Methodology

The overall objective of this chapter is to investigate the impact of release of floods maps and actual floods on the value of residential properties in Brisbane (Australia). To address this question, the actual prices of flood plain properties were compared to a hypothetical property or identical non-flood plain properties before and after the incidence (or pre- and post-incident). The transacted property prices were analysed on the basis that the price of a product or service is a proxy for the value of its utility. Therefore, HP theory was used to estimate the implicit price of each characteristic. As discussed in chapter one, the impact of the release of flood maps (2009) and subsequent flood in 2011 needed to be isolated. The following HP model was extended using DID estimation, taking into account the spatial interaction as well. The methodology was then extended to examine the temporal variation of flood-risk.

4.2.1 HEDONIC PROPERTY PRICE APPROACH

In an HP model, the price of a commodity can be attributed to a vector of characteristics which may include product characteristics, environmental factors and socio-economic factors. Given it is assumed that the market is in equilibrium, the marginal price of a housing attribute is equal to the MWTP for a change in the attribute (see, Rosen, 1974). The hedonic theory provides a methodology for identifying the structure of prices of each property attribute where also environmental risk can be considered as one of them.

As mentioned in chapter two, the HP model was first introduced in the 1960s and was later developed as a standard model in economic literature. Price theory

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predicts that the willingness to pay to avoid the disutility of flood risk should be reflected in a property price discount. When two houses are identical in all aspects except the flood risk, the house without the flood risk can be expected to sell at a higher price. That difference is the cost of flood or flood risk on the property

market11. According to Rosen (1974), the price of a commodity (p) is the function of

the vector of its characteristics (x). Mathematically it can be expressed as:

𝑝 = 𝑖(𝑥) Eq. 4.1

where, p is the market price of the property and x is the vector of characteristics

of the property. The characteristics can primarily be categorized as structural and neighbourhood characteristics, and environmental and flood variables.

The functional form shows the contribution of each attribute to property prices. The hedonic value of each characteristic can be expressed as partial derivatives. Hedonic regressions are the most commonly and widely accepted method for determining the effect on residential property values and other issues arising from events such as natural disasters.

The model takes the following form:

𝑙𝑐𝑝𝑖 = ∝𝑖+ ∑ 𝛽𝑖𝑥𝑖 + 𝜀𝑖 Eq. 4.2

where, p is the sale price as market price of the property, α is the constant term,

β is the vector of coefficient that reflects the influence of each characteristic (e.g. structural, neighbourhood and flood variables), x is the vector of characteristics of

the property and ‘ε’ is the error term.

The next challenge is to analyse and compare the impact of two incidences (the release of flood maps and an actual flood event) simultaneously. In this analysis, the following were considered:

• data on property transaction before flood risk information availability (pre-October, 2009),

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• between flood risk information being made available up until the flood incidence (October, 2009 to January, 2011) and

• post-flood (January, 2011 to March, 2013).

4.2.2 QUASI EXPERIMENTAL APPROACH

The mapping of flood prone areas in the BCC region in 2009 and the actual flood incidence in 2011 provides an interesting experiment to assess the impact of flood risk on property markets compared with the availability of public information. For this purpose, the Difference-in-differences (DID) method was employed. According to Woodridge (2007), this method can be applied to both repeated cross- sectional data and panel data. Research has demonstrated the applicability of this approach for both random as well as natural experiments (see, for example, Bin & Landry, 2013; Gawande et al., 2013; Parmeter & Pope, 2013).

DID rests on the premise of using two groups and two time periods where the second time period of one group is treated (the treated group in this study is flood- affected properties) and the other is not (the control group in this study is non flood- affected properties).

Considering the outcome before and after the treatment (flood) for treated groups (flood-affected properties), the average difference is the treatment effect which can be expressed as:

∆𝑃𝑡 = 𝑃�𝑡2− 𝑃�𝑡1 Eq. 4.3

In order to remove biases from the comparison over time (time trend), a control group was included. The comparison with the control group also removed differences between the treated and control groups. The outcome difference for the control group can be expressed as:

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The real treatment effect was given by subtracting the difference in average outcome in the control group (for the two time periods) from the difference in average outcome in the treated group (before and after the incident).

∆𝑃𝑡𝑐 = (𝑃�𝑡2− 𝑃�𝑡1) − (𝑃�𝑐2− 𝑃�𝑐1) Eq. 4.5