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].

Zillow Properties Listing dataset to access detailed real estate listings, including property prices, locations, and features. Popular use cases include market analysis, property valuation, and investment decision-making in the real estate sector.

Use our Zillow Properties Listing Information dataset to access detailed real estate listings, including property features, pricing trends, and location insights. This dataset is perfect for real estate agents, investors, market analysts, and property developers looking to analyze housing markets, identify investment opportunities, and assess property values.

Leverage this dataset to track pricing patterns, compare property features, and forecast market trends across different regions. Whether you're evaluating investment prospects or optimizing property listings, the Zillow Properties dataset offers essential information for making data-driven real estate decisions.

Dataset Features

• zpid: Unique property identifier assigned by Zillow.
• city: The name of the city where the property is located.
• state: The state in which the property is located.
• homeStatus: Indicates the current status of the property
• address: The full address of the property, including street, city, and state.
• isListingClaimedByCurrentSignedInUser: This field shows if the current Zillow user has claimed ownership of the listing.
• isCurrentSignedInAgentResponsible: This field indicates whether the currently signed-in real estate agent is responsible for the listing.
• bedrooms: Number of bedrooms in the property.
• bathrooms: Number of bathrooms in the property.
• price: Current asking price of the property.
• yearBuilt: The year the home was originally constructed.
• streetAddress: Specific street address (usually excludes city/state/zip).
• zipcode: The postal ZIP code of the property.
• isCurrentSignedInUserVerifiedOwner: This field indicates if the signed-in user has verified ownership of the property on Zillow.
• isVerifiedClaimedByCurrentSignedInUser: Indicates whether the user has claimed and verified the listing as the current owner.
• listingDataSource: The original source of the listing. Important for data lineage and trustworthiness.
• longitude: The longitudinal geographic coordinate of the property.
• latitude: The latitudinal geographic coordinate of the property.
• hasBadGeocode: This indicates whether the geolocation data is incorrect or problematic.
• streetViewMetadataUrlMediaWallLatLong: A URL or reference to the Street View media wall based on latitude and longitude.
• streetViewMetadataUrlMediaWallAddress: A similar URL reference to the Street View, but based on the property’s address.
• streetViewServiceUrl: The base URL to Google Street View or similar services. Enables interactive visuals of the property’s surroundings.
• livingArea: Total internal living area of the home, typically in square feet.
• homeType: The category/type of the home.
• lotSize: The size of the entire lot or land the home is situated on.
• lotAreaValue: The numerical value representing the lot area is usually tied to a measurement unit.
• lotAreaUnits: Units in which the lot area is measured (e.g., sqft, acres).
• livingAreaValue: The numeric value of the property's interior living space.
• livingAreaUnitsShort: Abbreviated unit for living area (e.g., sqft), useful for compact displays.
• isUndisclosedAddress: Boolean indicating if the full property address is hidden, typically used for privacy reasons.
• zestimate: Zillow’s estimated market value of the home, generated via its proprietary model.
• rentZestimate: Zillow’s estimated rental price per month, is helpful for rental market analysis.
• currency: Currency used for price, Zestimate, and rent estimate (e.g., USD).
• hideZestimate: Indicates whether the Zestimate is hidden from public view.
• dateSoldString: The date when the property was last sold, in string format (e.g., 2022-06-15).
• taxAssessedValue: The most recent assessed value of the property for tax purposes.
• taxAssessedYear: The year in which the property was last assessed.
• country: The country where the property is located.
• propertyTaxRate: The most recent tax rate.
• photocount: This column provides a photo count of the property.
• isPremierBuilder: Boolean indicating whether the builder is listed as a premier (trusted) builder on Zillow.
• isZillowOwned: Indicates whether the property is owned or managed directly by Zillow.
• ssid: A unique internal Zillow identifier for the listing (not to be confused with network SSID).
• hdpUrl: URL to the home’s detail page on Zillow (Home Details Page).
• tourViewCount: Number of times users have viewed the property tour.
• hasPublicVideo: This

Our Bulk Automated Valuation Model (AVM) is a service that uses mathematical modeling to determine current market values. AVMs integrate vast amounts of data, including sales prices, market trends, and geographic information, to estimate real estate values with minimal human intervention – often referred to as “Desktop Valuations”. These models are designed to provide objective and uniform evaluations, helping to standardize property valuations across the board.

What Does Our AVM Offer?

Our Automated Valuation Model (AVM) leverages cutting-edge technologies, the most recent methodologies, and is supported by the foremost data provider with the largest datasets in the industry. This ensures a swift, exceptionally accurate AVM that delivers the comprehensive insights you need.

