Buy U.S. Real Estate Neighbourhood Dataset
Accurate, Verified, and Ready-to-Use U.S. Property Listings Data for Market Analysis, Real Estate Intelligence & AI Models
Data Overview
Use Cases
APISCRAPY’s U.S. Real Estate Neighbourhood Dataset Price Comparison Dataset
| NeighborhoodID | NeighborhoodName | City | State | ZipCode | AvgHomePrice (USD) | AvgRent (USD) | Population | MedianIncome (USD) | WalkScore | TransitScore | SchoolRating | CrimeRate (per 1k) | AvgPropertyAge | ListingURL | SourceURL |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5001 | Downtown | Austin | TX | 78701 | $750,000 | $2,500 | 12,000 | $85,000 | 92 | 80 | 8 | 3.5 | 15 | Example Listing | Redfin Neighborhood |
| 5002 | Capitol Hill | Seattle | WA | 98102 | $950,000 | $3,200 | 10,500 | $95,000 | 88 | 85 | 9 | 2.8 | 25 | Example Listing | Zillow Neighborhood |
| 5003 | South End | Boston | MA | 02118 | $1,200,000 | $3,500 | 9,000 | $120,000 | 90 | 78 | 9 | 3.2 | 30 | Example Listing | Redfin Neighborhood |
| 5004 | Buckhead | Atlanta | GA | 30305 | $850,000 | $2,800 | 8,500 | $110,000 | 70 | 60 | 8 | 2.5 | 20 | Example Listing | Zillow Neighborhood |
| 5005 | Brickell | Miami | FL | 33131 | $780,000 | $2,700 | 15,000 | $90,000 | 85 | 75 | 7 | 3.0 | 18 | Example Listing | Redfin Neighborhood |
50 States
Nationwide Coverage
1M+ Agents
Verified Records
GDPR & HIPAA
Compliance-ready data.
2+ Years
Historical Data
Detailed Data Overview – U.S. Real Estate Neighbourhood Dataset
The U.S. Real Estate Neighbourhood Dataset provides a curated collection of property listings from multiple states across the United States. Each record includes verified property attributes such as address, property type, bedroom and bathroom counts, square footage, lot size, year built, and listing price, along with the original listing source URL.
Ideal for real estate agencies, property investors, data scientists, and housing market researchers, this dataset delivers granular residential data for price modeling, investment analysis, and location intelligence.
What Makes APISCRAPY’s Data Even More Powerful
APISCRAPY’s datasets go beyond basic records by delivering accuracy, scalability, and actionable intelligence that empower businesses to make confident, data-driven decisions.
Key Advantages:
- High Data Accuracy: Verified and validated through multi-source cross-checking.
- Custom Data Enrichment: Enhanced with additional attributes such as geo-coordinates, pricing trends, and property analytics.
- Regular Updates: Continuous data refresh ensures up-to-date insights aligned with market changes.
- AI-Ready Formats: Clean, structured data optimized for analytics, visualization, and machine learning applications.
- Domain Expertise: Curated by professionals with deep industry knowledge in real estate, finance, and urban analytics.
With APISCRAPY, you gain more than data i.e., you gain clarity, precision, and competitive advantage.
Get Data Now
APISCRAPY is an AI-driven web scraping and automation tool that converts any web data into ready-to-use data API. The tool is capable to extract data from websites, process data, automate workflows, classify data and integrate ready-to-consume data into database or deliver data in any desired format.
Get the Edge with Real-Time U.S. Real Estate Neighbourhood Dataset
Ready to power up your real estate analytics and market research with high-quality, structured U.S. Real Estate Neighbourhood Data? Gain a competitive edge by understanding housing trends and neighborhood-level insights long before your competitors.
Get a free quote for your project. Reach out today.
Jyotish 
Chief Data Officer
Scalable Web Scraping Solution
Guaranteed Data Quality Assurance
Flexible Integrations for Workflow
Maximum Privacy for Your Data
Web scraping
$25* per website
Data Fields Included in the U.S. Real Estate Neighborhood Dataset
Our dataset is meticulously structured to provide comprehensive property-level insights across multiple U.S. states. Each record includes key attributes required for market research, valuation modeling, and investment analysis. Custom fields can also be added to match your specific project or analytical needs.
