The average house price in Saskatchewan was about 320,912 Canadian dollars in 2024, and according to the forecast, is set to increase in the next two years. However, house price growth in the province is expected to be slower than the national average. In terms of home prices, Saskatchewan is one of the most affordable provinces for housing. Saskatchewan: key factsSaskatchewan is a province located between Alberta and Manitoba north of the Canada-United States border. In 2023, the population of Saskatchewan was over one million, which placed it as the sixth most populous Canada province. However, the population has been on the rise since 2006, so this may change in the future. Future of the housing marketThe number of housing starts in the province has been falling since 2012, which suggests that either supply is outstripping demand or that it’s simply not profitable enough for property developers. Some real estate experts in the region believe that the falling price of oil is causing the housing market slowdown because there are fewer jobs in the region as a result. However, they expect that the market will pick up again in the near future.

This table contains 204 series, with data for years 1981 - 2010 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (34 items: Canada; Newfoundland and Labrador; St. John's; Newfoundland and Labrador; Atlantic Region ...), New housing price indexes (3 items: Total (house and land);House only; Land only ...), Index base period (2 items: 1992=100;1997=100 ...).

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.

The average house price in the Canadian province of Manitoba in 2024 stood at 369,297 Canadian dollars. In the next two years, the house prices in the province are forecast to rise slightly, reaching 382,908 in 2025. Compared to other provinces, Manitoba was below the average for the country. Manitoba: key factsManitoba is a mid-sized Canadian province in terms of population and located between Saskatchewan, Nunavut and Ontario. However, its population had been trending upward since 2000 and shows no signs of slowing down. This suggests that demand for housing is also on the rise, which may explain the forecasted prices increases in the region. Affordability in ManitobaWeekly earnings of both salaried and hourly employees have also been on the rise in the province since 2001. Although the increase for salaried employees has been larger than for hourly employees. Nonetheless, this means that Manitobans have more money to save for and spend on buying a home. The number of housing starts in the province have varied over the past years.

In 2023, the rental market in Canada saw the lowest vacancy rate for rental apartments during the observed period. Approximately 1.5 percent of apartments were unoccupied in 2023, down from 1.9 percent the year below. Saskatchewan was the province with the highest vacancy rate, whereas Prince Edward Island and Nova Scotia had the lowest share of unoccupied apartments.

Note: The data release is complete as of August 14th, 2023. 1. (Added April 4th) Canada and Census Divisions = Early April 2023 2. (Added May 1st) Ontario, British Columbia, and Alberta Census Subdivisions (CSDs) = Late April 2023 3a. (Added June 8th) Manitoba and Saskatchewan CSDs 3b. (Added June 12th) Quebec CSDs = June 12th 2023 4. (Added June 30th) Newfoundland and Labrador, Prince Edward Island, New Brunswick, and Nova Scotia CSDs = Early July 2023 5. (Added August 14th) Yukon, Northwest Territories, and Nunavut CSDs = Early August 2023. For more information, please visit HART.ubc.ca. Housing Assessment Resource Tools (HART) This dataset contains 18 tables which draw upon data from the 2021 Census of Canada. The tables are a custom order and contains data pertaining to core housing need and characteristics of households. 17 of the tables each cover a different geography in Canada: one for Canada as a whole, one for all Canadian census divisions (CD), and 15 for all census subdivisions (CSD) across Canada. The last table contains the median income for all geographies. Statistics Canada used these median incomes as the "area median household income (AMHI)," from which they derived some of the data fields within the Shelter Costs/Household Income dimension. Included alongside the data tables is a guide to HART's housing need assessment methodology. This guide is intended to support independent use of HART's custom data both to allow for transparent verification of our analysis, as well as supporting efforts to utilize the data for analysis beyond what HART did. There are many data fields in the data order that we did not use that may be of value for others. 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 data fields: Geography: - Country of Canada, all CDs & Country as a whole - All 10 Provinces (Newfoundland, Prince Edward Island (PEI), Nova Scotia, New Brunswick, Quebec, Ontario, Manitoba, Saskatchewan, Alberta, and British Columbia), all CSDs & each Province as a whole - All 3 Territories (Nunavut, Northwest Territories, Yukon), all CSDs & each Territory as a whole Data Quality and Suppression: - 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. - Area suppression is used to replace all income characteristic data with an 'x' for geographic areas with populations and/or number of households below a specific threshold. If a tabulation contains quantitative income data (e.g., total income, wages), qualitative data based on income concepts (e.g., low income before tax status) or derived data based on quantitative income variables (e.g., indexes) for individuals, families or households, then the following rule applies: income characteristic data are replaced with an 'x' for areas where the population is less than 250 or where the number of private households is less than 40. Source: Statistics Canada - When showing count data, Statistics Canada employs random rounding in order to reduce the possibility of identifying individuals within the tabulations. Random rounding transforms all raw counts to random rounded counts. Reducing the possibility of identifying individuals within the tabulations becomes pertinent for very small (sub)populations. All counts greater than 10 are rounded to a base of 5, meaning they will end in either 0 or 5. The random rounding algorithm controls the results and rounds the unit value of the count according to a predetermined frequency. Counts ending in 0 or 5 are not changed. Counts of 10 or less are rounded to a base of 10, meaning they will be rounded to either 10 or zero. Universe: Full Universe: Private Households in Non-farm Non-band Off-reserve Occupied Private Dwellings with Income Greater than zero. Households examined for Core Housing Need: Private, non-farm, non-reserve, owner- or renter-households with incomes greater than zero and shelter-cost-to-income ratios less than 100% are assessed for 'Core Housing Need.' Non-family Households with at least one household maintainer aged 15 to 29 attending school are considered not to be in Core Housing...