I know where you will invest in the next year – Forecasting real estate investments with machine learning methods

2019 ◽  
Author(s):  
Marcelo Cajias ◽  
Jonas Willwersch ◽  
Felix Lorenz
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Real Estate Investments

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.

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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