Data Stories – Toronto

The economy is more than numbers and forecasts—it's the pulse of our communities, the foundation of opportunity, and a reflection of how we adapt to uncertainty. From inflation and trade pressures to population growth and urban development, Canada's economic story is evolving rapidly. In light of these transformations, do we need to rethink how we understand and respond to economic change?

Join the Chief Statistician of Canada, André Loranger, and a panel of experts for an in-person, data-driven discussion on the theme "Canada at a Crossroads: Resiliency, Adaptability and Change." Presented in partnership with Glendon College on Glendon Campus at York University this session will explore the major socio-economic shifts of the past five years, with a particular focus on Toronto and the province of Ontario. Topics will include Gross Domestic Product (GDP) trends, labour market dynamics, business challenges, and the demographic forces shaping our future.

An expert panel discussion and question period will follow immediately after the presentation. Participants are encouraged to share their perspectives on this vital topic.

We hope you'll join us for what promises to be a compelling and insightful conversation.

When: Tuesday, October 28, 2025, 1:00 pm - 3:00 pm EST

Where: Glendon College, Glendon Campus at York University, Room YH A100 (York Hall at Centre of Excellence)
2275 Bayview Avenue, North York, ON

Cost: Free

Registration: Registration is closed

Presenter and Panellist

André Loranger, Chief Statistician of Canada, Statistics Canada

Host

Carmen DaRosa, Director, Engagement and Data Services, Statistics Canada

Moderator

Jennifer Withington, Assistant Chief Statistician – Economic Statistics Field, Statistics Canada

Panellists

Daniel Brown

Chief Economist and Assistant Deputy Minister, Office of Economic Policy (OEP)
Ontario Ministry of Finance

Daniel Brown was appointed Chief Economist and Assistant Deputy Minister of the Office of Economic Policy (OEP) at the Ontario Ministry of Finance in April 2022. In this role, he leads the development of economic analysis, forecasting, and policy advice for the Ontario government, while also playing a central role in shaping statistical policy across the Ontario Public Service. His appointment builds on a distinguished career in both public and private sectors, with over two decades of leadership in economics, finance, and policy.

Prior to joining the Ontario Public Service, Daniel held senior roles within the United Kingdom's Civil Service, most recently as Director of Economics, Markets and Strategy at the Office of Rail & Road, the UK's independent transport regulator. He began his career at Her Majesty's Treasury, contributing to budget planning, business taxation, and economic modelling. In the private sector, he worked in private equity fundraising and investment, helping to launch OECD-focused infrastructure funds for 3i Group plc and advising major pension funds on infrastructure investment strategies.

Robert Hogue

Assistant Chief Economist
RBC

Robert Hogue is a member of the RBC Economics team, responsible for providing analysis and forecasts on the Canadian housing market and provincial economies. His publications include Housing Trends and Affordability, Provincial Outlook and provincial budget commentaries. In his functions, he is frequently called to comment on the economy both internally within RBC and externally with clients and the media.

Robert joined RBC in May 2008 after spending 15 years with another Canadian financial institution as a senior economist specializing in industrial analysis. At the outset of his career, he spent four years as an economist with Ontario Hydro, where he specialized in industrial and macroeconomic analysis. Robert holds a master's degree in economics from Queen's University and a bachelor's degree from Université de Montréal.

Meredith Lilly, PhD

Professor and Simon Reisman Chair in International Economic Policy
Norman Paterson School of International Affairs, Carleton University

Dr. Meredith Lilly is a full Professor and Simon Reisman Chair in International Economic Policy at Carleton University's Norman Paterson School of International Affairs. Her research focuses on North American trade relations, Canada's trade diversification strategy, economic sanctions, and the use of research evidence in policy making. Prior to her appointment at Carleton, she served as Foreign Affairs and International Trade Advisor to Canadian Prime Minister Stephen Harper. She was previously appointed at the University of British Columbia Okanagan and has worked in policy roles in several Canadian government departments. She engages regularly with foreign governments and diplomats and is a frequent media commentator on North American trade issues.

She is a member of C.D. Howe Institute's International Economic Policy Council, a non-resident scholar at Rice University's Baker Institute for Public Policy in Texas, an Advisory Board member for the Canadian Global Affairs Institute, and serves as the Academic Partner for Canada's delegation to the North American Forum.

