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February 2025 - February 2026
Detailed observation of presented data
Real Estate’s Facebook Ads cost per purchase ran hot and volatile across all countries, repeatedly diverging from the steadier global benchmark. The year opened expensive, spiked again in April, collapsed to a mid-year trough, then rebuilt through Q4. Compared to the overall market, Real Estate spent more to convert—and swung far more month to month—while the global baseline stayed in a tight band.
This analysis is based on $3B worth of advertising data from our dataset, which provides strong directional benchmarks. This analysis explores ad performance trends for the Real Estate industry across all countries compared to the global benchmark.
Across 2025, Real Estate’s median cost per purchase averaged $114, ranging from a high of $219.88 in April to a low of $12.50 in June. The year started at $213.10 in January and ended at $138.84 in December—down 35% from the New Year peak but still elevated versus the market.
Momentum whiplashed. Costs fell 32% from January to February ($213 → $145) and another 14% into March, then surged 76% into April’s high ($219.88). The sharpest break came in Q2: May to June plunged 92% ($147.87 → $12.50). A tentative July rebound (+56% to $19.51) accelerated in August (+380% to $93.60), softened in September ($69.53), and climbed steadily across Q4 ($90.57 → $97.31 → $138.84).
Volatility was the headline: the average absolute monthly move was about $51, versus roughly $1.6 for the global benchmark—more than 30 times choppier. Overall, Real Estate’s 2025 average of $114 was 2.2x the global median.
Quarterly rhythm framed the story:
By contrast, the global benchmark was remarkably stable: it averaged $52 for 2025, held between $47 and $55 most months, and drifted lower into year-end (January $53.15 to December $47.62, down 10%).
Real Estate costs sat above market most of the year, with two notable exceptions mid-year:
On average, the Real Estate industry across all countries priced in 121% higher cost per purchase than the global median and displayed dramatically higher volatility. While the global trend was flat to gently lower (tight $47–$55 band), Real Estate traced a far more jagged arc—early-year peaks, a Q2 cliff, a Q3 rebuild, and a Q4 reset to higher ground.
Understanding Facebook Ads cost-per-purchase benchmarks for the Real Estate industry across all countries helps marketers evaluate acquisition costs and compare performance to global patterns. This CPM-style, country-agnostic view sets a clear reference for Real Estate’s cost dynamics within Facebook Ads benchmarks and broader industry ad performance trends.
Insights & analysis of Facebook advertising costs
Facebook advertising costs vary based on many factors including industry, target audience, ad placement, and campaign objectives. In the Real Estate industry, Facebook ad costs can be influenced by seasonal trends and market competition. Geographic targeting affects ad costs based on market competition and user engagement in different regions. Different campaign objectives lead to varying costs based on how Facebook optimizes for your specific goals. The data shown represents median values across multiple campaigns, and individual results may vary based on ad quality, audience targeting, and campaign optimization.
We use the median CTR because the underlying distribution of click-through rates is highly skewed, with a small share of campaigns achieving extremely high CTRs. These outliers can inflate a simple average, making it less representative of what most advertisers actually experience. By using the median—which sits at the midpoint of all campaigns—we provide a more rigorous and realistic benchmark that reflects the true underlying data model and helps you set attainable performance expectations.
Note: This data represents industry median values and benchmarks. Your actual costs may vary based on specific targeting, ad creative quality, and campaign optimization.
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All data is sourced from over $3B in Facebook ad spend, collected across thousands of ad accounts that use Superads daily to analyze and improve their campaigns. Every data point is fully anonymized and aggregated—no individual advertiser is ever exposed.
This dataset updates frequently as new ad data flows in. It will only get bigger and better.
It depends on your product price and margins. Most brands aim for $10 to $50. For higher-ticket products, a higher CPA may be acceptable as long as you're maintaining a strong return on ad spend.
Higher-priced products typically have a higher CPA because people take longer to convert. That's not necessarily a problem if your margin can support it. You should measure CPA in context with AOV and LTV.
Your AOV may be increasing, which helps maintain ROAS even if CPA rises. You could also be facing higher CPMs, lower conversion rates, or creative fatigue.
Manual bidding can help if you're struggling to stay within target CPA. It's best used by experienced advertisers who can monitor performance and adjust regularly. It gives more control, but also requires more effort.
Increase budget gradually, rotate creative often, and avoid overlapping audiences. Scaling too quickly can lead to audience saturation and rising CPAs.
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