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.
Decision liquidity: AI is changing the timing of trust in homebuying
A decade ago, homebuying, mortgage, and insurance decisions moved through a linear process at a human pace. We evolved from pen and paper to connected technology, but one thing stayed the same — the buyer remained in the middle of every step.
Today, AI models can move buyers along a much more dynamic digital journey. In theory, that makes markets more efficient: risk is measured almost instantaneously, time and costs through the process are reduced and allocated fairly, and resources flow where demand is strongest. But in practice, AI creates a data-heavy, digital journey where even small shifts in data can have major impacts on a homebuyer’s future.
This a new kind of volatility that demands constant recalibration, putting stress on financial systems and the people depending on them to work.
The fraction of speed
Decision liquidity — the ability to gather supplemental information, update terms, valuations, and coverage in near real time — is reshaping the housing ecosystem. For markets, this dynamism is exciting. For consumers, it can feel unstable. Especially when changes appear with little explanation or guidance from a trusted advisor.
Most recent homebuyers in Cotality's From House to Home report agree the system as a whole has accelerated, demanding the fastest decision-making ever. That’s central to why they described the homebuying process as stressful or confusing.
Consider the old philosophical puzzle, “If a tree falls in the forest, and no one is around to hear it, does it make a sound?” In the same way, AI’s silent decision making sends waves across the home buying journey.
If mortgage rates adjust to minute shifts in credit risk and real-time monetary policy, if insurance premiums rise or fall with the latest environmental risk data, or if profile-dependent rental rate fluctuations happen and no person is available to explain why, people may hesitate to act.
Trust vs. convenience: AI tools vs. human professionals
When there’s a lack of knowledge around the decision points, there are consequences. Our survey found one in five people weren’t confident navigating the homebuying journey. Confidence dropped even lower for Gen Z where only one in four felt equipped. This matters because a lack of confidence can derail transactions.
A young couple may find a dream home within their price range only to have competing bids and higher risk tolerance of other buyers push the home beyond their monthly budget. Even if the buyer can afford the increased price, there is uncertainty if the appraisal will support it.
The sheer scale of the housing industry will (and should) favor the efficiencies AI offers, and individual incidents may just be collateral damage. However, as Cotality has noted, each gain in efficiency exposes new questions about fairness, transparency, and trust about how property is priced, financed, and insured. As AI permeates each part of the housing transaction, it risks creating the impression of a system moving too quickly to understand. That perception matters. People are looking for clear signals that point to the next step. Without them, they can pause transactions.
In Cotality’s research, buyers who hit the pause button correlate to deals falling through as agents, lenders, and insurers lose momentum. In other words, decision liquidity, when stripped of human context, high-confidence data, and AI transparency, casts doubt and becomes a drag on the very efficiency it was meant to deliver.
The knowledge gap
The challenge extends beyond consumer sentiment. Businesses also no longer rely on fixed reference points. Clear paper trails are fractured into an amalgamation of hundreds of variables that lenders, insurers, and regulators can struggle to reassemble when reviewing a decision or tracing an error. This information overload can lead to skewed outcomes and blur the lines of accountability, adding risk of misinterpretation.
Market fluidity demands new guardrails that record outcomes and the reasoning behind each automated change — the what and the why. Without that, questions can go unanswered, and disputes become nearly impossible to resolve.
Timing intelligence: Cotality’s CoreAI
At Cotality, every signal and input is clearly attributed to one of our 20,000-plus data sources and reviewed by human experts who cross-check and validate the results. By providing context through transparency, we help you build the explanations that your customers need for confident decisions.
Property professionals and homeowners alike tell us the system is fragmented. To bridge what they feel is a disconnected process, people look for guidance in the form of human intervention.
Our findings reflect the fact that interest in AI-led processes is growing, but humans remain a stabilizing, calming influence. For AI to become a full partner and unlock its full promise of efficiency, the key is timing and transparency.
Cotality frames this as “timing intelligence,” a structure on which CoreAI is designed. With thousands of algorithms and 16 petabytes of data representing 50 years of history and 5.5 billion records, data is designed to be the foundation for understanding. The aim goes beyond accurately pricing in risk. It’s about delivering those prices at moments when people are ready to make sense of them and act. Data should support decisions, not crowd them out.
