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 number of home sales in the United States peaked in 2021 at almost ************* after steadily rising since 2018. Nevertheless, the market contracted in the following year, with transaction volumes falling to ***********. Home sales remained muted in 2024, with a mild increase expected in 2025 and 2026. A major factor driving this trend is the unprecedented increase in mortgage interest rates due to high inflation. How have U.S. home prices developed over time? The average sales price of new homes has also been rising since 2011. Buyer confidence seems to have recovered after the property crash, which has increased demand for homes and also the prices sellers are demanding for homes. At the same time, the affordability of U.S. homes has decreased. Both the number of existing and newly built homes sold has declined since the housing market boom during the coronavirus pandemic. Challenges in housing supply The number of housing units in the U.S. rose steadily between 1975 and 2005 but has remained fairly stable since then. Construction increased notably in the 1990s and early 2000s, with the number of construction starts steadily rising, before plummeting amid the infamous housing market crash. Housing starts slowly started to pick up in 2011, mirroring the economic recovery. In 2022, the supply of newly built homes plummeted again, as supply chain challenges following the COVID-19 pandemic and tariffs on essential construction materials such as steel and lumber led to prices soaring.

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.

In 2022, the volume of commercial real estate transactions reached *** billion U.S. dollars, up from *** billion U.S. dollars in 2020. One of the reasons for the surge was the pandemic and the release of pent-up demand as the economy reopened. A real estate transaction refers to the process of passing the rights in a property unit from the seller to the buyer in return for an agreed upon sum. Effect of 2007-2008 credit crisis The U.S. real estate market reached its peak in 2007, just before the 2007-2008 credit crisis when the property market collapsed. The value of commercial property returns dropped between 2007 and 2009. Since 2010, the market has steadily recovered, and the volume of transactions climbed until 2015, and has levelled out since then. Types of commercial real estate The change in overall transaction volume is most likely impacted by the type of commercial properties which are more attractive to investors in a particular period. For instance, the interest in multifamily housing investment opportunities went down in the same period that interest in hotel investment opportunities went up.

Description:

• This dataset contains information about real estate property transactions.
• Each row represents a single property transaction.
• The dataset includes various attributes such as transaction dates, property locations, estimated and sale prices, property characteristics (e.g., type, residential status, number of rooms and bathrooms, carpet area), and property tax rates.
• The dataset provides insights into the real estate market, including trends in property prices, property types, and tax rates across different localities and years.
• It can be used for various analytical purposes such as market analysis, predictive modeling, and decision-making in the real estate industry.
• Researchers, analysts, and stakeholders in the real estate sector can utilize this dataset to understand market dynamics, assess property values, and make informed investment decisions.

Features:

Date: The date when the property transaction occurred. Year: The year of the property transaction. Locality: The locality or area where the property is located. Estimated Value: The estimated value of the property. Sale Price: The actual sale price of the property. Property: The type of property (e.g., Single Family). Residential: Indicates whether the property is residential or not. Num_rooms: The number of rooms in the property. Num_bathrooms: The number of bathrooms in the property. Carpet Area: The carpet area of the property. Property Tax Rate: The property tax rate applicable to the property. Face: The facing direction of the property (e.g., North, South, East).

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.

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.

1. Customers File (customers.csv)

• Description: This file contains information about clients involved in real estate transactions. It includes personal details such as name, surname, birth date, gender, and country, along with transaction-specific information like the purpose of the deal and the satisfaction level.
• Key Columns:
  • customerid: Unique identifier for the customer.
  • entity: Type of client, whether an individual or a company.
  • name and surname: First and last name of the customer.
  • birth_date: Customer's date of birth.
  • sex: Gender of the customer (Male/Female).
  • country and state: The country and state the customer is associated with.
  • purpose: Purpose of the transaction (e.g., Home purchase or Investment).
  • deal_satisfaction: Customer's satisfaction level with the transaction, ranging from 1 to 5.
  • mortgage: Indicates whether the transaction involved a mortgage (Yes/No).
  • source: How the customer was acquired (e.g., Website or Agency).

2. Properties File (properties.csv)

• Description: This file contains information about the properties sold, including building details, property type, area, price, and sale status.
• Key Columns:
  • id: Unique identifier for the property.
  • building: Number of the building where the property is located.
  • date_sale: The date when the property was sold.
  • type: Type of property (e.g., Apartment).
  • property#: The property number within the building.
  • area: Area of the property in square feet.
  • price: Sale price of the property.
  • status: Status of the sale (e.g., Sold).
  • customerid: The unique identifier of the customer associated with the property.

