A Spatial Regression Model for Predicting Prices of Short-Term Rentals in Athens, Greece

Short-term house rentals constitute a growing component of tourist accommodation in several countries and the determination of factors affecting rents is an important consideration in relevant studies. Short-term rentals have shown increasing trends in the city of Athens, Greece; however, this activ...

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Main Authors: Polixeni Iliopoulou, Vassilios Krassanakis, Loukas-Moysis Misthos, Christina Theodoridi
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:ISPRS International Journal of Geo-Information
Subjects:
Online Access:https://www.mdpi.com/2220-9964/13/3/63
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author Polixeni Iliopoulou
Vassilios Krassanakis
Loukas-Moysis Misthos
Christina Theodoridi
author_facet Polixeni Iliopoulou
Vassilios Krassanakis
Loukas-Moysis Misthos
Christina Theodoridi
author_sort Polixeni Iliopoulou
collection DOAJ
description Short-term house rentals constitute a growing component of tourist accommodation in several countries and the determination of factors affecting rents is an important consideration in relevant studies. Short-term rentals have shown increasing trends in the city of Athens, Greece; however, this activity has not been adequately studied. In this paper, spatial data of Airbnb rentals in Athens are analyzed in order to indicate the factors which are important for the spatial variation of rents. Factors such as property capacity, host attributes and review characteristics are considered. In addition, several locational attributes are examined. Regression analysis techniques are used to predict the cost per night, according to various explanatory factors, while the results of two models are presented: ordinary least squares (OLS) and geographically weighted regression (GWR). The results of the OLS model indicate several factors determining the rent, including capacity and host characteristics, as well as locational attributes. The GWR model produces more accurate results with a smaller number of independent variables. For the residuals analysis several additional amenities were examined that resulted in a small impact on rents. The unexplained spatial variation of rents may be attributed to neighborhood characteristics, socioeconomic conditions and special characteristics of the rentals.
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spelling doaj.art-9e386577e2c64f85b39ab50ed62f11fc2024-03-27T13:44:50ZengMDPI AGISPRS International Journal of Geo-Information2220-99642024-02-011336310.3390/ijgi13030063A Spatial Regression Model for Predicting Prices of Short-Term Rentals in Athens, GreecePolixeni Iliopoulou0Vassilios Krassanakis1Loukas-Moysis Misthos2Christina Theodoridi3Department of Surveying and Geoinformatics Engineering, University of West Attica, Egaleo Park Campus, Ag. Spyridonos Str., Egaleo, 12243 Athens, GreeceDepartment of Surveying and Geoinformatics Engineering, University of West Attica, Egaleo Park Campus, Ag. Spyridonos Str., Egaleo, 12243 Athens, GreeceDepartment of Surveying and Geoinformatics Engineering, University of West Attica, Egaleo Park Campus, Ag. Spyridonos Str., Egaleo, 12243 Athens, GreeceDepartment of Surveying and Geoinformatics Engineering, University of West Attica, Egaleo Park Campus, Ag. Spyridonos Str., Egaleo, 12243 Athens, GreeceShort-term house rentals constitute a growing component of tourist accommodation in several countries and the determination of factors affecting rents is an important consideration in relevant studies. Short-term rentals have shown increasing trends in the city of Athens, Greece; however, this activity has not been adequately studied. In this paper, spatial data of Airbnb rentals in Athens are analyzed in order to indicate the factors which are important for the spatial variation of rents. Factors such as property capacity, host attributes and review characteristics are considered. In addition, several locational attributes are examined. Regression analysis techniques are used to predict the cost per night, according to various explanatory factors, while the results of two models are presented: ordinary least squares (OLS) and geographically weighted regression (GWR). The results of the OLS model indicate several factors determining the rent, including capacity and host characteristics, as well as locational attributes. The GWR model produces more accurate results with a smaller number of independent variables. For the residuals analysis several additional amenities were examined that resulted in a small impact on rents. The unexplained spatial variation of rents may be attributed to neighborhood characteristics, socioeconomic conditions and special characteristics of the rentals.https://www.mdpi.com/2220-9964/13/3/63short term house rentalsAirbnbspatial analysisordinary least squares (OLS)spatial regressionAthens
spellingShingle Polixeni Iliopoulou
Vassilios Krassanakis
Loukas-Moysis Misthos
Christina Theodoridi
A Spatial Regression Model for Predicting Prices of Short-Term Rentals in Athens, Greece
ISPRS International Journal of Geo-Information
short term house rentals
Airbnb
spatial analysis
ordinary least squares (OLS)
spatial regression
Athens
title A Spatial Regression Model for Predicting Prices of Short-Term Rentals in Athens, Greece
title_full A Spatial Regression Model for Predicting Prices of Short-Term Rentals in Athens, Greece
title_fullStr A Spatial Regression Model for Predicting Prices of Short-Term Rentals in Athens, Greece
title_full_unstemmed A Spatial Regression Model for Predicting Prices of Short-Term Rentals in Athens, Greece
title_short A Spatial Regression Model for Predicting Prices of Short-Term Rentals in Athens, Greece
title_sort spatial regression model for predicting prices of short term rentals in athens greece
topic short term house rentals
Airbnb
spatial analysis
ordinary least squares (OLS)
spatial regression
Athens
url https://www.mdpi.com/2220-9964/13/3/63
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