The coronavirus (COVID-19) pandemic and the lockdowns during this period had an impact on the attitudes of prospective home buyers in the United Kingdom (UK) in different ways. On one hand, there was a large percentage of prospective home buyers of ** percent that said COVID-19 motivated them to buy homes between ********** and **********.
However, concerns of financial security and the home buying process being harder were also registered at high rates. ** percent of prospective home buyers were worried about their financial security, ** percent reported that lockdowns made it harder to buy homes. This shows that while the motivation and interest in buying homes was large, but the conditions of lockdown and the financial impact of the coronavirus (COVID-19) pandemic were a big barrier towards making purchases.

In a March 2020 survey, the development related to COVID-19 which had most affect home buying or selling plans in the United States was the drop in mortgage rates, which was cited by **** percent of the respondents. Fear of recession and stock market volatility followed behind at ** and ** percent, respectively. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

In a September 2020 survey among adults in the United States, many respondents said that the COVID-19 pandemic did not change their interest in buying a home. Millennials were most likely to have changed their homeownership plans: ** percent of Millennials were more interested in buying a home due to the COVID-19 pandemic compared with **** percent of Baby Boomers.In the United States, the 2020 homeownership rate reached **** percent.

In a March 2020 survey, only ***** percent of U.S. realtors said that COVID-19 had significantly decreased home buyer interest in their market. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

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...

In a September 2020 survey among adults in the United States, over half of respondents said that their interest in buying a home had not changed due to the COVID-19 pandemic (** percent). However, Hispanic respondents were more likely to have changed their plans (** percent) compared to white respondents (** percent). In the United States, the 2020 homeownership rate reached **** percent.

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.

In this study, we conducted conjoint analysis utilizing an internet-based survey by presenting consumers (n=583) with 12 different hypothetical at-home COVID test concepts that varied on five attributes (price, accuracy, time, where-to-buy, and method).

Home ownership persists as the primary way that families build wealth. Housing researchers and advocates often discuss the racial home ownership gap, particularly for Black and Hispanic households (Urban Institute, Pew Hispanic Center). Historical policies such as redlining, steering, and municipal underbounding have effects that stay with us today.This map shows the overall home ownership rate and the home ownership rate by race/ethnicity of householder in a chart in the pop-up. Map is multi-scale showing data for state, county, and tract.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.

Neighbourhood food environment exposures examined in models for take-home and out-of-home purchasing.

In a ************** survey among adults in the United States, around ** percent were more interested in buying a home after the outbreak of the coronavirus (COVID-19) pandemic. For ** percent of respondents, however, their interest had not changed due to the arrival of the pandemic. Interestingly enough, there were less women whose interest had not changed (** percent) than that there were men (** percent).In the United States, the 2020 homeownership rate reached **** percent.

Home Valuation Analytics Market Outlook

As per our latest research, the global home valuation analytics market size reached USD 4.8 billion in 2024 and is expected to grow at a CAGR of 11.2% during the forecast period, reaching USD 12.8 billion by 2033. This robust growth is primarily driven by the increasing adoption of data-driven decision-making in the real estate sector and the rising demand for accurate and real-time property valuation solutions. The market is experiencing a significant transformation as stakeholders across the real estate value chain seek analytical tools to enhance transparency, reduce risks, and optimize transaction efficiency.

One of the primary growth factors for the home valuation analytics market is the rapid digitalization of the real estate industry. As property transactions become increasingly complex and competitive, both buyers and sellers are turning to advanced analytics to gain deeper insights into market trends, property values, and risk factors. The proliferation of big data, artificial intelligence, and machine learning technologies has enabled the development of sophisticated valuation models that can process vast amounts of data from multiple sources, including historical sales, market trends, neighborhood analytics, and economic indicators. These innovations significantly enhance the accuracy and reliability of home valuations, making them indispensable for real estate agencies, financial institutions, and government bodies. Additionally, the integration of home valuation analytics with online property portals and mortgage platforms is streamlining the home buying and selling process, further propelling market growth.

Another key driver is the growing need for regulatory compliance and risk mitigation in property financing and investment. Financial institutions, such as banks and mortgage lenders, are under increasing pressure to adhere to stringent regulatory frameworks governing property appraisals and loan approvals. Home valuation analytics solutions offer automated, standardized, and auditable valuation processes, reducing the risk of human error and fraud. These solutions also facilitate portfolio management and risk assessment, allowing institutions to make more informed lending decisions and minimize exposure to market volatility. The trend towards remote property assessments, accelerated by the COVID-19 pandemic, has further underscored the value of digital valuation tools in maintaining business continuity and operational efficiency.

