Most "best cities for remote work" lists make the same mistake: they hand you one ranking. But the best workation destination is not a single answer, because the constraints are completely different depending on who you are. A freelancer can fly to Bali and stay six months. An employee is tied to a contract, social-insurance rules, and tax thresholds.
So I built three rankings instead of one, and open-sourced the underlying data (CC BY 4.0) so you can re-weight it for your own situation.
Germany really wants this
One number stood out while gathering the search data: Germany accounts for 52% of all global "workation" searches, by far the largest share worldwide.
| Country | Share of global "workation" searches |
|---|---|
| Germany | 52% |
| Rest of the World | 30% |
| Poland | 6% |
| India | 5% |
| Netherlands | 5% |
| United States | 3% |
import pandas as pd
url = ("https://raw.githubusercontent.com/DatapulseResearch/"
"workation-atlas/main/data/workation_search_share_by_country.csv")
df = pd.read_csv(url).sort_values(
"share_of_global_workation_searches_percent", ascending=True)
df.plot.barh(x="country",
y="share_of_global_workation_searches_percent",
legend=False, title="Global share of 'workation' searches")
Three rankings, three winners
Each ranking weights climate, cost of living, flight prices, infrastructure, safety, and (for employees) EU membership differently:
- Adventure Seeker (freelancers and founders): Bangkok, Kuala Lumpur, Chiang Mai, Da Nang, Ubud.
- Smart Spender (best value per euro): Jaipur, Bangkok, Da Nang, Chennai, Weligama.
- Easy Mover (employees, EU-only): Seville, Ericeira, Valencia, Faro, Rome.
The rankings barely overlap. Asia dominates the freelancer lists; EU/Southern Europe wins for employees, who in practice need an A1 certificate to stay in their home social-insurance system and have to watch the 183-day tax threshold. Bureaucracy, not preference, is what separates the lists.
ranks = pd.read_csv(
"https://raw.githubusercontent.com/DatapulseResearch/"
"workation-atlas/main/data/workation_city_rankings_top5.csv")
print(ranks.pivot(index="rank", columns="ranking", values="city"))
Data and method
- Rankings weight climate, cost of living, flights, infrastructure, safety, and EU/EFTA membership differently per traveller type.
- Search demand from Ahrefs global keyword data; analysis by DataPulse Research.
- Dataset: https://github.com/DatapulseResearch/workation-atlas
- Full study with methodology: https://www.datapulse.de/en/workation-atlas
What would your weighting be? Fork the CSV and post your own top 5 in the comments.
