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.

Cleaned House Prices Dataset

This dataset contains information on residential properties, focusing on various features that influence house prices. Each row represents a unique property, with the following columns:

Summary

This cleaned dataset is intended for use in various analyses related to housing prices, such as regression modeling and feature importance evaluation. The data provides a comprehensive overview of different attributes that may affect the pricing of houses in a given area. It can be utilized for predictive modeling in real estate price prediction tasks.

Data description

The 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.

Statistical concepts and definitions

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.

Statistical unit

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

This table shows the price development of newly built and existing dwellings purchased by households. Aside from the price indices, Statistics Netherlands also publishes figures on the number, average purchase price and total sum of the purchase prices of the sold dwellings.

Data available from: 1st quarter 2015 to 3rd quarter 2023

Status of the figures: The figures in this table that are associated with existing homes (PBK) are final. The figures in this table that are associated with new dwellings (PNK) are one period provisional and the figures in this table that are associated with the number of sold dwellings and the average purchase price and related to newly built dwellings and total figures are provisional. Since this table has been discontinued, the data is no longer finalized.

Changes as of 6th of October 2022: This statistic is calculated using a European harmonized method. The method for rounding figures has changed within the European guidelines. This method change has been implemented with the result that some figures have been adjusted by a maximum of 0.1 index point or 0.1% development. The figures therefore correspond to the figures on the eurostat website.

Changes as of 25th of April 2024: This table has been discontinued. This table is followed by House Prices: new and existing dwellings price index 2020=100. See paragraph 3.

Australian Housing Prices

Location, Size, Price, Etc

By Jeff [source]

About this dataset

This dataset contains information on 1000 properties in Australia, including location, size, price, and other details

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How to use the dataset

If you're looking for a dataset on Australian housing data, this is a great option. This dataset contains information on over 1000 properties in Australia, including location, size, price, and other details. With this data, you can answer questions like What is the average price of a home in Australia?, What are the most popular type of homes in Australia?, and more

Research Ideas

• This dataset can be used to predict hosing prices in Australia.
• This dataset can be used to find relationships between housing prices and location.
• This dataset can be used to find relationships between housing prices and features such as size, number of bedrooms, and number of bathrooms

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: RealEstateAU_1000_Samples.csv | Column name | Description | |:--------------------|:---------------------------------------------------------------------------------------| | breadcrumb | A breadcrumb is a text trail that shows the user's location within a website. (String) | | category_name | The name of the category that the listing belongs to. (String) | | property_type | The type of property being listed. (String) | | building_size | The size of the property's building, in square meters. (Numeric) | | land_size | The size of the property's land, in square meters. (Numeric) | | preferred_size | The preferred size of the property, in square meters. (Numeric) | | open_date | The date that the property was first listed for sale. (Date) | | listing_agency | The agency that is listing the property. (String) | | price | The listing price of the property. (Numeric) | | location_number | The number that corresponds to the property's location. (Numeric) | | location_type | The type of location that the property is in. (String) | | location_name | The name of the location that the property is in. (String) | | address | The property's address. (String) | | address_1 | The first line of the property's address. (String) | | city | The city that the property is located in. (String) | | state | The state that the property is located in. (String) | | zip_code | The zip code that the property is located in. (String) | | phone | The listing agent's phone number. (String) | | latitude | The property's latitude. (Numeric) | | longitude | The property's longitude. (Numeric) | | product_depth | The depth of the product. (Numeric) | | bedroom_count | The number of bedrooms in the property. (Numeric) | | bathroom_count | The number of bathrooms in the property. (Numeric) | | parking_count | The number of parking spaces in the property. (Numeric) | | RunDate | The date that the listing was last updated. (Date) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Jeff.

Anjuke is a leading real estate information service platform in China, providing a large amount of comprehensive, accurate, and reliable housing data. It aims to offer users a safe and convenient house-hunting experience. Therefore, this project involves scraping second-hand housing data from the Anjuke platform for the Shanghai area, to establish a regression prediction model for analysis.

