Existing Home Sales in the United States increased to 4100 Thousand in October from 4050 Thousand in September of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Monthly single-family home sales in Connecticut, 2001 through the present. Data updated monthly by the Connecticut Housing Finance Authority and tracked in the following dashboard: https://www.chfa.org/about-us/ct-monthly-housing-market-dashboard/.

CHFA has stopped maintaining the dashboard and associated datasets, and this dataset will no longer be updated as of 2022.

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.

House Price Prediction Dataset.

The dataset contains 2000 rows of house-related data, representing various features that could influence house prices. Below, we discuss key aspects of the dataset, which include its structure, the choice of features, and potential use cases for analysis.

1. Dataset Features

The dataset is designed to capture essential attributes for predicting house prices, including:

Area: Square footage of the house, which is generally one of the most important predictors of price. Bedrooms & Bathrooms: The number of rooms in a house significantly affects its value. Homes with more rooms tend to be priced higher. Floors: The number of floors in a house could indicate a larger, more luxurious home, potentially raising its price. Year Built: The age of the house can affect its condition and value. Newly built houses are generally more expensive than older ones. Location: Houses in desirable locations such as downtown or urban areas tend to be priced higher than those in suburban or rural areas. Condition: The current condition of the house is critical, as well-maintained houses (in 'Excellent' or 'Good' condition) will attract higher prices compared to houses in 'Fair' or 'Poor' condition. Garage: Availability of a garage can increase the price due to added convenience and space. Price: The target variable, representing the sale price of the house, used to train machine learning models to predict house prices based on the other features.

2. Feature Distributions

Area Distribution: The area of the houses in the dataset ranges from 500 to 5000 square feet, which allows analysis across different types of homes, from smaller apartments to larger luxury houses. Bedrooms and Bathrooms: The number of bedrooms varies from 1 to 5, and bathrooms from 1 to 4. This variance enables analysis of homes with different sizes and layouts. Floors: Houses in the dataset have between 1 and 3 floors. This feature could be useful for identifying the influence of multi-level homes on house prices. Year Built: The dataset contains houses built from 1900 to 2023, giving a wide range of house ages to analyze the effects of new vs. older construction. Location: There is a mix of urban, suburban, downtown, and rural locations. Urban and downtown homes may command higher prices due to proximity to amenities. Condition: Houses are labeled as 'Excellent', 'Good', 'Fair', or 'Poor'. This feature helps model the price differences based on the current state of the house. Price Distribution: Prices range between $50,000 and $1,000,000, offering a broad spectrum of property values. This range makes the dataset appropriate for predicting a wide variety of housing prices, from affordable homes to luxury properties.

3. Correlation Between Features

A key area of interest is the relationship between various features and house price: Area and Price: Typically, a strong positive correlation is expected between the size of the house (Area) and its price. Larger homes are likely to be more expensive. Location and Price: Location is another major factor. Houses in urban or downtown areas may show a higher price on average compared to suburban and rural locations. Condition and Price: The condition of the house should show a positive correlation with price. Houses in better condition should be priced higher, as they require less maintenance and repair. Year Built and Price: Newer houses might command a higher price due to better construction standards, modern amenities, and less wear-and-tear, but some older homes in good condition may retain historical value. Garage and Price: A house with a garage may be more expensive than one without, as it provides extra storage or parking space.

4. Potential Use Cases

The dataset is well-suited for various machine learning and data analysis applications, including:

House Price Prediction: Using regression techniques, this dataset can be used to build a model to predict house prices based on the available features. Feature Importance Analysis: By using techniques such as feature importance ranking, data scientists can determine which features (e.g., location, area, or condition) have the greatest impact on house prices. Clustering: Clustering techniques like k-means could help identify patterns in the data, such as grouping houses into segments based on their characteristics (e.g., luxury homes, affordable homes). Market Segmentation: The dataset can be used to perform segmentation by location, price range, or house type to analyze trends in specific sub-markets, like luxury vs. affordable housing. Time-Based Analysis: By studying how house prices vary with the year built or the age of the house, analysts can derive insights into the trends of older vs. newer homes.

5. Limitations and ...

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

These Kaggle datasets offer a comprehensive analysis of the US real estate market, leveraging data sourced from Redfin via an unofficial API. It contains weekly snapshots stored in CSV files, reflecting the dynamic nature of property listings, prices, and market trends across various states and cities, except for Wyoming, Montana, and North Dakota, and with specific data generation for Texas cities. Notably, the dataset includes a prepared version, USA_clean_unique, which has undergone initial cleaning steps as outlined in the thesis. These datasets were part of my thesis; other two countries were France and UK.

