With more than a decade of experience in local and national newsrooms, I'm passionate about exploring issues that matter most to people and communities鈥攆rom affordable housing to access to quality health care.
Each month, I search for and scroll through dozens of datasets to find trends and engaging stories for our newswire. In this post, I鈥檒l break down ways to distill data and tell stories without overloading an audience.
Whether you鈥檙e just starting to dabble in data journalism or you鈥檙e a beat reporter wanting to add hard numbers to your stories, navigating a spreadsheet with thousands of rows and dozens of columns can seem daunting. But like any good story, it鈥檚 a matter of finding the right source and knowing what questions to ask.
Housing is a perfect example of data that can be analyzed and localized in hundreds of ways (though I may be biased, as 麻豆原创鈥檚 resident real estate expert). What follows is how I tackle the dozens of real estate stories we publish each month. These tips can be applied to nearly any topic.
Countless datasets from government and private sources are available for free online. In my real estate coverage, I often refer to the Census Bureau, which offers data on for new construction and . Real estate websites (including Zillow, Realtor.com, and Apartment List) also make some of their data available to download. These are good sources for tracking home and rent prices in specific states, metros, or zip codes.
Regardless of the dataset, read its data dictionary or other supplemental files to understand what each column represents and identify any major caveats.
As a rule, reporters and editors act as translators. It鈥檚 our job to distill complex information鈥攊ncluding data鈥攊nto terms and visualizations readers can grasp and relate to.
On my beat, I typically write for average Americans: potential home buyers and sellers (who are usually buyers themselves). Much like a crime reporter would avoid using police jargon like 鈥渧ehicle鈥� or 鈥淎PB,鈥� in my real estate stories I explain and define common metrics like 鈥渕edian list price鈥� or 鈥渄ays on market,鈥� which may be unfamiliar terms for some readers鈥攅specially first-time homebuyers.
Gauge your target audience鈥檚 familiarity with data terminology, and define or simplify anything that may impede their understanding.
Context is key. Include a frame of reference for your audience, especially if your readers aren鈥檛 steeped in the subject matter.
If the story covers home prices or rents, show how they have changed from a decade, year, or month before. For local newsrooms, note how your county or metro compares to the rest of the state or country. Using supplemental data can also be helpful. For example, showing the difference between what homes are listed for and how much buyers typically pay can be a good barometer of how the market is doing overall.
No matter the topic, don鈥檛 overload your story with too much data. While it may be tempting to include everything and show off your data skills, too many numbers can be overwhelming and detract from the information you鈥檙e trying to convey.
You can continue to expect real estate data series monthly and quarterly. As for this May, we have stories for Asian American and Pacific Islander Heritage Month, summer travel, maternal health and .
Elena Cox is a Senior Data Reporter at 麻豆原创. After earning a master's degree in data journalism from Columbia University, she worked for Realtor.com and CBS News. She was awarded a Goldschmidt FRED fellowship in 2022 and was able to visit the Federal Reserve Bank of St. Louis to learn about economic data.
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