Existing Home Sales in the United States increased to 4030 Thousand in May from 4000 Thousand in April 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.

New Home Sales in the United States decreased to 623 Thousand units in May from 722 Thousand units in April 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.

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.

Single Family Home Prices in the United States increased to 422800 USD in May from 414000 USD in April of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

Pending Home Sales in the United States increased 1.10 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Pending Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Office of Policy and Management maintains a listing of all real estate sales with a sales price of $2,000 or greater that occur between October 1 and September 30 of each year. For each sale record, the file includes: town, property address, date of sale, property type (residential, apartment, commercial, industrial or vacant land), sales price, and property assessment. Data are collected in accordance with Connecticut General Statutes, section 10-261a and 10-261b: https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261a and https://www.cga.ct.gov/current/pub/chap_172.htm#sec_10-261b. Annual real estate sales are reported by grand list year (October 1 through September 30 each year). For instance, sales from 2018 GL are from 10/01/2018 through 9/30/2019. Some municipalities may not report data for certain years because when a municipality implements a revaluation, they are not required to submit sales data for the twelve months following implementation.

United States Existing Home Sales: US data was reported at 420,000.000 Unit in Sep 2018. This records a decrease from the previous number of 539,000.000 Unit for Aug 2018. United States Existing Home Sales: US data is updated monthly, averaging 436,000.000 Unit from Jan 1999 (Median) to Sep 2018, with 237 observations. The data reached an all-time high of 753,000.000 Unit in Jun 2005 and a record low of 218,000.000 Unit in Jan 2009. United States Existing Home Sales: US data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s USA – Table US.EB005: Existing Home Sales.

Housing Starts in the United States increased to 1321 Thousand units in June from 1263 Thousand units in May 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.

United States Existing Home Sales: Months Supply data was reported at 4.400 Month in Sep 2018. This records an increase from the previous number of 4.300 Month for Aug 2018. United States Existing Home Sales: Months Supply data is updated monthly, averaging 4.900 Month from Jan 1999 (Median) to Sep 2018, with 237 observations. The data reached an all-time high of 12.400 Month in Jul 2010 and a record low of 3.200 Month in Dec 2017. United States Existing Home Sales: Months Supply data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s USA – Table US.EB005: Existing Home Sales.

Total Housing Inventory in the United States increased to 1540 Thousands in May from 1450 Thousands in April of 2025. This dataset includes a chart with historical data for the United States Total Housing Inventory.

Zillow Properties Listing dataset to access detailed real estate listings, including property prices, locations, and features. Popular use cases include market analysis, property valuation, and investment decision-making in the real estate sector.

Use our Zillow Properties Listing Information dataset to access detailed real estate listings, including property features, pricing trends, and location insights. This dataset is perfect for real estate agents, investors, market analysts, and property developers looking to analyze housing markets, identify investment opportunities, and assess property values.

Leverage this dataset to track pricing patterns, compare property features, and forecast market trends across different regions. Whether you're evaluating investment prospects or optimizing property listings, the Zillow Properties dataset offers essential information for making data-driven real estate decisions.

