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Driving Factors Behind Airbnb Pricing - A Multilinear Regression Analysis
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2023 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Drivande faktorer bakom Airbnb-prisättning - En multilinjär regressionsanalys (Swedish)
Abstract [en]

With a high increase of users in the world's ever expanding sharing economy, Airbnb has become a customary solution in short term rentals of accommodations. In this market, it is the host's job to choose a pricing which sufficiently corresponds to what tenants are willing to pay. There can be multiple methods of choosing the price but this study aims to determine and evaluate which factors have a significant impact on short term rental pricing of housing and to what degree. By modelling this issue, the reader can make a better understanding of what to pay or charge for an accommodation. This study also serves as ground work for further investigations exploring nested and multi-leveled factors.

The study is limited to the Spanish short term rental market, taking a more in-depth look at the cities of Barcelona, Madrid and Palma. Moreover, listings between 2015 and 2017 are considered in the study. In the end, factors identified as significant on accommodation pricing were Entire Home, Accommodates, Bathrooms, Review Scores Rating etc.. Some of the factors are interchangeable as they have a miniscule effect on the accommodation pricing. Conversely, Entire Home and Accommodates is seen as absolute necessities for the model as they, together, explain two-thirds of the variations in price.

Abstract [sv]

Med en ständigt ökande användarskara, i världens ständigt expanderande delningsekonomi, har Airbnb blivit en allt vanligare lösning för korttidsuthyrning av boenden. På denna marknad är det värdens uppgift att välja en prissättning som tillräckligt motsvarar vad hyresgästerna är villiga att betala. Det kan finnas flera metoder för prissättning, men denna studie syftar till att bestämma och utvärdera vilka faktorer som har en betydande inverkan på prissättningen av bostäder tillgängliga för korttidsuthyrning. Genom modellering av detta problem kan läsaren få en bättre förståelse för vad man ska betala eller hur mycket man skall ta betalt för ett boende. Denna studie fungerar också som grundarbete för vidare undersökningar som utforskar inbäddade samt flernivåfaktorer.

Studien är begränsad till den spanska korttidsuthyrningsmarknaden, med fokus riktat mot städerna Barcelona, Madrid och Palma. Endast annonser mellan 2015 och 2017 tas i beaktning i studien. I slutändan identifierades de faktorer som har en betydande effekt på prissättningen av boenden som Entire Home, Accommodates, Bathrooms, Review Scores Rating med mera. Vissa av faktorerna är utbytbara eftersom de har en minimal effekt på prissättningen av boenden. Däremot anses Entire Home och Accommodates vara absolut nödvändiga för modellen eftersom de tillsammans förklarar två tredjedelar av variationerna i priset.

Place, publisher, year, edition, pages
2023.
Series
TRITA-SCI-GRU ; 2023-218
Keywords [en]
Regression Analysis, Applied Mathematics, Airbnb
Keywords [sv]
Regressionsanalys, Tillämpad Matematik, Airbnb
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-341849OAI: oai:DiVA.org:kth-341849DiVA, id: diva2:1823748
Subject / course
Applied Mathematics and Industrial Economics
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2024-01-03 Created: 2024-01-03 Last updated: 2024-01-03Bibliographically approved

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