Moreover, the rise of smart cities and urbanization is fueling demand for home valuation analytics in both developed and emerging markets. Governments and urban planners are leveraging these tools to assess property values, plan infrastructure investments, and implement property tax policies. The ability to analyze real-time data on property usage, neighborhood developments, and demographic changes is proving invaluable for policy formulation and resource allocation. As urban populations continue to grow, the need for scalable, automated, and transparent valuation solutions will only intensify, creating new opportunities for market expansion across residential, commercial, and industrial segments.

Regionally, North America currently leads the home valuation analytics market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, benefits from a mature real estate market, high digital adoption rates, and a strong presence of leading analytics providers. Europe is witnessing steady growth driven by regulatory harmonization and increased cross-border real estate investments. Meanwhile, Asia Pacific is emerging as a high-growth region, supported by rapid urbanization, rising property investments, and government initiatives to modernize land administration systems. Latin America and the Middle East & Africa are also showing promise, albeit from a lower base, as digital infrastructure improves and real estate markets mature.

Component Analysis

The home valuation analytics market by component is segmented into software and services, each playing a pivotal role in the ecosystem. The software segment, which includes valuation platforms, data visualization tools, and AI-driven modeling engines, holds the largest share of the market. These solutions enable users to automate complex valuation processes, generate real-time insights, and integrate data from diverse sources

United States CCI: Plans to Buy Within 6 Mos: sa: Home: Lived In data was reported at 2.100 % in Apr 2025. This records a decrease from the previous number of 2.300 % for Mar 2025. United States CCI: Plans to Buy Within 6 Mos: sa: Home: Lived In data is updated monthly, averaging 1.700 % from Feb 1967 (Median) to Apr 2025, with 637 observations. The data reached an all-time high of 4.700 % in Feb 2021 and a record low of 0.600 % in Feb 1975. United States CCI: Plans to Buy Within 6 Mos: sa: Home: Lived In data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H054: Consumer Confidence Index: Buying Plans & Intended Vacations. [COVID-19-IMPACT]

Hardware and home improvement stores’ revenue is forecast to rise at a compound annual rate of 1.4% over the five years through 2024 to reach €155.8 billion. Private spending on home renovation and maintenance, construction activity, environmental awareness and the number of households each play their part in determining sales. The EU and the UK enjoyed a housing market boom prior to 2023, when soaring mortgage rates deterred many from buying a new house. While demand for outfitting new houses is down, more Europeans are turning to repair, maintenance and renovation work on their existing properties, helping to raise sales of hardware and home improvement products. This trend accelerated during the COVID-19 pandemic, as people confined to their homes looked to refresh their surroundings and found themselves with more time to dedicate to DIY projects. Hardware and home improvement stores were deemed by many governments as essential businesses, allowing them to remain open during the lockdowns. In 2024, revenue growth is expected to be constrained by the cost-of-living crisis. Shoppers are increasingly price-sensitive and many are thinking twice before spending in response to intense inflationary pressures, cutting sales for many hardware and home improvement stores. Price inflation is expected to outweigh falling sales volumes, leading to revenue growth of 1% in 2024. Over the five years through 2029, hardware and home improvement stores’ revenue is slated to climb at a compound annual rate of 1.5% to reach €168 billion. Ever-growing levels of environmental awareness among Europeans will drive strong demand for sustainably sourced and energy-efficient products, like reclaimed wood and lithium-ion battery-powered hand tools. Competition from online-only retailers will continue to heat up, forcing hardware and home improvement stores to expand their in-store offerings to attract customers – augmented reality stations where shoppers can visualise their new products in their homes are one way retailers can try to do this.

