Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

Luxembourg had the highest average monthly salary of employees in the world in 2024 in terms of purchasing power parities (PPP), which takes the average cost of living in a country into account. Belgium followed in second, with the Netherlands in third.

About This Dataset

This dataset is the original 70-city version used in my first published research paper: “A Data-Driven Survey on Cost of Living and Salary Affordability in Indian Cities” (IJRASET, 2025) Link: https://www.ijraset.com/best-journal/a-datadriven-survey-on-cost-of-livingsalary-affordability-in-indian-cities

It was created using web-scraping techniques from LivingCost.org and converted to INR using a consistent USD→INR exchange rate. This dataset forms the foundational base for affordability analysis, exploratory data analysis (EDA), and benchmarking cost-of-living patterns across India.

The dataset includes 70+ Indian cities, with fields covering living cost, rent, salary, affordability ratio (“months covered”), and derived financial indicators. It is clean, structured, and suitable for beginner to intermediate analytics projects.

Why This Dataset?

This dataset is ideal for:

EDA practice for college & school projects

Correlation and regression analysis

Basic ML tasks (predicting salary, affordability, rent, etc.)

Urban economics mini-projects

Dashboard creation (PowerBI, Tableau)

Data cleaning and preprocessing assignments

It is designed to be simple enough for students but structured enough for real-world analysis.

Features Included

Each row represents a city/state-level affordability profile with:

Cost of living (USD & INR)

Rent for a single person (USD & INR)

Monthly after-tax salary (USD & INR)

Income after rent

“Months Covered” affordability ratio

Source URLs for verification

Exchange rate used

This makes the dataset both transparent and reliable for academic usage.

Data Quality

Web-scraped directly from LivingCost.org

Cleaned and standardized

Currency converted uniformly

Non-city entries flagged

Fully reproducible from the source

This dataset served as the master input for my peer-reviewed paper and has been validated through statistical analysis.

Intended Audience

Students (school, undergraduate, postgraduate)

Data science beginners

Educators needing real datasets for teaching

Analysts looking for quick EDA practice

Researchers exploring affordability or urban economics

Note

A more comprehensive 200+ city enhanced dataset (used in my second paper) will be uploaded soon, including ICT metrics, GDP, and extended affordability indicators.

In 2025, the Consumer Price Index (CPI) for medical professional services in the United States was at 432.46, compared to the period from 1982 to 1984 (=100). The CPI for hospital services was at 1,102.12.

Table showing the country rankings based in the different metrics analysed

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.

Quality of Life Index (higher is better) is an estimation of overall quality of life by using an empirical formula which takes into account purchasing power index (higher is better), pollution index (lower is better), house price to income ratio (lower is better), cost of living index (lower is better), safety index (higher is better), health care index (higher is better), traffic commute time index (lower is better) and climate index (higher is better).

Current formula (written in Java programming language):

index.main = Math.max(0, 100 + purchasingPowerInclRentIndex / 2.5 - (housePriceToIncomeRatio * 1.0) - costOfLivingIndex / 10 + safetyIndex / 2.0 + healthIndex / 2.5 - trafficTimeIndex / 2.0 - pollutionIndex * 2.0 / 3.0 + climateIndex / 3.0);

For details how purchasing power (including rent) index, pollution index, property price to income ratios, cost of living index, safety index, climate index, health index and traffic index are calculated please look up their respective pages.

Formulas used in the past

Formula used between June 2017 and Decembar 2017

We decided to decrease weight from costOfLivingIndex in this formula:

index.main = Math.max(0, 100 + purchasingPowerInclRentIndex / 2.5 - (housePriceToIncomeRatio * 1.0) - costOfLivingIndex / 5 + safetyIndex / 2.0 + healthIndex / 2.5 - trafficTimeIndex / 2.0 - pollutionIndex * 2.0 / 3.0 + climateIndex / 3.0);

The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.

The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for 2017 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.

Quality of life index, link: https://www.numbeo.com/quality-of-life/indices_explained.jsp

Happiness store, link: https://www.kaggle.com/unsdsn/world-happiness/home

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.

The Consumer Sentiment Index in the United States stood at 51 in November 2025. This reflected a drop of 2.6 point from the previous survey. Furthermore, this was its lowest level measured since June 2022. The index is normalized to a value of 100 in December 1964 and based on a monthly survey of consumers, conducted in the continental United States. It consists of about 50 core questions which cover consumers' assessments of their personal financial situation, their buying attitudes and overall economic conditions.

