The homebuyer sentiment in the United States worsened substantially in 2021 and remained negative until August 2025. As of August 2025, the net homebuyer sentiment measured negative **. This means that the share of respondents who thought it was a bad time to buy a home outweighed the share of respondents who said the contrary by 44 percent. The decline in sentiment is correlated with the falling homeowner affordability. In 2023, the U.S. homeowner affordability index fell to the lowest level on record.

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.

Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Oct 2025 about median and USA.

Nahb Housing Market Index in the United States increased to 38 points in November from 37 points in October of 2025. This dataset provides the latest reported value for - United States Nahb Housing Market Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 2025, India was the country with the highest increase in house prices since 2010 among the Asia-Pacific (APAC) countries under observation. In the second quarter of the year, the nominal house price index in India reached over 359 index points. This suggests an increase of 259 percent since 2010, the baseline year when the index value was set to 100. It is important to note that the nominal index does not account for the effects of inflation, meaning when adjusted for inflation, price growth in real terms was slower.

According to projections, Florida's real state market will be the worst affected by sea level rise in 2100 among coastal states in the U.S. By the end of the century, around ** percent of the housing units in the Sunshine State could be exposed to high risk of flooding.

Housing Index in China remained unchanged at -2.20 percent in October. This dataset provides the latest reported value for - China Newly Built House Prices YoY Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Summary presents the latest information about the HHF program in each of the 18 states and the District of Columbia, including the number of homeowners who have received assistance as well as trends in the local area housing market.

Innsbruck was the most expensive Austrian city to buy an apartment in, with average values of 7,700 euros per square meter in the first quarter of 2025. The price of an apartment in Graz was significantly lower at 4,590 euros per square meter.

The number of U.S. home sales in the United States declined in 2024, after soaring in 2021. A total of four million transactions of existing homes, including single-family, condo, and co-ops, were completed in 2024, down from 6.12 million in 2021. According to the forecast, the housing market is forecast to head for recovery in 2025, despite transaction volumes expected to remain below the long-term average. Why have home sales declined? The housing boom during the coronavirus pandemic has demonstrated that being a homeowner is still an integral part of the American dream. Nevertheless, sentiment declined in the second half of 2022 and Americans across all generations agreed that the time was not right to buy a home. A combination of factors has led to house prices rocketing and making homeownership unaffordable for the average buyer. A survey among owners and renters found that the high home prices and unfavorable economic conditions were the two main barriers to making a home purchase. People who would like to purchase their own home need to save up a deposit, have a good credit score, and a steady and sufficient income to be approved for a mortgage. In 2022, mortgage rates experienced the most aggressive increase in history, making the total cost of homeownership substantially higher. Are U.S. home prices expected to fall? The median sales price of existing homes stood at 413,000 U.S. dollars in 2024 and was forecast to increase slightly until 2026. The development of the S&P/Case Shiller U.S. National Home Price Index shows that home prices experienced seven consecutive months of decline between June 2022 and January 2023, but this trend reversed in the following months. Despite mild fluctuations throughout the year, home prices in many metros are forecast to continue to grow, albeit at a much slower rate.

The Housing Affordability Data System (HADS) is a set of files derived from the 1985 and later national American Housing Survey (AHS) and the 2002 and later Metro AHS. This system categorizes housing units by affordability and households by income, with respect to the Adjusted Median Income, Fair Market Rent (FMR), and poverty income. It also includes housing cost burden for owner and renter households. These files have been the basis for the worst case needs tables since 2001. The data files are available for public use, since they were derived from AHS public use files and the published income limits and FMRs. These dataset give the community of housing analysts the opportunity to use a consistent set of affordability measures. The most recent year HADS is available as a Public Use File (PUF) is 2013. For 2015 and beyond, HADS is only available as an IUF and can no longer be released on a PUF. Those seeking access to more recent data should reach to the listed point of contact.

Context

Wanting to further explore CNN + MLP hybrid modeling for housing prices, I (reasonably) cleaned and took a subset of the socal housing data made available by ted8080 at:

https://www.kaggle.com/ted8080/house-prices-and-images-socal/

His CSV cleaning code proved helpful as well as the fact that he (ted8080) had made a large list of images that needed to be cleaned. I wrote my own image cleaning code in Python, but used his list of bad images to clean the files using my code.

Content

The data set contains images and numeric data (including prices) for 2000 training and 1000 validation data. This is not an ideal split (typically it should be more like 2/3 and 1/3 split) but since the number of data is not large (on purpose) the validation set was made larger than usual. There is a clear demonstration of learning and not unreasonable price prediction achieved with this. One can also employ k-fold cross validation since the data set is not large.

NOTE NOTE NOTE: The images retain their original numeric label after cleaning and taking a subset, which means that the names may range from 0 to 3000+, even though there are only 2000 training and 1000 validation images. This is also true for the additional features CSV file which accompanies the images. The CSV house IDs range from 0 to 15000+ but only 3000 are actually used in the code. As a result, this labeling throws off the Kaggle column statistics displayed in the file pre-viewer.

Acknowledgements

Again, the original data are made available by ted8080 at the above Kaggle URL. I also acknowledge the helpful content at the PyTorch forum which had a nice discussion of CNN + MLP hybrid architectures useful for this work.

Inspiration

There are more detailed datasets out there for housing, which contain many more features/variables, but the purpose of this work was to extract as much performance from a small data set and model as possible.

Linear regressions (OLS) with fixed-effects for each property.

Graph and download economic data for All-Transactions House Price Index for Charlotte-Concord-Gastonia, NC-SC (MSA) (ATNHPIUS16740Q) from Q1 1977 to Q2 2025 about Charlotte, SC, appraisers, NC, HPI, housing, price index, indexes, price, and USA.