AimsTo explore changes in alcohol purchase and consumption during the first few months of the Covid-19 pandemic, and assess associations between increased alcohol purchase/use and socioeconomic and environmental factors.DesignSecondary data from a cross-sectional online survey conducted from 17 April to 25 June 2020.SettingThirty-eight countries from all continents of the world.ParticipantsA total of 37,206 adults (mean age:36.7, SD:14.8, 77% female) reporting alcohol purchasing and drinking habit before and during the pandemic.MeasurementsChanges in alcohol stock-up and frequency of alcohol use during the pandemic and increased alcohol stock-up and use were stratified by gender, age, education, household structure, working status, income loss, psychological distress, and country based on alcohol consumption per capita. The associations between increased alcohol stock-up/use and living with children, working from home, income loss and distress were examined using multivariate logistic regression, controlling for demographic factors.FindingsThe majority of respondents reported no change in their alcohol purchasing and drinking habits during the early pandemic period. Increased drinking was reported by 20.2% of respondents, while 17.6% reported decreased alcohol use. More than half (53.3%) of respondents experienced psychological distress, with one in five (20.7%) having severe distress. Female gender, being aged under 50, higher educational attainment, living with children, working from home, and psychological distress were all independently associated with increased alcohol drinking during lockdown. Limitations of the study were the non-representative sample, the data collection early in the pandemic, and the non-standard measurement of alcohol consumption.ConclusionIncreased psychological distress among people during the early pandemic period, resulted in increased alcohol consumption, especially among women with children working from home during lockdown.

Homebuilders have endured considerable volatility. Immigration into Canada has led to unprecedented population growth, exacerbating an existing housing crisis. New housing starts haven't kept up with the population growth, making homebuilders more critical than ever to meet housing needs. Home shortages and changes in buying behaviour supported homebuilders during the COVID-19 pandemic early in the recent five year period. Still, the pandemic's disruption to global supply chains didn't spare contractors, with equipment and material costs reaching unprecedented highs. Interest rate hikes in 2022 and 2023 slowed new relevant housing construction, spurring apartment building construction as consumers increasingly sought out renting. Also, the First Time Homebuyer Incentive, which seemed like a potential boon to homebuilders, largely lacked success and was repealed. Industry-wide revenue has been declining at a CAGR of 0.1% over the past five years – totaling an estimated $30.3 billion in 2025 – when revenue will climb an estimated 1.6%. The Bank of Canada raising rates in 2022 and 2023 led to a massive slowdown for homebuilders, even as the Canadian government tried to ramp up the number of housing units constructed. Higher interest rates make developers cautious about new projects, drive up construction costs for builders and push potential homebuyers out of the market. The Bank of Canada has decreased rates in 2024 and 2025 for the first time since 2022, potentially providing a boost to homebuilders. Labour shortages for home builders have hiked wage costs and hindered profit. Homebuilders will enjoy solid growth over the next five years. Interest rate cuts and low housing supply will spur downstream homebuying activity. Still, labour shortages and material costs will continue to strain contractors' capacity. These challenges will impact the broader construction sector, incentivizing federal and provincial governments to fund workforce development and tech adoption programs. Government initiatives like the First-Time Home Buyers’ Tax Credit, the First Home Savings Account (FHSA) and the Home Buyers Plan (HBP) will support homebuilding. Homebuilders' revenue is forecast to expand at a CAGR of 1.7% to $33.0 billion through the end of 2030.

The COVID-19 pandemic has impacted the daily lives of individuals across the world as multiple variants continue introducing new complexities. In December 2021, which is when we conducted our study, pressure to resume the normalcy of daily life was mounting as a new variant, Omicron, was rapidly spreading. A variety of at-home tests detecting SARS-CoV-2, known to the general public as “COVID tests,” were available for consumers to purchase. In this study, we conducted conjoint analysis utilizing an internet-based survey by presenting consumers (n = 583) with 12 different hypothetical at-home COVID test concepts that varied on five attributes (price, accuracy, time, where-to-buy, and method). Price was identified as the most important attribute, because participants were very price sensitive. Quick turnaround time and high accuracy were also identified as important. Additionally, although 64% of respondents were willing to take an at-home COVID test, only 22% reported they had previously taken the test. On December 21, 2021, President Biden announced the U.S. government would purchase 500 million at-home rapid tests and distribute them for free to Americans. Given the importance of price to participants, this policy of providing free at-home COVID tests was directionally appropriate.

