Comparing Tenant Protections and Eviction Risk Across Canada

Evictions have become an increasingly common feature of the Canadian rental housing system. While there is a growing body of research on experiences and rates of eviction in Canada, there has yet to be a comparative assessment of legislation regulating rental housing and its role in shaping eviction risk. Recent research from the NHAs’ Housing Precarity research cluster (RC1) begins to address this gap in a new report for the CMHC entitled “Jurisdictional Differences in Eviction Risk Across Canada: A Conceptual Model of Eviction Risk ”.

The report presents findings from a literature review and jurisdictional scan, assessing the wide range of legal frameworks that govern rent controls and evictions across Canada. Drawing on these findings, rubrics were developed to score the strength of tenant protections in each province and territory. Next, a conceptual model of eviction risk was created to assess and compare the factors that lead to a higher risk of eviction. The conceptual model was then applied to data from the Canadian Housing Survey (2021 and 2022), the Canadian Census (2021), and Ontario eviction filings (2017-2021).

Jurisdictional Scan

The research team conducted a Canada-wide jurisdictional scan to understand the legal frameworks that govern landlord-tenant relationships and found significant variation. While some provinces have strong tenant protections, others provide minimal safeguards. These differences suggest the legislative context in Canada is a significant but under-explored component of eviction risk.

The jurisdictional scan examined the following:

Drawing on findings from the literature review and jurisdictional scan, the team developed two main rubrics – one for comparing rent control and the other for assessing strength of tenant protections for no-fault evictions. Generally, provinces and territories with higher scores, tend to have stronger tenant protections, while those with lower scores tend to have weaker tenant protections.¹

The first rubric assesses the strength of rent controls – policies or laws that control how much a landlord can increase the rent for existing tenants (see Figure 1). Scoring was based on the presence of rent control, vacancy control, and whether there are exemptions such as above guideline rent increases (AGIs). Jurisdictions that scored highest were Prince Edward Island and Quebec. These were the only two jurisdictions that have a form of rent control and vacancy control, controlling how much a landlord can increase rent on a unit during and in between tenancies. Almost half of the country’s provinces and territories lack any form of rent control, resulting in a score of 0.

Figure 1: Rent Control Scores by Jurisdiction

The second rubric assesses the strength of protections against no-fault evictions. No-fault evictions, also known as landlord-factor evictions, are initiated by the landlord for reasons unrelated to the tenant, such as when the landlord plans to move into the unit, renovate, sell the unit, or demolish the property. A number of elements were considered in our assessment, such as the presence of filing fees, the length of notice and dispute periods, proactive measures against bad-faith evictions, compensation for tenants, the right to return after renovations (i.e. right of first refusal), penalties imposed on landlords for non-compliance, and targeted protections for vulnerable groups.

The scores illustrate the wide-range of policies across Canada, with some tenants provided with far stronger protections than others in cases of renovation, demolition, and conversion. Total scores ranged from a high of 8.5 in British Columbia and Quebec to a low of 1.5 in Newfoundland and Labrador (see Figure 2). The highest scoring provinces and territories offer a comprehensive set of protections such as minimum compensation, proactive safeguards against bad-faith evictions, and stronger procedural requirements. Newfoundland and Labrador is the lowest scoring province because it provides minimal tenant protections, leaving tenants in the province highly exposed to displacement pressures.

Figure 2: No-Fault Evictions due to Renovation, Demolition, or Conversion

The scores also indicate significant variation in the strength of protections for tenants facing eviction by a landlord who is claiming to repossess a unit for own-use or sale. Jurisdictions that scored higher did so through a combination of protections, such as proactive safeguards against bad-faith repossessions, fines, and targeted protections for vulnerable groups. Figure 3 highlights that protections against landlord repossession and sale tend to be weaker across the board than protections against renovation, demolition, or conversion. These findings suggest that while certain jurisdictions offer relatively stronger protections, most provinces leave tenants vulnerable to displacement through repossession or sale.

See the report for a full breakdown of these rubrics and how each area scored.

Figure 3: No-fault Evictions due to Repossession for Own-use or Sale

Measuring Eviction Risk: A Conceptual Model

While the decision to evict a tenant is made by a landlord, legislation can shape a landlord’s incentive and capacity to undertake an eviction. A landlord’s incentive to evict is influenced by larger market factors, such as rental vacancy rates, rent gaps, and the strength of provincial rent controls. A landlord’s capacity to evict depends on the legal structures which govern the landlord-tenant relationship and the willingness of governments to enforce those rules. It is also influenced by the strength of tenant organizing, as well as the landlord’s own resources such as time, money, and knowledge of the process. 