After data cleaning, the final constructed dataset contains a total of 175,128 records. Each record includes nearly 30 features, covering various aspects from basic information about the property to community characteristics and living environment features. The project also attempts to utilize textual content such as titles.

安居客是国内领先的房产信息服务平台，包含大量全面、精准、可靠的房屋数据，旨在为用户提供安心、便捷的找房服务。因此，本项目爬取安居客平台上海地区二手房数据，用于建立回归预测模型分析。

经过数据清洗，最终构建的数据集共包含175,128条记录。每条记录包括近30个特征，涵盖了从房源基本信息，到小区特点，居住环境特点等多方面，还尝试利用了标题等文本内容。

Annual mean and median property prices calculated by the GLA from Price Paid Data published on Land Registry website. Number of property sales also included. Data has been aggregated to Borough, Ward, MSOA, LSOA, Postcode Districts and Postcode Sectors. Caution should be used when analysing figures based on a low number of sales. Price Paid Data provides information on every residential property sale in England and Wales that has been lodged with HM Land Registry for registration. Download full price paid data from the Land Registry. Click on the image below to access an interactive dashboard using some of the data available from this page.

Quarterly median house prices for metropolitan Adelaide by suburb

This page is no longer being updated. Please use the UK House Price Index instead.

Mix-adjusted house prices, by new/pre-owned dwellings, type of buyer (first time buyer) and region, from February 2002 for London and UK, and average mix-adjusted prices by UK region, and long term Annual House Price Index data since 1969 for London.

The ONS House Price Index is mix-adjusted to allow for differences between houses sold (for example type, number of rooms, location) in different months within a year. House prices are modelled using a combination of characteristics to produce a model containing around 100,000 cells (one such cell could be first-time buyer, old dwelling, one bedroom flat purchased in London). Each month estimated prices for all cells are produced by the model and then combined with their appropriate weight to produce mix-adjusted average prices. The index values are based on growth rates in the mix-adjusted average house prices and are annually chain linked.

The weights used for mix-adjustment change at the start of each calendar year (i.e. in January). The mix-adjusted prices are therefore not comparable between calendar years, although they are comparable within each calendar year. If you wish to calculate change between years, you should use the mix-adjusted house price index, available in Table 33.

The data published in these tables are based on a sub-sample of RMS data. These results will therefore differ from results produced using full sample data. For further information please contact the ONS using the contact details below.
House prices, mortgage advances and incomes have been rounded to the nearest £1,000.
Data taken from Table 2 and Table 9 of the monthly ONS release.

Download from ONS website

This research data file contains the necessary software and the dataset for estimating the missing prices of house units. This approach combines several machine learning techniques (linear regression, support vector regression, the k-nearest neighbors and a multi-layer perceptron neural network) with several dimensionality reduction techniques (non-negative factorization, recursive feature elimination and feature selection with a variance threshold). It includes the input dataset formed with the available house prices in two neighborhoods of Teruel city (Spain) in November 13, 2017 from Idealista website. These two neighborhoods are the center of the city and “Ensanche”.

This dataset supports the research of the authors in the improvement of the setup of agent-based simulations about real-estate market. The work about this dataset has been submitted for consideration for publication to a scientific journal.

The open source python code is composed of all the files with the “.py” extension. The main program can be executed from the “main.py” file. The “boxplotErrors.eps” is a chart generated from the execution of the code, and compares the results of the different combinations of machine learning techniques and dimensionality reduction methods.

The dataset is in the “data” folder. The input raw data of the house prices are in the “dataRaw.csv” file. These were shuffled into the “dataShuffled.csv” file. We used cross-validation to obtain the estimations of house prices. The outputted estimations alongside the real values are stored in different files of the “data” folder, in which each filename is composed by the machine learning technique abbreviation and the dimensionality reduction method abbreviation.

