Use and interpret Housing Affordability data
Assessing variation in home ownership affordability across the nation.
Esri's Data Development team produces demographic data (known as Updated Demographics) for the United States using a variety of sources to update small areas, beginning with the latest U.S. Census base along with a mixture of other private sources to capture demographic change. Alongside Updated Demographics, Esri provides U.S. Census Bureau and American Community Survey (ACS) demographics as a point of reference for understanding growth in an area and to provide additional community details. Data tutorials educate both the novice and the expert analyst to learn more about a topic to properly incorporate Esri Demographics that are accessible within various products. In this tutorial, you will learn about the following:
- Esri's two key variables measuring affordability and how they are defined
- Why this data can be impactful in your work
- How to use and interpret the data
- Important data considerations
- Additional data resources
First, you'll learn what impacts housing affordability, what measures are available, and why using these can help analysts, planners, or policymakers quickly evaluate and track relative affordability.
Housing Affordability
The spatial and temporal variation in housing affordability, or the financial ability to purchase a home is a complex, multi-dimensional issue. The availability of affordable housing in an area is often influenced by many local factors including the labor market, supply and demand of housing, land use regulations, taxes, outside investment etc. Broader levels of housing affordability are often impacted by overall economic conditions and mortgage rates.
Broadly speaking, housing affordability is defined by the gap between median household income and median home value in an area. Often referred to as the share of income devoted to housing, many factors impacting affordability are considered in these types of indicators. Some may take into account commuting and transportation costs, while others may include housing supply or a rent-mortgage cost differential. While these measures are helpful in understanding local housing markets, the detailed and time-sensitive nature of the input data are not sufficient for broader geographic models. Esri's approach to housing affordability is driven largely by the gap between household income and home value. The result is two separate but similar measures --- a Housing Affordability Index (HAI), and the Percent of Income for Mortgage (POIFM).
Why use housing affordability data
In the United States, homeownership has long been a primary source of wealth accumulation making accessibility to affordable housing a primary concern for policy makers striving to advance economic opportunity. A lack of affordable housing can significantly impact economic well-being. Homeownership can meet basic shelter needs while also supporting economic mobility and household stability.
Households in unaffordable areas may be more likely to stretch their budget to afford a home, negatively impacting their overall financial condition. Purchasing a home is a significant transaction and generally represents the largest expense in a monthly budget. Understanding affordability can help assess local housing burden relative to housing demand and cost of living. Throughout this tutorial we'll demonstrate how Esri's measures can add more insight into your analysis.
Housing Affordability measures
Housing Affordability measures enable analysts to assess an area’s affordability relative to any other geographic area and investigate the relationship between affordability and income. Two complimentary data variables, created by Esri's Data Development team capture the financial ability to purchase a home:
Housing Affordability Index
This variable measures affordability using an index to quantify the ability of a typical household to purchase an existing home in an area.
Percent of Income for Mortgage
This variable provides a monthly budget perspective to examine the relationship between household income and mortgage payments (based on a median-valued home).
Data access
You can access Esri Demographics using Esri software and through apps like ArcGIS Business Analyst , ArcGIS for Excel , or ready to use maps from ArcGIS Living Atlas of the World . For use outside of the Esri platform data files are available in CSV, dBase, Excel, shapefile, or file geodatabase formats.
Contact an Esri data specialist with data questions at 800-447-9778 or send an email with your request to: datasales@esri.com
HAI calculation
Updated Housing variables are available for all Esri's standard geographic areas , as well as any user-defined polygon such as rings or drive times. The data are point-in-time estimates as of July 1.
The HAI model combines the national average effective mortgage rate from the Federal Housing Finance Agency with sources reflecting trends in the current housing market to derive a current borrowing rate. A 30-year conventional fixed mortgage is assumed with a down payment of 20 percent of the home price. Regional property tax rates from the latest American Community Survey are applied, and the model follows the Federal Housing Administration’s guidelines for debt service ratios. HAI is evaluated at the median value of household income and the median value of all owned dwellings within an area. HAI does not include payments towards Homeowners Insurance or Private Mortgage Insurance (PMI). The HAI is only computed for geographic areas with more than 50 owner households and top-coded to 350.
How to interpret HAI
HAI captures a single snapshot of affordability. The infographics below demonstrate the range of household incomes and home values represented in three different areas and how the relationship of these characteristics is captured in the HAI.
Here's what we can learn from mapping the HAI:
Mapping the HAI provides a quick and convenient way to spatially analyze local markets. Similar to the charts in the previous section, when the HAI is mapped it shows how local areas may differ throughout the U.S.
This county level map highlights areas where the value of HAI is 100 or higher. Recall, HAI values of 100 represent areas where, on average, have sufficient household income to qualify for a loan for a median-valued home.
In this map, only counties with a HAI of 200 or higher are highlighted to show where homes are twice (or more) as affordable, on average. As the map shows, many of these counties are found throughout the Midwest and Southern states.
Counties shaded in gray are those that have a HAI of 100 or lower, highlighting areas where housing can be more of a strain to afford. Many of these counties can be found along the East and West Coast.
POIFM calculation
Using the same borrowing rate and lending assumptions, the POIFM variable measures affordability from a monthly budget perspective. It calculates the percentage of median household income dedicated to monthly payments on a home priced at the median value. The POIFM does not incorporate Homeowners Insurance, PMI, or property taxes. The POIFM is only computed for geographic areas with more than 50 owner households.
