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 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.

The impact of the coronavirus epidemic (COVID-19) spread to all industries, and the real estate sector could not escape this health crisis. As a result, almost half of the French people intending to sell a property in ********** experienced delays in the process and ** percent had to postpone their sale project. For more information on the coronavirus pandemic (COVID-19), please consult our page: Statistics & Facts on the coronavirus (COVID-19).

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.

Number of Offers for Sale: Primary Housing: Smolensk Region data was reported at 1,208.000 Unit in Mar 2025. This records a decrease from the previous number of 1,253.000 Unit for Feb 2025. Number of Offers for Sale: Primary Housing: Smolensk Region data is updated monthly, averaging 1,720.000 Unit from Jan 2019 (Median) to Mar 2025, with 75 observations. The data reached an all-time high of 2,911.000 Unit in Jun 2021 and a record low of 489.000 Unit in Feb 2023. Number of Offers for Sale: Primary Housing: Smolensk Region data remains active status in CEIC and is reported by Sberbank. The data is categorized under Russia Premium Database’s Business and Economic Survey – Table RU.SJ003: Number of Offers for Sale: Primary Housing. [COVID-19-IMPACT]

The average sales price of new homes in the United States experienced a slight decrease in 2024, dropping to 512,2000 U.S. dollars from the peak of 521,500 U.S. dollars in 2022. This decline came after years of substantial price increases, with the average price surpassing 400,000 U.S. dollars for the first time in 2021. The recent cooling in the housing market reflects broader economic trends and changing consumer sentiment towards homeownership. Factors influencing home prices and affordability The rapid rise in home prices over the past few years has been driven by several factors, including historically low mortgage rates and increased demand during the COVID-19 pandemic. However, the market has since slowed down, with the number of home sales declining by over two million between 2021 and 2023. This decline can be attributed to rising mortgage rates and decreased affordability. The Housing Affordability Index hit a record low of 98.1 in 2023, indicating that the median-income family could no longer afford a median-priced home. Future outlook for the housing market Despite the recent cooling, experts forecast a potential recovery in the coming years. The Freddie Mac House Price Index showed a growth of 6.5 percent in 2023, which is still above the long-term average of 4.4 percent since 1990. However, homebuyer sentiment remains low across all age groups, with people aged 45 to 64 expressing the most pessimistic outlook. The median sales price of existing homes is expected to increase slightly until 2025, suggesting that affordability challenges may persist in the near future.

All data compiled into this dataset is available under public domain. This set is designed to provide some insight into sales trends across the state of Connecticut as well as the individual towns within. It is also specifically structured to highlight changes in trends due to the COVID-19 pandemic.

Variables

list_year: grand list year of the property (grand list years run from Oct. 1 through Sept. 30). town: name of the town that the property was sold in. population: population of the town that the property was sold in. residential_type: single family, two family, three family, four family, or condo. month: the month the sale was recorded. year: the year the sale was recorded. in_pandemic: boolean value indicating whether the selling date was after March 11, 2020. assessed_value: tax assessed value of the property at the time of the sale. sale_amount: final closing sale amount of the property. price_index: the Consumer Price Index (CPI) for that month/year. Used to normalize dollar values. norm_assessed_value: CPI-normalized assessed value (assessed_value / price_index * 100). norm_sale_amount: CPI-normalized sale amount (sale_amount / price_index * 100). norm_sales_ratio: CPI-normalized assessment to sale ratio (norm_assessed_value / norm_sale_amount). latitude: latitude for the property's town. longitude: longitude for the property's town.

Note: the original dataset also contained the street address and exact sale date for each record. Those variables were removed as they were not relevant to the analysis being conducted and to afford the individuals associated with each sale a stonger degree of personal privacy. Records from October 2000 to October 2010 from the original dataset were omitted due to timeliness issues. Records of non-residential types were omitted as they lacked enough historic records to be of consequence to the analysis.

Data sources

Real estate records: https://data.ct.gov/Housing-and-Development/Real-Estate-Sales-2001-2020-GL/5mzw-sjtu Township shapes: https://data.ct.gov/Government/Town-Boundary-Index-Map/evyv-fqzg Consumer price index: https://www.bls.gov/regions/new-england/data/consumerpriceindex_us_table.htm Town populations: https://www.connecticut-demographics.com/cities_by_population

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...

