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License information was derived automatically
Here's a short description of the dataset:
Serial Number: Is just a unique set of digits to identify each transaction
List year: This is the year that the particular property was put up for sale.
Date Recorded: Is the date that the transaction was completed. That is, the year the property was bought.
Town: The town where this property is located.
Address: The property's address.
Assessed Value: How much the property is generally considered to be worth.
Sale Amount: How much the property was actually sold for.
Sales Ratio: The ratio measures how close the selling price of the property is to it's assessed value.
Property Type: What kind of property it is.
Residential Type: If it is a residential property, what type is it.
Years until sold: Number of years before the property was finally sold
This dataset can be used for analysis and even machine learning projects. For those doing analysis, I invite you to try and answer these questions: * Average assessed value of properties from year to year? * Average sale amount of properties from year to year? * Average sales ratio of properties from year to year? * How long, on average, did it take for the different property types to get sold? * How long, on average, did it take for the different residential types to get sold? * Which towns saw the most property sales in 2021?
For those more interested in using this dataset in machine learning projects to forecast future property prices, I invite you also. Let's learn from your work.
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TwitterThe 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.
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TwitterIn the fourth quarter of 2024, the sales growth of the medium house market in Indonesia contracted by around **** percent. During the same period, sales in the large house segment had the highest growth, reaching over ** percent.
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Twitterhttps://www.usa.gov/government-works/https://www.usa.gov/government-works/
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.
| Column Name | Description |
|---|---|
| Serial Number | A unique identifier for each record in the dataset. |
| List Year | The grand list year in which the sale was recorded. |
| Date Recorded | The date when the sale was recorded. |
| Town | The town where the property is located. |
| Address | The address of the property. |
| Assessed Value | The assessed value of the property. |
| Sale Amount | The sales price of the property. |
| Sales Ratio | The sales ratio of the property. |
| Property Type | The type of the property (residential, apartment, commercial, industrial, or vacant land). |
| Residential Type | The type of residential property (if applicable). |
| Non Use Code | The non-use code associated with the property (if applicable). |
| Assessor Remarks | Remarks or comments provided by the assessor (if available). |
| OPM Remarks | Remarks or comments provided by the Office of Policy and Management (if available). |
| Location | The location of the property (if available). |
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TwitterThe 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.
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Twitterhttps://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain
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.
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TwitterReal Estate Sales 2001-2020 GL Metadata Updated: August 12, 2023
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. Access & Use Information Public: This dataset is intended for public access and use. Non-Federal: This dataset is covered by different Terms of Use than Data.gov. License: No license information was provided.
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License information was derived automatically
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.
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TwitterIn the fourth quarter of 2024, the sales growth of the large house market in Indonesia reached around **** percent. During the same period, the overall sales growth of the residential property market in Indonesia contracted by approximately ** percent.
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Twitterhttps://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain
Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q2 2025 about sales, housing, and USA.
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Twitterhttps://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain
Graph and download economic data for New Houses Sold by Sales Price in the United States, Total (NHSUSSPTA) from 2020 to 2024 about new, sales, housing, price, and USA.
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Twitterhttps://www.usa.gov/government-works/https://www.usa.gov/government-works/
The provided dataset contains information about real estate transactions in Connecticut for the year 2020. Each row in the dataset represents a single real estate transaction, and the columns provide details about each transaction.
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6286349%2Ffb5411354da1bff9948ada8097dc597b%2FScreenshot%202023-09-27%20103117.png?generation=1695803697508615&alt=media" alt="">
This dataset appears to contain information about various real estate transactions in Connecticut, including details about the properties, their assessed values, sale prices, and additional remarks or notes. The data can be used for various purposes, including real estate market analysis, property assessment accuracy assessment, and more.
https://media.giphy.com/media/l0IylQoMkcbZUbtKw/giphy.gif" alt="gif">
Please note that this dataset may require cleaning and preprocessing before performing any data analysis or visualization. Additionally, specific analyses or insights can be derived from this data depending on your research or analytical objectives.
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License information was derived automatically
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.
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TwitterIn October 2020, the North Shore City district recorded the largest annual change in residential property sales in New Zealand, with an increase of ***** percent compared to the same month in the previous year. The annual change in average residential house prices in this district increased by **** percent in the same period.
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TwitterResidential 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.
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TwitterHave been tracking the thailand housing market for a few years, in the last couple of years, for sale properties have been steadily increasing between 2020 and late 2021; coming into 2022, the housiing market seems went down in temrs of property listings,
https://barkingdata.com/static/upload/image/20220423/1650710255930743.png" alt="listings trend">
The attached dataset is generated via AI based public daa mining technology. Researchers can use this dataset to do various analysis because the number of fields of this dataset includes a lot of housing attributes such as property type, pricing, zipcode, living space size, bedrooms, bathrooms, city, state, lat/lng, home created date, land space, agent name, agent info, funished, premium type etc.. We specialize in web mining and web data harvesting from the world wide web (including mobile apps), we have built 5000+ datasets for researchers, analysts, scholars , retailers, ... Learn more from https://www.barkingdata.com
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TwitterThe House Price Index (HPI) measures inflation in the residential property market. The HPI captures price changes of all types of dwellings purchased by households (flats, detached houses, terraced houses, etc.). Only transacted dwellings are considered, self-build dwellings are excluded. The land component of the dwelling is included.
