Bloomberg News’s unprecedented analysis of New York City’s property tax system found pronounced unfairness in the valuation of condos and co-ops, which make up 83% of the city’s taxable property. By analyzing the income data that city officials use to value condos and co-ops, we found that they were adjusting the data in ways that benefited expensive properties while putting excessive burdens on lower-value buildings and renters. We gauged the system’s fairness by means of studies that compared recent sale prices to the values that city assessors determined. We used the data to find startling anecdotes and produce compelling visualizations.
Our New York story was part of a yearlong body of work on unfair property taxes, the causes behind them and the devastating effects they have on low-income families. Nationally, these stories helped start a conversation among local property tax officials and the Biden administration; those talks may improve local property valuations by giving assessors’ access to a national database of home values.
In New York, new Mayor Eric Adams has told Bloomberg News that he intends to overhaul the system.
The analysis was done entirely in R. It employed statistical techniques used by assessment officials across the country to measure the fairness and accuracy of property taxes. Using R allowed us to produce a clear, robust, and reproducible analysis that spanned several large datasets. While this analysis was the bedrock of our article, it also allowed us to produce a clear and comprehensive methodology.
We used Google Earth Studio, ffmpeg, and Svelte to produce an introductory video which gave readers a clear geographic overview of the story.
The interactive map of the city was made using Mapbox and Svelte. To emphasize the physical environment of New York City, we created a custom Mapbox buildings layer using a rendered 3D model from OpenStreetMap. The rendering was made with QGIS and Blender.
Throughout the article, charts and diagrams were made with Illustrator, ai2html, Svelte and D3.
What was the hardest part of this project?
This story was based on an in-depth analysis of millions of property tax records as well as mastery of a complex set of laws, regulations, and financial structures. The story was partly about how convoluted these regulations can be. We wanted to guide readers to an understanding of our findings without overwhelming them with details along the way.
One of our biggest challenges was in deciding how much complexity to expose to our readers. How much did we need to explain to the reader about concepts like net operating income, coefficient of dispersion, price-related differential, comparison properties, and capitalization rates? We worked on several iterations of “explainers” around these topics, some of which were moved to our methodology (which was detailed enough to deserve its own page), or scrapped entirely.
In the end we tried to deliver a story that described a complicated regulatory framework, walked readers through a rather sophisticated analysis, and told the human side of the story, without overwhelming them.
What can others learn from this project?
Compelling and important stories lay buried in seemingly mundane corners of government. Often, these stories go unnoticed because of complexities inherent in the subject matter and the inordinate amount of time it takes to master them. Yet, as we’ve shown with property taxes, the effort to ferret out stories can expose how government policies and practices tilt the playing field to favor some and burden others. Our story about New York City’s deeply unfair and inaccurate property tax system shows the significance of thorough reporting and comprehensive data analysis. But it also highlights the importance of connecting major findings to real people and visualizing them in clear, sophisticated ways. Everything must work together in order to hold the reader’s attention, to cut through the complexities and keep them focused on the key takeaways.