BR: Number of Residential Home Sales: Sao Paulo data was reported at 10,401.000 Unit in Feb 2025. This records an increase from the previous number of 6,755.000 Unit for Jan 2025. BR: Number of Residential Home Sales: Sao Paulo data is updated monthly, averaging 2,635.500 Unit from Jan 2004 (Median) to Feb 2025, with 254 observations. The data reached an all-time high of 11,397.000 Unit in Oct 2024 and a record low of 622.000 Unit in Jan 2017. BR: Number of Residential Home Sales: Sao Paulo data remains active status in CEIC and is reported by The Trade Union of Housing. The data is categorized under Global Database’s Brazil – Table BR.RKA007: Real Estate: São Paulo Residential Home Sales. [COVID-19-IMPACT]

The residential vacancy rate is the percentage of residential units that are unoccupied, or vacant, in a given year. The U.S. Census Bureau defines occupied housing units as “owner-occupied” or “renter-occupied.” Vacant housing units are not classified by tenure in this way, as they are not occupied by an owner or renter.

The residential vacancy rate serves as an indicator of the condition of the area’s housing market. Low residential vacancy rates indicate that demand for housing is high compared to the housing supply. However, the aggregate residential vacancy rate is lacking in granularity. For example, the housing market for rental units in the area and the market for buying a unit in the same area may be very different, and the aggregate rate will not show those distinct conditions. Furthermore, the vacancy rate may be high, or low, for a variety of reasons. A high vacancy rate may result from a falling population, but it may also result from a recent construction spree that added many units to the total stock.

The residential vacancy rate in Champaign County appears to have fluctuated between 8% and 14% from 2005 through 2022, reaching a peak near 14% in 2019. In 2023, this rate dropped to about 7%, its lowest value since 2005. However, this rate was calculated using the American Community Survey’s (ACS) estimated number of vacant houses per year, which has year-to-year fluctuations that are largely not statistically significant. Thus, we cannot establish a trend for this data.

The residential vacancy rate data shown here was calculated using the estimated total housing units and estimated vacant housing units from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Occupancy Status.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (25 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (4 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table SB25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Number of Offers for Sale: Secondary Housing: Sakhalin Region data was reported at 1,584.000 Unit in Mar 2025. This records an increase from the previous number of 1,454.000 Unit for Feb 2025. Number of Offers for Sale: Secondary Housing: Sakhalin Region data is updated monthly, averaging 2,261.000 Unit from Jan 2019 (Median) to Mar 2025, with 75 observations. The data reached an all-time high of 4,247.000 Unit in Oct 2021 and a record low of 1,406.000 Unit in Jan 2025. Number of Offers for Sale: Secondary Housing: Sakhalin Region data remains active status in CEIC and is reported by Sberbank. The data is categorized under Russia Premium Database’s Business and Economic Survey – Table RU.SJ004: Number of Offers for Sale: Secondary Housing. [COVID-19-IMPACT]

Number of Offers for Sale: Primary Housing: Samara Region data was reported at 2,590.000 Unit in Mar 2025. This records a decrease from the previous number of 3,448.000 Unit for Feb 2025. Number of Offers for Sale: Primary Housing: Samara Region data is updated monthly, averaging 4,476.000 Unit from Jan 2019 (Median) to Mar 2025, with 75 observations. The data reached an all-time high of 10,001.000 Unit in Jun 2021 and a record low of 2,235.000 Unit in Jun 2023. Number of Offers for Sale: Primary Housing: Samara Region data remains active status in CEIC and is reported by Sberbank. The data is categorized under Russia Premium Database’s Business and Economic Survey – Table RU.SJ003: Number of Offers for Sale: Primary Housing. [COVID-19-IMPACT]

The U.S. Covid 19 Diagnostics market is projected to be valued at $5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 8%, reaching approximately $10 billion by 2034.

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.

China's large population, the accelerating urbanization process, rising household disposable incomes, and strong economic expansion have all contributed to the development of the real estate market. As a result, demand for real estate agents in China has been rising to meet the expanding market volumes and requirements for higher transaction efficiency.Over the five years through 2025, industry revenue is anticipated to decrease at a CAGR of 3.3%, including a decline of 2.2% in 2025. A competitive market has led to speculation and inflated housing prices in recent years. As a result, the Chinese government has implemented property-purchasing and loan limitations, price restrictions, and housing tax reforms to regulate industry development and limit speculation. Since 2022, consumers' demand for real estate has declined due to the COVID-19 epidemic and economic downturn. In 2023, the newly constructed area of real estate decreased by 20.9% year-on-year, which was narrower than that in 2022, while the completed area of real estate in this year increased by 15.8%.Over the five years through 2030, ACMR-IBISWorld forecasts that China's Real Estate Agents industry will recover, with revenue increasing at a CAGR of 1.9%. Due to intensifying competition, the separation of real estate development and sales will continue. Outsourcing real estate sales operations will improve the operational efficiency of real estate developers and offer new opportunities for real estate intermediary service providers in the industry.

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.

