As of mid-2025, Port of Spain ranked as the second Latin American and Caribbean city with the highest cost of living. The capital of ******************* obtained an index score of ****, followed by the ********* capital, with **** points.

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.

The US Family Budget Dataset provides insights into the cost of living in different US counties based on the Family Budget Calculator by the Economic Policy Institute (EPI).

This dataset offers community-specific estimates for ten family types, including one or two adults with zero to four children, in all 1877 counties and metro areas across the United States.

Interesting Task Ideas:

1. See how family budgets compare to the federal poverty line and the Supplemental Poverty Measure in different counties.
2. Look into the money challenges faced by different types of families using the budgets provided.
3. Find out which counties have the most affordable places to live, food, transportation, healthcare, childcare, and other things people need.
4. Explore how the average income of families relates to the overall cost of living in different counties.
5. Investigate how family size affects the estimated budget and find counties where bigger families have higher costs.
6. Create visuals showing how the cost of living varies across different states and big cities.
7. Check whether specific counties are affordable for families of different sizes and types.
8. Use the dataset to compare living standards and economic security in different US counties.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

Checkout my other datasets

Employment-to-Population Ratio for USA

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Photo by Alev Takil on Unsplash

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.

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.

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.

Thumbnail Image by macrovector_official on Freepik

Missouri Economic Research and Information Center (MERIC) derives the cost of living index for each state by averaging the indices of participating cities and metropolitan areas in that state.

In general, the most expensive areas to live were Hawaii, Alaska, the Northeast, and the West Coast. The least expensive areas were the Midwest and Southern states.

Cities across the nation participate in the Council for Community & Economic Research (C2ER) survey on a volunteer basis. Price information in the survey is governed by C2ER collection guidelines which strive for uniformity.

The entries for Ontario, British Columbia, and Remote were added manually for my use case.

As of July 2025, the cost of living in Barrancabermeja was the highest among major Colombian cities. In total, the average cost per month amounted to *** U.S. dollars. Medellín followed in the ranking, with a monthly cost of living of *** U.S. dollars at that time.

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.

When the COVID-19 pandemic forced tens of millions of people to work remotely, some chose to relocate out of high-cost, large metro areas. Did people move to cheaper metros or give up in city living altogether? How many will follow in their footsteps, and what could their relocating mean for the places they choose?

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!

This work looks at the spiralling cost of living and the challenges facing Londoners including the rising poverty levels in the capital. The latest update is dated August 2022. The report and public attitudes and behaviour charts (published 1 February 2022) were republished (7 April 2022) to correct a calculation error. This error was due to manual calculation.

Annual indexes of price differences between 15 cities in all provinces and territories, as of October of the previous year, for a selection of products (goods and services) from the Consumer Price Index (CPI) purchased by consumers in each of the 15 cities. The combined city average index is 100.

Leading with the principle of ‘people-oriented urbanization,’ the adaptation of rural migrants in urban China has attracted increasing concerns from policy-makers and scholars. Today, China has proceeded to a new stage of urbanization. Many rural migrants prefer moving to cities near their home villages rather than to large cities, reflecting the changes in migration patterns and expectations of rural migrants. Although migrant adaptation has been repeatedly investigated in academia, researchers tend to address the topic in one host setting, while migrant adaptation in diverse urban settings has rarely been compared. This paper seeks to fill this research gap via a survey conducted in two cities with different urban settings in Jiangsu. The rural migrant adaptation experiences in the two cities are systematically compared. Our statistical results show that economic structure and living costs, on the one hand, and local regulations and socio-cultural environments, on the other hand, determine rural migrant adaptation experiences in different urban settings. Despite abundant employment opportunities in more-developed cities, the high living costs, working pressure, and strict institutional schemes significantly hamper rural migrant adaptation. In less-developed cities, limited employment opportunities and conservative socio-cultural environments hinder rural migrants from adapting in host societies. Our findings suggest that the governments of different cities need to tailor strategies to assist rural migrants in adapting in urban communities.

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.

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.

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.

This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

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.

VITAL SIGNS INDICATOR Poverty (EQ5)

FULL MEASURE NAME The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED December 2018

DESCRIPTION Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE U.S Census Bureau: Decennial Census http://www.nhgis.org (1980-1990) http://factfinder2.census.gov (2000)

U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html

For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.