Analyzing Spatial Variance of Airbnb Pricing Determinants Using Multiscale GWR Approach

Abstract

A sharing economy accommodation service like Airbnb, which provides trust between strangers to connect them for profiting from underutilized assets, was born and has thrived thanks to the innovations in the platform technology. Due to the unique structure of Airbnb, the pricing strategies of hosts are very different from the conventional hospitality industry. However, existing Airbnb pricing studies have limitations considering the varying scale of operation among hosts, spatial variances in pricing strategies, and crucial geographic information for estimating the influence of the pricing variables, as well as ignoring inter-city variances. In this research, we explored the spatially heterogeneous relationship between price and pricing variables using an innovative spatial approach, Multiscale Geographically Weighted Regression (MGWR). Analysis results for Airbnb listing in Log Angeles and New York in the US showed the effectiveness of MGWR regarding estimating the influence of pricing variables spatially. By revealing spatially heterogeneous and dependent relationships, this research fills gaps in Airbnb pricing research and deepens the understanding of the pricing strategies of the hosts.

The pricing of European airbnb listings during the pandemic: A difference-in-differences approach employing COVID-19 response strategies as a continuous treatment

“…Also applying a hedonic pricing model, Tong and Gunter (2020) determine that overall rating and characteristics indicative of the size of the listing have the strongest positive influence, whereas the number of reviews and distance from the city center exert the strongest negative influence on Airbnb prices in Barcelona, Madrid, and Seville. Recognizing the relevance of spatial factors in a study of listings in Los Angeles and New York, Hong and Yoo (2020) propose a multiscale geographically weighted regression (MGWR) approach for analyzing spatial variance of Airbnb pricing determinants. Despite this clear academic interest in Airbnb prices, the literature also notes differences between price and revenue determinants ( Sainaghi, Abrate, & Mauri, 2021 ).…”

Assessing work resumption in hospitals during the COVID‐19 epidemic in China using multiscale geographically weighted regression

“…Multiscale geographically weighted regression (MGWR) removes the assumption of consistent spatial scale in GWR and allows that the explanatory variables have various specific bandwidths to represent spatial local heterogeneity at various spatial scales (Fotheringham, Yang, & Kang, 2017). The MGWR technique has been applied extensively to examine the multiscale influences of the determinants on health, socioeconomics, and natural environment (Cupido, Fotheringham, & Jevtic, 2020; Fotheringham, Yue, & Li, 2019; Hong & Yoo, 2020; Iyanda et al., 2020; Mollalo, Vahedi, & Rivera, 2020; Oshan, Smith, & Fotheringham, 2020; Yang, Zhan, Lv, & Liu, 2019). In this study, we further examined the determinants of the hospital resumption rate at various spatial scales using the MGWR model described as follows:where , are the local intercepts and regression coefficients of explanatory variables with various optimal bandwidths, respectively, and bwi in denotes the specific bandwidth used for calibration of the i th conditional relationship.…”

Research on Design Strategy of B&B: Based on Text Mining and Machine Learning Method

“…Kalehbasti et al created a model for predicting the price of an Airbnb listing using property specifications, owner information, and customer reviews for the listing [1]. Hong and Yoo explored the spatially heterogeneous relationship between price and pricing variables using an innovative spatial approach, multiscale geographically weighted regression [2]. Voltes-Dorta et al presented a study about the drivers of Airbnb prices in Bristol using ordinary least squares and geographically weighted regression methods [3].…”