Note:- Only publicly available real estate data can be worked upon.

Discover the world of property insights with APISCRAPY's user-friendly services – Realtor Property Data, Realtor Data, and Realtor API. Designed for ease of use, our platform allows anyone, from real estate professionals to researchers and businesses, to effortlessly access publicly available property listings and Property owner Data.

Our Realtor Property Data service provides comprehensive details on property listings, while Realtor API ensures easy integration for streamlined access. Additionally, we offer Zillow Property Data, enriching your property insights with information from one of the leading property platforms.

Key Features:

Realtor Property Data: Dive into detailed property listings effortlessly with our user-friendly platform.

Realtor API Integration: Seamlessly integrate our Realtor API into your systems for easy access to property data.

Zillow Property Data: Enrich your property insights with data from Zillow, one of the leading property platforms.

Publicly Available Property Listings: APISCRAPY ensures access to publicly available property listings, making property data easily accessible.

Easy Integration: Our platform is designed for simplicity, allowing for easy integration into your existing systems.

Whether you're a real estate professional, researcher, or business looking for straightforward access to property information, APISCRAPY's services cater to your needs. Choose us for simple and efficient property data services, where ease of use and accessibility come together for your convenience.

In the realm of real estate data solutions, BatchData Property Data Search API emerges as a technical marvel, tailored for product and engineering leadership seeking robust and scalable solutions. This purpose-built API seamlessly integrates diverse datasets, offering over 600 data points, to provide a holistic view of property characteristics, valuation, homeowner information, listing data, county assessor details, photos, and foreclosure information. With state-of-the-art infrastructure and performance features, BatchData sets the standard for efficiency, reliability, and developer satisfaction.

Unraveling the Technical Prowess of BatchData Property Data Search API:

State-of-the-Art Infrastructure: At the heart of BatchData lies a state-of-the-art infrastructure that leverages the latest technologies available. Our systems are engineered to handle increased loads and growing datasets with ease, ensuring optimal performance without significant degradation. This commitment to technological advancement ensures that our data infrastructure and API systems operate at peak efficiency, even in the face of evolving demands and complexities.

Integration Capabilities: BatchData boasts integration capabilities that are second to none, thanks to our innovative data lake house architecture. This architecture empowers us to seamlessly integrate our data with any data platforms or pipelines in a matter of minutes. Whether it's connecting with existing data systems, third-party applications, or internal pipelines, our API offers limitless integration possibilities, enabling product and engineering teams to unlock the full potential of property data with minimal effort.

Developer Documentation: One of the hallmarks of BatchData is our clear and comprehensive developer documentation, which developers love. We understand the importance of providing developers with the resources they need to integrate our API seamlessly into their projects. Our documentation offers detailed guides, code samples, API reference materials, and best practices, empowering developers to hit the ground running and leverage the full capabilities of BatchData with confidence.

Performance Features: BatchData Property Search API is engineered for performance, delivering lightning-fast response times and seamless scalability. Our API is designed to efficiently handle increased loads and growing datasets, ensuring that users experience minimal latency and maximum reliability. Whether it's retrieving property data, conducting complex queries, or accessing real-time updates, our API delivers exceptional performance, empowering product and engineering teams to build high-performance applications and systems with ease. BatchData's APIs work for both residential real estate data and commercial real estate data.

Common Use Cases for BatchData Property Data Search API:

Powering Data-Driven Applications: Product and engineering teams can leverage BatchData Property Data Search API to power data-driven applications tailored for the real estate industry. Whether it's building real estate websites, mobile applications, or internal tools, our API offers comprehensive property data that can drive informed decision-making, enhance user experiences, and streamline operations.

Enabling Advanced Analytics: With BatchData, product and engineering leaders can unlock the power of advanced analytics and reporting capabilities. Our API provides access to rich property data, enabling analysts and researchers to uncover insights, identify trends, and make data-driven recommendations with confidence. Whether it's analyzing market trends, evaluating investment opportunities, or conducting competitive analysis, BatchData empowers teams to derive actionable insights from vast property datasets.

