In April 2020, the Sakha (Yakutiya) Republic recorded the most significant price drop in real estate prices in Russia with a roughly five percent price fall per square meter. In the Moscow and Leningrad Regions, the price of residential properties dropped by 3.2 and 3 percentage points per square meter over the given period, respectively.

Gross fixed capital formation for housing decreased significantly in several European countries in early 2020 but followed with a drop in the second quarter of the year with the coronavirus (COVID-19) outbreak. This translated into a halt of residential property investments. In countries like the United Kingdom (UK), Ireland, France, Spain, Italy, and Luxembourg the year-on-year percentage decrease was between ** and ** percent. Тhis was not the case with several countries that kept housing investment growing on an year-on-year basis in 2020: Greece, Hungary, Sweden, Denmark, and Czechia.

More in-depth data can be found in the report on the coronavirus impacting house prices in Europe in 2020 and 2021.

In this Economic Commentary , we compare characteristics of the 2000–2006 house-price boom that preceded the Great Recession to the house-price boom that began in 2020 during the COVID-19 pandemic. These two episodes of high house-price growth have important differences, including the behavior of rental rates, the dynamics of housing supply and demand, and the state of the mortgage market. The absence of changes in fundamentals during the 2000s is consistent with the literature emphasizing house-price beliefs during this prior episode. In contrast to during the 2000s boom, changes in fundamentals (including rent and demand growth) played a more dominant role in the 2020s house-price boom.

The statistic displays a **** year forecast for house price growth in the United Kingdom (UK) from 2020 to 2024, revised with the coronavirus (covid-19) impact on the market. According to the forecast, 2020 and 2021 will likely see a slower to no increase in house prices followed by a gradual recovery between 2022 and 2024. North West, North East, Yorkshire & the Humber, and Scotland prices are forecast to bounce back quicker than other UK regions with higher **** year price increase.

Residential Property Price Index: 18 Cities: Large data was reported at 107.304 2018=100 in Dec 2024. This records an increase from the previous number of 107.109 2018=100 for Sep 2024. Residential Property Price Index: 18 Cities: Large data is updated quarterly, averaging 102.588 2018=100 from Mar 2018 (Median) to Dec 2024, with 28 observations. The data reached an all-time high of 107.304 2018=100 in Dec 2024 and a record low of 99.532 2018=100 in Mar 2018. Residential Property Price Index: 18 Cities: Large data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Global Database’s Indonesia – Table ID.EF010: Residential Property Price Index: by Cities. [COVID-19-IMPACT]

The samples in this paper come from panel data of 35 large and medium-sized cities in China from 1999 to 2019(In order to avoid the impact of the COVID-19 Pandemic on the conclusions of this analysis, we use the data before the outbreak of the epidemic for empirical testing). Here, the variables adopted for assessing the housing bubble include price level, resident income, household population, the average wage of staff and land supply. Apart from the housing bubble index which is obtained via assessment, all the other basic data come from official statistics, including the Wind Economic Database, website of the People’s Bank of China, and National Bureau of Statistics website.

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.

This study examines the spatial dynamics of urban vegetation and its impact on housing prices in Chicago, analyzing data from both pre- and post-COVID-19 periods. Employing Ordinary Least Squares (OLS) and Multiscale Geographically Weighted Regression (MGWR) models, we assess how the effects of green spaces on property values vary across different neighborhoods. The OLS model generally indicates a positive correlation between increased vegetation and housing prices. In contrast, the MGWR model reveals that the benefits of urban green spaces to property values are not uniformly distributed and exhibit significant variability. Notably, in some South Side areas of Chicago, increases in green space correlate with declines in property values, a sensitivity that intensified post-pandemic, leading to notable price declines. Conversely, the North Side, characterized as a higher-income area, shows greater resilience to the impacts of both increased green spaces and the COVID-19 pandemic, with less susceptibility to economic downturns. This research underscores the intricate interplay between urban green spaces and economic factors, highlighting how local socio-economic conditions and urban planning strategies can influence the economic benefits of vegetation. The findings provide essential insights for urban policymakers and planners striving to promote sustainable development and equitable economic growth in urban environments.

