Find publicly available data for key factors that influence US home prices nationally. Then, build a data science model that explains how these factors impacted home prices over the last 20 years. Use the S&P Case-Schiller Home Price Index as a proxy for home prices.
- CSUSHPISA: Measures changes in residential property prices in the US.
- EVACANTUSQ176N: Estimates the number of vacant housing units in the US.
- GDP: Represents the total value of goods and services produced in the US.
- INTDSRUSM193N: Reflects interest rates or discount rates in the US.
- MSACSR: Measures the monthly supply of new houses in the US.
- PERMIT: Tracks the number of new housing units authorized by permits.
- UMCSENT: Reflects consumer sentiment about the economy.
- Analyze the correlation between these factors and CSUSHPISA.
- Develop a predictive model using machine learning to forecast CSUSHPISA based on these factors.
- Evaluate the model's accuracy and predictive power.
- Identify key factors contributing most significantly to changes in the home price index.
- Data Collection and Preparation:
- Gather historical data for the specified features.
- Clean and preprocess the data for analysis.
- Exploratory Data Analysis (EDA):
- Visualize relationships between features and CSUSHPISA.
- Identify correlations and patterns.
- Model Development:
- Build predictive models using regression or machine learning algorithms.
- Train and validate the models.
- Model Evaluation:
- Evaluate model performance using appropriate metrics.
- Fine-tune models for better accuracy.
- Insights and Reporting:
- Interpret model results to understand the impact of each feature on CSUSHPISA.
- Generate insights and recommendations based on the analysis.
- A robust predictive model that effectively estimates changes in the S&P/Case-Shiller U.S. National Home Price Index based on economic and housing-related factors.