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

Graph and download economic data for All-Transactions House Price Index for Los Angeles County, CA (ATNHPIUS06037A) from 1975 to 2024 about Los Angeles County, CA; Los Angeles; CA; HPI; housing; price index; indexes; price; and USA.

Real estate markets are of great importance for both local and international investors. Sydney and Melbourne are two dynamic markets where economic and social factors have significant impacts on property prices. Below is a detailed description of each feature:

1. Date: The date when the property was sold. This feature helps in understanding the temporal trends in property prices.
2. Price:The sale price of the property in USD. This is the target variable we aim to predict.
3. Bedrooms:The number of bedrooms in the property. Generally, properties with more bedrooms tend to have higher prices.
4. Bathrooms: The number of bathrooms in the property. Similar to bedrooms, more bathrooms can increase a property’s value.
5. Sqft Living: The size of the living area in square feet. Larger living areas are typically associated with higher property values.
6. Sqft Lot:The size of the lot in square feet. Larger lots may increase a property’s desirability and value.
7. Floors: The number of floors in the property. Properties with multiple floors may offer more living space and appeal.
8. Waterfront: A binary indicator (1 if the property has a waterfront view, 0 other- wise). Properties with waterfront views are often valued higher.
9. View: An index from 0 to 4 indicating the quality of the property’s view. Better views are likely to enhance a property’s value.
10. Condition: An index from 1 to 5 rating the condition of the property. Properties in better condition are typically worth more.
11. Sqft Above: The square footage of the property above the basement. This can help isolate the value contribution of above-ground space.
12. Sqft Basement: The square footage of the basement. Basements may add value depending on their usability.
13. Yr Built: The year the property was built. Older properties may have historical value, while newer ones may offer modern amenities.
14. Yr Renovated: The year the property was last renovated. Recent renovations can increase a property’s appeal and value.
15. Street: The street address of the property. This feature can be used to analyze location-specific price trends.
16. City: The city where the property is located. Different cities have distinct market dynamics.
17. Statezip: The state and zip code of the property. This feature provides regional context for the property.
18. Country: The country where the property is located. While this dataset focuses on properties in Australia, this feature is included for completeness.

If you like this dataset, please contribute by upvoting

BatchData is a premier data and technology solution helping businesses serving the real estate ecosystem achieve faster growth. BatchData specializes in providing accurate, granular B2C contact and property data for US property owners. Our Property Search API is the engine behind leading PropTech applications, predictive modeling engines, and high-volume sales operations.

With over 300+ unique search filters, we enable developers and data scientists to build highly specific "buy-boxes" and marketing audiences. Whether you are searching for high-equity homes in specific zip codes or identifying commercial properties based on zoning and lot size, BatchData delivers the "ground truth" you need. Visit www.batchdata.io to explore our documentation and start building.

The Power of Granular Search Unlike standard APIs that offer broad, surface-level data, BatchData allows you to query the US real estate market with surgical precision. Our API accepts complex boolean logic, allowing you to layer demographic profiles, mortgage history, and physical building characteristics to surface the exact properties that match your ideal customer profile (ICP).

Key Search Capabilities & Data Attributes Our API response leverages a massive schema of over 124 data points per property. You can search, filter, and retrieve data across these core categories:

1. Distressed Property & Motivated Seller Signals (Quick Lists) Identify off-market opportunities before they hit the MLS. Our API provides pre-calculated flags for immediate actionable intelligence:

Vacancy & Abandonment: Filter by USPS vacancy flags or properties where the mailing address differs from the situs address (Absentee Owners).

Financial Distress: Identify properties with active Notices of Default, Pre-Foreclosure filings, Tax Defaults (3+ years delinquent), or Involuntary Liens (HOA, mechanics, tax liens).

Ownership Fatigue: Target "Tired Landlords" (non-owner occupied, owned for 10+ years) or "Inherited" properties that are ripe for acquisition.

Equity Position: Search by calculated Equity Percentage or Loan-to-Value (LTV) ratios to find owners with high equity (Free & Clear) or low equity depending on your strategy.

1. Detailed Building & Lot Characteristics Go beyond bed/bath counts. Our physical property data is sourced from tax assessors and refined for accuracy:

Structural Details: Search by Construction Type (Masonry, Frame, etc.), Foundation Type, Roof Cover/Type (Gable, Hip, Flat), and Exterior Wall material.

Systems & Features: Filter properties by Air Conditioning Source (Central, Evaporative), Heating Fuel (Gas, Solar), and amenities like Pools, Patios, and Fireplaces.

Lot Intelligence: Access granular Zoning Codes, Topography, Lot Depth/Frontage, and Site Influence data to evaluate development potential.

1. Valuation & Market Analytics Power your underwriting models with our automated valuation metrics:

AVM (Automated Valuation Model): Access estimated market values with accompanying Confidence Scores and Standard Deviation metrics to assess reliability.

Investment Metrics: Utilize estimated Rent Amounts, Flip Profit history, and length of ownership to calculate potential ROI instantly.

