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Fig. 2.
A section of a coastal city with parcels contours. In red: recently-traded properties.
After buyers have submitted their bids, a seller checks how many bid offers he has
received. He chooses the highest bid to engage in price negotiations. The
transaction
price
is defined through a price negotiation procedure based on bid and ask prices and
relative market power of traders.
2.5
Real Estate Agent
It is challenging to model price expectations in urban property markets characterized
by high heterogeneity of goods, which are infrequently traded. We build upon the
previous research on agent-based modeling of urban land markets and introduce real
estate agents who observe successful transactions and form price expectations (Parker
and Filatova 2008; Gilbert et al. 2009; Ettema 2011; Magliocca et al. 2011). At the
end of each time step, which is equal to 1 month, all successful transactions got regis-
tered in a database together with all the attributes of the properties and traded agents.
Each time step real-estate agents update expectations about prices based on these
recent transactions. Specifically, real estate agent checks if there are enough transac-
tions to run a comparable sales analysis. If yes, then he runs a hedonic analysis on the
new transactions from the last 3 trading periods. If the number of realized transactions
is not sufficient to capture the variation on housing prices in the regression analysis,
then the horizon is extended yet for another month. Afterwards, these new coeffi-
cients got recorded into his memory. Then the real estate agent may decide to apply
one of the price learning strategies to suggest final asking price for a seller. Following
Magliocca et al (2011) we use some of the economic prediction models.
Regression analysis is realized by employing the R extension of Netlogo (Thiele
and Grimm 2010), what makes it possible to have a direct coupling of R script and
Netlogo ACE model.
