MISSION:
MAKE FIDI
A MODEL DISTRICT
FOR THE FUTURE OF CITIES
3 MAJOR CHALLENGES:
will reshape cities for better and worse; will force major resilience investment; and will drive upgrades to housing, mobility, power, health, and services.
INVENT CITY OBJECTIVES:
- Accelerate deployment of critical urban solutions.
- Create jobs, economic activity, and tax revenue for NYC.
GENERAL APPROACH:
Establish an anchored by an , powered by and amplified globally through
MODELED RESULTS FOR NYC:
- $4B in annual economic activity.
- 15,000 jobs.
- $380M in annual tax revenue.
AN 8 POINT
STRATEGY
because cost and benefit move decisions. The climate crisis could cost the global economy up to $38T annually by 2050, according to the Potsdam Institute for Climate Impact Research. The upside of action could exceed $43T by 2050 in new corporate revenues.
with more than 4B people in urban areas today and more than 6B projected by 2100. Older cities must modernize. Fast-growing cities must build. Both need better systems.
from interest to adoption. They control budgets, approvals, risk, procurement, operations, deployment, and scale.
puts companies, buyers, investors, agencies, talent, and partners in one place. Ideas move faster. Products get tested in context. Buyers compare solutions. Companies gain visibility. Sales cycles shrink. Deployment accelerates.
Kendall Square; 22@Barcelona; EUREF-Campus Berlin; Zhangjiang Science City; Dubai Internet City; and MaRS Discovery District show the model at work.
Invent City brings companies, buyers, investors, and business visitors to FiDi. It can fill vacant space, activate local streets, grow value chains, support jobs, and generate recurring tax revenue for New York City and the region.
will anchor the district with an Urban Trade Mart. It can turn vacant office space into commercial infrastructure: a place to demo products, meet customers, launch pilots, secure financing, build partnerships, and move deals toward deployment.
for sellers and buyers. Sellers gain access to customers, partners, investors, agencies, and project teams. Buyers can find, compare, test, finance, and deploy solutions faster.
makes the Trade Mart more powerful. Buildings, streets, rooftops, waterfront, transit, public space, energy systems, and underused real estate create a real-world test bed. Urban solutions can prove value in context.
because they turn attention into action. Buyers can see the product. Test the product. Compare alternatives. Ask questions. Understand ROI. Sellers gain credibility. Deals move faster. Events bring the market to FiDi. Digital tools take it global.
make selling more powerful because they turn a pitch into an experience. Buyers do not just hear the story. They walk into it. They see future streets, buildings, parks, rooftops, waterfronts, and city systems in action. They understand value faster. They trust the solution faster. They buy with more confidence. Roundtables and digital platforms take the experience global.
Buildings
Urban Agriculture
Consumer Products
Energy & Power
Urban Robotics
Urban AI
Wastewater
Water Systems
Transportation
Urban Logistics
gives Invent City instant global credibility. It concentrates capital, media, talent, tourism, culture, real estate, universities, NGOs, advertising, and decision-makers. That gives Invent City access to the people, money, attention, and institutions that can move a market.
gives Invent City leverage no other district can match. Wall Street capital. Global visibility. Deep transit. Airport access. Waterfront risk. Resilience projects. Vacant space. Hotels. Ferries. Blue Highway potential. City Hall nearby. FiDi has the assets, urgency, and audience to turn Invent City into a working model fast.
John Street
Nassau Street
S William and Beaver
Under the FDR
Broad Street @ NYSE
William Street @ Fosun Plaza
Wall Street
Gotham Park
is FiDi’s pain point — and Invent City’s opening. FiDi has more than 10M sf of vacant space and vacancy above 25%. Invent City can convert 1M sf into showrooms and activate another 2M sf for offices, events, support space, and trade-mart operations. Empty offices, retail, POPS, streets, parks, and public space create a faster, cheaper launch path than new construction. Owners gain tenants. Streets gain life. NYC gains jobs, activity, and recurring tax revenue.
