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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MDPI AG
2024-02-01
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Series: | ISPRS International Journal of Geo-Information |
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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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issn | 2220-9964 |
language | English |
last_indexed | 2024-04-24T18:12:37Z |
publishDate | 2024-02-01 |
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series | ISPRS International Journal of Geo-Information |
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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