Client Overview

A fast-growing real estate agency specializing in residential properties faced challenges in reaching a broader audience and maintaining up-to-date property listings. They lacked an efficient system to gather and process property data from multiple online sources, such as competitor websites and real estate listing platforms.

Challenge

The client needed a scalable solution to:
Aggregate property listings from various real estate platforms.
Identify and collect contact information for potential leads.
Maintain an updated database of properties and leads to avoid duplications and errors.
Access and utilize the collected data without reliance on local servers or infrastructure.

Solution Implemented

We built a Python-based web crawling solution that was hosted on the cloud, enabling the real estate agency to automatically gather property data and leads from multiple platforms.

  • Key Features of the Solution:

    Property Aggregation: Crawled popular real estate platforms to gather property details, including location, price, amenities, and contact information for sellers.

    Lead Capture: Extracted contact information (e.g., phone numbers, email addresses) of property owners and agents for direct outreach.

    Data Cleaning and Validation: Incorporated data validation processes to remove duplicates and standardize property listings for easier analysis.

    Custom Dashboard: Built a user-friendly dashboard for the client to visualize property listings, leads, and associated metrics in real-time.

    Cloud Deployment: Deployed the solution on Google Cloud Platform (GCP) to ensure scalability, reliability, and easy accessibility for the client.

  • Cloud Deployment Process

    Data Crawling Infrastructure: Used GCP Compute Engine for running web crawlers and storing raw data.

    Database Management: Stored processed data in a GCP Firestore NoSQL database, ensuring quick access and scalability.

    Automation: Leveraged Cloud Functions to automate scheduled crawling tasks and data updates.

    Visualization: Hosted the dashboard on App Engine for seamless access across the client’s team.

     

  • Implementation Steps

    Requirement Analysis: Worked with the client to identify key platforms, regions, and data fields to target.

    Crawler Development: Designed Python crawlers using Scrapy and Selenium to handle dynamic and static web content.

    Data Validation: Implemented scripts to clean and validate the data, ensuring accuracy and reliability.

    Integration: Integrated the solution with the client’s existing CRM system to streamline lead management.

Outcome

  • 30% Increase in Customer Base: The automated lead generation process enabled the client to connect with more potential buyers and sellers, expanding their customer base by 30% within six months.
  • Time Savings: The automation replaced hours of manual data collection, allowing the client to focus on closing deals and improving customer relationships.
  • Scalability: The cloud-hosted solution scaled effortlessly as the client’s operations grew, handling more platforms and data volume without additional infrastructure costs.
  • Real-Time Insights: The dashboard provided the client with up-to-date information on new properties and leads, enabling faster decision-making.

Client Testimonial

“The lead generation tool has revolutionized how we work. It’s like having a 24/7 assistant collecting data for us. The scalability and accuracy of the system have helped us expand our business significantly.”

Tools and Technologies Used

Python Libraries: Scrapy, Selenium

PandasDatabase: GCP Firestore (NoSQL)

Automation: GCP Cloud Functions

Framework: Flask for the dashboard

Cloud Platform: Google Cloud Platform (GCP)