Key information about China Gold Production

• China Real Residential Property Price Index was reported at 90.676 2010=100 in Jun 2025.
• This records a decrease from the previous number of 91.615 2010=100 for Mar 2025.
• China Real Residential Property Price Index data is updated quarterly, averaging 93.824 2010=100 from Jun 2005 to Jun 2025, with 81 observations.
• The data reached an all-time high of 112.991 2010=100 in Sep 2021 and a record low of 87.950 2010=100 in Jun 2005.
• China Real Residential Property Price Index data remains active status in CEIC and is reported by Bank for International Settlements.
• The data is categorized under World Trend Plus’s Association: Property Sector – Table RK.BIS.RPPI: Selected Real Residential Property Price Index: 2010=100: Quarterly. [COVID-19-IMPACT]

this graph was created in OnlinedatasetWorld:

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The Google Health COVID-19 Open Data Repository is one of the most comprehensive collections of up-to-date COVID-19-related information. Comprising data from more than 20,000 locations worldwide, it contains a rich variety of data types to help public health professionals, researchers, policymakers and others in understanding and managing the virus.

Dive into the data View the data in multi-layered graphs and charts, or for more technical users, download it into your systems or solutions to investigate specific topics of concern. The datasets provide current information on COVID-19 cases, deaths, vaccination rates, and hospitalizations. Customize your search with queries on weather, geography, and other variables. Using our visualizer, see contextualized results.

In the West Africa Economic Monetary Union (WAEMU) countries, COVID-19 is expected to affect households in many ways. First, governments might reduce social transfers to households due to the decline in revenue arising from the potential COVID-19 economic recession. Second households deriving income from vulnerable sectors such as tourism and related activities will likely face risk of unemployment or loss of income. Third an increase in prices of imported goods can also negatively impact household welfare, as a direct consequence of the increase of these imported items or as indirect increase of prices of local good manufactured using imported inputs. In this context, there is a need to produce high frequency data to help policy makers in monitoring the channels by which the pandemic affects households and assessing its distributional impact. To do so, the sample of the longitudinal survey is a sub-sample of the Enquête Harmonisée sur les Conditions de Vie des Ménages (EHCVM), a harmonized household survey conducted in 2018/19 household survey in the WAEMU countries.

For Burkina Faso, the survey, which is implemented by the Institut National de la Statistique et la Demographie (INSD), is conducted using cell phone numbers of household members collected during the 2018/19 EHCVM survey. The extensive information collected in the EHCVM provides a rich set of background information for the COVID-19 High Frequency Phone Survey of households. This background information can be leveraged to assess the differential impacts of the pandemic in the country. Every month, the sampled households will be asked a set of core questions on the key channels through which individuals and households are expected to be affected by the COVID-19-related restrictions. Employment, access to basic services, non-labor sources of income are channels likely to be impacted. The core questionnaire is complemented by questions on selected topics that rotate each month. This provides data to the government and development partners in near real-time, supporting an evidence-based response to the crisis.

Dataset Description: Pakistan Real Estate Prices (2018-2019)

Context

This dataset provides real estate price listings across various cities in Pakistan, capturing property details, pricing, locations, and listing dates. The data is valuable for market analysis, price forecasting, and inflation studies, making it a key resource for investors, researchers, and data scientists.

Source & Inspiration

The dataset is sourced from Zameen.com, Pakistan's leading real estate platform, containing 168,447 property listings from 2018 and 2019. The dataset helps analyze:

Market trends before COVID-19 Price fluctuations due to inflation Impact of location and property type on prices Forecasting future price movements Features & Data Columns Property Details: property_id, property_type, bedrooms, baths, Total_Area Location Info: location, city, province_name, latitude, longitude Financials: price (target variable), purpose (For Sale / For Rent) Time Features: date_added (listing date in YYYY-MM-DD format) Agency & Agent: agency, agent Meta: page_url (property page link)

Why This Dataset Matters?

Helps predict house prices using ML models like ARIMA, Prophet, LSTM Enables inflation tracking by observing price changes over time Provides insights into real estate investments in Pakistan

Abstract

In Chad, COVID-19 is expected to affect households in many ways. First, governments might reduce social transfers to households due to the decline in revenue arising from the potential COVID-19 economic recession. Second households deriving income from vulnerable sectors such as tourism and related activities will likely face risk of unemployment or loss of income. Third an increase in prices of imported goods can also negatively impact household welfare, as a direct consequence of the increase of these imported items or as indirect increase of prices of local good manufactured using imported inputs. In this context, there is a need to produce high frequency data to help policy makers in monitoring the channels by which the pandemic affects households and assessing its distributional impact. To do so, the sample of the longitudinal survey will be a sub-sample of the 2018/19 Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (Ecosit 4) in Chad.