1. Advanced Owner Demographics Understand the human element behind the property. Our demographic append allows you to segment properties based on the owner’s profile:

Financial Profile: Filter by Household Income, Net Worth, Discretionary Income, and Creditworthiness indicators.

Household Composition: Search by Marital Status, Presence of Children, Household Size, and Senior Owner status.

Lifestyle Indicators: Access data on interests such as Pet Ownership, Charitable Donations, and Investment activity (Stocks/Bonds, Real Estate).

1. Mortgage & Transaction History Trace the financial history of every rooftop:

Loan Details: Search by Loan Type (Conventional, FHA, VA, Reverse Mortgage), Interest Rate type (Fixed vs. Adjustable), and Lender Name.

Transaction Velocity: Analyze sales history including Prior Sale Price, Document Types (Deed, Quit Claim), and Distressed Transfer flags.

Use Cases for PropTech & Real Estate

For Real Estate Investors & Wholesalers: Automate your lead generation by programming the API to fetch new properties daily that meet your specific "Buy Box" (e.g., "Vacant Single Family Homes, 3+ Beds, Built after 1980, with >40% Equity"). Feed these leads directly into your CRM or cold-calling dialer.

For Home Services & Solar: Stop marketing to renters. Use the API to identify Owner-Occupied Single-Family Residences with specific roof types (e.g., Asphalt Shingle) and high discretionary income. Overlay this with "Old Roof" indicators (based on Year Built and Permit History) to target homeowners ready for replacement or solar upgrades.

For Financial Services & Lenders: Improve your risk models by integrating our Lien and Judgment data. Identify borrowers with "Free and Clear" properties for HELOC offers or target recent "Cash Buy...

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

House prices grew year-on-year in most states in the U.S. in the third quarter of 2025. Florida saw the largest decline at *** percent. The annual appreciation for single-family housing in the U.S. was *** percent, while in Illinois—the state where homes appreciated the most—the increase was **** percent. How have home prices developed in recent years? House price growth in the U.S. has been going strong for years. In 2024, the median sales price of a single-family home exceeded ******* U.S. dollars, up from ******* U.S. dollars five years ago. One of the factors driving house prices was the cost of credit. The record-low federal funds effective rate allowed mortgage lenders to set mortgage interest rates as low as *** percent. With interest rates on the rise, home buying has also slowed, causing fluctuations in house prices. Why are house prices growing? Many markets in the U.S. are overheated because supply has not been able to keep up with demand. How many homes enter the housing market depends on the construction output, whereas the availability of existing homes for purchase depends on many other factors, such as the willingness of owners to sell. Furthermore, growing investor appetite in the housing sector means that prospective homebuyers have some extra competition to worry about. In certain metros, for example, the share of homes bought by investors exceeded ** percent in 2025.

London Property Prices Dataset 200k+ records

Overview

This dataset offers a comprehensive snapshot of residential properties in London, capturing both historical and current market data. It includes property-specific information such as address, geographic coordinates, and various price estimates. Data spans from past transaction prices to present estimates for sale and rental values, making it ideal for real estate analysis, investment modeling, and trend forecasting.

Key Columns

• fullAddress: Complete address of the property.
• postcode: Postal code identifying specific areas in London.
• outcode: First part of the postcode, grouping properties into broader geographic zones.
• latitude & longitude: Geographic coordinates for mapping or location-based analysis.
• property details: Includes bathrooms, bedrooms, floorAreaSqM, livingRooms, tenure (e.g., leasehold or freehold), and propertyType (e.g., flat, maisonette).
• energy rating: Current energy rating, indicating the property’s energy efficiency.

Pricing Information

• Rental Estimates: Ranges for estimated rental values (rentEstimate_lowerPrice, rentEstimate_currentPrice, rentEstimate_upperPrice).
• Sale Estimates: Current sale price estimates with confidence levels and historical changes.
  • saleEstimate_currentPrice: Current estimated sale price.
  • saleEstimate_confidenceLevel: Confidence in the sale price estimate (LOW, MEDIUM, HIGH).
  • saleEstimate_valueChange: Numeric and percentage change in sale value over time.
• Transaction History: Date-stamped sale prices with historic price changes, providing insight into property appreciation or depreciation.

Potential Applications

This dataset enables a variety of analyses: - Market Trend Analysis: Track how property values and rents have evolved over time. - Investment Insights: Identify high-growth areas and property types based on historical and estimated price changes. - Geospatial Analysis: Use location data to visualize price distributions and trends across London.

Usage Recommendations

This dataset is well-suited for machine learning projects predicting property values, rent estimations, or analyzing urban property trends. With rich details spanning multiple facets of the real estate market, it’s an essential resource for data scientists, analysts, and investors exploring the London property market.

Build your application on bedrock, not sand.

In the world of PropTech, data accuracy isn't just a feature—it's the product. Yet, most developers struggle with the chaos of raw county assessor data: inconsistent zoning codes, missing square footage, and fragmented tax records. BatchData’s Core Property & Land Use dataset solves this by ingesting, cleaning, and normalizing data from thousands of municipal sources into a single, developer-ready schema.

