Are we automating too much? A critical view on AI and photo-based apps in property inspections

Across the property sector, from mainstream lettings to institutional portfolios, the adoption of AI-powered inspection tools has accelerated. Photo comparison apps, automation software, and data‑driven dashboards promise to transform property reporting with speed, scale, and efficiency. Yet, as adoption increases, so do questions around trust, accuracy, and fairness. Can AI understand context? Will landlords and tenants accept its judgment? And are we replacing something human, and essential, too soon?

The limits of automation: why AI still struggles with nuance

AI tools have made great strides in standardising documentation. Many systems can now detect changes between photos, highlight anomalies, and even label room layouts. But property inspections are not laboratory conditions. The accuracy of AI depends on lighting, angles, material types, and image consistency, all of which can vary dramatically from one property to the next, from one clerk to another, from one smartphone camera to another. A scuff on a wooden floor might be natural grain, genuine damage or in fact a stain. A patch on a wall could be a lighting artefact or a structural issue. Without trained oversight, automated systems are prone to both false positives and overlooked issues.

The broader concern lies in the illusion of precision. Because a platform generates a report quickly, with seemingly exact markers and visual flags, it may appear objective. But unless the system understands the property’s context, age, specification level, and history, it risks flattening judgment and misrepresenting reality.

When efficiency meets public scepticism

There is also a trust deficit to contend with. A growing body of public feedback shows that consumers remain wary of AI-based decisions in property and related services. Research from the Information Commissioner’s Office and consultancy company Cognizant shows that while automation is welcomed in administrative tasks, trust falls sharply when AI is used to make decisions with financial or legal implications.

This is especially relevant in sectors like property management and lettings, where AI is increasingly used to detect tenant-caused damage or justify deposit deductions. Stories from adjacent industries highlight the risks. Car rental firms such as Hertz have faced criticism for AI scanners that charge customers hundreds of pounds for barely visible dents or scratches (https://www.carscoops.com/2025/07/hertz-ai-complaints-are-spreading-faster-than-the-damage-it-flags/). Without a human in the loop, many felt the system was biased, opaque, or unfair. In a sector already fraught with disputes, property operators should take note. Transparency must accompany automation, or trust will erode.

AI’s potential in property: assist, not replace

Let's be clear, we are not a rejecting digital innovation. Hihouse has implemented a lot of automation tools to optimise our operations and this has been a transforming journey into our ability to operate efficiently, at scale with proven reliability. We support innovation and AI has clear benefits in property inspections. It can timestamp evidence, track changes over time, and help clerks identify recurring maintenance patterns or help with judgement. It brings a form of structure to chaos. But the strength of these systems lies in their ability to support, not supplant, professional judgment. This is our conclusion.

Well-trained human clerks do more than spot issues. They interpret them. They understand the implications of damage, the likelihood of wear, and the difference between cosmetic deterioration and actionable faults. They know that a water stain near a pipe might indicate a leak, while a smudge on a surface may be cleaning residue. These distinctions matter. And for legal defensibility, contextual judgment remains indispensable.

The legal risk of removing human oversight

Inventory reports are evidential documents. They are used in court, in tenancy deposit disputes, in insurance claims, and during regulatory audits. They must stand up to legal scrutiny. But that scrutiny often depends on reasoning, not just observation. What was the condition at the start of tenancy? Were changes reasonable? Was fair wear and tear correctly assessed? These are interpretive questions. They require human narrative, chronology, and sometimes explanation.

Fully automated reports rarely meet these standards. They lack the detailed annotations, photographic positioning, or narrative framing that adjudicators expect. And without a qualified professional’s name attached, accountability becomes unclear. In the event of a dispute, landlords and tenants may both find themselves unable to defend or challenge a claim credibly.

the road ahead: anticipating the evolution of ai in property

The next wave will take us beyond photo-based inspection. AI is beginning to power predictive analytics, weighting factors such as occupancy, asset performance, and weather patterns to forecast maintenance needs through systems like SnapInspect. Drone technology combined with thermal imaging is also improving rapid condition assessments, particularly for roofs and exteriors. The broader proptech ecosystem is coalescing around AI-native services, ranging from tenant screening to invoice processing and energy management. JLL’s research points toward a future where AI drives 90% of corporate real estate functions, operating in parallel with human experts rather than replacing them. Yet with expansion comes heightened responsibility: human context, legal transparency, and data governance must remain central.

This human-digital collaboration offers the best of both worlds. Technology provides consistency, structure, and efficiency. People provide reasoning, experience, and the ability to adapt to ambiguity. Our approach reflects a simple truth: in property, trust is built not by removing humans from the process, but by equipping them with better tools and sharper focus.