Information and Search on the Housing Market: An Agent-Based Model

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. We simulate a closed rental housing market with search and matching frictions, in which both landlord and tenant agents may be imperfectly informed of the characteristics of the market. The model hypotheses are set so as to match a rent posting search model in the spirit of search models of the labor market. In the simulations, landlords decide what rent to post based on the expected effect of the rent on the time-on-the-market (TOM) required to find a tenant. Each tenant observes their idiosyncratic preference for a random offer and decides whether to accept the offer or continue searching, based on their imperfect knowledge of the distribution of offered rents. The steady state to which the simulation evolves shows price dispersion, nonzero search times and vacancies. We further assess the effects of altering the level of information for landlords. Landlords are better off when they have less information. In that case they underestimate the TOM and so the steady-state of the market moves to higher rents. However, when landlords with different levels of information are present on the market, the better informed are consistently better off. The model setup also allows the analysis of market dynamics. It is observed that dynamic shocks to the discount rate can provoke overshoots in rent adjustments due in part to landlords use of outdated information in their rent posting decision. Terms of use: Documents in

Housing markets, signals and search

“…For instance, Carrillo (2012) sets out a computationally tractable stationary equilibrium model involving housing market search. A range of studies have developed agent-based models of market search to explore information implications for landlord and tenant decisions in rental markets that shape time on market, rent and vacancy outcomes (Gilbert, Hawksworth, & Swinney, 2009;McBreen, Goffette-Nagot, & Jensen, 2011;Sun & Manson, 2010). Genesove and Han (2011) have explored the price dynamics of housing markets with search, while Piazzesi and Schneider (2009) and Peterson (2012) share a similar aim of linking housing price dynamics and household information.…”

Modelling and Simulation of Selected Real Estate Market Spatial Phenomena

“…The use of simulation modelling of real estate markets has not yet been studied extensively (e.g., [14][15][16][17][18][19]). This study has mainly concerned the streamlining of the economic decisions taken (e.g., [16]), effectiveness of investments, inter alia, maximizing income from flat rental [15,20] or the effect of environmental and socio-economic factors on demand [19]. Simulation modelling in this case allowed for mimicking the probabilistic nature of real-life phenomena (e.g., [3,21]).…”

Using Kernel density estimation for modelling and simulating transaction location

“…The modelling of structures of the property market for cognitive purposes has been attempted by many researchers, however only a few of them have attempted to carry out market research with the use of simulation modelling (Diappi, Bolchi 2008;Mc Breen et al 2010;Bao et al 2012;Filatova 2015;Özbaş et al 2014;Vorel 2014). The great advantage of this specific research approach is the lack of limitations connected with the structure and degree of complexity characterizing the analysed system, as well as the possibility of including stochastic processes, hence allowing one to carry out the modelling of real systems with a high degree of complexity and high percentage of random factors (Von Malottki 2008).…”

“…This is of key importance for researchers focusing on the behaviours of the property market, where processes and relations may have a random nature. until now, simulation tests in the field of the property market have been focused on streamlining the economic decisions made (Bao et al 2012), the effectiveness of investing, inter alia, the maximization of profits from the rental sector (Mc Breen et al 2010), or the influences of environmental or social and economic features on demand (Vorel 2014). The simulations also aimed to analyse and explore price fluctuations on the property market, as well as analyse the dynamics of a local property market (Özbaş et al 2014;Diappi, Bolchi 2008;filatova 2015), while the spatial aspects of the modelled system were less addressed.…”