For years, the conversation around artificial intelligence in the built form in general has been dominated by caution. Privacy concerns have shaped both public debate and industry assumptions.

When we launched our most recent rental housing study, we expected those concerns to surface clearly, particularly around more visible technologies such as facial recognition.

Instead, the data told a different story. In this two-part column (Part II will be published on RENX next week), we explore what it tells us.

Across our national sample of Canadian renters from simplydbs’ 2025 Canadian Multi-Residential Satisfaction Study, a clear majority reported being comfortable with AI being integrated into building operations.

Depending on the application, roughly 60 per cent to 75 per cent of respondents expressed comfort or neutrality toward AI-enabled tools such as predictive maintenance, security systems and operational efficiencies. Even facial recognition, which we expected to be among the least accepted features, was viewed positively by a majority of respondents.

This outcome ran counter not only to our original thesis, but also to the public response seen during earlier high-profile urban technology initiatives that ultimately stalled amid concerns around data governance, scale, transparency and the role of private technology firms in public infrastructure.

Given that context, we chose to dig deeper into the data to understand whether this overall comfort held consistently across different demographic groups.

It is important to note that throughout this analysis, comfort and positive sentiment are defined as respondents who indicated an AI feature was essential, nice to have, or that they were indifferent to its use in building operations.

The analysis was supported by a robust response rate from Canadian renters living in rental buildings. This statistical strength gave us confidence to examine patterns beyond the topline results and explore where differences truly emerged.

Age isn’t the deciding factor - use case is

What we found particularly interesting in the age-based analysis was that there was no single age group that consistently represented either the most accepting or the most resistant cohort across all AI applications. Instead, the size of the “do not want” group shifted depending on the specific use case.

For example, acceptance of AI-enabled surveillance tools designed to flag suspicious behaviour was actually highest among older renters. Approximately 80 per cent of respondents aged 70 and over fell into the comfort grouping for this application, compared with just 64 per cent of renters aged 19 to 29. This suggests perceived safety benefits can outweigh privacy concerns for older residents in ways that are not always assumed.

The pattern reverses when looking at more convenience-driven tools. For AI-powered chatbots used for amenity bookings, renters aged 30 to 39 were roughly 15 percentage points more comfortable with the technology than those aged 70 and over. In this case, familiarity with digital self-service tools appears to play a stronger role for the younger generation.

There were also AI applications where age made remarkably little difference.

Features such as AI-assisted background checks and rental history verification, as well as AI tools that suggest ways to reduce energy usage or lower utility costs, showed consistently high comfort levels across all age groups. These tools were generally viewed as practical, low-risk and clearly beneficial, resulting in minimal variation in the size of the “do not want” group regardless of age.

Taken together, these findings reinforce the idea that acceptance of AI in building operations is driven more by purpose and perceived value than by age alone. Renters of all generations are willing to engage with AI but they evaluate each application on its own merits rather than through a single, age-based lens.

Provincial differences exist - but use case still leads

We observed a similar pattern when analyzing the data by province as we did with age. Comfort levels were not fixed by geography alone; instead, they shifted depending on the specific AI solution being proposed. In other words, no province was uniformly accepting or resistant across every application, the context of the technology mattered.

That said, one consistent finding stood out. Quebec reported the highest overall comfort levels across all 15 AI applications included in the survey, the most receptive province in the study. Regardless of application, renters in Quebec were more likely than those in any other province to fall into the comfort grouping.

The top three AI applications desired by Quebec renters were:

• package delivery management;
• personalized notifications related to rent, maintenance and community events; and
• AI tools that predict maintenance issues before they occur.

Notably, these same three applications also ranked highest in British Columbia.

This comparison is particularly interesting because British Columbia reported some of the lowest overall comfort levels nationally when viewed across all applications, though it is important to note comfort still remained a majority position. Even in the province with comparatively lower acceptance, renters were aligned with Quebec on which AI applications they found most valuable.

Taken together, these findings reinforce a broader theme in the data: while comfort with AI varies by province and by use case, renters across Canada consistently express a baseline comfort with this type of technology being incorporated into rental housing operations.

IN PART II NEXT WEEK: We'll explore the data based on citizenship, income, life stages and offer some concluding thoughts on what it all means both for tenants, and the owners and managers of multifamily buildings.