Austin Celestin, Urban Planning Graduate Student at NYU Wagner, on the City of Yes for Housing Opportunity proposal and its potential impact on New York City's housing crisis
2:40:39
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125 sec
Austin Celestin, an NYU Wagner urban planning graduate student, speaks in support of the City of Yes for Housing Opportunity proposal. He emphasizes the current housing crisis in New York City and how the proposal addresses various neighborhood needs while potentially alleviating some of the housing shortages.
- Celestin highlights personal experiences and statistics demonstrating the severity of the housing crisis in New York City.
- He praises the proposal's multi-faceted approach, including transit-oriented development, commercial conversions, Accessory Dwelling Units (ADUs), campus infill, and the removal of parking mandates.
- While supporting the plan, Celestin echoes Borough President Antonio Reynoso's sentiment that the city should consider doing even more to address the housing need.
- Current housing situation is unaffordable for many, including the speaker and his family
- Low vacancy rates, slow construction pace, high rents, and increasing homelessness are major issues
- Current zoning plan has reduced buildable capacity by 80% and led to housing loss in some neighborhoods
- The City of Yes proposal caters to different neighborhood needs and capacities
- Proposal includes various strategies like transit-oriented development, commercial conversions, ADUs, and lifting parking mandates
- The plan is modest but ambitious, covering about 20% of the housing need
- Urges the City Planning Commission to approve the City of Yes proposal and consider doing even more
[EXPERIMENTAL]
Which elements of City of Yes for Housing Opportunity were discussed in this testimony?
- Residential Conversions
- Parking Mandates
- ADU
- Transit-Oriented Development
- Campuses
The following are AI-extracted quotes and reasoning about which elements of the proposal were discussed in this testimony.
This is a quick, close approximation. Occasionally, the connection between a testimony's transcript and specific elements of City Planning's proposal is tenuous.
Read about this AI-generated analysis here.
Residential Conversions
"Commercial conversions for Midtown Manhattan"
This quote directly mentions commercial conversions, which is a key aspect of the Residential Conversions element of the proposal. It specifically mentions Midtown Manhattan as an area where this would be applicable.
Parking Mandates
"the underappreciated aspect of whipping parking mandates, which makes housing more expensive more expensive."
This quote directly mentions removing parking mandates, which is a key element of the proposal. The speaker acknowledges that parking mandates make housing more expensive, aligning with the proposal's aim to reduce housing costs by removing these mandates.
ADU
"ADU's full low density residential areas"
This quote directly mentions ADUs (Accessory Dwelling Units) in the context of low-density residential areas, which is a key aspect of the ADU element of the proposal.
Transit-Oriented Development
"transforming the development for those Attleboro communities near subways."
This quote refers to development near subways, which is a key aspect of transit-oriented development. The speaker is highlighting how the proposal caters to different neighborhood needs, including those near public transit.
Campuses
"campus infill for mid century developments"
This quote directly mentions campus infill, which is a key aspect of the Campuses element of the proposal. It refers to utilizing space in existing campus-like developments for new housing.
About this analysis:
This analysis is done by AI that reasons whether or not a quote from the testimony discusses a particular element of the proposal.
All the prompts and data are open and available on Github.
You can search for testimonies that mentioned a specific element in the table on the main meeting page.
When an element is explicitly stated in the testimony (e.g. "Universal Affordability Preference" or "UAP"), the analysis is accurate.
But the connection between a quote from the testimony and an element of the proposal is sometimes implicit.
In these cases, the AI might eagerly label a testimony as discussing a proposal when the connection is tenuous, or it might omit it entirely.