Efficient machine learning methods for real estate market analysis and city development

2021 ◽  
Author(s):  
Stanimir Kabaivanov ◽  
Veneta Markovska
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Real Estate Market ◽  
Market Analysis ◽  
City Development ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Efficient Machine

scholarly journals Machine-Learning-Based Prediction of Land Prices in Seoul, South Korea

2021 ◽  
Vol 13 (23) ◽  
pp. 13088
Author(s):  
Jungsun Kim ◽  
Jaewoong Won ◽  
Hyeongsoon Kim ◽  
Joonghyeok Heo
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Real Estate ◽  
South Korea ◽  
Price Volatility ◽  
Real Estate Market ◽  
Accurate Estimation ◽  
Learning Methods ◽  
Land Prices ◽  
Machine Learning Methods

The accurate estimation of real estate value helps the development of real estate policies that can respond to the complexities and instability of the real estate market. Previously, statistical methods were used to estimate real estate value, but machine learning methods have gained popularity because their predictions are more accurate. In contrast to existing studies that use various machine learning methods to estimate the transactions or list prices of real estate properties without separating the building and land prices, this study estimates land price using a large amount of land-use information obtained from various land- and building-related datasets. The random forest and XGBoost methods were used to estimate 52,900 land prices in Seoul, South Korea, from January 2017 to December 2020. The models were also separately trained for different land uses and different time periods. Overall, the results revealed that XGBoost yields a higher prediction accuracy. Whereas the XGBoost models were more accurate on the 2020 data than on the 2017–2020 data when analyzing residential areas, the random forest models were more accurate on the 2017–2020 data than on the 2020 data. Further analysis will extend the prediction model to consider submarkets determined by price volatility and locality.

MARTT: Automatic Markup of Taxonomic Descriptions with XML

2013 ◽  
Author(s):  
Hong Cui
Keyword(s):  
Machine Learning ◽  
Information Content ◽  
Large Scale ◽  
Learning Algorithms ◽  
General Purpose ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Taxonomic Descriptions ◽  
Efficient Machine

Despite the sub-language nature of taxonomic descriptions of animals and plants, researchers have warned about the existence of large variations among different description collections in terms of information content and its representation. These variations impose a serious threat to the development of automatic tools to structure large volumes of text-based descriptions. This paper presents a general approach to mark up different collections of taxonomic descriptions with XML, using two large-scale floras as examples. The markup system, MARTT, is based on machine learning methods and enhanced by machine learned domain rules and conventions. Experiments show that our simple and efficient machine learning algorithms outperform significantly general purpose algorithms and that rules learned from one flora can be used when marking up a second flora and help to improve the markup performance, especially for elements that have sparse training examples.Malgré la nature de sous-langage des descriptions taxinomiques des animaux et des plantes, les chercheurs reconnaissent l’existence de vastes variations parmi différentes collections de descriptions, en termes de contenu informationnel et de leur représentation. Ces variations présentent une menace sérieuse pour le développement d’outils automatiques pour la structuration de larges… 

Interpretable Machine Learning for Real Estate Market Analysis

2021 ◽  
Author(s):  
Felix Lorenz ◽  
Jonas Willwersch ◽  
Marcelo Cajias ◽  
Franz Fuerst
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Real Estate Market ◽  
Market Analysis ◽  
Interpretable Machine Learning

scholarly journals MASS EVALUATION OF RESIDENTIAL REAL ESTATE USING MACHINE LEARNING METHODS

2020 ◽  
Author(s):  
V. Chernushevych
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Residential Real Estate ◽  
Learning Methods ◽  
Mass Estimation ◽  
Machine Learning Methods ◽  
Real Estate Valuation

The essence of the concept of mass assessment is considered. The peculiarities of real estate valuation with the support of taxation in Ukraine are analyzed. The model of mass assessment of real estate with the use of machine learning methods is investigated. The results of modeling of mass estimation on the basis of gradient amplification are demonstrated.

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