Agent-Based Simulation of Short-Term Peer-to-Peer Rentals: Evidence from the Amsterdam Housing Market (Article Abstract)

N. Yorke-Smith

Agent-Based Simulation of Short-Term Peer-to-Peer Rentals: Evidence from the Amsterdam Housing Market (Article Abstract)

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Abstract

The full article published in Environment and Planning B studies the effect of a range of possible municipal policy measures on the peer-to-peer short-term rental market. The case study is the city of Amsterdam. A spatial agent-based simulation indicates that more lower income citizens remain in the city centre when regulation of the market is stronger, and that banning the touristic market restrains the overall increase in house prices, compared to the business-as-usual scenario. However, the feasibility of enforcement of regulation, and its libertarian consequences, must be considered.

Original language	English
Title of host publication	BNAIC/BeneLearn 2021
Subtitle of host publication	33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning
Editors	Edit Luis A. Leiva, Cédric Pruski, Réka Markovich, Amro Najjar, Christoph Schommer
Pages	709-711
Publication status	Published - 2021
Event	33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning - Esch-sur-Alzette, Luxembourg Duration: 10 Nov 2021 → 12 Nov 2021

Conference

Conference	33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning
Abbreviated title	BNAIC/BeneLearn 2021
Country/Territory	Luxembourg
City	Esch-sur-Alzette
Period	10/11/21 → 12/11/21

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Agent-Based Simulation of Short-Term Peer-to-Peer Rentals: Evidence from the Amsterdam Housing Market
Overwater, A. & Yorke-Smith, N., 2021, In: Environment and Planning B: Urban Analytics and City Science. 49, 1, p. 223-240 18 p.
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Yorke-Smith, N. / Agent-Based Simulation of Short-Term Peer-to-Peer Rentals: Evidence from the Amsterdam Housing Market (Article Abstract). BNAIC/BeneLearn 2021: 33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning. editor / Edit Luis A. Leiva ; Cédric Pruski ; Réka Markovich ; Amro Najjar ; Christoph Schommer. 2021. pp. 709-711

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title = "Agent-Based Simulation of Short-Term Peer-to-Peer Rentals: Evidence from the Amsterdam Housing Market (Article Abstract)",

abstract = "The full article published in Environment and Planning B studies the effect of a range of possible municipal policy measures on the peer-to-peer short-term rental market. The case study is the city of Amsterdam. A spatial agent-based simulation indicates that more lower income citizens remain in the city centre when regulation of the market is stronger, and that banning the touristic market restrains the overall increase in house prices, compared to the business-as-usual scenario. However, the feasibility of enforcement of regulation, and its libertarian consequences, must be considered. ",

author = "N. Yorke-Smith",

year = "2021",

language = "English",

pages = "709--711",

editor = "Leiva, {Edit Luis A. } and Pruski, {C{\'e}dric } and Markovich, {R{\'e}ka } and Najjar, {Amro } and Schommer, {Christoph }",

booktitle = "BNAIC/BeneLearn 2021",

note = "33rd Benelux Conference on Artificial Intelligence and<br/>30th Belgian-Dutch Conference on Machine Learning, BNAIC/BeneLearn 2021 ; Conference date: 10-11-2021 Through 12-11-2021",

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Yorke-Smith, N 2021, Agent-Based Simulation of Short-Term Peer-to-Peer Rentals: Evidence from the Amsterdam Housing Market (Article Abstract). in ELA Leiva, C Pruski, R Markovich, A Najjar & C Schommer (eds), BNAIC/BeneLearn 2021: 33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning. pp. 709-711, 33rd Benelux Conference on Artificial Intelligence and
30th Belgian-Dutch Conference on Machine Learning, Esch-sur-Alzette, Luxembourg, 10/11/21.

Agent-Based Simulation of Short-Term Peer-to-Peer Rentals: Evidence from the Amsterdam Housing Market (Article Abstract). / Yorke-Smith, N.
BNAIC/BeneLearn 2021: 33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning. ed. / Edit Luis A. Leiva; Cédric Pruski; Réka Markovich; Amro Najjar; Christoph Schommer. 2021. p. 709-711.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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N2 - The full article published in Environment and Planning B studies the effect of a range of possible municipal policy measures on the peer-to-peer short-term rental market. The case study is the city of Amsterdam. A spatial agent-based simulation indicates that more lower income citizens remain in the city centre when regulation of the market is stronger, and that banning the touristic market restrains the overall increase in house prices, compared to the business-as-usual scenario. However, the feasibility of enforcement of regulation, and its libertarian consequences, must be considered.

AB - The full article published in Environment and Planning B studies the effect of a range of possible municipal policy measures on the peer-to-peer short-term rental market. The case study is the city of Amsterdam. A spatial agent-based simulation indicates that more lower income citizens remain in the city centre when regulation of the market is stronger, and that banning the touristic market restrains the overall increase in house prices, compared to the business-as-usual scenario. However, the feasibility of enforcement of regulation, and its libertarian consequences, must be considered.

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BT - BNAIC/BeneLearn 2021

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A2 - Pruski, Cédric

A2 - Markovich, Réka

A2 - Najjar, Amro

A2 - Schommer, Christoph

T2 - 33rd Benelux Conference on Artificial Intelligence and<br/>30th Belgian-Dutch Conference on Machine Learning

Y2 - 10 November 2021 through 12 November 2021

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Agent-Based Simulation of Short-Term Peer-to-Peer Rentals: Evidence from the Amsterdam Housing Market (Article Abstract)

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Agent-Based Simulation of Short-Term Peer-to-Peer Rentals: Evidence from the Amsterdam Housing Market

Cite this