This has the advantage of conducting cost effectively welfare analysis without collecting new consumption data. The 30 minutes questionnaires covered many modules, including knowledge, behavior, access to services, food security, employment, safety nets, shocks, coping, etc. Data collection is planned for four months (four rounds) and the questionnaire is designed with core modules and rotating modules.

The main objectives of the survey are to: • Identify type of households directly or indirectly affected by the pandemic; • Identify the main channels by which the pandemic affects households; • Provide relevant data on income and socioeconomic indicators to assess the welfare impact of the pandemic.

Geographic coverage

National coverage, including Ndjamena (Capital city), other urban and rural

Analysis unit

• Households
• Individuals

Universe

The survey covered only households of the 2018/19 Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (ECOSIT 4) which excluded populations in prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Chad COVID-19 impact monitoring survey is a high frequency Computer Assisted Telephone Interview (CATI). The survey’s sample was drawn from the Enquête sur la Consommation des Ménages et le Secteur Informel au Tchad (Ecosit 4) which was conducted in 2018-2019. ECOSIT 4 is a survey with a sample size of 7,493 household’s representative at national, regional and by urban/rural. During the survey, each household was asked to provide a phone number of at least one member or a non-household member (e.g. friends or neighbors) so that they can be contacted for follow-up questions. The sampling of the high frequency survey aimed at having representative estimates by national and area of residence: Ndjamena (capital city), other urban and rural area. The minimum sample size was 2,000 for which 1,748 households (87.5%) were successfully interviewed at the national level. To account for non-response and attrition and given that this survey was the first experience of INSEED, 2,833households were initially selected, among them 1,832 households have been reached. The 1,748 households represent the final sample and will be contacted for the next three rounds of the survey.

Sampling deviation

None

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire is in French and has been administrated in French and local languages. The length of an interview varies between 20 and 30 minutes. The questionnaires consisted of the following sections: 1- Household Roster 2- Knowledge of COVID-19 3- Behavior and Social Distancing 4- Access to Basic Services 5- Employment and Income 6- Prices and Food Security 7- Other Impacts of COVID-19 8- Income Loss 9- Coping/Shocks 10- Social Safety Nets 11- Fragility 12. Gender based Violence (for the fourth wave) 13. Vaccine (for the fourth wave)

Cleaning operations

At the end of data collection, the raw dataset was cleaned by the INSEED with the support of the WB team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Response rate

The minimum sample expected is 2,000 households covering Ndjamena, other urban and rural areas. Overall, the survey has been completed for 1,748 households that is about 87.5 % of the expected minimal sample size at the national level. This provide reliable estimates at national and area of residence level.

According to Cognitive Market Research, the global Residential Real Estate market size was USD 32651.6 million in 2024. It will expand at a compound annual growth rate (CAGR) of 5.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 13060.64 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 9795.48 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 7509.87 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.5% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 1632.58 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 653.03 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.2% from 2024 to 2031.
The single-family homes category is the fastest growing segment of the Residential Real Estate industry

Market Dynamics of Residential Real Estate Market

Key Drivers for Residential Real Estate Market

Increasing population drives housing demand to Boost Market Growth

Increasing population drives housing demand by creating a need for more residential spaces to accommodate growing numbers of people. As population rises, particularly in urban and suburban areas, demand for housing expands, fueling the residential real estate market. This is especially evident in countries experiencing rapid urbanization, where people move to cities seeking better job opportunities, education, and lifestyle options, further increasing housing needs. Additionally, population growth often correlates with the formation of new households, such as young families or individuals moving out on their own, intensifying the demand for housing units. In response, developers and investors are motivated to build more residential properties, ranging from single-family homes to multifamily units, contributing to market growth and driving real estate values upward. For instance, The Ashwin Sheth Group aims to broaden its residential and commercial offerings in the Mumbai Metropolitan Region (MMR) of India.

Rising incomes and economic stability to Drive Market Growth

Rising incomes and economic stability drive the residential real estate market by boosting consumers’ purchasing power and confidence in long-term investments like homeownership. As incomes increase, people can afford larger down payments, qualify for higher loan amounts, and manage mortgage payments more comfortably, making home buying a more viable option. Economic stability, characterized by low unemployment rates and steady GDP growth, reinforces this confidence, as individuals feel secure in their financial situations. With greater disposable income, many consumers seek to upgrade to larger homes, buy second properties, or invest in luxury real estate, further fueling demand. This economic backdrop attracts both local and foreign investors, leading to more housing developments, increased property values, and a flourishing residential real estate market.

