Sensitivity Analysis of Machine Learning Models for the Mass Appraisal of Real Estate. Case Study of Residential Units in Nicosia, Cyprus
| Autor: | Thomas Dimopoulos, Nikolaos Bakas |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
general valuation
Artificial intelligence 010504 meteorology & atmospheric sciences Computer science real estate mass appraisals Cost approach Real estate CAMA Machine learning computer.software_genre algorithms Civil Engineering 01 natural sciences Field (computer science) 0502 economics and business Mass appraisals 050207 economics 0105 earth and related environmental sciences Valuation (finance) Mathematical models business.industry General valuation 05 social sciences Regression analysis AVM artificial intelligence Variety (cybernetics) Work (electrical) Transparency (graphic) Cyprus Engineering and Technology General Earth and Planetary Sciences business computer mathematical models Algorithms |
| Zdroj: | Remote Sensing Volume 11 Issue 24 Pages: 3047 |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs11243047 |
| Popis: | A recent study of property valuation literature indicated that the vast majority of researchers and academics in the field of real estate are focusing on Mass Appraisals rather than on the further development of the existing valuation methods. Researchers are using a variety of mathematical models used within the field of Machine Learning, which are applied to real estate valuations with high accuracy. On the other hand, it appears that professional valuers do not use these sophisticated models during daily practice, rather they operate using the traditional five methods. The Department of Lands and Surveys in Cyprus recently published the property values (General Valuation) for taxation purposes which were calculated by applying a hybrid model based on the Cost approach with the use of regression analysis in order to quantify the specific parameters of each property. In this paper, the authors propose a number of algorithms based on Artificial Intelligence and Machine Learning approaches that improve the accuracy of these results significantly. The aim of this work is to investigate the capabilities of such models and how they can be used for the mass appraisal of properties, to highlight the importance of sensitivity analysis in such models and also to increase the transparency so that automated valuation models (AVM) can be used for the day-to-day work of the valuer. |
| Databáze: | OpenAIRE |
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