Cotality’s approach treats timing as a risk variable in its own right. Solutions like OneHomeTM track micro-signals — response times, repeated document views, updated searches — that indicate hesitation before it turns into a stalled transaction. By correlating those signals with market-level trends, Cotality helps real estate agents, lenders, and insurers calibrate faster, but also smarter.
“Our systems measure readiness as carefully as rates,” notes Cotality President of Enterprise Data Solutions Devi Mateti. “We’re building models that move at digital speed but still leave space for human assurance. The motto is ‘be first, but first be right.’”
Clarity as currency
Of course, context also matters. Speed also relies on clarity and trust to close deals.
The economic logic is straightforward: trust expands transaction volumes. When participants believe the system reacts predictably, they act decisively. When they suspect the ground may shift beneath them, they retreat. AI’s strength is the speed and scale with which it can process information. However, that same edge can turn into a weakness when information is published without regard for how people make sense of it, or without a human in the loop to explain what’s happening and why it matters. At least for now, we see that when AI acts alone, it has the potential to erode the rhythm of decision-making that markets, and people, depend on.
The opportunity AI offers is the ability to build trust faster. Those who commit to explaining reasoning and sharing the underlying data driving the process will win.
That’s why Cotality has designed transparency into market models.
Each change is encoded clearly so that professionals can share current insights at just the right time for their customers to respond. For example, AutomatIQ Borrower shortened deal times by nearly four days and cut loan buybacks by 40%.
Buyers often describe the journey to homeownership as emotional whiplash: hope, anxiety, relief, doubt. Liquidity amplifies those swings.
A single percentage-point change in mortgage rate can alter a family’s budget for years. The technology that calculates those shifts in seconds must therefore be paired with communication that restores calm just as quickly.
The wider implication is that decision liquidity, the ability to update information, terms, valuations, and coverage in near real time, changes the role of professionals. Loan officers, brokers, agents, and insurers become interpreters of timing rather than mere executors of transactions. Their value lies in explaining why the number or decisions changed, and what to do next.
Cotality’s insight work points to a simple reality. As AI plays a larger role in property decisions, advantage will come from systems that connect the whole picture. CoreAI is built to hold that full view and deliver information in ways that help people stay grounded in what’s happening.
“Clarity is the new currency in the age of AI,” says Cotality International's Chief Data Officer, Craig Dargusch. “We’re in the business of interpreting the noise. Consumers are crying out for help on this front.”
Decision liquidity will define the next decade of housing and insurance, and the winners will be those who master its tempo by translating constant recalibration into predictable rhythm. Those who mistake motion for progress will be left behind.
The weight of a number
Eliana asked a simple question: how much is my house worth? The answer changed depending on where she looked. Different websites produced valuations that led to home equity line of credit (HELOC) offers ranging from enough to fund a full renovation to only enough for a basic landscaping project. The house was the same, and she had been told the market was steady. Yet the numbers kept shifting.
Those variations can give buyers and homeowners pause. Sometimes the uncertainty is enough to stop them moving at all, whether that means seeking finance for a long-overdue renovation or putting their home on the market.
These background calculations are increasingly part of the housing experience. Automated valuation models (AVMs) help shape what buyers, sellers, and homeowners believe they can do next. Yet AVMs do not all draw on data of the same quality. Stable outputs matter because when the market starts treating value as a reliable signal, people respond to it. Competition can intensify in neighborhoods that look more valuable. Insurance costs can rise or fall depending on whether a home appears resilient, or exposed.
What is an automated valuation model (AVM)?
Cotality’s automated valuation models use CoreAI to accurately estimate the value of a home based on decades of tax record, sales, and listing data. Our AVMs allow lenders, investors, and consumers to quickly and accurately assess home equity, risk, and market trends.
In other words, valuations must be explainable. AVMs compress loan origination timelines and facilitate transactions. This lack of friction in the process has led to homebuyers seeking assurance that the outputs are based on a defensible framework built on quality data.
