Agent-based simulation of short-term peer-to-peer rentals: Evidence from the Amsterdam housing market

Abstract

Gentrification, displacement and social exclusion are hot topics of debate in the city of Amsterdam, the Netherlands. A current phenomena is short-term rentals of private homes. In its peer-to-peer form, this phenomena has grown sharply, facilitated by services such as Airbnb. Its growth has caused controversies among communities in touristic areas of Amsterdam, since it contributes to a changed social fabric, increased housing prices and overall gentrification. In the Netherlands and elsewhere, municipal and national policy makers are interested to regulate short-term rentals. In order to provide insights into qualitative policy effects, we develop a micro-level agent-based simulation. Our spatial model simulates residential migration based on income and house pricing. The underlying economic theory is that of Smith’s rent-gap hypothesis. Our model is among the first agent-based approaches investigating this type of rental market, and the first to model a specific housing market, including social housing, in a geographically accurate way. Simulation analysis shows that the touristic rental market has caused considerable changes in housing prices and population development. We find 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.

An Agent-Based Market Analysis of Urban Housing Balance in The Netherlands

“…These models can analyse specific policies, as long as those policies can be described in terms of the economic variables; however, econometric models cannot accommodate individual-level behaviour and results. Because of this, these models struggle to predict emergent patterns caused by policy [16,26,32] (compare the point of [33]).…”

“…The closest are [42], who look at negotiation in Dutch property purchases using ABM, and [32], who look at the peer-to-peer rental market in Amsterdam, again using ABM.…”

Vacation rental market regulation and accommodation supply growth

“…Some works have conducted simulation studies to ascertain the potential effects of some specific policy regulations ex-ante. For the case of Amsterdam, Overwater and Yorke-Smith (2022) examine the qualitative macro-effects of maximum-night policy measures using agent-based modelling. Their simulations illustrate that a greater proportion of low-income citizens will live in the city when the regulation of the market is stronger.…”

Urbanism and Geographic Crises: A Micro-Simulation Lens on Beirut

“…Besides many other fields, it is now seen in geography, urban planning, and urban studies. For instance, agent-based models have been exploited to reproduce the evolution of the urban form (Stanilov & Batty, 2011); to model characteristic urban dynamics, including segregation, gentrification, and informal settlements (Patel et al, 2012;Picascia et al, 2015;Schelling, 1971); to plan urban land use (Ligtenberg et al, 2001;Zidar et al, 2017); to study the housing market (e.g., Yun & Moon, 2020;Zhang & Li, 2014), residential search behaviour (Huang et al, 2014), and the sharing economy (Overwater & Yorke-Smith, 2022); and more broadly in participatory urban planning (Ampatzidou et al, 2018). Hölscher and Frantzeskaki (2021, p. 1) categorise research about urban transformations into three categories: "in cities" studies "diverse factors, processes and dynamics driving place-based transformations"; "of cities" studies "outcomes of transformative changes in urban (sub-)systems"; and "by cities" studies "changes taking place on global and regional levels as a result of urbanisation and urban development approaches."…”