Restraint Factor for the Residential Real Estate Market

High Property Prices will Limit Market Growth

High property prices restrain the residential real estate market by making homeownership unaffordable for a significant portion of the population. As prices rise, potential buyers, particularly first-time homeowners and low- to middle-income families, may find it challenging to secure adequate financing or meet the necessary down payment requirements. This affordability crisis limits the pool of qualified buyers, leading to slower sales and potential stagnation in market growth. Additionally, high property prices can prompt increased demand for rental properties, shifting focus away from home purchases. In markets where prices escalate rapidly, even affluent buyers may hesitate, fearing potential market corrections. Consequently, elevated property values can create a barrier to entry, ultimately restricting the overall health and vibrancy of the residential real estate market.

Impact of Covid-19 on the Residential Real Estate Market

The COVI...

Abstract

In the WAEMU countries, COVID-19 is expected to affect households in many ways. First, governments might reduce social transfers to households due to the decline in revenue arising from the potential COVID-19 economic recession. Second households deriving income from vulnerable sectors such as tourism and related activities will likely face risk of unemployment or loss of income. Third an increase in prices of imported goods can also negatively impact household welfare, as a direct consequence of the increase of these imported items or as indirect increase of prices of local good manufactured using imported inputs. In this context, there is a need to produce high frequency data to help policy makers in monitoring the channels by which the pandemic affects households and assessing its distributional impact. To do so, the sample of the longitudinal survey will be a sub-sample of the 2018/19 household survey in each country.

For Mali, the survey which is implemented by the National Statistical Office (INSTAT), is conducted using cell phone numbers of household members collected during the 2018/19 survey. This has the advantage of conducting cost effectively welfare analysis without collecting new consumption data. The 35 minutes questionnaires covered 10 modules (knowledge, behavior, access to services, food security, employment, safety nets, shocks, etc…). Data collection is planned for six months (six rounds) and the questionnaire is designed with core modules and rotating modules. Survey data collection started on May 11th, 2020 and households are expected to be called back every three to four weeks.

The main objectives of the survey are to: • Identify type of households directly or indirectly affected by the pandemic; • Identify the main channels by which the pandemic affects households; • Provide relevant data on income and socioeconomic indicators to assess the welfare impact of the pandemic.

Geographic coverage

National coverage including rural and urban

Analysis unit

• Households
• Individuals

Universe

The survey covered only households of the 2018/19 survey which excluded populations in prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Mali COVID-19 impact monitoring survey is a high frequency Computer Assisted Telephone Interview (CATI). The survey’s sample was drawn from the population of the 2018/19 - Enquête Harmonisée des Conditions de Vie des Ménages (EHCVM) -, which was conducted between October 2018 and July 2019. EHCVM is itself a sample survey representative at national, regional and by urban/rural. For the 7,000 HHs in EHCVM, phone numbers were collected for about 90 percent of them. Each HH has between 1-4 phone numbers. The sampling, which was similar across WAEMU, aimed at having representative estimates by three zones: the capital city of Bamako, other urban areas and the rural area. The minimum sample size was 1,908 for which 1,766 were successfully interviewed, that is about 98 % of the expected minimal sample size at the national level. Given that Mali is conducting a phone survey for the first time, a total of 2,270 were drawn (25% increase) to take into account unknown non-response rates or presence of invalid numbers in the database.

The total number of completed interviews in round one is 1,766. The total number of completed interviews in round two is 1,935. The total number of completed interviews in round three is 1,901. The total number of completed interviews in round four is 1,797. The total number of completed interviews in round five is 1,766.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

All the interview materials were translated in french for the NSO. The questionnaire was administered in local languages with about varying length (30-35 minutes) and covered the following topics: 1- Household Roster 2- Knowledge of COVID-19 3- Behaviour and Social Distancing 4- Access to Basic Services 5- Employment and Income 6- Prices and Food Security 7- Other Impacts of COVID-19 8- Income Loss 9- Coping/Shocks 10- Social Safety Nets 11- Fragility 12- Governance and socio-political crisis

Cleaning operations

At the end of data collection, the raw dateset was cleaned by the NSO. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.

Response rate

The minimum sample expected is 1,809 households (with 603 households per domain). This sample was therefore 99% covered for Bamako, about 100% for other urban areas and 91% for rural areas. Overall, the minimum sample is 98% covered. This level of coverage provides reliable data at national level and for each domain.

