Context

This dataset contains Real Estate listings in the US broken by State and zip code.

Download

kaggle API Command !kaggle datasets download -d ahmedshahriarsakib/usa-real-estate-dataset

Content

The dataset has 1 CSV file with 10 columns -

1. realtor-data.csv (2,226,382 entries)
   • brokered by (categorically encoded agency/broker)
   • status (Housing status - a. ready for sale or b. ready to build)
   • price (Housing price, it is either the current listing price or recently sold price if the house is sold recently)
   • bed (# of beds)
   • bath (# of bathrooms)
   • acre_lot (Property / Land size in acres)
   • street (categorically encoded street address)
   • city (city name)
   • state (state name)
   • zip_code (postal code of the area)
   • house_size (house area/size/living space in square feet)
   • prev_sold_date (Previously sold date)

NB: 1. brokered by and street addresses were categorically encoded due to data privacy policy 2. acre_lot means the total land area, and house_size denotes the living space/building area

Acknowledgements

Data was collected from - - https://www.realtor.com/ - A real estate listing website operated by the News Corp subsidiary Move, Inc. and based in Santa Clara, California. It is the second most visited real estate listing website in the United States as of 2024, with over 100 million monthly active users.

Cover Image

Image by Mohamed Hassan from Pixabay

Disclaimer

The data and information in the data set provided here are intended to use for educational purposes only. I do not own any data, and all rights are reserved to the respective owners.

Inspiration

• Can we predict housing prices based on the features?
• How are housing price and location attributes correlated?
• What is the overall picture of the USA housing prices w.r.t. locations?
• Do house attributes (bedroom, bathroom count) strongly correlate with the price? Are there any hidden patterns?

Looking to analyze the real estate market across the USA? Our Redfin real estate dataset provides a detailed sample of property listings, including prices, addresses, property features, and images. This dataset is perfect for analysts, developers, and real estate enthusiasts looking to gain insights into housing trends and market dynamics.

The dataset includes fields such as price, currency, address, property details, number of beds and baths, square footage, listing status, images, and more, giving you a robust foundation for analysis.

You can explore the full dataset and download the sample from Redfin real estate dataset. This makes it easy to integrate into your analytics pipelines, machine learning models, or market research projects.

Whether you're building a property analytics dashboard, testing real estate algorithms, or simply exploring housing trends, this dataset provides rich, up-to-date information directly from Redfin listings across the USA.

Start analyzing the USA housing market today with our Redfin dataset sample and make data-driven decisions with confidence.

Portuguese Real Estate Listings

This weekly updated dataset contains the more than 100k real asking prices for real estate properties listed on Portuguese real estate websites. The data was legally and ethically scraped from several online platforms, ensuring compliance with the platforms' terms and conditions.

The dataset includes detailed information about each property listing, such as:

• Price: The asking price of the property in Euros.
• District, City, Town: The geographical location of the property, allowing for regional analysis.
• Type: The type of property (e.g., Apartment, House, Garage, Land).
• EnergyCertificate: The energy efficiency rating of the property, according to Portuguese energy certification standards.
• Area Measurements: Several columns related to the size and area of the property, including:
  • GrossPrivateArea: The private gross area of the property.
  • UsableArea: The area that can be used for living.
  • LivingArea: The actual livable space inside the property.
  • LotSize: The size of the land associated with the property.
  • BuiltArea: The total constructed area of the property.
• Rooms, Bedrooms, Bathrooms, WCs: Detailed breakdowns of the number of rooms, bedrooms, bathrooms, and water closets in the property.
• Floor: The floor on which the property is located.
• Parking, Garage: Information on parking availability and the presence of a garage.
• Additional Features: Whether the property includes amenities like elevators or electric car charging stations.
• ConstructionYear: The year the property was built.
• PublishDate: The date when the listing was published.

The dataset is suitable for a variety of analyses, including: - Price trends: Understanding the relationship between location, property features, and asking prices. - Energy efficiency: Investigating how energy certification impacts property values. - Geographical analysis: Exploring regional differences in real estate pricing across Portugal.