In 2024, the median household income in the United States was 83,730 U.S. dollars. This reflected an increase from the previous year. Household income The median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varied from state to state. In 2024, Massachusetts recorded the highest median household income in the country, at 113,900 U.S. dollars. On the other hand, Mississippi, recorded the lowest, at 55,980 U.S. dollars.Household income is also used to determine the poverty rate in the United States. In 2024, 10.6 percent of the U.S. population was living below the national poverty line. This was the lowest level since 2019. Similarly, the child poverty rate, which represents people under the age of 18 living in poverty, reached a three-decade low of 14.3 percent of the children. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.52 in 2024. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality, while a score of one indicates complete inequality.

This table shows the average House Price/Earnings ratio, which is an important indicator of housing affordability. Ratios are calculated by dividing house price by the median earnings of a borough.

The Annual Survey of Hours and Earnings (ASHE) is based on a 1 per cent sample of employee jobs. Information on earnings and hours is obtained in confidence from employers. It does not cover the self-employed nor does it cover employees not paid during the reference period. Information is as at April each year. The statistics used are workplace based full-time individual earnings.

Land Registry housing data are for the first half of the year only, so that they comparable to the ASHE data which are as at April.
Prior to 2006 data are not available for Inner and Outer London.

The lowest 25 per cent of prices are below the lower quartile; the highest 75 per cent are above the lower quartile.
The "lower quartile" property price/income is determined by ranking all property prices/incomes in ascending order.
The 'median' property price/income is determined by ranking all property prices/incomes in ascending order. The point at which one half of the values are above and one half are below is the median.

Regional data has not been published by DCLG since 2012. Data for regions has been calculated by the GLA. Data for 2014 has been calculated by the GLA.

Link to DCLG Live Tables

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
January 2023

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
1980-2000

U.S. Census Bureau: American Community Survey - https://data.census.gov/
2007-2021
Form C17002

CONTACT INFORMATION
vitalsigns.info@mtc.ca.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. 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 non-cash benefits (such as public housing, Medicaid and food stamps).

For the national poverty level definitions by year, see: US Census Bureau Poverty Thresholds - https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-thresholds.html.

For an explanation on how the Census Bureau measures poverty, see: How the Census Bureau Measures Poverty - https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-measures.html.

American Community Survey (ACS) 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

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.

The house price to income index in Europe declined in 13 of the 28 European countries in 2024, indicating that income grew faster than house prices. Portugal had the highest house price to income index ranking, with values exceeding ***** index points. Romania and Finland were on the other side of the spectrum, with less than 100 index points. The house price to income ratio is an indicator for the development of housing affordability across OECD countries and is calculated as the nominal house prices divided by nominal disposable income per head, with 2015 chosen as a base year. A ratio higher than 100 means that the nominal house price growth since 2015 has outpaced the nominal disposable income growth, and housing is therefore comparatively less affordable. In 2024, the OECD average stood at ***** index points.

Key information about Hong Kong SAR (China) Monthly Earnings

• Hong Kong SAR (China) Monthly Earnings stood at 2,485 USD in Jun 2025, compared with the previous figure of 2,457 USD in Mar 2025
• Hong Kong SAR (China) Monthly Earnings data is updated quarterly, available from Mar 1999 to Jun 2025, with an average number of 1,676 USD
• The data reached the an all-time high of 2,485 USD in Jun 2025 and a record low of 1,331 USD in Mar 2005

CEIC converts Monthly Earnings into USD. Census and Statistics Department provides Monthly Average Wage Rate in local currency. Federal Reserve Board average market exchange rate is used for currency conversions. Monthly Earnings cover the basic wages/salaries (including paid holidays and leave), commission and tips (tips received directly from customers are excluded), shift allowance, cost-of-living allowance, meal allowance and meal benefits, good attendance bonus, guaranteed year-end bonus, and other regular and guaranteed bonuses and allowances. Monthly Earnings prior to March 2009 are calculated from comparable discontinued series.


Further information about Hong Kong SAR (China) Monthly Earnings

• In the latest reports, Hong Kong SAR (China) Population reached 8 million people in Dec 2024
• Unemployment Rate of Hong Kong SAR (China) dropped to 4 % in Oct 2025
• The country's Labour Force Participation Rate dropped to 57 % in Oct 2025

Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

New Zealand has one of the highest house price-to-income ratios in the world; nonetheless, since the first quarter of 2022, the country's house price-to-income ratio started to trend downward. In the first quarter of 2025, the ratio was *****, a decrease from the same quarter of the previous year. This ratio was calculated by dividing nominal house prices by nominal disposable income per head, and is considered a measure of affordability. Homeownership dream New Zealand has been in what is widely considered a housing bubble. The disproportionately large increases in residential house prices have placed the dream of owning their own home out of reach for many in the country. In 2025, around ** percent of residential properties were sold for over a million New Zealand dollars. The majority of mortgage lending in the country went to owner-occupiers where the property was not their first home, with first-home buyers often struggling to secure a loan. In general, only New Zealand residents and citizens can buy homes in the country to live in, with new regulations tightening investment activity in that market. Rent affordability Due to New Zealand's high property prices, many individuals and families are stuck renting for prolonged periods. However, with rent prices increasing across the country and the share of monthly income spent on rent trending upwards in tandem with a highly competitive rental market, renting is becoming a less appealing prospect for many. The Auckland and Bay of Plenty regions had the highest weekly rent prices across the country as of December 2024, with the Southland region recording the lowest rent prices per week.

