Access to property data is no longer a competitive advantage in real estate – it's the baseline. To make successful investment decisions, real estate professionals need forward-looking predictive models presented in clear, actionable terms. Yet, a stark disconnect remains between investor ambition and legacy technology.
Introduction
The real estate industry doesn’t have a data problem – it has a delivery problem. While real estate investors agree that AI models and intuitive interfaces are the future, the industry’s push toward automated, forward-looking tools has hit a roadblock.
Everyone wants to move at light-speed, but technical and operational hurdles are holding investment teams back from true autonomy. It’s not about having more data – it's about getting the right data into the right hands instantly.
- 92% of investment decision-makers report that predictive AI models are missing or inadequate in their current platforms.
- 68% of investors said they consider native integration with cloud platforms a key requirement when evaluating future platforms, but only 26% currently access their data through a cloud marketplace.
- 68% of professionals are seeking advanced, self-service tools to build custom data queries without relying on IT support.
The investment ecosystem – from acquisitions to asset management – has spent the last decade aggregating parcel records, analyzing macroeconomic trends, and compiling historical valuation data. But the volume of this information has created a new challenge: data overload.
As market dynamics shift, real estate investors are realizing that access to information is table stakes. The competitive edge goes to those using modern models and delivery methods to extract actionable intelligence quickly. And our recent Cotality survey data shows this makes all the difference.
It’s no longer enough to log into a third-party platform to view a chart or export a static spreadsheet. The new standard is seamless data fluidity – delivering clean, AI-ready data directly into your workflows and autonomous agents.
When investment teams are forced to spend weeks cleaning poorly formatted inputs, data ROI vanishes. The market is pivoting toward intuitive, self-service tools that empower teams to query and analyze data in real time without IT bottlenecks.
In this environment, a platform's worth is measured not by its upfront cost, but by its intuitive features and the amount of time it takes to capitalize on them.
What this report covers
Cotality's Q3 market research surveyed qualified real estate investors and account decision-makers across the U.S., focusing on property professionals who maintain active accounts with third-party property data providers other than Cotality. Respondents span senior roles across asset management (36%), acquisitions (34%), and executive leadership (30%) within the real estate, proptech, and development sectors.
The report is organized around three core findings that carry direct operational implications for investors, asset managers, and the broader real estate ecosystem.
What tools and capabilities are shaping the future of real estate investment?
The future belongs to investors that abandon isolated, backward-looking metrics in favor of seamlessly integrated predictive intelligence – giving teams true analytical autonomy.
- The transition to AI-ready insights: Raw historical data is standard. Today’s true competitive advantage comes from predictive models, spatial linking (such as Cotality’s CLIP ID) and AI-ready datasets that allow teams to forecast market shifts and connect previously disjointed property attributes.
- The demand for seamless delivery: As investors prioritize speed to value, success increasingly depends on the ability to effortlessly route, clean, and contextualize property intelligence directly within modern cloud environments.
- The rise of self-service utility: In an environment flooded with complex data, true value is unlocked by empowering non-technical users with advanced, self-service tools that eliminate IT bottlenecks and shorten the time it takes to get actionable insights into the right hands.
Chapter 01: The predictive gap
For decades, the real estate market has tried to navigate today’s conditions using yesterday’s map. The industry standard for pricing assets and assessing risk often came down to historical comparables. But in today's market, past performance isn’t always a reliable indicator of future value.
Consider the foundation of most investment strategies: macroeconomic and housing trends. Today, more than 50% of real estate professionals rate the granularity of this macro data as "poor” or “fair” at best.
When allocating millions of dollars across complex portfolios, “fair” isn’t enough. Investment teams need more than a chart visualizing last quarter’s comps – they need to understand how local infrastructure shifts, zoning changes, and natural hazard risks will impact a specific asset’s value in the future.
While the market clearly recognizes the need for forward-looking intelligence, legacy technology continues to lag behind the demand. Nearly all investment decision-makers agree that predictive AI models for future property performance and market risk are inadequate or missing in their current tech stacks.
When it comes to the baseline property characteristics – square footage, structural characteristics, or basic boundaries – today’s legacy platforms meet basic expectations. In recent years, the industry has taken steps to build better forward-looking models.
Problems arise when investors attempt to zoom out from an individual parcel to analyze broader, dynamic market forces. While foundational property data is widely available, real estate investors report a glaring lack of macro-level context.
