Europe Real Estate Marketplace Analysis

? Project Overview

This project provides a comprehensive analysis of the European Real Estate market, focusing on identifying trends, market saturation, and the key factors influencing the time properties spend on the market.

?️ Data Pipeline & Architecture

I implemented a hybrid workflow to ensure a scalable and clean data structure:

1. Data Modeling & Preprocessing (Python)

Before importing into Power BI, I used Python (Pandas) to handle the heavy lifting of data preparation:

Cleaning: Handled missing values and ensured data type consistency.

Feature Engineering: Created logic for categorizing complex variables.

Star Schema Creation: Deconstructed the flat dataset into a relational structure, generating the foundational logic for Dimension and Fact tables.

2. BI Modeling (Power BI)

Schema: Implemented a Star Schema with a central Fact_listings table connected to Dim_Date, Dim_Property, and Dim_Features.

DAX: Developed advanced measures to calculate Market Speed, Average Price per SQM, and dynamic period-over-period comparisons.

? Key Insights

Market Saturation: The average Days On Market (DOM) is 172 days, indicating a significant slowdown across the European marketplace.

Uniformity: High DOM is consistent across various property types and features, suggesting a systemic market trend.

Decoupled Factors: Surprisingly, Sale Price, Monthly Rent, and Property Space showed no significant impact on market velocity (DOM).

? Advanced Categorization

Market Speed: Grouped DOM into 4 categories: (Very Fast, Normal, Slow, Very Slow).

Residential Segmentation: Categorized Bedrooms into logical clusters (1-2 Rooms, 3-4 Rooms, 5-6 Rooms, and Non-Residential).

? Recommendations

Analyze Macro Factors: Investigate external economic conditions (interest rates, local regulations) affecting demand.

Marketing Optimization: Improve digital visibility and exposure for listings with high DOM.

Strategic Targeting: Focus on high-demand niche segments to accelerate turnover.

? Technical Stack

Python: Pandas, NumPy (Data Cleaning & Modeling)

Power BI: Data Visualization & DAX

Architecture: Star Schema (Fact/Dimension Modeling)