Suggested Analysis and Tasks

1 Customer Insights: - Customer Segmentation: Group customers based on demographics, purpose, or deal satisfaction to understand different customer profiles. - Satisfaction Analysis: Investigate what factors (e.g., property price, area, or mortgage involvement) influence customer satisfaction levels. - Source Effectiveness: Analyze which acquisition sources (e.g., website or agency) yield the highest deal satisfaction.

2 Property Market Analysis: - Price Trends: Analyze how property prices vary over time or by location to identify market trends. - Demand Analysis: Determine which types of properties (e.g., apartments vs. houses) are most popular based on sales data. - Area vs. Price: Explore the relationship between property area and price to develop pricing models or evaluate property value.

3 Predictive Modeling: - Price Prediction: Build models to predict property prices based on features like area, type, and location. - Satisfaction Prediction: Create models to predict customer satisfaction using transaction details and demographics. - Likelihood of Sale: Develop a model to predict the likelihood of a property being sold based on its attributes and market conditions.

4 Geographical Analysis: - Heatmaps: Create heatmaps to visualize property sales and identify high-demand areas. - Country and State Trends: Examine how real estate trends differ between countries and states.

5 Mortgage Impact Study: - Mortgage vs. Non-Mortgage Analysis: Compare transactions that involved a mortgage to those that didn’t to study the impact on price, satisfaction, and deal closure speed.

6 Time Series Analysis: - Sales Over Time: Analyze property sales over different periods to identify seasonal trends or patterns. - Customer Birth Date Analysis: Study any correlations between customers’ birth years and their purchasing behavior.

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

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/">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.

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.

Residential property transactions in New York City plummeted in *********, reaching *** - the lowest figure registered during the period under observation. Just a year ago, the number of residential transactions amounted to *****. New York was hit hard by the COVID-19 pandemic in 2020, resulting in house price volatility and slowing sales activity.

The United States real estate brokerage market, valued at $197.33 billion in 2025, is projected to experience steady growth, driven primarily by a robust housing market, increasing urbanization, and the growing preference for professional real estate services. The market's Compound Annual Growth Rate (CAGR) of 2.10% from 2025 to 2033 indicates a consistent, albeit moderate, expansion. Key market segments include residential and non-residential properties, with sales and rental services as primary revenue streams. Major players such as Keller Williams, RE/MAX, and Coldwell Banker dominate the market, leveraging extensive networks and advanced technological tools to enhance client services. While competition is fierce, the market's growth is fueled by factors like rising home prices, increasing investor interest in real estate, and the continuing need for expert guidance in navigating complex real estate transactions. The market faces challenges such as fluctuating interest rates which can impact buyer affordability and economic downturns that can reduce both sales and rental activity, thereby influencing the overall market expansion. However, the long-term outlook remains positive, supported by the enduring demand for housing and the critical role of brokerage firms in facilitating real estate transactions. The increasing use of online platforms and proptech solutions is also expected to further shape the market landscape in the coming years. The segmentation by property type (residential and non-residential) and service type (sales and rental) provides valuable insights into market dynamics. Residential sales are likely to remain the largest segment, driven by demographic shifts and population growth. The non-residential segment, encompassing commercial properties, will likely experience growth influenced by business expansion and investment activities. The rental segment is expected to continue its growth, particularly in urban areas facing housing shortages. The competitive landscape features established national brands alongside smaller, localized firms. The success of individual firms will depend on their ability to adapt to technological advancements, offer specialized services, and build strong client relationships. Furthermore, government regulations and economic conditions will also continue to play a significant role in shaping the market's trajectory. Recent developments include: May 2024: Compass Inc., the leading residential real estate brokerage by sales volume in the United States, acquired Parks Real Estate, Tennessee's top residential real estate firm that boasts over 1,500 agents. Known for its strategic acquisitions and organic growth, Compass's collaboration with Parks Real Estate not only enriches its agent pool but also grants these agents access to Compass's cutting-edge technology and a vast national referral network., April 2024: Compass has finalized its acquisition of Latter & Blum, a prominent brokerage firm based in New Orleans. Latter & Blum, known for its strong foothold in Louisiana and other Gulf Coast metros, has now become a part of Compass. This strategic move not only solidifies Compass' presence in the region but also propels it to a significant market share, estimated at around 15% in New Orleans.. Key drivers for this market are: 4., Increasing Urbanization Driving the Market4.; Regulatory Environment Driving the market. Potential restraints include: 4., Increasing Urbanization Driving the Market4.; Regulatory Environment Driving the market. Notable trends are: Industrial Sector Leads Real Estate Absorption, Retail Tightens Vacancy Rates.