Optimizing Data Infrastructure: BatchData Property Data Search API can play a pivotal role in optimizing data infrastructure within organizations. By seamlessly integrating our API with existing data platforms and pipelines, product and engineering teams can streamline data workflows, improve data accessibility, and enhance overall data infrastructure efficiency. Our API's integration capabilities and performance features ensure that organizations can leverage property data seamlessly across their data ecosystem, driving operational excellence and innovation.

Conclusion: BatchData Property Data Search API stands at the forefront of real estate data solutions, offering product and engineering leaders a comprehensive, scalable, and high-performance API for accessing property data. With state-of-the-art infrastructure, seamless integration capabilities, clear developer documentation, and exceptional performance features, BatchData empowers teams to build data-driven applications, optimize data infrastructure, and unlock actionable insights with ease. As the real estate industry continues to evolve, BatchData remains committed to delivering innovative sol...

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.

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.

South Carolina Real Estate Dataset 2025

Dataset Overview

This comprehensive real estate dataset contains over 5,000 property listings from South Carolina, collected in 2025 from Realtor.com using apify api. The dataset captures diverse property types including single-family homes, condominiums, land parcels, townhomes, and other residential properties. This dataset provides a rich snapshot of South Carolina's real estate market suitable for predictive modeling, market analysis, and investment research.

Data Science Applications

• Price Prediction Models: Build regression models (Random Forest, XGBoost, Neural Networks) to predict property values based on size, location, bedrooms, and age
• Property Type Classification: Develop multi-class classifiers to categorize properties based on physical characteristics
• Market Segmentation: Apply clustering algorithms (K-means, DBSCAN) to identify distinct property segments and price brackets
• Time Series Analysis: Analyze construction trends and property age distributions to forecast future development patterns
• Investment Opportunity Detection: Create anomaly detection models to identify undervalued properties or outliers
• Feature Engineering: Generate derived features like price per square foot, bathroom-to-bedroom ratios for enhanced model performance

Column Descriptors

• type: Primary property category (single_family, condos, land, townhomes, multi_family, farm)
• sub_type: Detailed property classification (condo, townhouse, co_op)
• sqft: Property size in square feet
• baths: Number of bathrooms (decimal values indicate half baths)
• beds: Number of bedrooms
• stories: Number of floors/stories in the property
• year_built: Construction year of the property
• listPrice: Property listing price in USD

Ethically Obtained Data

This dataset was ethically scraped from publicly available listings on Realtor.com and is provided strictly for educational and learning purposes only. The data collection complied with ethical web scraping practices and contains only publicly accessible information. Users should utilize this dataset exclusively for academic research, educational projects, and learning data science techniques. Any commercial use is strictly prohibited.

The TovoData Real Estate API provides real-time access to the most current, accurate real estate data covering homeowners across the nation. All the data you need to know about an address: ownership, characteristics, valuation, mortgage, liens, market data, you name it – the TOVO Real Estate Data API delivers it.

Global Generative AI in Real Estate is segmented by Application (Property listings, Virtual tours, Market analysis, Customer engagement, Content creation), Type (SaaS, Platform, API, Custom, On-Premise) and Geography(North America, LATAM, West Europe, Central & Eastern Europe, Northern Europe, Southern Europe, East Asia, Southeast Asia, South Asia, Central Asia, Oceania, MEA)

This project combines data extraction, predictive modeling, and geospatial mapping to analyze housing trends in Mercer County, New Jersey. It consists of three core components: Census Data Extraction: Gathers U.S. Census data (2012–2022) on median house value, household income, and racial demographics for all census tracts in the county. It accounts for changes in census tract boundaries between 2010 and 2020 by approximating values for newly defined tracts. House Value Prediction: Uses an LSTM model with k-fold cross-validation to forecast median house values through 2025. Multiple feature combinations and sequence lengths are tested to optimize prediction accuracy, with the final model selected based on MSE and MAE scores. Data Mapping: Visualizes historical and predicted housing data using GeoJSON files from the TIGERWeb API. It generates interactive maps showing raw values, changes over time, and percent differences, with customization options to handle outliers and improve interpretability. This modular workflow can be adapted to other regions by changing the input FIPS codes and feature selections.

