DESARROLLO DE UN EXPERIMENTO

Researchers have often used market transaction price as a dependent variable in HP analysis, while some studies have analysed property market-related issues using rent prices or appraisal prices (see, for example, Hibiki & Managi, 2011). Moreover, as shown by Ma & Swinton’s study (2012) sale prices are more appropriate for valuing farmland than appraised values. Generally, Sale prices capture all environmental variations. Also, as defined by the HP theory, the market clearance price is regressed with possible property characteristics. However, it is assumed that the property market is perfect, and that buyers and sellers have equal bargaining power, and property information freely available to all parties.

The value of a property is not only be determined by its own characteristics but by other relevant factors. Acharya & Bennett (2001) showed the importance of other characteristics in determining the price of a property, such as neighbourhood socio- economic status, land use patterns and environmental characteristics. The property market can therefore be assumed to contain a number of structural variables, surrounding amenities, environmental characteristics and social factors. In order to capture the marginal effect of a particular characteristic, a large number of explanatory variables should be incorporated into the model and hence a long data set is required.

The quality and characteristics of a property are based on the structural

variables shown in Table 3.110. These attributes are number of bedrooms, number of

bathrooms, garage spaces, lot size, type of construction material (wooden or brick), carpeting or not and existence of a pool. Neighbourhood characteristics of a house include medium weekly income, medium weekly rent, and travelling distance to the nearest school and travelling distance to nearest shopping centre. Previous researchers have found both positive and negative effects of these variables. For example, Neelawala et al. (2012) found that the distance to school had a positive

10

effect on property value whereas other researchers have found that the quality of school was an important determinant of property values. As school zones are well defined in the BCC region, they may not affect property prices but the quality of the school may be an important determinant. Thus the quality of a school may be effectively employed in HP price analysis if a large number of suburbs are considered. Kilpatrick et al. (2007) found that the construction of a highway negatively affected property values but was a positive factor for properties closer to the access point.

Environmental characteristics are also important determinants of housing prices. Distance to parks, forests, green space and river views are the most commonly used environmental variables. According to Lansford & Jones (1995), distance variables are more important than views when it comes to valuing environmental amenities. These variables are measured using the GIS technique: the distribution of environmental and socioeconomic characteristics are displayed in Appendix C.3.

The importance of land use patterns or surrounding landscape has been explored through hedonic price analysis by a number of researchers (see, for example, Ham et al., 2012; Hamilton & Morgan, 2010; Bin et al., 2008; and Geoghegan et al., 1997). Hamilton & Morgan (2010) found that households were willing to pay higher prices for a more elevated view while Ham et al. (2012) showed that open space land use was heterogeneous and that the disaggregation of land use pattern provided better results. Therefore for this study, surrounding land use patterns were observed using Google Maps and GIS techniques for data collection.

The key variables in this study were those relating to floods. Their impacts on the property market have been studied using different perspectives. Some researchers have considered the influence of floodplain location (see, for example, Rambaldi et al., 2013; Samarasinghe & Sharp, 2010) whereas others have considered the actual flood incidence (see, for example, Bin & Landry, 2013). Most of these studies have used distance to water bodies or rivers to capture the flood influence, while a few have considered water depth as a key determinant of damage to residential properties (Merz et al., 2010). However the properties closer to water bodies also have positive amenities due to the view and outlook that it provides. The present study will,

57

therefore, include both direct distance to the nearest river or creek, as well as a proxy for the depth of flood water. Flood plain locations will also be included for analysis based on the released flood maps.

It is hypothesized that after the release of flood maps the closer the property is to a river, the lower the value when compared with properties that are farther away from a river with similar structural characteristics. This is based on the premise that residents have changed their minds after the release of flood maps and discount properties within the flood plain location compared to similar properties outside it. Most importantly, it is recognised that the depth of inundation may have a greater effect on values. On the other hand, it is also recognised the two variables of distance and elevation are not likely to capture the uneven distribution of land. For instance, if a property is close to the river but in an elevated place, the property is not likely to be at a risk of flooding. Hence, an interaction variable between distance and elevation is introduced to improve the accuracy of econometric modelling.

Table 3.1: Summary of description of variables Type of

variable

Variables Data sources

Dependant variable

Sale price RP Data

Structural variables

Number of bedrooms, number of bathrooms, number of garage spaces, construction material (wall, roof and floor), pool

RP Data, Google view

Environmental variables

Waterfront properties, greenery views

Google maps, near maps

Neighbourhood characteristics

Distance to transport, distance to the railway, distance to parks

Queensland government information service Socio-economic

variables

Median household income, household size

ABS Flood related

variables

Distance to river, elevation, flood risk mapping

BCC, Queensland government

information service,

Other variables Number of days on the market, date

of sale

RP Data

Other variables, such as a year dummy were created based on the research questions. The importance of proximity to the central business district has been investigated by previous researchers. According to Sirmans et al. (2005) the

significance and the sign of estimated coefficients for covariates vary depending on the study, and hence the consideration of the overall property market in the study area provided an important clue for selecting suitable covariates.