The dataset "aus_real_estate.csv" encapsulates comprehensive real estate information pertaining to Australia, showcasing diverse attributes essential for property assessment and market analysis. This dataset, comprising 5000 entries across 10 distinct columns, offers a detailed portrayal of various residential properties in cities across Australia.

The dataset encompasses crucial factors influencing property valuation and purchase decisions. The 'Price' column represents the property's cost, spanning a range between $100,000 and $2,000,000. Attributes such as 'Bedrooms' and 'Bathrooms' highlight the accommodation specifics, varying from one to five bedrooms and one to three bathrooms, respectively. 'SqFt' denotes the square footage of the properties, varying between 800 and 4000 square feet, elucidating their size and spatial dimensions.

The 'City' column encompasses major Australian urban centers, including Sydney, Melbourne, Brisbane, Perth, and Adelaide, delineating the geographical distribution of the properties. 'State' further categorizes the locations into New South Wales (NSW), Victoria (VIC), Queensland (QLD), Western Australia (WA), and South Australia (SA).

The dataset encapsulates temporal information through the 'Year_Built' attribute, spanning from 1950 to 2023, providing insights into the age and vintage of the properties. Moreover, property types are delineated within the 'Type' column, encompassing variations such as 'Apartment,' 'House,' and 'Townhouse.' The binary 'Garage' column signifies the presence (1) or absence (0) of a garage, while 'Lot_Area' provides an understanding of the land area, ranging from 1000 to 10,000 square feet.

This dataset offers a comprehensive outlook into the Australian real estate landscape, facilitating multifaceted analyses encompassing property valuation, market trends, and regional preferences. Its diverse attributes make it a valuable resource for researchers, analysts, and stakeholders within the real estate domain, enabling robust investigations and informed decision-making processes regarding property investments and market dynamics in Australia.

High-quality, free real estate dataset from all around the United States, in CSV format. Over 10.000 records relevant to Real Estate investors, agents, and data scientists. We are working on complete datasets from a wide variety of countries. Don't hesitate to contact us for more information.

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.

Zoopla UK properties dataset extracted bt crawl feeds team. Dataset having more than 80K+ records and 30 datapoints.

Dataset is available in CSV format

Site complexity: Difficult

Ready to download

Explore the Redfin USA Properties Dataset, available in CSV format. This extensive dataset provides valuable insights into the U.S. real estate market, including detailed property listings, prices, property types, and more across various states and cities. Perfect for those looking to conduct in-depth market analysis, real estate investment research, or financial forecasting.

Key Features:

• Comprehensive Property Data: Includes essential details such as listing prices, property types, square footage, and the number of bedrooms and bathrooms.
• Geographic Coverage: Encompasses a wide range of U.S. states and cities, providing a broad view of the national real estate market.
• Historical Trends: Analyze past market data to understand price movements, regional differences, and market trends over time.
• Geo-Location Details: Enables spatial analysis and mapping by including precise geographical coordinates of properties.

Who Can Benefit From This Dataset:

• Real Estate Investors: Identify lucrative opportunities by analyzing property values, market trends, and regional price variations.
• Market Analysts: Gain a deeper understanding of the U.S. housing market dynamics to inform research and reporting.
• Data Scientists and Researchers: Leverage detailed real estate data for modeling, urban studies, or economic analysis.
• Financial Analysts: Utilize the dataset for financial modeling, helping to predict market behavior and assess investment risks.

Download the Redfin USA Properties Dataset to access essential information on the U.S. housing market, ideal for professionals in real estate, finance, and data analytics. Unlock key insights to make informed decisions in a dynamic market environment.

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Send a request with just one click and explore detailed property listings, price trends, and housing data.
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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 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.

Gain a complete view of the real estate market with our Zillow datasets. Track price trends, rental/sale status, and price per square foot with the Zillow Price History dataset and explore detailed listings with prices, locations, and features using the Zillow Properties Listing dataset. Over 134M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Zpid
City
State
Home Status
Street Address
Zipcode
Home Type
Living Area Value
Bedrooms
Bathrooms
Price
Property Type
Date Sold
Annual Homeowners Insurance
Price Per Square Foot
Rent Zestimate
Tax Assessed Value
Zestimate
Home Values
Lot Area
Lot Area Unit
Living Area
Living Area Units
Property Tax Rate
Page View Count
Favorite Count
Time On Zillow
Time Zone
Abbreviated Address
Brokerage Name
And much more

Dataset

This dataset was created by Terry James

Released under Other (specified in description)

Contents

Explore the Redfin Canada Properties Dataset, available in CSV format and extracted in April 2022. This comprehensive dataset offers detailed insights into the Canadian real estate market, including property listings, prices, square footage, number of bedrooms and bathrooms, and more. Covering various cities and provinces, it’s ideal for market analysis, investment research, and financial modeling.

Key Features:

• Property Details: Includes crucial data such as listing price, property type, square footage, number of bedrooms and bathrooms, and more.
• Geo-Location Data: Provides geographical coordinates, allowing for spatial analysis and mapping.
• Market Trends: Offers historical data to analyze price trends and market fluctuations.

