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Real Estate Lead Generation and Web Application Development
  1. case
  2. Real Estate Lead Generation and Web Application Development

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Real Estate Lead Generation and Web Application Development

dataforest.ai
Real estate

Lead Generation & Data Access Challenges

Stantem requires a robust lead generation web application to efficiently search and target homeowners across the US. Key challenges include accessing and leveraging real estate data (property values), rapid search performance within a large dataset (150 million properties), and efficient data scraping from multiple sources. They also need to automate lead nurturing via email campaigns and provide advanced analytical tools.

About the Client

Stantem is a real estate lead generation automation company focused on the US market.

Project Goals

  • Develop a web application for real estate lead generation.
  • Enable efficient search and filtering of a large dataset of US properties.
  • Automate email campaigns to homeowners.
  • Provide tools for property value estimation.
  • Facilitate data comparison and analysis.
  • Implement subscription management and payment processing.

Functional Requirements

  • Property search (list and map views)
  • Email campaign functionality with a text editor
  • Property value range display
  • Advanced property comparison and analysis tools
  • User authentication and registration
  • Stripe integration for subscriptions and payments
  • Admin panel for managing users, subscriptions, and content

Technology Stack

Python
Django
ReactJS
Redis
AWS
ElasticSearch

External Integrations

  • Stripe (payment processing)
  • Google Maps API (map view)
  • Tiny text editor (email campaigns)

Non-Functional Requirements

  • High performance search (sub-second response time)
  • Scalability to handle a large and growing dataset
  • Security to protect user data and payment information
  • Reliability and uptime

Expected Business Impact

This project will enable Stantem to significantly increase lead generation efficiency, improve data accuracy, automate key processes, and enhance customer engagement. The anticipated impact includes increased sales conversions, reduced operational costs, and improved market competitiveness. Improved forecasting accuracy and reduced stockouts (if applicable to their business model) are also expected.

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