These steps include: - Removal of irrelevant features for statistical analysis. - Renaming variables for consistency across international datasets. - Adjustment of variable value ranges for a more refined analysis.

Unique aspects such as Redfin’s “hot” label algorithm, property search status, and detailed categorizations of property types (e.g., single-family residences, condominiums/co-ops, multi-family homes, townhouses) provide deep insights into the market. Additionally, external factors like interest rates, stock market volatility, unemployment rates, and crime rates have been integrated to enrich the dataset and offer a multifaceted view of the real estate market's drivers.

The USA_clean_unique dataset represents a key step before data normalization/trimming, containing variables both in their raw form and categorized based on predefined criteria, such as property size, year of construction, and number of bathrooms/bedrooms. This structured approach aims to capture the non-linear relationships between various features and property prices, enhancing the dataset's utility for predictive modeling and market analysis.

See columns from USA_clean_unique.csv and my Thesis (Table 2.8) for exact column descriptions.

Table 2.4 and Section 2.2.3, which I refer to in the column descriptions, can be found in my thesis; see University Library. Click on Online Access->Hlavni prace.

If you want to continue generating datasets yourself, see my Github Repository for code inspiration.

Let me know if you want to see how I got from raw data to USA_clean_unique.csv. Multiple steps include cleaning in Tableau Prep and R, downloading and merging external variables to the dataset, removing duplicates, and renaming columns for consistency.

Housing Starts in the United States decreased to 1307 Thousand units in August from 1429 Thousand units in July of 2025. This dataset provides the latest reported value for - United States Housing Starts - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The FHFA House Price Index (FHFA HPI®) is the nation’s only collection of public, freely available house price indexes that measure changes in single-family home values based on data from all 50 states and over 400 American cities that extend back to the mid-1970s. The FHFA HPI incorporates tens of millions of home sales and offers insights about house price fluctuations at the national, census division, state, metro area, county, ZIP code, and census tract levels. FHFA uses a fully transparent methodology based upon a weighted, repeat-sales statistical technique to analyze house price transaction data. ​ What does the FHFA HPI represent? The FHFA HPI is a broad measure of the movement of single-family house prices. The FHFA HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. This information is obtained by reviewing repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January 1975. The FHFA HPI serves as a timely, accurate indicator of house price trends at various geographic levels. Because of the breadth of the sample, it provides more information than is available in other house price indexes. It also provides housing economists with an improved analytical tool that is useful for estimating changes in the rates of mortgage defaults, prepayments and housing affordability in specific geographic areas. U.S. Federal Housing Finance Agency, All-Transactions House Price Index for Connecticut [CTSTHPI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CTSTHPI, August 2, 2023.

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).

This dataset centralizes for each year (between 2014 and 2023) several variables aggregated on the residential real estate market at the municipal level: - The number of mutations - The number of sales of houses and apartments - The proportion of sales of houses and apartments - The average price of goods sold - Average price per m2 of goods sold - The average area of goods sold The identification (and join) field of the municipalities is based on the INSEE code (COG 2022) These data are derived from a geolocated DVF database processing, here are the mutations taken into account: - Single-sales transfer (no batch sales) - Price between 15 000 € and 10 000 000 € - Surfaces of apartments (between 10m2 and 250m2) and surfaces of houses (between 10m2 and 400m2) - Price per m2 between 330 €/m2 and 15 000 €/m2 The methodology is explained in detail here > https://journals.openedition.org/cybergeo/39583

Python script used to examine how the marketing of properties explains neighborhood racial and income change using historical public remarks in real estate listings from Multiple Listing Services (MLS) collected and curated by CoreLogic.The primary dataset used for this research consists of 158,253 geocoded real estate listings for single-family homes in Mecklenburg County, North Carolina between 2001 and 2020. The historical MLS data which include public remarks is proprietary and can be obtained through purchase agreement with CoreLogic. The MLS is not publicly available and only available for members of the National Association of Realtors. Public remarks for homes currently listed for sale can be collected from online real estate websites such as Zillow, Trulia, Realtor.com, Redfin, and others.Since we cannot share this data, users need to, before running the script provided here, run the script provided by Nilsson and Delmelle (2023) which can be accessed here: https://doi.org/10.6084/m9.figshare.20493012.v1. This in order to get a fabricated/mock dataset of classified listings called classes_mock.csv. The article associated with Nilsson and Delmelle's (2023) script can be accessed here: https://www.tandfonline.com/doi/abs/10.1080/13658816.2023.2209803The user can then run the code together with the data provided here to estimate the threshold models together with data derived from the publicly available HMDA data. To compile a historical data set of loan/application records (LAR) for the user's own study are, the user will need to download data from the following websites:https://ffiec.cfpb.gov/data-publication/snapshot-national-loan-level-dataset/2022 (2017-forward)https://www.ffiec.gov/hmda/hmdaproducts.htm (2007-2016)https://catalog.archives.gov/search-within/2456161?limit=20&levelOfDescription=fileUnit&sort=naId:asc (for data prior to 2007)