AVM Data Details:

• Property Address
• FIPS
• Property ID
• APN
• Final Value
• High Value
• Low Value
• Confidence Score
• Standard Deviation
• Valuation Date
• Recording Time Stamp
• Recording Type

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.

Vietnam Rental And Sale Real Estate Dataset in Feb 2025

This dataset contains rental property listings from various cities and provinces across Vietnam. It includes details such as location, rental price, property area, number of bedrooms, and number of bathrooms. The data can be used for analyzing rental trends, comparing property prices across regions, and identifying patterns in Vietnam’s real estate market.

Dataset Overview

Geographic Coverage: Listings come from different provinces and major cities in Vietnam, including Hà Nội, Hồ Chí Minh City, Đà Nẵng, and other regions. Price (price): Represents the monthly rental cost and sale price, typically listed in Vietnamese đồng (VND). Some listings have negotiable pricing indicated as "Giá thỏa thuận". Area (area): Specifies the total available space in square meters (m²), ranging from small apartments to large commercial or industrial properties. Bedrooms (bedrooms_num) and Bathrooms (bathrooms_num): - If both values are greater than zero, the listing is likely a residential property such as an apartment, house, or villa. - If both values are zero, the listing may not be a traditional residential building but could be an office space, commercial property, warehouse, or vacant land available for rent. Example Listings

Real Estate Market size was valued at USD 79.7 Trillion in 2024 and is projected to reach USD 103.6 Trillion by 2031, growing at a CAGR of 5.1% during the forecasted period 2024 to 2031

Global Real Estate Market Drivers

Population Growth and Urbanization: In order to meet the demands of businesses, housing needs, and infrastructure development, there is a constant need for residential and commercial properties as populations and urban areas rise.

Low Interest Rates: By making borrowing more accessible, low interest rates encourage both individuals and businesses to make real estate investments. Reduced borrowing costs result in reduced mortgage rates, opening up homeownership and encouraging real estate investments and purchases.

Economic Growth: A thriving real estate market is a result of positive economic growth indicators like GDP growth, rising incomes, and low unemployment rates. Robust economies establish advantageous circumstances for real estate investment, growth, and customer assurance in the housing sector. Job growth and income increases: As more people look for rental or purchase close to their places of employment, housing demand is influenced by these factors. The housing market is driven by employment opportunities and rising salaries, which in turn drive home buying, renting, and property investment activity. Infrastructure Development: The demand and property values in the surrounding areas can be greatly impacted by investments made in infrastructure projects such as public facilities, utilities, and transportation networks. Accessibility, convenience, and beauty are all improved by improved infrastructure, which encourages real estate development and investment.

Government Policies and Incentives: Tax breaks, subsidies, and first-time homebuyer programs are a few examples of government policies and incentives that can boost the real estate market and homeownership. Market stability and growth are facilitated by regulatory actions that promote affordable housing, urban redevelopment, and real estate development.

Foreign Investment: Foreign capital can be used to stimulate demand, diversify property portfolios, and pump capital into the real estate market through direct property purchases or real estate investment funds. Foreign investors are drawn to the local real estate markets by favorable exchange rates, stable political environments, and appealing returns.

Demographic Trends: Shifting demographic trends affect housing preferences and demand for various property kinds. These trends include aging populations, household formation rates, and migration patterns. It is easier for real estate developers and investors to match supply with changing market demand when they are aware of demographic fluctuations.

Technological Innovations: New technologies that are revolutionizing the marketing, transactions, and management of properties include digital platforms, data analytics, and virtual reality applications. In the real estate industry, technology adoption increases market reach, boosts customer experiences, and increases operational efficiency.

Environmental Sustainability: Decisions about real estate development and investment are influenced by the growing knowledge of environmental sustainability and green building techniques. Market activity in environmentally aware real estate categories is driven by demand for eco-friendly neighborhoods, sustainable design elements, and energy-efficient buildings.

Abstract: The present article consists of the analysis of the relevant attributes in the formation of the prices of residential properties for sale in Conselheiro Lafaiete, MG, in 2016. A hedonic model was estimated from a multiple linear regression that allowed to associate the real estate price with the properties’ characteristics and its surroundings. The results suggest that the variables were relevant to explain the variability in real estate prices, and reflected the reality of the real estate market of Conselheiro Lafaiete.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q1 2025 about sales, median, housing, and USA.