Office vacancy
Vacant FiDi office space can become productive economic infrastructure faster than major redevelopment.
Retail vacancy
Vacant storefronts can become street-level showrooms, pop-ins, pilots, and visitor-facing demonstrations.
Lower cost
Existing FiDi space can be activated faster and at far lower cost than conversion or ground-up development.
RedevelopmentCosts
Faster revenue
A Trade Mart can move from vacant space to market activity much faster than major redevelopment.
Time toRevenue
Flexible rollout
The platform can start small, prove demand, and expand as companies, buyers, and capital follow.
FlexibleRollout
can put essential street services in one place. They can combine last-mile logistics, Blue Highway freight transfer, charging, battery exchange, micromobility, waste, storage, public bathrooms, worker support, shade, water, information, and emergency response.
Public Toilets
Packages
Bikes
Garbage
can add major public-realm upside, including Gotham Park. Invent City does not depend on it. But better streets can showcase water, mobility, vegetation, lighting, safety, public-space technology, and district life.
into smarter, faster, cleaner, safer places. Used well, AI can improve buildings, energy, mobility, logistics, healthcare, finance, media, government, public services, and daily urban life.
if deployed badly. It can automate bias, expand surveillance, displace work, hide accountability, weaken privacy, increase security risk, and put critical systems inside opaque black boxes.
will bring AI into the physical city. Robots can support delivery, inspection, cleaning, maintenance, security, construction, logistics, emergency response, and care — but only if cities test safety, labor impact, access, and public trust.
makes FiDi a real-world place to test urban AI. Companies can prove what saves money, reduces risk, improves service, protects people, and needs stronger oversight before cities scale it.
Urban AI
Waste Robotics
Transit Robotics
High-Rise Robotics
makes Invent City bigger than FiDi. Events, media, data, digital platforms, and AI keep the market active year-round. Founders, buyers, investors, policymakers, operators, delegations, and press can explore, compare, share, and engage across time zones. Interest turns into trust, leads, pilots, investment, and deployment.
Events
Live programming turns attention into meetings, pilots, partnerships, and deal flow.
Events
Media
Content expands the Trade Mart beyond FiDi and keeps buyers engaged between visits.
Media
Digital reach
Digital and AI channels keep discovery, comparison, and follow-up active 24/7.
DigitalReach
FOR NYC:
$4B IN ECONOMIC ACTIVITY,
15,000 JOBS,
$380M TAX REVENUES
Tourism brings outside dollars into New York. Invent City adds high-value business and innovation visitors who support FiDi hotels, restaurants, retail, culture, transit, events, and services.
| Tourism summary | Mid |
|---|---|
| Annual visitors | 1,000,000 |
| Traveler spending ($/yr) | $1,348,750,000 |
| Total economic impact ($/yr) | $2,090,562,500 |
| Tourism-supported jobs | 6,034 |
| Modeled taxes and fees ($/yr, no PIT) | $159,240,625 |
| Rounded taxes and fees ($/yr, no PIT) | $159,200,000 |
| Note: PIT excluded to avoid double counting with Trade Mart payroll impacts. | |
| Tourism summary | Low | Mid | High |
|---|---|---|---|
