Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

The house price to income ratio in Canada peaked in the second quarter of 2022, followed by a decline until the second quarter of 2025. The ratio measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. Canada's index score in the second quarter of 2025 amounted to *****, which means that house price growth has outpaced income growth by almost **** percent since 2015. Canadian home prices continue to grow House prices in Canada have steadily increased over the past decade, despite a very mild decline in 2023. This trend is forecast to continue until 2026, albeit at a lower rate than in the period between 2019 and 2022. In British Columbia, which has consistently been the most expensive province for housing, the average house price is expected to reach nearly *** million Canadian dollars in 2026. The rising homeownership costs have also affected rents. In 2024, the average two-bedroom apartment rent in Vancouver exceeded ***** Canadian dollars. Canadian incomes on the rise Incomes in Canada have steadily risen since 2000 and show no signs of slowing down in the near future. This should improve housing affordability, as long as home price growth slows down.

This statistic shows the house price to income ratio in Canada from 2013 to 2015 with forecasts for 2016 and 2017, by province. The house prices in British Columbia were *** times the average household income in 2015, but were set to increase to *** times average household income in 2016, then return to *** by 2017. For more recent data on housing affordability in the major Canadian markets click here.

Graph and download economic data for Residential Property Prices for Canada (QCAN628BIS) from Q1 1970 to Q1 2025 about Canada, residential, HPI, housing, price index, indexes, and price.

Data on resident buyers who are persons that purchased a residential property in a market sale and filed their T1 tax return form: number of and incomes of residential property buyers, sale price, price-to-income ratio by the number of buyers as part of a sale, age groups, first-time home buyer status, buyer characteristics (sex, family type, immigration status, period of immigration, admission category).

Prospective homebuyers in Vancouver, British Columbia, and Toronto, Ontario, needed an annual income of over ******* Canadian dollars in June 2025 to qualify for the average priced home. In Vancouver, this figure was approximately ******* Canadian dollars. British Columbia and Ontario, are Canada's most expensive provinces for housing. According to a January 2025 forecast by the Canadian Real Estate Association (CREA), the housing market is expected to grow in the next two years, which is likely to worsen home affordability.

Housing costs can represent a substantial financial burden to households, especially low-income households. The median of the ratio of housing costs over income gives an indication of the financial pressure that households face from housing costs. Another common measure of housing affordability presented in this indicator is the housing cost overburden rate, which measures the proportion of households or population that spend more than 40% of their disposable income on housing costs (in line with Eurostat methodology). For a discussion of different measures of housing affordability and their advantages and limits, please see indicator HC1.5 Overview of affordable housing indicators in the OECD Affordable Housing Database. For policy measures aiming to support households with housing costs, please see indicators in the PH2, PH3 and PH4 series. Housing costs can refer to: (1) a narrow definition based on rent and mortgage costs (principal repayment and mortgage interest); or (2) a wider definition that also includes the costs of mandatory services and charges, regular maintenance and repairs, taxes and utilities, which are referred to as “total housing costs” below. Housing costs are considered as a share of household disposable income, which includes social transfers (such as housing allowances) and excludes taxes. Income is equivalised for household size based on a common equivalence elasticity (the square root of household size) which implies that a household’s economic needs increase less than proportionally with its size. Housing costs refer to the primary residence. The data presented here are based on household survey microdata and concern national household or population level data.

The purpose of this study was to understand the relationship between happiness and housing prices in Canada. The happiness data were obtained from the General Social Survey between 2009 and 2013, asking respondents to report overall happiness level by using scale ranging between 1 to 10 points. House Price Indexes at the provincial level were constructed to cover the same period. The relationship between average house price change and average happiness was estimated using Ordinary Least Square and Logistic Regression techniques. Individual's characteristics were used as control variables. The study found that average happiness level is positively and significantly related to the change in housing prices for one group and not for another - for homeowners but not for renters. In addition, individuals with better health are much happier than individuals with poor health. Similarly, individuals with higher income are happier than individuals with less income. The implication of this study is that the government should design attractive policies to encourage homeownerships.

