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.

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.

This dataset provides values for AVERAGE HOUSE PRICES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Ireland, Italy, and Germany had some of the highest household electricity prices worldwide, as of March 2025. At the time, Irish households were charged around 0.45 U.S. dollars per kilowatt-hour, while in Italy, the price stood at 0.43 U.S. dollars per kilowatt-hour. By comparison, in Russia, residents paid almost 10 times less. What is behind electricity prices? Electricity prices vary widely across the world and sometimes even within a country itself, depending on factors like infrastructure, geography, and politically determined taxes and levies. For example, in Denmark, Belgium, and Sweden, taxes constitute a significant portion of residential end-user electricity prices. Reliance on fossil fuel imports Meanwhile, thanks to their great crude oil and natural gas production output, countries like Iran, Qatar, and Russia enjoy some of the cheapest electricity prices in the world. Here, the average household pays less than 0.1 U.S. dollars per kilowatt-hour. In contrast, countries heavily reliant on fossil fuel imports for electricity generation are more vulnerable to market price fluctuations.

The country with the highest annual household expenditure on video services worldwide was the United States in 2022, with households spending on average ***** U.S. dollars per year on TV and video streaming services. However, spending is forecast to decline in the U.S. in the upcoming years, while households in Western Europe are expected to increase their expenditure on video.

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.

This dataset contains information about the cost of living in almost 5000 cities across the world. The data were gathered by scraping Numbeo's website (https://www.numbeo.com).

Data dictionary

Indonesia Average Monthly Expenditure per Capita: Urban: Housing and Household Facilities: House Maintenance Cost data was reported at 12,491.000 IDR in 2018. This records an increase from the previous number of 8,444.000 IDR for 2017. Indonesia Average Monthly Expenditure per Capita: Urban: Housing and Household Facilities: House Maintenance Cost data is updated yearly, averaging 6,060.500 IDR from Dec 2003 (Median) to 2018, with 14 observations. The data reached an all-time high of 12,491.000 IDR in 2018 and a record low of 2,120.000 IDR in 2004. Indonesia Average Monthly Expenditure per Capita: Urban: Housing and Household Facilities: House Maintenance Cost data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC002: Average Monthly Expenditure per Capita: Urban.

Argentina: Cost of living index, world average = 100: The latest value from 2021 is 61.38 index points, a decline from 93.54 index points in 2017. In comparison, the world average is 79.81 index points, based on data from 165 countries. Historically, the average for Argentina from 2017 to 2021 is 77.46 index points. The minimum value, 61.38 index points, was reached in 2021 while the maximum of 93.54 index points was recorded in 2017.

The real per capita cosumer spending ranking is led by Iran with 120,324,699 U.S. dollars, while Vietnam is following with 49,388,580.61 U.S. dollars. In contrast, Zimbabwe is at the bottom of the ranking with 2.87 U.S. dollars, showing a difference of 120,324,696.13 U.S. dollars to Iran. Consumer spending, here depicted per capita, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data has been converted from local currencies to US$ using the average constant exchange rate of the base year 2017. The timelines therefore do not incorporate currency effects. The data is shown in real terms which means that monetary data is valued at constant prices of a given base year (in this case: 2017). To attain constant prices the nominal forecast has been deflated with the projected consumer price index for the respective category.

Guinea: Cost of living index, world average = 100: The latest value from 2021 is 47.59 index points, a decline from 50.48 index points in 2017. In comparison, the world average is 79.81 index points, based on data from 165 countries. Historically, the average for Guinea from 2017 to 2021 is 49.04 index points. The minimum value, 47.59 index points, was reached in 2021 while the maximum of 50.48 index points was recorded in 2017.

Key information about House Prices Growth

• US house prices grew 5.2% YoY in Dec 2024, following an increase of 5.4% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Dec 2024, with an average growth rate of 5.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

Russia Living Cost: Average per Month data was reported at 10,213.000 RUB in Dec 2018. This records a decrease from the previous number of 10,451.000 RUB for Sep 2018. Russia Living Cost: Average per Month data is updated quarterly, averaging 3,050.000 RUB from Mar 1992 (Median) to Dec 2018, with 108 observations. The data reached an all-time high of 10,451.000 RUB in Sep 2018 and a record low of 1.423 RUB in Jun 1992. Russia Living Cost: Average per Month 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.