This dataset offers a comprehensive snapshot of the Portuguese real estate market, enabling users to gain insights into current pricing, property types, and location-based factors influencing the market.

Extract detailed property data points — address, URL, prices, floor space, overview, parking, agents, and more — from any real estate listings. The Rankings data contains the ranking of properties as they come in the SERPs of different property listing sites. Furthermore, with our real estate agents' data, you can directly get in touch with the real estate agents/brokers via email or phone numbers.

A. Usecase/Applications possible with the data:

1. Property pricing - accurate property data for real estate valuation. Gather information about properties and their valuations from Federal, State, or County level websites. Monitor the real estate market across the country and decide the best time to buy or sell based on data

2. Secure your real estate investment - Monitor foreclosures and auctions to identify investment opportunities. Identify areas within special economic and opportunity zones such as QOZs - cross-map that with commercial or residential listings to identify leads. Ensure the safety of your investments, property, and personnel by analyzing crime data prior to investing.

3. Identify hot, emerging markets - Gather data about rent, demographic, and population data to expand retail and e-commerce businesses. Helps you drive better investment decisions.

4. Profile a building’s retrofit history - a building permit is required before the start of any construction activity of a building, such as changing the building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Use building permits to profile a city’s building retrofit history.

5. Study market changes - New construction data helps measure and evaluate the size, composition, and changes occurring within the housing and construction sectors.

6. Finding leads - Property records can reveal a wealth of information, such as how long an owner has currently lived in a home. US Census Bureau data and City-Data.com provide profiles of towns and city neighborhoods as well as demographic statistics. This data is available for free and can help agents increase their expertise in their communities and get a feel for the local market.

7. Searching for Targeted Leads - Focusing on small, niche areas of the real estate market can sometimes be the most efficient method of finding leads. For example, targeting high-end home sellers may take longer to develop a lead, but the payoff could be greater. Or, you may have a special interest or background in a certain type of home that would improve your chances of connecting with potential sellers. In these cases, focused data searches may help you find the best leads and develop relationships with future sellers.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

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

The data was scraped from the Magicbricks website. The following are the details of the dataset:

• Title: The title of the property listing.
• Price: The monthly rent of the property.
• Area: The total area of the property in square feet.
• BHK: The number of bedrooms in the property.
• Bathrooms: The number of bathrooms on the property.
• Furnished: Whether the property is furnished or not.
• Balconies: The number of balconies in the property.
• Floor: The floor number of the property.
• Ownership: The type of ownership of the property (i.e., freehold, leasehold, etc.).
• Facing: The direction the property faces.
• Amenities: The amenities that are available in the property or the surrounding area.
• Transaction Type: Whether the property is for sale or rent.
• Property Type: The type of property (i.e., apartment, house, villa, etc.).
• Location: The location of the property.
• Year of Construction: The year the property was built.
• Is Luxury: Whether the property is considered to be a luxury property.
• Description: A brief description of the property.
• Property Image: A link to the property image.

Key points in the dataset are :

1) This dataset can be used to gain insights into the rental market in Mumbai. For example, you could use the data to analyze the average rent for different types of properties, the most popular neighborhoods for renters, or the factors that affect the price of rent. You could also use the data to identify trends in the rental market, such as the increasing popularity of furnished apartments or the rising prices of luxury properties.

2) The dataset could also be used by real estate agents to help their clients find rental properties that meet their needs and budget. Additionally, the data could be used by developers to make informed decisions about the types of properties to build in Mumbai.

3) Overall, this dataset is a valuable resource for anyone who is interested in the rental market in Mumbai. It can be used to gain insights into the market, identify trends, and make informed decisions.

(Disclaimer: The data in this dataset has been gathered from publicly available sources. While the data is believed to be reliable and all privacy policies have been observed, No personal information such as email addresses, mobile numbers, or physical addresses hasn't been collected. I scrape data from the website Magicbricks to study the real estate market of Mumbai. ) Thank you !!!

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.

