Biggest New York City Developments Approved in August 2016

One of the best leading economic indicators with regard to real estate markets is construction permits. Construction permits can tell you where new projects are being built, how big those developments will be, what type of occupants those projects are for (commercial, residential, etc.), and a whole host of other insights.

Using this information, real estate developers and investors can project changes in supply. By understanding changes in supply, inquiring minds can get a better sense for how rents are changing and how neighborhoods are changing.

These are the biggest new building permits approved in New York City this August 2016:

August 2016 new building permits by square-footage.
August 2016 new building permits by square-footage.

This is built using the New York City Department of Buildings permit data which gets released on a monthly and weekly basis in Excel format. You can find this data here. What’s particularly great about this source of information is that it is timely. The PLUTO data that I love so much is only released yearly. NYC DOB permit data is released weekly.

In this post, I will explain the methodology I used to analyze permit data to visualize where the largest new developments currently in the pipeline are located.

The specific dataset I am using is located here (this will directly download the August 2016 zip file hosted on nyc.gov).

Due to computing constraints, I am limiting the permit data to the following rough locational boundaries:

The subset of permits that I am mapping falls roughly within this box.
The subset of permits that I am mapping falls roughly within this box.

This is important to note because the NYC DOB permit data is not geocoded, so I have to manually geocode it based on what we get from the DOB.

Data Analysis Sidebar: How to geocode non-geocoded New York City data

The best way I have found to manually geocode NYC data is to merge the non-geocoded data (permits) with geocoded data from PLUTO on a common field that accurately parallels location. Luckily, New York City’s borough-block-lot (BBL) system works perfectly.

Each line in PLUTO has a unique geocode (“the_geom”) corresponding to a unique borough-block-lot code (“borocode,” “block,” “lot”). And almost every other city datasource uses some form of BBL as an identifier. Depending on the source of data, use concatenations and your preferred lookup technique (index/match, vlookup, SELECT/WHERE, etc.) to pull geocodes from PLUTO into whatever datasource you’re looking at by matching BBLs.

In this case, I merged geocodes from PLUTO into the NYC DOB permit data, but the technique applies to pretty much every source of data New York City publishes. As you’ll see shortly, this allows me to map NYC DOB permit data through cartography software such as Carto.

The difficulty here is that the PLUTO data is enormous if you want to look at the entire city. There are millions of lots which translates into gigabytes of data even if you’re just downloading boro-block-lot information and no other tax lot data. That’s why most of my analyses are focused on Manhattan and a little bit of the outer boroughs. My Macbook Pro simply doesn’t have the processing power to handle all that data. (If somebody wants to donate a supercomputer or cloud-based solution to the data analytics cause then hit me up on LinkedIn.)

Back to the permits: Where are the biggest new projects?

Based on the data, these are the biggest developments that were approved in August 2016:

August 2016 NYC new building permits.
August 2016 NYC new building permits.

You’ll notice that the tallest new building (NB) permits are all in Manhattan, but there are a few projects in the Bronx that are very large from a square-footage basis.

The biggest project by far is 515 West 42 Street in Manhattan. A quick Google search confirms that this is a huge 350-unit residential building. Notice that The Real Deal posted this article on August 16 and they refer to the Department of Buildings data. This supports my earlier notion of the timeliness value provided by this weekly datasource.

You’ll also see a number of permits that don’t have any height or size information. This has to do with permit types. It appears that general construction permits will have this information, but structural and foundation permits will not. At this stage, I admittedly do not know a ton about the stages within the permitting process, but it’s something worth examining in greater detail in the future.

Now, here’s what we can create using the geocoding technique above:

Each dot represents a new building permit and the size of the dot represents the magnitude of the square-footage as stated on the permit. I factored out permits that do not have square-footage information and permits that were disapproved.

Again, please be aware that I cut the data off around the “e” in the “The Bronx” on that map. There were surely permits issued east of the “e,” but I cannot process all of the data with my current setup, so I focused more closely on Manhattan.

Next Steps

The value of this type of analysis is twofold. First, you have a visual aid that accurately represents where new developments are happening. Second, you have official quantification of both supply and type.

Building out this analysis over time and forecasting construction lengths (or finding estimates in public submittal data, if it exists) would give developers significant insight into the economic direction of the New York city real estate market. Add that to the list of things I need to do!

Which Zones Have The Tallest Buildings in New York City?

Last week, in my post about the correlation between allowable FARs and building heights, I mentioned that I would be delving deeper into which zones are home to the tallest buildings. That’s what I will be exploring in this article.

