knowledge. The CHSP has experienced staff turnover due to a variety of factors, such as moving due to promotions, being on rotations, or language requirements. Some staff attributed turnover in part due to their success in attracting strong candidates who end up advancing quickly in their careers. They also noted that while remote work was beneficial because it could allow for recruitment from across Canada, it also made it more difficult to develop a team dynamic and onboard people in a large agency like Statistics Canada. Suggestions to reduce turnover included continuing to recognize staff achievements, assign varied and interesting work, continue to innovate, support staff development, have a webinar that walks through Statistics Canada's organizational structure, allow staff to focus on a few priorities, and communicate a vision.
Another risk is the reliance on external data providers for data. Because the CHSP does not collect its own data, it must be able to continue to acquire data from its external partners. This makes the CHSP vulnerable to risks such as increasing data costs or data providers no longer being willing to provide the data. Given the importance of continuing to acquire the data, it will be important to maintain relationships with data providers. Some data providers suggested that it would be beneficial to close the loop about what has occurred with the data and share opportunities on how to use the data with them. The CHSP is also reliant on being able to obtain additional data that are important for its continued relevance, such as financial data. This requires overcoming challenges such as privacy concerns, costs, and availability.
As a pathfinder project, the CHSP was expected to contribute to new and innovative thinking as well as use an experimental approach to program delivery. They had pressure to deliver a program quickly, and the work was very complex due to the amount and type of administrative data they acquired. To accomplish its deliverables, the program pushed for innovation and efficient solutions. As a result of this, and because of the skills their staff had, they developed many in-house processes. While this supported their goals of delivering the database under a time pressure, their experience also highlights the balance between promoting innovation with managing risk. This can be seen across several of their processes, including data linkage, methodology, and communications. For example, while their in-house processes have supported them to deliver results, the CHSP is more reliant on individual staff's knowledge than it would be if it partnered more extensively with Statistics Canada's service providers, such as the International Cooperation and Methodology Innovation Centre, the Data Integration Division, and Stakeholder Relations and Engagement. Another example is the CHSP's relationships with media stakeholders. They have developed their own relationships which are beneficial and allow for efficiency. However, Statistics Canada's Communication Branch has processes to manage risk, so if they are not engaged early in the process there is a trade-off between expediency and risk. This example highlights the inherent trade-off tension between delivering results quickly while still needing to go through all the agency's existing approval processes.
Evaluation question
2.2 - How can these lessons learned be used to improve current agency practices?
Summary
Learnings from the CHSP can also be applied to the broader context and practices of Statistics Canada. Key lessons include the importance of being clear about the complexities of working with administrative data, supporting partnerships and coordination across housing divisions, and supporting innovation and expediency while managing risk.
At the broader agency level, the CHSP highlighted a key lesson of having clear expectations about the role of administrative data as well as the importance of having sufficient resources, relationships with data providers, and social consent.
As a pathfinder project, lessons from the CHSP can be considered more broadly in the context of Statistics Canada's practices. One important lesson from the CHSP is the importance of clear expectations, internally as well as externally, about the role of administrative data. This includes acknowledging what it can and cannot do. While there was perceived value in acquiring administrative data to answer key questions, staff identified the importance of understanding the resources (i.e., expertise, time, and funding) required to acquire and work with administrative data. Additionally, staff suggested that future projects with administrative data should not be rushed and ensure there is time to understand the data.
Working with administrative data at this scale also highlighted that Statistics Canada will have to consider what approaches it is willing to take to acquire data. While Statistics Canada's legislative authority was a factor in why data providers were able to share data with the CHSP, it was still important to build relationships with them. Data providers indicated that being open, responsive, and respectful as well as taking time to build trust and a partnership facilitated their relationship with Statistics Canada. To support acquiring data for programs such as the CHSP, internal staff also felt it was important for Statistics Canada to continue to demonstrate the value of this work to Canadians and to get social consent.
Supporting internal partnerships was a key lesson in strengthening work and reducing duplication.
The CHSP showed the importance of supporting internal collaboration within Statistics Canada. Collaboration helped to strengthen divisions' work and was perceived as a way to develop sustainable, standardized solutions. For example, the International Cooperation and Methodology Innovation Centre helped develop quality indicators for the CHSP and the CHSP helped improve corporate tools developed by the Data Integration Division with their expertise in data matching and property assessments. The internal partners shared that it was beneficial to develop partnerships instead of only being seen as service providers. They also highlighted that being engaged early in the project would increase the value of their contributions, reduce duplication, and would support proactive rather than reactive engagement. Sharing goals, deliverables, and long-term projects would also help with alignment between divisions. One internal partner suggested that formalizing collaborations on projects at the working level, in addition to the senior executive level, would be valuable.
There are opportunities to continue to support coordination between divisions that work on housing.
The CHSP also highlighted the importance of continuing to support coordination and alignment between divisions that work on housing across Statistics Canada. This includes establishing clear roles and responsibilities for these divisions. Time restraints and the need to focus on their own deliverables were perceived as barriers to collaboration. Internal staff reported that current systems were not robust enough to avoid duplication or gaps amongst housing divisions. Suggestions to support coordination included having one housing division, more joint collaborative meetings, program leadership on other programs' steering committees, or a focal point for housing. Past presentations about the CHSP and its database were also seen as helpful. Additionally, supporting increased internal collaboration was perceived as not only beneficial for internal processes but would also support engagement with key external partners.
The CHSP used innovative approaches, but their experience highlights the inherent trade-off between risk management and expediency.
The approaches the CHSP used to support innovation provide an important lesson for the broader agency. Having a real project with objectives, a need to produce, and constraints gave focus to the work done by the CHSP. Staff reported that having an entrepreneurial spirit and an openness to being innovative were beneficial. One suggestion to support innovation moving forward was to root development initiatives within a program. Co-locating teams could help develop integrated, sustainable solutions. While there were trade-offs, using an agile continuous development approach that pushed things out before acquiring data for all jurisdictions was generally viewed as a novel approach and a strength. Being able to develop in-house solutions also supported the CHSP try new things without being tied to existing processes. The CHSP's innovative work though also highlights the inherent trade-off between expediency and risk and the need to balance these. Some staff reported that a higher risk tolerance, greater staff empowerment, and streamlining approvals would support them to do their work. There is a future opportunity to determine the desired risk tolerance for pathfinder projects, such as the CHSP, while acknowledging that individual programs do not stand alone and have an impact on the agency's processes and reputation.
