Gradient Boosting–Based Machine Learning Methods in Real Estate Market Forecasting
- DOI
- 10.2991/aisr.k.201029.039How to use a DOI?
- Keywords
- real estate market analysis, forecasting, housing market, housing, real estate, residential real estate market value
- Abstract
Several approaches can be used to estimate the value of residential real estate. The sales comparison approach requires assessing a number of comparable residential properties and determining the degree of their compatibility. The cost approach requires using the information on all construction costs. The income approach requires a large amount of data on the market capacity, operation cost, expected operating expenses, and competitive opportunities. For the end customer or buyer and often for appraisers and realtors as well, these methods would involve processing a considerable amount of information. The sales comparison approach is used more frequently, since sufficient data for other approaches might not be publicly available. Nevertheless, all these methods are quite complex, and using them to estimate the value of a residential property can be time-consuming if performed without automated valuation models (AVMs). In the paper, the method of data collection is described, and the analysis based on these data is carried out. Moreover, the housing affordability index is determined (shows the number of years required to purchase a residential property). Finally, the most appropriate forecasting method with the least error is chosen, and the parameters of residential properties are determined and ranked according to the degree of their impact on the price.
- Copyright
- © 2020, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Nikita Fedorov AU - Yulia Petrichenko PY - 2020 DA - 2020/11/10 TI - Gradient Boosting–Based Machine Learning Methods in Real Estate Market Forecasting BT - Proceedings of the 8th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2020) PB - Atlantis Press SP - 203 EP - 208 SN - 1951-6851 UR - https://doi.org/10.2991/aisr.k.201029.039 DO - 10.2991/aisr.k.201029.039 ID - Fedorov2020 ER -