To the data!

The starting point, as is so often the case, is PLUTO data. I will be using the same data that I referenced in last week’s article. The data covers the entire island of Manhattan.

If you’ve read my last article, you already know that I’ve done some calculations about building heights by allowable FAR. I expanded that model to include zones and then sorted by building heights in order to figure out the tallest zones.

Here’s what I found:

Which zones have the tallest residential buildings in New York City?
Which zones have the tallest residential buildings in New York City?

This table shows zones on the Y-axis and allowable FAR along the X-axis. As expected from my last post, when we sort the zones in order from tallest to shortest (as I did above), we get a trend that moves up and to the right.

This trend exists because allowable FAR has a positive correlation with building heights. The higher the FAR, the taller the building, on average.

The column on the far right is added for reference as a measure of magnitude. It is the total number of buildings within a given zone in our sample.

This is how you read the chart. The first zone, C5-2.5, has a maximum allowable FAR of 10 (from the top row), an average building height of 32.0 stories (the green cell), and contains 9 residential buildings (the column on the right).

Next, I’m going to throw this data into Carto and do a visual spot check. Which 9 residential buildings are in C5-2.5? Does this make sense?

The residential buildings in zone C5-2.5.
The residential buildings in zone C5-2.5. (Color scale dictates height.)

If you know New York City real estate, the big red zone in the middle of the map will stick out like a sore thumb. That’s 432 Park, the 90-story residential skyscraper home to some of New York’s most expensive apartments. The dark orange building highlighted to its left is Museum Tower, a 53-story residential tower at 15 West 53rd Street. And to the south, just west of 3rd Avenue, is The Metropolis, a 48-story luxury rental building at 150 East 44th Street.

So it looks like we’re on to something. Simply modeling average building height by zone has enabled us to quickly isolate some valuable properties and create a hypothesis that zone C5-2.5 is great for tall buildings.

Warning: Digression Ahead

Now I want you to take a closer look at that map. Squint at the bottom left of the map between Park Avenue & Lexington Avenue and you’ll notice a very faint yellow rectangle. That’s 114 East 40th Street. And it’s only 9 stories tall. Weird.

In zone C5-2.5, we have 432 Park standing 90 stories tall, dominating the New York City skyline, and we have 114 East 40th Street rising just 9 stories.

Why? What other factors could create such an enormous disparity in building height?

Both buildings are C5-2.5 and both buildings are in the Midtown Special Purpose District (MiD). Zone clearly is not the only important factor in determining height.

432 Park’s lot area is about 10x the size of 114 East 40th Street’s lot area, but 432 Park has a building area of 745k SF whereas 114 East 40th has a building area of 26k SF — a 30x differential!

There is the added benefit of 432 Park being along a wide street, as explained in my post about setbacks. 114 East 40th Street was also built in the 1920s when the city was generally shorter.

But I think the key here, as reported by the New York Times in 2013, is air rights. 432 Park came with 115k SF of additional air rights before it was built. That enabled it to scale to great heights.

Unfortunately, there is no information about transferred or additional air rights in PLUTO data and I have not found a good source for this type of data, so we will have to live with the fact that we cannot account for air rights in our analyses automatically.

That said, we can still draw valuable conclusions. Zone C5-2.5 plus transferred air rights can lead to huge buildings. It’s something worth looking into for the other zones as well. For example, which sites are most favorable for applying transferred air rights?

Additionally, there is a lot more to building height than the zone in which a building sets and, therefore, there is a lot more to building height than allowable FAR (which is dictated by zone). Perhaps I’m inexperienced, but that’s news to me. The impact of air rights might be more meaningful than I previously thought.

Let’s get back on track.

That was a serious tangent. Valuable, but serious. Here’s the same breakdown for commercial land use:

Which zones have the tallest commercial buildings in New York City?
Which zones have the tallest commercial buildings in New York City?

It’s interesting that C6-6.5 has, on average, the tallest buildings, but it does not have the highest allowable FAR. I’m not exactly sure why that is, but I already bored all of you with one digression, so I won’t do it again.

Additionally, C5-3, C5-5, C6-6, C6-7, and C6-9 are all in the realm of 20-25 stories. If I were a real estate developer looking to build commercial skyscrapers, I would certainly focus on properties in these zones as a starting point.

In conclusion

This simple analysis has provided us with a foundation as to where the different zones stand in relation to commercial & residential building heights. I think it’s a quick and useful tool to help judge potential building height. On average, the zones towards the top of the list are going to be taller and inherently more valuable than the zones towards the bottom of the list. But that isn’t a definitive rule and it isn’t a suggestion that proper due diligence need not be performed.