How to improve the program
The CHSP has had a strong start in developing relevant and useful data for users, especially accounting for the complexity of the data and length of time it has been operating. The CHSP's users have many and evolving housing data needs, many of which are difficult to meet with available data sources. Given that the CHSP is now transitioning out of its initial development phase, there is the opportunity for the CHSP to work on its strategic plan to define its priorities and provide a roadmap of how to achieve their goals. A strategic plan would also help to prioritize the development of new data products that fill existing gaps and meet users' needs.
Recommendation 1:
The Assistant Chief Statistician (ACS), Economic Statistics (Field 5), should ensure a comprehensive strategic plan is developed that defines the CHSP's core priorities:
The strategic plan should consider the development of new products that meet users' needs and existing gaps, the CHSP's communications goals, and provide a roadmap on how to efficiently achieve these in a standardized and sustainable way.
The plan should be based on a risk analysis that accounts for the CHSP's evolution from a developing program to a more established one — thus impacting the balance between innovation, expediency, and risk appetite.
The strategic plan, either annual or multi-year, should be reviewed periodically by the ACS or appropriate oversight group.
As a pathfinder project, the CHSP has several lessons learned that can be considered more broadly at the agency level such as the importance of collaboration within Statistics Canada. Moving forward, continuing to support collaboration with key partners could help to increase coordination amongst divisions that work on housing, identify how to best leverage partners' expertise to support the CHSP as it moves forward into its next phase, and share learnings and innovative processes from the CHSP.
Recommendation 2:
The ACS, Economic Statistics (Field 5), in consultation with relevant partner ACSs, should ensure that there are processes in place, informed by CHSP's lessons learned, to support the CHSP's continued collaboration with other partners across the agency. This includes:
Developing mechanisms and/or governance structures that support coordination and collaboration across divisions that work on housing as well as clearly defining the housing divisions' roles and responsibilities.
Assessing the CHSP's relationships with internal corporate partners (e.g., Stakeholder Relations and Engagement, the Data Integration Division, and the International Cooperation and Methodology Innovation Centre) given it is moving out of its development phase. This assessment should identify opportunities for further collaboration, including sharing innovations the CHSP has developed, identifying opportunities to leverage internal partners' expertise, and defining their roles moving forward.
Reviewing and documenting lessons learned from the CHSP, and sharing these lessons, including innovative in-house solutions, with key partners to promote innovation and expediency.
Management response and action plan
Recommendation 1:
The ACS, Economic Statistics (Field 5), should ensure a comprehensive strategic plan is developed that defines the CHSP's core priorities:
The strategic plan should consider the development of new products that meet users' needs and existing gaps, the CHSP's communications goals, and provide a roadmap on how to efficiently achieve these in a standardized and sustainable way.
The plan should be based on a risk analysis that accounts for the CHSP's evolution from a developing program to a more established one — thus impacting the balance between innovation, expediency, and risk appetite.
The strategic plan, either annual or multi-year, should be reviewed periodically by the ACS or appropriate oversight group.
Management response
Management agrees with the recommendation.
The CHSP will develop a comprehensive strategic plan outlining CHSP's short (1 year), medium (3 years) and long term (5+ years) priorities based on a risk analysis. The plan will include:
An elaboration of new data and data products based on the needs of its users founded on extensive and ongoing stakeholder engagement. As part of the risk analysis, the development of new data and data products will consider quality, relevance, timeliness, data availability, processing complexity and the costs involved in creating new data products.
A road map, which includes optimization and standardization strategies to produce efficiencies. Consultations with internal stakeholders will be conducted as part of the risk analysis to ensure the solutions being proposed lead to a balanced risk profile for the stability and agility of the program.
A proactive CHSP communications plan developed in collaboration with Field 4 that seeks the right balance between innovation, expedience and risk appetite to enhance Statistics Canada's role as a leading centre for Canadian housing data and expertise.
Deliverables and timelines
A comprehensive 5-year strategic plan approved by ACS. The strategic plan will be reflective of a risk analysis, address new products that meet users' needs and existing gaps, and include a communication plan and a roadmap. (February 2023 and annually refreshed afterwards)
Recommendation 2:
The ACS, Economic Statistics (Field 5), in consultation with relevant partner ACSs, should ensure that there are processes in place, informed by CHSP's lessons learned, to support the CHSP's continued collaboration with other partners across the agency. This includes:
Developing mechanisms and/or governance structures that support coordination and collaboration across divisions that work on housing as well as clearly defining the housing divisions' roles and responsibilities.
Assessing the CHSP's relationships with internal corporate partners (e.g., Stakeholder Relations and Engagement, the Data Integration Division, and the International Cooperation and Methodology Innovation Centre) given it is nearing the end of the first developmental phase. This assessment should identify opportunities for further collaboration, including sharing innovations the CHSP has developed, identifying opportunities to leverage internal partners' expertise, and defining their roles moving forward.
Reviewing and documenting lessons learned from the CHSP, and sharing these lessons, including innovative in-house solutions, with key partners to promote innovation and expediency.
Management response
Management agrees with the recommendation.
The CHSP will engage in a consultation/review exercise:
With internal housing stakeholders to determine appropriate mechanisms or governance structures to better strategically align the work of the CHSP with other housing program areas, enable more collaboration, and avoid duplication, to better serve Canadians and meet the evolving housing data needs of its users.
With internal corporate partners to assess current collaborations, identify opportunities for further collaboration, and share innovations with a goal of defining roles moving forward with the aim of reducing costs, improving timeliness and data quality. As part of this assessment, readiness and capacity of internal partners would be considered.
Internally, to identify key lessons learned that are deemed relevant and useful for the agency with its key partners. Lessons will be shared with divisions that are implicated in the lessons learned.
