Should I Contact Him or Not? – Quantifying the Demand for Real Estate with Interpretable Machine Learning Methods

2021 ◽  
Author(s):  
Joseph-Alexander Zeitler ◽  
Marcelo Cajias
Keyword(s):  
Machine Learning ◽  
Real Estate ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Interpretable Machine Learning

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.

scholarly journals Development and Validation of Interpretable Machine Learning for Stroke Occurrence in Older, Community Chinese Dwellers

2021 ◽  
Author(s):  
Yafei Wu ◽  
Zhongquan Jiang ◽  
Shaowu Lin ◽  
Ya Fang
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Logistic Regression ◽  
High Risk ◽  
Prediction Models ◽  
Curve Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Interpretable Machine Learning ◽  
Extreme Gradient Boosting

Abstract Background: Prediction of stroke based on individuals’ risk factors, especially for a first stroke event, is of great significance for primary prevention of high-risk populations. Our study aimed to investigate the applicability of interpretable machine learning for predicting a 2-year stroke occurrence in older adults compared with logistic regression.Methods: A total of 5960 participants consecutively surveyed from July 2011 to August 2013 in the China Health and Retirement Longitudinal Study were included for analysis. We constructed a traditional logistic regression (LR) and two machine learning methods, namely random forest (RF) and extreme gradient boosting (XGBoost), to distinguish stroke occurrence versus non-stroke occurrence using data on demographics, lifestyle, disease history, and clinical variables. Grid search and 10-fold cross validation were used to tune the hyperparameters. Model performance was assessed by discrimination, calibration, decision curve and predictiveness curve analysis.Results: Among the 5960 participants, 131 (2.20%) of them developed stroke after an average of 2-year follow-up. Our prediction models distinguished stroke occurrence versus non-stroke occurrence with excellent performance. The AUCs of machine learning methods (RF, 0.823[95% CI, 0.759-0.886]; XGBoost, 0.808[95% CI, 0.730-0.886]) were significantly higher than LR (0.718[95% CI, 0.649, 0.787], p<0.05). No significant difference was observed between RF and XGBoost (p>0.05). All prediction models had good calibration results, and the brier score were 0.022 (95% CI, 0.015-0.028) in LR, 0.019 (95% CI, 0.014-0.025) in RF, and 0.020 (95% CI, 0.015-0.026) in XGBoost. XGBoost had much higher net benefits within a wider threshold range in terms of decision curve analysis, and more capable of recognizing high risk individuals in terms of predictiveness curve analysis. A total of eight predictors including gender, waist-to-height ratio, dyslipidemia, glycated hemoglobin, white blood cell count, blood glucose, triglycerides, and low-density lipoprotein cholesterol ranked top 5 in three prediction models.Conclusions: Machine learning methods, especially for XGBoost, had the potential to predict stroke occurrence compared with traditional logistic regression in the older adults.

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