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August 2025 - August 2026
Detailed observation of presented data
Real Estate cost-per-purchase in All countries ran consistently above the global benchmark and showed pronounced month-to-month swings: a mid-year low near $56 rising into two winter spikes above $160, then finishing the 12-month window at roughly $129. 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 Real Estate in All countries compared to the global benchmark.
Over the 12-month series (Jul 2025–Jun 2026) Real Estate cost-per-purchase averaged about $98.6, roughly double the global median of $49.9 over the same months. The series started at $56.22 in July 2025 and closed at $129.24 in June 2026 — a cumulative lift of about 130% from start to finish. The calendar high was $164.26 in December 2025, with a second peak of $160.93 in February 2026; the low point was $56.22 in July 2025. Monthly movements were large and frequent: absolute month-over-month changes averaged near 40% (for example, +41% into August, −21% into September, +46% into October and +48% into November). That pattern of sharp lifts and declines produced a jagged, high-amplitude trace rather than a smooth trend.
Seasonality is visible: the Real Estate CPAs climbed steadily into Q4, cresting in December, and then dropped sharply in early Q1 (December → January fell ~39%). Another surge in February pushed the metric back toward the year’s peak before a spring cooling through March and April. In this dataset Q4 and late-winter show the strongest pressure on cost-per-purchase, while midsummer and early spring months trend softer. These monthly rhythms create a sequence of spikes and rebounds rather than a sustained upward or downward slope across the whole year.
Compared to the global baseline, Real Estate in All countries was consistently above market. The smallest gap occurred in July 2025 when Real Estate costs were about 14% higher than the global median; typical months saw Real Estate 30–100% above baseline. At the extreme, December 2025 showed Real Estate costs roughly 230% higher than the global benchmark, and February 2026 was about 220% higher. Volatility contrasts sharply: Real Estate’s average month-to-month absolute change hovered around 40%, while the global benchmark averaged about a 6% monthly absolute change — making Real Estate notably more volatile than the overall market.
Understanding Cost Per Purchase benchmarks, Facebook Ads benchmarks, CPC trends, CPM analysis, CTR performance, country-specific ad costs, and industry ad performance for Real Estate in All countries provides a clear, data-grounded view of how purchase costs move through seasonal cycles and how they diverge from global norms.
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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