# Realtytrac Property Search Scraper (`alexist/realtytrac-property-search-scraper`) Actor

Scrape property listings from RealtyTrac with 22+ fields per record, including address, price, photos, beds/baths, and market status. Perfect for real estate investors, market analysts, and property aggregators seeking comprehensive property intelligence.

- **URL**: https://apify.com/alexist/realtytrac-property-search-scraper.md
- **Developed by:** [Alex](https://apify.com/alexist) (community)
- **Categories:** Automation, Developer tools, Real estate
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## RealtyTrac Property Scraper: Extract Real Estate Data at Scale

***

### What Is RealtyTrac?

RealtyTrac is one of the largest online real estate data platforms, offering detailed property information, tax assessments, and market intelligence across millions of U.S. residential and commercial properties. The platform aggregates data from public records, MLS feeds, and property databases, making it essential for investors, real estate professionals, and researchers. However, manually collecting property data from RealtyTrac is inefficient at scale — the **RealtyTrac Property Scraper** automates this workflow, extracting structured records directly from search results pages.

***

### Overview

The **RealtyTrac Property Scraper** extracts detailed property information from RealtyTrac search results, converting listings into clean, structured records with 22 key fields. It is ideal for:

- **Real estate investors** building lead lists and market datasets
- **Property aggregators** feeding property data into third-party platforms
- **Market researchers** analyzing regional property trends and valuations
- **Wholesalers** identifying off-market opportunities and distressed properties
- **Data analysts** building real estate intelligence dashboards

The scraper supports geographic targeting via latitude/longitude or ZIP code search, configurable item limits, and robust error handling for large-scale collection runs.

***

### Input Format

The scraper accepts a JSON configuration specifying target URLs and collection parameters:

```json
{
  "urls": [
    "https://www.realtytrac.com/california-city-ca/?lat=35.110455861764876&lng=-117.97103600000001&zoom=13.57159423828125&zipcode=93505"
  ],
  "max_items_per_url": 200,
  "ignore_url_failures": true
}
```

| Field | Type | Description |
|---|---|---|
| `urls` | array | RealtyTrac property search result URLs. Support geographic, ZIP code, and custom filtered searches |
| `max_items_per_url` | integer | Maximum properties to extract per URL (e.g., `200`). Higher values capture more results but may extend runtime |
| `ignore_url_failures` | boolean | If `true`, continues running even if individual URLs fail; if `false`, stops the entire run on first error |

**Pro tip:** RealtyTrac URLs encode location data (latitude, longitude, zoom level, ZIP) in the query string. Copy full URLs directly from the browser's address bar to preserve geographic targeting.

***

### Output Format

**Example output record:**

```json
{
  "id": "223311318",
  "type": "SFR",
  "street": "Hacienda",
  "suffix": "Ave",
  "addr": "16716 Hacienda Ave",
  "full_addr": "16716 Hacienda Ave, California City, CA 93505",
  "city": "California City",
  "state": "CA",
  "county": "Kern",
  "zip": "93505",
  "beds": 3,
  "baths": 2,
  "sqft": 2280,
  "lot_sqft": 111514,
  "value": 525000,
  "listed_on": null,
  "status": {
    "pre_foreclosure": true,
    "roi": 5.0258365
  },
  "status_date": 1784592000000,
  "lastupdate_date": 1782950400000,
  "bing_photo_url": "https://dudtfne5hk5fn.cloudfront.net/property-card-fallback_SRPD.webp",
  "image_url": "https://dudtfne5hk5fn.cloudfront.net/property-card-fallback_SRPD.webp",
  "coord": {
    "lat": 35.068752,
    "lon": -117.952425
  },
  "recently_sold_price": 540000,
  "from_url": "https://www.realtytrac.com/california-city-ca/?lat=35.110455861764876&lng=-117.97103600000001&zoom=13.57159423828125&zipcode=93505"
}
```

Each scraped property returns 22 structured fields:

#### Property Identification & Address

| Field | Meaning |
|---|---|
| `ID` | Unique RealtyTrac property identifier |
| `Type` | Property type (residential, commercial, land, etc.) |
| `Street` | Street name (e.g., "Main") |
| `Suffix` | Street suffix (e.g., "St", "Ave", "Rd") |
| `Address` | Full street address without city/state |
| `Full Address` | Complete address with city, state, ZIP |
| `City` | City name |
| `State` | State abbreviation (e.g., "CA") |
| `County` | County name |
| `ZIP` | ZIP code |

