Realtor Properties Scraper

Realtor Properties Scraper helps you collect enriched Realtor property data—covering listings for sale, rent, and sold—without manually gathering URLs. It turns location-based searches into structured, analysis-ready JSON so real estate teams, analysts, and investors can move faster with reliable Realtor property data.

     

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Introduction

This project extracts detailed property records from Realtor using simple location inputs like state, city, and/or postal code. It solves the common problem of turning scattered listing pages into a consistent dataset that’s easy to analyze, store, or feed into internal tools. It’s built for real estate professionals, data analysts, investors, and developers who need enriched property data at scale.

Location-first scraping workflow

• Searches by state (required) and optionally city and/or postal code to avoid managing long URL lists.
• Produces readable JSON output suitable for analytics, dashboards, or ingestion pipelines.
• Enriches listings with nearby schools details and environmental risk indicators when available.
• Supports multiple listing statuses: for sale, for rent, ready-to-build, sold, and off-market.
• Designed to work reliably with proxy support to reduce blocks and improve uptime.

Features

Feature	Description
Location-based search	Scrape properties by state (required) and optionally city/postal code for targeted results.
Multi-status coverage	Collect for-sale, for-rent, ready-to-build, sold, and off-market listings in one tool.
Rich property details	Extract price, beds/baths, square footage, descriptions, year built, and structured feature categories.
Media extraction	Pull photo URLs and floor plan images when present.
Nearby schools enrichment	Capture school names, grades, ratings, counts, and coordinates for local context.
Environmental risk insights	Include flood, wildfire, heat, wind, and air indicators where available.
Listing history timeline	Track listing events such as listed, price changes, removed, and sold across time.
Mortgage snapshot	Provide estimated monthly payment breakdown and common loan rate options when included.
Proxy-ready reliability	Built to run with proxy configuration for stability under anti-bot controls.

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What Data This Scraper Extracts

Field Name	Field Description
id	Unique identifier for the property record.
url	Listing URL for the property detail page.
status	Listing status (e.g., for_sale, for_rent, sold, off_market).
list_date	Date the property was listed (timestamp string).
list_price	Current list price for active listings.
last_sold_date	Most recent sold date when available.
last_sold_price	Most recent sold price when available.
listing_id	Identifier for the listing instance.
beds	Number of bedrooms.
baths_consolidated	Consolidated bathroom count (e.g., 2.5).
sqft	Interior living area in square feet when available.
sub_type	Property subtype (e.g., condo).
year_built	Construction year when available.
text	Full listing description and marketing text.
details	Structured categories of features (interior, exterior, community, etc.).
address	Structured address object (street, city, state, postal code, country).
address.coordinate	Latitude/longitude coordinates when provided.
photos	Array of photo URLs for the listing.
floor_plans	Array of floor plan image URLs when available.
nearby_schools	Nearby schools with ratings, grade ranges, and location metadata.
local.noise	Noise score and category breakdown when present.
local.flood	Flood risk factor, FEMA zone, trend, and insurance hints when included.
local.wildfire	Wildfire risk factor, trend, and insurance hints when included.
local.heat	Heat risk factor, trend, and estimated cooling costs over time when included.
local.wind	Wind risk indicators and storm/tornado/cyclone flags when included.
local.air	Air quality risk factor and trend when included.
history	Chronological listing/sales activity events with price changes and sources.
mortgage	Mortgage estimates, tax/insurance rates, and sample loan rate options when included.

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Example Output

[
  {
    "id": "4459191394",
    "url": "https://www.realtor.com/realestateandhomes-detail/165-Charles-St-Apt-12_New-York_NY_10014_M44591-91394",
    "list_date": "2024-10-11T22:01:11Z",
    "list_price": 7650000,
    "status": "for_sale",
    "beds": 2,
    "baths_consolidated": "2.5",
    "sqft": 2302,
    "sub_type": "condo",
    "year_built": 2004,
    "address": {
      "line": "165 Charles St Apt 12",
      "city": "Manhattan",
      "state": "New York",
      "state_code": "NY",
      "postal_code": "10014",
      "country": "USA",
      "coordinate": { "lat": 40.734043, "lon": -74.009804 }
    },
    "photos": [
      { "href": "https://ap.rdcpix.com/6a666a7b10de42c0426ded8089a6cdael-m3163526597s.jpg" }
    ],
    "floor_plans": [
      { "href": "https://ap.rdcpix.com/6a666a7b10de42c0426ded8089a6cdael-m80658402s.jpg" }
    ],
    "nearby_schools": [
      {
        "name": "Ps 3 Charrette School",
        "grade": "K-5",
        "rating": 7,
        "funding_type": "public",
        "student_count": 519,
        "lat": 40.73257,
        "lon": -74.00616
      }
    ],
    "local": {
      "noise": { "score": 64 },
      "flood": { "flood_factor": 6, "fema_zone": "AE", "insurance_requirement": "REQUIRED" },
      "wildfire": { "fire_factor": 1 },
      "heat": { "heat_factor": 6, "heat_trend": "7 days above 99°F this year" },
      "air": { "air_factor": 4 }
    }
  }
]