The HPI is available for all European Union Member States (except Greece), the United Kingdom (only until the third quarter of 2020), Iceland, Norway, Switzerland and Turkey. In addition to the individual country series, Eurostat produces indices for the euro area and for the European Union (EU). As from the first quarter of 2020 onwards, the EU HPI aggregate no longer includes the HPI from the United Kingdom.
The national HPIs are produced by National Statistical Offices (NSIs) and the European aggregates by Eurostat, by combining the national indices. The data released quarterly on Eurostat's website include the national and European price indices, weights and their rates of change.
In order to provide a more comprehensive picture of the housing market, house sales indicators are also provided. Available house sales indicators refer to the total number and value of dwellings transactions at national level where the purchaser is a household. Eurostat publishes in its database a quarterly and annual house sales index as well as quarterly and annual rates of change.
The HPI is based on market prices of dwellings. Non-marketed prices are ruled out from the scope of this indicator. Self-build dwellings, dwellings purchased by sitting tenants at discount prices or dwellings transacted between family members are out of the scope of the indicator. It covers all monetary dwelling transactions regardless of its type (e.g., carried out through a cash purchase or financed through a mortgage loan).
The HPI measures the price developments of all dwellings purchased by households, regardless of which institutional sector they were bought from and the purpose of the purchase. As such, a dwelling bought by a household for a purpose other than owner-occupancy (e.g., for being rented out) is within the scope of the indicator. The HPI includes all purchases of new and existing dwellings, including those of dwellings transacted between households.
The number and value of house sales cover the total annual value of dwellings transactions at national level where the purchaser is a household. Transactions between households are included. Transfers in dwellings due to donations and inheritances are excluded.
The house sales value reflect the prices paid by household buyers and include both the price of land and the price of the structure of the dwelling. The prices for new dwellings include VAT. Other costs related to the acquisition of the dwelling (e.g., notary fees, registration fees, real estate agency commission, bank fees) are excluded.
Each published index or rate of change refers to transacted dwellings purchased at market prices by the household sector in the corresponding geographical entity. All transacted dwellings are covered, regardless of which institutional sector they were bought from and of the purchase purpose.
more: https://ec.europa.eu/eurostat/cache/metadata/en/prc_hpi_inx_esms.htm
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TwitterThese 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/">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">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.
Further details for this statistical release, including data suitability and coverage, are included within the ‘Monthly property transactions completed in the UK with value of £40,000 or above’ quality report.
The latest release was published 09:30 28 November 2025 and was updated with provisional data from completed transactions during October 2025.
The next release will be published 09:30 09 January 2026 and will be updated with provisional data from completed transactions during November 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">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.
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License information was derived automatically
China Floor Space Sold: Year to Date: Residential: Existing House data was reported at 60,446.784 sq m th in Mar 2025. This records an increase from the previous number of 29,608.305 sq m th for Feb 2025. China Floor Space Sold: Year to Date: Residential: Existing House data is updated monthly, averaging 89,992.158 sq m th from Aug 2005 (Median) to Mar 2025, with 236 observations. The data reached an all-time high of 319,313.139 sq m th in Dec 2016 and a record low of 8,877.505 sq m th in Feb 2020. China Floor Space Sold: Year to Date: Residential: Existing House data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Real Estate Sector – Table CN.RKC: Commodity Building: Floor Space Sold: Monthly.
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TwitterIn the fourth quarter of 2024, the overall sales growth of the residential property market in Indonesia contracted by approximately ** percent. During the same period, the sales growth for small houses in Indonesia fell by over ** percent.
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TwitterApache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
Here's a short description of the dataset:
Serial Number: Is just a unique set of digits to identify each transaction
List year: This is the year that the particular property was put up for sale.
Date Recorded: Is the date that the transaction was completed. That is, the year the property was bought.
Town: The town where this property is located.
Address: The property's address.
Assessed Value: How much the property is generally considered to be worth.
Sale Amount: How much the property was actually sold for.
Sales Ratio: The ratio measures how close the selling price of the property is to it's assessed value.
Property Type: What kind of property it is.
Residential Type: If it is a residential property, what type is it.
Years until sold: Number of years before the property was finally sold
This dataset can be used for analysis and even machine learning projects. For those doing analysis, I invite you to try and answer these questions: * Average assessed value of properties from year to year? * Average sale amount of properties from year to year? * Average sales ratio of properties from year to year? * How long, on average, did it take for the different property types to get sold? * How long, on average, did it take for the different residential types to get sold? * Which towns saw the most property sales in 2021?
For those more interested in using this dataset in machine learning projects to forecast future property prices, I invite you also. Let's learn from your work.