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.

La Rioja was the Spanish region where the pandemic impact on real estate prices was higher compared to the previous year, with a decrease of almost 16% in the last quarter of 2020. The only place in Spain where there was an increase in comparison with the pre-pandemic data was in the autonomous city of Melilla.

Analysis of Spanish Apartment Pricing and Size Post-COVID-19

Investigating the Impact of the Pandemic

By [source]

About this dataset

This dataset provides an in-depth insight into Spanish apartment prices, locations and sizes, offering a comprehensive view of the effects of the Covid-19 crisis in this market. By exploring the data you can gain valuable knowledge on how different variables such as number of rooms, bathrooms, square meters and photos influence pricing, as well as key details such as description and whether or not they are recommended by reviews. Furthermore, by comparing average prices per square meter regionally between different areas you can get a better understanding of individual apartment value changes over time. Whether you are looking for your dream home or simply seeking to understand current trends within this sector this dataset is here to provide all the information necessary for both people either starting or already familiar with this industry

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How to use the dataset

This dataset includes a comprehensive collection of Spanish apartments that are currently up for sale. It provides valuable insight into the effects of the Covid-19 pandemic on pricing and size. With this guide, you can take advantage of all the data to explore how different factors like housing surface area, number of rooms and bathrooms, location, number of photos associated with an apartment, type and recommendations affect price.

• First off, you should start by taking a look at summary column which summarizes in one or two lines what each apartment is about. You can quickly search some patterns which could give important information about the market current situation during COVID-19 crisis.

• Explore more in depth each individual apartment by looking at its description section for example if it refers to particular services available like swimming pool or gymnasiums . Consequently those extra features usually bumps up the prices higher since buyers are keen to have such luxury items included in their purchase even if it’s not so affordable sometimes..

• Start studying locationwise since it might gives hint as to what kind preof city we have eirther active market in terms equity investment , home stay rental business activities that suggest opportunities for considerable return on investment (ROI). Even further detailed analysis such as comparing net change over time energy efficient ratings electrical or fuel efficiency , transport facilities , educational level may be conducted when choosing between several apartments located close one another ..

• Consider multiple column ranging from price value provided (price/m2 )to size sqm surface area measure and count number of rooms & bathrooms . Doing so will help allot better understanding whether purchasing an unit is worth expenditure once overall costs per advantages estimated –as previously acknowledged apps features could increase prices significantly- don’t forget security aspect major item critical home choice making process affording protection against Intruders ..

• An interesting but tricky part is Num Photos how many were included –possibly indicates quality build high end projects appreciate additional gallery mentioning quite informative panorama around property itself - while recomendation customarily assumes certain guarantees warranties unique promise provided providing aside prospective buyer safety issues impose trustworthiness matters shared among other future residents …

• Finally type & region column should be taken into account reason enough different categories identifies houses versus flats diversely built outside suburban villas contained inside specially designed mansion areas built upon special requests .. Therefore usage those two complementary field help finding right desired environment accompaniments beach lounge bar attract nature lovers adjacent mountainside

Research Ideas

• Creating an interactive mapping tool that showcases the average prices per square meter of different cities or regions in Spain, enabling potential buyers to identify the most affordable areas for their desired budget and size.
• Developing a comparison algorithm that recommends the best options available depending on various criteria such as cost, rooms/bathrooms, recommended status, etc., helping users make informed decisions when browsing for apartments online.
• Constructing a model that predicts sale prices based on existing data trends and analyses of photos and recommendations associated wit...

Residential real estate transactions saw both a decline as well as an increase during the coronavirus pandemic in 2020, depending on the country. In Denmark, for example, property sales increased by over ***** percent year-on-year in the second quarter of 2020. This was in stark contrast to the United Kingdom, where provisional and non-seasonal data suggested the country saw one of its largest drops in housing transactions since 2009. Some countries, on the other hand, already witnessed a decrease in their transactions before COVID-19 hit Europe. The housing trade inFrance, for example, suffered a large decrease in the first quarter of 2020, right before quarantine measures were enforced. Data for Germany, on the other hand, suggested that its housing market was still growing before the lockdown. Whether this was still the case in 2020 remains to be seen.