The house price data are collected from the official website of China's National Bureau of Statistics . We acquired the month-on-month growth data of house prices since January 2006, then compiled the house price index based on January 2006 as 100. The Shanghai Stock Exchange Index (SSEI) data which are treated as stock market prices are derived from the CSMAR database. After that, we calculate the monthly house price and stock price return as , where are proxied by the monthly house price index and SSEI, and represent the returns series. 157 observations from January 2006 to March 2019 are obtained.

The samples in this paper come from panel data of 35 large and medium-sized cities in China from 1999 to 2019(In order to avoid the impact of the COVID-19 Pandemic on the conclusions of this analysis, we use the data before the outbreak of the epidemic for empirical testing). Here, the variables adopted for assessing the housing bubble include price level, resident income, household population, the average wage of staff and land supply. Apart from the housing bubble index which is obtained via assessment, all the other basic data come from official statistics, including the Wind Economic Database, website of the People’s Bank of China, and National Bureau of Statistics website.

This service provides data on Federal Housing Administration (FHA) single family, Real Estate Owned (REO) properties that are up for sale. The U.S. Department of Housing and Urban Development's Real Estate Owned (REO) properties are the result of the Federal Housing Administration (FHA) paying a claim to a lending institution on a foreclosed property which was financed with an FHA Insured Mortgage, and the lender has transferred ownership of the property of to HUD. Typically, title to the property is not transferred (or the claim paid) until the previous owner is evicted from the property. Normally, after the home is transferred to HUD, the property will go up for auction on the HUD Home store website.

Context

So this data set is collected for completing a college project ,which is an android app for calculating the price of houses.

Content

This data is scraped from magic bricks website between june 2021 and july 2021 .

Acknowledgements

magicbricks.com

Inspiration

With the help of the data available one can make a regression model to predict house prices.

This dataset contains data about apartments in Tashkent (Uzbekistan). It was compiled by scraping the real estate website. Each row represents a house and each column represents the feature of a house. There are 300 rows and 6 columns: Price, Rooms, Area, Floor, Total Floor, Floor/Total_Flor. - Price shows the selling price of a house in Uzbeks som (not rental price). - Rooms shows the number of rooms in this apartment. - Area shows the area of the apartment in square meters. - Floor shows on which floor of the main building the apartment is located in. - Total floor shows the total number of floors in the building. - Floor/Total_Flor shows Floor divided by Total_Floor

Housing prices and number of transactions by dwelling type.

House sales not at full market value are excluded.

Ownership of this dataset remains with the Communities and Local Government (CLG). Information can only be reproduced if the source is fully acknowledged.

The Land Registry (LR) and CLG have provided these datasets drawn from the Land Register.

Information on outliers, that is transactions involving a very low or very high price, is included so that users can take their impact into account when using the data.

Available for Middle Layer Super Output Area (MSOA).

NOTE: This data has not been updated since 2009.

See more on the ONS NESS website.

Graph and download economic data for All-Transactions House Price Index for North Carolina (NCSTHPI) from Q1 1975 to Q3 2025 about NC, appraisers, HPI, housing, price index, indexes, price, and USA.

Federal Housing Administration Single-Family – Properties for SaleThis National Geospatial Data Asset (NGDA) dataset, shared as a Federal Housing Administration feature layer, displays single-family real estate owned (REO) properties that are up for sale in the United States. Per Housing and Urban Development (HUD), "The U.S. Department of Housing and Urban Development's Real Estate Owned (REO) properties are a result of the Federal Housing Administration (FHA) paying a claim to a lending institution on a foreclosed property which was financed with an FHA Insured Mortgage and the lender transferring ownership of the property to HUD. Typically, title to the property is not transferred (or the claim paid) until the previous owner is evicted from the property. Normally, after the home is transferred to HUD, the property will go up for auction on the HUD Home store website."FHA Single Family Property, Case Number:137-427167Data currency: current federal service (FHA Single Family REO Properties For Sale)NGDAID: 128 (FHA Single Family REO Properties for Sale - National Geospatial Data Asset (NGDA))For more information: The Federal Housing Administration (FHA); FHA Single Family HousingFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes."For other NGDA Content: Esri Federal Datasets