TY - GEN
T1 - Spatial Prediction of Housing Prices in Beijing Using Machine Learning Algorithms
AU - Yan, Ziyue
AU - Zong, Lu
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/7/3
Y1 - 2020/7/3
N2 - The real estate industry places key influence on almost every aspect of social economy given its great financing capacity and prolonged upstream and downstream industry chain. Therefore, predicting housing prices is regarded as an emerging topic in the recent decades. Hedonic Regression and Machine Learning Algorithms are two main methods in this field. This study aims to explore the important explanatory features and determine an accurate mechanism to implement spatial prediction of housing prices in Beijing by incorporating a list of machine learning techniques, including XGBoost, linear regression, Random Forest Regression, Ridge and Lasso Model, bagging and boosting, based on the housing price and features data in Beijing, China. Our result shows that compared to traditional hedonic method, machine learning methods demonstrate significant improvements on the accuracy of estimation despite that they are more time-costly. Moreover, it is found that XGBoost is the most accurate model in explaining and prediciting the spatial dynamics of housing prices in Beijing.
AB - The real estate industry places key influence on almost every aspect of social economy given its great financing capacity and prolonged upstream and downstream industry chain. Therefore, predicting housing prices is regarded as an emerging topic in the recent decades. Hedonic Regression and Machine Learning Algorithms are two main methods in this field. This study aims to explore the important explanatory features and determine an accurate mechanism to implement spatial prediction of housing prices in Beijing by incorporating a list of machine learning techniques, including XGBoost, linear regression, Random Forest Regression, Ridge and Lasso Model, bagging and boosting, based on the housing price and features data in Beijing, China. Our result shows that compared to traditional hedonic method, machine learning methods demonstrate significant improvements on the accuracy of estimation despite that they are more time-costly. Moreover, it is found that XGBoost is the most accurate model in explaining and prediciting the spatial dynamics of housing prices in Beijing.
KW - Housing Price
KW - Machine Learning Algorithms
KW - Prediction
KW - Spatial Modeling
UR - http://www.scopus.com/inward/record.url?scp=85090904137&partnerID=8YFLogxK
U2 - 10.1145/3409501.3409543
DO - 10.1145/3409501.3409543
M3 - Conference Proceeding
AN - SCOPUS:85090904137
T3 - ACM International Conference Proceeding Series
SP - 64
EP - 71
BT - Proceedings of the 2020 4th High Performance Computing and Cluster Technologies Conference, HPCCT 2020 and 3rd International Conference on Big Data and Artificial Intelligence, BDAI 2020
PB - Association for Computing Machinery
T2 - 4th High Performance Computing and Cluster Technologies Conference, HPCCT 2020 and the 3rd International Conference on Big Data and Artificial Intelligence, BDAI 2020
Y2 - 3 July 2020 through 6 July 2020
ER -