Our Methodology

The seven factors in our index are taken from nine different data sets.

Dollar losses per capita were taken from property damage estimates published in the National Climatic Data Center’s Storm Events Database for events occurring between Jan. 1, 1996, and Dec. 31, 2013. This period was chosen because the most thorough data for all types of weather events in NCDC’s database begins in 1996. The data were quality controlled to remove obvious errors. For several counties in the New York City area, where NCDC figures were missing or incomplete, damage from Superstorm Sandy was estimated using figures from the state of New York’s damage recovery request.

Total dollar losses over the 18-year period were divided by the 2013 Census estimated population for each geographic area. The score was standardized by assigning 100 points to the location with the highest value ($424,057.68 in Cameron Parish, Louisiana) and scaling all other counties proportionally using a logarithmic scale.

Deaths in homes per capita were taken from data published published in the National Climatic Data Center’s Storm Events Database for events occurring between Jan. 1, 1996, and Dec. 31, 2013. The database classifies storm-related deaths by the location in which they occurred; for our index, only the “Permanent Home” and “Mobile/Trailer Home” categories counted toward our index. Deaths from Hurricane Katrina in Louisiana and Mississippi were notably absent in the NCDC database; we estimated the number of deaths in homes using available data, including a 2008 study published in the journal Disaster Medicine and Public Health Preparedness.

Total deaths in homes over the 18-year period were divided by the 2013 Census estimated population for each geographic area.

The score was standardized by assigning 100 points to the location with the highest value (Kiowa County, Kansas) and scaling all other counties proportionally using a logarithmic scale.

For dollar losses and deaths, some events were categorized by National Weather Service zone rather than by county. In those cases the authors made every effort to assign the damage or death toll to the appropriate geographic area(s) based on available information, but where that was not possible, the damage or death toll was generally assigned to each county or equivalent area within the NWS zone proportional to its population.

Energy cost index was computed by first calculating the estimated amount of energy required to heat and cool a home, determined by computing the combined number of heating degree days and cooling degree days for each climate division in the contiguous U.S., using official 1981-2010 normals from the National Climatic Data Center. The values were then assigned to each county equivalent within the climate division. For counties (mainly in the West) split among two or more climate divisions, the climate division representing the most populated area of the county was used (generally determined using the location of the county’s center of population, as determined by the U.S. Census Bureau). For Alaska and Hawaii, values for each area were computed using representative climate sites within each county, borough or census area. For the District of Columbia, the value was computed using data from Reagan National Airport.

For each geographic area, the energy demand was multiplied by the average cost of electricity per kilowatt-hour in its state using data from the U.S. Energy Information Administration. While electricity is not the only means of heating and cooling homes, we used this as a reasonable proxy for energy cost.

The score was standardized by assigning 200 points to the location with the highest value (North Slope Borough, Alaska) and scaling all other counties proportionally using a linear scale. We assigned a maximum of 200 points to this category because unlike disasters, energy costs are an everyday fact of life for most homeowners.

Flood risk was determined using the total average annualized flood loss per capita for each county equivalent from FEMA’s HAZUS Average Annualized Flood Loss data. The score was standardized by assigning 100 points to the location with the highest value (Orleans Parish, Louisiana) and scaling all other counties proportionally using a logarithmic scale.

Fire risk was determined using the number of wildfires of 300 acres or larger in size for each county equivalent from 1994 to 2013, using the U.S. Geological Survey’s Federal Fire Occurrence Database. The number of fires was divided by the land area for each county equivalent. The score was standardized by assigning 100 points to the location with the most fires per unit area (Collier County, Florida) and scaling all other counties proportionally using a logarithmic scale.

Quake risk was determined using two sets of data from FEMA’s HAZUS MH Estimated Annualized Earthquake Losses data. The first was annualized estimated household displacement, indicating the number of households that can expect to be displaced from their homes due to earthquake damage per year, averaged over a very long period of time. This value is available at a state level. The second was the annualized earthquake loss ratio for major metropolitan areas. This is an estimate of the amount of dollar losses per year, proportional to the total value of all property, within selected Census Metropolitan Statistical Areas in seismic risk zones, averaged over a very long period of time. These values were assigned to each county or county equivalent within the listed MSAs. The score was standardized by assigning 50 points to the location with the highest value in each half of the index, and scaling all other areas proportionally for that metric using a logarithmic scale. The values were then combined, with a maximum possible score of 100.

Home damage was determined using two sets of data from FEMA. The first was Preliminary Damage Assessment Reports from FEMA documents. These documents described different categories of damage or destruction to homes in various natural disasters. Point values were assigned to the damage categories and multiplied by the number of homes in each damage category to come up with a “Composite Damage Index” for each state. This value was then standardized by assigning 50 points to the state with the highest value (Missouri), and scaling all other states proportionally for that metric using a logarithmic scale.

The second was federally approved housing assistance, in dollars, from federal disaster declarations involving natural events. (Man-made disasters such as explosions and chemical spills were not included.) The dollar figures were summed for each state. This value was then standardized by assigning 50 points to the state with the highest value (Louisiana), and scaling all other states proportionally for that metric using a logarithmic scale.

The home damage score was computed by adding the two values together for each state and assigning that score to all geographic areas within each state. The maximum possible score was 100, though no location actually earned 100 points.

Geographic units of analysis: We analyzed data for 3,111 geographical areas in the 50 states and the District of Columbia. For the vast majority of the country, these were counties. In Louisiana, parishes were used. We counted the independent cities of Baltimore, Maryland; St. Louis, Missouri; and Carson City, Nevada, as separate counties. In Virginia, we combined most independent cities with their adjacent or surrounding counties, following the practice established by the Bureau of Economic Analysis in the U.S. Department of Commerce, but the independent cities of Norfolk, Virginia Beach, Chesapeake, Suffolk, Portsmouth, and Newport News were analyzed separately. In Alaska we analyzed the data by borough; for the Unorganized Borough we used the census areas as defined by the U.S. Census Bureau. In Hawaii we used the counties, except that we combined Maui County with the very tiny Kalawao County. In the special case of Monroe County, Florida, we analyzed the mainland portion separately from the Florida Keys portion because they were treated separately in the federal fire risk data, and the stark difference between the two in geography, natural hazards, and population density supported extending that distinction across all other county-level categories.