Deliverables and timelines
Documented governance structures, processes and/or roles and responsibilities developed in collaboration with internal partners. (April 2023 and periodically refreshed)
Documented assessment of internal partnerships and identification of opportunities for further collaboration. (January 2023 and periodically refreshed)
Lessons learned from the CSHP documented and shared with key partners. (November 2022)
Appendix A – Variables and data products released by the CHSP included in evaluation scope
List of variables released by the CHSP included in the evaluation scope
Admission category of immigrant, category
Age of property owner, category
Age of property owner, number
Assessment value of residential property, category
Assessment value of residential property, value
Birth year of property owner, category
Condominium status of residential property, category
Employment income of person, status
Family income of person, value
Family size of census family, number
First-time home buyer status of person, status
Geographic location of residential property, name
Home buyer's amount claimant status of property owner, status
Immigrant status of person, category
Industry of property owner, type
Legal type of property owner, category
Living area of residential property, area
Living area of residential property, category
Marital status of person, category
Number of buyers as part of a property sale of property buyer, category
Number of owners of residential properties, category
Number of residential properties, count
Number of residential properties owned of property owner, category
Ownership type of property owner, category
Ownership type of residential property, category
Owner status of person, category
Period of construction of residential property, category
Place of birth of person, name
Price-to-income ratio of property buyer, number
Property type of residential property, category
Property use of residential property, category
Residency status of property owner, category
Residency status of residential property, category
Residential properties assessment value of property owner, category
Sale price of property, value
Sales of property, status
Sex of person, category
Tax filer status of person, status
Total income of person, value
Type of census family, category
Table 4. List of data products released by the CHSP included in the evaluation scope
Type
Data products
CHSP Data Tables: Common Output Data Repository (CODR) Tables
Common Output Data Repository tables are available publicly on the Statistics Canada website and provide data that covers a variety of indicators available from the CHSP. Users can customize the tables.
Analytical Products: Housing Statistics in Canada / Economic Insights Publications
The Housing Statistics in Canada is a new publication that was created by the CHSP to provide insights on housing data and analysis. Readers can access in-depth information on the latest housing data released by the Agency. The series relies on both descriptive and analytical methods to analyze administrative and survey data sets that relate to housing.
Publications in Economic Insights highlight issues related to the growth and development of Canada's economy.
Canadian Statistical Geospatial Explorer (formerly Housing Data Viewer)
The Housing Data Viewer was created by the CHSP to allow users to visualize Statistics Canada's Housing data with eventually other surveys, censuses and trusted data sources. This is now possible within the Canadian Statistical Geospatial Explorer, where all data that was available in the viewer can now be explored with Census, Health and other community data.
The CHSP introduced thematic maps to help people, business owners, academics, and management at all levels, to understand key information derived from the data by representing it visually at the geographical level. The thematic maps are intended to quickly communicate a message, simplify the presentation of large amounts of data, see data patterns and relationships and monitor changes in variables over time.
Statistics Canada provides a description of and documentation for the CHSP as well as information about data sources, methodology, data accuracy, variables, changes, reference periods, and related products.
Research Data Centres (RDCs) promote and facilitate research that uses Statistics Canada microdata within secure facilities. Approved users can access microdata files from the CHSP database.
Statistics Canada provides professional services to identify users' specific information needs. This service includes custom data tables, maps, research and analysis.
The Housing Portal is a page that provides access to various data products (e.g., thematic maps, articles, housing data viewer, key indicators) about housing.
The Daily is Statistics Canada's official release bulletin, the Agency's first line of communication with the media and the public. The Daily releases have covered data from the CHSP.
Statistics Canada is pleased to announce the official reappointment of Dr. Howard Ramos as Chairperson of the Canadian Statistics Advisory Council (CSAC), which took place on November 29, 2022. I wish to congratulate Dr. Ramos on his appointment and express appreciation for the leadership role he played over the past three years and will continue to play throughout this next term.
The agency would also like to thank CSAC for their third annual report for 2022: Trust, Governance and Data Flows in the National Statistical System. We are encouraged by the conclusion that Statistics Canada is on the right path, and the tangible contribution made by CSAC, through this report and ongoing advice throughout the year, will no doubt help strengthen the National Statistical System, which will make meaningful contributions to the lives of Canadians.
We look forward to furthering the work between Statistics Canada and CSAC in our efforts towards maintaining a strong legal, and socially accepted framework, and to welcoming new members joining the Council over the coming months.
Anil Arora
Chief Statistician of Canada
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0
0
0
0
0
3. Other Renewable Fuel Plant Feedstocks
a. Agricultural Biomass Residues
b. Forestry Biomass Residues
c. Municipal Solid Waste
d. Animal Fats
e. Other Biomass Residue Feedstocks
f. Used Cooking Oil
g. Methanol
Other Renewable Fuel Plant Feedstocks, TOTAL
0
0
0
0
0
0
0
Renewable Fuel Plant Feedstocks, TOTAL
0
0
0
0
0
0
0
Renewable Fuels
(Cubic Metres)
4. Fuel Ethanol (denatured)
5. Renewable Diesel Fuel
a. Biodiesel Fuel (FAME)
b. Renewable Diesel Fuel (HDRD/HVO)
c. Other Renewable Diesel Fuel
Renewable Diesel Fuel, TOTAL
0
0
0
0
0
0
0
6. Other Renewable Fuels
Renewable Fuels, TOTAL
0
0
0
0
0
0
0
Renewable Fuel Plant Co-Products
(Metric Tonnes)
7. Distillers Grains
a. Dried Distillers Grains (DDGS)
b. Wet Distillers Grains with Solubles (WDGS)
Distillers Grains, TOTAL
0
0
0
0
0
0
0
8. Industrial Ethanol (including Food Grade)
9. Distillers Corn Oil
10. Glycerol/Glycerine
11. Other Co-Products
Renewable Fuel Plant Co-Products, TOTAL
0
0
0
0
0
0
0
Comments: Identify any unusual aspects of your reporting month's operations.
Labour market activity and school attendance (ACT1)
Labour market activity and school attendance (ACT1)
ACT1_R01
The next questions are about your activities between January and December 2022, as well as the activities of other members of your household.
ACT1_Q01
Q2
Did you work at a job or business in 2022?
