Search Engine & API User Interface

In the complex landscape of real estate, our challenge extends beyond collection, we want making sense of this information to drive real value for our users.

Nasdaq real estate indices with Zillow’s data to capture a multi-dimensional view of the market and, we access and analyze extensive historical pricing data, enabling us to not only understand past but also anticipate the future housing values and market dynamics.

Our goal is to deepen real estate market understanding and empower consumers with comprehensive, accurate, and timely market data. We establish predictive models that help identifying high-potential investment opportunities and providing actionable insights to users.

Procurement

The Zillow Real Estate Data feed contains 56 real estate market indicators across four categories: Home Values, Rentals, Sales and Inventories. This indicators cover more than 78,200 regions in the United States. Regions are categorized into six types: State, County, Metro Area & USA, City, Neighborhood, and Zip Code.

Procurement of this data can be done through the Nasdaq Data Link API, where users can access the data using the Nasdaq datalink Python package. The API allows for filtering by certain indicators and regions, which can make more personalized analysis. To view pricing information and have full access to the Zillow Real Estate Data, users must log in with their Nasdaq Data Link account. This comprehensive dataset provides consumers with facts and knowledge about the real estate market while also connecting them with local professionals for assistance.

MongoDB: Tracking User Activity

To monitor and track user activity, we’ve integrated MongoDB, a NoSQL database known for its flexibility in handling unstructured data. Before public users can access our search engine, they are required to fill out a personal information survey. The collected data is then transformed into documents, which are immediately stored in MongoDB. This approach allows us to efficiently manage and query user information, providing insights into user behavior and preferences while ensuring scalability as our user base grows.

Integrating Python, Flask, PostgreSQL, and MongoDB

In the realm of web application development, choosing the right technologies is critical for achieving both functionality and scalability. For our project, we've strategically selected a stack that includes Python, Flask, PostgreSQL, and MongoDB. Each component plays a vital role in creating a seamless and efficient application, from handling user interactions to managing and storing data.

Python: The Backbone of Our Application

Python serves as the core programming language for our project, bridging both front-end and back-end activities. Its versatility and extensive libraries make it an ideal choice for web development. In our application, Python not only manages the logic and data flow but also acts as a conduit between the user interface and the backend systems. Whether it’s data retrieval, transformation, or visualization, Python’s robust ecosystem of packages empowers us to execute these tasks efficiently.

PostgreSQL: The Data Warehouse of Our Application

Our choice for a relational database management system is PostgreSQL, which serves as a critical component in our data architecture, functioning more like a data warehouse. Rather than storing raw data directly in PostgreSQL, we capture data through API interactions and store it in the database based on a predefined schema. This approach ensures that the data is both organized and readily accessible for future analysis, aligning with our goal of maintaining a structured and efficient data repository. PostgreSQL’s powerful query capabilities and support for complex data types make it an ideal solution for handling the captured data, allowing us to perform advanced analytics and reporting with ease.

Flask: API Framework

Flask, a lightweight yet powerful API framework, is our first key technology. We've leveraged Flask to define several API routes in Python, allowing users to interact directly with our backend databases via the user interface. Flask’s simplicity and flexibility enable rapid development while maintaining a clean and organized structure. This framework is pivotal in handling HTTP requests and responses, making it easier for our application to communicate with the underlying databases and deliver the necessary data to users.

API Main Page

User Survey

API Key and Password Security

For the sake of security and best practices, I have not included the Python file containing sensitive information like API keys and passwords in this blog. Protecting these credentials is crucial to maintaining the integrity of our application and preventing unauthorized access.

If you're interested in understanding how this API works on the backend or would like to see a demonstration of the API in action, feel free to reach out to me. I'd be happy to walk you through the process and show you a live demo.