Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

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.

Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
🔗 Request Redfin Real Estate Data

Context

Insurance companies collect multiple features of a House and select which houses can be insured and what amount they can charge the Premium from them. So here I have collected data from multiple insurance companies in USA where features with house prices are given

Content

This data set has many property details from address to their location co ordinates nad many other features, use them to predict the House price

Inspiration

Multiple regression datasets have been published every one unique in their own way, Use of location coordinates and some other co-ordinates are new here.

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

Real estate is a dynamic and ever-evolving industry that relies heavily on data to make informed decisions. One of the fundamental aspects of this industry is real estate listing data. This data encompasses detailed information about properties that are available for sale or rent in a given market. It plays a pivotal role in assisting buyers, sellers, real estate professionals, and investors in making well-informed choices. In this data brief, we will provide an overview of what real estate listing data is and highlight five key industry use cases.

Real Estate Listings Data Includes:

• Property Location
• 50+ Property and Building Characteristics
• School District Information
• List Date
• Listing Price - Maximum, Minimum, Sold Price
• Listing Status
• Number of Days on Market
• Listing Agent and Office

Autoscraping's Zillow USA Real Estate Data is a comprehensive and meticulously curated dataset that covers over 10 million property listings across the United States. This data product is designed to meet the needs of professionals across various sectors, including real estate investment, market analysis, urban planning, and academic research. Our dataset is unique in its depth, accuracy, and timeliness, ensuring that users have access to the most relevant and actionable information available.

What Makes Our Data Unique? The uniqueness of our data lies in its extensive coverage and the precision of the information provided. Each property listing is enriched with detailed attributes, including but not limited to, full addresses, asking prices, property types, number of bedrooms and bathrooms, lot size, and Zillow’s proprietary value and rent estimates. This level of detail allows users to perform in-depth analyses, make informed decisions, and gain a competitive edge in their respective fields.

Furthermore, our data is continually updated to reflect the latest market conditions, ensuring that users always have access to current and accurate information. We prioritize data quality, and each entry is carefully validated to maintain a high standard of accuracy, making this dataset one of the most reliable on the market.

Data Sourcing: The data is sourced directly from Zillow, one of the most trusted names in the real estate industry. By leveraging Zillow’s extensive real estate database, Autoscraping ensures that users receive data that is not only comprehensive but also highly reliable. Our proprietary scraping technology ensures that data is extracted efficiently and without errors, preserving the integrity and accuracy of the original source. Additionally, we implement strict data processing and validation protocols to filter out any inconsistencies or outdated information, further enhancing the quality of the dataset.

Primary Use-Cases and Vertical Applications: Autoscraping's Zillow USA Real Estate Data is versatile and can be applied across a variety of use cases and industries:

Real Estate Investment: Investors can use this data to identify lucrative opportunities, analyze market trends, and compare property values across different regions. The detailed pricing and valuation data allow for comprehensive due diligence and risk assessment.

Market Analysis: Market researchers can leverage this dataset to track real estate trends, evaluate the performance of different property types, and assess the impact of economic factors on property values. The dataset’s nationwide coverage makes it ideal for both local and national market studies.

Urban Planning and Development: Urban planners and developers can use the data to identify growth areas, plan new developments, and assess the demand for different property types in various regions. The detailed location data is particularly valuable for site selection and zoning analysis.

Academic Research: Universities and research institutions can utilize this data for studies on housing markets, urbanization, and socioeconomic trends. The comprehensive nature of the dataset allows for a wide range of academic applications.

Integration with Our Broader Data Offering: Autoscraping's Zillow USA Real Estate Data is part of our broader data portfolio, which includes various datasets focused on real estate, market trends, and consumer behavior. This dataset can be seamlessly integrated with our other offerings to provide a more holistic view of the market. For example, combining this data with our consumer demographic datasets can offer insights into the relationship between property values and demographic trends.

By choosing Autoscraping's data products, you gain access to a suite of complementary datasets that can be tailored to meet your specific needs. Whether you’re looking to gain a comprehensive understanding of the real estate market, identify new investment opportunities, or conduct advanced research, our data offerings are designed to provide you with the insights you need.

Problem Statement

👉 Download the case studies here

Investors and buyers in the real estate market faced challenges in accurately assessing property values and market trends. Traditional valuation methods were time-consuming and lacked precision, making it difficult to make informed investment decisions. A real estate firm sought a predictive analytics solution to provide accurate property price forecasts and market insights.

Challenge

Developing a real estate price prediction system involved addressing the following challenges:

Collecting and processing vast amounts of data, including historical property prices, economic indicators, and location-specific factors.

Accounting for diverse variables such as neighborhood quality, proximity to amenities, and market demand.

Ensuring the model’s adaptability to changing market conditions and economic fluctuations.

Solution Provided

A real estate price prediction system was developed using machine learning regression models and big data analytics. The solution was designed to:

Analyze historical and real-time data to predict property prices accurately.