Round one response rate was 77.8%. Round two response rate was 85.2%. Round three response rate was 83.7%. Round four response rate was 79.2%. Round five response rate was 79.7%.

Key information about US Nominal Residential Property Price Index

• United States Nominal Residential Property Price Index was reported at 232.573 2010=100 in Sep 2024.
• This records an increase from the previous number of 230.393 2010=100 for Jun 2024.
• US Nominal Residential Property Price Index data is updated quarterly, averaging 61.010 2010=100 from Mar 1970 to Sep 2024, with 219 observations.
• The data reached an all-time high of 232.573 2010=100 in Sep 2024 and a record low of 10.610 2010=100 in Mar 1970.
• US Nominal Residential Property Price Index data remains active status in CEIC and is reported by Bank for International Settlements.
• The data is categorized under World Trend Plus’s Association: Property Sector – Table RK.BIS.RPPI: Selected Nominal Residential Property Price Index: 2010=100: Quarterly.

Abstract

In the West Africa Economic Monetary Union (WAEMU) countries, COVID-19 is expected to affect households in many ways. First, governments might reduce social transfers to households due to the decline in revenue arising from the potential COVID-19 economic recession. Second households deriving income from vulnerable sectors such as tourism and related activities will likely face risk of unemployment or loss of income. Third an increase in prices of imported goods can also negatively impact household welfare, as a direct consequence of the increase of these imported items or as indirect increase of prices of local good manufactured using imported inputs. In this context, there is a need to produce high frequency data to help policy makers in monitoring the channels by which the pandemic affects households and assessing its distributional impact. To do so, the sample of the longitudinal survey is a sub-sample of the Enquête Harmonisée sur les Conditions de Vie des Ménages (EHCVM), a harmonized household survey conducted in 2018/19 household survey in the WAEMU countries.

For Burkina Faso, the survey, which is implemented by the Institut National de la Statistique et la Demographie (INSD), is conducted using cell phone numbers of household members collected during the 2018/19 EHCVM survey. The extensive information collected in the EHCVM provides a rich set of background information for the COVID-19 High Frequency Phone Survey of households. This background information can be leveraged to assess the differential impacts of the pandemic in the country. Every month, the sampled households will be asked a set of core questions on the key channels through which individuals and households are expected to be affected by the COVID-19-related restrictions. Employment, access to basic services, non-labor sources of income are channels likely to be impacted. The core questionnaire is complemented by questions on selected topics that rotate each month. This provides data to the government and development partners in near real-time, supporting an evidence-based response to the crisis.

The main objectives of the survey are to: • Identify type of households directly or indirectly affected by the pandemic; • Identify the main channels by which the pandemic affects households; • Provide relevant data on income and socioeconomic indicators to assess the welfare impact of the pandemic.

Phase 1 was conducted on a monthly basis during the period of June 2020 and July 2021 for11 Rounds. Phase 2 (starting from Round 12) was conducted on a bi-monthly basis starting in April 2022. Phase 3 (starting from Round 18) will be conducted on a bi-monthly basis, starting in July 2023.

Geographic coverage

National coverage, including Ouagadougou, rural and other urban

Analysis unit

• Households
• Individuals

Universe

The survey covered a sub-sample of the households of the 2018/19 - Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM) survey which excluded populations in prisons, hospitals, military barracks, and school dormitories.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the HFS is a subsample of the 2018/19 Harmonized Living Conditions Household Survey (EHCVM). The EHCVM 2018/19 is built on a nationally and regionally representative sample of households in Burkina Faso. EHCVM 2018/19 interviewed 7,010 households in urban and rural areas. In the EHCVM interview, households were asked to provide phone numbers of the household head, or a household member, or a non-household member (e.g. friends or neighbors) so that they can be contacted for follow-up questions. At least one valid phone number was obtained for 6877 households. These households established the sampling frame for the HFS. To obtain representative strata at the national, capital (Ouagadougou), urban, and rural level, the target sample size for the HFS is 1,800 household (assuming a 50% non-response rate the minimum required sample is 1479). To account for non-response and attrition, 2500 households were called in baseline round of the HFS. 1,968 households were fully interviewed during the first round of interviews. Those 1,968 households constitute the final successful sample and will be contacted in subsequent rounds of the survey.