Expertise: Canada-US relations; international trade policy; free trade negotiations; labour economics; economic sanctions.

In October 2025, questions measuring the Labour Market Indicators were added to the Labour Force Survey as a supplement.

Questionnaire flow within the collection application is controlled dynamically based on responses provided throughout the survey. Therefore, some respondents will not receive all questions, and there is a small chance that some households will not receive any questions at all. This is based on their answers to certain LFS questions.

Labour Market Indicators

ENTRY_Q01 / EQ 1 - From the following list, please select the household member that will be completing this questionnaire on behalf of the entire household.

SAT_Q01 / EQ 2 – Using a scale of 0 to 10, where 0 means “Very dissatisfied” and 10 means “Very satisfied,” in general, how satisfied are you with your main job or business? 

0 - Very dissatisfied 

1

2

3

4

5

6

7

8

9

10 - Very satisfied 

CHS_I01 – The following question is about the financial situation of your household. 

CHS_Q01 / EQ3 – Over the last month, that is since September 15 to today, how difficult or easy was it for your household to meet its financial needs in terms of transportation, housing, food, clothing and other necessary expenses? 

Would you say:

1. Very difficult
2. Difficult
3. Neither difficult nor easy
4. Easy
5. Very easy

Introduction

The Canadian Housing Statistics Program (CHSP) aims to provide detailed insights on residential properties in Canada and their owners. However, certain estimates are subject to limitations or may not be available for some jurisdictions or variable types because of differences in data sources, regional coverage and processing steps.

The purpose of this document is to help the reader interpret and use data from the CHSP. It outlines key data quality considerations and specific limitations affecting the availability and comparability of estimates across some domains.

To consult changes that are specific to a given reference year, please refer to the Summary of changes - Surveys and statistical programs - Canadian Housing Statistics Program (CHSP).

Data processing

• Random rounding is applied to all raw counts to protect the confidentiality of owners in the totals. Totals and subtotals may not equal the sum of components.
• Percentages are calculated from rounded counts.
• Averages and medians are calculated using only non-missing, non-null, and values greater than zero for the variables of interest (for example, assessment value, total living area and total income).
• Assessment value per square foot refers to the assessment value of a property divided by its total living area.
• Some property or owner characteristics are in the "unspecified" category either because the corresponding information was not received from the data provider or because there is no identifiable link connecting the property to the owner information. Therefore, users must take this limitation into account when interpreting the data.
• Previous reference period estimates are subject to revision.
• Each year, geocoding is updated based on the best available location information, which may result in slight variations in the counts of census subdivisions from one year to the next.

Universe of property tables

The following property tables (46-10-0093, 46-10-0094, 46-10-0095) are restricted to residential properties in Canada. The geographic location of a property is determined by its physical address. Mixed-use properties (e.g., residential and commercial) are included, but the property characteristics reported in the tables reflect only the residential portion of mixed-use properties. The universe covers residential properties across Canada. However, it does not cover residential properties located on reserves or collective dwellings. It also excludes commercial, industrial, and institutional properties.

Universe of owner tables

The following owners tables (46-10-0096, 46-10-0097, 46-10-0098) are restricted to resident owners who are persons occupying their residential property. An owner's geographic location is determined by the location of the occupied property.

In the case of Nunavut, where information on owner-occupied properties is unavailable, the universe includes all resident owners who are persons without restrictions on owner-occupancy. For owners with multiple properties, the geographic location and property characteristics are based on the residential property with the highest assessment value.

Universe of buyer table

The following buyers table (46-10-0099) is restricted to resident buyers who are persons who filed their T1 tax return form in the previous year and purchased a property in a market sale.

Data availability and limitations

Newfoundland and Labrador

• Estimates are not available at the provincial level and for the category "outside of census metropolitan areas (CMAs) and census agglomerations (CAs)."
• Estimates by property type are available only for the census subdivision of St. John's.

Prince Edward Island

• Estimates of total living area and assessment value per square foot are not available.

New Brunswick

• Estimates of total living area and assessment value per square foot for condominium apartments are not available.
• The "total, all property types" category excludes condominium apartments; therefore, users should consider this limitation when interpreting estimates of total living area and assessment value per square foot for this group.

Manitoba

• Estimates by property use of residential property are suppressed in many areas due to lower linkage quality.
• The estimate of the number of owner-occupied residential property is underestimated due to the quality of the linkage.

