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Airbnb Valuation : = A Machine Learning Approach.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Airbnb Valuation :/
Reminder of title:	A Machine Learning Approach.
Author:	Wyatt, Katherine.
Description:	1 online resource (189 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-07.
Contained By:	Masters Abstracts International85-07.
Subject:	Geography. -
Online resource:	click for full text (PQDT)
ISBN:	9798381220087

Airbnb Valuation : = A Machine Learning Approach.
Wyatt, Katherine.

Airbnb Valuation :A Machine Learning Approach. - 1 online resource (189 pages)

Source: Masters Abstracts International, Volume: 85-07.

Thesis (M.S.)--University of Arkansas, 2023.

Includes bibliographical references

This thesis uses a geospatially-enhanced, machine learning approach to investigate variations in rental success on the peer-to-peer property sharing website Airbnb.com. Geographic factors, listing attributes and amenities, customer response metrics, and host attributes are included in decision tree modeling to predict the short-term probability of receiving a review. The most important variables in increasing model accuracy are assessed and variations in the importance of these variables investigated using Shapley values.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381220087Subjects--Topical Terms:

654331
Geography.
Subjects--Index Terms:

Game theoryIndex Terms--Genre/Form:

554714
Electronic books.

Airbnb Valuation : = A Machine Learning Approach.
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500 $a Source: Masters Abstracts International, Volume: 85-07.
500 $a Advisor: Limp, Fred.
502 $a Thesis (M.S.)--University of Arkansas, 2023.
504 $a Includes bibliographical references
520 $a This thesis uses a geospatially-enhanced, machine learning approach to investigate variations in rental success on the peer-to-peer property sharing website Airbnb.com. Geographic factors, listing attributes and amenities, customer response metrics, and host attributes are included in decision tree modeling to predict the short-term probability of receiving a review. The most important variables in increasing model accuracy are assessed and variations in the importance of these variables investigated using Shapley values.
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538 $a Mode of access: World Wide Web
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650 4 $a Information science. $3 561178
653 $a Game theory
653 $a Geospatial data science
653 $a Machine learning
653 $a Spatiotemporal analysis
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