In my quest to find a comprehensive real estate/housing dataset for Bogotá, I encountered challenges that led me to explore alternative avenues. Consequently, I made the decision to employ web scraping techniques on various websites to compile the necessary information.

The dataset in question was procured in the year 2023, providing a snapshot of the real estate landscape at that particular time. However, it is crucial to acknowledge the potential presence of underlying biases within the dataset. This bias is a byproduct of the observation that real estate apps are not as widely embraced in Colombia compared to other regions. The limited popularity of these apps may have inadvertently restricted the dataset to predominantly include information about high-value housing.

This recognition prompts a level of caution when interpreting and utilizing the dataset, as its composition may not be fully representative of the entire spectrum of housing options in Bogotá. Users should be aware that the dataset may skew towards higher-end properties, potentially overlooking a more diverse range of real estate characteristics and values present in the city. Consequently, any analysis or conclusions drawn from this dataset should be approached with an awareness of these limitations and a consideration of potential biases introduced during the data collection process.

United States Sold Above Asking: Existing: Single Family Residential data was reported at 31.900 % in Mar 2023. This records an increase from the previous number of 26.000 % for Feb 2023. United States Sold Above Asking: Existing: Single Family Residential data is updated monthly, averaging 23.400 % from Jan 2012 (Median) to Mar 2023, with 135 observations. The data reached an all-time high of 64.400 % in May 2022 and a record low of 16.400 % in Jan 2015. United States Sold Above Asking: Existing: Single Family Residential data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB: Home Sold Above Asking.

United States Home Sales: Existing: Single Family Residential data was reported at 271.821 Unit th in Mar 2023. This records an increase from the previous number of 202.117 Unit th for Feb 2023. United States Home Sales: Existing: Single Family Residential data is updated monthly, averaging 338.667 Unit th from Jan 2012 (Median) to Mar 2023, with 135 observations. The data reached an all-time high of 497.307 Unit th in Jun 2021 and a record low of 173.984 Unit th in Jan 2023. United States Home Sales: Existing: Single Family Residential data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB: Home Sales.

The dataset is a catalog of major residential development projects in Somerset County, NJ. This includes Affordable Housing, Senior housing options, and Market-rate rentalsAffordable Housing Options: With New Jersey having some of the highest housing costs in the county, the state government has implemented several initiatives and programs to provide housing options for low- and moderate-income eligible households. In addition, several municipalities have implemented inclusionary zoning laws, that require property developers to allocate a certain percentage of the units for affordable housing. Somerset county has several affordable housing programs to help low-and moderate-income eligible households and first-time homebuyers, including the Mt. Laurel Doctrine, New Jersey Balanced Housing Program, HUD Public Housing Program, HUD Housing Choice Voucher Program (Section 8). This dataset provides a comprehensive list of all affordable housing projects in the county. The dataset includes ‘inclusionary’ developments that are comprised of both market-rate units and affordable units. It also includes municipality-sponsored and other 100% affordable housing projects, as well as affordable housing created through the redevelopment process. The total number of market rate and affordable housing units in each project is provided. Some projects include a blend of both rental and for-purchase units. Senior Housing Options: There are several housing options in Somerset County for older adults seeking assistance with daily living or those who want to maintain their independence or those who seek to live in communities designed for older adults. These options include – Active Adult Communities: These are communities designed for older adults who can live independently but want to live in a community specifically for older adults. They typically offer amenities such as fitness centers, swimming pools, and social activities. Many independent living communities also offer additional services such as transportation, housekeeping, and meals. Assisted Living Communities: These communities aid with daily living activities such as bathing, dressing, and medication management. They offer a range of services, depending on the level of care needed. Some assisted living communities also offer memory care services for individuals with dementia or Alzheimer's disease. Continuing Care Retirement Communities: These communities offer a continuum of care that includes independent living, assisted living, and skilled nursing care. This allows residents to "age in place" and receive additional care as needed without having to move to a different community. Senior Residence: These communities are restricted to residents who are 55 years of age or older. They typically offer amenities like active adult communities and may have additional features such as golf courses, community centers, and events. Market Rate Rentals: These properties are typically owned/operated by private landlords and are not considered affordable housing and are not subject to government subsidies. These include apartments, condominiums, town homes, single-family homes. The information included in this dataset represents a point-in-time (November 2023) and is subject to change. Furthermore, new, or alternative housing projects may be proposed in future years, which will be incorporated into subsequent dataset updates. Updates to this dataset will take place on an as-needed basis.

