New Home Sales in the United States increased to 676 Thousand units in February from 664 Thousand units in January 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.

Existing Home Sales in the United States increased to 4260 Thousand in February from 4090 Thousand in January 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.

This dataset has been published by the Office of the Real Estate Assessor of the City of Virginia Beach and data.virginiabeach.gov. The mission of data.virginiabeach.gov is to provide timely and accurate City information to increase government transparency and access to useful and well organized data by the general public, non-governmental organizations, and City of Virginia Beach employees.

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.

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.

This dataset is used for predicting house prices from both images and textual information. It is composed of 535 sample houses from California, USA.

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

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q4 2024 about sales, median, housing, and USA.

Median price paid for residential property in England and Wales, by property type and administrative geographies. Quarterly rolling annual data. Formerly HPSSA dataset 9

Existing Home Sales in Portugal increased to 36096 dwellings in the fourth quarter of 2024 from 32307 dwellings in the third quarter of 2024. This dataset includes a chart with historical data for Portugal Existing Home Sales.

Key information about House Prices Growth

• US house prices grew 5.2% YoY in Sep 2024, following an increase of 6.2% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Sep 2024, with an average growth rate of 5.5%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

Update Frequency: Yearly

Access to Residential, Condominium, Commercial, Apartment properties and vacant land sales history data.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

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

Prior to 1974 the data was based on surveys of existing house sales in Dublin carried out by the Valuation Office on behalf of the D. O. E. Since 1974 the data has been based on information supplied by all lending agencies on the average price of mortgage financed existing house transactions. Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. Data marked with n/a over the period 1969 and 1973 are not available. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Figure changed on the 27/6/16 as revised data received from the Local authority Includes houses and apartments, measured in €

Displacement risk indicator classifying census tracts according to single-family home sale prices in census tracts where at least 100 single-family homes exist. We classify arms-length transactions only along two dimensions:The median price of sales within the census tract for the specified year, balancing between nominal sale price and sale price per square foot.The change in median sale price (again balanced between nominal sale price and price per square foot) from the previous year.

This series does not include apartment prices. 2015 Figure changed on the 27/6/16 as revised data received from the Local authority Measured in €

The Detroit Land Bank Authority's (DLBA) Own It Now (OIN) program offers vacant homes for sale with a minimum offer price of $1,000. Homes are available on the DLBA website for sale 24 hours a day, 7 days a week and may be offered on at any time. Homes are sold as is and buyers are required to rehab the houses and ensure that they are occupied. The dataset provides information on the properties that have been successfully sold by the DLBA through the OIN program.

The Detroit Land Bank Authority (DLBA) works directly with individual buyers, as well as Community Partner organizations and developers to achieve their mission to return the city's blighted and vacant properties to productive use. They utilize a variety of sales programs to make homeownership and land purchases accessible to Detroiters. One of these programs is Own it Now (OIN).

Each row in the dataset represents an OIN home and includes data about the sale status, closing date for sold homes, sale price, and location information such as: address; parcel ID; and neighborhood.

For more information about the DLBA's sales programs click here.

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].

Context

This is a set of data created from imaginary data of house prices in an urban environment - Paris. I recommend using this dataset for educational purposes, for practice and to acquire the necessary knowledge. What I'm trying to do next is to create a classification dataset with same data from this dataset, I'll add a new column for class attribute ofc. Here is a classification dataset ---> classification dataset <---

Content

What's inside is more than just rows and columns. You can see house details listed as column names.

Description

All attributes are numeric variables and they are listed bellow:

• squareMeters
• numberOfRooms
• hasYard
• hasPool
• floors - number of floors
• cityCode - zip code
• cityPartRange - the higher the range, the more exclusive the neighbourhood is
• numPrevOwners - number of prevoious owners
• made - year
• isNewBuilt
• hasStormProtector
• basement - basement square meters
• attic - attic square meteres
• garage - garage size
• hasStorageRoom
• hasGuestRoom - number of guest rooms
• price - predicted value

Inspiration

Idea was to create dataset that is good for regression and that gives adequate results.