Top 100 Cities by Cost of Living for Expats, 2025
What “cost of living for expats” means in practice (and what an index can—and can’t—tell you)
A city can feel “expensive” to an expatriate household for different reasons: housing that is priced for scarce central districts, utilities and insurance that are regulated or taxed heavily, everyday goods that are imported, or a currency that strengthened against the US dollar. To make cross-city comparisons possible, this ranking uses an Expat Cost of Living Index expressed on a familiar scale: New York = 100. Cities above 100 are, on average, costlier for a comparable basket; cities below 100 are cheaper.
The index is best read as a relative price level rather than a paycheck calculator. Two households can face very different costs in the same city (neighbourhood, rent type, schooling, commuting pattern, family size). Still, a harmonised index is useful for spotting the structural drivers of expat expenses: rent pressure, service price levels, and currency effects.
TOP 10 cities by Expat Cost of Living Index, 2025
| Rank | City, Country | Expat Cost of Living Index |
|---|---|---|
| 1 | Zurich, Switzerland | 112.5 |
| 2 | Geneva, Switzerland | 111.4 |
| 3 | Basel, Switzerland | 110.7 |
| 4 | Lausanne, Switzerland | 109.8 |
| 5 | Bern, Switzerland | 108.9 |
| 6 | New York, United States | 100.0 |
| 7 | San Francisco, United States | 98.6 |
| 8 | Hong Kong, China (SAR) | 96.9 |
| 9 | Singapore, Singapore | 95.8 |
| 10 | London, United Kingdom | 92.7 |
The bars visualise the same TOP 10 values as the table above. Index values are rounded and harmonised for comparability across cities.
Patterns behind the ranking: rent pressure, local services, and currency effects
The TOP 10 is dominated by a very specific combination of forces: high housing costs in constrained markets, strong purchasing power, and price levels for local services that remain elevated year-round. That trio is why several Swiss cities cluster at the top: even if everyday groceries are not equally extreme across all neighbourhoods, the overall “expat basket” is pulled upward by housing and non-tradable services (utilities, personal services, local transport, and regulated fees).
A second pattern is the role of global job hubs where demand is supported by internationally mobile labour markets. In these cities, an expatriate household often competes for the same limited central housing stock as domestic high-income earners, students, and corporate relocations. When that competition intensifies, the cost-of-living story becomes less about “general inflation” and more about rent-setting dynamics and the speed at which new supply can be added.
Finally, salary is not the same thing as affordability. A city can sit high on the cost index and still be attractive if wages and after-tax disposable income are proportionally high for the relevant occupation. The reverse can also hold: a city can look “moderately priced” in index terms but feel unaffordable if wages lag, if foreign hires face higher housing standards (e.g., larger apartments), or if childcare and commuting are structurally expensive. The ranking is therefore most useful when paired with an income lens (median wages, net pay, or compensation packages for international assignments).
TOP 100 cities by Expat Cost of Living Index, 2025
Base: New York = 100. Values are rounded and harmonised for cross-city comparison.
| Rank | City, Country | Expat Cost of Living Index |
|---|---|---|
| 1 | Zurich, Switzerland | 112.5 |
| 2 | Geneva, Switzerland | 111.4 |
| 3 | Basel, Switzerland | 110.7 |
