Top 100 Cities by Housing Affordability (Price-to-Income Ratio), 2025
Reading the price-to-income ratio in cities (2025 snapshot)
The price-to-income ratio compares a typical home price in a city to a typical annual household income. It is commonly interpreted as “how many years of income” it would take to buy a home at the typical price. Lower values imply greater affordability; higher values imply that ownership is more disconnected from local earnings.
Top 100 median
≈ 16.0×
Half of the Top 100 sits above ~16× and half below.
Cities ≥ 20×
28
A sizeable “extreme” tier where prices are far ahead of incomes.
Top 10 range
28.6×–37.0×
The very top is in a different league from the middle of the list.
Top 10 least affordable cities by price-to-income ratio (2025)
The Top 10 is dominated by markets where housing prices have risen far faster than typical incomes, often alongside supply constraints, rapid urban demand growth, and credit dynamics that re-price housing faster than wages adjust.
Colombo
Sri Lanka
Kathmandu
Nepal
Beijing
China
Shanghai
China
Ho Chi Minh City
Vietnam
Tehran
Iran
Hong Kong
Hong Kong (China)
Manila
Philippines
Guangzhou
China
Bangkok
Thailand
Table 1 — Top 10 least affordable cities (2025)
| Rank | City | Country / territory | Price-to-income |
|---|---|---|---|
| 1 | Colombo | Sri Lanka | 37.0× |
| 2 | Kathmandu | Nepal | 36.6× |
| 3 | Beijing | China | 34.7× |
| 4 | Shanghai | China | 34.0× |
| 5 | Ho Chi Minh City | Vietnam | 34.0× |
| 6 | Tehran | Iran | 32.7× |
| 7 | Hong Kong | Hong Kong (China) | 29.1× |
| 8 | Manila | Philippines | 29.0× |
| 9 | Guangzhou | China | 28.6× |
| 10 | Bangkok | Thailand | 28.6× |
Direction reminder: higher ratio = less affordable. Values are shown as multiples of annual income (×).
Chart 1 — Top 10 least affordable cities (higher ratio = less affordable)
- Colombo — 37.0×
- Kathmandu — 36.6×
- Beijing — 34.7×
- Shanghai — 34.0×
- Ho Chi Minh City — 34.0×
- Tehran — 32.7×
- Hong Kong — 29.1×
- Manila — 29.0×
- Guangzhou — 28.6×
- Bangkok — 28.6×
Bars show the same values as Table 1. Values are displayed at one decimal place.
What tends to push the ratio higher
The ratio rises when housing prices outpace household incomes. In many cities, prices react quickly to credit conditions, investor demand, and supply constraints, while incomes adjust more slowly. Once a market moves into a high-ratio regime, it can remain “sticky” because land constraints, planning bottlenecks, and job clustering do not disappear quickly.
Demand composition matters as well. Cities with strong roles in finance, technology, migration, or “safe asset” demand can attract buyers whose purchasing power is only weakly connected to the local wage distribution. In those cases, the price-to-income ratio becomes less about average local earnings and more about how housing is priced relative to mobile capital.
Methodology
There is no single official global table that publishes a harmonised city-level price-to-income ratio for hundreds of cities. For this 2025 snapshot, the ranking uses the Property Prices Index by City (2025) dataset from Numbeo, focusing on the Price to Income Ratio field. The ratio is presented as a multiple of annual income (×) and is shown at one decimal place.
To keep the page useful for comparison, the ranking is ordered from the highest ratio (least affordable) to the lowest ratio within the Top 100 list. The metric is best treated as a screening indicator: it quickly signals whether ownership costs are likely to dominate budgets relative to typical local earnings.
Limitations are important. City-level datasets can differ in how “price” and “income” are proxied, and market composition (apartment size/quality, neighbourhood mix) can shift results. The ratio also does not capture mortgage interest rates, taxes, down payment constraints, household wealth, distributional inequality, or commuting trade-offs. For deeper analysis, pair the ratio with official house price indices, income trends, and housing cost burden measures.
Insights from the Top 100 distribution
The Top 100 is highly concentrated in the mid-to-high teens: 57 cities fall between 13× and 17×, while the “extreme” tail above 20× includes 28 cities. The median for the Top 100 is ≈ 16×, which helps separate routine affordability stress from truly exceptional disconnects between prices and incomes.
