PropertyPipeline

Real Estate ETL Pipeline

Zipco Real Estate Agency operates in the fast-paced and competitive world of real estate, where timely access to accurate information is crucial for success. . Our success factors likely include a strong understanding of local market dynamics, effective marketing strategies, and a commitment to client relationships. Their business focus may center on providing exceptional customer service, leveraging technology for efficient operations, and maintaining a robust online presence to attract leads.

However, the company is currently facing a significant data challenge that hinders its operational efficiency. The existing data processing workflow is inefficient, resulting in disparate datasets.

At Zipco Real Estate Agency, we encounter several pressing challenges within our data processing:

Inefficient Data Processing Workflow

Increased Operational Costs

Disparate Datasets and Inconsistent format

Compromised Data Quality

Rationale for the Project

Implementing a comprehensive ETL (Extract, Transform, Load) pipeline at Zipco Real Estate Agency is multifaceted, addressing the core challenges the company faces while also aligning with its strategic goals, and the desire to overcome existing data challenges, enhance operational efficiency, and position the company for sustainable growth and success in a competitive landscape.

Enhanced Operational Efficiency: By automating and streamlining data processing workflows, the ETL pipeline will significantly reduce the time and effort required to gather, clean, and prepare data.

Improved Data Quality and Consistency: The ETL process will standardize data formats and ensure that information from various sources is accurately integrated. This consistency enhances data quality, enabling agents and management to make informed decisions based on reliable and up-to-date information.

Timely Access to Critical Information: With a well-structured ETL pipeline, Zipco will be able to access critical property information and market insights in real-time. This timely access is essential for making quick decisions in a fast-paced real estate environment, ultimately leading to better service for clients and increased sales opportunities.

Cost Reduction: By minimizing manual data handling and reducing errors, the ETL pipeline can lead to significant cost savings. Lower operational costs can be redirected towards growth initiatives, marketing efforts, or enhancing customer service, thereby improving overall profitability.

Competitive Advantage: In the competitive real estate market, having access to high-quality, timely data can set Zipco apart from its competitors. By leveraging advanced data management capabilities, the agency can offer superior insights to clients, enhance marketing strategies, and respond more effectively to market trends.

Enhanced Decision-Making: With improved data quality and accessibility, management will be better equipped to make strategic decisions based on accurate insights and analytics. This informed decision-making can drive the agency's growth and help it navigate the complexities of the real estate market more effectively.

Aim of Project

Data Extraction

Data Cleaning and Transformation

Database Loading

Automation

Tools & Technologies

RapidAPI: This is used to extract real estate data.

Python: For data extraction, transformation, and loading (ETL process).

Postgres: A relational database for storing the property data.

Power BI: Data visualisation

Windows Task Scheduler / Cron: For scheduling periodic tasks to extract and load data.

GitHub: For version control and documentation. I got the real estate API from Realty Mole Property then I extracted and transformed via Python and loaded the transformed data into Postgress. Check link below to view the pipeline code, this solve Zipco Real Estate Agency problem as stated below

Below is the ERD from Schema in Postgres that the automated flow from the pipeline into Postgres

Data Modeling

Create a Star Schema: Fact Table: Central table containing metrics like property sales, area, bedrooms, etc. Dimension Tables: Reference tables for location, features, and sales details.

Schema Design: Location Dimension: Attributes like county, state, zipCode.

Features Dimension: Attributes like propertyType, zoning, features.

Sales Dimension: Attributes like sales_id, lastSalesDate, lastSalePrice etc

Fact Table: Links the dimensions table using their foreign keys

Below is the data modeling

I imported the data directly from Postgres to Power BI to building sales performance dashboard to show how much property was sold as at when and the location of each property sold and some other metrics were considered. Below is the dashboard

Task Scheduler: Open Task Scheduler. Create a new task, and set the trigger to run the Python ETL scripts & Power BI dashboard at a specified time (e.g., daily).

Set the action to run the Python script (python path_to_script.py) .& Power BI dashboard

I automated the refresh process using Task Schedular, this task will be run on a weekly basis to run the pipeline and also update the power Bi, so as to get the updated report on a weekly basis.