The "Source of Truth" for US Real Estate Covering over 155 million residential and commercial properties (99.8% of the US population), this dataset provides a high-definition digital twin of every parcel. We go beyond basic "beds and baths." Our proprietary ingestion engine standardizes granular building details—from roof types and foundation styles to heating systems and pool presence—allowing you to build precise filters and valuation models.

Why BatchData Property Intelligence?

Standardized Zoning & Land Use: We map thousands of local zoning codes (e.g., "R-1" vs "Res-Low") into a unified taxonomy, enabling you to programmatically identify "Single Family Residential" or "Vacant Land" across state lines without writing custom logic for every county.

Deep Building Characterization: Access over 75 distinct attributes related to the physical structure, essential for insurance underwriting, renovation estimation, and solar feasibility studies.

Historical Assessment Data: We track assessed values and tax amounts over time, giving you a longitudinal view of a property's holding costs and tax appreciation.

Key Statistics Coverage: 155,000,000+ Properties (Nationwide)

Attributes: 200+ Standardized Data Points

Update Frequency: Daily (Continuous Ingestion)

Property Types: Residential, Commercial, Industrial, Agricultural, Vacant Land

Geocoding: Roof-top precision Lat/Long coordinates

Detailed Data Schema & Attributes This dataset is organized into six logical clusters to support diverse use cases:

1. Building Characteristics (76+ Data Points)

Physical Structure: Living Area (Sq Ft), Building Area, Gross Area, Number of Stories, Year Built, Effective Year Built.

Room Counts: Bedrooms, Bathrooms (Full/Half/Quarter), Total Rooms.

Construction Details: Foundation Type (Slab/Basement/Pier), Roof Cover (Shingle/Tile/Metal), Roof Shape, Exterior Wall Type (Stucco/Brick/Siding), Frame Type.

Amenities & Systems: Pool (Yes/No), Pool Area, Fireplace (Count/Type), Heating/Cooling System Type, Garage Type/Size, Parking Spaces.

1. Land, Lot & Zoning (9+ Data Points)

Lot Geometry: Lot Size (Acres), Lot Size (Sq Ft), Lot Frontage, Lot Depth.

Land Use: Standardized Land Use Codes (e.g., SFR, Condo, Duplex, Vacant), County Land Use Descriptions.

Zoning: Standardized Zoning Codes, Zoning Descriptions, Topography, Site Influence (e.g., Corner Lot, Cul-de-sac).

1. Tax & Assessment (8+ Data Points)

Valuation: Total Assessed Value, Assessed Land Value, Assessed Improvement Value, Market Value.

Taxation: Total Tax Amount, Tax Year, Tax Area/Code, Exemption Status (e.g., Homestead).

History: Historical assessment data available for trend analysis.

1. Location & Geocoding (10+ Data Points)

Address: Standardized USPS Street Address, City, State, Zip, Zip+4.

Coordinates: Latitude, Longitude (Rooftop Precision).

Geo-Political: Census Tract, Block Group, Carrier Route.

1. Legal & Identification (20+ Data Points)

Identifiers: APN (Assessor Parcel Number), FIPS Code (State/County), Composite ID (BatchData Universal ID).

Legal Description: Full Legal Description, Subdivision Name, Tract Number, Block/Lot Number.

Deed Pointers: Last Sale Date, Last Sale Price (correlated with our Deed/Sales dataset).

Expanded Use Cases by Industry 1. PropTech & Search Portals

Search Experience: Power advanced filters like "Homes with a pool on >0.5 acres" or "Properties with basements built after 1990."

Listing Enrichment: Auto-populate listing pages with deep property details (Roof Type, Year Built, Tax History) to improve SEO and user engagement.

AVM Development: Train Automated Valuation Models using our granular "Improvement Value" and "Condition" data to predict market prices with higher accuracy.

1. Home Services & Energy (Solar/HVAC/Roofing)

Solar Feasibility: Use "Roof Shape," "Roof Cover," and "Building Area" combined with "Latitude/Longitude" to remotely qualify homes for solar panel installation.

Roofing Leads: Target properties with "Roof Material: Asphalt Shingle" built 15-20 years ago to identify homeowners likely needing a roof replacement.

HVAC Sizing: accurate "Living Area" and "Story Count" data helps estimators calculate load requirements before sending a truck.

1. Real Estate Investment (REI)

"Buy Box" Automation: Programmatically screen entire zip codes to find properties that match strict criteria (e.g., "3+ Beds, 2+ Baths, Built > 1980, Zoning: SFR").

Development Potential: Identify "Vacant Land" parcels with specific zoning codes (e.g., R-2 or R-3) ...

