Ensemble Learning Based Rental Apartment Price Prediction Model by Categorical Features Factoring

Neloy, Asif Ahmed; Haque, Halima; Islam, Md. Mahmud Ul

doi:10.1145/3318299.3318377

Cited by 22 publications

(15 citation statements)

References 28 publications

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“…Stacking: Since every type of machine learning model has its own limitations, some researchers have taken advantage of the stacking strategy so that the models can complement each other-and some strong performances in the real estate appraisal field have been achieved. For example, Neloy et al, (2019) selected a variety of commonly used machine learning models and advanced linear regression as the basic models when studying the apartment rental prices in Dhaka, Bangladesh, before comparing the prediction accuracy of the multiple ensemble learning methods [128]. The findings by Xu and Li (2021) show that the evaluation accuracy of the spatial econometric model is higher than that obtained via multiple linear regression, the accuracy of the machine learning model is much higher than the spatial econometric model.…”

Section: Machine Learningmentioning

confidence: 99%

The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data

Wei

Wang

et al. 2022

Land

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In the era of big data, advances in relevant technologies are profoundly impacting the field of real estate appraisal. Many scholars regard the integration of big data technology as an inevitable future trend in the real estate appraisal industry. In this paper, we summarize 124 studies investigating the use of big data technology to optimize real estate appraisal through the hedonic price model (HPM). We also list a variety of big data resources and key methods widely used in the real estate appraisal field. On this basis, the development of real estate appraisal moving forward is analyzed. The results obtained in the current studies are as follows: First, the big data resources currently applied to real estate appraisal include more than a dozen big data types from three data sources; the internet, remote sensing, and the Internet of things (IoT). Additionally, it was determined that web crawler technology represents the most important data acquisition method. Second, methods such as data pre-processing, spatial modeling, Geographic information system (GIS) spatial analysis, and the evolving machine learning methods with higher valuation accuracy were successfully introduced into the HPM due to the features of real estate big data. Finally, although the application of big data has greatly expanded the amount of available data and feature dimensions, this has caused a new problem: uneven data quality. Uneven data quality can reduce the accuracy of appraisal results, and, to date, insufficient attention has been paid to this issue. Future research should pay greater attention to the data integration of multi-source big data and absorb the applications developed in other disciplines. It is also important to combine various methods to form a new united evaluation model based on taking advantage of, and avoiding shortcomings to compensate for, the mechanism defects of a single model.

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Section: Machine Learningmentioning

confidence: 99%

The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data

Wei

Wang

et al. 2022

Land

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“…Stacking ensemble supports classification and regression. It is a linear combination of multiple base learning algorithms into a single, superior prediction function through a secondary learning process called meta-learning and it improves prediction accuracy and stability (Breiman, 1996: Kansara et al , 2018; Neloy et al , 2019). Stacking is a technique which is used to tackle an error of a model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Ensemble-based machine learning approaches are becoming the next generation cost estimation system in the construction sector. Ensemble ML techniques use multiple learning algorithms to achieve superior predictive efficiency, in terms of accuracy and stability, over any single learning algorithm (Breiman, 1996; LeDell, 2015; Kansara et al , 2018; Neloy et al , 2019). Ensemble learning methods, a synthesis of multiple model algorithms, have been widely applied to regression problems in several disciplines, such as demand forecasting model for supply chain (Kilimci et al , 2019); financial market prediction (Henrique et al , 2019); energy load prediction in residential buildings (Al-Rakhami et al , 2019); electricity load and price forecasting (Do, 2018; Agrawal et al , 2019; Zahid et al , 2019); house price prediction model in real estate market (Kansara et al , 2018; Neloy et al , 2019); prediction of computer Go player attributes (Moudrík and Neruda, 2015); warfarin dose estimation in the health sector (Ma et al , 2018); and software effort estimation (Banimustafa, 2018).…”

Section: Introductionmentioning

confidence: 99%

Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects

Meharie

Mengesha

Gariy

et al. 2021

ECAM

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PurposeThe purpose of this study to apply stacking ensemble machine learning algorithm for predicting the cost of highway construction projects.Design/methodology/approachThe proposed stacking ensemble model was developed by combining three distinct base predictive models automatically and optimally: linear regression, support vector machine and artificial neural network models using gradient boosting algorithm as meta-regressor.FindingsThe findings reveal that the proposed model predicted the final project cost with a very small prediction error value. This implies that the difference between predicted and actual cost was quite small. A comparison of the results of the models revealed that in all performance metrics, the stacking ensemble model outperforms the sole ones. The stacking ensemble cost model produces 86.8, 87.8 and 5.6 percent more accurate results than linear regression, vector machine support, and neural network models, respectively, based on the root mean square error values.Research limitations/implicationsThe study shows how stacking ensemble machine learning algorithm applies to predict the cost of construction projects. The estimators or practitioners can use the new model as an effectual and reliable tool for predicting the cost of Ethiopian highway construction projects at the preliminary stage.Originality/valueThe study provides insight into the machine learning algorithm application in forecasting the cost of future highway construction projects in Ethiopia.

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“…Finally, Neloy et al [44] develop a model for predicting the rental price of houses in Bangladesh through a website database of 3,505 homes with information on 19 characteristics. To develop the model, the following simple algorithms are selected for prediction: advance linear regression, neural network, random forest, support vector machine (SVM), and decision tree regressor.…”

Section: Literature Reviewmentioning

confidence: 99%

“…ese same aspects are highlighted in [55,56] in which the authors point out that web prices offer a valuable opportunity for statistical analysis due to the constant generation of information, their accessibility, and availability as well as there being little notable differences compared to offline prices. Within real estate, applications developed using web data are used byÖzsoy and Şahin [23] in Istanbul; Del Cacho [14] in Madrid; Larraz and Larraz and Población [57,58] in Spain; Pow et al [17] in Montreal; Larraz and Población [59] in Czech Republic; Nguyen [25] in the United States; Clark and Lomax [36] in England; Pérez-Rave et al [20] in Colombia; or Neloy et al [44] in Bangladesh.…”

Section: Empirical Applicationmentioning

confidence: 99%

A Fully Automated Adjustment of Ensemble Methods in Machine Learning for Modeling Complex Real Estate Systems

et al. 2020

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The close relationship between collateral value and bank stability has led to a considerable need to a rapid and economical appraisal of real estate. The greater availability of information related to housing stock has prompted to the use of so-called big data and machine learning in the estimation of property prices. Although this methodology has already been applied to the real estate market to identify which variables influence dwelling prices, its use for estimating the price of properties is not so frequent. The application of this methodology has become more sophisticated over time, from applying simple methods to using the so-called ensemble methods and, while the estimation capacity has improved, it has only been applied to specific geographical areas. The main contribution of this article lies in developing an application for the entire Spanish market that fully automatically provides the best model for each municipality. Real estate property prices in 433 municipalities are estimated from a sample of 790,631 dwellings, using different ensemble methods based on decision trees such as bagging, boosting, and random forest. The results for estimating the price of dwellings show a good performance of the techniques developed, in terms of the error measures, with the best results being achieved using the techniques of bagging and random forest.

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Ensemble Learning Based Rental Apartment Price Prediction Model by Categorical Features Factoring

Cited by 22 publications

References 28 publications

The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data

The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data

Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects

A Fully Automated Adjustment of Ensemble Methods in Machine Learning for Modeling Complex Real Estate Systems

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