Dataset Features

• zpid: Unique property identifier assigned by Zillow.
• city: The name of the city where the property is located.
• state: The state in which the property is located.
• homeStatus: Indicates the current status of the property
• address: The full address of the property, including street, city, and state.
• isListingClaimedByCurrentSignedInUser: This field shows if the current Zillow user has claimed ownership of the listing.
• isCurrentSignedInAgentResponsible: This field indicates whether the currently signed-in real estate agent is responsible for the listing.
• bedrooms: Number of bedrooms in the property.
• bathrooms: Number of bathrooms in the property.
• price: Current asking price of the property.
• yearBuilt: The year the home was originally constructed.
• streetAddress: Specific street address (usually excludes city/state/zip).
• zipcode: The postal ZIP code of the property.
• isCurrentSignedInUserVerifiedOwner: This field indicates if the signed-in user has verified ownership of the property on Zillow.
• isVerifiedClaimedByCurrentSignedInUser: Indicates whether the user has claimed and verified the listing as the current owner.
• listingDataSource: The original source of the listing. Important for data lineage and trustworthiness.
• longitude: The longitudinal geographic coordinate of the property.
• latitude: The latitudinal geographic coordinate of the property.
• hasBadGeocode: This indicates whether the geolocation data is incorrect or problematic.
• streetViewMetadataUrlMediaWallLatLong: A URL or reference to the Street View media wall based on latitude and longitude.
• streetViewMetadataUrlMediaWallAddress: A similar URL reference to the Street View, but based on the property’s address.
• streetViewServiceUrl: The base URL to Google Street View or similar services. Enables interactive visuals of the property’s surroundings.
• livingArea: Total internal living area of the home, typically in square feet.
• homeType: The category/type of the home.
• lotSize: The size of the entire lot or land the home is situated on.
• lotAreaValue: The numerical value representing the lot area is usually tied to a measurement unit.
• lotAreaUnits: Units in which the lot area is measured (e.g., sqft, acres).
• livingAreaValue: The numeric value of the property's interior living space.
• livingAreaUnitsShort: Abbreviated unit for living area (e.g., sqft), useful for compact displays.
• isUndisclosedAddress: Boolean indicating if the full property address is hidden, typically used for privacy reasons.
• zestimate: Zillow’s estimated market value of the home, generated via its proprietary model.
• rentZestimate: Zillow’s estimated rental price per month, is helpful for rental market analysis.
• currency: Currency used for price, Zestimate, and rent estimate (e.g., USD).
• hideZestimate: Indicates whether the Zestimate is hidden from public view.
• dateSoldString: The date when the property was last sold, in string format (e.g., 2022-06-15).
• taxAssessedValue: The most recent assessed value of the property for tax purposes.
• taxAssessedYear: The year in which the property was last assessed.
• country: The country where the property is located.
• propertyTaxRate: The most recent tax rate.
• photocount: This column provides a photo count of the property.
• isPremierBuilder: Boolean indicating whether the builder is listed as a premier (trusted) builder on Zillow.
• isZillowOwned: Indicates whether the property is owned or managed directly by Zillow.
• ssid: A unique internal Zillow identifier for the listing (not to be confused with network SSID).
• hdpUrl: URL to the home’s detail page on Zillow (Home Details Page).
• tourViewCount: Number of times users have viewed the property tour.
• hasPublicVideo: This

Autoscraping's Zillow USA Real Estate Data is a comprehensive and meticulously curated dataset that covers over 10 million property listings across the United States. This data product is designed to meet the needs of professionals across various sectors, including real estate investment, market analysis, urban planning, and academic research. Our dataset is unique in its depth, accuracy, and timeliness, ensuring that users have access to the most relevant and actionable information available.

What Makes Our Data Unique? The uniqueness of our data lies in its extensive coverage and the precision of the information provided. Each property listing is enriched with detailed attributes, including but not limited to, full addresses, asking prices, property types, number of bedrooms and bathrooms, lot size, and Zillow’s proprietary value and rent estimates. This level of detail allows users to perform in-depth analyses, make informed decisions, and gain a competitive edge in their respective fields.

Furthermore, our data is continually updated to reflect the latest market conditions, ensuring that users always have access to current and accurate information. We prioritize data quality, and each entry is carefully validated to maintain a high standard of accuracy, making this dataset one of the most reliable on the market.

Data Sourcing: The data is sourced directly from Zillow, one of the most trusted names in the real estate industry. By leveraging Zillow’s extensive real estate database, Autoscraping ensures that users receive data that is not only comprehensive but also highly reliable. Our proprietary scraping technology ensures that data is extracted efficiently and without errors, preserving the integrity and accuracy of the original source. Additionally, we implement strict data processing and validation protocols to filter out any inconsistencies or outdated information, further enhancing the quality of the dataset.

Primary Use-Cases and Vertical Applications: Autoscraping's Zillow USA Real Estate Data is versatile and can be applied across a variety of use cases and industries:

Real Estate Investment: Investors can use this data to identify lucrative opportunities, analyze market trends, and compare property values across different regions. The detailed pricing and valuation data allow for comprehensive due diligence and risk assessment.