- ListingID: Unique identifier assigned to each property record.
- Address: Full street address of the property.
- City: The city or locality where the property is located.
- State: The U.S. state abbreviation (e.g., FL, TX, IL).
- ZipCode: The postal code for accurate geographic mapping.
- PropertyType: Category of the property (e.g., Single Family, Townhouse, Condo, Duplex).
- Bedrooms: Number of bedrooms in the property.
- Bathrooms: Number of bathrooms (including half baths, if applicable).
- SquareFeet: Total interior living space in square feet.
- LotSize (sqft): Total lot or land area associated with the property.
- YearBuilt: The year the property was originally constructed.
- ListingPrice (USD): Current or last known listing price in U.S. dollars.
- Source URL: Verified external link to the original property listing (e.g., Redfin, Zillow).
Get the Edge with Real-Time U.S. Real Estate Neighbourhood Dataset
Ready to power up your litigation analytics and legal research with high-quality, structured Florida Accident Cases data? Gain a competitive advantage by understanding legal trends and judicial outcomes before your competition.
- Trusted by
Country Coverage
(4 Countries)
North America (2)
United States Of America
Canada
Australia (1)
Austraila
Europe (1)
United Kingdom
Suitable Company Sizes
Small Business
Medium-sized Business
Enterprise
750+
Happy Clients & Growing
2500+
Projects delivered
12+
Years of Sharing & Caring
Top Use Cases of the U.S. Real Estate Neighborhood Dataset
This dataset is more than just a list of records; it’s a strategic tool for a variety of critical applications:
Real Estate Market Research & Price Analysis
Gain a comprehensive view of housing trends and property values across different U.S. states. Analysts and researchers can use this dataset to identify market fluctuations, study price growth patterns, and evaluate neighborhood-level demand. It serves as a data-driven foundation for understanding regional housing market dynamics and long-term investment potential.
Investment Strategy & Portfolio Optimization
Use property-level insights to identify undervalued assets and emerging neighborhoods. Investors can analyze attributes such as year built, square footage, and listing price to calculate ROI and assess potential appreciation. This helps in creating optimized, geographically diverse real estate portfolios supported by empirical data.
Property Valuation & Predictive Modeling
Utilize property attributes and historical price data to train AI and machine learning models for automated property valuation. Real estate platforms and analytics firms can predict listing prices, detect anomalies, and forecast future market trends using structured, normalized data inputs. This enables businesses to make data-driven pricing decisions and improve appraisal accuracy.
Urban Planning & Development Insights
Government agencies, urban planners, and infrastructure consultants can use this dataset to assess residential density, zoning trends, and development needs. The data enables the identification of underdeveloped areas, supporting informed planning for new housing projects and sustainable city expansion.
Competitive Benchmarking for Real Estate Agencies
Real estate companies can benchmark their listings and pricing strategies against market averages in similar cities or ZIP codes. This data helps agencies optimize property pricing, tailor marketing campaigns, and evaluate their competitive position across different states and property categories.
Financial Risk & Mortgage Underwriting
Banks and mortgage institutions can integrate this dataset into risk assessment models to determine accurate property valuations and assess market volatility. Insights from listing prices and property types help underwriters improve pricing strategies, manage exposure, and refine credit scoring algorithms.
Business Intelligence & Visualization
Data analysts can connect this dataset to BI tools such as Tableau or Power BI to create dashboards that visualize price trends, property types, and geographic distribution. This supports data-driven decision-making across sales, marketing, and strategy teams in the real estate sector.
PropTech Application Development
Developers and startups in the property technology space can integrate this dataset into platforms for home discovery, automated appraisal, or investment recommendation. It provides the data backbone for AI-powered tools that enhance user experience and improve the accuracy of real-time insights.