Real Estate Email List is a premium mailing database for your needs. Most importantly, the list is the most popular site in the world. It is the largest data provider. Besides, the list is verified by human checks and automated software. You get new connections instantly. In addition, our expert team builds a qualified email list and checks the accuracy levels from millions of sources. The list is 95% accurate for giving the best results. Moreover, the dataset provides authentic service. This service can help you grow your business in a short time. Also, the leads link is ready for instant download. Furthermore, we give weekly updates and a bounce-back guarantee with Excel and CSV files. The leads give more information about your services. If you want a specific real estate email list, tell us. We make it for you properly. We provide new data for free to replace missing data.

Real Estate Email List provides a free sample for marketing campaigns. You can create any custom order with your desired areas. The leads ensure that you never get inactive email data. After visiting our website, List to Data, contact us. You can purchase this email list to make your business more competitive. The dataset is profitable. In conclusion, you can get instant results for your products and services. Real Estate Email Database gives you verified and updated contact details. Also, it helps you connect with property owners, agents, and investors directly. In fact, this dataset includes names, phone numbers, email addresses, and postal details. Therefore, you can reach the right people in the real estate market quickly. So, you get high-quality leads that can help you grow your business. Likewise, it covers both residential and commercial real estate sectors. As a result, you can target your audience more effectively. Real Estate Email Database is fresh and regularly updated. This way, your campaigns always reach active contacts. Also, the affordable price makes it suitable for businesses of any size.

Therefore, you can boost sales without spending too much. Furthermore, this Email database supports various marketing goals. For example, you can promote property listings, offer investment deals, or build long-term client relationships. Finally, choose our database to enjoy better leads, higher ROI, and steady business growth.

The dataset contains structural attributes, locational information and prices for more than 139 thousand apartments in the city of Tehran (Iran). The data was collected from the largest national real estate website using a web crawler and contains submission date, exact location, neighborhood name, base area, floor level, age of building, price per square meter, and total price for the entries of the past four years.

Success.ai’s Commercial Real Estate Dataset for the US and Canada covers brokers, agents, developers, and investors across the property ecosystem. Whether you’re selling software, leasing tools, investment services, or lead generation tools—this is your go-to dataset for North American real estate.

What You Get:
- Work email and phone (where available)
- Job title (e.g., Broker, Agent, Analyst, Developer)
- Company name, website, size, and real estate focus
- LinkedIn URLs and regional data

Use Cases:
- PropTech & CRM sales
- Real estate investment tools outreach
- Lead generation for listings & marketplaces
- Commercial financing or insurance sales

Context

The datasets contain real estate listings in Argentina, Colombia, Ecuador, Perú, and Uruguay. With information on number of rooms, districts, prices, etc. They include houses, apartments, commercial lots, and more.

The datasets origin from Properati Data which is a data division of Properati, the Latin American property search site. On their website you can find links to different tools and datasets to use freely for your projects. All you have to do is make sure you credit them for the data.

Content

What a minute the dataset is in Spanish?! Yes, so for that reason I have provided a translated overview below. Keep in mind that although Spanish is a single language, certain words and expressions may vary depending on the country and region, e.g. the word for apartment in Colombia "apartamento" is "departamento" in Argentina. But all of these are easy to translate with Google Translator.

Overview of Data

• type - Type of listing:
  • Propiedad (Property).
  • Desarrollo/Proyecto (Development/Project).
• country - Country in which the listing is published:
  • Argentina
  • Colombia
  • Ecuador
  • Perú
  • Uruguay
• id - id of the listing. It is not unique: if the listing is updated by the real estate agency (new version of the listing) a new record is created with the same id but different dates: registration and cancellation.
• start_date - Date of registration of the listing.
• end_date - Cancellation date of the listing.
• created_on - Date of registration of the first version of the listing.
• lat - Latitude of the property.
• lon - Longitud of the property.
• l1 - Administrative Level 1: Country of the property.
• l2 - Administrative Level 2: Usually the province of the property.
• l3 - Administrative Level 3: Usually the city of the property.
• l4 - Administrative Level 4: Usually the neighbourhood of the property.
• operation - Type of listing:
  • Venta (Sale).
  • Alquiler (Rent).
• type - Type of property:
  • Casa (House).
  • Departamento (Apartment).
  • PH (Horizontal Property).
• rooms - Number of rooms (useful for Argentina).
• bedrooms - Number of bedrooms (useful for the rest of the countries).
• bathrooms - Number of bathrooms.
• surface_total - Total area in m².
• surface_covered - Area covered in m².
• price - Price published in the listing.
• currency - Currency of published price.
• price_period - Payment periods:
  • Diario (Daily).
  • Semanal (Weekly).
  • Mensual (Monthly).
• title - Title of the listing (These are in Spanish).
• description - Description of the listing (In Spanish).
• status - Development status (Completed, Under construction, ...).
• name - Development name.
• short_description - Short listing description.

