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首页> 外文期刊>Journal of Business Research >Examining the predictors of successful Airbnb bookings with Hurdle models: Evidence from Europe, Australia, USA and Asia-Pacific cities
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Examining the predictors of successful Airbnb bookings with Hurdle models: Evidence from Europe, Australia, USA and Asia-Pacific cities

机译：用障碍模型检查成功Airbnb预订的预测因子：来自欧洲，澳大利亚，美国和亚太城市的证据

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摘要

Recent studies on Airbnb have examined the predictors of room prices, successful reservations and customer satisfaction. However, a preliminary investigation of the listings from twenty-two cities across four continents revealed that a significant number of Airbnb homes remained non-booked. Thus, Poisson count-regression techniques cannot efficaciously explain the effects of predictors of successful Airbnb bookings. To address this gap, we proposed a text mining framework using Hurdle-based Poisson and Negative Binomial regressions. We found that the superhost status, host response time, and communication with guests emerged as the most significant predictors irrespective of geographies. We also found that the instant booking option strongly influences the bookings across cities with incoming business visitors. Additionally, we presented a machine learning-based variable-importance scheme, which helps determine the top predictors of successful bookings, to design customized recommendations for attracting more guests and unique advertisement content on P2P accommodation platforms.
机译：最近关于AIRBNB的研究已经审查了房价的预测因子，成功的保留和客户满意度。但是，四大大陆的二十二个城市的上市初步调查显示，大量航空公司住宿仍未预订。因此，泊松数回归技术无法效力解释成功Airbnb预订的预测因子的影响。为了解决这一差距，我们提出了一种使用基于障碍的泊松和负二项式回归的文本挖掘框架。我们发现超级状态，主机响应时间和与客人的通信出现为最重要的预测因子，而不管地理位置。我们还发现即时预订选项强烈影响来自传入商务访客的城市的预订。此外，我们还提出了一种基于机器学习的可变重要性方案，有助于确定成功预订的最高预测因子，为吸引更多客人和P2P住宿平台上的独特广告内容设计定制的建议。

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