Data Analysis on the Influencing Factors of the Real Estate Price

He, Huang-Mei; Chen, Yi; Xiao, Jiaying; Chen, Xueqing; Lee, Zne-Jung

doi:10.37256/aie.222021966

Cited by 6 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GridSearch generates a grid of hyperparameter values and specifies which hyperparameters to use to control the learning process [32]. The number of input variables was set to five, following previous empirical studies [33]. The RandomForestRegressor algorithm in scikit-learn was used to analyze the RF model.…”

Section: Machine Learning Methods: Rf and Xgboostmentioning

confidence: 99%

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

Kim¹,

Won

Kim³

et al. 2021

Sustainability

View full text Add to dashboard Cite

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.

show abstract

Section: Machine Learning Methods: Rf and Xgboostmentioning

confidence: 99%

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

Kim¹,

Won

Kim³

et al. 2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…The procedure used to choose the optimum splitting feature for each node in the DT algorithm is known as feature selection [16]. To create a corresponding feature branch for each value and divide the related data set samples, the most often used feature selection is condensed.…”

Section: Decision Treementioning

confidence: 99%

Building Carbon Emissions Prediction Based on Deep Learning Network with Improved Particle Swarm Optimization

Chai,

Lin,

Wang

et al. 2023

Artificial Intelligence Evolution

View full text Add to dashboard Cite

Buildings' carbon emissions are the main contributor to climate change. The world needs to be able to foresee and further reduce construction carbon emissions if it wants to prevent the worst effects of climate change. The main challenge in carbon emission prediction for buildings is how to increase algorithm accuracy. Therefore, a novel technique for calculating carbon emissions is proposed in this study. The proposed technique uses improved particle swarm optimization (PSO) and deep neural network (DNN) to anticipate carbon emissions. DNN is employed in the proposed technique to forecast carbon emissions. The parameters used are fine-tuned using the improved PSO. Additionally, it chooses features that increase the predictability of carbon emissions. In this study, many methods for predicting carbon emissions are examined, including decision tree (DT), random forest (RF), support vector regression (SVR), DNN, and the proposed method. The outcomes demonstrate that the proposed technique can lower root mean square logarithmic error (RMSLE) prediction, and the final testing result of RMLSE is 0.3476 in this study. It demonstrates that the proposed method performs better than other alternatives when compared and has good implementation ability.

show abstract

“…The article by Chau et al (2005) analyses the already employed pricing indices in Hong Kong and provides a new transaction-based price index developed utilizing the repeated selling approach. He et al (2021) studied the significance of the input variable that is believed to influence the price, and as a result, they determined that the primary elements influencing the pricing are price, interior decoration, location, and status.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Türkiye'de Yeni Konut Fiyat Endeksini Etkileyen Faktörlerin Analizi

KOMŞUOĞLU YILMAZ

2023

Maliye Finans Yazıları

View full text Add to dashboard Cite

This study investigates the relationship between the weighted average interest rates applied to housing loans, housing construction labor cost and housing unit prices, and the housing price index for new dwellings for the period 2015-2021. In the time series, a structural break was detected in February 2019. In the results of the regression analysis, differences were determined for the January 2015-January 2019 period and the February 2019-August 2021 period.

show abstract

Data Analysis on the Influencing Factors of the Real Estate Price

Cited by 6 publications

References 15 publications

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

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

Building Carbon Emissions Prediction Based on Deep Learning Network with Improved Particle Swarm Optimization

Türkiye'de Yeni Konut Fiyat Endeksini Etkileyen Faktörlerin Analizi

Contact Info

Product

Resources

About