Market sizing often fails when household budgets and transport costs are not aligned across geographies. A ratio that means “burden” in Paris may mean “affordable” in Oslo. Without harmonization, comparisons mislead and decisions stall.
This dataset standardizes income, costs, and adoption into comparable ratios. You can size markets, test scenarios, and prioritize regions with confidence.
Select cities, regions, or countries to benchmark
Normalize income and mobility costs to one currency
Apply consistent income and cost definitions
Derive ratios that compare across geographies
Add one-line affordability comments for quick reading
This segment is activated with a blend of trusted sources and your own inputs
AI reasoning
Web intelligence
Open Datasets
Comparable affordability ratios across markets
Income and transport costs standardized to your taxonomy
Confidence scores for every enriched field
Export-ready dataset for sizing, strategy, and GTM
Clear ratios, aligned across geographies — ready for your models.
Your questions on this segment, answered
Your questions on this segment, answered
What alternatives exist if I don’t use a structured dataset?
You can rely on public statistics or one-off consulting reports, but these lack comparability and refresh. DIY data collection is also possible but time-intensive. A structured dataset ensures both speed and consistency.
How often should affordability data be refreshed, and can it sync to CRM or BI tools?
Refresh can be set from minutes to hours depending on volume. The dataset is structured for direct export to CRM, BI, or foresight dashboards. This keeps planning grounded in live data.
Can these datasets support foresight on new mobility models like MaaS or EVs?
Yes. Adoption indicators like MaaS penetration or cost ratios help forecast readiness. They show where budgets and habits align with future mobility models.
How are confidence scores calculated, and how do they ensure comparability across geographies?
Confidence reflects source availability, freshness, and the method used. Scores make clear which values are robust and which are modeled. This consistency allows valid cross-country and cross-city comparisons.
What makes Starzdata’s dataset different from public surveys or consulting reports?
It is built on a consistent taxonomy across geographies, not fragmented surveys. Every field comes with a confidence score and a transparent method. The dataset is workflow-ready, not static tables in a PDF
How can businesses use affordability benchmarks to estimate TAM and prioritize markets?
By comparing ratios across cities or regions, you see where households can actually afford new offers. This grounds TAM estimates in real budget constraints. It also helps prioritize geographies where spending room exists.
What is a mobility affordability ratio and why does it matter for market sizing?
It measures the share of household income spent on mobility. This ratio reveals whether transport is a budget enabler or constraint. For market sizing, it helps define realistic adoption potential and spending capacity.
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