Media & Market Journalism
Business journalists and research analysts can use this dataset to support data-driven stories about the U.S. housing market, neighborhood trends, and investment patterns. It helps uncover insights into market growth areas, affordability challenges, and shifts in home-buying behavior across states.
AI-Powered APISCRAPY’s U.S. Real Estate Neighborhood Dataset
Utilise the full potential of APISCRAPY’s structured data by integrating it with your AI and machine learning initiatives. APISCRAPY’s dataset is perfectly formatted to train and power intelligent systems for:
01.
Train AI models to predict the likely outcome of a case (e.g., verdict amount, liability finding) based on historical data and case attributes, giving you a powerful predictive edge.
02.
Use machine learning to identify patterns in judicial rulings and attorney strategies that lead to specific outcomes, offering a predictive edge in litigation.
03.
Develop AI-powered models that automatically score the risk of new insurance claims by comparing them to thousands of similar historical cases, streamlining the claims process and improving accuracy.
Easy Integration
Oracle
MySQL
MS SQL Server
PostgreSQL
Microsoft Access
Redis
MongoDB
Snowflake
Elastic search
IBM Db2
Sqlite
Cassandra
Databricks
Splunk
Azure SQL
Apache Hive
Google BigQuery
Neo4j
Data Listing Plan Comparison Table
The Data Listing Plan Comparison Table provides a concise overview of APISCRAPY’s available plans, allowing you to easily compare features, pricing, and benefits. It highlights key aspects such as data entries, update frequency, and support, helping you choose the plan that best fits your needs.
| Data Fields | Basic Plan | Premium Plan |
| Access to Core Data Fields | ❌ | |
| Advanced Data Fields | ❌ | |
| Contact/Email/Phone Data | ❌ | |
| Pricing Insights / Market Trends | ❌ | |
| Custom Field Requests | ❌ | |
| Real-Time Data Updates | ||
| Historical Data Access | ❌ | |
| API Access | ||
| Data Filtering & Query Options | ❌ | |
| Data Freshness | Weekly Updates | Daily or Real-Time |
| Priority Support | ❌ | |
| Usage Analytics Dashboard | ❌ | |
| SLA & Data Accuracy Guarantee | ❌ | |
| Data Access Quota (Searches) | 2,000 searches/month | Unlimited Searches |
| CRM Enrichment | Manual Only | Automatic Enrichment |
| CRM Filters / Integrations | Basic Filters Only | Advanced CRM Filtering |
| Dedicated Account Manager | ❌ |
Data Delivery Options
Delivery Methods
How the data is transferred or accessed by the user.
| S3 Bucket |
| SFTP |
| UI Export |
| REST API |
| SOAP API |
| Streaming API |
| Feed API |
| Google Cloud Storage (GCS) |
| Azure Blob Storage |
| Google Drive / Dropbox |
Update Frequency
How often the data is updated or made available.
| secondly |
| minutely |
| hourly |
| daily |
| weekly |
| monthly |
| quarterly |
| yearly |
| real-time |
| on-demand |
| Custom Schedule |
Data Formats
The file or structural format in which data is provided.
| .bin |
| .json |
| .xml |
| .csv |
| .xls |
| .sql |
| .txt |
| HTML (for human-readable previews) |
| PDF reports |
| ZIP (compressed packages) |
Industry Applications of APISCRAPY’s of the U.S. Real Estate Neighborhood Dataset
Our data serves a wide array of professional sectors:
Real Estate & Property Management
Real estate agencies, brokers, and listing platforms can use this dataset to analyze market trends, price fluctuations, and buyer preferences. It enables professionals to optimize pricing strategies, enhance property listings, and deliver accurate neighborhood insights to clients.
Investment & Private Equity
Investment firms and asset managers rely on granular property data to evaluate potential acquisitions and portfolio diversification. By integrating this dataset, analysts can identify undervalued markets, predict future appreciation, and assess risk across multiple U.S. regions all supported by verified property-level information.
Banking, Finance & Mortgage Lending
Banks, credit unions, and financial institutions can leverage this dataset to improve mortgage underwriting, collateral evaluation, and credit risk modeling. Accurate property and neighborhood data enable lenders to assess fair market value, refine loan-to-value ratios, and enhance decision-making in loan approvals and refinancing.