Palestine: Cost of living index, world average = 100: The latest value from is index points, unavailable from index points in . In comparison, the world average is 0.00 index points, based on data from countries. Historically, the average for Palestine from to is index points. The minimum value, index points, was reached in while the maximum of index points was recorded in .

Cuba: Cost of living index, world average = 100: The latest value from is index points, unavailable from index points in . In comparison, the world average is 0.00 index points, based on data from countries. Historically, the average for Cuba from to is index points. The minimum value, index points, was reached in while the maximum of index points was recorded in .

Abstract

The Household Income, Expenditure and Consumption Survey (HIECS) is of great importance among other household surveys conducted by statistical agencies in various countries around the world. This survey provides a large amount of data to rely on in measuring the living standards of households and individuals, as well as establishing databases that serve in measuring poverty, designing social assistance programs, and providing necessary weights to compile consumer price indices, considered to be an important indicator to assess inflation. The HIECS 2010/2011 is the tenth Household Income, Expenditure and Consumption Survey that was carried out in 2010/2011, among a long series of similar surveys that started back in 1955. The survey main objectives are:

• To identify expenditure levels and patterns of population as well as socio- economic and demographic differentials.

• To measure average household and per-capita expenditure for various expenditure items along with socio-economic correlates.

• To Measure the change in living standards and expenditure patterns and behavior for the individuals and households in the panel sample, previously surveyed in 2008/2009, for the first time during 12 months representing the survey period.

• To define percentage distribution of expenditure for various items used in compiling consumer price indices which is considered important indicator for measuring inflation.

• To estimate the quantities, values of commodities and services consumed by households during the survey period to determine the levels of consumption and estimate the current demand which is important to predict future demands.

• To define average household and per-capita income from different sources.

• To provide data necessary to measure standard of living for households and individuals. Poverty analysis and setting up a basis for social welfare assistance are highly dependent on the results of this survey.

• To provide essential data to measure elasticity which reflects the percentage change in expenditure for various commodity and service groups against the percentage change in total expenditure for the purpose of predicting the levels of expenditure and consumption for different commodity and service items in urban and rural areas.

• To provide data essential for comparing change in expenditure against change in income to measure income elasticity of expenditure.

• To study the relationships between demographic, geographical, housing characteristics of households and their income.

• To provide data necessary for national accounts especially in compiling inputs and outputs tables.

• To identify consumers behavior changes among socio-economic groups in urban and rural areas.

• To identify per capita food consumption and its main components of calories, proteins and fats according to its nutrition components and the levels of expenditure in both urban and rural areas.

• To identify the value of expenditure for food according to its sources, either from household production or not, in addition to household expenditure for non-food commodities and services.

• To identify distribution of households according to the possession of some appliances and equipments such as (cars, satellites, mobiles ,…etc) in urban and rural areas that enables measuring household wealth index.

• To identify the percentage distribution of income earners according to some background variables such as housing conditions, size of household and characteristics of head of household.

Compared to previous surveys, the current survey experienced certain peculiarities, among which :

1- The total sample of the current survey (26.5 thousand households) is divided into two sections:

a- A new sample of 16.5 thousand households. This sample was used to study the geographic differences between urban governorates, urban and rural areas, and frontier governorates as well as other discrepancies related to households characteristics and household size, head of the household's education status, etc.

b- A panel sample with 2008/2009 survey data of around 10 thousand households was selected to accurately study the changes that may have occurred in the households' living standards over the period between the two surveys and over time in the future since CAPMAS will continue to collect panel data for HIECS in the coming years.

2- The number of enumeration area segments is reduced from 2526 in the previous survey to 1000 segments for the new sample, with decreasing the number of households selected from each segment to be (16/18) households instead of (19/20) in the previous survey.

