Spatial Distribution and Location Characteristics of Airbnb in Seoul, Korea

Abstract

The popularity of the sharing economy, including organizations such as Airbnb and Uber, has increased rapidly over the last few years. Regarding Airbnb, several studies have demonstrated potential problems related to it, such as rising housing costs and worsening residential environments. Despite its popularity and possibility of problems, however, the locational characteristics of Airbnb have not been thoroughly examined in the urban planning and public policy fields. Therefore, this study analyzed the locational characteristics of Airbnb in Seoul and identified potential problems in urban planning. First, we analyzed an operation status of Airbnb in terms of the commercial host. After that, we identified spatial distributions of Airbnb and examined influential factors that affected the locational characteristics of Airbnb using a negative binomial regression model. The results of this study are as follows. First, a considerable number of units were operated by commercial hosts, in contrast to the original concept of Airbnb as being part of the sharing economy. This finding indicates that there would be a potential conflict between Airbnb businesses and housing policy in urban areas. Second, Airbnb units were mainly located in residential areas, especially around multiplexes and townhouses. This finding indicates that Airbnb may decrease the availability of housing in the housing market. Third, Airbnb units were located preferentially in areas adjacent to universities or subway stations, as well as in areas with high ratios of single-person households. Considering these characteristics of Airbnb and the directions of government housing policy, urban scholars and policymakers should pay close attention to the potential conflict between the spatial distributions of Airbnb units and housing policies.

Characteristics and influencing factors of Airbnb spatial distribution in China’s rapid urbanization process: A case study of Nanjing

“…In order to understand the relationship between the number of Airbnb listings and the distance to subway stations, the nearest neighbor analysis tool in ArcGIS was used to find the distance between each listing point and the nearest subway station, and then aggregated them to generate scatter-distribution maps [ 12 ]. As shown in the scatter plot of the number and distance of Airbnb listings ( Fig 4 ), there was an evident curve relationship between them, and the overall distribution was skewed, forming a U-shaped curve.…”

“…In the analysis of the spatial distribution of Airbnb in Seoul, South Korea, Ki and Lee (2019) used the negative binaries regression model to test the factors influencing Airbnb’s location characteristics. The study showed that Airbnb’s units preferred to be located in areas near universities or subway stations, as well as areas with a high proportion of single families [ 12 ]. Xu et al (2017), used the least-squares method and geographically weighted regression to study the distributive characteristics of Airbnb in London.…”

“…This study used the research methods of Ki and Lee (2019) and Xu et al (2017) [ 11 , 12 ] to study the spatial distribution of Airbnb in Nanjing, but the major contribution of this work was to further highlight the coupling relationship with the urban spatial structure. At the same time, it studied the driving factors of Airbnb’s development from the aspects of urban development and housing source, professional hosts, and rent gap.…”

“…In recent years, researchers have studied the spatial distribution characteristics of some cities considering the potential economic, social, and cultural impacts that Airbnb might have on communities. These studies showed that the distribution of Airbnb listing was related to city centers, major attractions, and transport accessibility [ 11 , 12 ].…”

A review of literature on the concept, impacts, and spatial interactions of sharing short-term rental platform

“…益" "实现共享互利" "增强社会资本" 和 "减少资源 消耗" 等 [2] ， 让人们相信， 它不仅仅是经济的变革， 更 将推动社会的发展。在全球经济衰退和社会问题 频发的背景下， 这种新颖的经济形式让人更加难以 抗拒 [3] [4] ， 至今， 该企业已在全球 10 万 多个城市中拥有超过 700 万套房源， 其规模和覆盖 范围已经可以与希尔顿和万豪等全球连锁酒店媲 美 [4][5] 。 Airbnb 平台的成功之处在于， 它降低了参与酒 店经营的门槛和获取信息的成本， 并借助互联网快 速实现了全球范围内的业务覆盖， 使得既有的非正 规短期租房活动发生了质的转变 [1] [5,[7][8][9] 。然而， 有关该平台具体 如何影响传统酒店行业， 又在多大程度上冲击了既 有市场， 现有研究还没有给出一个确切的结论。 Guttentag [4] 用熊彼得的 "破坏性创新(Destruc- [10] 。接着， Roma 等 [9] 发现低端 酒店与 Airbnb 之间存在价格竞争。其用户意向调 查也表明， 很多顾客主观上会将 Airbnb 视作中档酒 店的替代品。从这些研究看， Airbnb 成为了部分酒 店的替代产品 [11] 。 也有部分学者认为， 共享短租对酒店业的总体 发展有积极作用， 并且可以缓解部分地区酒店供应 不足的问题， 两者相互 "补足" 。Coyle 等 [12] 通过对 图 1 共享短租平台与酒店业和住房市场的关系 Fig. 1 Relationship between shared short-term rental platform, hotel industry, and housing market [13] 。不 仅如此， 西班牙 [14] 和维也纳 [15] 的城市案例也表明， Airbnb 可以为游客在城市中心区提供宽敞、 便宜的 住宿， 补充酒店服务的供应不足。该平台甚至为美 国俄勒冈州部分缺乏传统酒店服务覆盖的地区带 来新的旅游需求 [16] [24] 。 且有研究通过横向比较指出， 巴塞罗那 Airbnb 渗透 率 ② 较高比渗透率较低社区的长租房租金高 4% [25] 。 但随着实证案例的增加， 这种影响逐渐表现出 差异性。Coyle 等 [12] 从世界各国的案例来看， 平台房源总是大量 集聚在城市中心 [14,33] 、 旅游资源 [7,23,31] 和交通便利地 区附近 [22,34] ， 借以迎合游客的需求。这些资源与设 施是影响其空间分布呈现 "相似性" 的 "物质环境" 因子。 在 "相似性" 的基础上， 不同城市中 Airbnb 房源 的分布又各具特色。例如， 美国少数族裔社区往往 会成为 Airbnb 房源的 "飞地" 。在华盛顿、 奥斯汀、 波士顿和旧金山 [34] ， 很多距离城市中心 5 英里(1 英 里=1.61 km)内的少数族裔社区中(以低收入的非裔 美国人为主)， 几乎不存在短租房源。在布达佩斯 部分远离传统旅游资源的衰败城区中， "废墟酒吧" 的兴起促使附近 Airbnb 房源迅速增长 [38] 。保加利 亚为了带动国际旅游而大力支持共享短租平台的 发展， 在其首都索菲亚， Airbnb 房源多位于商业化、 中产阶级和特权人群集中的社区内 [37] 。而在韩国 首尔， 其房源往往分布在靠近大学且单身人群比例 较高的地区 [22]…”