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.

This file contains COVID-19 death counts and rates by month and year of death, jurisdiction of residence (U.S., HHS Region) and demographic characteristics (sex, age, race and Hispanic origin, and age/race and Hispanic origin). United States death counts and rates include the 50 states, plus the District of Columbia.Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file.Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death.Death counts should not be compared across jurisdictions. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly.The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington.Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf).Rate are based on deaths occurring in the specified week and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly) rate prevailed for a full year.Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

This dataset contains records of publicly reported data on COVID-19 testing in Ontario long-term care homes. It was collected between April 24, 2020 and March 30, 2023.

Summary data is aggregated to the provincial level. Reports fewer than 5 are indicated with <5 to maintain the privacy of individuals.

Data includes:

• Long-term care home COVID-19 summary data
• Long-term care homes with an active COVID-19 outbreak
• Long-term care homes no longer in a COVID-19 outbreak
• Long-term care home COVID-19 summary data by Public Health Unit (PHU)
• Long-term care home COVID-19 staff vaccination rates

An outbreak is defined as two or more lab-confirmed COVID-19 cases in residents, staff or other visitors in a home, with an epidemiological link, within a 14-day period, where at least one case could have reasonably acquired their infection in the long-term care home. Prior to April 7, 2021, the definition required one or more lab-confirmed COVID-19 cases in a resident or staff in the long-term care home.

Notes

February 21 to March 29, 2023: Data is only available for regular business days (for example, Monday through Friday, except statutory holidays)

March 12 – 13, 2022: Due to technical difficulties, data is not available.

September 8, 2022: The data dated September 6, 2022 represents data collected during the period of September 3, 4 and 5, 2022.

October 6, 2022: The data dated October 5, 2022 represents data collected during the period of October 1, 2, 3 and 4, 2022.

October 13, 2022: Due to technical difficulties, data for the date of October 9 is not available.

October 20, 2022: Due to technical difficulties, data for the dates of October 15, 16 is not available.

November 24, 2022: Due to technical difficulties, data is not available.

The data in this paper are divided into two main sections, which are data on the housing market and data on epidemic case information. The time span of the data sample is from December 1, 2019 to April 26, 2020.The original data of the housing market aspect such as the second-hand house price index in Wuhan and the surrounding provincial capital cities were obtained from Chain Home and Baidu Maps. Among them, there are 53,541 valid records of residential transactions in second-hand neighborhoods, with a final total of 347,720 after data cleaning (5582 in Wuhan; 5710 in Hefei; 7988 in Xi'an; 2066 in Changsha; 5910 in Zhengzhou; and 7464 in Chongqing).

Commercial banks are expected to help the federal government deflate Canada’s housing bubble after the COVID-19 (coronavirus) pandemic.

One of the expected impacts of the coronavirus (COVID-19) that brought the world to a halt in the first quarter of 2020 is the disruption to normal business activities and supply chains. The effect spreads through various industries and with the assumption of a ********* delay in construction activities, the forecast suggests property completions planned for 2020 in cities in the United Kingdom (UK) could decrease by more than *********, leading up to more completions in 2021 than originally planned. For more information on the Statista coverage of the coronavirus in the UK, see our report.

In a follow-up to his September article, “Commercial Banks Aid Canada’s Housing Market,” Lead Analyst Samuel Kanda explores deeper issues with Canada’s real estate market.

Context

Dataset aims to facilitate a state by state comparison of potential risk factors that may heighten Covid 19 transmission rates or deaths. It includes state by state estimates of: covid 19 positives/deaths, flu/pneumonia deaths, major city population densities, available hospital resources, high risk health condition prevalance, population over 60, means of work transportation rates, housing characteristics (ie number of large apartment complexes/seniors living alone), and industry information.

