Data has always mattered in real estate. Brokers have tracked comps for generations. Appraisers have built entire methodologies around comparable sales. Investors have modeled cash flows on spreadsheets for decades. But something is different about the current moment. The combination of AI’s arrival as a practical business tool, the intensifying competition among real estate platforms, and the growing recognition that proprietary data is a durable competitive moat has triggered a wave of acquisitions across the industry. Established real estate technology companies are buying analytics startups, data providers are absorbing forecasting platforms, and operators are acquiring tools that give them an edge in an increasingly data-driven market. The pace and breadth of that activity tells a story about where the industry believes value is going to be created over the next decade.

The single largest deal in the current cycle came in May 2026, when CoStar Group announced a definitive agreement to acquire Zonda, a leading provider of new home construction data, homebuilder software, and residential real estate marketplaces, for $800 million in cash. The acquisition is a revealing statement about CoStar’s strategic ambitions. The company has spent decades building the dominant data platform for commercial real estate, and the Zonda deal extends that logic into a market segment it has not previously owned. At the core of Zonda is a proprietary, lot-level database covering new home communities, land development activity, construction status, home sales, and builder operations, deeply embedded in builder workflows and widely used to support underwriting, land strategy, capital allocation, development planning, forecasting, and sales operations across the industry. CoStar isn’t just buying a product. It’s buying irreplaceable data that took years to build and would take years to replicate. According to CoStar, the annual value of new residential construction in the U.S. approaches $1 trillion, a market materially larger than the annual rent rolls of the institutional apartment and office sectors that CoStar Group has so successfully monetized. That context explains the price tag.

The Altus Group’s acquisition of Reonomy, completed in November 2021 for $201.5 million, was an earlier and in some ways more foundational expression of the same logic. Altus described the combination of Reonomy’s AI-powered data platform with its own suite of software, data and analytics capabilities as accelerating transformative innovation in AI predictive data analytics, better positioning the company technologically with data science and analytics expertise, and with a robust dataset to add analytics into workflows that not only look back at what happened and why, but look forward to machine learning informing what might happen next. The Reonomy platform brought with it an industry-leading collection of insights across more than 52 million tax parcels and over 38 million commercial properties, accounting for nearly all of the commercial inventory in the U.S. What Altus was paying for wasn’t just technology. It was the dataset underneath it, and the AI capability to connect it in ways that produced genuinely new insights rather than faster access to information people already had.

The pattern repeats across smaller deals with no less strategic clarity. In January 2026, ATTOM acquired key assets of ResiShares, including its analytics platform and proprietary technology, bringing ResiShares’ forecasting models and analytics tools into ATTOM’s national data platform, which covers most U.S. properties. ATTOM’s CEO framed the acquisition explicitly in terms of competitive positioning, describing it as strengthening the company’s competitive moat and bringing proven institutional-grade analytics and forecasting into its platform. ResiShares’ platform includes price and rent forecasting, neighborhood-level performance analysis, and modeling designed to identify market trends and risk, capabilities that extend ATTOM’s utility from a data repository into a predictive analytics engine. That shift from descriptive to predictive is exactly what makes an acquisition like this strategically meaningful rather than merely additive.

Grace Hill’s acquisition of HelloData, announced in May 2025, illustrates how the same logic is playing out in multifamily operations rather than capital markets. HelloData AI automates multifamily market analyses to provide leading insights into rents, concessions and amenities, drawing on publicly sourced data from over 35 million units across 5 million properties nationwide. Grace Hill, known primarily as a training and performance management platform for property management companies, didn’t acquire HelloData to enter a new market. It acquired the company to deepen the intelligence layer within a platform that its customers were already using every day. The deal reflects a growing understanding that the most defensible position in real estate software isn’t the workflow tool alone. It’s the workflow tool combined with the data that makes it smarter than the alternative. As Grace Hill’s CEO put it, the acquisition enables customers to tackle their most significant challenges with intelligent, data-informed tools.

The regulatory environment is also shaping acquisition strategy in ways that go beyond pure data accumulation. Real Estate Business Analytics’ acquisition of Markerr at the end of 2025 is a case in point. New York City-headquartered Markerr’s demand and supply insights, data and AI-driven predictive modeling enable REBA to deliver more complete intelligence, including clearly sourced and compliant internal and external data, for understanding real estate performance. The compliance dimension is deliberate and central to the deal’s logic. The acquisition came as algorithmic pricing lawsuits continue to reshape regulations around the country on how rental data can be used, with REBA’s CEO describing the combination of the Department of Justice’s RealPage settlements along with related laws in California, New York, and Seattle as establishing a new normal on rules for algorithmic pricing. In that environment, owning data that is clearly structured to meet regulatory requirements isn’t just a risk management decision, it’s a product differentiation strategy.

Taken together, these acquisitions describe an industry in the process of consolidating around data as the central source of competitive advantage. The companies doing the buying have concluded that building proprietary datasets from scratch is too slow, too expensive, and too uncertain to be the primary path to data leadership. Acquiring them, even at significant premiums, is faster and more defensible. The companies being acquired have generally built something that would take years to replicate: a proprietary dataset, a forecasting methodology, an AI model trained on a corpus of real estate information that no competitor can easily access. That combination of build-time and data moats is what commands the valuations and what makes the acquirers willing to pay them. The race to own real estate’s data layer is not slowing down. The deals being announced today are most likely a preview of what the next several years will look like as AI makes the gap between data-rich and data-poor real estate companies increasingly difficult to close.

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