Zurich, Lausanne, and Geneva were ranked as the most expensive cities worldwide with indices of ************************ Almost half of the 11 most expensive cities were in Switzerland.

Damascus in Syria was ranked as the least expensive city worldwide in 2023, with an index score of ** out of 100. The country has been marred by civil war over the last decade, hitting the country's economy hard. Other cities in the Middle East and North Africa, such as Tehran, Tripoli, and Tunis, are also present on the list. On the other hand, Singapore and Zurich were ranked the most expensive cities in the world.

The average for 2021 based on 165 countries was 79.81 index points. The highest value was in Bermuda: 212.7 index points and the lowest value was in Syria: 33.25 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

As of September 2025, Mumbai had the highest cost of living among other cities in the country, with an index value of ****. Gurgaon, a satellite city of Delhi and part of the National Capital Region (NCR) followed it with an index value of ****.  What is cost of living? The cost of living varies depending on geographical regions and factors that affect the cost of living in an area include housing, food, utilities, clothing, childcare, and fuel among others. The cost of living is calculated based on different measures such as the consumer price index (CPI), living cost indexes, and wage price index. CPI refers to the change in the value of consumer goods and services. The wage price index, on the other hand, measures the change in labor services prices due to market pressures. Lastly, the living cost indexes calculate the impact of changing costs on different households. The relationship between wages and costs determines affordability and shifts in the cost of living. Mumbai tops the list Mumbai usually tops the list of most expensive cities in India. As the financial and entertainment hub of the country, Mumbai offers wide opportunities and attracts talent from all over the country. It is the second-largest city in India and has one of the most expensive real estates in the world.

Addis Ababa, in Ethiopia, ranked as the most expensive city to live in Africa as of 2024, considering consumer goods prices. The Ethiopian capital obtained an index score of ****, followed by Harare, in Zimbabwe, with ****. Morocco and South Africa were the countries with the most representatives among the ** cities with the highest cost of living in Africa.

South Korea's capital Seoul had the highest cost of living among megacities in the Asia-Pacific region in 2024, with an index score of ****. Japan's capital Tokyo followed with a cost of living index score of ****. AffordabilityIn terms of housing affordability, Chinese megacity Shanghai had the highest rent index score in 2024. Affordability has become an issue in certain megacities across the Asia-Pacific region, with accommodation proving expensive. Next to Shanghai, Japanese capital Tokyo and South Korean capital Seoul boast some of the highest rent indices in the region. Increased opportunities in megacitiesAs the biggest region in the world, it is not surprising that the Asia-Pacific region is home to 28 megacities as of January 2024, with expectations that this number will dramatically increase by 2030. The growing number of megacities in the Asia-Pacific region can be attributed to raised levels of employment and living conditions. Cities such as Tokyo, Shanghai, and Beijing have become economic and industrial hubs. Subsequently, these cities have forged a reputation as being the in-trend places to live among the younger generations. This reputation has also pushed them to become enticing to tourists, with Tokyo displaying increased numbers of tourists throughout recent years, which in turn has created more job opportunities for inhabitants. As well as Tokyo, Shanghai has benefitted from the increased tourism, and has demonstrated an increasing population. A big factor in this population increase could be due to the migration of citizens to the city, seeking better employment possibilities.

Title: Top Cities Worldwide: Quality of Life Index 2024 Subtitle: Ranking the World's Best Cities for Living Based on Key Metrics

Source of Data: The dataset was collected from Numbeo.com, a publicly accessible database that provides data on various quality-of-life indicators across cities worldwide. Numbeo aggregates user-contributed data validated through statistical methods to ensure reliability.

Data Collection Method: Data was acquired through web scraping. Care was taken to follow ethical web scraping practices, adhering to Numbeo’s terms of service and respecting their robots.txt file.

