Analysis of massive volumes of data is burdensome and expensive, yet incredibly important to strategic decision making. Enter the index – an easy to understand alternative to analyzing raw source data. SP Group introduces our Neighborhood Distress Index (NDI).
An index is way to condense multiple variables into just one indicator without losing depth. An index normalizes data and provides context.
Indices have been, and continue to be, developed to measure a variety of topics from ecological diversity to housing affordability. What these indices have in common is their ability to:
- Combine relevant variables in a holistic index
- Create a single index that facilitates comparison
- Mitigate the risk of over-weighting a single variable
The Need for Granular Indices
Many indices use data representative of larger populations or broader geographic entities. An index that represents a broad geography, such as the county or state, has the potential to mask the variety in more granular regions, such as the zip codes or Census Tracts. Additionally, indices at more granular geographies allow for more targeted, data-driven interventions.
Take for example the Housing Affordability Index (HAI) released by the National Association of Realtors. The HAI measures whether a typical family earns enough income to qualify for a mortgage loan on a typical home based on the home price and income data.
While helpful in contextualizing housing affordability and measuring it over time, it does not shed light on affordability at granular regional levels. Calculating housing affordability at a granular level, like Census Tract is more useful in identifying pockets of affordable neighborhoods in an otherwise wealthier county. It is likely that this neighborhood would not show up as being “affordable” under the HAI, which is computed at a broader geographic level. (See our data visualization on Affordability at the Neighborhood Level).
Unveiling SP Group’s Neighborhood Distress Index (NDI)
SP Group has also developed a first of its kind Neighborhood Distress Index (NDI). Our index uses Census Tract data to assess neighborhood-level distress for all tracts in the US (over 70,000). The NDI is computed each year and includes the economic variables such as unemployment, poverty rate, median income, home values and educational attainment. Using our proprietary methodology, SP Group’s NDI allows Census Tracts to be compared with each other, as well as track changes over time.
Below is an example of the Neighborhood Distress Index for a census tract in Memphis, TN.