In addition to the 1,968 households successfully interviewed in Round 1, in Round 2, 242 additional households were sampled from the rural strata, in order to increase the representativeness in this domain. In Round 12, 231 additional households were selected from the rural stratum from the 2018/19 EHCVM sample. In Round 18, 858 additional households were selected from panel component of the 2021/22 EHCVM sample.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

BASELINE (Round 1): The Household Questionnaire provides information on demographics; knowledge regarding the spread of COVID-19; behavior and social distancing; access to basic services; employment.

Round 2: Household Roster; Access to Basic Services; Employment (with a focus on non-farm enterprises); Food Security; Shocks; Fragility, conflict, and violence.

Round 3: Household Roster; Knowledge regarding the spread of COVID-19; Behavior and social distancing; Access to Basic Services; Employment (with a focus on farm household activities); Food Security; Other revenues; Social protection.

Round 4: The following modules were administered in Round 4: Household Roster; Access to Basic Services; Credit; Employment and revenue (with a focus on livestock activities); Food Security; Other revenues; Shocks; Fragility, Conflict and Violence.

Round 5: Household Roster; Knowledge regarding the spread of COVID-19; Behavior and social distancing; Access to Basic Services; Education at individual level; Employment; Food Security; Other revenues; Social protection.

Round 6: Household Roster; Access to Basic Services; Education; Employment and revenues (with a focus on harvest activities and revenues from crop selling); Food Security; Other revenues; Shocks; Fragility, conflict and violence.

Round 7: Household Roster; Access to Basic Services; Education; Employment and revenues (with a focus on harvest activities and revenues from crop selling); Food Security; Other revenues; Shocks; Fragility, conflict and violence.

Round 8: Household Roster; Early Child Development; Access to Basic Services; Employment and revenues; Food Security; Other revenues; Shocks; Fragility, conflict and violence.

Round 9: Household Roster; Access to Basic Services; Employment and revenues; Food Security and Other revenues.

Round 10: Household Roster; Mental health; Knowledge regarding the spread of COVID-19; Behavior and social distancing; Covid-19 Testing and Vaccination; Access to Basic Services; Credit; ; Employment and revenue (with a focus on livestock activities); Food Security; Other revenues; Shocks; Concerns regarding the impact of COVID-19 on personal health and financial wealth of the household; Fragility, Conflict and Violence

Round 11: Household basic information; Access to Basic Services; Employment and revenue (with a focus on agricultural activities); Food Security; Other revenues; Concerns regarding the current situation; Social Safety Nets.

Round 12: Household Roster; Covid-19 Vaccination; Access to Health Care; and Employment and Income.

Round 13: Household Roster; Access to Health Care; Credit; Employment and Income; Food Security; Other Revenues; and Economic Sentiments.

Round 14: Household Roster; Access to Health Care; Vaccination; Concerns; Economic Sentiments.

Round 15: Household Roster; Displacement; Education; Access to basic foodstuffs; Employment and Income; Food Security; Other Revenues; Economic Sentiments; Items Price.

Round 16: Household Roster; Access to Health Care; Vaccination; Agriculture; Livestock; Shocks; Climate Change; Economic Sentiments; Items Price.

Round 17: Household Roster; Access to Basic Foodstuffs; Access to HealthCare – individual level; Credit; Employment and Income; Food Security; and Other Revenues.

Round 18: Household Roster; Access to Basic Goods and Services; Access to Health Care – individual level; Price of items; Employment and Income; Food Security; Food Consumption Score; Economic Sentiments; and Subjective Welfare.

Round 19: Household Roster; Access to Basic Goods and Services; Access to Health Care – individual level; Price of items; Employment and Income; Food Security; Shocks; Food Consumption Score; Economic Sentiments; and Subjective Welfare.

Round 20: Households Roster; Access to basic goods and services; Access to Health Care - Individual level; Price ofItems; Employment and Income; Food Security; Food Consumption Score; Economic Sentiments; SubjectiveWelfar.

Round 21: Household Roster; Access to Basic Goods and Services; Education; Price of items; Employment and Income; Agriculture; Livestock; Food Security; Food Consumption Score; Economic Sentiments; Subjective Welfare.

Round 22: Household Roster; Household Mobility; Access to Basic Goods and Services; Price of items; Access to Health Care - individual level; Employment and Income; Food Security; Food Consumption Score; Shocks; Economic Sentiments; and Subjective Welfare.

Round 23: Household Roster; Access to Basic Goods and Services; Price of items; Employment and Income; Food Security; Food Consumption Score; Economic Sentiments; and Subjective Welfare.

