The National Association of Realtors (NAR) has been recognized by Meta as a leader in digital innovation, securing the prestigious 2026 Agency Award for Best Use of Automation. The accolade follows a highly successful, AI-powered advertising campaign designed to reach prospective first-time homebuyers with greater precision and lower costs than traditional marketing methods.
The Meta Agency Awards are designed to celebrate the most effective uses of the platform’s advertising ecosystem, specifically honoring agencies and advertisers that leverage Meta’s sophisticated toolsets to drive measurable, high-impact business results. This year’s competition was particularly rigorous, with more than 400 submissions reviewed from across North America and Latin America. NAR’s victory in the Best Use of Automation category places the trade group at the forefront of a shifting digital landscape where machine learning is increasingly replacing manual targeting.
At the heart of the award-winning entry was NAR’s Consumer Ad Campaign, a strategic initiative aimed at demystifying the home-buying process and highlighting the value of working with a Realtor. To optimize the campaign’s reach, NAR partnered with the global advertising agency Havas and Meta’s internal teams to implement a structured test of Meta’s Advantage+ Audience tool. This AI-driven system represents a significant departure from the manual audience segmentation that has defined digital advertising for over a decade.
The Evolution of Audience Targeting
For years, real estate marketers relied on manual parameters to find potential clients. This traditional approach typically involved selecting specific age ranges, geographic locations, and a list of interests—such as "real estate investing," "mortgage loans," or "home improvement." While effective in the past, these manual segments can often be limiting, failing to capture the nuance of a modern consumer’s journey.
By utilizing Meta’s Advantage+ Audience suite, NAR allowed Meta’s machine-learning algorithms to take the lead. The system analyzed a vast array of engagement, audience, and conversion signals in real-time, identifying prospective first-time buyers who might not have fit into a pre-defined manual category but whose online behavior suggested they were actively in the market for a home. This "black box" approach to targeting uses historical data and real-time interactions to find users with the highest propensity to engage with the content, effectively casting a wider but more accurate net.
The results of the structured test were stark. According to the data released by NAR, the AI-driven campaign generated 432,000 landing page views. More importantly, the use of automation led to a 29% reduction in the cost per view and a 25% reduction in total spend when compared to the trade group’s traditional targeting strategies. These figures represent a significant increase in efficiency, allowing NAR to stretch its marketing budget further while delivering a higher volume of traffic to its educational resources.
A Strategic Pivot in a Challenging Market
