VAMPIRE stands for Vulnerability Assessment for Mortgage, Petrol and Inflation Risks and Expenditure. The VAMPIRE index was created in a study of six Australian Cities to assess the risk to households arising from the combined impact of rising mortgage costs, high fuel prices and pressures from inflation. Jago Dodson and Neil Sipe developed the methodology in 2006 at Griffith University and the paper that details their methodology can be found here. Abley Transportation Consultants has applied the VAMPIRE index developed by Dodson and Sipe to New Zealand.
The index is constructed from four indicator variables obtained from the 2006 Statistics New Zealand census data that are combined to provide a composite fuel price and mortgage vulnerability index and can be mapped at the geographic level of census meshblocks. A meshblock is the smallest unit of measurement undertaken within census. The VAMPIRE index assesses the average vulnerability of households at a meshblock level rather than indicating the specific vulnerability of individual households.
The variables used are:
The use of these variables merits explanation. The first three variables i.e. both the car dependence variables and the median weekly income level indicate the extent of car dependence for urban travel as used in the original study undertaken by Dodson and Sipe in 2005. That study developed an index titled ‘Vulnerability Index for Petroleum Energy Rises’ or VIPER. The paper that details that methodology can be found here. The addition of the Mortgages variable transforms the VIPER methodology into the VAMPIRE methodology. A description of the variable is then:
Together, these four variables provide an indication of the spatial representation of household mortgage and oil vulnerability. The VAMPIRE index is constructed by combining the four variables. As with VIPER the variable combinations are scored according to car dependence, income and mortgage by assigning an index according to the percentiles shown in Table 1.
High levels of car ownership, journey to work by car and mortgage tenure received high index values while low levels of household incomes received lower scores. Thus a census meshblock with high levels of car ownership, journey to work by car, income and mortgages would receive a score of 15 (5+5+0+5).
Table 1 Index Value assigned relative to percentile for each Indicator
|Car own ≥ 2||JTW by car||Income||Mortgage|
The four variables selected for their contribution to the VAMPIRE index are not equal in their weighting. Rather, the variables have been weighted according to their proportional contribution to the overall VAMPIRE score. Thus of a total possible VAMPIRE score of 30, five points are provided by each of the car ownership and journey to work variables while ten points each are provided by the income and mortgage scores.
The respective weightings of the four indicators used to calculate an overall VAMPIRE index are presented in Table 2.
Table 2 Variable weighting for VAMPIRE
|Car own ≥ 2||JTW by car||Income||Mortgage|
The original VIPER and VAMPIRE studies conducted an analysis for a number of Australian cities. As shown in Table 1, resultant scores are assigned depending upon the corresponding percentile assigned to each geographic unit (meshblocks in the case of New Zealand) within the study area. Consequently, the results for a single meshblock can only be compared with the results of other meshblocks that participated in the analysis of the study area.
Some of Statistics New Zealand data is unavailable because the sample sizes are too small and are deemed to be confidential. Data for three of the four indicators has been sourced at a meshblock level, however the household ownership rates have only been released at Area Unit level the is an amalgamation of a number of meshblocks. In these cases the Area Unit value has been assigned to all meshblocks within the Area Unit.
There is no definitive level of vulnerability and rather the VAMPIRE simply provides relativity compared to the whole of the study area. It can therefore only be used as a broad indicator. Even so, it does provide guidance, especially in terms of direction i.e. the VAMPIRE identifies more or less vulnerable areas compared to other areas.
The average VAMPIRE score by definition is 15 out of 30. While it is plausible that values for a particular location may be as low as 0 (very low vulnerability i.e. good) or as high as 30 (very high vulnerability i.e. bad) the scores form a tight bell shape curve around the average. Across all towns and cities in New Zealand the average scores range between 9 and 22.
It is also important to recognise that because the VAMPIRE analysis requires all the data to be segregated into percentiles, if the best site within the study area still represents poor vulnerability, the VAMPIRE may indicate low vulnerability, but in practice the site is simply the ‘best of the worst’. It is therefore important to understand the results of the whole study area i.e. the whole of New Zealand, before undertaking detailed analysis of other study areas or disaggregating data.
To provide a qualitative interpretation of the results, the following descriptions are provided that compare the suburb against the national average:
|Much less vulnerable||highest 20% of suburbs i.e. 80th to 100th percentile|
|Slightly less vulnerable||next 20% of suburbs i.e. 60th to 80th percentile|
|Similar vulnerability to||within 10% of median result* i.e. 40th to 60th percentile|
|Slightly more vulnerable||next 20% of suburbs i.e. 20th to 40th percentile|
|Much more vulnerable||lowest 20% of suburbs i.e. 0 to 20th percentile|