Note: April 22, 2025: Updates to "CHN by income and HH size_v3". --------------------------------------------------------------------------------------------------------------------------------- Note: April 16, 2025: Updates to the following files have been made on April 9th and 16th: "CHN by income and HH size_v2", "cd_hh_projections_v2", "csd_hh_projections_v2", and "CMAs_all data_v3". --------------------------------------------------------------------------------------------------------------------------------- Note: March 31, 2025 files "Data_Element_1a" & "...1b" updated to v3 to include additional geographies (CDs and PTs) in the calculation of households close to rail transit. --------------------------------------------------------------------------------------------------------------------------------- Note: This dataset as of March 31st, 2025 now contains data on all 12 data elements, including core housing need among "gender diverse" households (formerly called "2SLGBTQ+" households) in table "Data_Element_ 3". That table (i.e. Data_Element_3) now also includes core housing need data on those priority populations reported in HART's HNA Tool. Two other outputs were migrated from that HNA Tool into this Federal HNA Template dataset: Income Categories and Affordable Shelter Costs, Percentage of Households in Core Housing Need by Income Category and Household Size, and 2021 Affordable Housing Deficit. (HICC Section 3.6), and Projected Households by Household Size and Income Category (HICC Section 6.1.1) This Borealis dataset has been updated accordingly to include that data: "AMHI.csv" (2021 AMHI and dollar ranges of income and shelter cost categories) "cd_hh_projections.csv" (Projected households in 2031 for CDs) "csd_hh_projections.csv" (Projected households in 2031 for CSDs) "CHN by income and HH size.csv" (2021 core housing need by income and household size) The geographical scope of the dataset has also been expanded. Before March 31st, only CSDs were included. As of March 31st, data on CDs, provinces/territories, the country of Canada, and CMA/CAs has been added. Not all data is available for all geographies: Data from CMHC's Rental Market Survey and Starts and Completions Survey are reported at the CSD level within CMAs/CAs. Results for provinces/territories/Canada are reported, but data for CDs is not. Since these surveys may not include all CSDs within a given CD, we have not attempted to aggregate this CSD data into CDs. Data from any custom census order by HART does not include CMA/CAs. We are able to aggregate the data by CSD into CMA/CAs, but all income and shelter cost data had been categorized based on the AMHI of the CSD as part of the original order (i.e. whether a household is "Very Low" income or "Low" income depends on the median household income of the CSD that the household lives in). This will lead to some inaccuracy and ambiguity of interpretation for the income or shelter cost data reported for CMAs. Data on "gender diverse" households is only available from Statistics Canada for geographies with a population count greater than 50,000 as of the 2021 census. This represents a total of 239 geographies (incl. Canada and the provinces/territories). Due to the low number of CSDs with this data, we have not attempted to aggregated this to the CMA/CA level. Data for CMAs/CAs will be added to the tool by mid-April 2025, but the source data has been summarized and included in this dataset: "CMAs_all data.csv" (All available data for CMAs and CAs) --------------------------------------------------------------------------------------------------------------------------------- Update (March 14, 2025): Tables "Data_Element_1a" and "...1b" have been updated to exclude some non-rail rapid transit stops that were erroneous included, notably in Winnipeg. --------------------------------------------------------------------------------------------------------------------------------- For more information, please visit HART.ubc.ca. Housing Assessment Resource Tools (HART) This database was created to accompany the dashboard on HART's website called the "Federal Housing Needs Assessment Template." URL: https://hart.ubc.ca/federal-hna-template/. This dashboard presents housing-related data to help communities complete the Housing Needs Assessment template requested by the Government of Canada as a requirement for certain funding applications. For more information on that template, please visit the Government of Canada's website (https://housing-infrastructure.canada.ca/housing-logement/hna-ebml/template-modele-eng.html). This dataset represents the underlying data used to populate HART's dashboard. The data contains some public and custom data from Canada's Census of Population (author: Statistics Canada), public data from the Canada Mortgage and Housing Corporation (CMHC) regarding it's Rental Market Survey as well as it's Starts and Completions Survey, private...

New housing price index (NHPI). Monthly data are available from January 1981. The table presents data for the most recent reference period and the last four periods. The base period for the index is (201612=100).

The house price for Ontario is forecast to decrease by eight percent in 2023, followed by a minor increase of one percent in 2024. From roughly 932,000 Canadian dollars, the average house price in Canada's second most expensive province for housing is expected to fall to 861,000 Canadian dollars in 2024. After British Columbia, Ontario is Canada's most expensive province for housing. Ontario Ontario is the most populated province in Canada, located on the eastern-central side of the country. It is an English speaking province. To the south, it borders American states Minnesota, Michigan, Ohio, Pennsylvania, and New York. Its provincial capital and largest city is Toronto. It is also home to Canada’s national capital, Ottawa. Furthermore, a large part of Ontario’s economy comes from manufacturing, as it is the leading manufacturing province in Canada. The population of Ontario has been steadily increasing since 2000. The population in 2018 was an estimated 14.3 million people. The median total family income in 2016 came to 83,160 Canadian dollars. Ontario housing market The number of housing units sold in Ontario is projected to rise until 2024. Additionally, the average home prices in Ontario have significantly increased since 2007.