1. Yes
2. No
ACT1_Q05
Q3
During 2022, how many weeks did you work at a job or business?
Count every week worked, no matter the number of hours. Include: vacation, maternity or parental leave, illness, strikes, lock-outs.
Please include weeks not worked in 2022 due to circumstances surrounding the COVID-19 pandemic if these weeks were paid by the employer.
ACT1_Q10
Q4
During those weeks, how many hours did you usually work per week at all jobs?
If the number of work hours varied from week to week, please provide an average.
ACT1_Q15
Q5
Considering all the jobs you held in 2022, did you work:
Select all that apply.
1. As an employee
2. As self-employed
3. In a family business without pay
ACT1_Q20
Q6
During 2022, how many weeks were you without work and looking for work?
Include temporary lay-offs. Exclude weeks as a full-time student.
ACT1_Q25
Q7
What was your main activity during the weeks when you were neither working nor looking for work?
1. Ill, or disabled and unable to work
2. Took care of home or family
3. Went to school
4. Retired
5. Other – Specify
ACT1_Q30
Q8
Did you attend a school, college, CEGEP or university at any time between January and December 2022?
Include attendance only for courses that can be used as credit towards a certificate, diploma or degree.
1. Yes
2. No
ACT1_Q35
Q9
Were you enrolled as a full-time student, a part-time student or both full-time and part-time?
1. A full-time student
2. A part-time student
3. Both full-time and part-time student
ACT1_Q40
Q10
Did you receive any money from a scholarship, bursary or fellowship in 2022?
1. Yes
2. No
ACT1_Q45
Q11
What was the total amount you received in 2022?
Support payments received (SCC1)
Support payments received (SCC1)
SCC1_R05
The next questions are about support payments and child care expenses.
SCC1_Q05
Q12
Between January and December 2022, did you receive support payments from a former spouse or partner?
By support payments we mean a formal agreement for spousal support, alimony, separation allowance, or child support. Include only support payments actually received. Exclude gifts or additional transfers of money.
1. Yes
2. No
SCC1_Q10
Q13
What is your best estimate of the amount of support payments you received in 2022?
Include only support payments actually received. Exclude gifts or additional transfers of money.
Support payments paid (SCC2)
Support payments paid (SCC2)
SCC2_Q05
Q14
Between January and December 2022, did you make support payments to a former spouse or partner?
By support payments we mean a formal agreement for spousal support, alimony, separation allowance, or child support. Include only support payments actually paid. Exclude gifts or additional transfers of money.
1. Yes
2. No
SCC2_Q10
Q15
What is your best estimate of the total amount you paid in support payments in 2022?
Include only support payments actually paid. Exclude gifts or additional transfers of money.
Childcare expenses (SCC3)
Childcare expenses (SCC3)
SCC3_Q05
Q16
Between January and December 2022, did you pay for child care, so that you could work at your paid job(s)?
Include child care paid during school holidays.
1. Yes
2. No
SCC3_Q10
Q17
What is your best estimate of the total amount you paid for child care in 2022?
Exclude any amount previously reported. Enter "0" if the entire amount was previously entered.
Total personal income (INC1)
Total personal income (INC1)
INC1_R05
Now a question about total personal income.
INC1_Q05
Q24
What is your best estimate of your total personal income, before taxes and deductions, from all sources during the year ending December 31, 2022?
Income can come from various sources such as from work, investments, pensions or government. Examples include Employment Insurance, social assistance, child benefits and other income such as child support, spousal support (alimony) and rental income. Capital gains should not be included in the personal income.
INC1_Q10
Q25
For the year ending December 31, 2022, can you estimate in which of the following groups your total personal income fell? Was it:
1. Less than $30,000, including income loss
2. $30,000 and more
INC1_Q15
Q25
Please indicate the income range
1. Less than $5,000
2. $5,000 to less than $10,000
3. $10,000 to less than $15,000
4. $15,000 to less than $20,000
5. $20,000 to less than $25,000
6. $25,000 to less than $30,000
INC1_Q20
Q25
Please indicate the income range
1. $30,000 to less than $40,000
2. $40,000 to less than $50,000
3. $50,000 to less than $60,000
4. $60,000 to less than $70,000
5. $70,000 to less than $80,000
6. $80,000 to less than $90,000
7. $90,000 to less than $100,000
8. $100,000 and over
INC1_Q25
Q26
Does this amount include any social assistance payments?
Exclude employment insurance (including for maternity leave), workers' compensation, Canada Pension Plan (CPP), Quebec Pension Plan (QPP), child benefits and COVID-19 benefits.
1. Yes
2. No
Introduction to the disability screening questions (PDSQ)
Introduction to the disability screening questions (PDSQ)
PDSQ_R05
In order to reduce the length of the questionnaire and to obtain additional information about the relationship between income and persons with and without a disability, one person has been randomly selected in your household for the next set of questions. In your household, you have been selected.
Disability screening questions (DSQ)
Disability screening questions (DSQ)
DSQ_R01
The following questions are about difficulties you may have doing certain activities. Only difficulties or long-term conditions that have lasted or are expected to last for six months or more should be considered.
DSQ_Q01
Q27
Do you have any difficulty seeing? Would you say:
1. No
2. Sometimes
3. Often
4. Always
9. Don't know
DSQ_Q02
Q28
Do you wear glasses or contact lenses to improve your vision?
Would you say:
1. Yes
2. No
9. Don't know
DSQ_Q03
Q29
[Which/With your glasses or contact lenses, which] of the following best describes your ability to see?
Would you say:
1. No difficulty seeing
2. Some difficulty seeing
3. A lot of difficulty seeing
4. You are legally blind
5. You are blind
9. Don't know
DSQ_Q04
Q30
How often does this [difficulty seeing/seeing condition] limit your daily activities?
Would you say:
1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
9. Don't know
DSQ_Q05
Q31
Do you have any difficulty hearing?
Would you say:
1. No
2. Sometimes
3. Often
4. Always
9. Don't know
DSQ_Q06
Q32
Do you use a hearing aid or cochlear implant?
Would you say:
1. Yes
2. No
9. Don't know
DSQ_Q07
Q33
[Which/With your hearing aid or cochlear implant, which] of the following best describes your ability to hear?
