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.

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

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.

Extract of Cambridge Assessing Department on-line property database file for the most recently released fiscal year. Contains residential, condo, commercial and exempt data. Please refer to Cambridge's property database website for official assessment data: https://www.cambridgema.gov/propertydatabaseThis Feature Service will be updated annually with the latest fiscal year's data.The following fields are contained in the property database table.Field NameDescriptionPIDInternal Unique Parcel IDGISIDLink to ML in GIS Parcels layerBldgNumBuilding Number on ParcelAddressParcel AddressUnitUnit NumberStateClassCodeState Classification CodePropertyClassClassification Code descriptionZoningZoning (Unofficial)Map/LotAssessor's Map and Lot IDLandAreaLand area in square feetYearOfAssessmentFiscal Year of Assessment for this recordTaxDistrictDistrict for valuation groupingResidentialExemptionReceiving Residential exemption for fiscal yearBuildingValueAssessed value of building improvements on the parcelLandValueAssessed value of land on the parcelAssessedValueTotal assessed valueSalePricePrice listed for last deed transfer for the parcelBook/PageBook and Page number from the registry of deeds for last deed transactionSaleDateDate of last deed transactionPreviousAssessedValueTotal assessed value for the prior fiscal yearOwner_NameName of owner of record for the date of assessmentOwner_CoOwnerNameName of co-owner of record for the date of assessmentOwner_AddressAddress of owner of record for the date of assessmentOwner_Address2Second line of address of owner of record for the date of assessmentOwner_CityCity of owner of record for the date of assessmentOwner_StateState of owner of record for the date of assessmentOwner_ZipZip code of owner of record for the date of assessmentExterior_StyleBuilding style descriptionExterior_occupancyBuilding occupany, or use, type descriptionExterior_NumStoriesNumber of stories for the buildingExterior_WallTypeExterior wall material descriptionExterior_WallHeightAverage height of floors in a commercial or apartment buildingExterior_RoofTypeRoof structure descriptionExterior_RoofMaterialRoof material descriptionExterior_FloorLocationFloor level for condominium unitsExterior_ViewView quality rating for condominiumsInterior_LivingAreaFinished area of buildingInterior_NumUnitsNumber of units in a commercial or apartment buildingInterior_TotalRoomsTotal number of rooms in a condominium or residential buildingInterior_BedroomsTotal number of bedrooms in a condominium or residetential buildingInterior_KitchensKitchen description in condominium unitInterior_FullBathsCount of full bathrooms in a condominium unit or residential buildingInterior_HalfBathsCount of half bathrooms in a condominium unit or residential buildingInterior_FireplacesCount of fireplaces in residential buildingsInterior_FlooringDescription of primary floor cover materialInterior_LayoutLayout description for condominium unitInterior_LaundryInUnitYes or No flag for in unit laundry for condominiumSystems_HeatTypeHeat system type descriptionSystems_HeatFuelHeat fuel type descriptionSystems_CentralAirCentral air conditioning system indicatorSystems_PlumbingRating of plumbing system for commercial buildingCondition_YearBuiltActual year built of buildingCondition_InteriorConditionDescription of interior condition of residential buildingCondition_OverallConditionDescription of overall condition of buildingCondition_OverallGradeDescription of overall grade of buildingParking_OpenNumber of open parking spaces for residential building or condominium unitParking_CoveredNumber of covered parking spaces for residential building or condominium unitParking_GarageNumber of garage parking spaces for residential building or condominium unitUnfinishedBasementGrossUnfinished basement areaFinishedBasementGrossFinished basement area

The dataset is created with almost 49,000 rows, spanning a simulated period of four years (2020-2023). The generated dataset is saved in a CSV file named 'smart_home_dataset.csv'. It is a valuable resource for analyzing smart home behavior, energy consumption patterns, and decision-making scenarios related to offloading computational tasks.

Dataset for Smart Home Appliances:

Unix Timestamp: Represents the Unix timestamp corresponding to each data entry, providing a standardized time format. Transaction ID: Assigns a unique identifier to each transaction or entry in the dataset, ensuring distinction between different observations. Appliance Usage (Television, Dryer, Oven, Refrigerator, Microwave): Binary indicators (0 or 1) indicating whether each corresponding smart home appliance is in use (1) or not (0). Voltage Metrics (Line Voltage, Voltage, Apparent Power): • Line Voltage: The voltage level in the electrical line. • Voltage: The voltage level at the specific location. • Apparent Power: The combination of real and reactive power in the electrical system. Energy Consumption (kWh): Represents the energy consumed, generated randomly within a specified range. Bandwidth (kbps): Bandwidth in a smart home system refers to the capacity or speed at which data can be transmitted between devices. It is crucial in facilitating smooth communication, enabling seamless control, monitoring, and automation of various home functions. Offloading Decision: Randomly selects between 'Local' and 'Remote' to simulate decision-making for offloading computational tasks.