Revenue for the Real Estate Development and Management industry in China is expected to decrease at a CAGR of 9.4% over the five years through 2025. This trend includes an expected decrease of 8.8% in the current year.Since August 2020, the People's Bank of China and the China Banking and Insurance Regulatory Commission have proposed three debt indicators for real estate development and management companies through which the company's financial health can be rated. This new policy has exacerbated the company's debt pressure, making it unable to repay old debts by borrowing new debt. Some real estate companies faced a liquidity crisis.In 2022, the city's lockdown and laying-off caused by COVID-19 epidemic led to the pressure of delaying the delivery of houses. The industry's newly constructed and completed areas decreased significantly throughout the year. In addition, the epidemic has also impacted sales of the industry, and some sales offices have been forced to close temporarily. In 2022, the sales area of commercial housing decreased by 24.3%, and the sales of commercial housing decreased significantly by 29.8%.Industry revenue will recover at an annualized 0.8% over the five years through 2030. Over the next five years, the industry's drag on GDP will weaken, and industry growth will stabilize. However, high housing prices have become a major social problem in China. Under the measures on the principle that residential real estate is used for living, not speculation, the financial attributes of real estate will gradually weaken, and housing prices will tend to stabilize.

According to Cognitive Market Research, the global Prefabricated Home market size was USD 145142.6 million in 2024. It will expand at a compound annual growth rate (CAGR) of 7.00% 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 58057.04 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 43542.78 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 33382.80 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.0% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 7257.13 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.4% 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 2902.85 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.7% from 2024 to 2031.
The Wood held the highest Prefabricated Home market revenue share in 2024.

Market Dynamics of Prefabricated Home Market

Key Drivers for Prefabricated Home Market

Rising Urbanization to Increase the Demand Globally

The market for prefabricated homes is heavily influenced by the growing urbanization, which creates a need for quick and inexpensive housing options. Because many people cannot afford traditional solutions due to the rapid increase in metropolitan housing prices relative to income growth, prefabricated home affordability is a concern. Their cost-effectiveness offers a suitable alternative. Additionally, they occupy faster than traditional procedures because of their modular nature, which is anticipated to reduce holding costs. Prefabricated homes are an attractive choice for those searching for affordable and timely housing options in urban areas due to their affordability and speed. There is a growing prefabricated housing market as seen by the increasing prevalence of prefabricated homes in the commercial, industrial, and residential sectors. Their potential in a bigger prefabricated home market is demonstrated by the increase in sales. Furthermore, the sustainability, efficiency, and quality of prefabricated homes are increasing due to ongoing technological advancements in construction techniques, materials, and designs. These advancements facilitate the continuous expansion of the industry and establish prefabricated homes as a flexible and appealing option for several sectors.

Innovative Home Design to Propel Market Growth

Consumer preference for sustainable living, energy efficiency, and customisation in homes is rising. Prefabricated homes, with their modern designs, satisfy these criteria. Technological advancements like 3D printing and AI-driven design tools have made complex prefab designs possible, increasing the market's attractiveness. Emerging lifestyles such as tiny homes are driving innovative prefab solutions tailored to specific groups. New designs are appealing to a wide range of customers, which drives market expansion by drawing in new clientele. Premiumization occurs when prefabricated homes with superior design and craftsmanship command higher prices, hence raising the overall market value. A competitive prefabricated homes market environment is created by innovative designs that enable producers to stand out from the competition and appeal to design-conscious buyers.

Restraint Factor for the Prefabricated Home Market

Perception and Stereotypes to Limit the Sales

Changing the public's perspective and the preconceptions around prefabricated homes is one of the main obstacles. Many individuals still hold outmoded beliefs about prefabricated homes, believing them to be of worse quality or offering fewer customizing options than traditional residences. Negative perceptions limit penetration by keeping potential buyers out of the market. Stereotypes about poor quality affect price points and reduce perceived value. A tarnished reputation deters talent and investors, which impedes the industry's expansion.

Impact of Covid-19 on the Prefabricated Home Market

There have been positive as well as negative impacts of the COVID-19 epidemic on the prefabricated home market. People started lookin...

Number of Offers for Sale: Secondary Housing: Kostroma Region data was reported at 1,172.000 Unit in Mar 2025. This records a decrease from the previous number of 1,183.000 Unit for Feb 2025. Number of Offers for Sale: Secondary Housing: Kostroma Region data is updated monthly, averaging 2,941.000 Unit from Jan 2019 (Median) to Mar 2025, with 75 observations. The data reached an all-time high of 8,904.000 Unit in Jan 2020 and a record low of 1,112.000 Unit in Jan 2025. Number of Offers for Sale: Secondary Housing: Kostroma Region data remains active status in CEIC and is reported by Sberbank. The data is categorized under Russia Premium Database’s Business and Economic Survey – Table RU.SJ004: Number of Offers for Sale: Secondary Housing. [COVID-19-IMPACT]