Regional patterns in the Top 100 are uneven. Asia carries a high share of the extreme end, with a regional median of ≈ 27.3× in this list. Europe’s median is ≈ 15.2× and the Americas’ median is ≈ 16.0×. These medians are computed from the Top 100 list and therefore reflect the most stressed markets rather than “typical” cities globally.
What this means for readers
For relocation planning, the ratio is a fast way to anticipate whether housing will be the dominant cost pressure. A city can be expensive in absolute terms yet relatively manageable if incomes are also high; conversely, a city with lower headline prices can be less affordable if typical earnings are much lower.
For household budgeting, a high ratio often translates into higher leverage needs, longer saving horizons, or heavier reliance on transfers and family support. For market monitoring, a persistently elevated ratio can indicate structural scarcity and stronger sensitivity to interest-rate shocks.
FAQ
Is a lower price-to-income ratio always “better”?
Lower ratios generally imply greater affordability, but they do not automatically mean higher quality of life. Cities with low ratios can still have high rent burdens, long commutes, weaker job markets, or limited housing quality. Use the ratio as one input, not a full scorecard.
Why can some cities look extreme even if prices have cooled recently?
Ratios can stay high when incomes lag behind prices over long periods. Even if prices stop rising, affordability may not improve quickly unless incomes catch up or housing supply expands enough to compress prices.
Does the ratio reflect mortgages and interest rates?
No. The ratio compares price levels to income levels. It does not include mortgage rates, down payments, taxes, or borrowing constraints, all of which can meaningfully change the lived affordability of buying.
Why can two cities with similar prices have very different ratios?
Because incomes differ. A similar home price can be far more affordable in a city where typical household earnings are higher. That’s why the ratio is often more informative than prices alone for cross-city comparison.
What ratio is often treated as “affordable” in simple frameworks?
A common plain-language benchmark is around 3× annual income, but thresholds vary by country and reporting framework. Many cities in this Top 100 list are far above that level, which helps explain persistent affordability debates.
Should renters care about the price-to-income ratio?
Yes, indirectly. High ownership price-to-income ratios often coexist with broader housing scarcity and pressure on rents, though the strength of the link depends on local regulation, construction pipelines, and tenure structure.
Table 2 — Top 100 cities by housing affordability (price-to-income ratio), 2025
This table lists the Top 100 least affordable cities in the 2025 snapshot, ordered from the highest price-to-income ratio (Rank 1) to the lowest ratio within this Top 100 list (Rank 100).
| Rank | City | Country / territory | Price-to-income (×) |
|---|---|---|---|
| 1 | Colombo | Sri Lanka | 37.0× |
| 2 | Kathmandu | Nepal | 36.6× |
| 3 | Beijing | China | 34.7× |
| 4 | Shanghai | China | 34.0× |
| 5 | Ho Chi Minh City | Vietnam | 34.0× |
| 6 | Tehran | Iran | 32.7× |
| 7 | Hong Kong | Hong Kong (China) | 29.1× |
| 8 | Manila | Philippines | 29.0× |
| 9 | Guangzhou | China | 28.6× |
| 10 | Bangkok | Thailand | 28.6× |
| 11 | Mumbai | India | 28.4× |
