Homebuying meets AI: Why trust still has a human face

Artificial intelligence continues to make inroads into industries once defined by human relationships, and real estate is no exception. From automated property valuations to chat-based listing assistants, the idea of an “AI realtor” is no longer speculative. Yet new survey data suggests that when it comes to one of life’s biggest financial and emotional decisions—buying a home—many people remain hesitant. Notably, women appear significantly more cautious than men about replacing human agents with machines.

A recent survey by Home Marketing Services reveals a clear gender divide in attitudes toward AI-only real estate agents. While just over half of men (54.47%) said they do not want AI replacing human realtors, opposition increases sharply among women, with 74.81% preferring human guidance. Only around a quarter of women said they would opt for an AI-driven agent, compared with nearly half of men.

These figures underscore an important point: real estate is not simply a transactional market. It is a domain where trust, judgement and personal experience matter and where decisions often carry long-term consequences.

Unlike many digital services, homebuying cannot be easily reduced to a set of data points. A property is more than its square footage, price or location on a map. It is a living environment, shaped by factors such as safety, community, commute, schools and future resale value.

Bob Lovell, founder of Home Marketing Services, captures this complexity succinctly. Purchasing a home, he suggests, involves navigating a web of considerations—financial, emotional and practical. It is not merely a search and compare exercise, but a process of balancing risk and uncertainty.

Survey results suggest that women may be more attuned to this broader context. Rather than focusing solely on property attributes, many consider the “whole living situation”—including family dynamics, potential risks and what might go wrong after the purchase.

From a scientific and behavioural perspective, this aligns with research into decision-making under uncertainty. Individuals who weigh multiple contextual factors often display a more cautious approach, particularly when outcomes are difficult to reverse. A housing purchase, once completed, is rarely easily undone.

The Canadian perspective: Trust and regulation

In Canada, these dynamics are reinforced by a regulatory environment that places strong emphasis on consumer protection and professional accountability. Real estate agents are governed by provincial bodies, such as the Real Estate Council of Ontario (RECO) which impose obligations around disclosure, fiduciary duty and ethical conduct.

These frameworks are built on the expectation that agents act as advocates for their clients, guiding them through inspections, negotiations and contractual obligations. Translating this into an AI-only model is far from straightforward.

Canadian buyers must also contend with a complex market. Issues such as housing affordability, regional price disparities and variable financing conditions add layers of uncertainty. In this context, the value of local knowledge becomes particularly salient.

While AI tools can provide data, they may struggle to interpret these subtleties. A model may identify a property as “good value” based on historical pricing but fail to account for emerging local risks or qualitative factors that an experienced agent might recognise.

Despite these reservations, AI clearly has a role to play in modern real estate. Its ability to process large datasets quickly offers tangible advantages. Prospective buyers can compare properties, analyse price trends and access relevant information without needing to consult multiple sources.

AI can also improve accessibility. For first-time buyers, particularly those unfamiliar with real estate jargon, conversational AI tools can help explain terminology and outline the steps involved in a transaction. In Canada, where mortgage rules and qualification criteria can be complex, this type of support can be valuable.

Furthermore, AI can help streamline the early stages of the buying process. By organising search criteria, shortlisting properties and generating questions for agents, it enables buyers to approach viewings more prepared.

The risks: Loss of advocacy and situational awareness

However, the survey findings suggest that for many the perceived risks of removing the human element outweigh these benefits. One concern is the loss of advocacy. A human agent acts as an intermediary, representing the buyer’s interests and negotiating with sellers. This role becomes particularly important when deals become complex or contentious. AI, while capable of recommending strategies, cannot assume responsibility or accountability in the same way.

Another limitation is situational awareness. Real estate decisions often rely on subtle cues—how a property “feels,” whether something appears out of place, or whether there are signs of underlying issues. These are judgements that currently remain beyond the capabilities of automated systems.

In addition, there is the issue of liability. In Canada, agents are subject to professional standards and can be held accountable for errors or omissions. With AI systems, responsibility becomes less clear. If an algorithm misinterprets data or provides inadequate guidance, determining liability may be more challenging.

Gender differences and risk perception

The gender divide highlighted in the survey may reflect broader differences in risk perception. Studies have consistently shown that women, on average, tend to be more risk-averse in financial decision-making contexts. This is not a weakness, but often a rational response to uncertainty, particularly in high-stakes scenarios.

In real estate, where transactions involve substantial financial commitments and long-term consequences, this cautious approach may lead to a stronger preference for human support. The presence of an agent provides not only expertise but also reassurance—a factor that should not be underestimated.

For the real estate industry AI adoption is likely to succeed where it complements human expertise rather than attempts to replace it. The most effective model may be a hybrid one. AI can handle data analysis, property comparisons and administrative tasks, while human agents focus on interpretation, negotiation and relationship-building. This approach leverages the strengths of both.

Ultimately, the survey underscores a fundamental point: technology alone does not generate trust. In areas where decisions are deeply personal and financially significant, people continue to value human interaction. For many women, this trust appears particularly important. The preference for human agents reflects not resistance to technology per se, but a recognition of the limitations of current AI systems in addressing the full complexity of real estate decisions.