The influence of the neighbourhood environment on peer-to-peer accommodations: A random forest regression analysis

Jiang, Hongqiang; Lin, Mei; Wei, Ye; Zheng, Rumin; Yan-hua, Guo

doi:10.1016/j.jhtm.2022.02.028

Cited by 23 publications

(13 citation statements)

References 52 publications

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“…Similarly, the performances of random forest and multiple regression has been reported from the research findings in [18]. How the neighbourhood environment can influence peerto-peer accommodation when using random forest is the finding reported in [19]. It seems that random forest is very promising, and it is also one of the suggested algorithms from AutoModel used in this research together with Decision Tree [20] and Support Vector Machine [21].…”

Section: B Office Rent Predictions With Machine Learningmentioning

confidence: 58%

Rapid Modelling of Machine Learning in Predicting Office Rental Price

Mohd¹,

Harussani²,

Masrom³

2022

IJACSA

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This study demonstrates the utilization of rapid machine learning modelling in an essential case of the real estate industry. Predicting office rental price is highly crucial in the real estate industry but the study of machine learning is still in its infancy. Despite the renowned advantages of machine learning, the difficulties have restricted the inexpert machine learning researchers to embark on this prominent artificial intelligence approach. This paper presents the empirical research results based on three machine learning algorithms namely Random Forest, Decision Tree and Support Vector Machine to be compared between two training approaches; split and crossvalidation. AutoModel machine learning has accelarated the modelling tasks and is useful for inexperienced machine learning researchers for any domain. Based on real cases of office rental in a big city of Kuala Lumpur, Malaysia, the evaluation results indicated that Random Forest with cross-validation was the best promising algorithm with 0.9 R squared value. This research has significance for real estate domain in near future, by applying a more in-depth analysis, particularly on the relevant variables of building pricing as well as on the machine learning algorithms.

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Section: B Office Rent Predictions With Machine Learningmentioning

confidence: 58%

Rapid Modelling of Machine Learning in Predicting Office Rental Price

Mohd¹,

Harussani²,

Masrom³

2022

IJACSA

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“…RF model is a decision tree‐based machine learning algorithm (Breiman, 2001). The algorithm circumvents the problem of multivariate covariance by calculating the nonlinear effects of variables; it does not require variable selection and improves the interpretability of the model by feature importance ranking and producing biased dependency graphs (Jiang et al ., 2022). We created RF regression models using meteorological factors (T, Pre, and RH), topographic factors (elevation, slope, and NDVI), and anthropogenic factors (LUT, GDP, and POP) as explanatory variables and AOD as the dependent variable.…”

Section: Methodsmentioning

confidence: 99%

“…The disadvantages of these methods include that they only consider linear relationships between AOD and aerosol drivers, that they do not adequately quantify the relative importance of driving factors in the spatial variation of AOD, and that they do not consider the effects of interactions between factors that affect the spatial variation of AOD. In contrast, the random forest (RF) model and the geographical detector are not affected by multivariate covariance (Wang and Xu, 2017; Jiang et al ., 2022). A RF is a machine learning algorithm developed in recent years that performs better than a neural network in classification and regression.…”

Section: Introductionmentioning

confidence: 99%

Spatial heterogeneity and driving factors of aerosol in Western China: Analysis on multiangle implementation of atmospheric correction–aerosol optical depth in Xinjiang over 2001–2019

Ding

Jin

2022

Intl Journal of Climatology

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Knowledge of aerosol dynamics is essential to combating atmospheric aerosol pollution, and there is a growing interest in aerosol changes and their drivers. However, the effects of interactions between natural and anthropogenic drivers of aerosol are not well understood. Here, we analyse changes in aerosol optical depth (AOD) in Xinjiang, China using the multiangle implementation of atmospheric correction aerosol products over 2001–2019 and investigate the driving factors of aerosol dynamics using a random forest (RF) and a geographical detector. The results show the dominant AOD changes in the quasi‐period of 3.21 months, 7.86 months, 1.19 years, for seasonal, half‐year, and interannual variations of aerosols. The increasing and then decreasing nonlinear trends were observed in the interannual AOD variation during 19 years period. The importance ranking results of the two models indicated that meteorological factors dominated the spatial distribution of AOD in Xinjiang (72.73% for RF and 65.78% for the geographic detector), and that interactions between factors enhanced the explanatory power in AOD changes. In addition, the influence of anthropogenic factors on AOD was increasing in North Xinjiang, and the influence of precipitation and population on AOD was increasing in East Xinjiang. However, the influence of drivers on AOD in South Xinjiang was basically constant over time, showing the spatially heterogeneous relationship between AOD and drivers. This study emphasized the spatial heterogeneity and driving factors of small‐scale aerosols in arid regions and so can guide targeted air pollution prevention and control in local areas.

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Section: Modelmentioning

confidence: 99%

“…This study assumes that the service provides provide only one type of service through the platform rather than multiple services. In practice, there are various types of services on the home-sharing platform (Jiang et al, 2022). For example, Airbnb provides services including apartments, personal residences, inns, seascape villas, and garden houses (Casamatta et al, 2022).…”

Section: Modelmentioning

confidence: 99%

The entry quality threshold setting and commission rate contract selection of a peer-to-peer service sharing platform

Chi

Fan

Wang

2022

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PurposeIn recent years, some peer-to-peer (P2P) service sharing platforms have improved their service quality by setting an entry quality threshold for service providers. Considering consumers' heterogeneous preferences for service quality and commission rate, it is worth studying how to select the commission rate contract for a P2P platform under a predetermined entry quality threshold for service providers.Design/methodology/approachIn this study, the platform's profit-maximizing model is constructed under two proposed contracts: unilateral commission rate contract and bilateral commission rate contract. The optimal entry quality threshold and the optimal commission rate are obtained. This study also explores the impacts of cross-side network externality and service price on a platform's optimal decisions and social welfare.FindingsResults show that it is always advantageous for the platform to adopt the bilateral commission rate contract, which is closely related to the strength of cross-side network externality, service price and quality sensitivity coefficient. Under certain conditions, adopting the unilateral commission rate contract can reduce platform profit and service provider surplus, and improve consumer surplus and social welfare.Originality/valueThis study analyzes the unilateral commission rate contract and the bilateral commission rate contract of the platform, and discusses which contract is beneficial to the platform, consumers and service providers. In addition, this study provides a basis for the operation decision of a P2P service sharing platform and the pricing decision of service providers.

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The influence of the neighbourhood environment on peer-to-peer accommodations: A random forest regression analysis

Cited by 23 publications

References 52 publications

Rapid Modelling of Machine Learning in Predicting Office Rental Price

Rapid Modelling of Machine Learning in Predicting Office Rental Price

Spatial heterogeneity and driving factors of aerosol in Western China: Analysis on multiangle implementation of atmospheric correction–aerosol optical depth in Xinjiang over 2001–2019

The entry quality threshold setting and commission rate contract selection of a peer-to-peer service sharing platform

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