SMU Data Science Review
Abstract
This paper presents a comprehensive study examining the real estate market potential in the dynamic urban landscapes of Frisco and Plano, Texas. Combining traditional real estate analysis with cutting-edge machine learning techniques, the study aims to predict home prices and assess investment feasibility. Leveraging these findings, the study proposes a strategic focus on predictive modeling and investment potential identification, emphasizing the continual refinement of machine learning models with updated data to accurately forecast changes in the real estate market. By harnessing the predictive power of these models, investors can identify high-growth areas and optimize their investment decisions, thus capitalizing on emerging trends and investment hot-spots in Frisco and Plano. This study highlights the potential of advanced analytical tools in guiding investors toward lucrative real estate opportunities in rapidly developing urban environments.
Recommended Citation
Hernandez, Joey; Chang, Danny; Gutierrez, Santiago; and Huggins, Paul
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"Predictive Analysis of Local House Prices: Leveraging Machine Learning for Real Estate Valuation,"
SMU Data Science Review: Vol. 8:
No.
1, Article 12.
Available at:
https://scholar.smu.edu/datasciencereview/vol8/iss1/12
Creative Commons License
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