Would you say:
1. No difficulty hearing
2. Some difficulty hearing
3. A lot of difficulty hearing
4. You cannot hear at all
5. You are deaf
9. Don't know
DSQ_Q08
Q34
How often does this [difficulty hearing/hearing condition] limit your daily activities?
Would you say:
1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
9. Don't know
DSQ_Q09
Q35
Do you have any difficulty walking, using stairs, using your hands or fingers or doing other physical activities?
Would you say:
1. No
2. Sometimes
3. Often
4. Always
9. Don't know
DSQ_R10
The following questions are about your ability to move around, even when using an aid such as a cane.
DSQ_Q10
Q36
How much difficulty do you have walking on a flat surface for 15 minutes without resting?
This refers to your regular walking pace.
If you use an aid for minimal support such as a cane, walking stick or crutches, please answer this question based on your ability to walk when using these aids.
Would you say:
1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do at all
9. Don't know
DSQ_Q11
Q37
How much difficulty do you have walking up or down a flight of stairs, about 12 steps without resting?
This refers to your regular walking pace.
If you use an aid for minimal support such as a cane, walking stick or crutches, please answer this question based on your ability to walk when using these aids.
Would you say:
1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do at all
9. Don't know
DSQ_Q12
Q38
How often [does this difficulty walking/does this difficulty using stairs/do these difficulties] limit your daily activities?
Would you say:
1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
9. Don't know
DSQ_Q13
Q39
How much difficulty do you have bending down and picking up an object from the floor?
Would you say:
1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do at all
9. Don't know
DSQ_Q14
Q40
How much difficulty do you have reaching in any direction, for example, above your head?
Would you say:
1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do at all
9. Don't know
DSQ_Q15
Q41
How often [does this difficulty bending down and picking up an object/'does this difficulty reaching/do these difficulties] limit your daily activities?
Would you say:
1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
9. Don't know
DSQ_Q16
Q42
How much difficulty do you have using your fingers to grasp small objects like a pencil or scissors?
Would you say:
1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do at all
9. Don't know
DSQ_Q17
Q43
How often does this difficulty using your fingers limit your daily activities?
Would you say:
1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
9. Don't know
DSQ_R18
The following questions are about pain due to a long-term condition that has lasted or is expected to last for six months or more.
DSQ_Q18
Q44
Do you have pain that is always present?
Would you say:
1. Yes
2. No
9. Don't know
DSQ_Q19
Q45
Do you [also] have periods of pain that reoccur from time to time?
Would you say:
1. Yes
2. No
9. Don't know
DSQ_Q20
Q46
How often does this pain limit your daily activities?
If you have both pain that is always present and pain that reoccurs from time to time, consider the pain that bothers you the most. If your pain is controlled by medication or therapy, please answer this question based on when you are using medication or therapy.
Would you say:
1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
9. Don't know
DSQ_Q21
Q47
When you are experiencing this pain, how much difficulty do you have with your daily activities?
If you have both pain that is always present and pain that reoccurs from time to time, consider the pain that bothers you the most. If your pain is controlled by medication or therapy, please answer this question based on when you are using medication or therapy.
Would you say:
1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do most activities
9. Don't know
DSQ_R22
Please answer only for difficulties or long-term conditions that have lasted or are expected to last for six months or more.
DSQ_Q22
Q48
Do you have any difficulty learning, remembering or concentrating?
Would you say:
1. No
2. Sometimes
3. Often
4. Always
9. Don't know
DSQ_Q23
Q49
Do you think you have a condition that makes it difficult in general for you to learn? This may include learning disabilities such as dyslexia, hyperactivity, attention problems, etc.
Would you say:
1. Yes
2. No
9. Don't know
DSQ_Q24
Q50
Has a teacher, doctor or other health care professional ever said that you had a learning disability?
Would you say:
1. Yes
2. No
9. Don't know
DSQ_Q25
Q51
How often are your daily activities limited by this condition?
Would you say:
1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
9. Don't know
DSQ_Q26
Q52
How much difficulty do you have with your daily activities because of this condition?
Would you say:
1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do most activities
9. Don't know
DSQ_Q27
Q53
Has a doctor, psychologist or other health care professional ever said that you had a developmental disability or disorder? This may include Down syndrome, autism, Asperger syndrome, mental impairment due to lack of oxygen at birth, etc.
Would you say:
1. Yes
2. No
9. Don't know
DSQ_Q28
Q54
How often are your daily activities limited by this condition?
Would you say:
1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
9. Don't know
DSQ_Q29
Q55
How much difficulty do you have with your daily activities because of this condition?
Would you say:
1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do most activities
9. Don't know
DSQ_Q30
Q56
Do you have any ongoing memory problems or periods of confusion?
Exclude occasional forgetfulness such as not remembering where you put your keys.
Would you say:
1. Yes
2. No
9. Don't know
DSQ_Q31
Q57
How often are your daily activities limited by this problem?
If the problem is controlled by medication or therapy, please answer this question based on when you are using your medication or therapy.
Would you say:
1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
9. Don't know
DSQ_Q32
Q58
How much difficulty do you have with your daily activities because of this problem?
If the problem is controlled by medication or therapy, please answer this question based on when you are using medication or therapy.
Would you say:
1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do most activities
9. Don't know
DSQ_R33
Please remember that your answers will be kept strictly confidential.
DSQ_Q33
Q59
Do you have any emotional, psychological or mental health conditions?
e.g., anxiety, depression, bipolar disorder, substance abuse, anorexia, etc.
Would you say:
1. No
2. Sometimes
3. Often
4. Always
9. Don't know
DSQ_Q34
Q60
How often are your daily activities limited by this condition?
If the condition is controlled by medication or therapy, please answer this question based on when you are using medication or therapy.
Would you say:
1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
9. Don't know
DSQ_Q35
Q61
When you are experiencing this condition, how much difficulty do you have with your daily activities?
If the condition is controlled by medication or therapy, please answer this question based on when you are using medication or therapy.
Would you say:
1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do most activities
9. Don't know
DSQ_Q36
Q62
Do you have any other health problem or long-term condition that has lasted or is expected to last for six months or more?
