Case study of Airbnb listings in Berlin : Hedonic pricing approach to measuring demand for tourist accommodation characteristics

The main purpose of this degree project is to reveal the Airbnb customer’s preferences and quantify the impact of non-market factors on the market price of tourist accommodation in Berlin, Germany. The data retrieved from Airbnb listings, publicly available on Inside Airbnb (2017), was supplemented...

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Main Author: Haubeltova, Libuse
Format: Others
Language:English
Published: Högskolan Dalarna, Nationalekonomi 2018
Subjects:
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:du-29979
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spelling ndltd-UPSALLA1-oai-DiVA.org-du-299792019-05-07T05:14:47ZCase study of Airbnb listings in Berlin : Hedonic pricing approach to measuring demand for tourist accommodation characteristicsengHaubeltova, LibuseHögskolan Dalarna, Nationalekonomi2018Sharing economy accommodationAirbnbhedonic pricingmachine learninglinear regressionordinary least squares method (OLS)Berlin.EconomicsNationalekonomiThe main purpose of this degree project is to reveal the Airbnb customer’s preferences and quantify the impact of non-market factors on the market price of tourist accommodation in Berlin, Germany. The data retrieved from Airbnb listings, publicly available on Inside Airbnb (2017), was supplemented on indicator of sharing economy accommodation using machine learning method in order to distinguish between amateur and business-running professional hosts. The main aim is to examine the consumers’ preferences and quantify the marginal effect of "real sharing economy" accommodation and other key variables on market price. This is accomplished by model approach using hedonic pricing method, which is used to estimate the economic value of particular attribute. Surprisingly, our data indicates the negative impact of sharing economy indicator on price. The set of motivations of consumers, which determine their valuation of Airbnb listings, was identified. The trade-off between encompass and parsimony of the set was desired in order to build an effective model. Calculation of proportion of explained variance showed that the price is affected mainly by number of accommodated persons, degree of privacy, number of bedrooms, cancellation policy, distance from the city centre and sharing economy indicator in decreasing order. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:du-29979application/pdfinfo:eu-repo/semantics/openAccess
collection NDLTD
language English
format Others
sources NDLTD
topic Sharing economy accommodation
Airbnb
hedonic pricing
machine learning
linear regression
ordinary least squares method (OLS)
Berlin.
Economics
Nationalekonomi
spellingShingle Sharing economy accommodation
Airbnb
hedonic pricing
machine learning
linear regression
ordinary least squares method (OLS)
Berlin.
Economics
Nationalekonomi
Haubeltova, Libuse
Case study of Airbnb listings in Berlin : Hedonic pricing approach to measuring demand for tourist accommodation characteristics
description The main purpose of this degree project is to reveal the Airbnb customer’s preferences and quantify the impact of non-market factors on the market price of tourist accommodation in Berlin, Germany. The data retrieved from Airbnb listings, publicly available on Inside Airbnb (2017), was supplemented on indicator of sharing economy accommodation using machine learning method in order to distinguish between amateur and business-running professional hosts. The main aim is to examine the consumers’ preferences and quantify the marginal effect of "real sharing economy" accommodation and other key variables on market price. This is accomplished by model approach using hedonic pricing method, which is used to estimate the economic value of particular attribute. Surprisingly, our data indicates the negative impact of sharing economy indicator on price. The set of motivations of consumers, which determine their valuation of Airbnb listings, was identified. The trade-off between encompass and parsimony of the set was desired in order to build an effective model. Calculation of proportion of explained variance showed that the price is affected mainly by number of accommodated persons, degree of privacy, number of bedrooms, cancellation policy, distance from the city centre and sharing economy indicator in decreasing order.
author Haubeltova, Libuse
author_facet Haubeltova, Libuse
author_sort Haubeltova, Libuse
title Case study of Airbnb listings in Berlin : Hedonic pricing approach to measuring demand for tourist accommodation characteristics
title_short Case study of Airbnb listings in Berlin : Hedonic pricing approach to measuring demand for tourist accommodation characteristics
title_full Case study of Airbnb listings in Berlin : Hedonic pricing approach to measuring demand for tourist accommodation characteristics
title_fullStr Case study of Airbnb listings in Berlin : Hedonic pricing approach to measuring demand for tourist accommodation characteristics
title_full_unstemmed Case study of Airbnb listings in Berlin : Hedonic pricing approach to measuring demand for tourist accommodation characteristics
title_sort case study of airbnb listings in berlin : hedonic pricing approach to measuring demand for tourist accommodation characteristics
publisher Högskolan Dalarna, Nationalekonomi
publishDate 2018
url http://urn.kb.se/resolve?urn=urn:nbn:se:du-29979
work_keys_str_mv AT haubeltovalibuse casestudyofairbnblistingsinberlinhedonicpricingapproachtomeasuringdemandfortouristaccommodationcharacteristics
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