3- Some additional questions that showed to be important based on previous surveys results, were added, such as:

a- Collect the expenditure data on education and health on the person level and not on the household level to enable assessing the real level of average expenditure on those services based on the number of beneficiaries.

b- The extent of health services provided to monitor the level of services available in the Egyptian society.

c- Smoking patterns and behaviors (tobacco types- consumption level- quantities purchased and their values).

d- Counting the number of household members younger than 18 years of age registered in ration cards.

e- Add more details to social security pensions data (for adults, children, scholarships, families of civilian martyrs due to military actions) to match new systems of social security.

f- Duration of usage and current value of durable goods aiming at estimating the service cost of personal consumption, as in the case of imputed rents.

4- Quality control procedures especially for fieldwork, are increased, to ensure data accuracy and avoid any errors in suitable time, as well as taking all the necessary measures to guarantee that mistakes are not repeated, with the application of the principle of reward and punishment. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality. Data is a public good, in the interest of the region, and it is consistent with the Economic Research Forum's mandate to make micro data available, aiding regional research on this important topic.

Geographic coverage

National

Analysis unit

1- Household/family

2- Individual/Person

Universe

The survey covered a national sample of households and all individuals permanently residing in surveyed households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of HIECS, 2010-2011 is a self-weighted two-stage stratified cluster sample, of around 26500 households. The main elements of the sampling design are described in the following:

1- Sample Size It has been deemed important to collect a smaller sample size (around 26.5 thousand households) compared to previous rounds due to the convergence in the time period over which the survey is conducted to be every two years instead of five years because of its importance. The sample has been proportionally distributed on the governorate level between urban and rural areas, in order to make the sample representative even for small governorates. Thus, a sample of about 26500 households has been considered, and was distributed between urban and rural with the percentages of 47.1 % and 52.9, respectively. This sample is divided into two parts: a- A new sample of 16.5 thousand households selected from main enumeration areas. b- A panel sample with 2008/2009 survey data of around 10 thousand households.

2- Cluster size The cluster size in the previous survey has been decreased compared to older surveys since large cluster sizes previously used were found to be too large to yield accepted design effect estimates (DEFT). As a result, it has been decided to use a cluster size of only 16 households (that was increased to 18 households in urban governorates and Giza, in addition to urban areas in Helwan and 6th of October, to account for anticipated non-response in those governorates: in view of past experience indicating that non-response may almost be nil in rural governorates). While the cluster size for the panel sample was 4 households.

3- Core Sample The core sample is the master sample of any household sample required to be pulled for the purpose of studying the properties of individuals and families. It is a large sample and distributed on urban and rural areas of all governorates. It is a representative sample for the individual characteristics of the Egyptian society. This sample was implemented in January 2010 and its size reached more than 1 million household (1004800 household) selected from 5024 enumeration areas distributed on all governorates (urban/rural) proportionally with the sample size (the enumeration area

The average for 2021 based on 11 countries was 67.5 index points. The highest value was in Uruguay: 100.24 index points and the lowest value was in Suriname: 43.15 index points. The indicator is available from 2017 to 2021. Below is a chart for all countries where data are available.

Kazakhstan Cost of Living: Average per Capita data was reported at 28,620.000 KZT in Oct 2018. This records a decrease from the previous number of 28,690.000 KZT for Sep 2018. Kazakhstan Cost of Living: Average per Capita data is updated monthly, averaging 13,073.000 KZT from Oct 2000 (Median) to Oct 2018, with 217 observations. The data reached an all-time high of 29,146.000 KZT in Aug 2018 and a record low of 3,983.000 KZT in Oct 2000. Kazakhstan Cost of Living: Average per Capita 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.

Panama: Cost of living index, world average = 100: The latest value from 2021 is 82.58 index points, an increase from 72.83 index points in 2017. In comparison, the world average is 79.81 index points, based on data from 165 countries. Historically, the average for Panama from 2017 to 2021 is 77.71 index points. The minimum value, 72.83 index points, was reached in 2017 while the maximum of 82.58 index points was recorded in 2021.

For a couple with 2 children, where one parent earned the average wage, and the other parent earned 67 percent of the average wage. The U.S. and Ireland had the most expensive childcare among OECD countries, with net childcare costs taking up ** and ** percent of net household income, respectively.