Gives property, or parcel, ownership together with value information, which ensures fair assessment of Boston taxable and non-taxable property of all types and classifications. To preserve their integrity, the identifiers PID, CM_ID, GIS_ID, ZIPCODE, and MAIL_ZIPCODE all are marked with an underscore ("_") as the last character.

Year-specific documentation for the FY2008 through FY2013 files is not currently available, but the format of those files is equivalent to that described in the FY2014 documentation.

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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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

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.

The Canada Trademarks Dataset

18 Journal of Empirical Legal Studies 908 (2021), prepublication draft available at https://papers.ssrn.com/abstract=3782655, published version available at https://onlinelibrary.wiley.com/share/author/CHG3HC6GTFMMRU8UJFRR?target=10.1111/jels.12303

Dataset Selection and Arrangement (c) 2021 Jeremy Sheff

Python and Stata Scripts (c) 2021 Jeremy Sheff

Contains data licensed by Her Majesty the Queen in right of Canada, as represented by the Minister of Industry, the minister responsible for the administration of the Canadian Intellectual Property Office.

This individual-application-level dataset includes records of all applications for registered trademarks in Canada since approximately 1980, and of many preserved applications and registrations dating back to the beginning of Canada’s trademark registry in 1865, totaling over 1.6 million application records. It includes comprehensive bibliographic and lifecycle data; trademark characteristics; goods and services claims; identification of applicants, attorneys, and other interested parties (including address data); detailed prosecution history event data; and data on application, registration, and use claims in countries other than Canada. The dataset has been constructed from public records made available by the Canadian Intellectual Property Office. Both the dataset and the code used to build and analyze it are presented for public use on open-access terms.

Scripts are licensed for reuse subject to the Creative Commons Attribution License 4.0 (CC-BY-4.0), https://creativecommons.org/licenses/by/4.0/. Data files are licensed for reuse subject to the Creative Commons Attribution License 4.0 (CC-BY-4.0), https://creativecommons.org/licenses/by/4.0/, and also subject to additional conditions imposed by the Canadian Intellectual Property Office (CIPO) as described below.

Terms of Use:

As per the terms of use of CIPO's government data, all users are required to include the above-quoted attribution to CIPO in any reproductions of this dataset. They are further required to cease using any record within the datasets that has been modified by CIPO and for which CIPO has issued a notice on its website in accordance with its Terms and Conditions, and to use the datasets in compliance with applicable laws. These requirements are in addition to the terms of the CC-BY-4.0 license, which require attribution to the author (among other terms). For further information on CIPO’s terms and conditions, see https://www.ic.gc.ca/eic/site/cipointernet-internetopic.nsf/eng/wr01935.html. For further information on the CC-BY-4.0 license, see https://creativecommons.org/licenses/by/4.0/.

The following attribution statement, if included by users of this dataset, is satisfactory to the author, but the author makes no representations as to whether it may be satisfactory to CIPO:

The Canada Trademarks Dataset is (c) 2021 by Jeremy Sheff and licensed under a CC-BY-4.0 license, subject to additional terms imposed by the Canadian Intellectual Property Office. It contains data licensed by Her Majesty the Queen in right of Canada, as represented by the Minister of Industry, the minister responsible for the administration of the Canadian Intellectual Property Office. For further information, see https://creativecommons.org/licenses/by/4.0/ and https://www.ic.gc.ca/eic/site/cipointernet-internetopic.nsf/eng/wr01935.html.