As we saw with our comparison of 432 Park and 115 East 40th Street, zone is just one piece of the pie in attempting to figure out the key drivers behind building heights. I will continue to explore these drivers and use them to identify value where possible.

Identifying New York City’s Biggest Real Estate Development Targets

Many real estate developers dream about making their mark on New York City’s skyline. Building big creates a legacy that can be seen miles away. From a business perspective, height creates value and a sustainable competitive advantage. Views from the clouds command equally stratospheric premiums and only a few buildings can give prospective buyers such views.

The reasons why developers want to build big buildings are obvious, so how do they identify the sites where building big is possible?

In this post, I will examine a model I built, with the help of a few handy tools such as Carto (previously CartoDB) & NYC PLUTO, that outlines the real estate growth potential of New York City and helps identify the biggest possible development sites in the city.

Disclaimer

I said I will model New York City, but I am only modeling Manhattan south of Central Park. I understand that New York City extends beyond Manhattan. Likewise, the methodology discussed henceforth extends beyond the data set I used. Please do not hurt me.

Background Information

The basis of this article is a project I did at NYU Stern for Urban Systems, a real estate class taught by Paul Romer. (A second disclaimer: Paul’s personal blog is the inspiration for my own.) In this class, we were challenged to ask questions about cities, perform quick analyses, and come up with general solutions. That is the methodology I will use here. This model is not intended to be a comprehensive review on any individual property. It is intended to paint broad strokes over the city in regard to real estate development potential.

Let’s start!

While I’d love to be able to quickly judge which sites can grow the tallest, height is a function of a number of zoning code components such as tower regulations, setback requirements, air rights, special district privileges, etc. Modeling all of that is too complex for now. A quicker way to get a feel for the development potential of any site is reviewing its Floor-Area Ratio (FAR).

A primer on Floor-Area Ratio (FAR)

Floor-Area Ratio is how a real estate developer determines the total floor space a building can have on any given plot of land.

For example, let’s take a lot that is 100′ wide by 100′ long. That lot has 10,000 SF of area. Assume the zoning code states that lot has an FAR of 5.0. Multiply the lot area by the FAR and you get 50,000 SF of total floor space that can be developed. This total is usually called ZFA, or Zoning Floor Area. It’s your floor space limit.

Simple enough, right?

The other important concept is that the Zoning Floor Area can be molded however you see fit (within the bounds of the zoning code). You can build a building with 5 floors on a 10,000 SF footprint using the entire site, you can build a building with 10 floors on a 5,000 SF footprint using half the site, or you can build any other combination as long as the total floor area of the building does not exceed 50,000 SF. The NYC Zoning Code Glossary has a nice picture to hammer home this concept:

Floor area can be divvied up however you see fit.
Floor area can be divvied up proportionally.

This picture also shows how FAR is related to height. Tall buildings need high FARs because tall buildings have a lot of floor space, but FAR isn’t a perfect metric for height because there are other constraints on height. I would certainly bet that there is a correlation between height and FAR, but I have never done a study. Maybe one day…

But I digress. Now that we’ve gone through Floor-Area Ratio and Zoning Floor Area, we have to figure out which properties have the most potential. For that, it’s best to jump into the model.

Building the Model

Every good model starts with great data. Thanks to New York City’s Open Data Plan, which provides that the city must release a ridiculous amount of data to the public, real estate developers with an eye for data analytics have access to tons of great data.

The specific data we need comes from PLUTO, Property Land Use Tax lot Output, which is aggregated by New York City. PLUTO contains zoning information, square footages, building types, tax values, dimensions, and a host of other fields all the way down at the individual lot level indexed by geocodes. This means we can put this data on an actual map!

My favorite tool for getting a handle on PLUTO Data is Chris Whong’s PLUTO Data Downloader. You can select an area, download the data to a CSV file, and import it right into Excel.

Once the data is in Excel, it’s easy to build in metrics. I focused on a few key fields:

lotarea: Area of the lot
builtfar:
Total building area / lotarea
commfar:
Maximum allowable commercial FAR
facilfar:
Maximum allowable facility FAR
residfar: 
Maximum allowable residential FAR

New York City provides a handy data dictionary if you want more detailed definitions about the various fields.

Using these fields, I came up with a metric I call “Square Foot Growth Potential,” or SFGP. Conceptually, SFGP is the difference between the square footage of floorspace a building currently has and the square footage of floorspace the lot can support as dictated by the zoning code.