Acknowledgements & Inspiration

I want to thank Properati Data for providing the datasets free of charge. Especially, datasets on real estate listings that can be difficult to come by without spending time on creating crawlers and finding websites that will allow for crawling.

The inspiration and reason I came by the datasets in the first place was through my personal project on predicting apartment prices in Buenos Aires.

Additional Information

Data was downloaded the May 24 2020.

This Dataset is a rich collection of real estate listings sourced from the popular real estate website, Bina.az. This dataset comprises 39,300 rows and includes 10 columns, each containing essential information about properties available for sale various locations. This valuable dataset serves as a foundational resource for comprehensive real estate market analysis, property valuation, and housing market research.

Dataset Details:

Number of Rows: 39,300 Number of Columns: 10 Column Names and Descriptions:

1.Price: This column indicates the listed price of the property, offering insights into market trends and pricing variations.

2.Location: The "Location" column specifies the geographical details of the property, including the city, district, nearest metro stations. Location is a critical factor for real estate decision-making.

3.Rooms: This column represents the number of rooms in the property. Knowing the room count is crucial for prospective buyers or renters to assess the property's suitability for their needs.

4.Square: The "Square" column contains information about the total area of the property in square meters. Property size is an essential factor for assessing space and value.

5.Floor: This column indicates the floor on which the property is situated. For those interested in apartments, the floor number can be a critical factor.

6.New Building: The "New Building" column contains binary values (e.g., 0 or 1) to indicate whether the property is in a newly constructed building. This information is valuable for those seeking modern or recently built properties.

7.Has Repair: This column contains binary values to indicate whether the property has undergone any repairs or renovations. Repair status can influence a buyer's decision.

8.Has Bill of Sale: This column contain binary values to indicate whether a legal bill of sale exists for the property, ensuring the legitimacy of the transaction.

9.Has Mortgage: The "Has Mortgage" column contains binary values to indicate whether the property has an existing mortgage.

This dataset is a powerful tool for a wide range of applications, including:

Market Analysis: Real estate professionals and analysts can leverage this dataset to conduct market research, assess pricing dynamics, and understand property preferences.

Property Valuation: Property appraisers and valuation experts can use this data to estimate property values based on attributes like location, size, and condition.

Housing Market Research: Academics, policymakers, and researchers can explore this dataset to gain insights into housing market trends, affordability, and the prevalence of mortgages and repairs.

Homebuyers and Renters: Individuals seeking properties can filter and search through the dataset to identify suitable homes based on their specific criteria, such as price, location, room count, and more.

The Bina.az Real Estate Dataset empowers data-driven decision-making within the real estate sector and serves as a valuable resource for anyone interested in the real estate market.

If you want to see scraping code follow this link: https://github.com/AzadShahvaladov/Bina.azDataScraping

Property currently or historically owned and managed by the City of Chicago. Information provided in the database, or on the City’s website generally, should not be used as a substitute for title research, title evidence, title insurance, real estate tax exemption or payment status, environmental or geotechnical due diligence, or as a substitute for legal, accounting, real estate, business, tax or other professional advice. The City assumes no liability for any damages or loss of any kind that might arise from the reliance upon, use of, misuse of, or the inability to use the database or the City’s web site and the materials contained on the website. The City also assumes no liability for improper or incorrect use of materials or information contained on its website. All materials that appear in the database or on the City’s web site are distributed and transmitted "as is," without warranties of any kind, either express or implied as to the accuracy, reliability or completeness of any information, and subject to the terms and conditions stated in this disclaimer.