Real Estate Sales 730 Days

City of Hartford real estate sales for the past 2 years

By [source]

About this dataset

This dataset contains data on City of Hartford real estate sales for the last two years, with comprehensive records including property ID, parcel ID, sale date, sale price and more. This dataset is continuously updated each night and sourced from an official reliable source. The columns in this dataset include LocationStartNumber, ApartmentUnitNumber, StreetNameAndWay, LandSF TotalFinishedArea, LivingUnits ,OwnerLastName OwnerFirstName ,PrimaryGrantor ,SaleDate SalePrice ,TotalAppraisedValue and LegalReference - all valuable information to anyone wishing to understand the recent market trends and developments in the City of Hartford real estate industry. With this data providing detailed insights into what properties are selling at what time frame and for how much money – let’s see what secrets we can learn from examining the City of Hartford real estate activity!

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• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains helpful information about homes sold in the Hartford area over the past two years. This data can be used to analyze trends in real estate markets, as well as monitor sales activity for various areas.

In order to use this dataset, you will need knowledge of EDA (Exploratory Data Analysis) such as data cleaning and data visualization techniques. You will also need a basic understanding of SQL queries and Python scripting language.

The first step is to familiarize yourself with the columns and information contained within the dataset by analyzing descriptive statistics like mean, min, max etc. Next you can filter or “slice” the data based on certain criteria or variables that interest you - such as sale date range, location (by street name or zip code), sale price range, type of dwelling unit etc. After using various filters for analysis it is important to take an error-check step by looking for outliers or any discrepancies that may exist - this will ensure more accuracy in results when plotting graphs and visualizing trends via software tools like Tableau and Power BI etc.

Next you can conduct exploratory analysis through plot visualizations of relationships between buyer characteristics (first & last name) vs prices over time; living units vs square footage stats; average price per bedroom/bathroom ratio comparisons etc – all while taking into account external factors such as seasonal changeovers that could affect pricing fluctuations during given intervals across multiple neighborhoods - use interactive maps if available ets. At this point it's easy to compile insightful reports containing commonalities amongst buyers and begin generalizing your findings with extrapolations which allow us gain a better understanding of current market conditions across different demographic spectrums being compared ie traditional Vs luxury properties – all made possible simply through dedicated research with datasets like these!

Research Ideas

• Analyzing market trends in the City of Hartford's real estate industry by tracking sale prices and appraised values over time to identify regions who are being under or over valued.
• Conducting a predictive analysis project to predict future sales prices, annual appreciation rates, and key features associated with residential properties such as total finished area and living units for investment purposes.
• Studying the impact of local zoning laws on property ownership and development by comparing sale dates, primary grantors, legal references, street names and ways in a given area over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: real-estate-sales-730-days-1.csv | Column name | Description | |:------------------------|:---------------------------------------------------------------| | LocationStartNumber | The starting number of the location of the property. (Integer) | | ApartmentUnitNumber | The apartment unit number of the property. (Integer) | | StreetNameAndWay | The st...

Unlock the full potential of the short-term rental market with our comprehensive Airbnb Listing Data. This dataset provides a granular, 360-degree view of listing performance, property characteristics, and market dynamics across key global geographies. Designed for Real Estate Investors, Property Managers, Hedge Funds, and Travel Analysts, our data serves as the backbone for data-driven decision-making in the hospitality sector.

Whether you are looking to optimize pricing strategies, identify high-yield investment neighborhoods, or analyze amenity trends, this dataset delivers the raw intelligence required to stay ahead of the competition. We capture high-fidelity signals on listings, availability, pricing, and reviews, allowing you to model supply and demand with precision.

Key Questions This Data Answers Our data is structured to answer the most pressing commercial questions in the short-term rental industry. By leveraging our granular fields, analysts can immediately address:

Market Composition: What is the exact distribution of property types (Entire Home vs. Private Room vs. Shared) in a specific market? Understand supply saturation instantly.

Amenity ROI: Which amenities are most common in top-performing listings? Correlate features (e.g., Pools, Hot Tubs, Wi-Fi speeds) with Occupancy Rates and ADR (Average Daily Rate) to determine the ROI of renovations.

Pricing Intelligence: How does nightly price vary by neighborhood, seasonality, and property type? Visualize price elasticity and identify arbitrage opportunities between sub-markets.

Geospatial Density: What is the density of listings in different geographical areas? Pinpoint "hot zones" for tourism and identify underserved areas ripe for new inventory.

Performance Benchmarking: How do my listings compare to the top 10% of competitors in the same zip code?

Comprehensive Use Cases 1. Market Analysis & Competitive Positioning Gain a competitive edge by understanding the landscape of any target city.

Competitor Mapping: Track the growth of listing supply in real-time. Identify which property managers control the market share.

Saturation Analysis: Avoid over-supplied markets. Use density metrics to find neighborhoods with high demand but low inventory.

Trend Forecasting: Analyze historical data to predict future supply shifts and market saturation points before they occur.

1. Pricing Strategy & Revenue Management Move beyond static pricing. Our data enables dynamic pricing models based on real-world market conditions.

Attribute-Based Pricing: Quantify exactly how much a "Sea View" or "King Bed" adds to the nightly rate.

Seasonality Adjustments: Optimize calendars by analyzing historical price surges during holidays, events, and peak seasons.