How to interpret POIFM
Most financial experts recommend not spending more than 25 to 28 percent of your monthly income on your mortgage payment. While this is a good rule of thumb it does not always apply in all markets. This, however, is a useful gauge to view the POIFM through to understand how burdened a population is by their mortgage payment.
Below are three examples based on the rule above:
...if an area has a POIFM value of 10, households (on average) allocate an estimated 10 percent of their income towards a mortgage, deeming householders are comfortable with their mortgage payments.
...if an area has a POIFM value of 26, households (on average) allocate an estimated 26 percent of their income towards a mortgage, deeming householders fall within the recommended acceptable range with their mortgage payments.
...if an area has a POIFM value of 60, households (on average) allocate an estimated 60 percent of their income towards a mortgage, deeming householders are likely more heavily burdened by their mortgage payments.
Here's what we can learn from mapping the POIFM:
Mapping the POIFM variable allows the analyst to evaluate areas where the mortgage burden is high.
The U.S. value is 20.6 percent. This means, on average, households must earmark nearly 21 percent of their household income to service a mortgage.
On this map, counties containing a yellow circle have a POIFM as low or lower than the U.S.
As POIFM percentages increase (from green to blue, to dark gray) beyond the national average, we can easily identify where housing is relatively less affordable. Similar to the HAI, the map highlights the more expensive coastal areas.
Charting POIFM data can be extremely useful and complimentary to your map analysis. With the above POIFM map that was created in ArcGIS Pro, a chart was generated to rank counties (in descending order) by the POIFM for the state of Florida.
A market identified as unaffordable using either of Esri's summary measures does not render the entire market unaffordable. These measures are designed as a comparative analytical tool and serves as an excellent metric for understanding housing or economic well-being in an area. Explore more Esri's Housing Affordability data and other key housing demographics
How to enhance your housing affordability data analysis
Up to this point, we have defined Esri's Housing Affordability data variables, as well as how to interpret both. Next, lets investigate the relationship between household income and housing affordability, and the related impact on migration.
The relationship map below includes the HAI, median household income, and the compound annual household growth rate. The map symbology used is based around an HAI of 100 and the U.S. median household income for every Core Based Statistical Area, or CBSA. The size of the circle reflects household growth between 2020 and 2022, while the color distinguishes the relationship between income and affordability. Dark red CBSAs are areas with above median household income and most affordable, while pink represents areas with below median household income but high on the affordability scale. Grey circles represent CBSAs that have generally unaffordable housing markets while darker grey shaded metros have lower income relative to lighter grey shaded areas.
Relationship style map using the HAI, median household income, and household growth.
This type of map is a powerful way to study relationships between socioeconomic characteristics. Analyzing the connection between median household income and affordability and superimposing recent household change can reveal migration patterns driven by affordability. For example, relative to other metropolitan areas on the West Coast, Bend, Oregon metropolitan area shows significantly higher growth (as shown by the bubble size), even though it is flagged as Low HAI-High Income.
A similar pattern holds true for other metropolitan areas in the region that like Bend, Oregon are further inland and present in neighboring Mountain West states. Examples include Bozeman, Montana and Heber, Utah. Why would these metros in the Mountain West be growing so much if they are unaffordable? Of course, there could be many explanations. One explanation may be that affordability is a relative measure built using only one point on the income-home value continuum. Bend, Oregon may be an unaffordable metro area but continues to grow because it is more affordable relative to other metros in the region. Homeowners who have built up significant equity in their homes also view the affordability picture differently. Bend is unaffordable when compared to the U.S. as a whole, but Californians seeking more affordable living may view this metro as affordable.
One can also glean from the map that affordability drives household growth. California and the Northeast are experiencing slower growth while more affordable areas in the South and Mountain West are growing faster. The household growth rate included in this map is from the most recent 2020 decennial census so growth patterns may be influenced by pandemic-related migration flows. Whether households move for better weather, locate closer to family, or own a more spacious home, this type of map can shed some insight for the desirability of affordable markets.
Given the complexities of weighing affordable housing against competing economic factors, it is always advantageous to couple housing affordability measures with other socioeconomic indicators to enhance your analysis.
Data review and considerations
When using the HAI, index values of 100 or higher indicate higher levels of affordability in an area.
When using the POIFM data, percentages that are considered “high” highlight the increasing share of income necessary to cover a mortgage payment.
The U.S.'s housing as a whole, is generally affordable. Deeper analysis in pockets of the country reveal the full spectrum of affordability. Rural or small-towns are typically more affordable, while coastal areas are generally considered the most unaffordable areas to live.
The interpretation of the HAI variable is distinct from other index variables found within the Esri datasets that quantify geographic differences between a local area relative to the nation as a whole. HAI includes property tax; POIFM does not. POIFM assumes you can make the down payment on a house and focuses on the monthly expenditure or share of budget.
Next steps
In this tutorial, you learned about the basics of Housing Affordability data, how to interpret the data, and the significant impact it has on communities. Additional data tutorials in two learn series are available. Click on the links below for continued data exploration, learning, and ways to access the data.
Learn more
Data methodologies
Housing Affordability data are developed using data from a variety of sources that include regional property tax rates from the latest ACS, along with Esri's model that follows the Federal Housing Administration's guidelines for debt service ratios. Represented as point-in-time estimates as of July 1, the data is available for Esri’s standard geographic areas and for any user-defined polygon such as a ring or drive time. Read the Esri Updated Demographics Methodology Statement for more information
Frequently asked questions
Use our data reference page to help answer additional questions about Esri Demographics.
Helpful links
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