Content

The Data Includes:

1) Covid 19 Outcome Stats:

Covid_Death : Covid Deaths by State

Covid_Positive : Covid Positive Tests by State

2) US Major City Population Density by State: CBSA_Major_City_max_weighted_density

3) KFF Estimates of Total Hospital Beds by State:

Kaiser_Total_Hospital_Beds

4) 2018 Season Flu and Pneumonia Death Stats:

FLUVIEW_TOTAL_PNEUMONIA_DEATHS_Season_2018

FLUVIEW_TOTAL_INFLUENZA_DEATHS_Season_2018

5)US Total Rates of Flu Hospitalization by Underlying Condition:

Fluview_US_FLU_Hospitalization_Rate_....

6) State by State BRFSS Prevalance Rates of Conditions Associated with Higher Flu Hospitalization Rates

BRFSS_Diabetes_Prevalance BRFSS_Asthma_Prevalance BRFSS_COPD_Prevalance
BRFSS_Obesity BMI Prevalance BRFSS_Other_Cancer_Prevalance BRFSS_Kidney_Disease_Prevalance BRFSS_Obesity BMI Prevalance BRFSS_2017_High_Cholestoral_Prevalance BRFSS_2017_High_Blood_Pressure_Prevalance Census_Population_Over_60

7)State by state breakdown of Means of Work Transpotation:

COMMUTE_Census_Worker_Public_Transportation_Rate

8) State by state breakdown of Housing Characteristics

9) State by State breakdown of Industry Information

Acknowledgements

Links to data sources:

https://worldpopulationreview.com/states/

https://covidtracking.com/data/

https://gis.cdc.gov/GRASP/Fluview/FluHospRates.html https://www.kff.org/health-costs/issue-brief/state-data-and-policy-actions-to-address-coronavirus/#stateleveldata

https://data.census.gov/cedsci/table?q=United%20States&tid=ACSDP1Y2018.DP05&hidePreview=true&vintage=2018&layer=VT_2018_040_00_PY_D1&cid=S0103_C01_001E

Census Tables: ACSST1Y2018.S1811 ACSST1Y2018.S0102 ACSST1Y2018.S2403 ACSST1Y2018.S2501 ACSST1Y2018.S2504

https://www.census.gov/library/visualizations/2012/dec/c2010sr-01-density.html

https://gis.cdc.gov/grasp/fluview/mortality.html

Inspiration

I hope to show the existence of correlations that warrant a deeper county by county analysis to identify areas of increased risk requiring increased resource allocation or increased attention to preventative measures.

We’ve examined how pandemic-related to disruption to office working, retail operations and the hospitality sector has affected the real estate market.

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 bipartisan CARES Act; and the Paycheck Protection Program and Health Care Enhancement Act (PPPHCEA); and the Coronavirus Response and Relief Supplemental Appropriations (CRRSA) Act provided $178 billion in relief funds to hospitals and other healthcare providers on the front lines of the coronavirus response. The Department of Health and Human Services through the Health Resources and Services Administration is allocating $2 billion in incentive payments to nursing home facilities that reduce both COVID-19 infection rates relative to their county and mortality rates against a national benchmark.

This file contains COVID-19 death counts and rates by month and year of death, jurisdiction of residence (U.S., HHS Region) and demographic characteristics (sex, age, race and Hispanic origin, and age/race and Hispanic origin). United States death counts and rates include the 50 states, plus the District of Columbia.

Deaths with confirmed or presumed COVID-19, coded to ICD–10 code U07.1. Number of deaths reported in this file are the total number of COVID-19 deaths received and coded as of the date of analysis and may not represent all deaths that occurred in that period. Counts of deaths occurring before or after the reporting period are not included in the file.

Data during recent periods are incomplete because of the lag in time between when the death occurred and when the death certificate is completed, submitted to NCHS and processed for reporting purposes. This delay can range from 1 week to 8 weeks or more, depending on the jurisdiction and cause of death.

Death counts should not be compared across jurisdictions. Data timeliness varies by state. Some states report deaths on a daily basis, while other states report deaths weekly or monthly.