RevPAR Optimization: Balance Occupancy and ADR to maximize Revenue Per Available Room (RevPAR).

1. Real Estate Investment & Valuation For investors and funds, this data acts as a fundamental layer for asset valuation.

Cap Rate Calculation: Combine our revenue data with property values to estimate potential yields and Cap Rates for prospective acquisitions.

Investment Scouting: Filter entire regions by "High Occupancy / Low Price" to find undervalued assets.

Due Diligence: Validate seller claims regarding income potential with independent, third-party data history.

1. Property Type & Amenity Distribution Analysis Understand what guests actually want.

Amenity Gap Analysis: Identify amenities that are in high demand (high search volume) but low supply in specific neighborhoods.

Renovation Planning: Data-driven insights on whether installing A/C or allowing pets will significantly increase booking conversion.

Data Dictionary & Key Attributes Our schema is designed for financial modeling and granular analysis. We provide over 50 distinct fields per listing, including calculated financial metrics for Trailing Twelve Months (TTM) and Last 90 Days (L90D).

Listing Identity & Characteristics:

listing_id: Unique identifier for the listing

listing_name & cover_photo_url: Title and main visual

listing_type & room_type: Property classification (e.g., villa, entire home)

amenities: Comprehensive list of offered features

min_nights & cancellation_policy: Booking rules and restrictions

instant_book & professional_management: Operational indicators

Property Specs & Capacity:

guests, bedrooms, beds, baths: Full capacity details

latitude, longitude, city, state, country: Precise geospatial coordinates

photos_count: Quantity of listing images

Host Intelligence:

host_id & host_name: Primary operator details

cohost_ids & cohost_names: Extended management team details

superhost: Quality badge status

Financial Performance (TTM - Trailing 12 Months):

ttm_revenue & ttm_revenue_native: Total gross revenue generated

ttm_avg_rate (ADR): Average Daily Rate achieved

ttm_occupancy & ttm_adjusted_occupancy: Raw vs. Adjusted (excluding owner blocks) occupancy

ttm_revpar & ttm_adjusted_revpar: Revenue Per ...

This dataset contains detailed information on current real estate listings in Houston, Texas, sourced from Zillow, and provides a comprehensive snapshot of the Houston housing market as of 5th June 2024.

The data was extracted from Zillow using a combination of two scraping tools from Apify: Zillow ZIP Code Scraper 🔗 https://apify.com/maxcopell/zillow-zip-search and Zillow Details Scraper 🔗 https://apify.com/maxcopell/zillow-detail-scraper.

The data includes key details for each listing for sale, such as:

• 📍 Complete address, city, state, zip code, latitude/longitude coordinates
• 🏡 Property type (single family, condo, apartment, etc.)
• 💵 Listing price
• 🛏️ Number of bedrooms and bathrooms
• 📐 Square footage
• 🌳 Lot size in acres (if applicable)
• 🏗️ Year of construction
• 🏘️ HOA fees (if applicable)
• 💸 Property tax history
• ✨ Amenities such as rooftop terraces, concierge services, etc.
• 🏫 Nearby schools and their GreatSchools ratings
• 🧑‍💼 Property and listing agents, brokers, and their contact information
• 🕒 Availability for tours and open houses
• 🖼️ Links to listing photos

With 25,900 current listings, this dataset is ideal for in-depth analysis of the Houston housing market and the Houston real estate market. Potential use cases include:

• Comparing listing prices, price per square foot across different neighborhoods, property types
• Mapping listings to visualize the spatial distribution of for-sale inventory
• Analyzing the age of for-sale housing stock from year-built data
• Evaluating typical HOA fees, and property taxes for listings
• Identifying listings with sought-after amenities
• Assessing school quality near listings from GreatSchools ratings
• Contacting listing agents programmatically using the included agent info

Whether you're a real estate professional, market researcher, data scientist, or just curious about the Houston housing market, this dataset provides a wealth of information to explore. You can start investigating Houston real estate today.

Graph and download economic data for All-Transactions House Price Index for Massachusetts (MASTHPI) from Q1 1975 to Q4 2025 about MA, appraisers, HPI, housing, price index, indexes, price, and USA.

Description

The dataset contains comprehensive records of real estate sales, each with a sales price of $2,000 or more, spanning from October 1 to September 30 of each year. These meticulous records are diligently maintained by the Office of Policy and Management.

Contents

1. Date Recorded: Denotes the date when the property sale information was recorded.
2. List Year: Indicates the year in which the property sale was listed.
3. Town: Identifies the town where the property is situated.
4. Address: Specifies the address of the property, pinpointing its location.
5. Assessed Value: Reveals the assessed value of the property, aiding in valuation assessments.
6. Sale Amount: Represents the actual sale amount or price at which the property was transacted.
7. Sales Ratio: Provides insight into the ratio of the sale amount to the assessed value, offering a comparative perspective.
8. Property Type: Categorizes the property type into residential, apartment, commercial, industrial, or vacant land.
9. Residential Type: Further categorizes residential properties for detailed analysis.
10. Longitude: Provides the longitudinal coordinate of the property location.
11. Latitude: Provides the latitudinal coordinate of the property location.