#### Property Features

| Field | Meaning |
|---|---|
| `Beds` | Number of bedrooms |
| `Baths` | Number of bathrooms |
| `Square Feet` | Interior living area (sq ft) |
| `Lot Square Feet` | Total lot size (sq ft) |

#### Valuation & Market Status

| Field | Meaning |
|---|---|
| `Value` | Current assessed value or estimated property value |
| `Recently Sold Price` | Last sale price (if property recently sold) |
| `Listed On` | Date property was listed for sale |
| `Status` | Current market status (e.g., Active, Sold, Pending, Withdrawn) |
| `Status Date` | Date of the last status change |
| `Last Update Date` | Most recent data update timestamp |

#### Images & Location

| Field | Meaning |
|---|---|
| `Bing Photo URL` | Aerial/map view image from Bing Maps |
| `Image URL` | Property photo or listing image URL |
| `Coordinates` | Latitude and longitude (geocoordinates) for map integration |

***

### How to Use

1. **Build search URLs** — Navigate to RealtyTrac.com, apply filters (location, property type, price range), and copy the resulting search page URL from your browser.
2. **Format input** — Paste URLs into the `urls` array. Start with `max_items_per_url: 50-100` for test runs.
3. **Configure collection** — Set `ignore_url_failures: true` for large batches to prevent interruptions.
4. **Run the scraper** — Execute the actor and monitor progress in the run log.
5. **Export & process** — Download results as JSON or CSV; import into Excel, Python, or your CRM for analysis.

**Best practices:**

- Use narrow geographic filters (ZIP code or city) to focus collection and reduce runtime.
- Enable `ignore_url_failures` when scraping 10+ URLs simultaneously.
- Check `Recently Sold Price` and `Last Update Date` to identify stale listings.
- Cross-reference `Coordinates` with mapping tools for neighborhood analysis.

***

### Use Cases & Business Value

- **Investment analysis:** Build databases of properties matching investment criteria (price range, beds, lot size) in target markets
- **Market intelligence:** Track price trends, days on market, and supply by analyzing historical snapshots
- **Lead generation:** Identify active or recently sold properties for direct mail, cold calling, or networking
- **Property aggregation:** Feed RealtyTrac data into internal platforms, MLS integrations, or mobile apps
- **Comparative market analysis (CMA):** Collect comps for property valuation without manual research

The RealtyTrac Property Scraper accelerates workflows that would otherwise require hours of manual clicking, enabling data-driven decisions and competitive advantages in real estate operations.

***

### Conclusion

The **RealtyTrac Property Scraper** transforms property search data into actionable intelligence. With 22 fields per record and flexible URL targeting, it supports investors, wholesalers, researchers, and developers in scaling their real estate operations. Extract property data reliably and integrate it into your analytics pipeline or business workflow.

# Actor input Schema

## `urls` (type: `array`):

Add the URLs of the property list urls you want to scrape. You can paste URLs one by one, or use the Bulk edit section to add a prepared list.

## `ignore_url_failures` (type: `boolean`):

If true, the scraper will continue running even if some URLs fail to be scraped.

## `max_items_per_url` (type: `integer`):

The maximum number of items to scrape per URL.

## Actor input object example

```json
{
  "urls": [
    "https://www.realtytrac.com/california-city-ca/?lat=35.110455861764876&lng=-117.97103600000001&zoom=13.57159423828125&zipcode=93505"
  ],
  "ignore_url_failures": true,
  "max_items_per_url": 100
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "urls": [
        "https://www.realtytrac.com/california-city-ca/?lat=35.110455861764876&lng=-117.97103600000001&zoom=13.57159423828125&zipcode=93505"
    ],
    "ignore_url_failures": true,
    "max_items_per_url": 100
};

// Run the Actor and wait for it to finish
const run = await client.actor("alexist/realtytrac-property-search-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "urls": ["https://www.realtytrac.com/california-city-ca/?lat=35.110455861764876&lng=-117.97103600000001&zoom=13.57159423828125&zipcode=93505"],
    "ignore_url_failures": True,
    "max_items_per_url": 100,
}

# Run the Actor and wait for it to finish
run = client.actor("alexist/realtytrac-property-search-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "urls": [
    "https://www.realtytrac.com/california-city-ca/?lat=35.110455861764876&lng=-117.97103600000001&zoom=13.57159423828125&zipcode=93505"
  ],
  "ignore_url_failures": true,
  "max_items_per_url": 100
}' |
apify call alexist/realtytrac-property-search-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,alexist/realtytrac-property-search-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/3iL90lEotjhV6N04x/builds/UGu7l9zQGkIGlctvU/openapi.json