Shelter-cost-to-income ratio by tenure including presence of mortgage payments and subsidized housing for Canada, provinces and territories, census divisions and census subdivisions. Includes residence on or off reserve, and household type including census family structure.

To illustrate a more complete picture of housing affordability over time, the housing affordability measure Payment as Percent of Income (PPI) was constructed from the perspective of a new home buyer on a given year: - earning the median regional after-tax (estimated quarter to quarter) income - purchasing a home through a 25-year variable rate mortgage with a minimum downpayment - financing their mortgage at the current prime rate - and purchasing their home at the median price of each category for their region. Taking the combination of house prices, income, and mortgage rates into account illustrates a more comprehensive picture of housing affordability over time. These factors are used to calculate the quarterly mortgage PPI Sources of Data: Income Data, Prime Rates, Consumer Price Index : Statistics Canada (StatsCan); House Prices: BC Assessment Authority (BCA)

This dataset includes one dataset which was custom ordered from Statistics Canada.The table includes information on housing suitability and shelter-cost-to-income ratio by number of bedrooms, housing tenure, age of primary household maintainer, household type, and income quartile ranges for census subdivisions in British Columbia. The dataset is in Beyond 20/20 (.ivt) format. The Beyond 20/20 browser is required in order to open it. This software can be freely downloaded from the Statistics Canada website: https://www.statcan.gc.ca/eng/public/beyond20-20 (Windows only). For information on how to use Beyond 20/20, please see: http://odesi2.scholarsportal.info/documentation/Beyond2020/beyond20-quickstart.pdf https://wiki.ubc.ca/Library:Beyond_20/20_Guide Custom order from Statistics Canada includes the following dimensions and variables: Geography: Non-reserve CSDs in British Columbia - 299 geographies The global non-response rate (GNR) is an important measure of census data quality. It combines total non-response (households) and partial non-response (questions). A lower GNR indicates a lower risk of non-response bias and, as a result, a lower risk of inaccuracy. The counts and estimates for geographic areas with a GNR equal to or greater than 50% are not published in the standard products. The counts and estimates for these areas have a high risk of non-response bias, and in most cases, should not be released. Housing Tenure Including Presence of Mortgage (5) 1. Total – Private non-band non-farm off-reserve households with an income greater than zero by housing tenure 2. Households who own 3. With a mortgage1 4. Without a mortgage 5. Households who rent Notes: 1) Presence of mortgage - Refers to whether the owner households reported mortgage or loan payments for their dwelling. 2015 Before-tax Household Income Quartile Ranges (5) 1. Total – Private households by quartile ranges1, 2, 3 2. Count of households under or at quartile 1 3. Count of households between quartile 1 and quartile 2 (median) (including at quartile 2) 4. Count of households between quartile 2 (median) and quartile 3 (including at quartile 3) 5. Count of households over quartile 3 Notes: 1) A private household will be assigned to a quartile range depending on its CSD-level location and depending on its tenure (owned and rented). Quartile ranges for owned households in a specific CSD are delimited by the 2015 before-tax income quartiles of owned households with an income greater than zero and residing in non-farm off-reserve dwellings in that CSD. Quartile ranges for rented households in a specific CSD are delimited by the 2015 before-tax income quartiles of rented households with an income greater than zero and residing in non-farm off-reserve dwellings in that CSD. 2) For the income quartiles dollar values (the delimiters) please refer to Table 1. 3) Quartiles 1 to 3 are suppressed if the number of actual records used in the calculation (not rounded or weighted) is less than 16. For cases in which the renters’ quartiles or the owners’ quartiles (figures from Table 1) of a CSD are suppressed the CSD is assigned to a quartile range depending on the provincial renters’ or owners’ quartile figures. Number of Bedrooms (Unit Size) (6) 1. Total – Private households by number of bedrooms1 2. 0 bedrooms (Bachelor/Studio) 3. 1 bedroom 4. 2 bedrooms 5. 3 bedrooms 6. 4 bedrooms Note: 1) Dwellings with 5 bedrooms or more included in the total count only. Housing Suitability (6) 1. Total - Housing suitability 2. Suitable 3. Not suitable 4. One bedroom shortfall 5. Two bedroom shortfall 6. Three or more bedroom shortfall Note: 1) 'Housing suitability' refers to whether a private household is living in suitable accommodations according to the National Occupancy Standard (NOS); that is, whether the dwelling has enough bedrooms for the size and composition of the household. A household is deemed to be living in suitable accommodations if its dwelling has enough bedrooms, as calculated using the NOS. 'Housing suitability' assesses the required number of bedrooms for a household based on the age, sex, and relationships among household members. An alternative variable, 'persons per room,' considers all rooms in a private dwelling and the number of household members. Housing suitability and the National Occupancy Standard (NOS) on which it is based were developed by Canada Mortgage and Housing Corporation (CMHC) through consultations with provincial housing agencies. Shelter-cost-to-income-ratio (4) 1. Total – Private non-band non-farm off-reserve households with an income greater than zero 2. Spending less than 30% of households total income on shelter costs 3. Spending 30% or more of households total income on shelter costs 4. Spending 50% or more of households total income on shelter costs Note: 'Shelter-cost-to-income ratio' refers to the proportion of average total income of household which is spent on shelter costs. Household Statistics (8) 1....