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.

After a steep decline, starting in 2010 and culminating in the bottom value of 20,438 euros, average annual wages in Portugal increased to unprecedented figures. By 2021, the mean wage per year of work was 22,251 euros, despite the more than 200-euro drop that followed in 2022. By 2024, annual wages rose to 23,177 euros. The salary increase is not enough to tackle living expenses The surge in salaries is even more evident when considering minimum annual wages. Following a period of stagnation between 2011 and 2014, the Portuguese minimum annual salary has been on a hike, reaching 11,480 euros by 2024. This rise is relevant when considering the impact of growing inflation on the prices of goods and services, as it restores some purchasing power to workers. Nevertheless, 50 percent of Portuguese people stated, in 2023, that their professional income was mostly not enough to face up to living expenses. Youngsters and women struggle the most when it comes to unemployment and salary amounts The unemployment rate in the country was at six percent in the second quarter of 2024, having decreased from the previous quarter. People between 16 and 24 years of age were the most affected by unemployment, with shares of 22 percent in the same quarter. Women followed as the second most impacted by this social issue. The gender gap is also notorious when it comes to wages. In 2022, women’s average monthly salaries were over 13 percent lower than the salaries of their male counterparts, which impairs women in the tackling of the rising cost of living.

In 2024, the average monthly income per capita in Vietnam reached approximately *** million Vietnamese dong, indicating a slight increase from the previous year. 2020 and 2021 were particularly difficult years for the country’s population when per capita income decreased due to the negative impacts of the COVID-19 epidemic. Income distribution in Vietnam As a rapidly developing country in Southeast Asia, Vietnam has made significant efforts to improve income distribution among its population. One of the key factors contributing to a more balanced income distribution is Vietnam’s robust economic growth. Although the COVID-19 pandemic posed challenges to the country’s economy, Vietnam has been enjoying gradual GDP growth over the past few years, which explains the increase in job opportunities and higher wages for many Vietnamese citizens. Over the years, the Vietnamese government has implemented various policies and strategies to reduce the poverty rate and narrow the income gap in the country. However, the difference in income between urban and rural areas is inevitable. According to a governmental report in 2022, earnings per capita improved steadily across the whole country regardless of area; nonetheless, the monthly average income in urban areas was 1.5 times higher than that of their rural counterparts. Among the five major cities, Ha Noi and Ho Chi Minh City recorded the highest income per capita due to their higher living expenses compared to other areas. Monthly household expenditures in Vietnam While Vietnam has made noticeable progress in reducing poverty and improving income distribution, challenges remain in shaping the overall living standard for the population. The cost of living varies across different regions, with urban areas generally having higher expenses compared to rural areas. The largest portions of household expenditures are mainly used for nutrition, followed by housing, transportation, and healthcare. Education and entertainment also contributed to the monthly expenses, especially after the COVID-19 pandemic recovery and many restrictions were lifted in the country.

As of 2022, Seychelles was the African country with the highest estimated minimum gross monthly wage, standing at ****** U.S. dollars. It was followed by Morocco at ****** U.S. dollars and South Africa ****** U.S. dollars. Among the selected nations, only **** countries had a minimum wage above *** U.S. dollars on the continent. Minimum wage adjustments Legislations regarding minimum wages vary significantly across countries. The minimum remuneration of employees is usually proportionate to a specific area's cost of living. Determining a minimum wage aims to increase employees' living conditions while reducing poverty and inequality. Due to rising prices and inflation, governments occasionally adjust the minimum salary. In Africa, Sierra Leone experienced the highest increase in the minimum wage in recent years, with a growth of almost ** percent between 2010 and 2019. However, governments can also lower minimum wages. Liberia and Burundi reduced the lowest possible remuneration by around ** percent and ***** percent, respectively, between 2010 and 2019. Widespread informal employment Despite legislation in force, minimum wages are not always guaranteed. In fact, several forms of employment allow employers to avoid paying minimum wages. In addition, undeclared work remains a common practice in many countries worldwide. The situation is particularly critical in some African countries. According to estimates, over ** percent of the working population in Niger, The Democratic Republic of Congo, Benin, and Madagascar engaged in informal employment between 2019 and 2023. In Egypt and South Africa, the share stood at ** percent and ** percent, respectively. Seychelles had the lowest rate on the continent at around ** percent.