| 12 | Taipei | Taiwan | 28.4× |
| 13 | Shenzhen | China | 27.8× |
| 14 | Hangzhou | China | 27.3× |
| 15 | Seoul | South Korea | 26.8× |
| 16 | Phnom Penh | Cambodia | 25.7× |
| 17 | Hanoi | Vietnam | 24.7× |
| 18 | Suzhou | China | 23.5× |
| 19 | Tel Aviv-Yafo | Israel | 23.3× |
| 20 | Algiers | Algeria | 23.2× |
| 21 | Singapore | Singapore | 23.2× |
| 22 | Bogota | Colombia | 22.1× |
| 23 | Yerevan | Armenia | 22.0× |
| 24 | Lisbon | Portugal | 21.1× |
| 25 | Brasilia | Brazil | 21.0× |
| 26 | Alexandria | Egypt | 21.0× |
| 27 | Rio de Janeiro | Brazil | 20.9× |
| 28 | Buenos Aires | Argentina | 20.3× |
| 29 | Beirut | Lebanon | 19.8× |
| 30 | Split | Croatia | 19.1× |
| 31 | London | United Kingdom | 18.6× |
| 32 | Caracas | Venezuela | 18.6× |
| 33 | Medellin | Colombia | 18.2× |
| 34 | Belgrade | Serbia | 18.2× |
| 35 | Santiago | Chile | 18.1× |
| 36 | Sao Paulo | Brazil | 17.9× |
| 37 | Moscow | Russia | 17.6× |
| 38 | Mexico City | Mexico | 17.6× |
| 39 | Prague | Czech Republic | 17.4× |
| 40 | Cairo | Egypt | 17.4× |
| 41 | Lahore | Pakistan | 17.4× |
| 42 | Chengdu | China | 17.3× |
| 43 | Nairobi | Kenya | 17.1× |
| 44 | Paris | France | 16.9× |
| 45 | Curitiba | Brazil | 16.9× |
| 46 | Kazan | Russia | 16.6× |
| 47 | Tirana | Albania | 16.6× |
| 48 | Ulaanbaatar | Mongolia | 16.0× |
| 49 | Milan | Italy | 16.0× |
| 50 | Tokyo | Japan | 16.0× |
| 51 | Kiev (Kyiv) | Ukraine | 15.9× |
| 52 | Kaliningrad | Russia | 15.7× |
| 53 | Saint Petersburg | Russia | 15.7× |
| 54 | Baku | Azerbaijan | 15.5× |
| 55 | Sarajevo | Bosnia And Herzegovina | 15.5× |
| 56 | Novi Sad | Serbia | 15.5× |
| 57 | Krasnodar | Russia | 15.5× |
| 58 | Bratislava | Slovakia | 15.4× |
| 59 | Nizhny Novgorod | Russia | 15.4× |
| 60 | Pristina | Kosovo (Disputed Territory) | 15.4× |
| 61 | Banja Luka | Bosnia And Herzegovina | 15.2× |
| 62 | Florianopolis | Brazil | 15.1× |
| 63 | Munich | Germany | 15.0× |
| 64 | Porto | Portugal | 15.0× |
| 65 | Naples | Italy | 15.0× |
| 66 | Guadalajara | Mexico | 14.9× |
| 67 | Vienna | Austria | 14.9× |
| 68 | Ufa | Russia | 14.8× |
| 69 | Monterrey | Mexico | 14.8× |
| 70 | Antalya | Turkey | 14.8× |
| 71 | Casablanca | Morocco | 14.7× |
| 72 | Athens | Greece | 14.7× |
| 73 | Brno | Czech Republic | 14.5× |
| 74 | Lima | Peru | 14.4× |
| 75 | Zurich | Switzerland | 14.4× |
| 76 | Guatemala City | Guatemala | 14.4× |
| 77 | Porto Alegre | Brazil | 14.3× |
| 78 | Istanbul | Turkey | 14.3× |
| 79 | Ljubljana | Slovenia | 14.2× |
| 80 | Bishkek | Kyrgyzstan | 14.2× |
| 81 | New York, NY | United States | 14.2× |
| 82 | Rome | Italy | 13.9× |
| 83 | Tbilisi | Georgia | 13.9× |
| 84 | Skopje | North Macedonia | 13.7× |
| 85 | Budapest | Hungary | 13.7× |
| 86 | Vilnius | Lithuania | 13.6× |
| 87 | Odessa (Odesa) | Ukraine | 13.5× |
| 88 | Nis | Serbia | 13.5× |
| 89 | Warsaw | Poland | 13.5× |
| 90 | Belo Horizonte | Brazil | 13.5× |
| 91 | Thessaloniki | Greece | 13.5× |
| 92 | Tashkent | Uzbekistan | 13.5× |
| 93 | Florence | Italy | 13.4× |
| 94 | Victoria | Canada | 13.2× |
| 95 | Dhaka | Bangladesh | 13.2× |
| 96 | Yekaterinburg | Russia | 13.2× |
| 97 | Tunis | Tunisia | 13.2× |
| 98 | Novosibirsk | Russia | 13.1× |
| 99 | Montevideo | Uruguay | 13.0× |
| 100 | Sydney | Australia | 13.0× |
Source: Numbeo — Property Prices Index by City (2025), “Price to Income Ratio”. Regions are grouped for analysis (Asia, Europe, Americas, MENA, Africa, Oceania).
