House Price INDEX (HDI) Analysis

Overview

Explore historical house price trends using the Federal Housing Finance Agency (FHFA) House Price Index (HPI) dataset. The project employs Python for a comprehensive analysis, focusing on understanding trends, geographic patterns, methodology, economic indicators, and dataset exploration.

Objectives

1. Understanding Trends: Analyze HPI to identify periods of growth or decline.
2. Geographic Analysis: Examine regional house price fluctuations.
3. Methodology Understanding: Gain insights into the transparent HPI methodology.
4. Economic Indicators: Assess HPI as an economic indicator.
5. Dataset Exploration: Describe the FHFA HPI dataset.

Reasons for Choosing HPI Dataset

• Comprehensive Coverage: Covers all 50 states and 400+ cities.
• Longitudinal Data: Extends back to the mid-1970s for historical insights.
• Transparency: Utilizes a transparent weighted, repeat-sales statistical technique.
• Decision-Making Insights: Offers timely indicators for real estate, finance, and economic decisions.
• Relevance to Housing Economists: A valuable tool for housing economists, providing granularity for nuanced analyses.

Getting Started

Clone the Repository:

git clone https://github.com/elaaatif/DATA-ANALYSIS.git

some screenshots of the presentation :