Our Automated Valuation Model (AVM) Data is a service that uses mathematical modeling to determine current market values. AVM data includes sales prices, property characteristics, market trends, and geographic information, to estimate real estate values with minimal human intervention.

In 2022, housing prices in Belgium rose. According to the forecast, 2023 and 2024 will follow with a slight increase of two percent. Consumers signal much uncertainty on, for example, development of unemployment, which can hamper the housing market.

Belgium’s housing prices development

For years, house prices in Belgium followed a similar growth pattern to the country’s economy. Residential property prices grew when Belgium's economy performed well but stagnated when the economy slowed down. Since 2020, however, growth has accelerated. In 2022, the average house price exceeded 319,000 euros, up from 298,000 euros the year before.

The Belgian economy faces an uncertain future

Belgium’s real estate market is closely connected to the economic performance of the country. According to a 2022 forecast, the Belgian economy was predicted to grow by 2.1 percent in 2023. This prediction reflected inflation, supply chain disruptions impacting domestic demand, as well as (a lack of) international trade impacting Belgian growth.

The United States Residential Real Estate Market is Segmented by Property Type (Apartments and Condominiums, and Villas and Landed Houses), by Price Band (Affordable, Mid-Market and Luxury), by Business Model (Sales and Rental), by Mode of Sale (Primary and Secondary), and by Region (Northeast, Midwest, Southeast, West and Southwest). The Market Forecasts are Provided in Terms of Value (USD)

Graph and download economic data for Real Residential Property Prices for China (QCNR628BIS) from Q2 2005 to Q1 2025 about China, residential, HPI, housing, real, price index, indexes, and price.

Key information about House Prices Growth

• US house prices grew 5.2% YoY in Dec 2024, following an increase of 5.4% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Dec 2024, with an average growth rate of 5.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

Graph and download economic data for Commercial Real Estate Prices for United States (COMREPUSQ159N) from Q1 2005 to Q3 2024 about real estate, commercial, rate, and USA.

Spatial equilibrium implies that distant factors are correlated with local prices through market mechanisms. Using this logic, we develop a novel approach for handling price endogeneity in land use models. We combine a control function approach with a duration model to identify the impact of prices in influencing land conversion. We find that failure to control for endogeneity results in large differences in elasticities. Specifically, we find an elasticity of 2.06 compared to 0.67 in a model without instrumentation. This difference is significant as it suggests that price-based policies, such as green taxes, are likely more effective in altering development patterns than would be expected from a naïve estimation that ignores price endogeneity.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Dow Jones U.S. Real Estate: A True Reflection of the Market?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

1) Data Introduction • The Real Estate DataSet consists of 506 examples, including home prices in the Boston suburbs and various residential and environmental characteristics.

2) Data Utilization (1) Real Estate DataSet has characteristics that: • The dataset provides 13 continuous variables and one binary variable, including crime rate, house size, environmental pollution, accessibility, tax rate, and population characteristics. (2) Real Estate DataSet can be used to: • House Price Forecast: It can be used to develop a regression model that predicts the median price (MEDV) of a house based on various residential and environmental factors. • Analysis of Urban Planning and Policy: It can be used for urban development and policy making by analyzing the impact of residential environmental factors such as crime rates, environmental pollution, and educational environment on housing values.

Residential Real Estate Market is Segmented by Property Type (Apartments & Condominiums, and Landed Houses & Villas), by Price Band (Affordable, Mid-Market, and Luxury/Super-prime), by Business Model (Sales and Rental), by Mode of Sale (Primary and Secondary), and by Region (North America, South America, Europe, Asia-Pacific, and Middle East & Africa). The Market Forecasts are Provided in Terms of Value (USD).

Our dataset features comprehensive housing market data, extracted from 250,000 records sourced directly from Redfin USA. Our Crawl Feeds team utilized proprietary in-house tools to meticulously scrape and compile this valuable data.

Key Benefits of Our Housing Market Data:

• In-Depth Market Analysis: Gain insights into the real estate market with up-to-date data on recently sold properties.
  
  
• Price Trend Identification: Track and analyze price trends across different cities.
  
  
• Accurate Price Estimation: Estimate property values based on key factors such as area, number of beds and baths, square footage, and more.
  
  
• Detailed Real Estate Statistics: Access detailed statistics segmented by zip code, area, and state.

Unlock the Power of Redfin Data for Real Estate Professionals

Leveraging our Redfin properties dataset allows real estate professionals to make data-driven decisions. With detailed insights into property listings, sales history, and pricing trends, agents and investors can identify opportunities in the market more effectively. The data is particularly useful for comparing neighborhood trends, understanding market demand, and making informed investment decisions.

