The Forecasting repository is dedicated to advancing the accuracy and applicability of trend forecasts, particularly focusing on demographics and socially valuable variables across the Richmond Region. Initially employing SARIMAX and Facebook Prophet models in the Forecasting_1 branch, this project is an ongoing effort to explore and integrate more complex forecasting techniques. Our goal is to refine our predictions, offering even more accurate and reliable insights into housing market trends by ZIP code.
- Enhance Forecasting Accuracy: Continuously improve upon the foundational SARIMAX model by exploring advanced forecasting methods and integrating them into our analysis.
- Expand Regional Coverage: While currently focused on the Richmond Region, future developments aim to apply these forecasting techniques to additional areas, broadening the project's scope and impact.
- Strengthen Model Optimization: Leverage automation and machine learning to fine-tune model parameters, ensuring optimal forecasting performance across diverse datasets.
- Foster Community Contributions: Encourage the community to contribute new ideas, models, and approaches to enhance the project's forecasting capabilities and applicability.
- Automated Model Optimization: Initial models (SARIMAX and Facebook Prophet) automatically select the best parameters for accurate forecasting, with plans to extend this automation to new models.
- Comprehensive Regional Analysis: Provides detailed ZHVI forecasts for each ZIP code in the Richmond Region, with ambitions to expand to new regions.
- Visual Accuracy Assessment: Utilizes Test_RMSE maps and other visualization tools to evaluate and illustrate forecast accuracy.
- Identification of Growth Potentials: Analyzes data to highlight areas with significant growth potential, aiding stakeholders in making informed decisions.
Accurate forecasting in the real estate market is invaluable for investors, policymakers, homeowners, and potential buyers. By continually enhancing our forecasting models and techniques, this project seeks to be a cornerstone resource for data-driven decision-making in real estate.
To engage with this project, clone the repository and follow the setup instructions in the README.md files of the respective branches. Each branch, starting with Forecasting_1 for SARIMAX and Facebook Prophet models, contains specific setup and operational guidelines.
Contributions are crucial to the success and evolution of this project. Whether you have suggestions for new models, improvements to existing techniques, or want to extend the analysis to new regions, your input is highly valued. Please refer to the CONTRIBUTING.md file for guidelines on how to contribute.
This project is licensed under the Apache License 2.0 - see the LICENSE.md file for details.