Affordable Home Prices are calculated based on household income for the second to sixth decile. Affordability is calculated based on housing costs not exceeding 30% of the households' gross income. Each decile represents 10% of households. The deciles are organized by income level where the first decile is the lowest 10% of households with the lowest income and the tenth decile is the top 10 % of households with the highest income. This is calculated for each of the 47 Service Manager Areas.

The dataset titled "Overview of affordable housing indicators" is a comprehensive resource that provides insights into the affordability of housing across OECD member countries. The data spans from 2010 to 2020 and is updated annually. The dataset, published by the Organisation for Economic Co-operation and Development (OECD) on May 27, 2021, is available in PDF format and can be accessed openly. However, the OECD restricts the posting of its material on the internet, although linking, sharing, and embedding are permitted. The dataset does not contain data about individuals or identifiable individuals. The metadata for this dataset was created on November 15, 2023, and last modified on April 8, 2025. The dataset provides a range of economic indicators related to housing affordability, including house-price-to-income and housing-expenditure-to-income ratio measures. It also includes more data-intensive indicators such as residual income measures, which focus on the income households have left after paying for housing. The dataset is tagged with keywords such as Affordability, Affordable Housing, Economic Indicators, Expenditure, Housing Potential, Income, and Indicator. The dataset is owned by the OECD, and they can be contacted via telephone or fax for any queries. The dataset is available in English and the description of the dataset is provided. The dataset's source and location are provided, but the license is not specified.

The average resale house price in Canada was forecast to reach nearly ******* Canadian dollars in 2026, according to a January forecast. In 2024, house prices increased after falling for the first time since 2019. One of the reasons for the price correction was the notable drop in transaction activity. Housing transactions picked up in 2024 and are expected to continue to grow until 2026. British Columbia, which is the most expensive province for housing, is projected to see the average house price reach *** million Canadian dollars in 2026. Affordability in Vancouver Vancouver is the most populous city in British Columbia and is also infamously expensive for housing. In 2023, the city topped the ranking for least affordable housing market in Canada, with the average homeownership cost outweighing the average household income. There are a multitude of reasons for this, but most residents believe that foreigners investing in the market cause the high housing prices. Victoria housing market The capital of British Columbia is Victoria, where housing prices are also very high. The price of a single family home in Victoria's most expensive suburb, Oak Bay was *** million Canadian dollars in 2024.

House prices capture the financial burden of purchasing a dwelling, and their development over time is measured by a (real) house price index. The evolution of rental prices can be monitored over time by the (real) rent price index. Alternatively, house prices can be compared to income (price-to-income ratio) as a measure of the affordability of owning a dwelling. If the price-to-income ratio is above (below) their long-term average, house prices are considered to be overvalued (undervalued). Meanwhile, the OECD database on regional house price indices shows how house price developments vary across regions and cities within countries (for further discussion, see the OECD National and Regional House Price Indices Database, as well as OECD, 2020a).

Explore the Redfin Canada Properties Dataset, available in CSV format and extracted in April 2022. This comprehensive dataset offers detailed insights into the Canadian real estate market, including property listings, prices, square footage, number of bedrooms and bathrooms, and more. Covering various cities and provinces, it’s ideal for market analysis, investment research, and financial modeling.

Key Features:

• Property Details: Includes crucial data such as listing price, property type, square footage, number of bedrooms and bathrooms, and more.
• Geo-Location Data: Provides geographical coordinates, allowing for spatial analysis and mapping.
• Market Trends: Offers historical data to analyze price trends and market fluctuations.

Who Can Use This Dataset:

• Real Estate Professionals: Evaluate market trends and property values to better advise clients or guide investment decisions.
• Investors: Analyze the Canadian housing market to identify investment opportunities and potential returns.
• Data Analysts and Researchers: Use this dataset to study market dynamics, urban development, or economic factors influencing the real estate sector.
• Financial Analysts: Incorporate the data into financial models to forecast market behavior and investment outcomes.

Download the Redfin Canada Properties Dataset to access valuable information on the Canadian housing market, perfect for anyone involved in real estate, finance, or data analysis.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.