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.

(API) Abrdn Property Income: Renting Out the Future?

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

BatchData's property listings data provides comprehensive insights with over 140 data points and nationwide listing data inclusive of For Sale By Owner (FSBO) listings across the United States. Updated daily in most markets, the data includes:

• Listing Details: property listings descriptions, property characteristics, pricing, days on market, and more.
• Agent Information: agent names, license numbers, contact details, listing counts, and listing histories.
• Broker Information: Broker names, locations, URLs, emails, phone numbers, and licensing information.
• Additional Details: Information about schools, neighborhoods, subdivisions, and tax data.

Common Use Cases: - Recruiting Teams: Enhance talent acquisition by analyzing agents' listing counts, close rates, property types, and client profiles. - Proptech Software & Marketplaces: Integrate current and historical listings to create detailed property profiles, advanced search features, and robust analytics. - Home Service Providers: Target marketing and outreach efforts to homeowners, whether they are preparing to move or have recently relocated. - Real Estate Agents & Investors: Identify undervalued properties, connect with buyers/sellers based on activity, analyze market trends, and develop effective marketing strategies.

Our property listings data can be delivered in a variety of formats to suit your needs. Choose from API integration for seamless, real-time data access, bulk data delivery for extensive datasets, S3 bucket storage for scalable cloud solutions, and more. This flexibility ensures that you can incorporate our comprehensive property information into your systems efficiently and effectively, whether you're building a new platform, enhancing existing tools, or conducting in-depth analyses.

This repository contains data and codes that support the findings of the study.- PPD-EPC open dataset with the enriched spatial analyses scores and UPRN.- Batch Geocoding Notebook of PPD-EPC dataset with GeoPy - Here API- PyQGIS codes for proximity, terrain, and visibility spatial analyses.- Jupyter Notebook of Machine Learning algorithms for mass property valuation.

The Zoopla Dataset provides a detailed repository of information covering property listings available on the Zoopla platform. Tailored to support businesses, researchers, and analysts in the real estate sector, this dataset delivers valuable insights into market trends, property valuations, and consumer preferences within the real estate market.

With key attributes such as property details, pricing data, location information, and listing history, users can conduct thorough analyses to refine property investment strategies, assess market demand, and identify emerging trends.

Whether you're a real estate agent seeking to enhance your property listings, a researcher investigating trends in the housing market, or an analyst aiming to refine investment strategies, the Zoopla Dataset serves as an essential resource for unlocking opportunities and driving success in the competitive landscape of real estate

Searchable online database of homes for sale, rent, and not currently on the market, with value estimator, market report, and real-estate trend tool. Users search by location (neighborhood, city, zip code, address) and parameters, such as property specifications, pricing, and keyword. Registration allows for favorite listing saving, customized property e-mail alerts, and other privileges. Users can also access real-estate listing data through an API.

The municipality’s assessment of the housing market situation in particular housing for the elderly in the municipality. Balance, surplus or deficit of housing. Housing deficits do not always mean that there are housing social problems such as overcrowding or extensive subletting as a widespread phenomenon. Housing deficits can mean that there is a dynamic economy in the municipality, where increased income leads to increased demand for housing. The fact that a municipality reports a deficit on housing means in many cases that it is difficult to move to, or within the municipality. surplus of housing means that there are constantly more vacant dwellings, or homes for sale, than is demanded. The existence of unleashed apartments in a single residential area does not necessarily mean that the local housing market is characterised by a surplus. A surplus of housing does not necessarily mean that there are suitable housing in relation to the demand and/or need in the municipality. Special forms of housing for the elderly refer to housing in accordance with Chapter 5, Section 5 of the Social Services Act. In order to be able to live in special housing, you need an aid assessment and a decision from the municipality.