All the interview materials were translated in French for the INSD. The questionnaire was administered in local languages with about varying length (about 25 minutes).

Cleaning operations

At the end of data

Key information about Hong Kong SAR (China) Gold Production

• Hong Kong SAR (China) Real Residential Property Price Index was reported at 129.683 2010=100 in Jun 2025.
• This records an increase from the previous number of 129.024 2010=100 for Mar 2025.
• Hong Kong SAR (China) Real Residential Property Price Index data is updated quarterly, averaging 49.873 2010=100 from Dec 1979 to Jun 2025, with 183 observations.
• The data reached an all-time high of 198.526 2010=100 in Sep 2018 and a record low of 28.660 2010=100 in Sep 1984.
• Hong Kong SAR (China) Real Residential Property Price Index data remains active status in CEIC and is reported by Bank for International Settlements.
• The data is categorized under World Trend Plus’s Association: Property Sector – Table RK.BIS.RPPI: Selected Real Residential Property Price Index: 2010=100: Quarterly. [COVID-19-IMPACT]

Key information about Peru Gold Production

• Peru Real Residential Property Price Index was reported at 109.464 2010=100 in Jun 2025.
• This records an increase from the previous number of 108.677 2010=100 for Mar 2025.
• Peru Real Residential Property Price Index data is updated quarterly, averaging 120.669 2010=100 from Mar 1998 to Jun 2025, with 110 observations.
• The data reached an all-time high of 156.972 2010=100 in Jun 2014 and a record low of 57.159 2010=100 in Sep 2006.
• Peru Real Residential Property Price Index data remains active status in CEIC and is reported by Bank for International Settlements.
• The data is categorized under World Trend Plus’s Association: Property Sector – Table RK.BIS.RPPI: Selected Real Residential Property Price Index: 2010=100: Quarterly. [COVID-19-IMPACT]

Abstract

Like the rest of the world, Sudan has been experiencing the unprecedented social and economic impact of the COVID-19 pandemic. From restrictions on movement to school closures and lockdowns, the economic situation worsened, and commodity prices soared across the country. Results from the first six rounds of the High-Frequency Phone survey indicated that household welfare was negatively affected. The situation led to the loss of employment and income, decreased access to essential commodities and services, and food insecurity, particularly among the poor and vulnerable Sudanese. Moreover, the inability to access food and medicine degraded in July/August 2021 despite a slight amelioration in February/April 2021.

After COVID-19 in 2020, Sudan experienced situations that are more likely to compromise the recovery process. Political instability, unrest, and protests occurred before and after the military takeover in October 2021. Meanwhile, Sudan Central Bank devalued the currency, which may increase the already high commodities price. Besides, Sudan encountered historic flooding since the onset of the rainy season between May and June 2022. To monitor and assess the dynamics of the impacts of the country's economic and political situation (high inflation, social unrest, food shortages, asset loss, displacement, etc.) on households' welfare, another round of the Sudan High-Frequency Phone survey took place in June to August 2022.

Similar to the six previous rounds, the survey was conducted using mobile phones and covered all 18 states of Sudan. Round 7 sample is composed of 2816 Households from both urban and rural areas of Sudan. This sample allows us to draw statistical inferences about the Sudanese population at the national and rural/urban levels. The risk of nonresponse was a concern, so efforts were made to minimize this risk, including follow-up with respondents who failed to respond and keep the interviews short (15–20 minutes) to reduce respondent fatigue.

The questions are similar to the previous six rounds of the High-Frequency Phone survey but with added context. Households are asked about the key channels through which individuals and households are expected to be affected by the exchange rate distortions, country political instability, or flooding that occurred in May/June 2022, as well as how they have recovered from the COVID-19 pandemic impacts. Furthermore, questions cover a range of topics/themes including, but not limited to, health conditions, access to health facilities, access to other social services, availability of common food and non-food items (including medicines), nutrition and food security, employment/labor, income, assets, coping strategies, remittances, subjective welfare, climate/weather events, and the safety nets assistance.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Sampling procedure

The sampling methodology adopted for the implementation of this survey is probabilistic. Each of the units in the targeted population of the study must have a nonzero and known probability of selection. The sample was stratified by rural/urban for all 18 states. The distribution of the sub-sample between states and rural/urban is proportional to the size of the individuals owning mobile phones, i.e., not equal allocation. The selection of the individual phones (the households) is random, i.e., with equal probability, using a systematic sample procedure in the list (frame) of phones. This allows for extrapolating the results of the sample to the target population and estimating the precision of the results obtained. However, the implementation of this approach requires the availability of an adequate sampling frame containing all the units of the population without omissions or duplications.

