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(Arthur et al. 1997; Kirman and Vriend 2001; Tesfatsion and Judd 2006) land is a
good with very diverse attributes. The same house in a different location may have a
disproportionally different price as do two houses with different structural characte-
rizes in the same neighborhood. Modeling price expectations in housing markets
needs an introduction of mediator who learns the efficient price of any unique house
and who participates often in transactions of such infrequently-purchased good as a
house (Parker and Filatova 2008; Gilbert et al. 2009; Ettema 2011; Magliocca et al.
2011). 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. Adaptive expectations about property prices in the areas with in-
creasing hazard probabilities, which real estate agents and households form, may
experience abrupt changes that cardinally alter the trend of spatial development and
the price trend.
The innovativeness of this paper is threefold: (i) in comparison to economic studies
of land use our ABM explicitly simulates the emergence of property prices and spatial
patterns under adaptive price expectations of heterogeneous agents, including the
emergence of cardinally new trends in prices and spatial development, (ii) in compari-
son to other agent-based land markets, which are stylized abstract models (Parker and
Filatova 2008; Gilbert et al. 2009; Ettema 2011; Magliocca et al. 2011), the current
model makes step forward towards empirical modeling of ABM land markets by
using actual hedonic studies and distribution of households preferences; (iii) in com-
parison to other empirical spatial ABMs modeling urban phenomena (Robinson et al.
2007; Brown et al. 2008) our ABM has a fully modeled land market with adaptive
price expectations, which allows for the emergence prices and may lead to qualitative-
ly different trends in spatial patterns (Parker et al. 2011).
2.2
Case-Study and Data
The model is applied to two coastal towns in Carteret county, North Carolina. The
area is in general low lying and is prone to flooding with probability of 1:100 and
1:500 in certain zones. For ACE model initialization we employ spatially referenced
data from multiple GIS data-sets on the locations of residential housing, coastal amen-
ities (measured in terms of distance from coastal water and sound, and a boolean
measure of waterfront), flood probabilities, distances to the CBD and national parks,
and data on structural characteristics of properties (age, sq.ft, lot size, number of
rooms and etc). The ACE land market model is programmed in Netlogo (Wilensky
1999) and vector data is uploaded using GIS extension. In addition, we use hedonic
analysis (Bin et al. 2008) based on the real estate transactions from 2000 to 2004 after
a period of active hurricane seasons from mid 1990s to 2003.
2.3
Buyers' Behavior
There are 3 main trading agents in the model (Figure 1): buyers, sellers and real-estate
agents.
