How CommunityScale forecasts households by income group

This post examines a key element of housing needs assessment: projecting future household income distribution. In planning for housing needs—whether for the next 5 years or 10—forecasting not only the number of households but also their income distribution is crucial.

We can access past income distributions through the ACS 5-year B19001 dataset. For this example we use Pontiac, Michigan. The ACS income distribution is delivered in current-year income data bins. As shown in the chart below, this results in a skewed perception of increasing affluence over time. The reason is that B19001’s income bins, denominated in the dollars of their respective years, don’t reflect changes to purchasing power due to inflation. For instance, $1.00 in 2010 is equivalent to $1.40 in 2023.

To correct this, CommunityScale adjusts the counts in each income bin based on statistical techniques that incorporate inflation using the CPI calculator, rebalancing them to 2023 dollars. This allows us to observe real income distribution shifts over time as shown below. However, this adjusted data is still overly detailed for practical use in housing needs assessments.

To address this, CommunityScale further consolidates the data into bins based on HUD Median Family Income (MFI, also known as Area Median Income, AMI) for 2023, which stands at $94,700 for Pontiac's region.

Now we have historic and forecast data that are expressed in 2023 dollars and broken down by 30%, 60% (the HUD housing threshold), 80%, 100%, and 120% of MFI, which are bins that align with distinct housing demand profiles.

This refined analysis reveals a decrease in households earning below 30% MFI since 2014. This trend suggests a continued decline in the very low-income bracket and growth in the 30-60% MFI group.

With these insights, we can more accurately forecast housing needs. By analyzing income distributions for 2023, noting changes since 2010, and using these trends to predict future incomes, we can pinpoint growth-contributing income groups and recommend targeted housing solutions.

While this analysis is focused on income distribution, a comparison of household income distribution against cost burden is informative, which we will cover in a future post.


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