National Association of Realtors wins Meta 2026 Agency Award for AI ad automation
The National Association of Realtors (NAR) was announced as the winner of Meta’s 2026 Agency Award for Best Use of Automation. The award recognized an AI‑powered campaign that used Meta’s Advantage+ Audience targeting to attract first‑time homebuyers more efficiently and at lower cost. More than 400 submissions from North America and Latin America were evaluated for the award.
Key points
- NAR won Meta’s 2026 Agency Award for Best Use of Automation for its AI-powered first‑time homebuyer campaign.
- The campaign used Meta Advantage+ Audience targeting, outperforming NAR’s traditional targeting with lower cost and higher engagement.
- Test showed a 9‑point lift in intent to use a Realtor and increased traffic to lead‑generation content.
NAR partnered with advertising agency Havas and Meta to run a structured test that compared the Advantage+ Audience approach with NAR’s traditional targeting. Meta’s machine‑learning system evaluated engagement, audience and conversion signals to expand the audience beyond manually defined segments. According to NAR’s chief marketing and communications officer Bennett Richardson, the campaign reached prospective buyers more effectively while reducing costs, delivering greater value to members. The test showed a 9‑point lift in intent to use a Realtor and increased traffic to educational and lead‑generation content, suggesting AI‑optimized audiences can stretch marketing budgets for housing marketers.
NAR wins Meta 2026 award for AI ad automation
housingwire.com · 21 September 2026
The National Association of Realtors (NAR) won Meta’s 2026 Agency Award for Best Use of Automation for an AI-powered campaign that drove more first-time homebuyer traffic at a lower cost, the trade group announced on Friday.
The Meta Agency Awards recognize agencies and advertisers that use Meta’s advertising tools in ways that deliver measurable business results. More than 400 submissions from across North America and Latin America were considered for this year’s awards, according to the announcement.
The Best Use of Automation category highlights campaigns that leverage Meta’s AI-driven automation tools, particularly its Advantage+ suite, to improve performance, scale and business outcomes. NAR’s winning entry centered on its Consumer Ad Campaign and its use of Meta Advantage+ Audience targeting to reach prospective first-time homebuyers.
NAR said it partnered with advertising agency Havas and Meta to run a structured test that compared Advantage+ Audience with NAR’s traditional targeting strategy. Meta’s machine-learning system evaluated engagement, audience and conversion signals to identify additional prospective first-time buyers beyond NAR’s manually defined audience segments.
“This recognition shows our strategic plan in action. We are modernizing how NAR works for members by using new tools to help them get to their next transaction more efficiently,” Bennett Richardson, NAR chief marketing and communications officer, said in a statement. “We reached prospective homebuyers more effectively while reducing costs and delivering greater value to our members.”
Meta has been pushing broader adoption of its Advantage+ automation tools as housing and mortgage marketers seek more efficient ways to reach renters and would-be buyers amid high rates and lean inventory. For real estate brokerages and agents, first-time buyers remain a critical source of new business, but they can be expensive to identify and convert using traditional demographic or interest-based targeting alone.
For housing professionals, NAR’s test results suggest AI-optimized audiences can stretch marketing budgets while driving more engaged traffic to educational and lead-generation content. The 9-point lift in intent to use a Realtor also underscores how brand and membership campaigns can translate into potential business for agents, not just awareness for the trade group.
This article was generated using HousingWire Automation and reviewed by a HousingWire editor before publication.
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