Aktuálne číslo: 2/2023
Archív
DATA SHARING – WAY TO IMPROVE REAL ESTATE VALUATION QUALITY: LITHUANIAN CASE
With the growing quality of life, the construction sector is also growing, and more and more people are willing to acquire their own housing and settle in it. For financing purposes, many are applying to banks for housing loans, therefore it is highly important to adequately determine the value of real estate in order to create mutually suitable conditions for financing the purchase. In most cases, the current assessment of value is based on the subjective opinion of experts. In this paper, the authors propose to create a database ensuring the sufficient and comprehensive amount of data securing the accurate valuation of real estate. The creation of an accurate and comprehensive database with constantly updated and processed data would allow appraiser to work with limited amount of data while performing the assessment of real estate, to automatically capture causal relations between explanatory variables and asset prices, as well as to determine the short-term market value of the property, which becomes increasingly important.
Vydanie: 2017/2 Strany: 53-58 Klasifikácia JEL:
DOI:
Kľúčové slová: valuation, market value, data, database, data sharing
Sekcia: SOCIAL AND ECONOMIC DEVELOPMENT
Kontakty:
Prof. Jadvyga Čiburienė
Kaunas University of Technology,
School of Economics and Management,
Department of Economics;
e-mail: jadvyga.ciburiene@ktu.lt
Rusnė Jegelavičiūtė
Real estate appraiser-assistant,
Lituka ir ko, Ltd;
e-mail: rusnejegelaviciute@gmail.com
Literatúra:
Banking business overview. (2016). The Bank of Lithuania. 2016, III quater (in lithuaniain, ISSN 2335-8319), [on-line], [cit.: 03-20-2016]. Available at: https://www.lb.lt/banku_apzvalga_2016_m_iii_ketv
Bardhan, A., Kroll, C. A. (2007). Globalization and the Real Estate Industry: Issues, Implications, Opportunities. Sloan Industry Studies Annual Conference, Cambridge
Catella Research. (2015). Big data in the real estate sector a big opportunity or a big threat? [on-line], [cit.: 03-20-2016]. Available at: https://www.catella.com/Documents/Germany%20Property%20Funds/02_Research/01_Studien/Catella%20Resarch_Big_Data_%202015_english.pdf.
Chaphalkar, N. B., Sandbhor, S. (2013). Use of Artificial Intelligence in Real Property Valuation. International Journal of Engineering and Technology, Vol 5, No 3.
Dudek-Dyduch, E. (2011). Information system for real estate valuation. Automatyka. Krakow, Vol.15.
EVS. (2016). European Valuation Standards. The European Group of Valuers' Association. [on-line], [cit.: 03-20-2016]. Available at: http://www.tegova. org/data/bin/a5738793c0c61b_EVS_2016.pdf
EY. (2014). Big data Changing the way businesses compete and operate. [on-line], [cit.: 03-20-2016]. Available at: http://www.ey.com/Publication/vwLUAssets/EY_-_Big_data:_changing_the_way_businesses_operate/%24FILE/EY-Insights-on-GRC-Big-data.pdf.
Morano, P., Tajani, F., Torre, C. M. (2015). Artificial intelligence in property valuations An application of artificial neural networks to housing appraisal. Advances in Environmental Science and Energy Planning, ISBN: 978-1-61804-280-4, Spain, p. 23-29.
Schulz, R. (2003). Valuation of Properties and Economic Models of Real Estate Markets. Dissertation. Berlin, p. 11.
Zhou, L., Liang, S. L., Zhang, S. (2015) Database Construction of Real Estate Assessment Based on Big Data. 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering, p. 92-96