Details of Repository Contents:

This repository includes a number of .zip archives which expand into folders containing either scripts for construction and analysis of the dataset or data files comprising the dataset itself. These folders are as follows:

• /csv: contains the .csv versions of the data files
• /do: contains Stata do-files used to convert the .csv files to .dta format and perform the statistical analyses set forth in the paper reporting this dataset
• /dta: contains the .dta versions of the data files
• /py: contains the python scripts used to download CIPO’s historical trademarks data via SFTP and generate the .csv data files

If users wish to construct rather than download the datafiles, the first script that they should run is /py/sftp_secure.py. This script will prompt the user to enter their IP Horizons SFTP credentials; these can be obtained by registering with CIPO at https://ised-isde.survey-sondage.ca/f/s.aspx?s=59f3b3a4-2fb5-49a4-b064-645a5e3a752d&lang=EN&ds=SFTP. The script will also prompt the user to identify a target directory for the data downloads. Because the data archives are quite large, users are advised to create a target directory in advance and ensure they have at least 70GB of available storage on the media in which the directory is located.

The sftp_secure.py script will generate a new subfolder in the user’s target directory called /XML_raw. Users should note the full path of this directory, which they will be prompted to provide when running the remaining python scripts. Each of the remaining scripts, the filenames of which begin with “iterparse”, corresponds to one of the data files in the dataset, as indicated in the script’s filename. After running one of these scripts, the user’s target directory should include a /csv subdirectory containing the data file corresponding to the script; after running all the iterparse scripts the user’s /csv directory should be identical to the /csv directory in this repository. Users are invited to modify these scripts as they see fit, subject to the terms of the licenses set forth above.

With respect to the Stata do-files, only one of them is relevant to construction of the dataset itself. This is /do/CA_TM_csv_cleanup.do, which converts the .csv versions of the data files to .dta format, and uses Stata’s labeling functionality to reduce the size of the resulting files while preserving information. The other do-files generate the analyses and graphics presented in the paper describing the dataset (Jeremy N. Sheff, The Canada Trademarks Dataset, 18 J. Empirical Leg. Studies (forthcoming 2021)), available at https://papers.ssrn.com/abstract=3782655). These do-files are also licensed for reuse subject to the terms of the CC-BY-4.0 license, and users are invited to adapt the scripts to their needs.

The python and Stata scripts included in this repository are separately maintained and updated on Github at https://github.com/jnsheff/CanadaTM.

This repository also includes a copy of the current version of CIPO's data dictionary for its historical XML trademarks archive as of the date of construction of this dataset.

By using this dataset you acknowledge the following:Kansas Open Records Act StatementThe Kansas Open Records Act provides in K.S.A. 45-230 that "no person shall knowingly sell, give or receive, for the purpose of selling or offering for sale, any property or service to persons listed therein, any list of names and addresses contained in, or derived from public records..." Violation of this law may subject the violator to a civil penalty of $500.00 for each violation. Violators will be reported for prosecution.By accessing this site, the user makes the following certification pursuant to K.S.A. 45-220(c)(2): "The requester does not intend to, and will not: (A) Use any list of names or addresses contained in or derived from the records or information for the purpose of selling or offering for sale any property or service to any person listed or to any person who resides at any address listed; or (B) sell, give or otherwise make available to any person any list of names or addresses contained in or derived from the records or information for the purpose of allowing that person to sell or offer for sale any property or service to any person listed or to any person who resides at any address listed."

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.

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.

Product contains one data file (.csv format) for each year from 2006-2022. Records provide information about family demographics, dwelling characteristics, home value, income, years in residence & detailed geographic identifiers. Note: These data files are large (9-14GB each) and cannot be delivered through the Borealis platform. Please contact the Map and Data Library to arrange access: https://mdl.library.utoronto.ca/about/contact-form.

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.

Unlock the potential of your business with our meticulously curated dataset of products from The Home Depot. This extensive dataset includes detailed information on thousands of products, ranging from building materials and tools to home decor and appliances. Whether you're conducting market analysis, price comparison, or developing a retail strategy, this dataset provides invaluable insights.

The dataset features product names, descriptions, SKUs, prices, availability, and more, enabling you to gain a competitive edge in the market. Each entry is carefully organized to facilitate easy integration into your existing systems or databases.

Utilize this data to:

• Analyze product trends: Understand what products are popular and track pricing changes over time.
• Enhance retail strategies: Improve your inventory management, pricing strategies, and product offerings.
• Market research: Gain insights into consumer preferences and product performance in the home improvement sector.

For a closer look at the product-level data we’ve extracted from Home Depot, including pricing, stock status, and detailed specifications, visit the Home Depot dataset page. You can explore sample records and submit a request for tailored extracts directly from there.

