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Title:
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MEXICO CITY'S AIRBNB LISTING PRICE ANALYSIS
USING REGRESSION |
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Author(s):
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Daniela A. Gomez-Cravioto, Ramon E. Diaz-Ramos, Virginia I. Contreras-Miranda
and Francisco J. Cantu-Ortiz |
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ISBN:
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978-989-8704-19-1 |
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Editors:
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Piet Kommers and Guo Chao Peng |
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Year:
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2020 |
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Edition:
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Single |
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Keywords:
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AirBnb, Mexico, CRISP-DM, Logistic Regression, GAM, Quantile Regression |
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Type:
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Full |
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First Page:
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138 |
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Last Page:
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148 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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The AirBnb platform provides users with the option of renting their vacant spaces as tourist accommodations and
competing with traditional accommodation enterprises. However, since AirBnb gives the user the freedom to establish
the price of their listing, a challenge is placed on the owner to determine the most appropriate number. This study
analyses the information from listings in Mexico City to determine how the listing attributes can be used as predictors for
a new listing's price. The study uses statistical methods and machine learning techniques to analyze the information
scraped from AirBnb's website, which is publicly available at the Inside AirBnb webpage. In this work, an experiment
was made to compare a quantile regression, logistic regression, and a generalized additive model to find the most suitable
technique for predicting an AirBnb listing's price. The models were compared based on the residual standard error,
R squared, and AIC. The results show that the generalized additive model provides the best fit for the dataset explaining
60% of the variance. |
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