Housing Affordability, or the financial ability to purchase a home, is driven largely by the gap between household income and home value. Housing affordability is a complex, multidimensional issue influenced by the balance between housing supply and demand, the labor market, and mortgage rates by way of Federal monetary policy. This story map introduces two new measures developed by Esri to gain insight into housing affordability:
Housing Affordability Index (HAI)
Percent of Income for Mortgage (POIFM)
The HAI and Percent of Income for Mortgage data are not only powerful tools to analyze and understand casual trends in the real estate market, but are also invaluable indicators of change for policy makers. Housing affordability has wide ranging effects including influencing migration patterns and affecting the ability of renters to become home owners.
Housing Affordability has many definitions. Some take into account commuting and transportation costs, others include housing supply or the rent-mortgage 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. Multiple definitions inspired Esri's Data Development team to create two complimentary approaches to understand housing affordability for an area.
The first approach measures housing affordability using an index to quantify the ability of a typical resident to purchase an existing home in an area. We call this the Housing Affordability Index, or HAI. The model employs a national average effective mortgage rate from the Federal Housing Finance Agency, an interest rate of 4.5 percent, and a 30-year mortgage is assumed with a down payment of 20 percent of the home price. Regional property tax rates are determined from the latest American Community Survey and Esri’s model follows the Federal Housing Administration’s guidelines for debt service ratios.
Similar to the HAI data, Esri's second approach for measuring housing affordability employs a monthly budget perspective. This measure uses the percentage of median household income dedicated to monthly payments on a home priced at the median value. We call this the Percent of Income for Mortgage (POIFM). Again, an interest rate of 4.5 percent, and a 30-year mortgage is assumed with a down payment of 20 percent of the home price.
Looking at the U.S. as a whole, the 2018 HAI stands at 124, down from a peak of 158 in 2010. An HAI of 100 represents an area that on average has sufficient household income to qualify for a loan on a home valued at the median home price.
An index greater than 100 suggests homes are easily afforded by the average area resident. An HAI less than 100 indicates homes are less affordable (and the median income is not enough to purchase a median valued home). In these unaffordable areas, home buyers are likely to place a larger down payment or service high interest rate loans. In other words, homeowners would be stretching their income further, putting them at greater risk of default.
Please note that the housing affordability index is not applicable in areas with no households or primarily contains rental units. Esri’s home value estimates cover owner-occupied homes only.
Similarly, the U.S. Percent of Income for Mortgage is 18 percent. This means, on average, householders spend 18 percent of their household income on mortgage payments. The POIFM does not include home insurance, private mortgage insurance (PMI), or property taxes.
To develop effective housing affordability measures, Esri had to consider some very important factors that guide change in the marketplace.
One of the key factors in understanding changes in housing affordability is to examine trends on household income, home value and mortgage rates.
Housing Affordability remained stable between 2000 and 2005, despite a 46 percent gain in median home value and an 18 percent gain in median income during the same period. Fueled by favorable and plentiful financing options, the first half of the decade saw unprecedented acceleration in home prices while maintaining affordability.
As mortgage rates fell to historic lows, more households could afford to buy homes. When monetary policy reversed course and the Federal Reserve began to raise short-term rates, teaser rates on risky loans increased which strained many household budgets. Hence, the higher debt servicing caused a rise in delinquencies, defaults, and foreclosures. The collapse of the housing market that fueled the financial meltdown in 2008 ultimately led the country into a long and deep recession. The latter half of the decade saw housing affordability gains fueled by declining home prices and further declining mortgage rates.
In the last six-year post-recession period, housing affordability has waned. While mortgage rates have remained steady during most of this period, housing affordability has fallen lower than pre-recession, pre-housing boom levels. On average, the pace of recovery in household income has simply not kept up with the pace of recovery in the housing market. The recent decline in affordability is driven by rising interest rates, compounded by tight inventories.
With this high-level overview in mind, let’s take a closer look at the U.S. housing market.
Mapping housing affordability can provide valuable insight into local markets. To illustrate, we'll demonstrate the disparities that impact housing affordability using both measures.
Our first example is a county-level thematic map of HAI to identify areas above or below the national average. Counties shaded in the darkest red represent the least affordable housing, while counties shaded in the darkest blue are the most affordable.
Like the west coast, many northeastern metropolitan areas lack affordable housing. In this example, let's explore the New York market using Esri's Percent of Income for Mortgage (POIFM) data. Using the same mapping technique areas shaded in dark gray represent areas with the smallest mortgage payment relative to household income. The darker red shaded areas highlight the increasing share of income necessary to cover a mortgage. Households within many U.S. counties spend 40 percent or more of their income on a mortgage.
The darker red areas represent counties where more household income is required to pay towards a monthly mortgage; counties shaded in gray depict areas where less income is required.
Areas in white represent counties at or around the 18 percent national average.
Evaluating and tracking housing affordability is important to researchers, planners and policymakers alike. Esri’s HAI and Percent of Income for Mortgage data not only provides that insight, but also shows you where and by what degree, nationwide.
Housing Affordability measures are a complement to other Esri Demographics. Starting with Esri’s 2019 demographic release, the Housing Affordability Index and Percent of Income for Mortgage data sets are available for all geographic levels.
You can purchase the data sets as ad-hoc databases by contacting our data sales team at: datasales@esri.com or call our toll free number at: 1-800-292-2224.
The data can also be accessed through various Esri products including:
• ArcGIS Business Analyst
• ArcGIS Community Analyst
• ArcGIS Maps for Office
• ArcGIS Maps for Power BI
• ArcGIS GeoEnrichment Service
This story map was created by Esri's Data Development Team with the Esri Story Map Cascade app template.
Led by chief demographer Kyle R. Cassal, Esri's data development team has more than a 40-year history of excellence in market intelligence. The team's economists, statisticians, demographers, geographers, and analysts produce independent small-area demographic and socioeconomic estimates and forecasts for the United States. The team develops exclusive demographic models and methodologies to create market-proven datasets, many of which are now industry benchmarks such as Tapestry™ Segmentation, Consumer Spending, Market Potential, and annual Updated Demographics. Esri® Demographics power ArcGIS® through dynamic web maps, data enrichment, reports, and infographics.
Learn more about Esri Demographics.
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