Data Collection

The dataset's compilation and maintenance are overseen by the Office of Policy and Management, ensuring accuracy and reliability. Records are meticulously compiled annually, encompassing sales transactions occurring within the fiscal year timeframe (October 1 to September 30).

Purpose

This dataset serves as a valuable resource for discerning real estate trends, evaluating property assessments, and comprehending market dynamics over time. Researchers, policymakers, real estate professionals, and analysts can leverage this dataset for myriad purposes such as conducting market research, performing property valuations, analyzing trends, and formulating informed policy decisions.

Applications and Utility

The dataset on real estate sales transactions has numerous applications and utilities across various domains: 1. Market Research: Analyze trends, patterns, and fluctuations in real estate sales over time. 2. Property Valuation: Assess property values accurately by comparing sale amounts with assessed values. 3. Policy Development: Formulate housing policies and urban planning strategies based on insights from the dataset. 4. Investment Analysis: Identify lucrative opportunities in the real estate market by analyzing historical sales data. 5. Geospatial Analysis: Visualize real estate sales patterns and identify hotspots using longitude and latitude coordinates. 6. Predictive Modeling: Develop predictive models to forecast real estate sales trends and market demand. 7. Market Intelligence: Gain valuable market intelligence to make informed business decisions and stay competitive.

File Format: Expect the dataset to be structured in a tabular format, most likely as a CSV (Comma-Separated Values) file or a similar structured format.

Usage Notes: It's essential to acknowledge that the dataset exclusively encompasses real estate sales with a sales price of $2,000 or greater, encompassing transactions within the specified fiscal year period. Users should exercise caution as the dataset may contain missing values or inherent inaccuracies typical in real estate transaction records.

Dataset Link: Real Estate Sales Dataset (2001-2018)

By providing access to this dataset, the Office of Policy and Management endeavors to foster transparency and facilitate comprehensive research and analysis within the dynamic realm of the real estate sector.

Unlock the Potential of U.S. National MLS Real Estate Data

Discover the wealth of information encapsulated in licensing bulk MLS (Multiple Listing Service) data, a cornerstone of the real estate realm. From property particulars to market trends, delve into the significance and multifaceted utility of MLS data across diverse industries.

MLS Real Estate Data includes:

• Property Information: Address, size, layout, condition, amenities, and more.
• Price History: Historical price changes, listing dates, and sales dates.
• Geographic Insights: Location, neighborhood information, school districts, and proximity to amenities.
• Property Photos: MLS images of properties (see the condition of a property inside and out.)
• Agent/Broker Information: Certain details about the listing agent or broker as well as their notes on properties.
• Market Dynamics: Data on local real estate market conditions, including inventory levels, price trends, and days on the market.

Last updated on 22 Feb 2025

Introduction

This dataset provides comprehensive information on property sales in England and Wales, sourced from the UK government's HM Land Registry. Although the government site claims to update on the same day each month, actual updates can vary. To bridge this update variation gap, our fully automated ETL pipeline retrieves the official government data on a daily basis. This ensures that the dataset always reflects the most current transaction data available.

ETL Process

Our ETL (Extract, Transform, Load) process is designed to automate the data update and publishing workflow: 1. Extract:
The pipeline uses web scraping to retrieve the latest data from the official government website. This step is necessary as the site does not offer an API. 2. Transform:
Before loading the data, the ETL pipeline processes the dataset to ensure consistency and usability. As part of the transformation stage, the first column (Transaction_unique_identifier) is removed. This column is dropped during staging to focus on the most relevant transactional information. The column removal successfully reduces the data file size from almost 6GB to 3.1GB, and therefore will greatly increase the data analysis efficiency, and reduces the chance of kernal error/restart. 3. Load:
Finally, the transformed data is loaded into the dataset.

The transformed data is loaded into the dataset in two parts: - Complete Data (pp-complete.csv): This file encompasses all records from January 1995 to the present. The complete data file is replaced during each update to reflect any corrections or additional historical data. The first column is price. - Monthly Data: A separate monthly file is amended each month. This monthly archive ensures a complete record of updates over time, allowing users to track changes and trends more granularly.

Summary of Results

The dataset (pp-complete.csv) contains records of property sales dating back to January 1995, up to the most recent monthly data. It covers various types of transactions—from residential to commercial properties—providing a holistic view of the real estate market in England and Wales.

Column Descriptions

The original data includes the following columns: - Transaction_unique_identifier
- price
- Date_of_Transfer
- postcode
- Property_Type
- Old/New
- Duration
- PAON
- SAON
- Street
- Locality
- Town/City
- District
- County
- PPDCategory_Type
- Record_Status - monthly_file_only

Note: As part of the transformation process, the Transaction_unique_identifier column is removed from the final published pp-complete.csv data file. Therefore the first column of the pp-complete.csv file is price.