Simply put, the industry has mastered the “what" and the “where.” The deficit lies in "what's next."
Because baseline property records have reached parity across major providers, sophisticated investment teams are shifting their focus toward advanced analytical solutions. They are no longer evaluating providers based on the size of their database, but by their platform’s ability to turn disparate variables and complex trends into a cohesive, forward-looking forecast.
To build accurate predictive models, AI algorithms need deep, hyper-local context – but the building blocks for this are missing from many legacy platforms. When asked to identify deficits with their current providers, 40% of investors pointed to inadequate zoning data and land-use restrictions. Another 26% cited missing or inaccurate ownership history and portfolio-wide mapping.
Machine learning models thrive on precision. An AI algorithm must ingest exact parcel boundaries, dynamic zoning regulations, and environmental exposure of a neighborhood to accurately forecast its development potential. Without this granular, contextual data, AI models pull insights from the shallow end of the pool. They can't predict the future of a property if they don’t understand the dynamic, real-world variables acting upon it.
This contextual gap explains why 94% of investment professionals said they now consider AI-ready datasets to be a critical requirement when evaluating future data platforms.
Prioritization of features if evaluating new platforms in the future
Data source: Cotality survey, 2026
Some providers claim their data is AI-ready simply because they offer an API – but this is nothing more than a delivery method. If you pipe messy, unstructured, or disjointed parcel records into a machine learning model, the result won’t be reliable enough to drive high-conviction investment decisions.
True AI-ready property data isn’t just delivered – it’s cleaned, standardized, and enhanced with semantic layers, giving AI the context it needs to make meaningful conclusions and correlations.
When datasets are truly AI-ready, it means they have already been validated by geospatial scientists, structured by software engineers, contextualized by domain experts, and anonymized by ethics and compliance teams. The result is reliable, real-time data that can be fed directly into your AI workflows, freeing up your team to focus on strategy rather than data cleaning.
According to a global study by McKinsey, AI has the potential to unlock between $110 billion and $180 billion in value for the real estate sector. This projection demonstrates the opportunity for firms that are able to successfully use AI to predict real estate market movements. If your team is part of the 92% operating without AI models, you could be left behind.
Still, generating meaningful insights is only half of the battle. The other half is making those insights instantly available across your organization. In many cases, it’s not a data availability problem – it's a data deployment problem.
Chapter 02: Speed needs direction for value
With predictive analytics, your analysts can uncover compelling market trends. But how easily can they share them? Forty percent of investors said their current data providers offer inadequate built-in integration with their major CRMs or property management platforms, making it difficult for real estate professionals with boots on the ground to access and act on key insights.
It’s also challenging to share real-time insights with senior leadership. Sixty-two percent of respondents also said they lack automated reporting tools for key stakeholders. And when your team has to manually export data, build custom charts, and draft a slide deck just to show leadership a forecasted trend, you’ve already lost your momentum.
How does predictive intelligence help? It changes how every division operates:
- For portfolio managers: It enables dynamic risk modeling, adjusting portfolio valuations based on real-time shifts in climate risks, property conditions, or local zoning laws.
- For acquisitions teams: It delivers hyper-local forecasts, allowing you to identify and underwrite undervalued assets before the broader market catches on.
- For leadership & stakeholders: It automatically synthesizes complex forecasts into standardized reports, eliminating the manual reporting bottleneck.
The future of real estate investment isn’t just about having the best data. It is about deploying that data seamlessly and arming every professional in your firm with the foresight to act decisively.
Reported deficits with automation and integration
Data source: Cotality survey, 2026
To achieve this level of cross-organizational intelligence, the underlying data infrastructure must be fluid. Professionals across the investment sector recognize that traditional data delivery methods are failing to keep up with the speed of the market. However, a significant disconnect remains between the infrastructure investment teams’ needs and the infrastructure they’re currently using.
While 68% said they consider native integration with cloud platforms to be a key requirement, only 26% currently consume their real estate data via cloud marketplaces. This 42-point gap highlights a critical transition that is still underway.
Traditional API pulls and flat-file transfers are effective, but they take time. To get ahead of the competition, leading firms are looking for Zero-ETL (Extract, Transform, Load) delivery. Zero-ETL bypasses the extraction and ingestion phases. It allows property and market data to be delivered directly into the cloud environments you already occupy, such as Snowflake, Databricks, and Google Cloud.