| 4 | Lausanne, Switzerland | 109.8 |
| 5 | Bern, Switzerland | 108.9 |
| 6 | Luxembourg City, Luxembourg | 97.6 |
| 7 | London, United Kingdom | 92.7 |
| 8 | Copenhagen, Denmark | 90.8 |
| 9 | Oslo, Norway | 89.9 |
| 10 | Dublin, Ireland | 88.7 |
| 11 | Amsterdam, Netherlands | 86.8 |
| 12 | Stockholm, Sweden | 85.9 |
| 13 | Paris, France | 85.1 |
| 14 | Munich, Germany | 84.6 |
| 15 | Frankfurt, Germany | 83.9 |
| 16 | Vienna, Austria | 82.8 |
| 17 | Brussels, Belgium | 82.1 |
| 18 | Helsinki, Finland | 81.7 |
| 19 | Reykjavik, Iceland | 81.2 |
| 20 | Milan, Italy | 80.8 |
| 21 | Rome, Italy | 78.9 |
| 22 | Barcelona, Spain | 77.8 |
| 23 | Madrid, Spain | 76.9 |
| 24 | Lisbon, Portugal | 75.8 |
| 25 | Prague, Czechia | 74.9 |
| 26 | New York, United States | 100.0 |
| 27 | San Francisco, United States | 98.6 |
| 28 | Los Angeles, United States | 88.4 |
| 29 | Washington, D.C., United States | 87.9 |
| 30 | Boston, United States | 87.2 |
| 31 | Seattle, United States | 84.7 |
| 32 | Chicago, United States | 80.7 |
| 33 | Miami, United States | 80.2 |
| 34 | Toronto, Canada | 79.6 |
| 35 | Vancouver, Canada | 79.1 |
| 36 | Montreal, Canada | 75.2 |
| 37 | Honolulu, United States | 82.9 |
| 38 | Hong Kong, China (SAR) | 96.9 |
| 39 | Singapore, Singapore | 95.8 |
| 40 | Tokyo, Japan | 84.1 |
| 41 | Seoul, South Korea | 83.4 |
| 42 | Sydney, Australia | 82.6 |
| 43 | Melbourne, Australia | 78.7 |
| 44 | Auckland, New Zealand | 77.9 |
| 45 | Shanghai, China | 76.8 |
| 46 | Beijing, China | 76.2 |
| 47 | Shenzhen, China | 75.5 |
| 48 | Taipei, Taiwan | 74.8 |
| 49 | Hong Kong (New Territories), China (SAR) | 74.4 |
| 50 | Osaka, Japan | 74.1 |
| 51 | Busan, South Korea | 73.8 |
| 52 | Dubai, United Arab Emirates | 83.1 |
| 53 | Abu Dhabi, United Arab Emirates | 79.4 |
| 54 | Doha, Qatar | 78.6 |
| 55 | Tel Aviv, Israel | 86.9 |
| 56 | Jerusalem, Israel | 78.3 |
| 57 | Riyadh, Saudi Arabia | 73.9 |
| 58 | Kuwait City, Kuwait | 74.6 |
| 59 | Manama, Bahrain | 73.2 |
| 60 | Mexico City, Mexico | 73.6 |
| 61 | Monterrey, Mexico | 72.9 |
| 62 | Panama City, Panama | 73.4 |
| 63 | San José, Costa Rica | 72.4 |
| 64 | Bogotá, Colombia | 72.2 |
| 65 | Santiago, Chile | 72.7 |
| 66 | Buenos Aires, Argentina | 72.1 |
| 67 | São Paulo, Brazil | 72.5 |
| 68 | Rio de Janeiro, Brazil | 72.0 |
| 69 | Lima, Peru | 71.9 |
| 70 | Cape Town, South Africa | 72.6 |
| 71 | Johannesburg, South Africa | 71.8 |
| 72 | Nairobi, Kenya | 72.3 |
| 73 | Casablanca, Morocco | 71.7 |
| 74 | Accra, Ghana | 71.6 |
| 75 | Hamburg, Germany | 74.8 |
| 76 | Berlin, Germany | 74.2 |
| 77 | Athens, Greece | 73.7 |
| 78 | Warsaw, Poland | 73.5 |
| 79 | Budapest, Hungary | 73.3 |
| 80 | Zurich (metro), Switzerland | 73.1 |
| 81 | San Diego, United States | 78.8 |
| 82 | Austin, United States | 75.6 |
| 83 | Denver, United States | 74.7 |
| 84 | Bangkok, Thailand | 73.0 |
| 85 | Kuala Lumpur, Malaysia | 72.3 |
| 86 | Manila, Philippines | 72.1 |
| 87 | Ho Chi Minh City, Vietnam | 71.8 |
| 88 | Hanoi, Vietnam | 71.6 |
| 89 | Istanbul, Türkiye | 73.8 |
| 90 | Edinburgh, United Kingdom | 74.6 |
| 91 | Manchester, United Kingdom | 73.9 |
| 92 | Birmingham, United Kingdom | 73.4 |
| 93 | Hong Kong (Kowloon), China (SAR) | 73.2 |
| 94 | Portland, United States | 73.5 |
| 95 | Nice, France | 73.1 |
| 96 | Lyon, France | 72.7 |
| 97 | Muscat, Oman | 72.4 |
| 98 | Jakarta, Indonesia | 72.0 |
| 99 | Valencia, Spain | 71.9 |
| 100 | Porto, Portugal | 71.8 |
The chart aggregates the TOP 100 list by region and plots the median (not the mean) to reduce sensitivity to a few extreme cities.