Address data Explanation - Postcode: The postal code where the property is located. - PAON (Primary Addressable Object Name): Typically the house number or name. - SAON (Secondary Addressable Object Name): Additional information if the building is divided into flats or sub-buildings. - Street: The street name where the property is located. - Locality: Additional locality information. - Town/City: The town or city where the property is located. - District: The district in which the property resides. - County: The county where the property is located. - Price Paid: The price for which the property was sold.

Legal and Ethical Considerations

Ownership and Attribution This dataset is the property of HM Land Registry and is released under the Open Government Licence (OGL). If you use or publish this dataset, you are required to include the following attribution statement:

>"Contains HM Land Registry data © Crown copyright and database right 2021. This data is licensed under the Open Government Licence v3.0."

Usage Guidelines

The data can be used for both commercial and non-commercial purposes.

The OGL does not cover third-party rights, which HM Land Registry is not authorized to license. For any other use of the Address Data, you must contact Royal Mail.

Suggested Usages

Market Trend Analysis: Understand the ups and downs of the property market over time. Investment Research: Identify potential areas for property investment. Academic Studies: Use the data for economic research and studies related to the housing market. Policy Making: Assist government agencies in making informed decisions regarding housing policies. Real Estate Apps: Integrate the data into apps that provide property price information services.

By using this dataset, you agree to abide by the terms and conditions as specified by HM Land Registry. Failure to do so may result in legal consequences.

This dataset is scraped from zillow.com (only for educational purposes).

This data can be used if you are looking for making a recommendation system. Since there are features like nearby_city that can help for the recommendation engine. Note that this is raw dataset. And no cleaning is done.

Here's the information about the dataset.

📑 Dataset Features Below is a detailed explanation of the dataset columns:

Basic Property Information url: URL of the property listing on Zillow.

image_url: Featured image of the property.

address: Full property address.

region: Region/city where the property is located (e.g., 731-733 Carew St, Springfield, MA 01104 → Springfield).

property_type: Type of property (Single-family, Multi-family, Condo, Townhouse).

year_built: Year the property was built.

Property Details beds: Total number of bedrooms.

baths: Total number of bathrooms.

sqft: Total built-up area (in square feet).

sqft_lot: Lot area (square feet).

sqft_lot_cleaned: Cleaned version of sqft_lot.

sqft_lot_categories: Categorized lot size feature.

price_per_sqft: Price per square foot.

price: Total property price (target variable).

Financials estimated_monthly_payment: Estimated monthly mortgage/rent payment.

Interior & Exterior Features interior_features: Details of interior amenities.

other_rooms: Additional rooms in the house.

appliances: Appliances provided with the property.

parking_total_spaces: Total available parking spaces.

parking_garage_spaces: Garage capacity (in cars).

parking_uncovered_spaces: Total uncovered parking spaces.

parking_parking_features: Special features of parking space.

Utilities utilities_Electric: Details about electric utilities.

utilities_Sewer: Sewer-related utilities.

utilities_Water: Water utilities.

utilities_Utilities: Any other utility-related info.

Accessibility & Mobility walk_score: Walkability score (out of 100).

bike_score: Bike-friendliness score (out of 100).

transit_score: Public transportation availability (out of 100).

Nearby Schools elementary_school_name: Closest elementary school.

elementary_school_distance: Distance to elementary school.

middle_school_name: Closest middle school.

middle_school_distance: Distance to middle school.

high_school_name: Closest high school.

high_school_distance: Distance to high school.

Environmental Risks flood_risk: Flood risk (out of 10).

fire_risk: Fire risk (out of 10).

wind_risk: Wind risk (out of 10).

air_risk: Air quality risk (out of 10).

heat_risk: Heat risk (out of 10).

Geographic Context nearby_cities: Top 5 nearby cities relative to the property location.

Historical Data property_history: Past price and sale history of the property.

The average price per square foot of floor space in new single-family housing in the United States decreased after the great financial crisis, followed by several years of stagnation. Since 2012, the price has continuously risen, hitting ****** U.S. dollars per square foot in 2024. In 2024, the average sales price of a new home exceeded ******* U.S. dollars. Development of house sales in the U.S. One of the reasons for rising property prices is the gradual growth of house sales between 2011 and 2020. This period was marked by the gradual recovery following the subprime mortgage crisis and a growing housing sentiment. Another significant factor for the housing demand was the growing number of new household formations each year. Despite this trend, housing transactions plummeted in 2021, amid soaring prices and borrowing costs. In 2021, the average construction cost for single-family housing rose by nearly ** percent year-on-year, and in 2022, the increase was even higher, at close to ** percent. Financing a house purchase Mortgage interest rates in the U.S. rose dramatically in 2022 and remained elevated until 2024. In 2020, a homebuyer could lock in a 30-year fixed interest rate of under ***** percent, whereas in 2024, the average rate for the same mortgage type was more than twice higher. That has led to a decline in homebuyer sentiment, and an increasing share of the population pessimistic about buying a home in the current market.