Columns Description:

The dataset includes the following columns:

• Rank: City ranking based on the Quality of Life Index.
• City: Name of the city.
• Country: Country where the city is located.
• Quality of Life Index: Overall index measuring quality of life, calculated based on various sub-indices.
• Purchasing Power Index: Measures relative purchasing power in the city.
• Safety Index: Indicates how safe the city is based on crime rates.
• Health Care Index: Reflects the quality and accessibility of healthcare services.
• Cost of Living Index: Represents the cost of living, including housing, food, and transportation.
• Property Price to Income Ratio: A measure of housing affordability, calculated as the ratio of property prices to average incomes.
• Traffic Commute Time Index: Average time spent commuting within the city.

Limitations and Considerations:

• User-Generated Data: Since data on Numbeo is user-contributed, it may be subject to biases.
• Data Update Frequency: As Numbeo updates its data regularly, the dataset represents a snapshot in time and may require periodic updates.

Usage Note: The dataset is intended for research and analytical purposes. Users should verify the data's applicability for their specific use cases, considering the limitations mentioned above.

West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

About the Dataset

This dataset contains Quality of Life indices for various countries around the globe, extracted from the Numbeo website. The data provides valuable metrics for comparing countries based on several aspects of living standards, which can assist in decisions such as choosing a place to live or analyzing global trends in quality of life.

OBS: The code to generate this dataset is presented on: https://www.kaggle.com/code/marcelobatalhah/web-scrapping-quality-of-life-index

Columns in the Dataset

1. Rank:
   The global rank of the country based on its Quality of Life Index according to Year (1 = highest quality of life).

2. Country:
   The name of the country.

3. Quality of Life Index:
   A composite index that evaluates the overall quality of life in a country by combining other indices, such as Safety, Purchasing Power, and Health Care.

4. Purchasing Power Index:
   Measures the relative purchasing power of the average consumer in a country compared to New York City (baseline = 100).

5. Safety Index:
   Indicates the safety level of a country. A higher score suggests a safer environment.

6. Health Care Index:
   Evaluates the quality and accessibility of healthcare in the country.

7. Cost of Living Index:
   Measures the relative cost of living in a country compared to New York City (baseline = 100).

8. Property Price to Income Ratio:
   Compares the affordability of real estate by dividing the average property price by the average income.

9. Traffic Commute Time Index:
   Reflects the average time spent commuting due to traffic.

10. Pollution Index:
    Rates the level of pollution in the country (air, water, etc.).

11. Climate Index:
    Rates the favorability of the climate in the country (higher = more favorable).

12. Year:
    Year when the metrics were extracted.

Key Insights from the Dataset

• The Quality of Life Index aggregates multiple indicators, making it a useful single metric to compare countries.
• Specific indices such as Safety Index or Health Care Index allow for focused analysis on areas like security or healthcare quality.
• Cost of Living Index and Purchasing Power Index can help determine the affordability of living in each country.

How the Data Was Collected

• The dataset was built using web scraping techniques in Python.
• The data was extracted from the "Quality of Life Rankings by Country" page on Numbeo.
• Libraries used:
  • requests for retrieving webpage content.
  • BeautifulSoup for parsing the HTML and extracting relevant information.
  • pandas for organizing and storing the data in a structured format.

Possible Applications

1. Relocation Decision Making:
   Use the dataset to compare countries and identify destinations with high quality of life, safety, and healthcare.

2. Global Analysis:
   Perform exploratory data analysis (EDA) to identify trends and correlations across quality of life metrics.

3. Visualization:
   Plot global maps, bar charts, or other visualizations to better understand the data.

4. Predictive Modeling:
   Use this dataset as a base for machine learning tasks, like predicting Quality of Life Index based on other metrics.

This Cost of International Education dataset compiles detailed financial information for students pursuing higher education abroad. It covers multiple countries, cities, and universities around the world, capturing the full tuition and living expenses spectrum alongside key ancillary costs. With standardized fields such as tuition in USD, living-cost indices, rent, visa fees, insurance, and up-to-date exchange rates, it enables comparative analysis across programs, degree levels, and geographies. Whether you’re a prospective international student mapping out budgets, an educational consultant advising on affordability, or a researcher studying global education economics, this dataset offers a comprehensive foundation for data-driven insights.