The ten (10) United States Department of Health and Human Services (HHS) regions include the following jurisdictions. Region 1: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont; Region 2: New Jersey, New York; Region 3: Delaware, District of Columbia, Maryland, Pennsylvania, Virginia, West Virginia; Region 4: Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee; Region 5: Illinois, Indiana, Michigan, Minnesota, Ohio, Wisconsin; Region 6: Arkansas, Louisiana, New Mexico, Oklahoma, Texas; Region 7: Iowa, Kansas, Missouri, Nebraska; Region 8: Colorado, Montana, North Dakota, South Dakota, Utah, Wyoming; Region 9: Arizona, California, Hawaii, Nevada; Region 10: Alaska, Idaho, Oregon, Washington.

Rates were calculated using the population estimates for 2021, which are estimated as of July 1, 2021 based on the Blended Base produced by the US Census Bureau in lieu of the April 1, 2020 decennial population count. The Blended Base consists of the blend of Vintage 2020 postcensal population estimates, 2020 Demographic Analysis Estimates, and 2020 Census PL 94-171 Redistricting File (see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/2020-2021/methods-statement-v2021.pdf).

Rate are based on deaths occurring in the specified week and are age-adjusted to the 2000 standard population using the direct method (see https://www.cdc.gov/nchs/data/nvsr/nvsr70/nvsr70-08-508.pdf). These rates differ from annual age-adjusted rates, typically presented in NCHS publications based on a full year of data and annualized weekly age-adjusted rates which have been adjusted to allow comparison with annual rates. Annualization rates presents deaths per year per 100,000 population that would be expected in a year if the observed period specific (weekly) rate prevailed for a full year.

Sub-national death counts between 1-9 are suppressed in accordance with NCHS data confidentiality standards. Rates based on death counts less than 20 are suppressed in accordance with NCHS standards of reliability as specified in NCHS Data Presentation Standards for Proportions (available from: https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf.).

This map shows households that spend 30 percent or more of their income on housing, a threshold widely used by many affordable housing advocates and official government sources including Housing and Urban Development. Census asks about income and housing costs to understand whether housing is affordable in local communities. When housing is not sufficient or not affordable, income data helps communities: Enroll eligible households in programs designed to assist them.Qualify for grants from the Community Development Block Grant (CDBG), HOME Investment Partnership Program, Emergency Solutions Grants (ESG), Housing Opportunities for Persons with AIDS (HOPWA), and other programs.When rental housing is not affordable, the Department of Housing and Urban Development (HUD) uses rent data to determine the amount of tenant subsidies in housing assistance programs.Map opens in Atlanta. Use the bookmarks or search bar to view other cities. Data is symbolized to show the relationship between burdensome housing costs for owner households with a mortgage and renter households:This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

Accelerated Russian ruble devaluation, caused by the coronavirus (COVID-19) expansion and sinking oil prices, generated an increasingly popular fear of a possible mortgage rate growth in the country. Consequently, the residential real estate demand growth led to increased prices in the secondary market. The highest increase was marked in Krasnoyarsk at two percent, while Moscow made it in the top three with a 1.5 percent increment on average.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Provisional COVID-19 death counts, rates, and percent of total deaths, by jurisdiction of residence

  Description

This file contains COVID-19 death counts, death rates, and percent of total deaths by jurisdiction of residence. The data is grouped by different time periods including 3-month period, weekly, and total (cumulative since January 1, 2020). United States death counts and rates include the 50 states, plus the District of Columbia and New York City. New York state… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/provisional-covid-19-death-counts-rates-and-percen.

The website plans.fr, which lists more than 1,000 house plans online, has listed price increases in construction since 2020. These increases are due to several factors: — Re 2020 replacing the ROE 2012 — COVID with shortages of materials and craftsmen — High inflation of raw materials (+ 60 % on steel,...) The rises in the price of new housing since 2020 are delusional and have never been seen in recent history.