Urban Planning & Government Agencies
Urban planners and municipal authorities can use the dataset to monitor housing density, infrastructure needs, and urban growth patterns. It supports data-backed policy formulation for housing development, zoning decisions, and city expansion, helping create more sustainable and balanced communities.
PropTech & Real Estate Technology
Technology-driven real estate companies and startups can integrate this dataset into property discovery platforms, investment tools, and automated valuation models (AVMs). The dataset’s clean, structured format allows for seamless integration into APIs and applications, powering AI-based solutions for valuation, recommendation, and forecasting.
Business Intelligence & Consulting
Consulting firms and data analysts can incorporate this dataset into BI platforms to uncover market trends, price movements, and regional performance. It helps organizations identify growth opportunities, analyze competitor pricing strategies, and make data-informed recommendations to clients in the real estate and financial sectors.
Academic Institutions & Research Organizations
Universities, research bodies, and policy institutes can utilize this dataset to study housing affordability, urbanization trends, and socio-economic disparities. It enables empirical research in urban economics, housing policy, and demographic analysis using verified, structured property data.
Insurance & Actuarial Firms
Insurance providers and actuaries can analyze property characteristics and neighborhood data to assess exposure, pricing, and claim risk. Understanding patterns in property age, type, and size supports more accurate underwriting and helps insurers manage real estate-related risk portfolios effectively.
Data Analytics & AI Companies
AI developers and analytics service providers can integrate this dataset into predictive models for property price forecasting, investment scoring, and market simulations. Its standardized data structure allows for easy machine learning integration, enabling companies to build scalable and intelligent property analytics systems.
Our assurance to security & quality
Why You Should Go With APISCRAPY
Experience
12+ years
of sharing and caring.
Projects
2500+ Projects
delivered with a smile.
Retention
98%
engagement success rate.
Quick
Scale-up
as per your requirement.
Certified
ISO 9001 & 27001
data quality & security.
Clients
750+
served across industries.
Compliant with the Highest Data Privacy Standards
APISCRAPY is proudly certified by the Ethical Web Data Collection Initiatives. We collect only publicly available, business-related information—never private or sensitive data. We strictly avoid scraping content behind login-protected or restricted areas.
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FAQs about the U.S. Real Estate Neighborhood Dataset
What is the U.S. Real Estate Neighborhood Dataset?
The U.S. Real Estate Neighborhood Dataset is a curated collection of verified residential property listings from multiple states across the United States. It includes key property details such as address, type, price, size, year built, and source URL making it ideal for real estate analytics, market research, and AI modeling.
Where can I get high-quality Florida accident cases data for legal research?
You can get APISCRAPY’s expertly curated, verified, and structured dataset directly from us. We specialize in providing litigation data for lawyers and legal professionals.
What type of properties are included in the dataset?
The dataset covers a wide range of residential property types, including single-family homes, townhouses, condos, and duplexes. Commercial properties can also be added upon request as part of a custom dataset enrichment service.
How often is the dataset updated?
The dataset is refreshed regularly to ensure accuracy and relevance. Updates include the latest property listings, price changes, and newly built homes. Clients can also opt for scheduled updates (weekly, monthly, or quarterly) depending on their project needs.
Is the dataset verified for accuracy?
Yes. Each record undergoes multi-source verification, cross-checked against leading real estate platforms like Zillow, Realtor.com, and Redfin. This ensures that the data is highly accurate, complete, and reliable for analytical and commercial applications.
In what format is the dataset delivered?
The dataset is delivered in AI-ready structured formats such as CSV, JSON, Excel, or SQL, ensuring seamless integration into data analytics tools, visualization dashboards, or machine learning pipelines.
Can I request data for specific states, cities, or ZIP codes
Absolutely. The dataset can be customized based on geographic filters such as state, city, county, or ZIP code. This allows real estate analysts, investors, and data scientists to focus on specific regions or market segments relevant to their research or operations.
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