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.

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.

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.

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.

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.

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.

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.

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.

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.

Small Space Furniture Market size was valued at USD 3.28 Billion in 2023 and is projected to reach USD 4.61 Billion by 2031, growing at a CAGR of 5.0% during the forecast period 2024-2031.

Global Small Space Furniture Market Drivers

Urbanization: As more people move to urban areas, the demand for smaller living spaces increases. Apartments and compact homes are becoming more common, leading to a greater need for furniture designed for small spaces. Rising Housing Costs: In many cities, housing costs are skyrocketing, prompting individuals and families to opt for smaller, more affordable living units. This shift encourages the need for furniture that maximizes functionality in limited square footage.

Global Small Space Furniture Market Restraints

High Competition: The market is characterized by a large number of established players as well as new entrants, leading to intense competition. This can pressure profit margins and make it difficult for smaller companies to gain market share. Cost Constraints: The price sensitivity of consumers, especially in urban areas where small space living is common, can limit the pricing strategies of manufacturers. High production costs may force companies to pass costs onto consumers, potentially reducing demand.

Saga Prefecture is located on the island of Kyushu in western Japan. With a population of 839,458, it is one of the least populated prefectures in Japan. Every year, Saga City holds the “Saga International Balloon Festival”. Many people who live in Saga Prefecture attend the event, along with visitors from all over Japan and the world, with attendance typically in the millions. Saga City is considered to be a part of the Fukuoka-Kitakyushu metropolitan area. Agriculture and forestry dominate the economy of Saga Prefecture; it is the largest producer of mandarin oranges and mochigome in Japan. Saga Prefecture is also famous for its porcelain production.

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: CR: City of Sevastopol data was reported at 15,762.000 RUB in 2024. This records an increase from the previous number of 14,519.000 RUB for 2023. Living Cost: Average per Month: CR: City of Sevastopol data is updated yearly, averaging 14,219.000 RUB from Dec 2021 (Median) to 2024, with 4 observations. The data reached an all-time high of 15,762.000 RUB in 2024 and a record low of 11,380.000 RUB in 2021. Living Cost: Average per Month: CR: 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.HF001: Living Cost.

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: 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.

Population with Income per Capita below Living Cost: % of Total: NW: City of St Petersburg data was reported at 3.500 % in 2024. This records a decrease from the previous number of 4.400 % for 2023. Population with Income per Capita below Living Cost: % of Total: NW: City of St Petersburg data is updated yearly, averaging 9.250 % from Dec 1995 (Median) to 2024, with 30 observations. The data reached an all-time high of 33.100 % in 1999 and a record low of 3.500 % in 2024. Population with Income per Capita below Living Cost: % of Total: 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 Demographic and Labour Market – Table RU.GA015: Population with Income per Capita below Living Cost.

Barcelona, Madrid, and Donostia - San Sebastian were some of the most expensive cities to rent a house in Spain in February 2025. Barcelona, which is the capital of Catalonia, led the list with an average price of **** euros per square meter. Madrid followed closely in the second position with an average square meter of rental residential property cost of **** euros.