Market Analysis: Market researchers can leverage this dataset to track real estate trends, evaluate the performance of different property types, and assess the impact of economic factors on property values. The dataset’s nationwide coverage makes it ideal for both local and national market studies.

Urban Planning and Development: Urban planners and developers can use the data to identify growth areas, plan new developments, and assess the demand for different property types in various regions. The detailed location data is particularly valuable for site selection and zoning analysis.

Academic Research: Universities and research institutions can utilize this data for studies on housing markets, urbanization, and socioeconomic trends. The comprehensive nature of the dataset allows for a wide range of academic applications.

Integration with Our Broader Data Offering: Autoscraping's Zillow USA Real Estate Data is part of our broader data portfolio, which includes various datasets focused on real estate, market trends, and consumer behavior. This dataset can be seamlessly integrated with our other offerings to provide a more holistic view of the market. For example, combining this data with our consumer demographic datasets can offer insights into the relationship between property values and demographic trends.

By choosing Autoscraping's data products, you gain access to a suite of complementary datasets that can be tailored to meet your specific needs. Whether you’re looking to gain a comprehensive understanding of the real estate market, identify new investment opportunities, or conduct advanced research, our data offerings are designed to provide you with the insights you need.

These National Statistics provide monthly estimates of the number of residential and non-residential property transactions in the UK and its constituent countries. National Statistics are accredited official statistics.

England and Northern Ireland statistics are based on information submitted to the HM Revenue and Customs (HMRC) Stamp Duty Land Tax (SDLT) database by taxpayers on SDLT returns.

Land and Buildings Transaction Tax (LBTT) replaced SDLT in Scotland from 1 April 2015 and this data is provided to HMRC by https://www.revenue.scot/" class="govuk-link">Revenue Scotland to continue the time series.

Land Transaction Tax (LTT) replaced SDLT in Wales from 1 April 2018. To continue the time series, the https://gov.wales/welsh-revenue-authority" class="govuk-link">Welsh Revenue Authority (WRA) have provided HMRC with a monthly data feed of LTT transactions since July 2021.

LTT figures for the latest month are estimated using a grossing factor based on data for the most recent and complete financial year. Until June 2021, LTT transactions for the latest month were estimated by HMRC based upon year on year growth in line with other UK nations.

LTT transactions up to the penultimate month are aligned with LTT statistics.

Go to Stamp Duty Land Tax guidance for the latest rates and information.

Go to Stamp Duty Land Tax rates from 1 December 2003 to 22 September 2022 and Stamp Duty: rates on land transfers before December 2003 for historic rates.

The latest release was published 09:30 27 June 2025 and was updated with provisional data from completed transactions during May 2025.

The next release will be published 09:30 31 July 2025 and will be updated with provisional data from completed transactions during June 2025.

https://webarchive.nationalarchives.gov.uk/ukgwa/20240320184933/https://www.gov.uk/government/statistics/monthly-property-transactions-completed-in-the-uk-with-value-40000-or-above" class="govuk-link">Archive versions of the Monthly property transactions completed in the UK with value of £40,000 or above are available via the UK Government Web Archive, from the National Archives.

Homes Sold: sa: All Residential: Asheville, NC data was reported at 663.481 Unit th in Jul 2020. This records an increase from the previous number of 552.070 Unit th for Jun 2020. Homes Sold: sa: All Residential: Asheville, NC data is updated monthly, averaging 570.170 Unit th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 741.436 Unit th in Jul 2016 and a record low of 364.010 Unit th in Jul 2012. Homes Sold: sa: All Residential: Asheville, NC data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB018: Homes Sold: by Metropolitan Areas: Seasonally Adjusted.

Pending Home Sales MoM in the United States increased to 1.80 percent in May from -6.30 percent in April of 2025. This dataset includes a chart with historical data for the United States Pending Home Sales MoM.