In addition to market factors, legal structures, and the attributes of individual landlords, the reason for eviction is also an important factor to include in a model of eviction risk. Our model distinguishes between two types of eviction filings: tenant-factor (i.e., non-payment of rent or behavioural issues) and landlord-factor (i.e., repossession for own use, demolition, or renovations). Landlord-factor evictions are commonly known as “no-fault” evictions. Figure 4 below illustrates a conceptual model to assess eviction risk in Canada.

Figure 4: Total Eviction Risk Model

Source: David Wachsmuth, 2025

Several variations of the eviction risk model were developed and applied to a number of datasets. Three models were developed using 2021 Canadian Housing Survey (CHS) data including: 1) household-level data, 2) household and Census Metropolitan Area (CMA) level data, and 3) household-level data, CMA-level data, and scoring results from the jurisdictional scan for each province and territory.

A fourth model was developed using 2022 CHS data and includes household and provincial-level variables. The 2022 CHS Public Use Microdata File (PUMF) did not report respondent CMAs so it was not possible to include those variables.

In examining household variables alone, the eviction risk model found:

• Single parent households with children were more likely to have experienced a forced move than households with two parents. Households with two parents and children have similar risk of eviction as households with no children.
• Canadian born renters were more likely to report a forced move than non-Canadian born respondents, controlling for other factors.
• Respondents who were evicted from their previous rental accommodations were more likely to report negative mental health than respondents who left their previous rental accommodations of their own volition.
• People who spend between 30% and 50% of their income on housing are more likely to report being evicted.

After adding CMA variables, the model found:

• Both household employment and employment levels in the wider CMA are linked to eviction risk. People who are not employed are more likely to say they’ve been evicted, and people living in areas with lower overall employment are also more likely to experience eviction.

After adding provincial level variables identified in the jurisdictional scan, the conceptual model found:

• Households living in provinces with stronger rent controls, weaker landlord-factor eviction prevention laws, and strong tenant-factor eviction laws were less likely to have reported an eviction as the cause of their last move. Because these results are susceptible to reverse causation and omitted‑variable bias, and given the limitations of the study design, any inferences about the relationship between eviction policy and eviction prevalence should be made with considerable caution.

Applying the Conceptual Model of Eviction Risk (Ontario)

The research team also built two models to assess eviction risk in Ontario. The models were created using eviction filings from 2017-2021, obtained by the research team through Freedom of Information requests, as well as publicly available 2021 Census data. The first model focuses on eviction filings related to unpaid rent. The second model looks at all other types of eviction filings (cases where the reason was not non-payment of rent).

Key findings:

• Communities with relatively high rents see more filings for non-payment of rent, while areas with rapidly rising rents see more eviction filings under other categories.
• Non-payment filings are more common in communities with larger racialized populations, and less common in areas with many immigrants or high levels of core housing need.
• Tenant-related evictions tend to occur in more vulnerable communities, reflecting economic hardship and discrimination, while landlord-driven evictions are more common in neighbourhoods where rent growth creates financial incentives to replace existing lower-rent tenants with higher-rent tenants.

Key Takeaways and Policy Implications

This report provides important takeaways for advocates and policymakers:

• The Strength of Tenant Protections is Highly Uneven across Canada: The jurisdictional scan reveals that the provincial and territorial frameworks governing residential tenancies differ substantially in their protections for tenants.

• Tenant Protections Could Be Strengthened Across the Country: Especially concerning is the fact that almost half of Canada’s provinces and territories have no rent controls at all. Furthermore, the policies of those that do have rent controls are frequently riddled with exemptions, which are eroding the effectiveness of otherwise strong protections. These exemptions are leaving a growing number of tenants in unregulated units and leaving many others to face repeated above-guideline rent increases. They also create financial incentives for landlords to force out long-standing tenants. Jurisdictions with no rent control or that allow lease terminations without cause leave tenants more vulnerable to the destabilizing effects of an eviction.

• More Accessible and Standardized Eviction Data is Needed: There are clear limitations to what can currently be measured and understood regarding evictions in Canada. The absence of standardized and accessible eviction data, particularly regarding informal evictions, means that researchers can only capture the tip of the iceberg of forced moves. Though the privacy and protection of tenants must be a forefront consideration in data management, without improvements, policymakers, tenant advocates and researchers will continue to work with an incomplete understanding of eviction risk. Improvements to data systems should include maintaining consistent data on eviction filings, court outcomes, sales transactions, and building ownership information, as well as developing mechanisms to track informal evictions.

1. It is important to bear in mind that the scoring is not meant to be an ‘absolute’ measurement of the strength of a jurisdiction’s tenant protections, but rather a comparative score. A key limitation of the scoring approach is that it is based on an assessment of what is written in the legislation and regulations, rather than on what occurs in practice. Further, the jurisdictional scan was primarily conducted between May and September 2025, so changes to any legislation and rental regulations (e.g., Quebec’s recent regulatory changes) after this period were not reflected in the analysis. ↩︎