The real estate transaction value in the 'Residential Real Estate Transactions' segment of the real estate market in the United States was modeled to be ************* U.S. dollars in 2024. Between 2017 and 2024, the real estate transaction value rose by *********** U.S. dollars, though the increase followed an uneven trajectory rather than a consistent upward trend. The real estate transaction value will steadily rise by *********** U.S. dollars over the period from 2024 to 2029, reflecting a clear upward trend.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Residential Real Estate Transactions.

Real estate transactions - annual version corresponds to the dataset describing transactions of real rights on real estate property such as recorded by the FPS Finance for registration purposes. This dataset is composed of five classes. The first class shows, at the national level, for each cadastral nature and for each type of transaction, the number of real estate property concerned by a transaction as well as market values of these transactions. The second class includes this information at the level of the three regions. The following classes do the same at the level of provinces, arrondissements, municipalities. The dataset can be freely downloaded as a zipped CSV.

Graph and download economic data for Nonfinancial Corporate Business; Residential Real Estate at Market Value, Transactions (BOGZ1FU105035023A) from 1946 to 2024 about market value, real estate, transactions, nonfinancial, residential, business, and USA.

Graph and download economic data for Households; Owner-Occupied Real Estate Including Vacant Land and Mobile Homes at Market Value, Transactions (BOGZ1FU155035015A) from 1946 to 2024 about Mobile, land, market value, vacancy, real estate, transactions, households, housing, and USA.

The Real Estate Sales and Brokerage industry has faced headwinds recently, mainly because of high mortgage rates. Between 2022 and 2023, the Federal Reserve raised its benchmark interest rate 11 times to manage inflation. Although reduced several times since, the aftermath remains prevalent, with mortgage rates still significantly higher than the levels of 2019-2021. This has stifled homebuyer demand, resulting in reduced home sales and pressure on related sectors. Agents and brokers are adjusting to this new reality, with many would-be homeowners delaying or reconsidering their purchasing plans. The office market has also been impacted, facing high vacancy rates. Despite the challenges, there are indicators of resilience in the industry. Housing inventory has increased, alleviating some buying pressures and providing more options for buyers. Brokers and agents are shifting their strategies, focusing more on marketing and price negotiations. Home prices have continued to climb, benefiting agents and brokerages whose commission relies on selling prices. In the office market, despite an increase in vacancies, sales of buildings have been on the rise; brokers have found opportunities by focusing on high-quality assets, such as Class A office spaces. Nonetheless, because of the industry's robust performance from 2020 to 2021, revenue has climbed at a CAGR of 0.7% over the past five years, reaching $240.0 billion in 2025. 2025 revenue will climb an estimated 0.6% as home price appreciation and a rebound in commercial sales volume will fuel tepid growth. The 'higher for longer' mortgage rate environment will persist, but reductions in interest rates will make new building constructions less expensive, leading to a gain in apartment complex constructions and benefiting real estate professionals. Supply constraints will gradually ease as housing starts are projected to strengthen, resulting in a more balanced and sustainable market. The industry will also see technological advancements with a greater reliance on AI-driven lead generation, virtual staging and automated transaction tools. Federal efforts to alleviate housing shortages through regulatory reforms and the use of federal lands for housing construction may boost the industry. Overall, industry revenue will gain at a CAGR of 1.8% to reach $262.6 billion in 2030.

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.

The "City of Hartford Real Estate Sales 730 Days" dataset provides an invaluable window into the dynamic world of urban property transactions within Hartford. Encompassing a comprehensive collection of real estate sales data from the past two years leading up to the present day, this dataset offers a real-time glimpse into the city's property market. Updated on a nightly basis, it captures the ebb and flow of real estate transactions, enabling stakeholders to track trends, assess market values, and gain insights into the ever-evolving real estate landscape of Hartford.

Uses and Applications: The utility of the "City of Hartford Real Estate Sales 730 Days" dataset extends across various sectors, from real estate professionals and developers to policymakers, analysts, and prospective buyers. Real estate agents and property developers can leverage this data to identify emerging trends, determine property values, and strategize their investment decisions. Prospective buyers and investors can gain a comprehensive understanding of the market dynamics, aiding them in making informed choices. Urban planners and policymakers can utilize this dataset to assess neighborhood growth, identify areas of development potential, and formulate policies that align with the city's real estate landscape. Financial analysts can scrutinize the data to provide insights into economic trends and market fluctuations. Overall, this dataset empowers stakeholders with accurate, up-to-date information, fostering a well-informed real estate ecosystem that contributes to the development and prosperity of Hartford's urban fabric.

Real Estate Sales 2001-2020 State of Connecticut

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.