Comprehensive Federal Tax Lien Data by CompCurve Unlock unparalleled insights into tax lien records with CompCurve Federal Tax Lien Data, a robust dataset sourced directly from IRS records. This dataset is meticulously curated to provide detailed information on federal tax liens, unsecured liens, and tax-delinquent properties across the United States. Whether you're a real estate investor, financial analyst, legal professional, or data scientist, this dataset offers a treasure trove of actionable data to fuel your research, decision-making, and business strategies. Available in flexible formats like .json, .csv, and .xls, it’s designed for seamless integration via bulk downloads or API access, ensuring you can harness its power in the way that suits you best.

IRS Tax Lien Data: Unsecured Liens in Focus At the heart of this offering is the IRS Tax Lien Data, capturing critical details about unsecured federal tax liens. Each record includes key fields such as taxpayer full name, taxpayer address (broken down into street number, street name, city, state, and ZIP), tax type (e.g., payroll taxes under Form 941), unpaid balance, date of assessment, and last day for refiling. Additional fields like serial number, document ID, and lien unit phone provide further granularity, making this dataset a goldmine for tracking tax liabilities. With a history spanning 5 years, this data offers a longitudinal view of tax lien trends, enabling users to identify patterns, assess risk, and uncover opportunities in the tax lien market.

Detailed Field Breakdown for Precision Analysis The Federal Tax Lien Data is structured with precision in mind. Every record includes a document_id (e.g., 2025200700126004) as a unique identifier, alongside the IRS-assigned serial_number (e.g., 510034325). Taxpayer details are comprehensive, featuring full name (e.g., CASTLE HILL DRUGS INC), and, where applicable, parsed components like first name, middle name, last name, and suffix. Address fields are equally detailed, with street number, street name, unit, city, state, ZIP, and ZIP+4 providing pinpoint location accuracy. Financial fields such as unpaid balance (e.g., $15,704.43) and tax period ending (e.g., 09/30/2024) offer a clear picture of tax debt, while place of filing and prepared_at_location tie the data to specific jurisdictions and IRS offices.

National Coverage and Historical Depth Spanning the entire United States, this dataset ensures national coverage, making it an essential resource for anyone needing a coast-to-coast perspective on federal tax liens. With 5 years of historical data, users can delve into past tax lien activity, track refiling deadlines (e.g., 01/08/2035), and analyze how tax debts evolve over time. This historical depth is ideal for longitudinal studies, predictive modeling, or identifying chronic tax delinquents—key use cases for real estate professionals, lien investors, and compliance experts.

Expanded Offerings: Secured Real Property Tax Liens Beyond unsecured IRS liens, CompCurve enhances its portfolio with the Real Property Tax Lien File, focusing on secured liens tied to real estate. This dataset includes detailed records of property tax liens, featuring fields like tax year, lien year, lien number, sale date, interest rate, and total due. Property-specific data such as property address, APN (Assessor’s Parcel Number), FIPS code, and property type ties liens directly to physical assets. Ownership details—including owner first name, last name, mailing address, and owner-occupied status—add further context, while financial metrics like face value, tax amount, and estimated equity empower users to assess investment potential.

Tax Delinquent Properties: A Wealth of Insights The Real Property Tax Delinquency File rounds out this offering, delivering a deep dive into tax-delinquent properties. With fields like tax delinquent flag, total due, years delinquent, and delinquent years, this dataset identifies properties at risk of lien escalation or foreclosure. Additional indicators such as bankruptcy flag, foreclosure flag, tax deed status, and payment plan flag provide a multi-dimensional view of delinquency status. Property details—property class, building sqft, bedrooms, bathrooms, and estimated value—combined with ownership and loan data (e.g., total open loans, estimated LTV) make this a powerhouse for real estate analysis, foreclosure tracking, and tax lien investment.

Versatile Formats and Delivery Options CompCurve ensures accessibility with data delivered in .json, .csv, and .xls formats, catering to a wide range of technical needs. Whether you prefer bulk downloads for offline analysis or real-time API access for dynamic applications, this dataset adapts to your workflow. The structured fields and consistent data types—such as varchar, decimal, date, and boolean—ensure compatibility with databases, spreadsheets, and programming environments, making it easy to integrate into your ...

The dataset mainly provides actual information about pre-sale house transactions declared by applicants nationwide, including actual transaction prices and key attributes such as area, land use zoning, and other information. (Provide MANIFEST.CSV, schema-main.csv, schema-build.csv, schema-land.csv, schema-park.csv) Released once on the 1st, 11th, and 21st of each month.