Enhance Your Real Estate Research with Custom Filters and Analysis

Our Redfin dataset is not only extensive but also customizable, allowing users to apply filters based on specific criteria such as property type, listing status, and geographic location. This flexibility enables researchers and analysts to drill down into the data, uncovering patterns and insights that can guide strategic planning and market entry decisions. Whether you're tracking the performance of single-family homes or exploring multi-family property trends, this dataset offers the depth and accuracy needed for thorough analysis.

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

Zoopla Properties Listing dataset to explore detailed property information, including pricing, location, and features. Popular use cases include real estate market analysis, property valuation, and investment research.

Use our Zoopla Properties Listing Information dataset to explore detailed property listings, including property details, pricing, location, and market trends across various regions. This dataset provides valuable insights into property valuations, consumer preferences, and real estate dynamics, enabling businesses and researchers to make data-driven decisions.

Tailored for real estate professionals, investors, and market analysts, this dataset supports market trend analysis, property valuation assessments, and investment strategy development. Whether you're evaluating property investments, tracking market conditions, or conducting competitive analysis, the Zoopla Properties Listing Information dataset is a key resource for navigating the real estate landscape.

Dataset Features

• url: The original listing URL on Zoopla.
• property_type: Type of property (e.g., Flat, Detached, Terraced).
• property_title: Title or headline of the listing.
• address: Full postal address of the property.
• google_map_location: Geographical coordinates (latitude, longitude).
• virtual_tour: Link to a virtual walkthrough or 360° tour.
• street_view: Link to the Google Street View of the property.
• url_property: Zoopla-specific property page URL.
• currency: Currency in which the property is priced.
• deposit: Security deposit required (typically for rentals).
• letting_arrangements: Letting details (e.g., short-term, long-term).
• breadcrumbs: Category breadcrumbs for location and type navigation.
• availability: Availability status (e.g., Available now, Under offer).
• commonhold_details: Information about commonhold ownership.
• service_charge: Annual service charge (for leasehold properties).
• ground_rent: Annual ground rent cost.
• time_remaining_on_lease: Lease duration remaining in years.
• ecp_rating: Energy Performance Certificate rating.
• council_tax_band: Council tax band.
• price_per_size: Price per square meter or foot.
• tenure: Tenure type (Freehold, Leasehold, etc.).
• tags: Descriptive tags (e.g., New build, Chain-free).
• features: List of property features (e.g., garden, garage, en-suite).
• property_images: URLs to property photos.
• additional_links: Other related links (e.g., brochures, agents).
• listing_history: Changes in price, listing dates, and status over time.
• agent_details: Information about the listing agent or agency.
• points_ofInterest: Nearby landmarks or facilities (schools, transport).
• bedrooms Number of bedrooms.
• price: Listed price of the property.
• bathrooms: Number of bathrooms.
• receptions: Number of reception rooms (living, dining, etc.).
• country_code: Country code of the listing (e.g., GB for UK).
• energy_performance_certificate: Detailed EPC documentation or summary.
• floor_plans: URL or data related to property floor plans.
• description: Detailed property description from the listing.
• price_per_time: Price frequency for rentals (e.g., per week, per month).
• property_size: Area of the property (in sq ft or sq m).
• market_stats_last_12_months: Market stats for the area over the past year.
• market_stats_renta_opportunities: Data on rental yields and opportunities.
• market_stats_recent_sales_nearby: Sales history for nearby properties.
• market_stats_rental_activity: Local rental activity trends.
• uprn: Unique Property Reference Number for UK properties.
• listing_label: Label/category of the listing.

Distribution

• Data Volume: 44 Columns and 95.92K Rows
• Format: CSV

Usage

This dataset is ideal for a variety of high-impact applications:

• Property Valuation Models: Train ML models to estimate market value using features like size, location, and amenities.
• Real Estate Market Analysis: Identify pricing trends, demand patterns, and neighbourhood growth over time.
• Investment Research: Analyse rental yields, price per square foot, and historical price changes for investment opportunities.
• Recommendation Systems: Develop intelligent recommendation engines for property buyers and renters.
• Urban Planning & Policy Making: Use location and infrastructure data to guide city development.
• Sentiment & Description Analysis: NLP-driven insights from listing descriptions and agent narratives.

Coverage

• Geographic Coverage: Global
• Time Range: Ongoing collection; historical data may span multiple years

License

CUSTOM

Please review the respective licenses below:

1. Data Provider's License
   -