Shawn Telford, Chief Valuation Officer at Cotality, notes that as automation becomes embedded across the mortgage ecosystem, explanations become part of the service itself. “A property valuation isn’t just a number — it reflects trust in the value. As automation increases, we have to be able to show where that result came from.” Traceability becomes credibility.
Technology-driven valuation models are probabilistic by design. Their outputs move as inputs change, sometimes by a narrow margin. This can be enough to alter a decision, causing lenders to refine exposure and insurers to redraw comfort zones. For homebuyers, a data discrepancy can alter their future. Differences in data determine what gets financed, which loans close and when, and what doesn’t happen at all. Cotality analysts describe this accumulation of behavior and outputs as algorithmic gravity.
Explanations earn trust
Though house prices are still largely driven by supply and demand, models run by lenders commonly determine valuations used to determine lending risks.
Broadly speaking, AVMs draw on comparable sales, geographic market signals, environmental exposure, satellite imagery, and market momentum to support lending decisions that must move quickly, consistently, compliantly, and at scale. These collateral valuation and risk management systems ingest market data at scale to provide institutions with both the full picture of the market, and the ability to assess one address at a time. This lets them manage risk across thousands of properties without losing coherence.
But it’s only part of the equation. Buyers must also be willing to trust a valuation provided by technology.
Cotality’s housing trends research shows that trust in the role technology plays in homebuying processes varies across generations. Older buyers, shaped by years of working through professionals, tend to accept model-led outcomes as part of the environment they know. Younger buyers arrive with sharper expectations around explanation and visibility, particularly when property valuation outcomes affect access to credit, affordability, or the timing of life decisions already under strain.
A Cotality survey of recent homebuyers in 2025 found that trust is often higher in roles such as attorneys working with buyers, where explanation is key and the buyer feels seen and heard at the right times as opposed to technology derived and delivered outputs. The number of touchpoints doesn’t matter so much as the alignment of those touchpoints within the buyer’s decision cycle.
To understand what it might look like moving forward as AI plays a growing role in decisions, Cotality asked future buyers how much they would prefer an AI tool versus a human professional across key tasks in the homebuying process. The answer was clear: buyers expect humans and technology to work together.
Signaling trust in tech
Data source: Cotality survey, 2025
At its simplest, a property valuation is a reading of current risk and future value. Yet that number carries real consequences for buyers. They want to know why a home is worth what it is.
That demand for explainability is showing up in a growing desire for human oversight and real-time accountability alongside technology-based valuation models. The models bring scale, consistency, and fresh data. People still want someone who can stand behind the number.
How do AVMs work in the modern property market?
Trust now drives technology adoption. That’s why the algorithms behind Cotality’s AVMs are built on top of a strict data governance structure that ensures every calculation is reliable and repeatable so that you can have confidence in your answers.
Oversight now means shared understanding — understanding how signals behave over time, how assumptions propagate, and how aggregated logic settles into everyday practice.
Cotality’s focus is to maintain a shared understanding of the market at scale. That means turning data points into answers people can trust. Using CoreAI-powered platforms, Cotality brings together the geographic and borrower risk signals that shape valuation models and the decisions that follow. Governance, expertise, and repeated testing help ensure those insights can scale and still be explained and understood with confidence.
“Explainability is a stabilizing force. When institutions can articulate how value is formed, confidence holds, even through volatility. Investors stay measured, regulators stay constructive, and consumers stay engaged,” says Craig Dargusch, Chief Data Officer for Cotality International.
For households, clarity tends to restore agency. A valuation that can be understood and contextualized signals a system that recognizes the difference between probability at scale and consequence at home. It moves conversations beyond the proverbial ‘black box.’
Trust is the killer app
As AI becomes more embedded in housing decisions, its reach begins to shape the market and influence behavior. And the questions facing the industry in this area are already in motion. Automated valuation sits in the middle of housing decision-making for good reason — it is effective and fast. What will continue to develop is how institutions such as lenders take on responsibility for how AI-powered signals travel and are explained to homebuyers.
Cotality builds transparency into its CoreAI strategy to put trust and people at the center. When signals are correctly interpreted, outputs are simply explained, and assumptions are made visible, the results are understood and lending institutions can care for the individuals moving within it.