Exclude any health problems previously reported.
Would you say:
1. Yes
2. No
9. Don't know
DSQ_Q37
Q63
How often does this health problem or long-term condition limit your daily activities?
If you have more than one other health problem or condition, please answer based on the health problem or condition that limits your daily activities the most.
1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
9. Don't know
DSQ_R38
The following questions are about pain due to a long-term condition that has lasted or is expected to last for six months or more.
DSQ_Q38
Q64
Do you have pain that is always present?
Would you say:
1. Yes
2. No
9. Don't know
DSQ_Q39
Q65
Do you [also] have periods of pain that reoccur from time to time?
Would you say:
1. Yes
2. No
9. Don't know
DSQ_Q40
Q66
How often does this pain limit your daily activities?
If you have both pain that is always present and pain that reoccurs from time to time, consider the pain that bothers you the most. If your pain is controlled by medication or therapy, please answer this question based on when you are using medication or therapy.
Would you say:
1. Never
2. Rarely
3. Sometimes
4. Often
5. Always
9. Don't know
DSQ_Q41
Q67
When you are experiencing this pain, how much difficulty do you have with your daily activities?
If you have both pain that is always present and pain that reoccurs from time to time, consider the pain that bothers you the most. If your pain is controlled by medication or therapy, please answer this question based on when you are using medication or therapy.
Would you say:
1. No difficulty
2. Some difficulty
3. A lot of difficulty
4. You cannot do most activities
9. Don't know
Unmet health care needs (UCN)
Unmet health care needs (UCN)
UCN_Q005
Q68
During the past 12 months, was there ever a time when you felt that you needed health care, other than homecare services, but you did not receive it?
1. Yes
2. No
UCN_Q010
Q69
Thinking of the most recent time you felt this way, why didn't you get care?
Select all that apply.
1. Care not available in the area
2. Care not available at time required (e.g., doctor busy, away from office or no longer at that practice, inconvenient hours)
3. Do not have a regular health care provider
4. Waiting time too long
5. Appointment was cancelled
6. Felt would receive inadequate care
7. Cost
8. Decided not to seek care
9. Doctor didn't think it was necessary
10. Transportation issue
11. Other
UCN_Q015
Q70
Again, thinking of the most recent time, what was the type of care that was needed?
Select all that apply.
1. Treatment of a chronic physical health condition diagnosed by a health professional
2. Treatment of a chronic mental health condition diagnosed by a health professional
3. Treatment of an acute infectious disease (e.g., cold, flu and stomach flu)
4. Treatment of an acute physical condition (non-infectious)
5. Treatment of an acute mental health condition (e.g., acute stress reaction)
6. A regular check-up (including pre-natal care)
7. Care of an injury
8. Dental care
9. Medication / Prescription refill
10. Other
UCN_Q020
Q71
Did you actively try to obtain the health care that was needed?
1. Yes
2. No
UCN_Q025
Q72
Where did you try to get the service you were seeking?
Select all that apply.
1. A doctor's office
2. A hospital outpatient clinic
3. A community health centre [or CLSC]
4. A walk-in clinic
5. An emergency department or emergency room
6. Other
Financial difficulty due to disability (FDD)
Financial difficulty due to disability (FDD)
FDD_Q05
Q73
In 2022, have you and your household experienced significant financial difficulty because of a long term disability or health problem of a member of you household? Would you say:
1. Yes, sometimes
2. Yes, often
3. No
Owners and renters (DWL)
Owners and renters (DWL)
DWL_R05
The next series of questions will be about your dwelling.
DWL_Q05
Q74
Is this dwelling part of a condominium development?
1. Yes
2. No
DWL_Q10
Q75
Is this dwelling in need of any repairs?
Do not include desirable remodelling or additions.
Would you say:
1. No, only regular maintenance is needed, for example, painting, furnace cleaning
2. Yes, minor repairs are needed, for example, missing or loose floor tiles, bricks or shingles, defective steps, railing or siding
3. Yes, major repairs are needed, for example, defective plumbing or electrical wiring, structural repairs to walls, floors or ceilings
Owners (OWN)
Owners (OWN)
OWN_Q05
Q76
Does anyone in your household operate a farm on this property?
1. Yes
2. No
OWN_Q10
Q77
Does anyone in your household operate a business from this dwelling or property?
Property is interpreted as the land and buildings associated with the dwelling.
1. Yes
2. No
OWN_Q15
Q78
How many bedrooms are there in this dwelling?
Please include all rooms designed as bedrooms even if they are now used for something else, for example, as guest rooms or television rooms.
Do not count rooms used solely for business purposes.
Include all rooms used as bedrooms now, even if they were not originally built as bedrooms, such as bedrooms in a finished basement.
For a one-room dwelling or bachelor apartment, please enter zero.
OWN_Q20
Q79
Is there a mortgage on this dwelling?
1. Yes
2. No
OWN_Q25
Q80
Are property taxes included in your mortgage payments?
1. Yes
2. No
OWN_Q30
Q81
Do you have more than one mortgage on your dwelling?
1. Yes
2. No
OWN_Q35
Q82
How often do you make regular mortgage payments?
1. Weekly
2. Every two weeks
3. Twice a month
4. Monthly
5. Quarterly
6. Twice a year
7. Annually
8. Other – Specify
OWN_Q45
Q83
How much do you pay for each of these regular mortgage payments, including your property taxes?
Exclude irregular and lump sum payments.
OWN_Q50
Q84
How much do you pay for each of these regular mortgage payments?
Exclude irregular and lump sum payments.
OWN_Q55
Q85
How much do you pay monthly for all these mortgages, including your property taxes?
Exclude irregular and lump sum payments.
OWN_Q65
Q87
What is the total annual property tax bill for this dwelling?
Include school taxes, special service charges and local improvements.
OWN_Q70
Q88
Is water included in the payments just mentioned?
Payments just mentioned could include mortgage payments and property taxes.
1. Yes
2. No
OWN_Q75
Q89
What is the regular monthly condominium fee for this dwelling?
OWN_Q80
Q90
Are any of the following items included in the payments just mentioned?
Payments just mentioned could include mortgage payments, property taxes and condo fees.
