Machine Learning Approaches for Modelling Real Estate Systems
Session
Management, Business and Economics
Description
In the intricate domain of real estate, precise property valuation remains paramount for a spectrum of economic endeavors. The 2008 global economic downturn spotlighted the limitations of conventional valuation methods, prompting the exploration of innovative, objective techniques. This paper investigates the incorporation of machine learning in modeling intricate real estate systems. We discuss the evolution from traditional appraisal methods to Automated Valuation Models (AVM) and the subsequent challenges these models face. Drawing from global research and case studies, we highlight the adaptability, accuracy, and potential of machine learning techniques in real estate valuation. Our findings underscore machine learning's transformative role in enhancing property appraisal, offering a forward-looking perspective on the future of real estate valuation.
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-550-95-6
Location
UBT Lipjan, Kosovo
Start Date
28-10-2023 8:00 AM
End Date
29-10-2023 6:00 PM
DOI
10.33107/ubt-ic.2023.179
Recommended Citation
Hoxha, Visar; Berisha, Lorana; and Hoxha, Jehona, "Machine Learning Approaches for Modelling Real Estate Systems" (2023). UBT International Conference. 34.
https://knowledgecenter.ubt-uni.net/conference/IC/MBE/34
Machine Learning Approaches for Modelling Real Estate Systems
UBT Lipjan, Kosovo
In the intricate domain of real estate, precise property valuation remains paramount for a spectrum of economic endeavors. The 2008 global economic downturn spotlighted the limitations of conventional valuation methods, prompting the exploration of innovative, objective techniques. This paper investigates the incorporation of machine learning in modeling intricate real estate systems. We discuss the evolution from traditional appraisal methods to Automated Valuation Models (AVM) and the subsequent challenges these models face. Drawing from global research and case studies, we highlight the adaptability, accuracy, and potential of machine learning techniques in real estate valuation. Our findings underscore machine learning's transformative role in enhancing property appraisal, offering a forward-looking perspective on the future of real estate valuation.