Hampton Roads: Home of the least affordable homes in Virginia

Entry type: Single project

Country/area: United States

Publishing organisation: WHRO Public Media [https://whro.org/], including WHRO Journalism (the station’s newsroom) and the Virginia Center for Investigative Journalism. VCIJ is part of WHRO [https://whro.org/community/31846-whro-public-media-expands-their-journalism-efforts-hiring-veteran-investigative-journalists] but also has a separate website [https://vcij.org/].

Organisation size: Small

Publication date: 2022-10-19

Language: English

Authors: Ryan Murphy, Louis Hansen, Mechelle Hankerson, Katherine Hafner, Jeff South, Cameron Houck, Chris Tyree


WHRO Journalism is a three-year-old newsroom at WHRO Public Media, an NPR member station in Hampton Roads, Virginia. The newsroom’s four-person staff works with freelancers to accomplish work that serves the region with accuracy, nuance and creativity. The Virginia Center for Investigative Journalism, also part of WHRO Public Media, provides in-depth and watchdog reporting on issues vital to Virginians. Hankerson directed the project; Murphy, Hansen and Hafner did on-the-ground reporting; South provided data analysis and visualizations; and Houck and Tyree shot photographs.

Project description:

WHRO Public Media, supported by the Pulitzer on Crisis Reporting, explored a common concern: Why is housing so expensive, and why can’t people access it?

WHRO used data from myriad sources on rent, home prices, mortgages, income and other indicators going back more than 60 years.

Reporters gave the audience historical context by showing how the market ebbs and flows, and how it’s notably different now.

Reporters gave a face to the numbers with stories from residents looking for housing. The interviews confirmed what the numbers showed: It’s expensive to live here.

Impact reached:

We made the data available to reporters around the state (https://bit.ly/cost-burdened) to investigate affordability and affordability factors in their own metro areas. We also invited other media outlets to republish our articles. For example, VPM, the public radio station in Richmond, shared the WHRO stories with its audience:


Locally, we’ve been approached by some former municipal officials and advocacy groups interested in planning community events around the information we’ve found and the topic of housing affordability.

The inaugural story had the highest Instagram engagement in October 2022 on WHRO’s account (5,666 reach and 436 reactions). Instagram is where we reach younger members of our audience, engaging irregular news consumers with local content that resonates with them

In the last three months of 2022, several of our data-driven articles about housing were among our top-read stories online. For instance, the introductory story (“Housing in Hampton Roads is less affordable than Northern Virginia” – https://whro.org/news/local-news/33139-housing-in-hampton-roads-is-less-affordable-than-northern-virginia-and-many-other-pricey-areas) logged 1,892 unique pageviews, with readers spending an average of 5:34 minutes on the page.

And using locality and pricing data, we published “In a small Eastern Shore community descended from slavery, a grassroots affordable housing model expands” – https://whro.org/news/local-news/33770-in-a-small-eastern-shore-community-descended-from-slavery-a-grassroots-affordable-housing-model-expands. It had 2,749 unique pageviews, with an average of 4:34 minutes spent on the page).

Techniques/technologies used:

We compiled housing-related data from multiple sources (such as the U.S. Census Bureau, the U.S. Department of Housing and Urban Development, Zillow and Clear Capital) for multiple years (1960 through 2021) at multiple geographic levels (nationwide, states, metro areas, counties and cities). The statistics included housing prices, rents, mortgage payments, income and the proportion of residents who spent 30% or more of their income on housing (the definition of “cost-burdened,” according to the Census Bureau and other researchers).

In some cases, we were able to download and easily import the data; in other cases, we had to scrape it from websites or PDFs. We used OpenRefine to clean the data and Microsoft Access to join datasets for different years so that we could examine longitudinal trends. Most of our analysis was done with Microsoft Access and Microsoft Excel.

