Existing Home Sales in the United States decreased to 3930 Thousand in June from 4040 Thousand in May 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.

Total Housing Inventory in the United States decreased to 1530 Thousands in June from 1540 Thousands in May of 2025. This dataset includes a chart with historical data for the United States Total Housing Inventory.

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.

Our dataset features comprehensive housing market data, extracted from 250,000 records sourced directly from Redfin USA. Our Crawl Feeds team utilized proprietary in-house tools to meticulously scrape and compile this valuable data.

Key Benefits of Our Housing Market Data:

• In-Depth Market Analysis: Gain insights into the real estate market with up-to-date data on recently sold properties.
  
  
• Price Trend Identification: Track and analyze price trends across different cities.
  
  
• Accurate Price Estimation: Estimate property values based on key factors such as area, number of beds and baths, square footage, and more.
  
  
• Detailed Real Estate Statistics: Access detailed statistics segmented by zip code, area, and state.

Unlock the Power of Redfin Data for Real Estate Professionals

Leveraging our Redfin properties dataset allows real estate professionals to make data-driven decisions. With detailed insights into property listings, sales history, and pricing trends, agents and investors can identify opportunities in the market more effectively. The data is particularly useful for comparing neighborhood trends, understanding market demand, and making informed investment decisions.

Enhance Your Real Estate Research with Custom Filters and Analysis

Our Redfin dataset is not only extensive but also customizable, allowing users to apply filters based on specific criteria such as property type, listing status, and geographic location. This flexibility enables researchers and analysts to drill down into the data, uncovering patterns and insights that can guide strategic planning and market entry decisions. Whether you're tracking the performance of single-family homes or exploring multi-family property trends, this dataset offers the depth and accuracy needed for thorough analysis.

Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
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New Home Sales in the United States increased to 627 Thousand units in June from 623 Thousand units in May 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.

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.

Data on the number of residential properties sold, sale price and number of buyers by sale type, property type and period of construction.

Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

Number of residential property sales in England and Wales, by property type and administrative geographies. Annual data.

UK Government House Price Index (HPI) data, up to and including August 2022.

**UPDATE (19/4/2023): Version 2 of this dataset contains data up to February 2023 in the file 'UK-HPI-full-file-2023-02.csv' ** The dataset consists of various metrics calculated from individual property transaction records. This data is stored on a regional basis with records made for each date period split based on averages across property type (Flat, Terraced, SemiDetached, Detached), method of purchase (Cash, Mortgage), buyer type (First Time Buyer, Former Owner Occupier) and property status (New Build, Existing (Old)).

Each of these subsets has data regarding average prices (normal and seasonally adjusted), sales volume, 12-month percentage price change, 1-month percentage price change, plus an index value which uses hedonic regression - matching sale price data with the attributes of a property (such as number of bedrooms, floor space, etc) to give an overview of the market.

Contains HM Land Registry data © Crown copyright and database right 2020. This data is licensed under the Open Government Licence v3.0.

Graph and download economic data for Monthly Supply of New Houses in the United States (MSACSR) from Jan 1963 to Jun 2025 about supplies, new, housing, and USA.

This dataset contains data on all Real Property parcels that have sold since 2013 in Allegheny County, PA.

Before doing any market analysis on property sales, check the sales validation codes. Many property "sales" are not considered a valid representation of the true market value of the property. For example, when multiple lots are together on one deed with one price they are generally coded as invalid ("H") because the sale price for each parcel ID number indicates the total price paid for a group of parcels, not just for one parcel. See the Sales Validation Codes Dictionary for a complete explanation of valid and invalid sale codes.

Sales Transactions Disclaimer: Sales information is provided from the Allegheny County Department of Administrative Services, Real Estate Division. Content and validation codes are subject to change. Please review the Data Dictionary for details on included fields before each use. Property owners are not required by law to record a deed at the time of sale. Consequently the assessment system may not contain a complete sales history for every property and every sale. You may do a deed search at http://www.alleghenycounty.us/re/index.aspx directly for the most updated information. Note: Ordinance 3478-07 prohibits public access to search assessment records by owner name. It was signed by the Chief Executive in 2007.

