An Agent-Based Simulation of Rental Housing Markets

Abstract

We simulate a closed rental housing market with search and matching frictions, in which both landlord and tenant agents are imperfectly informed of the characteristics of the market. Landlords, who observe a random sample of market offered rents, 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. Tenants are heterogeneous in income. 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 offered ret distribution.The steady state to which the simulation evolves shows price dispersion, nonzero search times and vacancies. We further assess the effects of increasing information on either side of the market. Tenants' information level has a positive effect on their welfare. Conversely, 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 allows the analysis of the 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that dynamic shocks to the discount rate can provoke overshoots in rent adjustments due in part to landlords use of outdated information in their rent posting decision.

Is home sharing driving up rents? Evidence from Airbnb in Boston

“…Researchers have used search theory to model the sensitivity of housing prices and sales volume to demand and/or supply conditions given imperfect information (Head, Lloyd-Ellis and Sun, 2014), as well as to account for the role of brokers (Yavas, 1994). Researchers have also extended this model to rental housing (McBreen, Goffette-Nagot and Jensen, 2009). Typically, this research suggests that market tightness, the ratio of vacant homes offered for sale/rent to those seeking to buy/rent, is one of the mechanisms through which demand or supply changes affect price (Novy-Marx, 2009).…”

Disentangling Wine Judges’ Consensus, Idiosyncratic, and Random Expressions of Quality or Preference

“…Bayer, Ferreira, and McMillan (2003) examine a real-estate market, including the idiosyncratic utility of home i to buyer k . Mc Breen, Goffette-Nagot, and Jensen (2009) evaluate a market for rental housing and find that “idiosyncratic tastes give some monopoly power” to landlords (p. 5). Hastings, Kane, and Staiger (2006) analyze school choice, including the “idiosyncratic preference of a student” for school i (p. 11).…”

“…All of those authors define and model idiosyncratic preference and quality ratings as having a distribution around c i . Mc Breen et al (2009) assume that ε j , i has a normal distribution. Bayer et al (2003) and Hastings et al (2006) assume that ε j , i has an extreme value distribution.…”

A spatially based artificial neural network mass valuation model for land consolidation

“…artificial neural networks (ANNs) (Garcia et al, 2008;Kathman, 1993;Nguyen and Cripps, 2001;Pao, 2008), expert systems (Kilpatrick, 2011), and case-based reasoning (Gonzalez and Laureano-Ortiz, 1992) have arisen during the last two decades. Moreover, newer methods such as agent-based models (Breen et al, 2009) and genetic algorithms have also been applied in the area of land valuation more recently (Ahn et al, 2012). Among the AI techniques, ANNs (Fausett, 1994), which attempt to simulate the functioning of the human brain, have been the most widely used for valuation during the last decade (Garcia et al, 2008;Kathman, 1993;Kontrimas and Verikas, 2011).…”