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.

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.

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.

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!

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.

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.

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.

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.

Co-Living Space Market Outlook

According to our latest research, the Global Co-Living Space market size was valued at $21.4 billion in 2024 and is projected to reach $72.6 billion by 2033, expanding at a robust CAGR of 14.2% during the forecast period of 2024–2033. The primary driver fueling this substantial growth is the rising demand for affordable and flexible housing solutions among urban millennials and young professionals worldwide. As urbanization accelerates and the cost of living in major cities continues to soar, co-living spaces offer a compelling alternative by combining affordability, convenience, and a sense of community. This evolving lifestyle preference, coupled with technological advancements in property management and digital platforms, is reshaping the residential real estate landscape and positioning co-living as a mainstream solution for the future of urban living.

Regional Outlook

North America currently commands the largest share of the global co-living space market, accounting for nearly 35% of total market revenue in 2024. This dominance is attributed to the region’s mature real estate infrastructure, high urbanization rates, and a robust ecosystem of tech-enabled property management companies. Cities such as New York, San Francisco, and Toronto have witnessed a surge in co-living developments, driven by a growing population of young professionals and students seeking cost-effective and socially engaging living arrangements. Furthermore, favorable regulatory frameworks and the proliferation of venture-backed startups have accelerated the adoption of co-living models, making North America a benchmark for operational excellence and innovation in the sector.

The Asia Pacific region is emerging as the fastest-growing market, projected to register a remarkable CAGR of 17.8% from 2024 to 2033. This growth trajectory is propelled by rapid urban migration, a burgeoning middle class, and escalating property prices in metropolitan hubs like Beijing, Mumbai, Singapore, and Sydney. Governments in the region are increasingly supportive of alternative housing formats to address urban housing shortages, while real estate developers and institutional investors are ramping up investments in co-living projects. The region’s youthful demographic profile and cultural openness to shared living further catalyze market expansion, positioning Asia Pacific as a critical engine for future growth in the global co-living space market.

In emerging economies across Latin America, the Middle East, and Africa, the adoption of co-living spaces is gaining momentum but faces unique challenges. Limited awareness, regulatory ambiguities, and varying cultural perceptions of shared living can hinder rapid adoption. However, as urbanization intensifies and the demand for affordable housing rises, these markets present significant untapped potential. Localized demand is being addressed through partnerships with universities, corporations, and local governments, while regulatory reforms and pilot projects are gradually paving the way for broader acceptance. Despite infrastructural and policy hurdles, the long-term outlook for co-living in these regions remains optimistic, especially as global operators and investors begin to explore these nascent markets.

Report Scope

Home Furniture Rental Market Outlook

The global home furniture rental market size was valued at approximately USD 5.3 billion in 2023 and is projected to reach USD 9.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.9%. The market is witnessing significant growth due to the increasing trend of transient living, urbanization, and the growing preference for cost-effective and flexible furnishing solutions among millennials and expatriates.

One of the primary growth factors for the home furniture rental market is the rising mobility of the urban population. As more people move frequently for job opportunities, education, or lifestyle choices, the need for temporary and flexible furnishing solutions has risen. Renting furniture offers a cost-effective alternative to purchasing, especially for those who do not wish to invest heavily in permanent home setups. This trend is particularly prevalent in metropolitan cities where the cost of living is high, and housing is often rented rather than owned.

Additionally, the growth of e-commerce platforms has spurred the demand for home furniture rentals. Online platforms offer a wide range of furniture options, easy rental terms, and convenient delivery and pickup services. These platforms often provide users with the flexibility to customize rental periods and replace furniture as their needs change. The digital transformation and the increasing penetration of the internet have made it easier for customers to explore and rent furniture online, thereby driving market growth.

Environmental sustainability is another key factor contributing to the market's expansion. Renting furniture aligns with the principles of the circular economy by promoting the reuse and recycling of furniture items. Companies in the home furniture rental market are increasingly adopting environmentally friendly practices, such as refurbishing and repurposing used furniture, which appeals to environmentally conscious consumers. This sustainable approach not only reduces waste but also attracts a growing segment of consumers who prioritize eco-friendly choices.

Regionally, the Asia Pacific market is anticipated to witness significant growth due to rapid urbanization and the increasing middle-class population. Countries like India and China are seeing a surge in demand for rental furniture, driven by the influx of young professionals and students in urban areas. North America and Europe also continue to be strong markets for furniture rental, driven by high mobility rates, a well-established rental culture, and the presence of numerous rental service providers. Latin America and the Middle East & Africa are emerging markets with potential for future growth as awareness and acceptance of furniture rental services increase.

Product Type Analysis

The home furniture rental market is segmented by product type into living room furniture, bedroom furniture, dining room furniture, office furniture, and others. Living room furniture accounts for a significant share of the market due to the high demand for sofas, coffee tables, and entertainment units. This segment is driven by consumers looking to furnish their living spaces without the long-term commitment of purchasing. Additionally, the flexibility to change decor based on trends and personal preferences makes renting living room furniture an attractive option.

Bedroom furniture is another substantial segment in the home furniture rental market. The demand for beds, wardrobes, and nightstands is particularly high among young professionals and students who frequently relocate. Renting bedroom furniture allows these consumers to avoid the hassle and cost of moving heavy and bulky items. The convenience of renting, coupled with the ability to upgrade furniture as needed, drives the growth of this segment.

Dining room furniture, including dining tables and chairs, also holds a notable market share. This segment is favored by consumers who host social gatherings or have fluctuating living arrangements. Renting dining room furniture provides the flexibility to accommodate varying numbers of guests and adapt to different dining spaces. Moreover, it allows consumers to experiment with different styles and layouts without the financial burden of purchasing new furniture.

The office furniture segment has gained prominence, especially with the rise of remote work and home offices. As more people set up dedicated workspaces at home, the demand for rental office furniture, such as desks, chairs, and storage units, h

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.

Geneva stands out as Europe's most expensive city for apartment purchases in early 2025, with prices reaching a staggering 15,720 euros per square meter. This Swiss city's real estate market dwarfs even high-cost locations like Zurich and London, highlighting the extreme disparities in housing affordability across the continent. The stark contrast between Geneva and more affordable cities like Nantes, France, where the price was 3,700 euros per square meter, underscores the complex factors influencing urban property markets in Europe. Rental market dynamics and affordability challenges While purchase prices vary widely, rental markets across Europe also show significant differences. London maintained its position as the continent's priciest city for apartment rentals in 2023, with the average monthly costs for a rental apartment amounting to 36.1 euros per square meter. This figure is double the rent in Lisbon, Portugal or Madrid, Spain, and substantially higher than in other major capitals like Paris and Berlin. The disparity in rental costs reflects broader economic trends, housing policies, and the intricate balance of supply and demand in urban centers. Economic factors influencing housing costs The European housing market is influenced by various economic factors, including inflation and energy costs. As of April 2025, the European Union's inflation rate stood at 2.4 percent, with significant variations among member states. Romania experienced the highest inflation at 4.9 percent, while France and Cyprus maintained lower rates. These economic pressures, coupled with rising energy costs, contribute to the overall cost of living and housing affordability across Europe. The volatility in electricity prices, particularly in countries like Italy where rates are projected to reach 153.83 euros per megawatt hour by February 2025, further impacts housing-related expenses for both homeowners and renters.

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.

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.