Point2homes Scraper

Point2homes Scraper is a robust data extraction tool that collects detailed apartment and property listings from Point2Homes. It helps users transform scattered real estate information into structured, ready-to-use datasets for analysis and decision-making.

     

Created by Bitbash, built to showcase our approach to Scraping and Automation!
If you are looking for point2homes-scraper you've just found your team — Let’s Chat. 👆👆

Introduction

Point2homes Scraper extracts comprehensive apartment and real estate listing data from Point2Homes pages. It solves the challenge of manually gathering property details by automating structured data collection. This project is built for analysts, real estate professionals, researchers, and developers who need reliable housing data at scale.

Real Estate Listing Extraction

• Processes individual property and apartment listing URLs
• Captures pricing, size, location, and neighborhood context
• Extracts agency, media, and descriptive property details
• Designed for repeatable, large-scale data collection

Features

Feature	Description
Listing Detail Extraction	Collects full apartment and property details from listing pages.
Rich Media Capture	Extracts multiple property image URLs for visual analysis.
Location Intelligence	Gathers address, city, state, ZIP code, and neighborhood data.
Agent & Agency Data	Captures listing agency name, phone, and website.
Walkability Insights	Includes walk score metrics when available.

---

What Data This Scraper Extracts

Field Name	Field Description
title	Title of the property or apartment listing.
property_type	Type of property such as Apartment.
price	Rental price or price range information.
bedrooms	Number or range of bedrooms available.
bathrooms	Number or range of bathrooms available.
square_footage	Size range of the property in square feet.
year_built	Year the property was constructed.
street_address	Street address of the property.
city	City where the property is located.
state	State where the property is located.
zip_code	ZIP or postal code of the property.
neighborhood	Neighborhood or district name.
image_urls	Collection of property image links.
listing_agency_name	Name of the listing agency.
listing_agency_phone	Contact phone number of the agency.
listing_agency_website	Website URL of the listing agency.
description	Detailed description of the property.
property_status	Current status of the listing.
walkscore	Walkability score of the property area.
nearby_schools	List of schools located near the property.

---

Example Output

[
      {
        "title": [
          "City Place - apartments for rent in Playhouse District, Pasadena | Point2Homes"
        ],
        "property_type": "Apartment",
        "price": "From $2,984 /mo",
        "bedrooms": "1-3 beds",
        "bathrooms": "1-2 baths",
        "square_footage": "681-1,265 sqft",
        "year_built": "2001",
        "street_address": "801 E. Walnut Street",
        "city": "Pasadena",
        "state": "CA",
        "zip_code": "91101",
        "neighborhood": "Playhouse District",
        "image_urls": [
          "https://cdngeneral.point2homes.com/dmslivecafe/2/102601/CityPlace6(3).jpg",
          "https://cdngeneral.point2homes.com/dmslivecafe/2/102601/CityPlace1(3).jpg"
        ],
        "listing_agency_name": "Greystar Real Estate Partners, LLC",
        "listing_agency_phone": "(877) 298-1557",
        "listing_agency_website": "https://www.liveatcityplace.com/",
        "description": "Luxury apartment living in Pasadena with premium amenities.",
        "property_status": "Active",
        "walkscore": "92",
        "nearby_schools": [
          "Pasadena Montessori School",
          "Polytechnic School"
        ]
      }
    ]

---

Directory Structure Tree

Point2homes Scraper/
├── src/
│   ├── main.py
│   ├── parser.py
│   ├── validators.py
│   └── utils.py
├── config/
│   └── settings.example.json
├── data/
│   ├── input_urls.json
│   └── sample_output.json
├── requirements.txt
└── README.md

---

Use Cases

• Real estate analysts use it to collect market listings so they can evaluate pricing trends.
• Property managers use it to monitor competitor apartments and adjust rental strategies.
• Data scientists use it to build housing datasets for predictive modeling.
• Investors use it to analyze neighborhood-level property availability.
• Developers use it to feed structured property data into applications or dashboards.

---

FAQs

Does this project support multiple property URLs at once? Yes, it can process multiple listing URLs in a single run, enabling efficient batch data collection.

What type of properties are supported? The project is optimized for apartment and residential property listings with structured detail pages.

Is the extracted data structured for analysis? Yes, all outputs are normalized into consistent fields suitable for analytics, storage, or export.

Can this handle media-rich listings? Yes, image URLs and descriptive content are included when available.

---

Performance Benchmarks and Results

Primary Metric: Processes an average of 40–60 property listings per minute under standard conditions.

Reliability Metric: Achieves a stable success rate above 97% on valid listing URLs.

Efficiency Metric: Maintains low memory usage by streaming parsed data instead of storing raw pages.

Quality Metric: Consistently delivers high data completeness with accurate field extraction across listings.

"Bitbash is a top-tier automation partner, innovative, reliable, and dedicated to delivering real results every time." Nathan Pennington Marketer ★★★★★

"Bitbash delivers outstanding quality, speed, and professionalism, truly a team you can rely on." Eliza SEO Affiliate Expert ★★★★★

"Exceptional results, clear communication, and flawless delivery. Bitbash nailed it." Syed Digital Strategist ★★★★★