The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Foundations of AI and Machine Learning in Real Estate Valuation: An Analysis Using the California Housing Prices Dataset With Python Implementations
|
|
Author(s): Olimjon Yalgashev (Samarkand International University of Technology, Uzbekistan), Arul Kumar Natarajan (Samarkand International University of Technology, Uzbekistan)and Mohammad Gouse Galety (Samarkand International University of Technology, Uzbekistan)
Copyright: 2025
Pages: 36
Source title:
Data Analytics and AI for Quantitative Risk Assessment and Financial Computation
Source Author(s)/Editor(s): Mohammad Gouse Galety (Samarkand International University of Technology, Uzbekistan), Jimbo Henri Claver (Samarkand Interntional University of Technology, Uzbekistan), A. V. Sriharsha (Mohan Babu University, India), Narasimha Rao Vajjhala (University of New York Tirana, Tirana, Albania)and Arul Kumar Natarajan (Samarkand International University of Technology, Uzbekistan)
DOI: 10.4018/979-8-3693-6215-0.ch001
Purchase
|
Abstract
This chapter delves into the application of Artificial Intelligence (AI) and Machine Learning (ML) within the field of real estate valuation, utilizing the California Housing Prices dataset to demonstrate practical implementations. By employing and contrasting various regression models, including linear Regression, decision trees, and ensemble methods like Random Forest and Gradient Boosting, this study highlights the capabilities and limitations of these approaches. The research meticulously evaluates each model's performance, offering a comprehensive analysis that underscores the significant potential of AI and ML to enhance predictive accuracy and efficiency in real estate markets. Through detailed data preprocessing, model application, and performance evaluation, the chapter provides valuable insights into the integration of sophisticated AI methodologies in the valuation process, making it accessible and actionable for both practitioners and researchers.
Related Content
|
Frederic Andres.
© 2027.
14 pages.
|
|
Kalsoom Safdar, Khairul Najmy Abdul Rani, Mohd Aminudin Jamlos, Siti Julia Rosli, Muhammad Usman Younus, Zanab Safdar.
© 2027.
27 pages.
|
|
Bani Adam, Binastya Anggara Sekti, Muhammad Adi Zacky Zahran.
© 2027.
24 pages.
|
|
Swetha Margaret T. A., Renuka Devi D..
© 2027.
31 pages.
|
|
Maurice Saluschke, Michael Schulz.
© 2027.
30 pages.
|
|
Mirjam Sepesy Maučec, Gregor Donaj.
© 2027.
16 pages.
|
|
Jorge A. Ruiz-Vanoye, Ocotlan Diaz-Parra, Ricardo A. Barrera-Cámara, Alejandro Fuentes-Penna, Francisco R. Trejo-Macotela, Jaime Aguilar-Ortiz, Eric Simancas-Acevedo.
© 2027.
21 pages.
|
|
|