The following columns were added 4/14/2023:

• Sales Status
• Sale Offering Status
• Sale Offering Reason
• Square Footage - City Estimate
• Land Value (2022) -- Note: The year will change over time.

The following columns were added 3/19/2024:

• Application Use
• Grouped Parcels
• Application Deadline
• Offer Round
• Application URL

Listings include individual properties and historic districts. All listings have a point and most also have boundaries depicted by a polygon. This data set excludes restricted listings (such as archaeological sites) whose locations are protected. Each listing’s data includes property name, address, county, city, listing date, associated Multiple Property Listing (where applicable) and National Historic Landmark listing date (where applicable). Data is updated semi-annually.

Comprehensive proprietary research analyzing 312,367 assumable mortgage homes from 2023-2025 across all 50 states, including interest rates, savings analysis, state distribution, price ranges, and down payment requirements.

Source

The data was scraped from one of the most popular real estate listings website nieruchomosci-online, data contains only properties for rent.

Content

Dataset consists of 31 columns representing rough location of the property, price, size, amount of rooms and more.

The Military Bases dataset was last updated on November 11, 2025 and are defined by Fiscal Year 2024 data, from the Office of the Assistant Secretary of Defense for Energy, Installations, and Environment and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The dataset depicts the authoritative locations of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas world-wide. These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Assistant Secretary of Defense for Energy, Installations, and Environment. Only sites reported in the BSR or released in a map supplementing the Foreign Investment Risk Review Modernization Act of 2018 (FIRRMA) Real Estate Regulation (31 CFR Part 802) were considered for inclusion. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD. While every attempt has been made to provide the best available data quality, this data set is intended for use at mapping scales between 1:50,000 and 1:3,000,000. For this reason, boundaries in this data set may not perfectly align with DoD site boundaries depicted in other federal data sources. Maps produced at a scale of 1:50,000 or smaller which otherwise comply with National Map Accuracy Standards, will remain compliant when this data is incorporated. Boundary data is most suitable for larger scale maps; point locations are better suited for mapping scales between 1:250,000 and 1:3,000,000. If a site is part of a Joint Base (effective/designated on 1 October, 2010) as established under the 2005 Base Realignment and Closure process, it is attributed with the name of the Joint Base. All sites comprising a Joint Base are also attributed to the responsible DoD Component, which is not necessarily the pre-2005 Component responsible for the site. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529039

Basically this data scraped from a website of real estate the data is not perfect there is some issue like not having values and very small data but its very accurate and i cleaned it very well and didnt change anything so its accurate u can use it i didn't DO EDA ON it bcs i want big data i will try to scrap big data then will to and post here till then is someone fond it use full then use it Thankyou!!! The data contains the following columns:

About: A description of the property, including size, type, and location. Price: The listed price of the property. Agent: The name of the real estate agent or agency handling the sale. Similar: Information about similar listings, usually by the same agent or in the same area. Built Up Area: The total built-up area of the property. Bathroom: The number of bathrooms in the property. Location: The specific location of the property. Ownership: The type of ownership (typically whether it's by an agent or the owner). images-num: The number of images available for the property. Bedroom: The number of bedrooms in the property.

Tax liens are a method the government uses to secure an interest in unpaid tax debt. This dataset represents information about county, municipal, school district, and water/sewer tax liens by parcel (and property identification number, where available). This dataset includes the name of the municipality, county or school district filing, the date that the lien was filed, and the tax amount at the date of filing. This data is based on records that were filed dating back to 1995. This dataset will be updated with the previous month's filings as new data becomes available (typically, close to the beginning of the month). Delinquent Tax Docket numbers are not unique identifiers. Instead, users need to combine the Delinquent Tax Docket number, the tax year, and the lien description. This dataset represents our best effort to describe the state of tax liens. Users are encouraged to consult the Allegheny County Department of Court Records web site, as it is the definitive and most reliable source for this information: https://dcr.alleghenycounty.us/ More detailed and up-to-date information on each lien can be found on that site.