Data powers decisions in the property market, but the outcomes are lived by people.
A decade ago, homeownership was defined by the name on the Land Registry deed. It was clear who was liable for questions and changes to value, risk, payments, and conveyancing. Artificial intelligence is altering this rhythm, taking real estate from an asset class defined by days of conversations and contracts to a dynamic recalibration of inputs in seconds.
The speed at which AI can assess mortgage portfolios and analyse thousands of properties has transformed property into a new kind of financial instrument. What was once a physical asset made of sticks and bricks now exists as a digital model to be scrutinised at every angle by anyone, anywhere.
While AI holds the promise of data liquidity and efficient knowledge transfer, what happens if it miscalculates equity, inflates value, or overlooks structural risk? Where do you look for answers?
Blurred accountability and the UK consumer
The question of accountability is critical for UK firms operating under the FCA’s Consumer Duty. While automation offers benefits, the Duty requires firms to act in good faith and avoid foreseeable harm to retail customers. When algorithms make key decisions—such as a property’s acceptable valuation or a borrower’s affordability—the accountability cannot simply be outsourced to the code.
The knowledge gap is clear: traditional UK housing regulation assumes clear lines of liability: owner, mortgage lender, insurer, regulator.
AI-driven systems blur those lines. When valuations and borrower eligibility are made by algorithms trained on opaque datasets, legal recourse could become uncertain.
Accuracy without accountability gambles on future outcomes for lenders, valuers, brokers, and homeowners alike. Working with partners like Cotality illuminates the layers of data, governance, and analysis behind the models so you can have confidence in Automated Valuation Models (AVMs), property risk profiles.
Encoding responsibility and regulation
The UK regulatory landscape is still catching up. Oversight focuses responsibility on the firm and the individual carrying out the regulated activity. With the Bank of England and FCA monitoring the systemic risk of AI adoption, firms must demonstrate that their AI systems are governed, auditable, and non-discriminatory.
Data quality is human responsibility
There is no longer a distinct divide between human expertise and technological insights. They work in tandem. But to provide accurate answers, transparency and accountability must remain the guardrails guiding AI’s development and increasing presence in the property market.
That’s why Cotality positions people at the connection point between data validation and model transparency. So when the FCA asks, “who’s responsible?”, the answer isn’t “the algorithm.”
Trust is transparent
“AI shouldn’t replace the chain of accountability; it should strengthen it,” says Cotality Chief Data and Analytics Officer John Rogers. “Our goal is to make every decision auditable, from the dataset to the doorstep. That's why we ensure our data is compliant with over 130 state and federal laws and regulations.”
As AI intermediates more of that value, market stability will depend less on the brilliance of the code than on the clarity of the rules governing it.
Lenders want risk mitigation; regulators want compliance and transparency; families want security. All three require trust that the system can be explained when it fails.
The future of property may well be fractional, algorithmic, and dynamic. But the responsibility for what happens when models meet real lives must have a clear centre.