For example, if a 10,000 SF lot with an FAR of 5.0 currently has a building with only 15,000 SF of floorspace–equivalent to a built FAR of 1.5–then the Square Foot Growth Potential is 35,000 SF.

Mathematically, SFGP is defined as such:

SFGP = ( Allowable FAR – Built FAR ) * Lot Area

We already have the Built FAR (builtfar) and Lot Area (lotarea), but we don’t have one field for Allowable FAR. We have three: commfar, facilfar, and residfar. To be conservative, I assume the Allowable FAR is equal to the lowest FAR contained in those three fields.

Note that I’m only running this calculation on properties where Allowable FAR is greater than Built FAR. For this exercise, I don’t care about buildings that are somehow larger than what the zoning code allows (via transfer rights, FAR bonuses, etc.). Additionally, I don’t factor in any FAR bonuses beyond what is already in the data.

That’s all that needs to be done to identify targets with high growth potential, but that alone won’t provide anyone with anything useful. There is too much noise. We need to filter it out.

Filtering Out the Noise

The data from PLUTO includes every tax lot in New York City. That means prime development targets like apartment buildings, vacant lots, and gas stations, but it also means public parks, schools, hospitals, government facilities, and other buildings that would be very difficult to develop. Additionally, some buildings are landmarked or located in historic districts. The following fields help us account for these factors:

bldgclass: Building class codes
histdist:
 Historic district
landmark: Landmark
ownertype: Tax lot ownership type

Building class codes allow us to distinguish between the different types of buildings. There are over 200 different categories and subcategories ranging from bungalow-style one family dwellings to outdoor pools to orphanages. A full list of the building codes is available in Appendix C of the data dictionary.

I excluded over 80 building codes from my analysis including, but not limited to:

  • Luxury hotels and dormitories
  • Hospitals, infirmaries, and nursing homes
  • Churches, synagogues, and convents
  • Asylums, orphanages, and detention centers
  • Concert halls, museums, and libraries
  • Parks, playgrounds, and public pools
  • Airports, piers, and docks
  • Utility facilities, transportation infrastructure, and railroads
  • Various government-owned land types
  • Schools, colleges, and seminaries
  • Court houses, post offices, and cemeteries

You can see that the building codes contain a great deal of valuable information that helps us hone our analysis.

Next, I factored out any property that is within a historic district or deemed a landmark.

Lastly, I factored out any lot with an ownertype of ‘O’ or ‘C’. An ownertype of ‘C’ means the lot is owned by the city. An ownertype of ‘O’ means the lot is owned by a public authority, state, or federal government.

All of this checking is an attempt to weed out properties that, in reality, would be significantly more difficult to redevelop than your standard, privately-owned townhouse, apartment, or office building.

The End Result

After downloading the data, building the metrics, and cleaning things up, the dataset is ready for use.

As I mentioned before, this data is geocoded, so we can easily plop it onto a map. That’s where Carto becomes extraordinarily valuable.

Using Carto and geocoded data, we can turn an Excel model into a map and visualize the analysis I outlined above. For your viewing pleasure, I filtered the map to include only lots with over ~20,000 SFGP.

Here’s what the final product looks like:

If you browse around the map, you’ll be sure to notice some of the most well-known recent developments like 432 Park (zoned as 53 East 57th Street), boasting 252k SFGP, and forthcoming 80 Essex (part of Essex Crossing), with 130k SFGP.

You might also discover some sites that you didn’t know were in the works. I didn’t know Silverstein is looking to develop 514 11th Avenue, which we model at over 800k SFGP, into a super-tall tower until this model highlighted that property. (Turns out Silverstein may back out, though.)

What interests me most are the underdeveloped sites in prime locations. For example, our model tabs 420 7th Avenue at 133k SFGP and the site is heavily trafficked next to Madison Square Garden, but the building appears to be a one-story Bank of America/Modell’s according to Google Maps.

+130k SFGP in the heart of New York City.
Over 130k SFGP in the heart of New York City.

I wonder what’s going on with that…

Closing Remarks

As you can see, PLUTO + Carto can be a very powerful combination for real estate developers. Building this model only took a couple hours of work, but it provides me with the ability to scan the entire city and find opportunities with high growth potential visually. Adjusting Carto filters on SFGP, lot dimensions, and number of floors further enables me to pinpoint buildings of interest.

As I continue exploring real estate and New York City, I’m sure I’ll be diving into things I discovered from this simple tool. I hope it piques your interest as much as it does mine.