Saskatchewan

• Provincial estimates exclude the census subdivision of Prince Albert.
• Owner-related variables are not available because of missing owners' information.

Alberta

• Estimates by property use and residency status of residential property are suppressed due to low data quality.
• The number of resident owners who are persons occupying a residential property, which represent the universe of the owner tables, is underestimated due to the low linkage quality. Therefore, the number of owners should be interpreted with caution.

Yukon

• Estimates by property use and residency status of residential property are available only for the census subdivision (CSD) of Whitehorse.
• The number of resident owners who are persons occupying a residential property, which represent the universe of the owner tables, is underestimated due to the low linkage quality outside the CSD of Whitehorse. Therefore, the number of owners outside this CSD should be interpreted with caution.

Northwest Territories

• Data are available only for the CA of Yellowknife.
• Estimates by property type and period of construction are not available.
• Estimates of total living area and assessment value per square foot are not available.

Nunavut

• Estimates by property type, period of construction and property use are not available.
• Estimates of total living area and assessment value per square foot are not available.

Variable-specific limitations

Property use of residential property

• The property use indicator is suppressed outside CMAs and CAs due to low linkage quality. It may also be removed in certain regions where its reliability has been deemed insufficient.
• For the most recent period of construction, the property use indicator is less precise. Consequently, these estimates should be used with caution.

Owner-occupancy

• The quality of the linkage is unreliable outside CMAs and CAs, leading to an underestimation of the number of resident owners who are persons occupying a residential property, which represents the universe of the owner tables (except for Nunavut). Therefore, Census Subdivisions (CSDs) located outside CMAs and CAs are not included in the owner tables. Although aggregate estimates for the category "outside of census metropolitan areas (CMAs) and census agglomerations (CAs)" are still provided, the number of owners in this category should be interpreted with caution.

Assessment value

• Because provinces and territories have varying assessment periods and assessment roll durations from one region to another, it is not possible to make precise comparisons between the assessment values of properties located in different provinces or territories. To obtain the reference years for property assessment values, please refer to the document linked on the CHSP web page: Reference years of the property stock and assessment values, by province and territory.

Number of residential properties owned

• The number of properties owned by the property owner is limited to residential properties that are within a given province.

Composite quality indicator

The composite quality indicators (CQI) combine multiple individual quality indicators (QIs) representing the quality of various CHSP data processing steps (for example, coding, geocoding, linkage, imputation). The CQIs are available for certain tables, such as the following:

Table 46-10-0093-01 Residential properties by characteristics, property use and ownership type

Table 46-10-0094-01 Residential properties by characteristics and residency status.

The CQI letter grades are defined as follows:

A – Excellent: All domain variables and the variable of interest are of excellent quality.

B – Very good: All domain variables and the variable of interest are of very good to excellent quality.

C – Good: The quality of some of the domain variables or the variable of interest is considered good, while all the other variables are of very good to excellent quality.

D – Acceptable: The quality of some of the domain variables or the variable of interest is considered acceptable, while all the other variables are of good to excellent quality.

E – Use with caution: The quality of several domain variables or the variable of interest is considered poor.

F – Too unreliable to be published

This webinar, organized in partnership with the LIS Cross-National Data Center in Luxembourg, will provide an introduction to the use of the structured LIS database for the study of income concepts. This session will also demonstrate how to access and analyze data through the LIS Remote-Execution System “LISSY” with hands-on exercises using Stata to produce descriptive statistics and indicators.

Key topics include:

• Overview of the LIS database, including geographic and temporal coverage and variable content
• Selection of welfare measures in LIS database
• Accessing LIS data via the LIS Remote-Execution System, "LISSY"
• Practical exercises using Stata with LISSY to conduct basic statistical analysis
  • Producing basic descriptive statistics
  • Producing inequality and poverty measures

Speaker: Heba Omar, Assistant Director of Operations, LIS Cross-National Data Center in Luxembourg

To register for the English webinar, please fill out the following form:

• Wednesday, November 5, 2025, 10:00am - 11:30am PT / 11:00am - 12:30pm MT / 12:00pm - 1:30pm CT / 1:00pm - 2:30pm ET / 2:00pm – 3:30pm AT

To register for the French webinar, please fill out the following form:

• Friday, November 14, 2025, 6:30am – 8:00am PT / 7:30am - 9:00am MT / 8:30am - 10:00am CT / 9:30am - 11:00am ET / 10:30am - 12:00pm AT

Please Note: Registrations will close for the English session on October 15th, 2025, and for the French session on October 22nd, 2025.

