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NYC Opportunity Zones: The Potential in Queens

For this case study, we will focus on zoning deltas at a large scale in Queens. We’ll start with a simple query: take the potential total gross buildable area of a parcel, and find the difference between that and the total square footage of any existing buildings in it. This results in the unbuilt area potential of the parcel.

We will sort the unbuilt area potential by three different variables: Year Built, Zoning Code Type and ZIP Code.

By targeting the unbuilt area, we can reveal specific development value-add scenarios within Queens’ Opportunity Zones. The graphs below provide an aggregation of unbuilt area which clarify the “what” (Year Built, Zoning Code Type) and the “where” (ZIP Code) to achieve our ultimate goal: figuring out what the best development scenario is.

There are over 26,000 Opportunity Zone parcels in Queens. For simplicity, we’ve removed all the parcels that are zoned for single family.  This reduces are dataset to approximately 5,000 parcels.

Year Built & Total Gross Buildable Delta

This graph shows unbuilt potential in blue.

Above, we show all the parcels by year built, from 1890 to 2017, and the potential total gross buildable delta (the difference between existing and zoned) in blue.

Zoning Type & Total Gross Buildable Delta

The graph above visualizes the unbuilt area per Zoning Type within the 5,000 parcels. This is an aggregation of the unbuilt totals of all the parcels within that specific Zoning Code type. More on this, below.

Zipcode and Total Gross Buildable Delta

Lastly, the graph above shows unbuilt area by ZIP Code. Again, this is an aggregation of all the parcels within each specific ZIP Code. This provides local insight into where to target our search for value-add development scenarios.

Conclusion

Anything to the left of the zero on the x-axis is total gross buildable area that exceeds the potential total gross buildable by FAR only [1]. What does this imply? Probably zoning bonuses or overlays that incentivize development, which owners and builders have taken advantage of via negotiations with the city, waivers, warrants, etc.  This gives the parcels a total gross buildable area that is larger than the total allowed based solely on FAR. Interesting to note: most, if not all, of this trend in the first graph occurs after 1986 [2].  We could also assume that, over time, owners and developers have become more sophisticated by maximizing the total built area of their parcels and/or that municipalities have become more restrictive.  Either way, there seems to be less opportunity the more recent a building was built.

Anything to the right of the zero on the x-axis implies potential total gross buildable area. Again, this an aggregation based on Year Built,  Zoning Type or ZIP Code. This is unbuilt area that currently exists for developers and owners to target.  

The awesome part of the dataset (not shown here) is that we know the details of all 5,000 parcels that comprise these visualizations and aggregations.  Meaning, we have the addresses of each parcel, the Year Built, Zoning Code Type, ZIP Code and many more, so your research and analysis teams can easily model and run preliminary analysis of specific parcels, gained from the insights above, on a per parcel scale.

If you are interested in Deepblocks data, please contact us for cities we currently have for sale, here.

If you want to analyze any given parcel in Queens, don’t forget to check out our software Deepblocks.


Notes

  1. The dataset does not include bonuses, overlays, or exceptions that may increase the total gross buildable area allowed.  The potential total gross buildable area is an FAR multiple only.

  2. This trend confirms a few assumptions - newer developments maximize the total area they are allowed to build and new buildings are built utilizing the newest rules allowed by code.

Author Olivia Ramos
Founder and CEO of Deepblocks, holds master's degrees in Architecture from Columbia University and Real Estate Development from the University of Miami. Her achievements before Deepblocks include designing Big Data navigation software for the Department of Defense's DARPA Innovation House and graduating from Singularity University's Global Solutions and Accelerator programs.