Provide actionable insights on market trends, enabling better investment strategies.

Identify undervalued properties and potential growth areas for investors.

Development Steps

Data Collection

Collected extensive datasets, including property listings, sales records, demographic data, and economic indicators.

Preprocessing

Cleaned and structured data, removing inconsistencies and normalizing variables such as location, property type, and size.

Model Development

Built regression models using techniques such as linear regression, decision trees, and gradient boosting to predict property prices. Integrated feature engineering to account for location-specific factors, amenities, and market trends.

Validation

Tested the models using historical data and cross-validation to ensure high prediction accuracy and robustness.

Deployment

Implemented the prediction system as a web-based platform, allowing users to input property details and receive price estimates and market insights.

Continuous Monitoring & Improvement

Established a feedback loop to update models with new data and refine predictions as market conditions evolved.

Results

Increased Prediction Accuracy

The system delivered highly accurate property price forecasts, improving investor confidence and decision-making.

Informed Investment Decisions

Investors and buyers gained valuable insights into market trends and property values, enabling better strategies and reduced risks.

Enhanced Market Insights

The platform provided detailed analytics on neighborhood trends, demand patterns, and growth potential, helping users identify opportunities.

Scalable Solution

The system scaled seamlessly to include new locations, property types, and market dynamics.

Improved User Experience

The intuitive platform design made it easy for users to access predictions and insights, boosting engagement and satisfaction.

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.

1. Real estate transaction information includes location, area, total price, etc. 2. This dataset is updated every 10 days.

Our Realtor.com (Multiple Listing Service) dataset represents one of the most exhaustive collections of real estate data available to the industry. It consolidates data from over 500 MLS aggregators across various regions, providing an unparalleled view of the property market.

Features:

Property Listings: Each listing provides comprehensive details about a property. This includes its physical address, number of bedrooms and bathrooms, square footage, lot size, type of property (e.g., single-family home, condo, townhome), and more.

Photographs and Virtual Tours: Visuals are crucial in the property market. Most listings are accompanied by high-quality photographs and, in many cases, virtual or 3D tours that allow potential buyers to explore properties remotely.

Pricing Information: Listings provide asking prices, and the dataset frequently updates to reflect price changes. Historical price data, which includes initial listing prices and any subsequent reductions or increases, is also available.

Transaction Histories: For sold properties, the dataset provides information about the date of sale, the sale price, and any discrepancies between the listing and sale prices.

Agent and Broker Information: Each listing typically has associated details about the property's real estate professional. This might include their name, contact details, and affiliated brokerage.

Open House Schedules: Open house dates and times are listed for properties that are actively being shown to potential buyers.

1. Analytical Insights:

Market Trends: By analyzing the dataset over time, one can glean insights into market dynamics, such as the rate of price appreciation or depreciation in certain areas, the average time properties stay on the market, and seasonality effects.

Neighborhood Data: With comprehensive geographical data, it becomes possible to understand neighborhood-specific trends. This is invaluable for potential buyers or real estate investors looking to identify burgeoning markets.

Price Comparisons: Realtors and potential buyers can benchmark properties against similar listings in the same area to determine if a property is priced appropriately.

1. Utility:

For Industry Professionals and Analysts: Beyond buyers and sellers, the dataset is a trove of information for real estate agents, brokers, analysts, and investors. They can harness this data to craft strategies, predict market movements, and serve their clients better.

1. Real estate transaction information, including property location, area, total price, and other details. 2. This dataset is updated every 10 days. (Shiding District)

Real estate datasets from various websites cover all major real estate data points including: property type, size, location, price, bedrooms, baths, address, history, images, and much more. Popular use cases include: forecast housing demand, analyze price fluctuations, improve customer satisfaction, see past prices to monitor market trends, and more.

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.

Context

This dataset contains various details (price,area,floors etc) about houses in Nepal.

Content

This dataset includes following columns Title ------------------------------------->Tile of post Address ---------------------------------->Address of property City--------------------------------------->Last word in address (usually the district) Price-------------------------------------->Price in NRs. Bedroom --------------------------------->No of bedrooms Bathroom--------------------------------->No of bathrooms (0/ NA for land) Floors------------------------------------->No of floors (0/ NA for land) Parking----------------------------------->No of parking space Face-------------------------------------->Direction property is facing Year-------------------------------------->Year of construction (Not available for some houses) Views-------------------------------------->No of views (Determines how visually attractive the house is) Area-------------------------------------->Area occupied by property (Anna,ropani,kattha,sq feet) Road-------------------------------------->Road size and width as give by seller Road Width-------------------------------->Road width extracted from given string Road Type--------------------------------->Road type extracted from given string (Graveled,Blacktop etc) Build Area--------------------------------->Area occupied by house ((Anna,ropani,kattha,sq feet)

Posted ----------------------------------->Date property was posted relative to scraping date (views/posted ratio may be a good indicator for how visually appealing the house is)

Amenities ------------------------------->Amenities(Garden,Security etc). House with more amenities are typically premium ones

Note that converting area to a standard unit is the most challenging part as there are variety of units (anna,ropani,kattha etc) used.Also absence of floors means the property is a land.