Existing Home Sales in Ireland increased to 2913 Units in May from 2908 Units in April of 2025. This dataset provides - Ireland Existing Home Sales- actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset is a snapshot from October 2022 of all 48 homes in a section of a neighborhood nearby a large university in Central Florida. All of the homes are single family homes featuring a garage, a driveway, and a fenced-in backyard. Data was gathered by hand (keyboard) via a collection of sites, including Zillow, Realtor, Redfin, Trulia, and Orange County Property Appraiser. All homes were built in the same year in the early 2000's and feature central air and all other utilities typical of contemporary suburban homes in the United States. The area is close to a university and a large portion of renters are college students and young professionals, as well as families and older adults.

There are 30 columns:

• HID: House ID, a unique identifier for each house (int from 1 to 48, not the actual address number) -Sqft: The Square Footage of the Interior of the house (int) -LandSqft: The Total Square Footage of the land (int) -Neighbors: The number of homes directly adjacent to each house (int) -Stories: The number of stories in each house (int) -Pool: Does the house have a pool (int, 0 for 'No', 1 for 'Yes') -Bedrooms: The number of bedrooms in each house (int) -Bathrooms: The number of bathrooms (full or half) in each house (int) -DateLastSold: The date on which the house was last sold (datetime) -PropertyTaxes2022: The annual property taxes for 2022 (float) -OwnedByBank: Is the house owned by a bank (int, 0 for 'No', 1 for 'Yes') -OuterPortion: Is the house on the Outer Portion of the Neighborhood (int, 0 for 'No', 1 for 'Yes') -NextToLoudRoad: Is the house directly adjacent to a loud road (int, 0 for 'No', 1 for 'Yes') -PriceLastSold: Price that the house was last sold for (float) -Zestimate: Zillow's Price Estimate for the house (float) -RentZestimate: Zillow's Estimate for the Monthly Price of rent for the house (float) -RealtorcomEstimate: Realtor dot com's Estimate for the house (float) -RedfinEstimate: Redfin's Estimate for the house (float) -TruliaEstimate: Trulia's Estimate for the house (float) -OCPALandValue2022: The Land Value on the county's 2022 records (float) -OCPABuildingValue2022: The Building Value on the county's 2022 records (float) -OCPAFeaturesValue2022: The Features Value on the county's 2022 records (float) -OCPAMarketValue2022: The Market Value on the county's 2022 records (float) -OCPAAssessedValue2022: The Assessed Value on the county's 2022 records (float), AKA what homeowners are taxed on -OCPALandValue2021: The Land Value on the county's 2021 records (float) -OCPABuildingValue2021: The Building Value on the county's 2021 records (float) -OCPAFeaturesValue2021: The Features Value on the county's 2021 records (float) -OCPAMarketValue2021: The Market Value on the county's 2021 records (float) -OCPAAssessedValue2021: The Assessed Value on the county's 2021 records (float), AKA what homeowners are taxed on -Notes: any notes on any of the homes (str)

Note that while the dataset is exhaustive in that it has all of the houses, some homes are missing some columns, typically because a home did not feature a estimate on a site or the one home not found on the property appraiser's site. This also is therefore not a randomized dataset, so the only population of homes that it can be used to infer on are those within this specific portion of the neighborhood. Personally, I am going to use the dataset to practice a couple of aspects of real-world data: Cleaning, Imputing, and Exploratory Data Analysis. Mainly, I want to compare different approaches to filling in the missing values of the dataset, then do some Model Building with some additional Dimensionality Reduction.

Homes Sold: sa: All Residential: Chicago, IL data was reported at 8,693.019 Unit th in Jul 2020. This records an increase from the previous number of 6,102.253 Unit th for Jun 2020. Homes Sold: sa: All Residential: Chicago, IL data is updated monthly, averaging 7,955.512 Unit th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 8,778.475 Unit th in Apr 2017 and a record low of 5,341.268 Unit th in May 2020. Homes Sold: sa: All Residential: Chicago, IL data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB018: Homes Sold: by Metropolitan Areas: Seasonally Adjusted.