This dataset is comprised of the final assessment rolls submitted to the New York State Department of Taxation and Finance – Office of Real Property Tax Services by 996 local governments. Together, the assessment rolls provide the details of the more than 4.7 million parcels in New York State.

The dataset includes assessment rolls for all cities and towns, except New York City. (For New York City assessment roll data, see NYC Open Data [https://opendata.cityofnewyork.us])

For each property, the dataset includes assessed value, full market value, property size, owners, exemption information, and other fields.

Tip: For a unique identifier for every property in New York State, combine the SWIS code and print key fields.

The municipality’s assessment of the housing market situation for young people, aged 19-25, in the municipality. Balance, surplus or deficit of housing. Housing deficits do not always mean that there are housing social problems such as overcrowding or extensive subletting as a widespread phenomenon. Housing deficits can mean that there is a dynamic economy in the municipality, where increased income leads to increased demand for housing. The fact that a municipality reports a deficit on housing means in many cases that it is difficult to move to, or within the municipality. surplus of housing means that there are constantly more vacant dwellings, or homes for sale, than is demanded. The existence of unleashed apartments in a single residential area does not necessarily mean that the local housing market is characterised by a surplus. A surplus of housing does not necessarily mean that there are suitable housing in relation to the demand and/or need in the municipality.

Market Analysis for Generic API The global Generic API market is estimated to reach a value of XXX million by 2033, exhibiting a CAGR of XX% from 2025 to 2033. The growth is primarily driven by the increasing prevalence of chronic diseases, rising demand for affordable healthcare options, and government initiatives to promote generic drug use. Other drivers include the expiration of patents on branded drugs and technological advancements that make it easier to produce generic APIs. Major trends in the market include the consolidation of the generic API industry, the increasing adoption of biosimilars, and the globalization of the supply chain. Restraints to growth include regulatory challenges, competition from branded drug manufacturers, and intellectual property concerns. The market is segmented by application (pharmaceutical, veterinary, nutraceuticals, etc.) and type (small molecules, large molecules, biologics, etc.). Key players in the industry include TEVA Pharmaceuticals, Sun Pharmaceutical Industries Ltd, Pfizer, and Novartis.

Content

Every January, Finance mails New York City property owners a Notice of Property Value (NOPV). This important notice has information about your property’s market and assessed values. Finance determines your property’s value every year, according to State law. The Cityʼs property tax rates are applied to the assessed value to calculate your property taxes for the next tax year. You get your first tax bill for the year in June. If you believe the values or property descriptions on the NOPV are not correct.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Photo by Gus Ruballo on Unsplash

Fair Market RentsThis National Geospatial Data Asset (NGDA) dataset, shared as a Department of Housing and Urban Development (HUD) feature layer, displays fair market rents (FMR) in the United States. According to HUD, "Fair Market Rents (FMRs) represent the estimated amount (base rent + essential utilities) that a property in a given area typically rents for. The data are primarily used to determine payment standard amounts for the Housing Choice Voucher program. However, FMRs are also used to determine initial renewal rents for expiring project-based Section 8 contracts, determine initial rents for housing assistance payment (HAP) contracts in the Moderate Rehabilitation Single Room Occupancy program (Mod Rehab), rent ceilings for rental units in both the HOME Investment Partnerships program and the Emergency Solution Grants (ESG) program, calculate of maximum award amounts for Continuum of Care recipients and the maximum amount of rent a recipient may pay for property leased with Continuum of Care funds, and calculate flat rent amounts in Public Housing Units."Milwaukee-Waukesha-West Allis, WI Metropolitan Statistical Area (MSA)Data currency: current Federal service (Fair Market Rents)NGDAID: 122 (Fair Market Rents (Fair Market Rents For The Section 8 Housing Assistance Payments Program) - National Geospatial Data Asset (NGDA))For more information, please visit: Fair Market RentsSupport documentation: Fair Market Rents (FMRs)For feedback, please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes."For other NGDA Content: Esri Federal Datasets