Real estate ads in Russia are published on the websites avito.ru, realty.yandex.ru, cian.ru, sob.ru, youla.ru, n1.ru, moyareklama.ru. The ads-api.ru service allows you to upload real estate ads for a fee. The parser of the service works strangely and duplicates real estate ads in the database if the authors extended them after some time. Also in the Russian market there are a lot of outbids (bad realtors) who steal ads and publish them on their own behalf. Before publishing this dataset, my task was to select the original ad from a bunch of ads. Russian real estate services allow ad authors to manually write data about an apartment or house. Therefore, it often happens that a user can publish an ad with errors or typos. Also, the user may not know, for example, the type of walls near his house. The user also specifies the address of the object being sold. He may make a mistake and simply indicate the address, for example, "Moscow". Which street? Which house? We will never know.

Dataset

The real estate market in Russia is of two types, in the dataset it is used as object type 0 - Secondary real estate market; 2 - New building. I found it necessary to determine the geolocation for each ad address and add the coordinates to this dataset. Also there is a number of the region of Russia. For example, the number of the Chuvash region is 21. Additionally, there is a house number that is synchronized through the federal public database of the Federal Tax Service "FIAS". Since the data is obtained through a paid third party service, I cannot publish the results, however, I can anonymize them and publish parameters such as Street ID and House ID. Basically, all houses are built from blocks such as brick, wood, panel and others. I marked them with numbers: building type - 0 - Don't know. 1 - Other. 2 - panel. 3 - Monolithic. 4 - Brick. 5 - blocky. 6- Wooden

The number of rooms can also be as 1, 2 or more. However, there is a type of apartment that is called a studio apartment. I've labeled them "-1".

Ideas

I hope that the publication of this dataset will improve developments in the field of global real estate. You can create apartment price forecasts. You can analyze real estate markets. You can understand that there is a need to publish free real estate datasets. And much more

Others

The license for this dataset is public, you can use it in your scientific research, design work and other works. The only condition is the publication of a link to this dataset. You can send suggestions (or complaints) on the dataset by mail daniilakk@gmail.com

Graph and download economic data for Existing Home Sales (EXHOSLUSM495S) from Oct 2024 to Oct 2025 about headline figure, sales, housing, and USA.

The Housing Affordability Index value in the United States plummeted in 2022, surpassing the historical record of ***** index points in 2006. In 2024, the housing affordability index measured **** index points, making it the second-worst year for homebuyers since the start of the observation period. What does the Housing Affordability Index mean? The Housing Affordability Index uses data provided by the National Association of Realtors (NAR). It measures whether a family earning the national median income can afford the monthly mortgage payments on a median-priced existing single-family home. An index value of 100 means that a family has exactly enough income to qualify for a mortgage on a home. The higher the index value, the more affordable a house is to a family. Key factors that drive the real estate market Income, house prices, and mortgage rates are some of the most important factors influencing homebuyer sentiment. When incomes increase, consumer power also increases. The median household income in the United States declined in 2022, affecting affordability. Additionally, mortgage interest rates have soared, adding to the financial burden of homebuyers. The sales price of existing single-family homes in the U.S. has increased year-on-year since 2011 and reached ******* U.S. dollars in 2023.

The German prefabricated houses market, valued at €7.30 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 5.43% from 2025 to 2033. This expansion is driven by several key factors. Increasing urbanization and a shortage of skilled labor in traditional construction are pushing consumers and developers towards faster, more efficient prefabricated building solutions. Furthermore, growing environmental awareness is fueling demand for sustainable building materials and construction methods, a key advantage of prefabricated housing. Government initiatives promoting sustainable and affordable housing also contribute to market growth. The market is segmented by house type, with single-family homes currently dominating, although multi-family units are expected to see increased growth due to rising population density in urban areas. Leading players like Deutsche Fertighaus Holding, Bien Zenker, and ALHO Systembau GmbH are driving innovation and market competition, offering diverse designs and technological advancements to meet evolving customer preferences. While potential restraints like fluctuating raw material prices and regulatory changes exist, the overall market outlook remains positive, with significant growth potential throughout the forecast period. The market's growth trajectory is influenced by evolving consumer preferences. Demand for customizable and energy-efficient prefabricated homes is rising, prompting manufacturers to incorporate smart home technologies and sustainable materials into their designs. This trend, coupled with the ongoing need for affordable housing solutions, will likely shape the industry's development in the coming years. The regional focus remains predominantly on Germany, but expansion into neighboring European markets could be a significant growth avenue for established players and new entrants. A robust supply chain and continued investment in research and development are crucial for maintaining the industry's competitive edge and achieving the projected growth targets. The success of the German prefabricated housing market depends on effectively addressing these dynamics and adapting to emerging technological advancements and evolving consumer expectations. Recent developments include: November 2022: Bien-Zenker, one of the largest manufacturers of prefabricated houses in Europe, planned to open its newest model house in Bad Vilbel in December 2022. This house will show the company's 360° sustainability strategy., February 2022: DFH Group, manufacturers of prefabricated houses in Germany, began construction work of the DFH Performance and Innovation Center (LIZ) in the Simmern Industrial Park. The facility is spread across 32,000 square meters of area. This facility will be used as the training center, including a test hall and conference area.. Notable trends are: Prefabricated Buildings are Witnessing Significant Growth.

New Home Sales in the United States increased to 800 Thousand units in August from 664 Thousand units in July of 2025. This dataset provides the latest reported value for - United States New Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Review of estimates for the climate change impact in Chile’s Agriculture sector (relative to no climate change scenario).