Countries around the world have resorted to issuing stay-at-home orders to slow viral transmission since the COVID-19 pandemic. During the lockdown, access to public park plays a central role in the public health of surrounding communities. However, we know little about how such an unprecedented policy may exacerbate the preexisting unequal access to green space (i.e., green space justice). To address this research void, we used difference-in-difference models to examine socioeconomic disparities, urban-rural disparities, and mobility disparities in terms of public park access in the United States. Our national analysis using the weekly mobile phone movement data robustly suggests the following three key findings during COVID-19: (1) The elderly, non-college-educated people, poor people, and blacks are less likely to visit public parks frequently, while unemployed people appear to be the opposite. (2) Compared to rural areas, populations in urban neighborhoods appear to visit public parks more frequently and they generally go to larger parks to minimize the risk of infection. (3) Populations in neighborhoods with higher private vehicle ownership or those with a higher density of transit stops would more frequently visit and travel a longer distance to public parks during the stay-at-home order. Our results imply that conventional inequality in green space access may still exist and even become worse during COVID-19, which could negatively impact people's health during isolation. We suggest that special attention should be paid to park-poor neighborhoods during the pandemic and in the post-pandemic recovery phase.

Description of area characteristics and outcome variables over time, take-home reporters (n = 1,221).

COVID-19 Stats & Trends

Context

This dataset seeks to provide insights into what has changed due to policies aimed at combating COVID-19 and evaluate the changes in community activities and its relation to reduced confirmed cases of COVID-19. The reports chart movement trends, compared to an expected baseline, over time (from 2020/02/15 to 2020/02/05) by geography (across 133 countries), as well as some other stats about the country that might help explain the evolution of the disease.

Content

1. Grocery & Pharmacy: Mobility trends for places like grocery markets, food warehouses, farmers' markets, specialty food shops, drug stores, and pharmacies.
2. Parks: Mobility trends for places like national parks, public beaches, marinas, dog parks, plazas, and public gardens.
3. Residential: Mobility trends for places of residence.
4. Retail & Recreation: Mobility trends for places like restaurants, cafes, shopping centers, theme parks, museums, libraries, and movie theaters.
5. Transit stations: Mobility trends for places like public transport hubs such as subway, bus, and train stations.
6. Workplaces: Mobility trends for places of work.
7. Total Cases: Total number of people infected with the SARS-CoV-2.
8. Fatalities: Total number of deaths caused by CoV-19.
9. Government Response Stringency Index: Additive score of nine indicators of government response to CoV-19: School closures, workplace closures, cancellation of public events, public information campaigns, stay at home policies, restrictions on internal movement, international travel controls, testing policy, and contact tracing.
10. COVID-19 Testing: Total number of tests performed.
11. Total Vaccinations: Total number of shots given.
12. Total People Vaccinated: Total number of people given a shot.
13. Total People Fully Vaccinated: Total number of people fully vaccinated (might require two shots of some vaccines).
14. Population: Total number of inhabitants.
15. Population Density per km2: Number of human inhabitants per square kilometer.
16. Health System Index: Overall performance of the health system.
17. Human Development Index (HDI): Summary index based on life expectancy at birth, expected years of schooling for children and mean years of schooling for adults, and GNI per capita.
18. GDP (PPP) per capita: Gross Domestic Product (GDP) per capita based on Purchasing Power Parity (PPP), taking into account the relative cost of local goods, services and inflation rates of the country, rather than using international market exchange rates, which may distort the real differences in per capita income.
19. Elderly Population (percentage): Percentage of the population above the age of 65 years old.

References & Acknowledgements

Bing COVID-19 data. Available at: https://github.com/microsoft/Bing-COVID-19-Data COVID-19 Community Mobility Report. Available at: https://www.google.com/covid19/mobility/ COVID-19: Government Response Stringency Index. Available at: https://ourworldindata.org/grapher/covid-stringency-index Coronavirus (COVID-19) Testing. Available at: https://github.com/owid/covid-19-data/blob/master/public/data/testing/covid-testing-all-observations.csv Coronavirus (COVID-19) Vaccination. Available at: https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv List of countries and dependencies by population. Available at: https://www.kaggle.com/tanuprabhu/population-by-country-2020 List of countries and dependencies by population density. Available at: https://www.kaggle.com/tanuprabhu/population-by-country-2020 List of countries by Human Development Index. Available at: http://hdr.undp.org/en/data Measuring Overall Health System Performance. Available at: https://www.who.int/healthinfo/paper30.pdf?ua=1 List of countries by GDP (PPP) per capita. Available at: https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD List of countries by age structure (65+). Available at: https://data.worldbank.org/indicator/SP.POP.65UP.TO.ZS

Authors

• Diogo Silva, up201706892@fe.up.pt