Prediction and Analysis of Chengdu Housing Rent Based on XGBoost Algorithm

Yue, Ming; Zhang, Jie; Qi, Jiaming; Liao, Tian; Wang, Maolin; Zhang, Lingli

doi:10.1145/3422713.3422720

Cited by 11 publications

(8 citation statements)

References 2 publications

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Section: Literature Reviewmentioning

confidence: 99%

“…(2021) apply the neural network to forecast rental values of residential properties in Cape Town, South Africa, and obtain accuracy of 66.67%. Ming et al. (2020) compare the random forest, light gradient boosting and XGBoost for forecasting rental prices in Chengdu and find the XGBoost optima with accuracy of 85%.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As compared to house price forecasting, research on rental price forecasting through machine learning (e.g. Clark and Lomax, 2018;Embaye et al, 2021;Hu et al, 2019;Li, 2018;Li and Li, 1996;Ma et al, 2018;Ma and Liu, 2019;Ming et al, 2020;Odubiyi et al, 2019;Oshodi et al, 2020Oshodi et al, , 2021Oyedeji Joseph et al, 2018;Oyedeji and Oyewale, 2018;Rafatirad, 2017;Tsai and Pan, 2014;Wang and Cao, 2019;Zhang et al, 2019) seems relatively scare. Hu et al (2019) explore the random forest, extra-trees, gradient-boosting, support vector, multi-layer perceptron neural network and k-nearest neighbor when building housing rent prediction models for Shenzhen in China in October 2017 and February 2018 and find that all of these algorithms, except for the support vector, generally present good performance with the random forest and extra-trees being the leaders.…”

Section: Introductionmentioning

confidence: 99%

“…Oshodi et al (2021) apply the neural network to forecast rental values of residential properties in Cape Town, South Africa, and obtain accuracy of 66.67%. Ming et al (2020) compare the random forest, light gradient boosting and XGBoost for forecasting rental prices in Chengdu and find the XGBoost optima with accuracy of 85%. Ma et al (2018) explore the linear regression, regression tree, random forest and gradient boosting regression trees to estimate warehouse rental prices in Beijing and find the machine learning models perform better than the linear regression with the random forest being optimal.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Rent index forecasting through neural networks

Zhang

2021

JES

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PurposeChinese housing market has been growing fast during the past decade, and price-related forecasting has turned to be an important issue to various market participants, including the people, investors and policy makers. Here, the authors approach this issue by researching neural networks for rent index forecasting from 10 major cities for March 2012 to May 2020. The authors aim at building simple and accurate neural networks to contribute to pure technical forecasting of the Chinese rental housing market.Design/methodology/approachTo facilitate the analysis, the authors examine different model settings over the algorithm, delay, hidden neuron and data spitting ratio.FindingsThe authors reach a rather simple neural network with six delays and two hidden neurons, which leads to stable performance of 1.4% average relative root mean square error across the ten cities for the training, validation and testing phases.Originality/valueThe results might be used on a standalone basis or combined with fundamental forecasting to form perspectives of rent price trends and conduct policy analysis.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Rent index forecasting through neural networks

Zhang

2021

JES

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“…The LBS data used in this study were derived from Baidu, the leading LBS provider and big data operator in China. The accumulative datasets over six months can reach more than 1 trillion, which is used to generate these population products by the XGBOOST machine learning algorithm [45]. The accuracy of these data mining results exceeds 90% (Urban population data product, from Baidu map big data.…”

Section: Lbs Datamentioning

confidence: 99%

Are Electric Vehicles Reshaping the City? An Investigation of the Clustering of Electric Vehicle Owners’ Dwellings and Their Interaction with Urban Spaces

Kang

Kan

Lin

2021

IJGI

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With the rapid development of electric vehicles (EVs) around the world, debates have arisen with regard to their impacts on people’s lifestyles and urban space. Mining spatio-temporal patterns from increasingly smart city sensors and personal mobile devices have become an important approach in understanding the interaction between human activity and urban space. In this study, we used location-based service data to identify EV owners and capture the distribution of home and charging stations. The research goal was to investigate that how the urban form in regions under rapid urbanization is driven by EV use, from a geographical perspective. Using a case study of the expanding metropolis of Beijing, GIS-based spatial statistical analysis was conducted to characterize the spatial-pattern of the homes of EV owners as well as their charging preferences. Our results indicate that the spatial clustering of the homes of EV owners in non-urban central areas—suburban areas—is significantly higher than that in urban central areas. According to the records of visits to charging stations, the spatial interaction distance between the dwellings of EV owners and their visits to charging stations exhibits significant distance attenuation characteristics. 88% of EV owners in this research travels within 40 km (Euclidean distance) between housing and charging stations. At the same time, there were significant differences in the spatial patterns between working days and non-working days which are affected by commuting activities. The three types of urban spatial interaction patterns were identified and categorized by visualization. This transformation to EV use in the city influences several aspects of people’s decisions and behaviors in life. Understanding the impacts will provide valuable information for the development of EVs and their implications in the electrification of transportation, smart planning, and sustainable urbanization.

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Diabetes Analysis with a Dataset Using Machine Learning

Chang

Javvaji

et al. 2022

Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases

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Prediction and Analysis of Chengdu Housing Rent Based on XGBoost Algorithm

Cited by 11 publications

References 2 publications

Rent index forecasting through neural networks

Rent index forecasting through neural networks

Are Electric Vehicles Reshaping the City? An Investigation of the Clustering of Electric Vehicle Owners’ Dwellings and Their Interaction with Urban Spaces

Diabetes Analysis with a Dataset Using Machine Learning

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