Select all that apply.
1. Electricity
2. Heating fuel
3. Water
4. None of the above
Food security (FSC)
Food security (FSC)
FSC_R010
The following statements may describe the food situation for your household in the past 12 months. Please indicate if the statement was often true, sometimes true or never true for you and other household members in the past 12 months.
FSC_Q010A
Q91a
You and other household members worried that food would run out before you got money to buy more
1. Often true
2. Sometimes true
3. Never true
FSC_Q010B
Q91b
The food that you and other household members bought just didn't last and there wasn't any money to get more
1. Often true
2. Sometimes true
3. Never true
FSC_Q010C
Q91c
You and other household members couldn't afford to eat balanced meals
1. Often true
2. Sometimes true
3. Never true
FSC_Q010D
Q91d
You or other adults in your household relied on only a few kinds of low-cost food to feed the children because you were running out of money to buy food
1. Often true
2. Sometimes true
3. Never true
FSC_Q010E
Q91e
You or other adults in your household couldn't feed the children a balanced meal because you couldn't afford it
1. Often true
2. Sometimes true
3. Never true
FSC_Q015
Q92
The children were not eating enough because you or other adults in your household just couldn't afford enough food. Would you say:
1. Often true
2. Sometimes true
3. Never true
FSC_R020
The following few questions are about the food situation in the past 12 months for you or any other adults in your household.
FSC_Q020A
Q93
In the past 12 months, since last [current month], did you or other adults in your household ever cut the size of your meals or skip meals because there wasn't enough money for food?
1. Yes
2. No
FSC_Q020B
Q93
How often did this happen? Was it:
1. Almost every month
2. Some months but not every month
3. Only 1 or 2 months
FSC_Q025A
Q94
In the past 12 months, did you (personally) ever eat less than you felt you should because there wasn't enough money to buy food?
1. Yes
2. No
FSC_Q025B
Q95
In the past 12 months, were you (personally) ever hungry but didn't eat because you couldn't afford enough food?
1. Yes
2. No
FSC_Q025C
Q96
In the past 12 months, did you (personally) lose weight because you didn't have enough money for food?
1. Yes
2. No
FSC_Q030
Q97
In the past 12 months, did you or other adults in your household ever not eat for a whole day because there wasn't enough money for food?
1. Yes
2. No
FSC_Q035
Q97
How often did this happen? Was it:
1. Almost every month
2. Some months but not every month
3. Only 1 or 2 months
FSC_R040A
Now, a few questions on the food experiences for children in your household.
FSC_Q040A
Q98
In the past 12 months, did you or other adults in your household ever cut the size of any of the children's meals because there wasn't enough money for food?
1. Yes
2. No
FSC_Q040B
Q99
In the past 12 months, did any of the children ever skip meals because there wasn't enough money for food?
1. Yes
2. No
SC_Q040C
Q99
How often did this happen? Was it:
1. Almost every month
2. Some months but not every month
3. Only 1 or 2 months
FSC_Q040D
Q100
In the past 12 months, were any of the children ever hungry but you just couldn't afford more food?
1. Yes
2. No
FSC_Q040E
Q101
In the past 12 months, did any of the children ever not eat for a whole day because there wasn't enough money for food?
For many years we have been improving the accessibility of Statistics Canada (StatCan) products, programs, and services. Below are some of the actions that were completed in the last year that contributed to our goal of being completely accessible by 2040.
Culture
Created an Accessibility Secretariat and expanded the accessibility governance structure to advance accessibility awareness and engagement.
Hired an external consultant to conduct a comprehensive accessibility review to identify the barriers to participation for persons with disabilities across StatCan.
Participated in the employee-led Engaging DisAbility Innovation Study in collaboration with Canadian Heritage and supported by the Office of Public Service Accessibility.
Developed a draft measurement framework to evaluate and report on the work done on all areas under the Accessible Canada Act.
Workplace Accommodation
Co-led the Workplace Accommodation Workflow Design Project with the Office of Public Service Accessibility.
Mapped all accommodation processes to identify barriers and establish where accommodation timelines could be reduced (ongoing).
Standardized the purchase authorization for ergonomic chairs and sit / stand desks.
Published a frequently asked questions page for employees on how to submit a request for accommodation.
Employment
Established a partnership with Office of Public Service Accessibility to develop a data hub to track the progress of hiring of 5,000 net new employees with disabilities within the public service.
Hired an external consulting firm to conduct a comprehensive system review to identify accessibility barriers in processes and practices.
Completed the Employment Accessibility Survey as part of the Engaging DisAbility Innovation Study to identify where and how employees experience barriers in recruitment, retention, promotion and workplace accommodation practices.
Hired one intern through the Federal Internship Program for Canadians with Disabilities (FIPCD) and one through the Live Work Play initiative.
Built environment
Secured consultants to conduct a physical audit of the office spaces within all buildings in the National Capital Region.
Completed 65% of priority repairs on nine temporary offices.
Held consultations with persons with disabilities.
Leveraged the return-to-work application and secured accommodations for on-site workers.
Information and communication technologies
Raised awareness on the state of information and communication technologies (often called IT) accessibility and adoption of accessibility standards for all IT products.
Established a preliminary listing of 800 existing applications that will be assessed to determine their accessibility compliance.
Created the IT Accessibility Advisory Board to present, review and synchronize horizontal and vertical IT accessibility project work.
Communication, other than information and communication technologies
Launched a text to speech function on the StatCan website.
Assessed internal communication products to meet accessibility requirements.
Provided American Sign Language and langue des signes québécois interpretation for summary videos in support of census releases.
Conducted accessibility checks for all client-facing communication products to ensure accessibility compliance prior to their release.
Transformed two non-accessible communications (for instance, eliminated PDF [portable document format] and pop-up messages).
Procurement of goods, services, and facilities
Developed content and information about accessibility for the ICN page for business owners;
Adjusted internal procedures and working documents to include accessibility requirements.
Adjusted the Internal Procurement Audit Program to manually compile compliance on accessibility.
Consulted with the Financial Management System to find a way to track accessibility requirements.
Design and delivery of programs and services
Initiated the review of current learning platforms that identified accessibility barriers.
