Apartments.com is one of the largest rental listing platforms in the United States. It covers thousands of cities and provides detailed data on each listing — including price, location, photos, tenant reviews, and building amenities.

However, accessing this data at scale is not possible through the website alone. That's where an Apartments.com scraper API comes in. By extracting data from Apartments.com, you can:

• Track rental price trends across cities and neighborhoods
• Identify investment opportunities before they become widely known
• Build comparison tools or rental market dashboards
• Enrich your CRM or data warehouse with up-to-date listing data
• Automate competitive intelligence for property managers

A rich data source for real estate professionals

In addition to basic listing details, Apartments.com provides neighborhood descriptions and nearby schools. Moreover, it includes transportation scores, points of interest, and tenant reviews.

As a result, the data goes well beyond a simple price list. It gives you a full picture of each property and its surroundings — which is exactly what makes this API valuable for data-driven decisions.

Example of a listing page on Apartments.com

Our Apartments.com scraper is useful for a wide range of users. In particular, it fits well for:

• Real estate investors — monitor price trends and spot undervalued opportunities
• Property managers — benchmark your listings against the competition
• Data analysts — build rental market datasets for research or reporting
• Developers — integrate real estate data into apps, dashboards, or tools
• Proptech startups — power your product with live market data

ScrapingBot's Real Estate API handles the hard work for you: it renders JavaScript, rotates proxies, and solves CAPTCHAs automatically. You simply pass the Apartments.com listing URL, and the API returns clean, structured JSON data.

What the scraper collects

For each Apartments.com listing page, the API extracts the following categories of data:

Key listing data — monthly rent, bedrooms, bathrooms and surface area extracted in a single API call

Tenant reviews and property manager responses — all extractable via the reviews API fields

Overview of the data returned by the API for a single listing

Points of interest with travel time and distance — extracted via the pointOfInterest API fields

The API returns a structured JSON object. Here is a simplified example with the most useful fields:

{
  "title": "Woods on LaMonte Apartments",
  "description": "A quiet community in the Garden Oaks neighborhood...",
  "monthlyRent": "$1,200 - $2,400",
  "numberOfBedrooms": "1 - 3 bd",
  "numberOfBathrooms": "1 - 2.5 ba",
  "surfaceArea": "650 - 1,200",
  "surfaceAreaUnit": "sqft",
  "postalCode": "77092",
  "city": "Houston",
  "state": "TX",
  "road": "LaMonte Lane",
  "rating": {
    "value": 2.9,
    "total": 5,
    "reviewsCount": 22
  },
  "marketedBy": {
    "phoneNumber": "(571) 741-4115",
    "todaysHours": "9am - 6pm"
  },
  "pricing": [
    {
      "name": "1 Bedroom",
      "rent": "$1,200",
      "deposit": "$500",
      "numberOfBedrooms": "1",
      "numberOfBathrooms": "1",
      "surfaceArea": "650 sqft",
      "AvailableUnits": [
        {
          "unit": "A101",
          "price": "$1,200",
          "availability": "2024-06-01"
        }
      ]
    }
  ],
  "reviews": [
    {
      "stars": 4,
      "publishingDate": "2024-01-15",
      "title": "Great location",
      "review": "Close to everything, quiet neighborhood.",
      "propertyManagerResponse": "Thank you for your feedback!"
    }
  ],
  "transportation": {
    "scores": [
      { "name": "Walk Score", "score": 57, "total": 100 },
      { "name": "Bike Score", "score": 54, "total": 100 }
    ]
  }
}

Key fields reference

Here are the most commonly used fields and what they contain:

Walk Score, Transit Score and Bike Score — extracted via the transportation.scores API fields

Transit options and airports with travel time — extracted via the transportation.transport API fields

Nearby public and private schools with ratings — extracted via the education.schools API fields

Nearby colleges and universities with travel time — extracted via the education.colleges API fields

Lease options and property details — extracted via the fees and property info API fields

Lease details, pet policies and fees — extracted via the fees.feesPolicies API fields

Apartment features and amenities — extracted via the amenities.apartmentAmenities API fields

Community amenities — extracted via the amenities.communityAmenities API fields

About section and unique features — extracted via the about.description and about.uniqueFeatures API fields

Floor plan details with available units, prices and availability dates — extracted via the pricing.AvailableUnits API fields

The API returns clean JSON — no parsing or cleaning required

Getting started takes just a few lines of code. Below is an example in Python using the Real Estate API endpoint:

import requests

USERNAME = "your_username"
API_KEY  = "your_api_key"

url = "https://api.scraping-bot.io/scrape/real-estate"

payload = {
    "url": "https://www.apartments.com/woods-on-lamonte-houston-tx/",
    "realEstateOptions": {
        "city": "Houston"
    }
}

response = requests.post(
    url,
    json=payload,
    auth=(USERNAME, API_KEY)
)

data = response.json()

print(data["title"])        # "Woods on LaMonte Apartments"
print(data["monthlyRent"])  # "$1,200 - $2,400"
print(data["rating"])       # {"value": 2.9, "total": 5, "reviewsCount": 22}

Available SDKs

In addition to Python, ScrapingBot provides official SDKs for Node.js, PHP, and Java. Furthermore, you can use raw HTTP calls from any language or tool that supports REST APIs.

The API integrates cleanly into any data pipeline or backend application

ScrapingBot uses a credit-based pricing model. Each API call consumes a set number of credits depending on the endpoint and options used. In addition, all plans include access to the full Real Estate API feature set.

Available plans

Moreover, the free tier includes 100 credits every month — enough to run tests and validate your use case before committing to a paid plan.