Here are the short keys and their descriptions for each feature present in this dataset: PropertyID: unique identifier for each property xrCompositeLandUseID: code representing the type of land use for the property xrBuildingTypeID: code representing the type of building on the property ParcelID: unique identifier for the parcel of land LocationStartNumber: starting number of the property's street address ApartmentUnitNumber: unit number of the property's apartment, if applicable StreetNameAndWay: name of the street where the property is located xrPrimaryNeighborhoodID: code representing the primary neighborhood where the property is located LandSF: land square footage of the property TotalFinishedArea: total finished square footage of the property LivingUnits: number of living units in the property OwnerLastName: last name of the property's owner OwnerFirstName: first name of the property's owner PrimaryGrantor: name of the primary grantor of the property SaleDate: date when the property was sold SalePrice: sale price of the property TotalAppraisedValue: total appraised value of the property LegalReference: legal reference of the property xrSalesValidityID: code representing the sales validity of the property xrDeedID: code representing the type of deed for the property

Number of residential property sales in England and Wales, by property type and electoral ward. Annual data.

Analysis of ‘Current Tax Sale list’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/0c5b8262-1651-4344-a459-9d4f9fa8417f on 28 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains all properties that are eligible for tax sale as of May 6, 2021. Note: properties may be removed from the sale daily and this dataset only represents a snapshot in time as of May 6. This dataset does not constitute an official copy of the list.

The data include owner-occupied properties. On May 3rd, 2021, Mayor Scott announced that tax lien certificates on these properties would not be sold, however they are included in these data for reference. Use the field "BEING_REMO" to filter out properties that will no longer be sold based on Mayor Scott’s announcement on May 3.

Data Dictionary

Field Name Description

BLOCK The block number for the property.

LOT The lot number for the property.

OWNERSHIP INDICATOR Indicator for type of ownership on property. H = Owner occupied principal residence D = Dual use N Not owner occupied

LAND USE CODE The land use code for the parcel. R = residential C = commercial I = Industrial

OWNER NAME The name of the owner of the property.

TAX BASE The value of the property.

CITY TAX The annual city property tax based on the assesed value of the property.

STATE TAX The annual state property tax based on the assesed value of the property.

TOTAL TAXES The sum of the "City Tax" and "State Tax" columns.

TOTAL 3 YEAR TAXES DUE The total remaining taxes owed for the property.

TOTAL LIENS DUE The sum of the columns "Total 3 Year Taxes Due" and "Total Lien". This is the current total amount of money owed including liens and past due taxes.

TOTAL LIEN The total amount of liens on the property.

YEARS ELIGIBLE FOR SALE The number of years the property has been eligible for tax sale in the past.

DEED DATE The date that ownership of the property was transferred to the owner.

COUNCIL DISTRICT The city council district where the property is located.

NEIGHBORHOOD The neighborhood where the property is located.

WHEN SOLD The last time the tax lien certificate on the property was sold. Street Address The street number and street name of the property. City The city the property is in (Baltimore). State The state the property is in (Maryland). ZIP The ZIP code of the property. Latitude The latitude of the property. Longitude The longitude of the property.

--- Original source retains full ownership of the source dataset ---

This dataset represents real estate assessment and sales data that is updated on a quarterly basis by the Real Estate Assessor’s Office. This dataset contains information for properties in the city including: Acreage, Square footage, GPIN, Street Address, year built, current land value, current improvement values, and current total value. The information is obtained from Real Estate Assessor’s Office ProVal records database.

Dataset Overview

This dataset provides a detailed snapshot of real estate properties listed in Dubai, UAE, as of August 2024. The dataset includes over 5,000 listings scraped using the Apify API from Propertyfinder and various other real estate websites in the UAE. The data includes key details such as the number of bedrooms and bathrooms, price, location, size, and whether the listing is verified. All personal identifiers, such as agent names and contact details, have been ethically removed.