Description

Potential Uses

• Budget Planning Prospective students can filter by country, program level, or university to forecast total expenses and compare across destinations.
• Policy Analysis Educational policymakers and NGOs can assess the affordability of international education and design support programs.
• Economic Research Economists can correlate living-cost indices and tuition levels with enrollment rates or student demographics.
• University Benchmarking Institutions can benchmark their fees and ancillary costs against peer universities worldwide.

Notes on Data Collection & Quality

• Currency Conversions All monetary values are unified to USD using contemporaneous exchange rates to facilitate direct comparison.
• Living Cost Index Derived from reputable city-index publications (e.g., Numbeo, Mercer) to standardize disparate cost-of-living metrics.
• Data Currency Exchange rates and fee schedules should be periodically updated to reflect market fluctuations and policy changes.

Feel free to explore, visualize, and extend this dataset for deeper insights into the true cost of studying abroad!

Kazakhstan Cost of Living: Average per Capita: City: Almaty data was reported at 32,029.000 KZT in Oct 2018. This records a decrease from the previous number of 32,475.000 KZT for Sep 2018. Kazakhstan Cost of Living: Average per Capita: City: Almaty data is updated monthly, averaging 15,920.000 KZT from Oct 2000 (Median) to Oct 2018, with 217 observations. The data reached an all-time high of 32,640.000 KZT in Aug 2018 and a record low of 4,577.000 KZT in Oct 2000. Kazakhstan Cost of Living: Average per Capita: City: Almaty data remains active status in CEIC and is reported by The Agency of Statistics of the Republic of Kazakhstan. The data is categorized under Global Database’s Kazakhstan – Table KZ.H012: Cost of Living: Average per Capita.

Kazakhstan Cost of Living: Average per Capita: City: Shymkent data was reported at 26,400.000 KZT in Oct 2018. This records an increase from the previous number of 26,207.000 KZT for Sep 2018. Kazakhstan Cost of Living: Average per Capita: City: Shymkent data is updated monthly, averaging 26,195.000 KZT from Jun 2018 (Median) to Oct 2018, with 5 observations. The data reached an all-time high of 26,400.000 KZT in Oct 2018 and a record low of 24,740.000 KZT in Jul 2018. Kazakhstan Cost of Living: Average per Capita: City: Shymkent data remains active status in CEIC and is reported by The Agency of Statistics of the Republic of Kazakhstan. The data is categorized under Global Database’s Kazakhstan – Table KZ.H012: Cost of Living: Average per Capita.

Living Cost: Average per Month: CF: City of Moscow data was reported at 17,740.000 RUB in Dec 2020. This records a decrease from the previous number of 18,029.000 RUB for Sep 2020. Living Cost: Average per Month: CF: City of Moscow data is updated quarterly, averaging 9,158.000 RUB from Sep 2001 (Median) to Dec 2020, with 78 observations. The data reached an all-time high of 18,029.000 RUB in Sep 2020 and a record low of 2,295.000 RUB in Sep 2001. Living Cost: Average per Month: CF: City of Moscow data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.

Dataset Name: City Happiness Index

Dataset Description:

This dataset and the related codes are entirely prepared, original, and exclusive by Emirhan BULUT. The dataset includes crucial features and measurements from various cities around the world, focusing on factors that may affect the overall happiness score of each city. By analyzing these factors, we aim to gain insights into the living conditions and satisfaction of the population in urban environments.