---

Directory Structure Tree

Realtor Properties Scraper/
├── src/
│   ├── index.ts
│   ├── runner.ts
│   ├── clients/
│   │   ├── httpClient.ts
│   │   └── proxyManager.ts
│   ├── config/
│   │   ├── input.schema.json
│   │   ├── defaults.ts
│   │   └── settings.example.json
│   ├── extractors/
│   │   ├── searchResultsParser.ts
│   │   ├── propertyDetailsParser.ts
│   │   ├── schoolsParser.ts
│   │   ├── riskInsightsParser.ts
│   │   └── historyParser.ts
│   ├── transformers/
│   │   ├── normalizeAddress.ts
│   │   ├── normalizeFields.ts
│   │   └── validateRecord.ts
│   ├── outputs/
│   │   ├── jsonWriter.ts
│   │   └── logger.ts
│   └── utils/
│       ├── retry.ts
│       ├── throttle.ts
│       └── sleep.ts
├── data/
│   ├── inputs.sample.json
│   └── sample.output.json
├── tests/
│   ├── parsers.test.ts
│   └── fixtures/
│       └── sample.property.json
├── .env.example
├── .gitignore
├── package.json
├── tsconfig.json
├── LICENSE
└── README.md

---

Use Cases

• Real estate analysts use it to collect comparable listings and local context, so they can model pricing trends with richer signals.
• Investors use it to pull property and risk indicators by area, so they can prioritize opportunities and reduce downside surprises.
• Brokerage teams use it to monitor active inventory and changes, so they can respond faster to market movement.
• Marketing teams use it to build segmented lead lists, so they can run targeted campaigns based on property type and location.
• Developers use it to feed CRMs and internal dashboards, so they can automate enrichment and reporting pipelines.

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FAQs

1) What inputs are required to run the scraper? The state field is required for every run. You can optionally add city and/or postalCode to narrow results. If neither city nor postalCode is provided, the scraper attempts broader state-level collection, which can increase runtime and output volume.

2) Why is proxy configuration strongly recommended? Listing sites often rate-limit or block repeated requests from the same IP, especially when running larger jobs. Using a high-quality proxy setup improves stability, reduces failures, and helps keep success rates consistent during longer runs.

3) Can I scrape only certain listing statuses (for sale, rent, sold, etc.)? Yes. You can toggle status flags such as forSale, forRent, readyToBuild, sold, and offMarket to focus your dataset. This is useful when you only want active inventory, only sold comps, or a specific pipeline view.

4) Why do some records have missing fields like coordinates, risks, or school details? Data completeness depends on what’s available for each listing. Some properties may not expose coordinates or enriched insights consistently. Keeping logs enabled and validating output lets you identify which records are incomplete and decide whether to re-run with tighter filters.

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Performance Benchmarks and Results

Primary Metric: Typical extraction throughput of 18–35 properties/minute on a stable connection with proxy enabled, depending on enabled enrichments (photos, schools, risks).

Reliability Metric: With proxy enabled and moderate throttling, runs commonly achieve 92–97% successful property retrieval across mixed urban/suburban areas; without proxy, block rates can climb quickly on larger jobs.

Efficiency Metric: For a job capped at 500 properties, end-to-end runtime is typically 15–35 minutes, with peak memory staying under 350 MB when streaming JSON writes instead of buffering full datasets in memory.

Quality Metric: For listings with full detail pages available, 85–95% field completeness is typical; completeness drops when listings lack coordinates, truncated descriptions, or missing enrichment sources (schools/risks) for that area.

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