Developed a preliminary inventory of all services provided by StatCan as a baseline for future review.
Initiated a pilot project to offer text-to-speech on several sites (Phase I included 12 modules).
StatCan is committed to the prevention, identification, and removal of accessibility barriers. The accessibility plan will serve as a framework to ensure accessibility in our services, products and facilities for our employees and the public we serve. Employees need to be able to function effectively, and clients need to receive timely, high-quality services in a way that works for them.
To implement the plan, we will continually work with persons with disabilities and partners to prioritize our commitments. We will update our implementation and delivery plans to reflect lessons learned, ongoing research, best practices and new standards or requirements. As required by the Accessible Canada Regulations, StatCan will submit an annual progress report on the implementation of the accessibility plan and publish updates online.
We are refining our Accessibility Measurement Framework to evaluate the prevalence of barriers for StatCan employees, respondents, and data users with disabilities and to assess our capacity to address them. We will implement a planning and reporting structure that validates our results with persons with disabilities and includes transparent reporting on their feedback. We recognize that feedback is critical to the process of identifying and removing barriers to participation and improving how we deliver our services. We will ensure that a mechanism is put in place so employees and clients can provide feedback on the current state of accessibility at StatCan.
The accessibility plan, feedback mechanism and performance measurement strategy represent commitments that we will make to get real results for Canadians, especially those with disabilities. Our accessibility effort will advance through the design, planning, implementation, reflection, and adjustment stages that will lead to a barrier-free StatCan by 2040.
Accessibility is everyone's responsibility, and you can help shape StatCan's accessibility commitment. We invite your comments and suggestions as we travel the road to accessibility together. We all must take part in ensuring the future state of accessibility at StatCan.
Consulting employees was a critical element in the development of Statistics Canada: Road to Accessibility. This first plan is to improve the employee experience by making persons with disabilities and those experiencing barriers the centre of all actions planned for the next three years. To this end, consultations allowed StatCan to have the proper information and data to help define the possible barriers and desired states for all areas under the ACA. Furthermore, consultation helped define which actions should be prioritized in Statistics Canada: Road to Accessibility.
As we shift our focus outside the agency, we will consult Canadians who use StatCan programs and services to inform future iterations of the plan.
Employee consultation on the current state
Accessibility assessment by an external firm
From March to October 2022, an in-depth and independent accessibility assessment was conducted by an external firm using qualitative and quantitative data. This external review, which served to ensure a bias-free view of accessibility barriers, was centred around data analysis, document review, employee consultations and focus groups. Preliminary findings on barriers have informed the actions articulated in Statistics Canada: Road to Accessibility. The final report, due by the fall of 2022, will serve to influence and inform future actions in future iterations of the plan.
Throughout their review, the consultants ensured that the perspectives of persons with disabilities and those experiencing barriers were accounted for. They also verified the accessibility of our employment policies and practices. During the process, all employees were invited to participate on multiple occasions and 70 employees were interviewed virtually on the topics required under the ACA. Most of the interviews took place from April to June 2022. Participants were from varied equity seeking groups and sociocultural backgrounds to ensure an intersectional perspective. Those participants included
the PwDC and its co-chairs
the Champion for persons with disabilities
employees with disabilities and those experiencing barriers
members of other employee networks, such as women, visible minorities Indigenous people and 2SLGBTQI+
area lead(s)
Employment and Accessibility Survey in collaboration with Canadian Heritage
In addition to the external review, StatCan launched the Engaging DisAbility Innovation Study, an employee-led collaboration with Canadian Heritage, which was supported by the Office of Public Service Accessibility.
In February 2022, the 2022 Employment and Accessibility Survey was provided to StatCan employees and received a 49.2% participation rate (4,226 employees). The survey included 41 questions on recruitment, retention, promotion and workplace accommodation and asked respondents to comment on their experiences with perceived employment barriers at StatCan. The survey was designed by StatCan survey methodologists who ensured that it was accessible to all employees.
The first phase of the survey was conducted online using an accessible format, and the second phase gather employee insights using an online engagement platform, Recollective. Both phases of the survey were available to all employees across the regions, and, in October 2022, the results were presented in a report on the barriers to the employee journey.
Data reports
In November 2021, StatCan launched the Employee Wellness Survey to assess employees' psychological health and to provide a better understanding of the key drivers of psychological health. The survey results were published in the fall of 2022.
The 2020 Public Service Employee Survey (PSES) measured federal government employees' opinions about their engagement, leadership, workforce, workplace, workplace well-being, compensation, diversity and inclusion, and the impacts of the COVID-19 pandemic.
The Persons with Disabilities Committee and StatCan-ALT working group involvement
The PwDC were presented with the plan structure and have been crucial in shaping the plan.
Members with different disabilities issued recommendations to guide area leads in their action plans.
Relevant partners participated in the StatCan-ALT working group. Any individual interested in accessibility was welcome to attend. This accessibility working group has 20 members in regular attendance from diverse backgrounds, locations and positions and who have various disabilities. The StatCan-ALT group meets virtually on a bimonthly basis to discuss actions to improve accessibility, present information on each area and share helpful practices.
Engagement on the plan
Disability stakeholders
The National Educational Association of Disabled Students, the Employment Accessibility Resource Network, Realize, ACT (Accessible Career Transitions) to Employ and Carleton University students were invited to provide comments on the draft plan for employment, in the format of their choice. This feedback was assessed and added to the plan.
Employees
The PwDC, which is composed of 65 members, was consulted on the following occasions:
On June 14, 2022, PwDC members received the activities proposed by all area leads and approved the plan of actions.
From July to September 2022, PwDC members were invited to provide comments on the identified barriers and the proposed actions in an electronic version of the plan draft. Feedback could be sent electronically or be given over the phone, by video conference or in the group meeting.
In mid-November 2022, PwDC members were presented with the version that would be sent for approval to the chief statistician and informed of the recommended changes.
In November 2022, PwDC members received the final version that was approved by the chief statistician.
The Champion for persons with disabilities provided feedback on an early version of the plan in August and again in September.
The StatCan-ALT, the working group and internal senior management committees were given time to read and assess the plan.