Dataset Description This dataset contains rental property listings scraped from Tonaton.com, one of Ghana's leading online classifieds platforms. It provides valuable information on rental prices across various regions in Ghana, along with other property details. The dataset is designed to support analysis, visualization, and modeling of rental prices in the Ghanaian real estate market.

Features The dataset includes the following columns: - url: The link to the listing. - name: The headline or title of the rental property listing. - price: The rental price of the property in Ghanaian Cedis (GHS). - category: The type of rental property (e.g., apartment, house, room, office). - bedrooms: The number of bedrooms available in the property. - bathrooms: The number of bathrooms available in the property. - floor_area: The floor area of the property in square meters. - location: The address location where the property is located. - condition: Condition of the property e.g new, used, off-plan etc. - amenities: Amenities provided in the property. - region: Geographic administrative region of the property location. - locality: Represent the town or city where the property is located. - parking_space: Indicates if there is parking space available. - is_furnished: Indicates if the property is furnished. - lat: Longitude location of the property. - lng: Latitude location of the property.

Potential Use Cases

Real Estate Market Analysis: Analyze rental price trends across different locations and property types in Ghana.
Price Prediction Models: Train machine learning models to predict rental prices based on features like location, property type, and number of bedrooms.
Geographical Insights: Investigate how location impacts rental prices across urban and rural areas in Ghana.
Consumer Trends: Study patterns in rental property preferences, such as the most popular types of properties or regions.

Source The data was scraped from Tonaton.com as of November, 2024. Please note that this dataset reflects the listings available during that period and may not include all rental properties in Ghana.

Disclaimer This dataset is shared for educational and research purposes only. It is not intended for commercial use or to reproduce Tonaton.com’s proprietary information. Users are responsible for ensuring their use complies with Tonaton.com’s terms and conditions.

Conflict of Interest There are no conflicts of interest associated with the creation or use of this dataset.

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

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This dataset provides a comprehensive record of property listing price changes over time, including detailed property attributes, location information, and event types for each price change. It enables in-depth analysis of real estate market dynamics, pricing strategies, and property value trends across regions and property types.

VITAL SIGNS INDICATOR List Rents (EC9)

FULL MEASURE NAME List Rents

LAST UPDATED October 2016

DESCRIPTION List rent refers to the advertised rents for available rental housing and serves as a measure of housing costs for new households moving into a neighborhood, city, county or region.

DATA SOURCE real Answers (1994 – 2015) no link

Zillow Metro Median Listing Price All Homes (2010-2016) http://www.zillow.com/research/data/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) List rents data reflects median rent prices advertised for available apartments rather than median rent payments; more information is available in the indicator definition above. Regional and local geographies rely on data collected by real Answers, a research organization and database publisher specializing in the multifamily housing market. real Answers focuses on collecting longitudinal data for individual rental properties through quarterly surveys. For the Bay Area, their database is comprised of properties with 40 to 3,000+ housing units. Median list prices most likely have an upward bias due to the exclusion of smaller properties. The bias may be most extreme in geographies where large rental properties represent a small portion of the overall rental market. A map of the individual properties surveyed is included in the Local Focus section.

Individual properties surveyed provided lower- and upper-bound ranges for the various types of housing available (studio, 1 bedroom, 2 bedroom, etc.). Median lower- and upper-bound prices are determined across all housing types for the regional and county geographies. The median list price represented in Vital Signs is the average of the median lower- and upper-bound prices for the region and counties. Median upper-bound prices are determined across all housing types for the city geographies. The median list price represented in Vital Signs is the median upper-bound price for cities. For simplicity, only the mean list rent is displayed for the individual properties. The metro areas geography rely upon Zillow data, which is the median price for rentals listed through www.zillow.com during the month. Like the real Answers data, Zillow's median list prices most likely have an upward bias since small properties are underrepresented in Zillow's listings. The metro area data for the Bay Area cannot be compared to the regional Bay Area data. Due to afore mentioned data limitations, this data is suitable for analyzing the change in list rents over time but not necessarily comparisons of absolute list rents. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Due to the limited number of rental properties surveyed, city-level data is unavailable for Atherton, Belvedere, Brisbane, Calistoga, Clayton, Cloverdale, Cotati, Fairfax, Half Moon Bay, Healdsburg, Hillsborough, Los Altos Hills, Monte Sereno, Moranga, Oakley, Orinda, Portola Valley, Rio Vista, Ross, San Anselmo, San Carlos, Saratoga, Sebastopol, Windsor, Woodside, and Yountville.

Inflation-adjusted data are presented to illustrate how rents have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself. Percent change in inflation-adjusted median is calculated with respect to the median price from the fourth quarter or December of the base year.