Supported shortlist
84 cities across 25 countries
The city layer can support a broader shortlist than the currently published SEO surface, so new rollout batches do not require architectural changes.
Methodology and credibility
How the Relocation Decision Engine works, what data it uses, and where the limits are.
This tool is built for people who want a practical relocation planning shortcut before they commit serious time, money, or paperwork. It estimates likely monthly costs, savings room, relocation risk, and a shortlist of destinations that fit your household, income, and preferences.
It is designed to support early decision-making, not to replace legal, immigration, tax, housing, or personal financial advice.
Published city guides
80 cities live across 25 countries
Only the current published and indexable batch is surfaced in public guides, sitemap discovery, and most internal-link hubs.
Live enrichment
28 Teleport-enabled destinations
Mapped cities can use a richer city profile before the engine scores affordability and fit.
City publishing is staged on purpose. Some destinations are fully supported in the catalog and content system before they are promoted into the public sitemap and hub discovery flow, which keeps rollout quality more controlled.
Scoring model
How recommendations are calculated
The engine combines multiple relocation factors using a weighted scoring model. It does not rely on one single metric like rent alone.
Affordability and cost of living
The engine builds an estimated monthly budget using rent, food, transport, utilities, healthcare, and lifestyle costs. Household size, children, rent comfort, remote work, and cost priority all affect the estimate.
Salary fit and savings room
Your entered salary is compared with estimated monthly costs to see whether the move leaves breathing room. If you plan to take a local job, the tool avoids assuming remote-level income in every market.
Safety, visa, and day-to-day practicality
Safety signals and visa difficulty are part of the score so the tool does not treat cheap destinations as automatically better if the move looks harder to execute or riskier in daily life.
Climate, language, and work fit
Climate preference, English friendliness, and remote-work or career suitability help separate destinations that look financially possible from those that also fit the way you want to live and work.
1
Budget and salary baseline
The calculator estimates a working monthly budget and compares it with expected income after normalizing currencies when needed.
2
Fit and friction signals
Safety, visa difficulty, climate match, English friendliness, and remote or career suitability are layered in so the output is not just a price comparison.
3
Ranking and risk flagging
Cities are ranked by score, then risk is raised when estimated affordability looks tight or the monthly savings picture turns negative.
If you choose a preferred country, the model can give it a small nudge, but it does not override affordability, risk, or overall fit.
Data inputs
What data we use
The tool mixes structured app data, live enrichment where available, and a few explicit planning assumptions. It is better understood as a decision-support model than a database of exact real-world prices.
Curated city and country dataset
The app ships with a curated city catalog that includes fallback cost, rent, grocery, transport, safety, climate, English-friendliness, visa, and remote-work signals. That snapshot acts as the baseline coverage layer.
Teleport enrichment when available
For mapped cities, the engine can enrich the profile with Teleport urban-area scores and salary data. If the fetch fails or a city has no mapping, the engine falls back to the internal dataset rather than leaving the result blank.
Salary and currency normalization
Non-euro salaries are converted into EUR before scoring. The app tries to use cached exchange-rate data and falls back to internal reference rates if live conversion is unavailable.
Editorial assumptions inside the calculator
The budget model includes transparent assumptions such as household multipliers, childcare impact, healthcare buffers, lifestyle spend, and rent-comfort adjustments. These are estimates used to make comparisons more realistic, not exact household budgets.
Reading results
How to interpret the results
The strongest way to use the tool is to treat it as a shortlist and budget-pressure test.
The top recommendation is the best match for the inputs you gave, not a guarantee that it will be your best choice in real life.
The alternatives matter. Small changes in salary, rent expectations, or visa tolerance can reshuffle the shortlist.
Budget and savings figures are directional estimates designed to help you compare destinations before deeper research.
Risk levels rise when projected costs leave very little monthly room or push the move into a negative savings position.
Limits
What the tool does not guarantee
Trust improves when the limits are clear. This calculator is useful, but it is not authoritative for every relocation decision.
This is not legal, immigration, tax, or financial advice.
Visa pathways and residency rules can change quickly and may depend on nationality, income source, and paperwork that the tool does not fully model.
Salary estimates are not job offers and cannot predict what a specific employer will pay you.
Some locations use fallback reference values rather than freshly fetched city profiles.
Neighborhood choice, schooling, childcare, healthcare, and personal lifestyle can move real costs far away from the estimate.
Reality check
Why some results may differ from real life
Even a careful model cannot fully reproduce your exact move. Real relocation costs and experience depend on many local and personal details.
Rent can vary dramatically by neighborhood, apartment quality, lease length, and how quickly you need to move.
Exchange rates, local inflation, and utility costs can change between planning and relocation.
Families may face private schooling, childcare, insurance, or larger-housing costs that exceed simple averages.
Remote work opportunities, office requirements, and local hiring conditions vary by employer and experience level.
Language comfort, commute expectations, climate tolerance, and social support can change whether a place feels like a good fit.
Freshness
How data freshness works
Result pages already expose source and freshness labels so you can tell whether the answer came from a richer city profile or from a reference estimate.
Live city data
When a city is mapped to Teleport, the app can enrich the profile with urban-area scores and salary data. Those responses are cached and revalidated daily.
Fallback dataset
If live enrichment is unavailable or fails, the engine uses the app's curated city snapshot so recommendations still work without pretending the data is live.
Recently updated / Static estimate
Result pages surface human-readable freshness labels so you can see whether a recommendation came from a fresher city profile or from a reference estimate.
Next step
Use the calculator with the methodology in mind.
The tool is most useful when you treat it as a practical planning aid: narrow a shortlist, compare alternatives, then verify the important visa, housing, salary, and tax details independently.