{"title":"What Factors drives the Airbnb Listing’s Prices?","authors":"Mwigeka, Samwel","doi":"10.22158/ibes.v4n1p26","DOIUrl":null,"url":null,"abstract":"The main aim of this study is to assess the impact of Airbnb listing attributes on price. Pricing is widely acknowledged to be one of the most critical factors determining the long-term success of the accommodation industry (Hung et al., 2010). The study has applied OLS regression in assessing the relationship between price and its determinants.The study has shown that there exist significant impacts of the variables on price. The variables such as number of beds, number reviews, room type dummies, property type dummies and neighborhood dummies have shown the impact of prices for the New York City, supported by Wang and Nicolau (2017), Portolan (2013), and Dogru and Pekin (2017).In supplementing OLS results the study has employed variance-weighted least square and feasible generalized least square which they possess stronger estimation properties than OLS.The results of this research provide instructions and direction to main tourism stakeholders and policymakers, and at the same time assist facility owners in shaping prices helping them to create instruments for future planning.","PeriodicalId":343833,"journal":{"name":"International Business & Economics Studies","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Business & Economics Studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22158/ibes.v4n1p26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The main aim of this study is to assess the impact of Airbnb listing attributes on price. Pricing is widely acknowledged to be one of the most critical factors determining the long-term success of the accommodation industry (Hung et al., 2010). The study has applied OLS regression in assessing the relationship between price and its determinants.The study has shown that there exist significant impacts of the variables on price. The variables such as number of beds, number reviews, room type dummies, property type dummies and neighborhood dummies have shown the impact of prices for the New York City, supported by Wang and Nicolau (2017), Portolan (2013), and Dogru and Pekin (2017).In supplementing OLS results the study has employed variance-weighted least square and feasible generalized least square which they possess stronger estimation properties than OLS.The results of this research provide instructions and direction to main tourism stakeholders and policymakers, and at the same time assist facility owners in shaping prices helping them to create instruments for future planning.
本研究的主要目的是评估Airbnb上市属性对价格的影响。定价被广泛认为是决定住宿行业长期成功的最关键因素之一(Hung et al., 2010)。本研究运用OLS回归来评估价格与其决定因素之间的关系。研究表明,各变量对价格存在显著影响。在Wang和Nicolau(2017)、Portolan(2013)和Dogru和Pekin(2017)的支持下,诸如床位数量、评论数量、房间类型假人、财产类型假人和社区假人等变量显示了价格对纽约市的影响。本文采用方差加权最小二乘法和可行广义最小二乘法对OLS结果进行了补充,它们具有比OLS更强的估计性能。本研究的结果为主要的旅游利益相关者和政策制定者提供了指导和方向,同时帮助设施所有者制定价格,帮助他们为未来的规划创造工具。