In this survey, the sampling frame is provided by the phone lists. Considerable efforts were made to compile the frame using multiple lists of phone numbers collected during the implementation of various projects/surveys during the last few years at the household level across the country. This reduces the chances of having more than one phone number per household. Moreover, the interviewers double-checked during data collection that only one number was called for each selected surveyed household. Therefore, selecting individual phone numbers is the same as selecting households. It is worth noting that for West Kordofan and Central Darfur, the proportionality of rural/urban cannot be done according to the size of phones since there are no details for rural/urban. So, the size of the rural and urban populations (projection 2020) was used instead.

In Sudan, under the present federal system, the state is considered a semiautonomous entity mandated to take care of the affairs of the citizen, provide governance, and be responsible for planning, policy formulation, and implementation of the annual program. Consequently, the sample needed to cover all 18 states of the country. The sample is conceived to provide reliable estimates for the country (urban and rural) and to give statistically meaningful results at the national level.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

BASELINE (ROUND 1): One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Knowledge regarding the spread of COVID-19 - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, financial services) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets

ROUND 2: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Knowledge regarding the spread of COVID-19 - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, financial services, water, transportation, housing, internet, energy) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets ROUND 3: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, financial services) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets ROUND 4: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Demographics - Youth module screening - Behavior and social distancing - Access to basic goods and services (medicines, staple food, health, education, transportation, fuel) - Employment - Income loss - Food insecurity experience - Welfare - Shocks and Coping strategies - Social safety nets ROUND 5: One questionnaire, the Household Questionnaire, was administered to all households in the sample. Respondent were asked to think about each child in their household for the education question. The Household Questionnaire provides information on: - Demographics - Mental health of the respondent - Children education.

ROUND 6: One questionnaire, the Household Questionnaire, was administered to all households in the sample. One youth per household is interviewed in the youth section of the questionnaire. The Questionnaire provides information on: - Demographics - Access to basic goods (medicines, staple food) - Youth employment - Youth job search - Youth aspirations and expectations - Youth skills and mental health.

ROUND 7: One questionnaire, the Household Questionnaire, was administered to all households in the sample. The Household Questionnaire provides information on: - Geography - Access to basic goods and services (medicines, staple food, health, education, water, housing, electricity) - Employment - Income loss - Food insecurity experience - Welfare - Experience of Climate/Weather events - Shocks and Coping strategies

Response rate

BASELINE (ROUND 1): A total of 4,032 households were successfully interviewed during the first round of data collection (conducted during June 16–July 5, 2020). Selected households from each state include both rural and urban households, with the representation of each state in the final sample being proportional to the state’s population relative to the overall population. Households who refused to tell their location (mode of living and state) were dropped to minimize bias. The final sample size accounts 4,027 households.

ROUND 2: Interviewers attempted to contact and interview all 4,032 households that were successfully interviewed in the baseline of the Sudan HFS on COVID-19. 2,989 households were successfully interviewed in the second round. However, households who refused to tell their location (mode of living and state) were dropped to minimize bias. The final sample size accounts 2,987 households.

ROUND 3: Interviewers attempted to contact and interview all 4,032 households that were successfully interviewed in the Baseline of the Sudan HFS on COVID-19. 2,990 households were successfully interviewed in the third round. Households who refused to tell their location (mode of living and state) were dropped to minimize bias. The final sample size accounts 2,987 households.

ROUND 4: Interviewers attempted to contact and interview all 4,032 households that were successfully interviewed in the Baseline of the Sudan

Key information about Japan Gold Production

• Japan Real Residential Property Price Index was reported at 121.828 2010=100 in Jun 2025.
• This records a decrease from the previous number of 124.004 2010=100 for Mar 2025.
• Japan Real Residential Property Price Index data is updated quarterly, averaging 179.103 2010=100 from Mar 1955 to Jun 2025, with 282 observations.
• The data reached an all-time high of 189.276 2010=100 in Mar 1991 and a record low of 12.662 2010=100 in Mar 1955.
• Japan Real Residential Property Price Index data remains active status in CEIC and is reported by Bank for International Settlements.
• The data is categorized under World Trend Plus’s Association: Property Sector – Table RK.BIS.RPPI: Selected Real Residential Property Price Index: 2010=100: Quarterly. [COVID-19-IMPACT]