The dataset consists of the following features:

• City: Name of the city.
• Month: The month in which the data is recorded.
• Year: The year in which the data is recorded.
• Decibel_Level: Average noise levels in decibels, indicating the auditory comfort of the citizens.
• Traffic_Density: Level of traffic density (Low, Medium, High, Very High), which might impact citizens' daily commute and stress levels.
• Green_Space_Area: Percentage of green spaces in the city, positively contributing to the mental well-being and relaxation of the inhabitants.
• Air_Quality_Index: Index measuring the quality of air, a crucial aspect affecting citizens' health and overall satisfaction.
• Happiness_Score: The average happiness score of the city (on a 1-10 scale), representing the subjective well-being of the population.
• Cost_of_Living_Index: Index measuring the cost of living in the city (relative to a reference city), which could impact the financial satisfaction of the citizens.
• Healthcare_Index: Index measuring the quality of healthcare in the city, an essential component of the population's well-being and contentment.

With these features, the dataset aims to analyze and understand the relationship between various urban factors and the happiness of a city's population. The developed Deep Q-Network model, PIYAAI_2, is designed to learn from this data to provide accurate predictions in future scenarios. Using Reinforcement Learning, the model is expected to improve its performance over time as it learns from new data and adapts to changes in the environment.

Living Cost: Labour Force: Average per Month: NW: City of St Petersburg data was reported at 13,074.000 RUB in Dec 2020. This records an increase from the previous number of 12,826.000 RUB for Sep 2020. Living Cost: Labour Force: Average per Month: NW: City of St Petersburg data is updated quarterly, averaging 6,800.000 RUB from Mar 2002 (Median) to Dec 2020, with 76 observations. The data reached an all-time high of 13,074.000 RUB in Dec 2020 and a record low of 2,403.000 RUB in Mar 2002. Living Cost: Labour Force: Average per Month: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF002: Living Cost: Labour Force.

Average Household Income: Tanjung Pinang Municipality data was reported at 10,904,826.000 IDR in 2018. Average Household Income: Tanjung Pinang Municipality data is updated yearly, averaging 10,904,826.000 IDR from Dec 2018 (Median) to 2018, with 1 observations. The data reached an all-time high of 10,904,826.000 IDR in 2018 and a record low of 10,904,826.000 IDR in 2018. Average Household Income: Tanjung Pinang Municipality data remains active status in CEIC and is reported by Statistics Indonesia. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HB003: Cost of Living Survey (SBH-2018): Average Monthly Household Income: by Cities.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂

Living Cost: Pensioners: Average per Month: SF: City of Sevastopol data was reported at 9,360.000 RUB in Dec 2020. This records an increase from the previous number of 9,346.000 RUB for Sep 2020. Living Cost: Pensioners: Average per Month: SF: City of Sevastopol data is updated quarterly, averaging 8,253.000 RUB from Sep 2014 (Median) to Dec 2020, with 26 observations. The data reached an all-time high of 9,514.000 RUB in Jun 2019 and a record low of 4,841.000 RUB in Sep 2014. Living Cost: Pensioners: Average per Month: SF: City of Sevastopol data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF003: Living Cost: Pensioner.

Fukuoka Prefecture is located on the island of Kyushu in western Japan. Its capital, Fukuoka City, is the largest city on the island of Kyushu and the 6th largest city in Japan, with a population of 1.4 million people. Fukuoka City is considered to be one of the best cities in the world to live. It's home to many popular festivals, but the biggest and oldest is the Hakata Dontaku, dating back 800 years, with an attendance of over 2 million people each year, the highest in Japan. Kokura Castle, located in Kitakyushu City, is a popular destination for tourists. Fukuoka Prefecture also has the highest population of Yakuza members, the highest number of gun-related crimes, and the highest number of youth crimes in all of Japan.

Living Cost: Average per Month: NW: City of St Petersburg data was reported at 11,910.000 RUB in Dec 2020. This records an increase from the previous number of 11,685.000 RUB for Sep 2020. Living Cost: Average per Month: NW: City of St Petersburg data is updated quarterly, averaging 6,123.500 RUB from Mar 2002 (Median) to Dec 2020, with 76 observations. The data reached an all-time high of 11,910.000 RUB in Dec 2020 and a record low of 2,117.000 RUB in Mar 2002. Living Cost: Average per Month: NW: City of St Petersburg data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Household Survey – Table RU.HF001: Living Cost.