300+
The number of data scientists building, validating, and monitoring CoreAI models
99.5%
The percentage of direct-sourced data underpinning 2.5 million algorithms
130+
Federal and state regulation integrations to ensure compliance
Each statistic signals AI’s potential to make the property industry faster and smarter — especially when people are put at the center of this technology’s development.
We’re looking at how to interpret signals, connect risks, and guide the industry toward a future where intelligence moves fast — but accountability never falls behind.
Introduction
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Artificial intelligence is changing what it means to own, value, and protect a home.
It’s embedding itself deeper within the property market to quickly facilitate everyday tasks and quietly adjust asset values and risk exposure. But automated assistance often moves faster than human confidence can follow. And this recalibration is only just beginning.
AI-powered innovation is increasing its influence as this technology becomes widely accessible. It’s possible that human-in-the-loop processes soon will collapse into a single automated step. Interpersonal touchpoints may dissolve into automated profiles built on buyer preferences and risk tolerance to instantly produce property recommendations paired with a list of lenders that someone is pre-qualified to work with — and all communications are already coordinated with a homebuyer’s calendar.
Simplifying the complex homebuying process sounds ideal. However, each gain in efficiency exposes new questions about fairness, transparency, and trust about how property is priced, financed, and insured.
The AI conversation is no longer about speed. Nor is it just about data. It’s about who can be trusted to establish confidence when questions arise about the integrity of automated outcomes generated by the chain of information.
Cotality’s latest AI series explores this transformation from the ground up:
- How accountability shifts when property ownership becomes digital
- Why opacity in home valuation threatens market stability
- What happens when real-time decisions outpace human readiness
- Where digital twins blur the line between forecast and fact
At every step, the series returns to the same principle: data powers decisions in the property market, but the outcomes are lived by people.
Who really owns the data?
Artificial intelligence has altered the rhythm of the property market. It has democratized information, recalibrated expectations behind data-driven decisions, and accelerated answers to the point of near instantaneity. It has also left industry professionals and homebuyers alike wondering who is responsible for questions surrounding changes in value, risk, payments, and transactions.
The speed at which AI can assess portfolios and analyze thousands of properties has transformed property data 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 scrutinized from 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
As property professionals increasingly rely on AI-powered tools, many feel that automated tasks and programmed responses leave them with unanswered questions at a time when homebuyers are clamoring for more transparency, not less.
“AI is unmatched at efficiency,” explained Cotality’s head of Data Science Amy Gromowski. “But efficiency without accountability is acceleration without brakes. The next frontier is less about better models and more about better mechanisms for explaining and auditing them.”
Being able to trace, account for, and explain automated results is the new currency of building long-term trust in business. And that is what homebuyers are searching for. According to a recent Cotality housing trends survey, homebuyers still, on the whole, want human explanation, not just results.
Cotality found that this sentiment stems from a perceived lack of information, and AI is further blurring the lines of accountability by conflating data with understanding. Compared to other generations, our survey found that Gen Z felt less informed about the current real estate market (63%) versus the broader population (73%).
A lack of understanding amplifies risk. AI calculates its responses, but it can misinterpret signals — and that’s how a market can diverge from the reality on the ground.
To safeguard against this divergence and ensure real-time accuracy, professionals need partners who prioritize transparency. Working with partners like Cotality illuminates over 20,000 layers of data built on over 50 years of research to capture the signals that matter. To withstand scrutiny and deliver clarity, our insights are backed by technical and transparent governance that keeps humans in the loop for that extra cross-check of analysis. By providing transparency to what’s behind the models, you can have confidence in home valuations, property risk profiles, and borrower eligibility adjustments made on the fly.
Safeguarding the market
Behind the abstractions sits a basic truth: every digital asset is still connected to someone’s home. When someone places their trust in a model without critical validation, inconsistencies can create lived consequences like higher premiums, higher interest rates, or a loss in value.
Who then is responsible when answers are flawed — the data scientist, the platform, or the model itself?
Cotality’s research team sees these questions of responsibility as a simple need for transparency. When data enters the system through over 20,000 sources — insurers, lenders, local taxing jurisdictions — each hand-off creates potential drift. CoreAI — our holistic, layered approach to artificial intelligence — unifies these inputs through 2.5 million algorithms and a CLIP ID (an instant link between all instances of a property across millions of data points), making them traceable and interpretable at every step.
Transparency in data cannot be treated as a technical preference but as a required market safeguard.
Data quality is human responsibility
One of the most significant obstacles to effective AI use in property decisions is quality control. The ubiquitous AI warning “garbage in, garbage out” holds truer than ever.
And quality is still associated with human oversight.
People pay attention to where their information is coming from, especially during the homebuying process. According to our survey, recent buyers preferred people over AI tools across every major task — 63% for finding a home (vs. 12% for AI), 62% for finding a mortgage (vs. 15%), and 59% for homeowners insurance (vs. 16%).
But preferences don’t always dictate reality. There is no longer a distinct divide between human expertise and technological insight. They work in tandem. Accuracy and actionability are the results of transparency and accountability guardrails that guide AI’s development and presence in the property market.
That’s why CoreAI eliminates that friction by positioning people at the connection between data validation and model transparency. So when someone asks, “who’s responsible for this result?”, 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 the value stored in property, market stability will depend less on the brilliance of the code than on the clarity of explanation. That’s what Cotality strives for. With high-fidelity data embedded directly into over 200 models, our teams perform thousands of manual and automated checks to help you uncover clear signals from over 5.5 billion property records.
Investors want liquidity; regulators want transparency; families want security. All three require trust that the system can be explained—and held accountable—when it fails.
A buyer may never read a model audit, but they feel its results in the interest rate on their mortgage or the premium on their insurance policy. Transparency, in that sense, isn’t an abstract principle; it’s the difference between a stable payment and a lost home.
The future of property may well be automated, algorithmic, and dynamic. But the responsibility for what happens when models meet real lives must have a clear connection center of accountability.