About the LIS Cross-National Data Center in Luxembourg

The LIS Cross-National Data Center collects and harmonizes datasets containing information on income, employment, wealth, and demographics, spanning over 50 countries and over 1,000 country-year datasets from the 1960s to 2023. These datasets are made publicly available in two databases: the Luxembourg Income Study (LIS) and Luxembourg Wealth Study (LWS). They offer a unique comparative perspective on economic disparities across different countries and time periods, aiding researchers in uncovering the underlying drivers of these inequalities.

Introduction

The Labour Force Survey (LFS) is the source of Canada’s official unemployment rate and one of the primary sources of labour market information for Canadians and policy makers. Statistics Canada’s Centre for Labour Market Information is undertaking a series of studies to better understand respondent experience with the LFS. These studies are conducted in collaboration with the Privy Council Office’s Impact and Innovation Unit and will include one-on-one and group discussions with individuals who have recently completed their participation in the LFS.

Consultative engagement objectives

The engagement activity will gather feedback from those who had been selected to participate in the LFS to better understand their motivations for participation, barriers to timely participation and reactions to LFS communications. Topics of discussion will include trust in Statistics Canada, the clarity and tone of communication materials, and the usability of different survey completion modes. The findings will inform upcoming initiatives to enhance respondent experiences and lay the groundwork for future experiments and interventions aimed at improving survey operations.

How to get involved

Individuals who wish to obtain more information, can contact us at consultativeengagement-mobilisationconsultative@statcan.gc.ca

Statistics Canada is committed to respecting the privacy of consultative engagement participants. All personal information created, held, or collected by the agency is kept strictly confidential; it is protected by the Privacy Act. For more information on Statistics Canada's privacy policies, please consult the privacy notice.

Results

Summary results of the engagement initiatives will be published online when available.

Consultative engagement objectives

The Census of Environment (CoE) program is committed to providing comprehensive biophysical information on the extent and condition of Canada’s ecosystems, linking it to socio-economic data and informing Canadians about the many values of nature's ecosystem services This integrated approach is designed to support policy development and investment decisions at national, provincial, and regional levels.

As a relatively new program, the CoE would like to gather feedback on client satisfaction with its products and tools. This input will help assess the usefulness of the program’s outputs during its first five years and inform future planning and improvements.

As part of this initiative, an e-form was created to gather users’ perspectives, relevance, usability, accessibility, quality and impact of CoE products.

How to get involved

To provide your feedback on CoE products, please fill out the following e-form: Census Of Environment Feedback Form.

If you would like to obtain more information on this engagement initiative or are interested in participating, please contact us by email at consultativeengagement-mobilisationconsultative@statcan.gc.ca.

Statistics Canada is committed to respecting the privacy of consultation participants. All personal information created, held, or collected by the agency is kept strictly confidential; it is protected by the Privacy Act. For more information on Statistics Canada's privacy policies, please consult the privacy notice.

Results

Summary results of the engagement initiatives will be published online when available.

On this page

• Apply now for a census job!
• Local knowledge matters – be part of the 2026 Census
• Work that matters — Apply now!
• Jobs in select northern and remote communities for the 2026 Census!

Apply now for a census job!

The 2026 Census is coming soon, and Statistics Canada is hiring in select northern and remote communities.

Recruiting community members who know the people, the land and the language is key to a successful census – local knowledge makes a difference.

Census information is used to help plan services like employment, child care, education and health care. That’s why it’s so important that every community is counted.

Joining the census team is a great way for people to earn income while helping to shape their community’s future.

Apply now and tell a friend: census.gc.ca/jobs-north.

Local knowledge matters – be part of the 2026 Census

For the 2026 Census, Statistics Canada is hiring in select northern and remote communities. Local workers bring valuable insight related to the people, the culture, and the land.

This local knowledge is key to collecting accurate information that helps plan for essential services like child care, education, and health care.

Whether you're looking to broaden your skills, or earn extra income, a census job is a meaningful way to make a difference.

Apply now at census.gc.ca/jobs-north.

Work that matters — Apply now!

