After years of caution and privacy concerns dominating the conversation around the use of artificial intelligence in the built form, we decided to seek hard data from Canadians in our most recent rental housing study.

What we found challenges previous perceptions. In Part II of our look at this rapidly evolving issue, we explore how citizenship, income and life stage affect opinions. Then, we offer a few concluding thoughts on the data. 

Looking for Part I of this column on the AI study? You can find it right here

The figures are from a national sample of Canadian renters who responded to simplydbs’ 2025 Canadian Multi-Residential Satisfaction Study.

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

How citizenship influences AI perception

When examining patterns by citizenship, the data revealed a more pronounced and consistent difference than we observed in the age or provincial groupings, particularly between Canadian citizens by birth and international students. Across all 15 AI applications measured, we saw an average comfort gap of approximately 25 percentage points between these two groups.

The smallest differences appeared in more operational and low-friction tools, such as package delivery management and personalized notifications related to rent, maintenance and community events. These features were broadly accepted regardless of citizenship status, suggesting that clear, practical benefits help bridge demographic differences.

The largest gaps emerged in more visible or personalized technologies, including AI concierge services (voice-controlled assistance in-unit or via app), facial recognition for increased security, and AI-assisted background checks and rental history verification. These applications prompted greater caution among Canadian-born respondents compared to international students, who were generally more comfortable with their use.

That said, it is important to emphasize that a majority of respondents across nearly all citizenship categories expressed comfort with the AI solutions tested. Whether respondents identified as Canadian by birth, Canadian naturalized, international students or another category, acceptance remained the dominant response for most tools.

There were only two exceptions.

Chatbots for rental and maintenance inquiries recorded a comfort level of 48 per cent among Canadian-born respondents, making them the only tools that fell just below a majority threshold within any citizenship grouping. Even in these cases, all other citizenship categories still showed majority comfort.

Overall, the citizenship analysis reinforces a key theme from the broader study: while levels of comfort vary by background and by application, resistance to AI in rental housing operations remains limited, and acceptance is widespread across renter demographics.

AI acceptance transcends income brackets

When analyzed by income, the results were notably consistent, with only small variations of a few percentage points between income groups. Across all income brackets, a clear majority of respondents expressed comfort with AI being integrated into rental housing operations across all 15 solutions evaluated.

That said, some patterns did emerge.

While still supported by a majority, chatbot-based tools,  particularly those used for rental and maintenance inquiries, tended to rank as the least supported AI applications across income groups. In contrast, package delivery management and personalized notifications related to rent, maintenance and community events consistently ranked among the most supported solutions, regardless of income level.

Overall, the income-based analysis reinforces the broader finding that comfort with AI in building operations is widespread, with differences driven more by the type of application than by household income.

Different life stages, similar comfort with AI

Finally, we examined the data through the lens of employment and life-stage status. Once again, the same overarching pattern emerged: each of the 15 AI applications we evaluated was considered independently, and no single employment or life-stage group consistently ranked as either the most accepting or the most resistant.

Across all groups measured, a majority of respondents expressed comfort with AI being integrated into rental housing operations.

That said, several meaningful distinctions surfaced.

Full-time students ranked above full-time employees for most of the AI applications evaluated, indicating a generally higher level of comfort with AI-enabled tools. However, there were notable exceptions.

Full-time employees reported higher comfort levels than students for AI surveillance monitors designed to flag suspicious behaviour, AI systems that predict maintenance issues before they occur, and package delivery management, all applications closely tied to building safety, reliability and operational continuity.

Retired respondents continued to show distinct preferences that aligned closely with the overall dataset. This group demonstrated a stronger appetite for package delivery management and personalized notifications related to rent, maintenance and community events, while showing less enthusiasm for chatbot-based tools.

Retired renters also reported high comfort levels with both AI surveillance monitors for suspicious behaviour and predictive maintenance technologies.

Taken together, the employment and life-stage analysis reinforces a central finding of the study: acceptance of AI in rental housing is driven less by work status and more by how directly each tool addresses residents’ day-to-day needs.

A new chapter for AI in rental housing

Taken together, the findings from this study point to a clear evolution in renter attitudes toward AI in rental housing operations. While earlier public debates around large-scale technology initiatives were shaped by uncertainty about how emerging tools might be used, today’s renters appear to be taking a different view.

Across age, province, citizenship, income and work status, most renters expressed comfort with AI when it is applied thoughtfully and with a clear purpose. Rather than reacting to AI as a single concept, renters are evaluating individual tools based on the value they deliver to safety, reliability, communication and day-to-day convenience.

For housing providers, this shift represents both an opportunity and a call to action.

AI should no longer be viewed solely as a back-of-house operational tool, but as a way to leverage real-time and predictive data to better understand resident needs not just historically, but in the moment and into the future.

Providers that begin exploring and implementing AI solutions now will be better positioned to anticipate issues, personalize services and respond to changing resident expectations in a more proactive and informed way.

When deployed transparently and responsibly, AI has the potential to become a core component of delivering the rental experience residents say they want today and will expect tomorrow.