Building an Automated Real Estate Listing Registration and Information Extraction System with n8n and GPT-4 Vision: From Zillow, Realtor.com Scraping to Custom Notifications
Stop manual real estate listing searches! Combine n8n and GPT-4 Vision to build a powerful system that automatically scrapes listing information from real estate platforms like Zillow and Realtor.com, extracts key information through image analysis, and provides custom user notifications. You'll save time and gain market competitiveness.
1. The Challenge / Context
The real estate market is rapidly changing, with new listings constantly emerging. Investors and prospective homebuyers need to grasp these changes in real-time and quickly find properties that suit their needs. However, checking platforms like Zillow and Realtor.com daily and manually collecting and analyzing listing information is a very cumbersome and time-consuming task. Furthermore, it's difficult to accurately understand a property's condition or features with just text information. Before the advent of GPT-4 Vision, extracting real estate information through image analysis was a very complex and costly technology. Now, automated scraping and image analysis make it possible to collect and analyze real estate information more efficiently and accurately.
2. Deep Dive: n8n (No-code Automation Platform)
n8n is a powerful open-source workflow automation platform. It allows you to build complex workflows by connecting various services and applications without coding. It offers diverse functionalities such as web scraping, data transformation, API calls, database integration, and email/Slack notifications, enabling users to customize workflows as desired. The core is its node-based visual interface, where each node performs a specific task, and nodes are connected to define data flow. This makes it easy to visualize and manage complex backend logic.
3. Step-by-Step Guide / Implementation
Now, let's take a detailed look at the steps to build an automated real estate listing registration and information extraction system using n8n and GPT-4 Vision.
Step 1: n8n Installation and Basic Setup
First, you need to install n8n. n8n can be installed in various ways, including Docker, npm, and n8n Cloud. Here, we will explain the installation method using Docker as an example.
# Create a Docker Compose file.
version: '3.7'
services:
n8n:
image: n8nio/n8n
restart: always
ports:
- 5678:5678
volumes:
- ~/.n8n:/home/node/.n8n
environment:
- N8N_BASIC_AUTH_ACTIVE=true
- N8N_BASIC_AUTH_USER=your_username
- N8N_BASIC_AUTH_PASSWORD=your_password
- NODE_ENV=production
# Run Docker Compose.
docker-compose up -d
In the code above, you need to change `your_username` and `your_password` to your desired values. After running Docker Compose, you can access the n8n interface by navigating to `localhost:5678` in your web browser.
Step 2: Building a Web Scraping Workflow (Zillow Example)
Build a workflow to scrape listing information from Zillow. Use the HTTP Request node to fetch the listing page for a specific area on Zillow, and the HTML Extract node to extract the desired information. You can use a Python script node to perform data cleaning and transformation.
// HTTP Request Node Configuration
{
"nodes": [
{
"parameters": {
"url": "https://www.zillow.com/homes/san-francisco-ca/",
"method": "GET",
"responseFormat": "string"
},
"name": "HTTP Request",
"type": "n8n-nodes-http.httpRequest",
"typeVersion": 1,
"position": [
200,
200
]
},
{
"parameters": {
"mode": "html",
"selector": ".list-card-info a",
"output": "text"
},
"name": "HTML Extract",
"type": "n8n-nodes-html.htmlExtract",
"typeVersion": 1,
"position": [
400,
200
],
"connections": {
"main": [
[
{
"node": "Function",
"type": "main",
"index": 0
}
]
]
}
},
{
"parameters": {
"jsCode": "items.forEach(item => {\n item.json.url = 'https://www.zillow.com' + item.json.text;\n delete item.json.text;\n});\n\nreturn items;"
},
"name": "Function",
"type": "n8n-nodes-base.function",
"typeVersion": 1,
"position": [
600,
200
],
"connections": {
"main": [
[
{
"node": "Set",
"type": "main",
"index": 0
}
]
]
}
},
{
"parameters": {
"values": [
{
"name": "url",
"value": "={{$json.url}}",
"type": "string"
}
],
"options": {}
},
"name": "Set",
"type": "n8n-nodes-base.set",
"typeVersion": 1,
"position": [
800,
200
],
"connections": {
"main": [
[
{
"node": "HTTP Request1",
"type": "main",
"index": 0
}
]
]
}
},
{
"parameters": {
"url": "={{$json.url}}",
"method": "GET",
"responseFormat": "string"
},
"name": "HTTP Request1",
"type": "n8n-nodes-http.httpRequest",
"typeVersion": 1,
"position": [
1000,
200
],
"connections": {
"main": [
[
{
"node": "HTML Extract1",
"type": "main",
"index": 0
}
]
]
}
},
{
"parameters": {
"mode": "html",
"selector": "h1.Text-c11n-8-14-0__sc-aiai24-0.krsKbz",
"output": "text"
},
"name": "HTML Extract1",
"type": "n8n-nodes-html.htmlExtract",
"typeVersion": 1,
"position": [
1200,
200
],
"connections": {
"main": [
[
{
"node": "Function1",
"type": "main",
"index": 0
}
]
]
}
},
{
"parameters": {
"jsCode": "items.forEach(item => {\n item.json.title = item.json.text;\n delete item.json.text;\n});\n\nreturn items;"
},
"name": "Function1",
"type": "n8n-nodes-base.function",
"typeVersion": 1,
"position": [
1400,
200
],
"connections": {
"main": [
[
{
"node": "Set1",
"type": "main",
"index": 0
}
]
]
}
},
{
"parameters": {
"values": [
{
"name": "title",
"value": "={{$json.title}}",
"type": "string"
}
],
"options": {}
},
"name": "Set1",
"type": "n8n-nodes-base.set",
"typeVersion": 1,
"position": [
1600,
200
],
"connections": {
"main": [
[
{
"node": "Console",
"type": "main",
"index": 0
}
]
]
}
},
{
"parameters": {
"message": "Title: {{$json.title}}"
},
"name": "Console",
"type": "n8n-nodes-base.console",
"typeVersion": 1,
"position": [
1800,
200
]
}
],
"connections": {}
}
The code above is a workflow that extracts the URL of each listing from the Zillow listing page, then accesses that URL to extract the listing's title. It uses the `.list-card-info a` selector to extract links from the listing list, and then accesses each link to extract the title using the `h1.Text-c11n-8-14-0__sc-aiai24-0.krsKbz` selector.
Step 3: Image Analysis using GPT-4 Vision
Extract images from the listing page and perform image analysis using the GPT-4 Vision API. GPT-4 Vision offers various functionalities such as object recognition within images, text extraction, and scene understanding. For example, it
