Immigration is on the rise and significantly contributing to Canada’s population and economic growth. But where will newcomers live when they arrive in Canada? How can we close the gap between immigrant-driven housing supply and demand? In this two-part blog series, we’ll explore how immigration impacts the Canadian economy, the lack of housing supply, and how big data can help Canadian groups address this significant challenge.
The lack of housing supply is a challenge even more complex for Canadian immigrants. As much as 26.6% of recent immigrants faced core housing needs compared to 12.7% of all Canadians as of 2016. In a study on immigrant housing needs, affordability was cited as the biggest barrier facing new immigrants, followed by declining relative incomes. Approximately 74% of newcomers are renters in their first five years and nearly 25% of newcomer tenants pay more than half of family income for rent. Another study also underlined the lack of housing supply for large families, which disproportionately affects immigrant families (20% of immigrant households in Toronto have 5 or more people), quality of housing, lack of resettlement information, and suitability of housing to employment opportunities and services.
Immigrants also face barriers such as an inadequate supply of housing, fragmentation of agency services, cultural barriers, and landlord reluctance due to financial or discriminatory reasons. For non-European and non-Chinese origin immigrants, homeownership rates were exceedingly low at 32%, with respondents citing discrimination based on income (though recent studies have shown this may be in itself be influenced by lack of recognition of degrees/licenses and discriminatory hiring practices).
Knowing the needs of the country, housing market, and immigrants, we must prioritize housing supply solutions. Technological innovations and big data have increasingly become a part of solving supply/demand issues. Compared to traditional methods, the use of data solutions enables predictive, accurate, and timely policy implementation to drive supply accommodation and better meet immigrant needs.
Two notable cases relating to refugees and migration show examples of data analytics for immigration patterns. During the height and aftermath of the 2015 surge of asylum seekers in Europe, the International Organization for Migration tabulated and released weekly figures for arrivals, deportations, and resettlement of refugees, helping policymakers, researchers, and non-governmental organizations to decipher trends and create predictive settlement and migration policies.
Similarly, Statistics Canada and Immigration Canada have already utilized data to ascertain information and trends of resettled Syrian refugees in Canada, from arrival to average family size. This has been used to help organizations accommodate immigrants’ basic everyday needs and provide/find appropriate housing, resulting in more seamless integration. Using this tool on a wider scale to analyze hundreds of thousands of new arrivals would certainly be a path towards a more cohesive housing policy.
To support Canada’s economic potential, efforts must be made to appropriately plan and accommodate population growth. As more newcomers come to Canada to start their new lives and contribute to society, we must ensure housing supply keeps up with demand, while overcoming existing housing barriers and challenges. Implementing a data aggregate and analytics tool for this purpose could be a major step in ensuring that Canada is ready to accommodate newcomers and plan for the future economy.