This house price per square metre dataset is created through complex address-based matching between the Land Registry’s Price Paid Data (LR-PPD) and property size information from the Domestic Energy Performance Certificates (EPC) data published by the Department for Levelling Up, Housing and Communities (DLUHC, formerly MHCLG). Details of the data linkage are published in the UCL Open: Environment along with the related linkage code via the UK Data Service ReShare repository.

During this data linkage process, the transactions assigned as category B (Additional Price Paid entry) and other property types are removed. Here we publish our latest limited attribute version of the uncorrected house price per square metre dataset in England and Wales with the LR-PPD data (1/1/1995-26/2/2021) and Domestic EPCs data (the sixth version, up to 20/9/2020) downloaded on 1/4/2021 for non-commercial purpose. This uncorrected version of house price per square metre dataset records over 18 million transactions with 16 variables in England and Wales since 1995. Unlike in our published article, in this uncorrected version we have not removed transactions with any improbable price per square metre values - i.e. where either the transaction price or total floor area values are null, 0 or too low to be realistic. This uncorrected version of the data will offer the most flexibility for researchers.

We offer technical validation and data cleaning code via the UKDA ReShare repository to help users evaluate the representation of the linked data for a given time period. The data cleaning code shows our methods for cleaning up unlikely floor size records before using this data in analysis. Users can create their own rules and undertake this clean-up process based on their own experience and research aims.

This limited attribute version is published by local authority (2021 version). Details of the 16 variables are described in the explanation file. The National Statistics Postcode Lookup NSPL (May 2021 version) is used to assign the local authority unit for your production of area-based statistics. Users can match historical changes in LA boundaries by choosing appropriate aggregations using, for instance ONSPD, and the postcode variable in our dataset.

An extended version of this dataset containing additional variables is available from UK Data Service Reshare service. Users can directly access this full version dataset (tranall_link_01042021.zip) via the following link: https://reshare.ukdataservice.ac.uk/855033/ . Accompanying LR-PPD and EPC data are also supplied through the ReShare service. Users who would like to attach their own additional variables from the LR-PPD data are advised to use the transactionid variable to link to the LR-PPD (LRPPD_01042021.zip). Users who would like to attach additional variables from the EPC data are advised to use the id variable to link to the sixth version Domestic EPCs (epc6_id.zip).

The 2024 update

The 2024 updated version of the house price per square metre dataset extends the data coverage to the end of 2024 ( hpm_la_2024.zip ). This new version is the result of linking LR-PPD data (01/01/1995–31/10/2024) and Domestic EPCs data (up to 31/10/2024), downloaded on 26/12/2024 for non-commercial purposes. It records over 22 million transactions in England and Wales since 1995.

Unlike the previous versions, this updated removes the id variable (created by the authors) and adds the lmk_key variable (originally from the Domestic EPCs dataset). This change was made because the lmk_key serves as a unique identifier with no duplicate records since 2024.

The match rate of the linked data varies over time; therefore, we recommend users carefully choose the time coverage and validate the data coverage using the match rate. Please note that publicly available Domestic EPCs data starts in 2008, resulting in an extremely low match rate for the period between 1995 and 2008.

The National Statistics Postcode Lookup (November 2024 version) is used to assign local authorities (2023 version) for this publication. An extended version of this dataset, containing addition

London Property Prices Dataset 200k+ records Overview This dataset offers a comprehensive snapshot of residential properties in London, capturing both historical and current market data. It includes property-specific information such as address, geographic coordinates, and various price estimates. Data spans from past transaction prices to present estimates for sale and rental values, making it ideal for real estate analysis, investment modeling, and trend forecasting.

Key Columns fullAddress: Complete address of the property. postcode: Postal code identifying specific areas in London. outcode: First part of the postcode, grouping properties into broader geographic zones. latitude & longitude: Geographic coordinates for mapping or location-based analysis. property details: Includes bathrooms, bedrooms, floorAreaSqM, livingRooms, tenure (e.g., leasehold or freehold), and propertyType (e.g., flat, maisonette). energy rating: Current energy rating, indicating the property’s energy efficiency. Pricing Information Rental Estimates: Ranges for estimated rental values (rentEstimate_lowerPrice, rentEstimate_currentPrice, rentEstimate_upperPrice). Sale Estimates: Current sale price estimates with confidence levels and historical changes. saleEstimate_currentPrice: Current estimated sale price. saleEstimate_confidenceLevel: Confidence in the sale price estimate (LOW, MEDIUM, HIGH). saleEstimate_valueChange: Numeric and percentage change in sale value over time. Transaction History: Date-stamped sale prices with historic price changes, providing insight into property appreciation or depreciation. Potential Applications This dataset enables a variety of analyses:

Market Trend Analysis: Track how property values and rents have evolved over time. Investment Insights: Identify high-growth areas and property types based on historical and estimated price changes. Geospatial Analysis: Use location data to visualize price distributions and trends across London. Usage Recommendations This dataset is well-suited for machine learning projects predicting property values, rent estimations, or analyzing urban property trends. With rich details spanning multiple facets of the real estate market, it’s an essential resource for data scientists, analysts, and investors exploring the London property market.