United States Sold Above Asking: New Construction: Single Family Residential data was reported at 14.200 % in Mar 2023. This records an increase from the previous number of 13.300 % for Feb 2023. United States Sold Above Asking: New Construction: Single Family Residential data is updated monthly, averaging 22.500 % from Jan 2012 (Median) to Mar 2023, with 135 observations. The data reached an all-time high of 32.200 % in Jul 2021 and a record low of 13.300 % in Feb 2023. United States Sold Above Asking: New Construction: Single Family Residential data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB: Home Sold Above Asking.

Based on the RWI-GEO-RED data that base on the data provided by ImmobilienScout24 hedonic housing price indices are estimated. The indices are on the grid level, district/county and municipality level. We conduct a hedonic price regression that covers characteristics of the object as well as regional fixed effects. The hedonic regression is estimated separately for houses for sale as well as apartments for rent and for sale. We also offer a combined index which combines the individual housing types into one index. There are three different specifications: First, the overall time development from 01/2008 to 11/2023 on grid level given yearly and quaterly; Second, cross-regional differences for each year separately and time development within one region from 01/2018 to 11/2023 (municipality, district and grid level); third, the time-region fixed effect between 2008 and 2023, which is used to determine the price changes for all three region types to the base year of 2008 or year-quarter 2008-Q1. RWI-GEO-REDX Other The data is based on the data set RWI-GEO-RED, that collects all offers for private housing on ImmobilienScout24 between January 2008 and November 2023. ImmobilienScout24 is the largest listing website for real estate in Germany. The price indices are estimated labor market region, district and municipality level.

Gross-Profit-Margin Time Series for Landsea Homes Corp. Landsea Homes Corporation (Nasdaq: LSEA) is a publicly traded residential homebuilder based in Dallas, Texas that designs and builds best-in-class homes and sustainable master-planned communities in some of the nation's most desirable markets. The company has developed homes and communities in New York, Boston, New Jersey, Arizona, Colorado, Florida, Texas and throughout California in Silicon Valley, Los Angeles, and Orange County. Landsea Homes was honored as the Green Home Builder 2023 Builder of the Year, after being named the 2022 winner of the prestigious Builder of the Year award, presented by BUILDER magazine, in recognition of a historical year of transformation. An award-winning homebuilder that builds suburban, single-family detached and attached homes, mid-and high-rise properties, and master-planned communities, Landsea Homes is known for creating inspired places that reflect modern living and provides homebuyers the opportunity to "Live in Your Element." Our homes allow people to live where they want to live, how they want to live " in a home created especially for them. Driven by a pioneering commitment to sustainability, Landsea Homes' High Performance Homes are responsibly designed to take advantage of the latest innovations with home automation technology supported by Apple. Homes include features that make life easier and provide energy savings that allow for more comfortable living at a lower cost through sustainability features that contribute to healthier living for both homeowners and the planet. Led by a veteran team of industry professionals who boast years of worldwide experience and deep local expertise, Landsea Homes is committed to positively enhancing the lives of our homebuyers, employees, and stakeholders by creating an unparalleled lifestyle experience that is unmatched. As of June 24, 2025, Landsea Homes Corporation operates as a subsidiary of The New Home Company Inc.

Price-Earnings-Ratio Time Series for Vonovia SE. Vonovia SE operates as an integrated residential real estate company in Europe. It operates through four segments: Rental, Value-Add, Recurring Sales, and Development. The company offers property management services; property-related services; and value-added services, including maintenance and modernization of residential properties, craftsmen and residential environment organization, condominium administration, cable TV, metering, energy supply, and insurances services. It also involved in the sale of individual condominiums and single-family houses; and project development activities to build new homes. In addition, the company engages in the sale of projects to investors, construction of rental apartments, and construction of new properties on existing land held in the portfolio. Further, it provides My Vonovia, a mobile application to organize service requests and appointments, track the status of requests online in real time, and view all Vonovia documents. The company was formerly known as Deutsche Annington Immobilien SE and changed its name to Vonovia SE in August 2015. Vonovia SE was founded in 1998 and is headquartered in Bochum, Germany.