Statistics Canada is hiring in select northern and remote communities. From understanding the land to knowing the connections between families, local voices carry insights that can’t be found anywhere else.

Working on the 2026 Census is more than just a job — it’s a way to help ensure your community is accurately counted and represented.

Why census data matters

Accurate census data provides a clear picture of who lives in each community and what their needs are. Governments, community organizations, and service providers can use this information to plan and fund programs that support people where they live. Census data can help determine how many teachers and classrooms are needed, what health services and clinics should be available, how housing programs are funded, where to improve roads and transportation, and how to support language programs.

In northern and remote communities, accurate information is essential for planning for the future. That begins with local people leading the way.

If you are ready to take on work that matters, now is the time. Join the 2026 Census team.

Apply now at census.gc.ca/jobs-north.

Jobs in select northern and remote communities for the 2026 Census!

Are you looking for meaningful work that fits your schedule and can help make a difference in your community? Statistics Canada is hiring for the 2026 Census in select northern and remote communities.

By hiring local people who know the land, the language, and the community, the census can collect more accurate information that reflects the realities of northern and Indigenous communities. This information can help support planning for vital services such as employment, childcare, education, and healthcare.

Important to know

• Pay is $35.79 per hour for non-supervisory positions and $37.79 per hour for supervisory positions, plus authorized expenses.
• The timeframe for census jobs is from January to April 2026, depending on the position and location.
• You must be available to work flexible hours, including days, evenings and weekends.

Your contribution to the census can help your community plan and shape its future – apply now!

Apply now and tell a friend!
census.gc.ca/jobs-north

Statistics Canada’s use of generative artificial intelligence (GenAI) is governed in accordance with the Government of Canada’s Treasury Board Secretariat (TBS) policy and guidance on the responsible use of artificial intelligence (AI). For more information, consult the Guide on the use of generative artificial intelligence.

At Statistics Canada, we have embraced TBS’s guidance by embedding our use of GenAI within our own AI Strategy and a robust internal framework tailored to the sensitive and specialized nature of our statistical work. Statistics Canada’s strategy requires employees to adhere to clearly defined best practices, which are specific to our business needs. Employees must follow privacy and security protocols and seek approval before deploying new GenAI tools for production use.

Our approach includes

• mandatory staff validation and review of all GenAI-assisted outputs prior to publication
• enhanced privacy and data protection measures aligned with the Statistics Act
• clear documentation and approval processes for the adoption of new AI tools
• ongoing staff training and support for responsible AI use.

We are committed to transparency, responsible innovation and privacy protection in all our activities. Statistics Canada’s guiding principles and use of GenAI tools are continuously monitored and reviewed to remain relevant and reflect the evolving technology and regulatory landscape, ensuring that our practices meet the highest ethical and professional standards.

How Statistics Canada uses generative artificial intelligence

To improve efficiency and enhance our services, we may use government-approved GenAI tools to help create our information and communications products. All AI-assisted outputs are reviewed and validated by Statistics Canada staff before publication.

The following are examples of how we may use GenAI:

• drafting and editing reports and discussion papers
• editing documents to ensure plain and inclusive language
• conducting preliminary research and compiling a list of sources
• drafting presentations, outlines, speaking notes, meeting minutes and other written materials
• summarizing and organizing comments and feedback
• reviewing and summarizing research materials
• drafting and editing emails and messages
• translating brief text segments
• producing videos and images.

Protecting your privacy while using generative artificial intelligence tools

Safeguarding the privacy and confidentiality of all information entrusted to us is our highest priority. Statistics Canada is dedicated to upholding strict data protection standards and complying with all relevant legislation when using GenAI tools. Every use of AI respects our legal and ethical obligations, ensuring that no personal or sensitive data are exposed or misused during the process.

For more on how we protect your data and foster public trust, please visit the Trust Centre pages dedicated to privacy and confidentiality commitments.

Unlocking innovation through responsible artificial intelligence use

By embracing GenAI with a commitment to responsibility, we unlock opportunities to design innovative services that respond more precisely to the evolving needs of those we serve. Harnessing these advanced tools allows us not only to enhance the quality of our existing offerings but also to streamline processes, increase efficiency and deliver greater value to Canadians. Responsible integration of AI empowers us to adapt swiftly and thoughtfully, ensuring that new solutions remain aligned with our high standards of service and integrity.