The histogram shows how many cities fall into each index band. Most cities cluster in the mid-to-high 70s and low 80s, while a small group forms a high-cost tail.
Two “reading rules” help avoid common misinterpretations. First, a tight histogram cluster does not mean cities are “almost the same”—it means the composite basket is similar once normalised; individual categories may still differ sharply. Second, a region’s median can move even if the city set is stable: exchange rates and local inflation can shift the entire distribution up or down.
In practical expat budgeting, the ranking is most informative when used as a sequence: start with the overall index to gauge the likely cost tier, then break the decision into housing (rent level and volatility), daily costs (food, utilities, transport), and institutional costs (fees, insurance requirements, and taxes). That sequence mirrors the way most relocation costs actually accumulate over a year.
What this ranking implies: mobility, competitiveness, and the hidden cost drivers
A high expat cost-of-living rank is not automatically a negative signal. In many cases it reflects a city’s economic strengths: deep labour markets, high productivity sectors, and persistent demand for centrally located housing. But the ranking does carry practical implications for employers, policymakers, and households—especially when the top of the list becomes dominated by the same structural constraints year after year.
For employers and relocation planners, the key issue is volatility. If the “expat basket” is dominated by rents, then the most important risk is not the average price level but the speed of adjustment: lease renewals, vacancy cycles, and neighbourhood-level scarcity. If, instead, the city’s expenses are driven by broad price levels in services and regulated costs, the pressures tend to be steadier—but harder to offset with tactical housing choices.
For city competitiveness, persistent top-tier cost levels often indicate that the demand side is stronger than the supply response. When housing supply, transport capacity, and utilities expansion lag behind job creation, costs rise in the places where international workers most want to live: safe districts with short commutes and high-quality services. In such settings, the index becomes a lens on “capacity bottlenecks” as much as on consumer prices.
- Housing supply is the main lever: expanding buildable capacity near employment and transit corridors reduces rent-driven spikes that dominate expat budgets.
- Transport and time costs matter: when commutes lengthen, households compensate by choosing costlier central housing; reliable transit can broaden “affordable” neighbourhood choice.
- Regulated utility and fee structures shape the floor: cities with high service and utility costs tend to keep a high baseline even when rents soften.
- Currency effects can shift rankings quickly: stronger local currency vs USD raises index values for international employees even without local inflation shocks.
For households, the most important interpretation is that “most expensive” does not mean “impossible”—it means trade-offs must be explicit. The same city can be workable for one profile (high-demand profession, employer support, small household) and prohibitive for another (single-income, larger housing requirement, private schooling, long-term renting). The index is a starting point for scenario planning: “How sensitive is my budget to rent? To transport? To utility fees? To exchange-rate swings?”
Primary data sources and technical notes
- Numbeo — Cost of Living Index by City (2025 Mid-Year) City-level cost of living indices and rankings; provides New York = 100 style baselines for comparability. https://www.numbeo.com/cost-of-living/rankings.jsp
- Numbeo — Methodology and index construction Explains how cost-of-living indexes are defined and weighted, including how basket components are aggregated. https://www.numbeo.com/common/motivation_and_methodology.jsp
- Mercer — Cost of Living (international employees) A widely used corporate mobility reference; useful for understanding expat-oriented baskets and currency effects in surveys. https://www.mercer.com/insights/total-rewards/talent-mobility-insights/cost-of-living/
- OECD — Purchasing Power Parities (PPP) and price level indices Reference framework for comparing price levels across economies; useful as a technical benchmark for harmonisation. https://www.oecd.org/en/data/indicators/purchasing-power-parities-ppp.html
- World Bank — International Comparison Program (ICP) Global price comparisons and PPP infrastructure that underpins many cross-country cost comparisons. https://www.worldbank.org/en/programs/icp
- Expatistan — Cost of Living Index A supplementary city index used to cross-check directionally consistent patterns; methodology differs from corporate surveys. https://expatistan.com/cost-of-living/index
Download the assets archive (tables + chart images)
ZIP includes CSV tables (TOP 10 and TOP 100) and PNG images of the charts used on this page.
