Machine Learning Price Prediction on Green Building Prices

Jamil, Syafiqah; Mohd, Thuraiya; Masrom, Suraya; Rahim, Normaliza Abd

doi:10.1109/isiea49364.2020.9188114

Cited by 10 publications

(4 citation statements)

References 36 publications

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“…Decision Tree Regressor presents the best performances for predicting green building prices. [69] Aims to decrease the error of Zillow's price estimation LR, Gradient Boosting, Grid search and cross-validation to avoid overfitting.…”

Section: Minrem and Olsmentioning

confidence: 99%

Applications, Opportunities and Challenges of AI in the Real Estate Sector

Antão,

Ribeiro,

Pereira

2024

Preprint

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The article presents a Multivocal Literature Review (MLR) on the use of Artificial Intelligence (AI) in the real estate sector, aiming to analyze existing applications, literature gaps, and current challenges. The methodology involved defining keywords, searching online repositories, and applying inclusion/exclusion criteria. In total, 185 documents were selected and reviewed. Findings underscore the significance of areas such as personalized strategies for clients, real estate recommendation systems, and process automation. A literature gap was identified regarding the analysis of performance and accuracy of these applications, indicating the need for further research and technological development to meet the demands of the real estate sector.

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Section: Minrem and Olsmentioning

confidence: 99%

Applications, Opportunities and Challenges of AI in the Real Estate Sector

Antão,

Ribeiro,

Pereira

2024

Preprint

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“…For machine learning models, Mohd et al (2020) examine the linear regression, decision tree, random forest, Ridge and least absolute shrinkage and selection operator (LASSO) approaches for studying green building price predictions and find that the random forest outperforms the other four models. Jamil et al (2020) also investigate these five models when studying green building price predictions and find that the decision tree has the best performance. Kim et al (2013) compare accuracy of the regression analysis, support vector machine and NN for construction cost estimations at early stages of construction projects and find that the NN is optimal.…”

Section: Introductionmentioning

confidence: 99%

Office property price index forecasting using neural networks

Zhang

2023

JFMPC

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Purpose The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021. Design/methodology/approach The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios. Findings The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases. Originality/value The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

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“…For office property price forecasting through machine learning models, Mohd et al(2020b) examine the linear regression, decision tree, random forest, ridge and lasso approaches for studying green building price predictions and find that the random forest outperforms the other four models. Jamil et al (2020) also investigate these five models when studying green building price predictions and find that the decision tree has the best performance. Kim et al (2013) compare accuracy of the regression analysis, support vector machine and neural network for construction cost estimations at early stages of construction projects and find that the neural network is the optimal.…”

Section: Introductionmentioning

confidence: 99%

“…(2020b) examine the linear regression, decision tree, random forest, ridge and lasso approaches for studying green building price predictions and find that the random forest outperforms the other four models. Jamil et al. (2020) also investigate these five models when studying green building price predictions and find that the decision tree has the best performance.…”

Section: Introductionmentioning

confidence: 99%

Composite property price index forecasting with neural networks

Xu,

Zhang

2023

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PurposeThe Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.Design/methodology/approachThe goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.FindingsThe authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.Originality/valueResults here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

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Machine Learning Price Prediction on Green Building Prices

Cited by 10 publications

References 36 publications

Applications, Opportunities and Challenges of AI in the Real Estate Sector

Applications, Opportunities and Challenges of AI in the Real Estate Sector

Office property price index forecasting using neural networks

Composite property price index forecasting with neural networks

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