MODELOS PARA UN SOLO SIJSTRATO

Most countries are subject to some natural disasters. Extreme weather events that cause these disasters, such as floods, droughts, bushfires and cyclones are likely to be more frequent in the future if global climate change forecasts are accurate (Salinger, 2005). Disasters can be categorized as hydro-meteorological (floods, storms, drought), geophysical (earthquakes, tsunami, volcanic eruptions) and biological (epidemics and insect infestations) of which hydro-meteorological disasters are considered the most damaging (Cavallo & Noy, 2010). According to Keskitalo (2013) water-related hazards comprise 90% of natural hazards.

Floods harms lives, animals and properties. Stress, loss of one's memorable items, health problems, temporary or permanent displacement, and disruption of education and work can be some of the more serious negative repercussions. The damage caused by such disasters can be categorized into direct and indirect, and each can be further categorized as tangible and intangible (Merz et al., 2010). Direct damage costs include the cost to properties, agriculture and infrastructure, while disruption of services, loss of production and loss of property values are considered indirect costs. The increase in natural disasters worldwide makes it necessary to examine their likely impact. Policy makers are particularly interested in assessing the direct and indirect costs associated with natural disasters. The direct damage costs of natural disasters have been extensively investigated, but indirect costs have not. Recently, Cavallo & Noy (2010) reviewed empirical work on the cost of natural disasters focusing on the post evaluation of direct costs and the data sources for those studies. They found that the estimated costs of natural disasters were heterogeneous regardless of country. Many researchers have also investigated the indirect costs associated with natural disasters for residents by means of estimating the impact on property values, as many environmental problems are related to land and house markets.

Different models have been adopted to study the impact of natural disasters on property markets. For example, Breisinger et al. (2012) investigated the economy- wide impact of floods using computable general equilibrium models. However most researchers have used the HP pricing model, repeat sales price analysis and price indexes to examine the HP pricing model that was widely applied.

A number researchers have attempted to study the effect of natural disasters and climate change on property markets (see, for example, Bustic et al., 2011 Nikolaos et al., 2011; Schlenker et al., 2005). Butsic et al. (2011) applied the HP model to simulate the impact of global warming on real estate prices in Canada finding a likely negative impact of climate change on housing prices. Moreover, the paper compared four locations in Canada and found substantial heterogeneity in the impact of climate change on housing prices. Similarly, the influence of natural hazards may vary between geographical locations due to its inherent geographical characteristics. In general most of these researchers have adopted basic HP pricing models.

The economic impact of earthquakes and cyclones has been extensively examined, such as the Kobe earthquake (Horwich, 2000), Hurricane Katrina (Hallegatte, 2008) and the Haiti earthquake (Cavallo & Noy, 2010). Most of these studies only examined the direct economic costs, although Cavallo & Noy (2010) investigated both the direct and indirect costs. All these studies focused on post estimation of damage costs rather than studying their effect on the property market or changes in property value.

Another significant natural disaster is wildfires but only a handful of studies have investigated the risk of wildfires on the property market. Athukorala et al. (2012) show that buyers in the study areas paid higher prices to live close to green spaces despite these areas being known as wildfire-prone areas. Their results indicated that residents either discounted or were unaware of the risks of wildfires. Muller & Loomis (2008) investigated wildfire risks on nearby properties using the spatial econometric techniques. Similarly, Donovan et al., (2007) examined the impact that information available to residents on wildfire risks had on housing prices in Colorado Spring’s wild land-urban interface and showed that ‘pre-website overall

wildfire risk ratings’ were positively related to house prices. Their results suggested

that the positive amenity values of the house and neighbourhood characteristics that affect a house’s wildfire risks outweighed the perceived loss in house utility resulting from those risks.

Natural disasters can have negative as well as positive externalities, especially on natural ecosystems. Cunado & Ferreira (2014) analysed data on flood events in 135 countries and concluded that these events had a significant impact on economic

growth. Moreover, the results showed a very large positive effect in developed countries and within the agricultural sector. Fomby et al. (2013) also confirmed the positive effects of natural disasters on economic growth. A further advantage is that following a natural disaster, some sectors such as construction and those associated with rebuilding are likely to boom. Events like floods can also increase soil fertility and increase agricultural productivity. Some reports have argued that such incidences are a natural phenomenon and are necessary for the stability of ecosystems. However, this research has looked only at overall economic growth and not largely considered indirect effects. Infrequent natural disasters can result in huge welfare losses compared with more frequent events (Barro, 2009). The next section discusses the relevant literature on the impact of floods on property values.