Chart 2 — Histogram of price-to-income ratios (Top 100, 2025)
Each bar counts how many cities fall into a ratio range. A long right tail means that a small set of extreme markets can dominate headlines even if many cities cluster in the mid-to-high teens.
| Ratio range | Count |
|---|---|
| 13–15× | 35 |
| 15–17× | 22 |
| 17–19× | 13 |
| 19–21× | 4 |
| 21–23× | 5 |
| 23–25× | 5 |
| 25–27× | 2 |
| 27–29× | 6 |
| 29–31× | 2 |
| 31–33× | 1 |
| 33–35× | 3 |
| 35–37× | 1 |
| ≥ 37× | 1 |
Bins use 2× steps for readability. Counts sum to 100.
Chart 3 — Median price-to-income ratio by region (Top 100, 2025)
Bars show regional medians computed from the Top 100 list. Medians reduce the influence of extreme outliers and help indicate whether affordability stress is concentrated in a few outlier cities or is widespread across a region within this Top 100 set.
| Region | Median | n |
|---|---|---|
| Asia | 27.3× | 25 |
| Europe | 15.2× | 43 |
| Americas | 16.0× | 20 |
| MENA | 18.6× | 10 |
| Africa | 17.1× | 1 |
| Oceania | 13.0× | 1 |
n is the number of cities from each region appearing in the Top 100 list.
Interpretation: what the 2025 city ranking suggests
The Top 100 list illustrates how affordability stress can be structural. High price-to-income ratios tend to persist in markets where housing supply responds weakly to demand shocks, where access to credit capitalises into prices faster than wages move, and where ownership demand is boosted by migration, investor demand, or strong job clustering.
The distribution also matters: when many cities cluster in a mid-range band (here, the mid-to-high teens), the “typical” stressed market looks different from the headline extremes. A few cities at 30×+ can dominate attention, but the broader policy and household reality is often shaped by the much larger group in the 13×–17× range.
How to use the price-to-income ratio well
- Screening: identify markets where ownership costs are likely to overwhelm budgets relative to typical earnings.
- Context: pair the ratio with local income growth, house price indices, rent-to-income and housing cost burden measures.
- Structure: interpret persistent high ratios as a signal of supply constraints and strong price sensitivity to credit conditions.
- Comparability: treat cross-city comparisons cautiously when “price” and “income” proxies differ across sources.
Policy takeaway: what the ratio tends to signal (without prescribing actions)
- Supply sensitivity: persistently high ratios often coincide with a constrained supply response to demand shocks.
- Distributional pressure: rising ratios widen the gap between typical earnings and market prices, increasing reliance on credit and transfers.
- Macro linkages: high valuations relative to income can amplify vulnerability when rates rise or employment weakens.
- Spatial trade-offs: when central housing becomes unaffordable, commuting adjustments and stronger intra-city price gradients often follow.
For comparative analysis, the most informative approach is to combine this ratio with official indicators that track price growth and incomes over time. That helps distinguish short-term cycles from longer-run affordability regimes.
Primary sources and technical notes
City-level price-to-income is not published as one universal official global table. This page uses a 2025 city snapshot dataset for the ranking values and pairs it with official documentation on affordability concepts and national/region-level housing statistics for interpretation.
-
Numbeo — Property Prices Index by City (2025): city snapshot table used for the ranking (“Price to Income Ratio”).
https://www.numbeo.com/property-investment/rankings.jsp?title=2025 -
OECD — Affordable Housing Database (HM1.2): definition and construction of price-to-income style indicators.
https://webfs.oecd.org/els-com/Affordable_Housing_Database/HM1-2-Housing-prices.pdf -
UN Statistics Division — SDG 11.1.1 metadata: international metadata referencing house-price-to-income concepts and thresholds used in some reporting contexts.
https://unstats.un.org/sdgs/metadata/files/Metadata-11-01-01.pdf -
UN-Habitat — Urban Data Digest: global housing affordability context (price-to-income and rent-to-income framing).
https://unhabitat.org/sites/default/files/2020/06/urban_data_digest_the_global_housing_affordability_challenge.pdf -
Eurostat — Housing price statistics database: EU official tables and methodology for housing price indicators.
https://ec.europa.eu/eurostat/web/housing-price-statistics/database -
U.S. FHFA — House Price Index (HPI): official repeat-sales house price indices for the United States.
https://www.fhfa.gov/data/hpi -
World Bank / IMF — Global Housing Watch listing: cross-country housing market indicators (including price-to-income series) for benchmarking context.
https://data360.worldbank.org/en/dataset/IMF_GHW
Data are presented as a 2025 snapshot and shown at one decimal place. For formal policy work, always consult original statistical releases and methodological notes.