With no syncing, custom builds, or pipeline maintenance, the external data sits natively alongside your portfolio data, ready to be queried instantly. For acquisitions teams, this means you layer external forecasts on top of your own internal data without waiting days for IT to merge the databases.
This transition to seamless cloud delivery is driven by a growing demand for speed. When acquiring new datasets, you might expect analysts to immediately begin building models and underwriting deals. But today’s investors face a different reality – with 74% of professionals encountering unexpected formatting or data structure changes when integrating a new provider’s data.
While 86% of investment professionals said it took one to three months for data to be integrated and accessed by users, there’s a clear desire to expedite this process. Only 8% achieved full integration in less than one month, and 94% of those who experienced technical or operational hurdles said those hurdles ultimately delayed their launch plans.
These types of challenges carry a cost. When pipelines break or formatting is inconsistent across data sources, highly paid data scientists are forced to abandon strategic work, wasting valuable time scrubbing fields, mapping variables, and tracking down missing API credentials. In a high-stakes environment, time kills deals. Every hour your team spends cleaning a dataset is an hour not spent generating real value. Top-tier firms are realizing that the most comprehensive dataset in the world can be a liability if it takes months to make it useable.
Technical and operational hurdles experienced during the integration process
Data source: Cotality survey, 2026
With this in mind, an overwhelming 92% of investors said they prioritize rapid implementation, expecting full data integration within three months of purchase. If a tech provider's data requires complex, custom ingestion protocols that drag implementation into the next quarter, the ability to capitalize on current market conditions is compromised before the new system even goes live.
For investment teams, data fluidity is becoming an operational necessity. Don’t look at it as buying data. Look at it as buying speed to value. The upper hand will go to firms that understand the difference – prioritizing platforms that put the power directly in the hands of frontline real estate professionals.
Chapter 03: Speaking to the data
There's a lingering misconception that budget constraints are the primary barrier to technology adoption in real estate, but the data tells a different story. Our survey revealed that only 10% of respondents chose their current provider because it offered the lowest price. Forty percent of decision-makers rejected an alternative platform specifically because the solution did not justify the cost – even though it fit their available budget.
When evaluating a new technology partner, transparent pricing is the required ticket just to enter the conversation. But while price certainly matters, the procurement process for real estate investors isn’t about finding the cheapest option. Modern investment teams have the capital to invest in premium platforms, assuming their features justify the cost.
This begs the question: how do you measure the value of a data platform? It's not a simple calculation of subscription cost versus deals won. It’s determined by how effectively the platform empowers frontline professionals and accelerates your speed to accurate, actionable insights.
Integration timeframes, delivery methods, and the sophistication of advanced, forward-looking models can all impact the long-term value provided by a specific tool. If a provider has a premium price tag, it must deliver a premium capability that directly drives ROI.
For some, the key differentiator here is usability. If your acquisitions team is looking at the same pre-packaged charts as every other firm in your market, it can be hard to get a leg up. The advantage lies in the ability to manipulate data in ways your competitors cannot.
Our research highlights a massive hunger for this capability. Sixty-eight percent of investors expressed a strong desire for seamless self-service query tools, which would allow them to easily build custom data queries without technical support.
This signals a major shift in how investment teams operate. In the past, if an asset manager wanted to cross-reference a specific set of zoning restrictions with localized employment shifts, they had to submit a ticket to the data engineering team and wait days for a custom report. True utility means empowering an asset manager to run that analysis on their own.
This is where the concept of AI-ready data comes full circle. When your property data is clean, structured, and rich with semantic context, it can be fed directly into generative AI applications like ChatGPT, Claude, and Gemini – allowing you to ask property questions in natural language and receive answers rooted in verified data.
This removes the technical barrier, allowing investors on the frontline to analyze the market, test hypotheses, and generate hyper-local forecasts simply by having a dialogue with the data. It’s the ultimate form of self-service utility: giving every professional the ability to extract bespoke intelligence instantly, securely, and with absolute confidence in the underlying data.
AI-ready datasets provide the foundation, and seamless cloud delivery methods provide the underlying speed. But it is advanced, self-serve utility that ultimately puts the power directly into the hands of the investor. For sophisticated teams, that level of empowerment is always worth the investment.