Who Can Use This Dataset:

• Real Estate Professionals: Evaluate market trends and property values to better advise clients or guide investment decisions.
• Investors: Analyze the Canadian housing market to identify investment opportunities and potential returns.
• Data Analysts and Researchers: Use this dataset to study market dynamics, urban development, or economic factors influencing the real estate sector.
• Financial Analysts: Incorporate the data into financial models to forecast market behavior and investment outcomes.

Download the Redfin Canada Properties Dataset to access valuable information on the Canadian housing market, perfect for anyone involved in real estate, finance, or data analysis.

This is a collection of CSV files that contain assessment data. The files in this extract are:Primary Parcel file containing primary owner and land information;Addn file containing drawing vectors for dwelling records;Additional Address file containing any additional addresses that exist for a parcel;Assessment file containing assessed value-related data;Appraisal file containing appraised value-related data;Commercial file containing primary commercial data;Commercial Apt containing commercial apartment data;Commercial Interior Exterior dataDwelling fileEntrance data containing data from appraisers' visits;Other Buildings and Yard ImprovementsSales FileTax Rate File for the current billing cycle by taxing district authority and property class; and,Tax Payments File containing tax charges and payments for current billing cycle.In addition to the CSV files, the following are included:Data Dictionary PDF; and,St Louis County Rate Book for the current tax billing cycle.

Information about real estate transactions is considered to be public information in Minnesota. The data is collected by the Minnesota Department of Revenue. Revenue provides the database each year to the University of Minnesota Department of Applied Economics for use in educational programming. The data on farmland sales is anonymized and certain items are uploaded to the University of Minnesota website “Minnesota Land Economics” where reports can be generated by county or other boundaries. Total acres in the parcel, tillable acres in the parcel, and price can be viewed or downloaded down to the township level. This database includes the original data files provided by Revenue, including all real estate transactions, in CSV format, for the years 2010-2021. The yearly files cover October 1 to September 30. The data for some years is split into several files. A CSV file is included with column names and descriptions for 2018, and another with the column names for 2021.

This dataset mainly provides real information declared by applicants nationwide for real estate transactions, pre-sale house transactions, and leases, including actual price and main attributes such as area, and land use zoning. (Provide MANIFEST.CSV, schema-main.csv, schema-build.csv, schema-land.csv, schema-park.csv) Published once on the 1st, 11th, and 21st of this month.

This dataset mainly provides actual information on real estate lease transactions declared by declarants nationwide, including actual prices and main attributes such as area and usage zones. (Provide MANIFEST.CSV, schema-main.csv, schema-build.csv, schema-land.csv, schema-park.csv) Published once on the 1st, 11th, and 21st of each month.

A csv export of data from FRPP, a database of Federal real property for civilian agencies.

Real estate transactions 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 seven classes. The first class shows, at the national level, for each cadastral nature and for each type of transaction, the number of parcels 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, cadastral divisions and statistical sectors. The dataset can be freely downloaded as a zipped CSV.

Real estate sales - Profile of the buyers corresponds to the dataset describing the profile of the buyers (natural persons) of real estate. This dataset is composed of seven classes. The first class shows, at the national level, for each cadastral nature and by price range the number of real estate property that was sold as well as the number of buyers broken down by age and gender categories. 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, cadastral divisions and statistical sectors. The dataset can be freely downloaded as a zipped CSV.

The Box and Parcel Label Dataset is a collection of images and corresponding bounding box labels designed for object detection and localization tasks. The dataset is organized in a hierarchical structure consisting of a main folder containing two subfolders: "Images" and "Labels". It is intended for research and development purposes in the field of computer vision, particularly focusing on object detection and recognition.

Dataset Structure:

Main Folder:

The main folder serves as the root directory of the dataset. Contains two subfolders: "Images" and "Labels". Images Subfolder:

The "Images" subfolder contains a collection of 200 images. Each image depicts various scenarios containing boxes and parcels. The images are of diverse resolutions and may vary in dimensions. Image formats may include commonly used formats such as JPEG, PNG, etc. Labels Subfolder:

The "Labels" subfolder contains the bounding box annotations corresponding to the images in the "Images" subfolder. Each image in the "Images" subfolder has a corresponding label file in this subfolder. The labels are stored in a structured format, typically in formats like XML, JSON, or CSV, detailing the bounding box coordinates, class labels, and other relevant metadata. Each label file corresponds to an image, providing accurate localization information for objects such as boxes and parcels within the image.

Explore the Redfin Canada Real Estate Data, last extracted in June 2022 and available in CSV format. This robust dataset contains over 100,000 records, offering detailed insights into the Canadian housing market.

It includes comprehensive data on property listings, prices, square footage, and more across various cities and provinces.

Ideal for real estate analysis, market trend research, and investment planning, this dataset is a valuable resource for professionals seeking in-depth understanding of the Canadian real estate landscape.