We shared the data with all members of our reporting team with Google Sheets (https://bit.ly/cost-burdened). We also used Datawrapper to create visualizations (https://bit.ly/housing-vizes) that guided the reporting. We embedded many of these charts and maps in our published reports.

The WHRO newsroom used a Google Form to solicit first-person experiences of housing to gauge what issues people face and ensure diversity of sources, especially geographically. This feature was also open to people with questions or suggestions. The reporters relied on strong knowledge of the local community to source first-person stories as well.

Context about the project:

From a data standpoint, we faced several challenges. For one thing, each year (and each geographic level) is a separate dataset. We needed data from seven decennial censuses (1960 through 2020) and from recent American Community Surveys. (The initial reporting was based on analysis of the 2020 ACS. But we timed the launch of our series with the release of the 2021 ACS so that it would reflect the most recent statistics available. After the Census Bureau released the 2021 ACS on Sept. 15, 2022, we updated all of our data analyses, visualizations and story drafts.)

Wrangling the census data alone required downloading more than 30 different datasets. The Census Bureau’s downloadable data bank contains the decennial censuses only since 2000. Older data is available from the bureau’s FTP site and from the Inter-university Consortium for Political and Social Research at the University of Michigan. The historical datasets were stored in various formats, including CSV files, ASCII Summary Tape Files and PDF books. Parsing the datasets required a range of strategies.

Joining data could be complicated because during the decades we examined, changes occurred in certain FIPS codes (the numerical designation the U.S. government assigns to each geographic unit), metro area definitions and boundaries, and even metro area names. (Hampton Roads is synonymous with the Virginia Beach-Norfolk-Newport News, VA-NC MSA. However, the area previously was called the Norfolk-Virginia Beach-Newport News MSA – and before then, the Norfolk-Portsmouth MSA.)

In addition, we had to adjust historical information to current conditions, like record-setting inflation. Using the Consumer Price Index from the U.S. Bureau of Labor Statistics, we presented all median household income values and median home values as 2022 dollars [https://bit.ly/cpi-adjusted-whro].

Another technological challenge involved WHRO’s website, which is in the early stages of a redesign and is currently not optimal for data and other multimedia presentations. Using third-party programs like Datawrapper allowed us to work around those limitations.

While reporting, we did run into some sources who felt concerned that speaking about their housing challenges would endanger their access to housing. This was especially noticeable when writing about cities that were displacing and destroying affordable units while approving large-scale market and luxury developments. That story required us to talk with people who lived in transient and temporary housing, so it was important to explain our process to those sources to make them more comfortable sharing their story.

What can other journalists learn from this project?

Although data provided a foundation for our reporting, it’s crucial to keep the focus on people. That is especially true in radio because numbers are hard to convey over the air. We put human voices and faces to our data by finding people struggling to afford housing. And we showed how Hampton Roads compares to similar metro areas. That was validating for our readers, and it drove notable engagement, especially among audiences we don’t typically interact with.

Housing affordability is tricky to measure because it involves two factors: not only the cost of housing but also the level of income. In the Washington, D.C., metro area, for example, home prices and rents are high – but so are salaries; as a result, about 31% of the households there spend 30% or more of their income on housing (the definition of “cost-burdened”). In Hampton Roads, home prices and rents are lower than in the D.C. area – but salaries are, too. And so 34% of the households in Hampton Roads spend 30% or more of their income on housing.

We used comparisons readers can easily relate to – such as the ratio between median salaries and median housing prices over time. The story “First-Time Homebuyers In Virginia Face Rising Prices And Fierce Competition” [https://vcij.org/stories/scrimp-scroll-square-off-first-time-homebuyers-in-virginia-face-rising-prices-and-fierce-competition] noted:

It’s not just a gut feeling from first-time home buyers that it’s harder than ever to buy a house — in many ways, it’s much tougher than their parents and grandparents had it.

Consider the relation of home prices and household income over three generations. In 1960, the median home value in Virginia was roughly twice a household’s annual income. Today, home prices are more than four times what a family earns in a year.

Project links: