We identify how disaster risks affect housing prices and rent in the US. There are various types of disasters including earthquakes, hail, heat waves, tsunamis, and wildfires. While the outcomes of disasters are likely disruptive, we do not know how each disaster type leads to lower housing values. As explained in our recent post, for instance, housing prices in high-fire-risk regions are relatively higher; homebuyers prefer housing in the suburbs even if they get vulnerable to wildfire risk. In this regard, it is critical to have a sense of the type of disasters that are associated with lower housing values.
In this post, we use the National Risk Index developed by the US Federal Emergency Management Agency (FEMA). The National Risk Index is a dataset and online tool to help illustrate the U.S. communities most at risk for 18 natural hazards: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather (see https://hazards.fema.gov/nri/map). The risk index for each of the hazard types is provided at the census tract level, in which we analyze their association with housing value and rent from the Census data. See the figure below for an example of the National Risk Index.
By using the latest American Community Survey data, we tested multiple regression models to understand how the hazard risk affects housing prices and rent. We here only focus on the census tracts we address in our product. Since the urban context across the US is heterogeneous, we apply a state-level fixed model (i.e., we try to capture the effect of hazard risk on housing values, while removing the housing value differences across states).
The results are shown below including the coefficients of each hazard type for two outcome variables (i.e., housing value and rent value). For instance, an increase in the risk of Avalanche by 1 would lead to -$6159.6 for housing values and -$3.6 for rent values. While we would expect negative coefficients in general, there were some hazard types that showed a positive one. As we can expect, the risk of wildfire showed a positive association with housing and rent values. In addition, Tsunami showed positive coefficients (e.g., $39755.7 and $36.8), in which the results imply that the housing market cares less about tsunamis because they occur rarely. Based on the coefficient values, it is also possible to have a gist of the hazard types that are highly influential on housing and rent values.
From these simple analyses, we can understand the hazard types that should be adopted when we model and predict housing values. For example, an increase in heatwave risks by 1 reduces both housing value by $1904.1 and rent value by $2.6. We also see that the expected climate change in the future is likely to have large influence on housing values.
Q & A?
Head of Urban Research
Doctoral student in Urban Planning at USC
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