Price Listing Predictions and Forthcoming Analysis of Airbnb

Garlapati, Anusha; Garlapati, Kavya; Malisetty, Neeraj; Krishna, Doredla Radha; Narayana, Gayathri

doi:10.1109/icccnt51525.2021.9579773

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Several recent papers based on the study of hedonic listing prices related to amenities or other factors (determinants of the Airbnb market as inputs or exogenous variables) have applied AI methods to consider the sharing accommodation market (Chattopadhyay and Mitra, 2020; Garlapati et al, 2021; Moreno-Izquierdo et al, 2018; Priambodo and Sihabuddin, 2020, among others).…”

Section: Econometric Methodsmentioning

confidence: 99%

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Modelling prices and volatilities in the sharing economy

Pérez-Rodríguez,

Hernández,

Andrada-Félix

2023

Tourism Economics

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This paper examines whether short-term rental listings in the sharing accommodation market take account of market risk in their pricing. To do so, we estimate time-varying market risks, and forecast price changes using daily supply-price time series. The empirical analysis was conducted using daily data for the Canary Islands sharing accommodation market for the period January 2016 to September 2021. The following main results were obtained. First, individual listings face systematic risks that are lower than the average market listing return, but multi-unit hosts are more sensitive to market index variations than single-unit hosts. Second, there is a time-varying but long-range dependence on market risk, indicating a slow reversion to the mean level of volatility. Price changes also reflect negative long-range dependence or anti-persistence. Third, volatility does not affect price adjustments in the market (no evidence of risk-return trade-off) for types of hosts and lodgings. Fourth, models can be used to perform risk management using value-at-risk approaches, and market risks are greater for houses and single-unit hosts in the GBP market. Finally, prices can be predicted in different horizons using long-range dependence models.

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Section: Econometric Methodsmentioning

confidence: 99%

“…Finally, Garlapati et al (2021) used several machine learning paths to study the demand for Airbnb properties in New York City, comparing prices by means of various algorithms and identifying the most accurate and efficient models.…”

Section: Forecasting Tourism Pricesmentioning

confidence: 99%

Modelling prices and volatilities in the sharing economy

Pérez-Rodríguez,

Hernández,

Andrada-Félix

2023

Tourism Economics

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“…Acquah et al (2019),Adanu et al (2020),Alabi et al (2014), AmoabengNti (2020),Amuzu (2018),Asampong et al (2015),Azodo et al (2017),Balasubramanian and Karthikeyan (2017),Burns et al (2019),Chen et al (2011), Cong et al (2018,Dai et al (2020),Davis et al (2019),Decharat and Kiddee (2020),Fischer et al (2020),Forti et al (2020),Garlapati (2016),Grant et al (2013),Huo et al (2019),Jaiswal et al (2015),Jibiri et al (2014),Laskar and Pal (2013),Leclerc and Badami (2020),Machete (2017),Maphosa and Maphosa (2020),Meenambal (2011),Nwagwu and Okuneye (2016), Noel-Brune et al (2013),Park et al (2017),Peluola (2016),Purchase et al (2020), Seith et al, (2019,Soetrisno and Delgado-Sabori (2020),Yu et al (2017),Zhang et al, (2020) …”

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confidence: 99%

The impact of electronic-electrical waste on human health and environment: A systematic literature review

Sonny¹,

Samali²,

Bosco³

et al. 2023

J. Eng. Technol. Res.

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Sub-Saharan Africa has become one of the leading destinations for used equipment or Electronic-Electrical waste (e-waste-or-EEW) from developed economies, making its management more urgent than ever. Most studies have articulated the serious impact of e-waste disposal to the environment and public human health. Little attention or consideration has been devoted to the management of e-waste in Uganda despite the existence of laws, policies, and guidelines. Comparatively, much more attention has been paid on the disposal of plastics. In this study, using systematic literature review (SLR), the effect of irresponsible e-waste recycling and disposal on the environment and human health were amplified. Initially, articles were retrieved from four electronic databases using keywords on the environmental and health impact of e-waste and zeroed on only Google scholar and Science Direct because they constituted over 90 percent of the identified articles. Consequently, 35 papers were selected and analyzed from 8,319 papers that were assessed for eligibility to address the research questions. The SLR revealed plausible results associated with irresponsible disposal and exposure to e-waste. Specifically, the outcomes include premature births, abortions, damage to the kidney, the nervous and blood systems, skin diseases, lung cancer, respiratory disorders, and chronic brain damage. Toxic elements are detrimental to the environment since they affect the food system and water systems. Consequently, practical and technological solutions were provided and recommendations to minimize future catastrophe from e-waste to policymakers, theoretical contributions and prospects for future research were made.

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