[OC] Average monthly rent: 1-bedroom flat vs average monthly equivalised net income across EU capitals Comparison
![[OC] Average monthly rent: 1-bedroom flat vs average monthly equivalised net income across EU capitals Comparison](/api/images/reddit-maps/1u1g763_1781049603311.jpg)
Data Analysis
What This Visualization Shows
This data visualization displays "[OC] Average monthly rent: 1-bedroom flat vs average monthly equivalised net income across EU capitals" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on For average monthly rents, the published value for the Netherlands refers to The Hague rather than Amsterdam, so I used The Hague.
Rent values come exclusively from Eurostat: [https://ec.europa.eu/eurostat/databrowser/view/prc\_colc\_rents/default/table?lang=en](https://ec.europa.eu/eurostat/databrowser/view/prc_colc_rents/default/table?lang=en)
For the flat and house categories used in the rent data, Eurostat covered selected neighbourhoods in each surveyed city. Methodology/source booklet: [https://ec.europa.eu/eurostat/documents/6939681/0/Booklet\_2026\_rents\_2025\_e\_Final.pdf/d2cd0065-f017-16a7-dfa2-7dad9d6fa84b?t=1766065004758](https://ec.europa.eu/eurostat/documents/6939681/0/Booklet_2026_rents_2025_e_Final.pdf/d2cd0065-f017-16a7-dfa2-7dad9d6fa84b?t=1766065004758)
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For mean equivalised net income, I used Eurostat ilc\_di03 annual mean equivalised net income values for 2025, which refer to the 2024 income reference year, divided by 12: [https://ec.europa.eu/eurostat/databrowser/view/ilc\_di03/default/table?lang=en](https://ec.europa.eu/eurostat/databrowser/view/ilc_di03/default/table?lang=en)
These are country-level figures, not city-specific wages, and they refer to mean equivalised net household income, not individual salaries.
There values used here are filtered by age class 18–64, meaning the final average is calculated only for people aged 18 to 64. The income measure is still based on total household net income adjusted for household size and composition.
In the equivalence scale (modified OECD) used by Eurostat, the first adult counts as 1.0, each additional household member aged 14 or over counts as 0.5, and each child under 14 counts as 0.3. Source: [https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary%3AEquivalised\_disposable\_income](https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary%3AEquivalised_disposable_income)
Example: if John earns €20,000 net per year, Mary earns €20,000, and John’s grandfather, aged 67, earns €10,000, and they all live in the same household, total household net income is €50,000. With an equivalence scale of 2.0, the household’s equivalised net income is €25,000 per year. This value is then assigned to each household member.
With the 18–64 filter, John and Mary would each be counted in the final average with an equivalised net income of €25,000 per year, while the grandfather would not be counted in that final average. However, the grandfather’s income and household weight still affect the household’s equivalised income.
Source: [citycostatlas.com](http://citycostatlas.com) / citycostatlas on Instagram. On the website, you can compare different metrics with each and see how they relate, view city rankings based on various metrics, and use an interactive map that instantly displays the data., which allows us to understand complex relationships and insights within the data through visual storytelling.
Deep Dive into the Topic
Economic data visualization plays a crucial role in understanding market trends, financial performance, and economic patterns across different sectors and regions. This type of data analysis helps economists, policymakers, and business leaders make informed decisions based on quantitative insights.
Economic indicators such as GDP growth, unemployment rates, inflation, and market performance are complex datasets that require sophisticated visualization techniques to communicate effectively. Interactive charts and graphs can reveal trends over time, compare performance across different markets, and highlight correlations between various economic factors.
The significance of economic data visualization extends beyond academic research. Financial institutions use these visualizations for risk assessment, investment strategies, and market analysis. Governments rely on economic data visualization to track policy effectiveness, plan budgets, and communicate economic status to citizens. Businesses use economic trend analysis to forecast demand, plan expansion, and assess market opportunities.
Data Analysis and Insights
The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.
Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.
The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.
Significance and Applications
This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.
For analysts, researchers, and decision-makers, this type of data visualization provides essential insights for strategic planning and performance optimization. Whether addressing operational challenges, market analysis, or policy development, understanding data patterns helps create more effective strategies and solutions.
The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.
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About the Author

Alex Cartwright
Senior Data Visualization Expert
Alex Cartwright is a renowned data visualization specialist and infographic designer with over 15 years of experience in...