Data Science Applications

Given the size and structure of this dataset, it is ideal for the following data science applications:

• Price Prediction Models: Predicting the price of properties based on features like location, size, and furnishing status.
• Market Analysis: Understanding trends in the Dubai real estate market by analyzing price distributions, property types, and locations.
• Recommendation Systems: Developing systems to recommend properties based on user preferences (e.g., number of bedrooms, budget).
• Sentiment Analysis: Extracting and analyzing sentiments from the property descriptions to gauge the market's tone.

This dataset provides a practical foundation for both beginners and experts in data science, allowing for the exploration of real estate trends, development of predictive models, and implementation of machine learning algorithms.

# Column Descriptors

• title: The listing's title, summarizing the key selling points of the property.
• displayAddress: The public address of the property, including the community and city.
• bathrooms: The number of bathrooms available in the property.
• bedrooms: The number of bedrooms available in the property.
• addedOn: The timestamp indicating when the property was added to the listing platform.
• type: Specifies whether the property is residential, commercial, etc.
• price: The listed price of the property in AED.
• verified: A boolean value indicating whether the listing has been verified by the platform.
• priceDuration: Indicates if the property is listed for sale or rent.
• sizeMin: The minimum size of the property in square feet.
• furnishing: Describes whether the property is furnished, unfurnished, or partially furnished.
• description: A more detailed narrative about the property, including additional features and selling points.

# Ethically Mined Data

This dataset was ethically scraped using the Apify API, ensuring compliance with data privacy standards. All personal data such as agent names, phone numbers, and any other sensitive information have been omitted from this dataset to ensure privacy and ethical use. The data is intended solely for educational purposes and should not be used for commercial activities.

# Acknowledgements

This dataset was made possible thanks to the following:

• Apify: For providing the API to ethically scrape the data.
• Propertyfinder and various other real estate websites in the UAE for the original listings.
• Kaggle: For providing the platform to share and analyze this dataset.

-**Photo by** : Francesca Tosolini on Unsplash

Use the Data Responsibly

Please ensure that this dataset is used responsibly, with respect to privacy and data ethics. This data is provided for educational purposes.

This dataset represents real estate assessment and sales data made available by the Office of the Real Estate Assessor. This dataset contains information for properties in the city, including acreage, square footage, GPIN, street address, year built, current land value, current improvement value, and current total value. The information is obtained from the Office of the Real Estate Assessor ProVal records database. This dataset is updated daily on weekdays.

Description in Spanish, original page The data in this dataset was collected by Properati.

Context

One of the best applications of data science and machine learning in general is the real estate business. This data set provides data for those who want to make data analysis and use of machine learning models to perform multiple tasks and generate new insights.

Content

It consists of a .csv where each row contains a publication. The .csv contains no missing data, this means that it is almost ready for use and model training. The only thing necessary is to convert the "string" type data into numerical data.

Columns

id - Notice identifier. It is not unique: if the notification is updated by the real estate agency (new version of the notification) a new record is created with the same id but different dates: registration and cancellation.

operation_type - Type of operation (these are all sales, can be removed).

l2 - Administrative level 2: usually province

l3 - Administrative level 3: usually city

lat - Latitude.

lon - Longitude.

price - Price published in the ad.

property_type - Type of property (House, Apartment, PH).

rooms - Number of rooms (useful in Argentina).

bathrooms - Number of bathrooms.

start_date - Date when the ad was created.

end_date - Date of termination of the advertisement.

created_on - Date when the first version of the notice was created.

surface_total - Total area in m².

surface_covered - Covered area in m².

title - Title of the advertisement.

description - Description of the advertisement.

ad_type - Type of ad (Property, Development/Project).

Acknowledgements

The data in this dataset was collected by Properati.