| Annual visitors | 1,000,000 | 1,000,000 | 1,000,000 |
| Traveler spending ($/yr) | $1,035,000,000 | $1,348,750,000 | $1,768,750,000 |
| Total economic impact ($/yr) | $1,604,250,000 | $2,090,562,500 | $2,741,562,500 |
| Tourism-supported jobs | 6,034 | 6,034 | 6,034 |
| Modeled taxes and fees ($/yr, no PIT) | $123,684,375 | $159,240,625 | $206,796,875 |
| Rounded taxes and fees ($/yr, no PIT) | $123,700,000 | $159,200,000 | $206,800,000 |
| Note: PIT excluded to avoid double counting with Trade Mart payroll impacts. | |||
| 1. Base assumptions: 1.0M visitors/yr | Visitors/year | Avg nights | Hotel room-nights | Visitor-days |
|---|---|---|---|---|
| International overnight | 250,000 | 5 | 1,250,000 | 1,250,000 |
| Domestic overnight | 250,000 | 2 | 500,000 | 500,000 |
| Domestic day | 500,000 | 0 | 0 | 500,000 |
| Total | 1,000,000 | — | 1,750,000 | 2,250,000 |
| 2. Spending inputs | Low | Mid | High |
|---|---|---|---|
| Hotel room-nights | 1,750,000 | 1,750,000 | 1,750,000 |
| ADR ($/room-night) | $250 | $325 | $425 |
| International other spend ($/day) | $350 | $450 | $600 |
| Domestic overnight other spend ($/day) | $200 | $275 | $350 |
| Domestic day other spend ($/day) | $120 | $160 | $200 |
| Note: ADR = average daily hotel room rate. “Other spend” = non-hotel visitor spending. | |||
| 2A. Hotel and other traveler spending | Low ($/yr) | Mid ($/yr) | High ($/yr) |
|---|---|---|---|
| Hotel revenue | $437,500,000 | $568,750,000 | $743,750,000 |
| Other spending (non-hotel) | $597,500,000 | $780,000,000 | $1,025,000,000 |
| Total traveler spending | $1,035,000,000 | $1,348,750,000 | $1,768,750,000 |
| Formula: Other spend = sum of (visitor-days by segment × spend/day by segment). | |||
| 2B. Total economic impact | Low | Mid | High |
|---|---|---|---|
| Traveler spending ($/yr) | $1,035,000,000 | $1,348,750,000 | $1,768,750,000 |
| Impact ratio | 1.55x | 1.55x | 1.55x |
| Total economic impact ($/yr) | $1,604,250,000 | $2,090,562,500 | $2,741,562,500 |
| Rounded total economic impact ($/yr) | $1,604,000,000 | $2,091,000,000 | $2,742,000,000 |
| Formula: Total economic impact = traveler spending × 1.55. | |||
| 3. Tourism-supported jobs | Input / Output | Value |
|---|---|---|
| NYC visitors (2024) | 64.3M visitors | |
| NYC tourism-supported jobs (2024) | 388,000+ jobs | |
| Jobs per 1M visitors | 6,034+ jobs | |
| IC annual visitors (assumption) | 1.0M visitors | |
| IC tourism-supported jobs (modeled) | 6,034+ jobs | |
| Formula: Jobs per 1M visitors = 388,000 / 64.3. | ||
| 4A. Hotel room taxes and fees | Low | Mid | High |
|---|---|---|---|
| NYS sales tax on hotel rooms (4.0%) | $17,500,000 | $22,750,000 | $29,750,000 |
| NYC sales tax on hotel rooms (4.5%) | $19,687,500 | $25,593,750 | $33,468,750 |
| MCTD sales tax on hotel rooms (0.375%) | $1,640,625 | $2,132,813 | $2,789,063 |
| NYC hotel occupancy tax (5.875%) | $25,703,125 | $33,414,063 | $43,695,313 |
| NYC per-room hotel tax ($2.00 × room-nights) | $3,500,000 | $3,500,000 | $3,500,000 |
| NYS hotel unit fee ($1.50 × room-nights) | $2,625,000 | $2,625,000 | $2,625,000 |
| 4B. Sales tax on other spending | Low | Mid | High |
|---|---|---|---|
| NYS sales tax (4.0%) | $23,900,000 | $31,200,000 | $41,000,000 |
| NYC sales tax (4.5%) | $26,887,500 | $35,100,000 | $46,125,000 |
| MCTD sales tax (0.375%) | $2,240,625 | $2,925,000 | $3,843,750 |
| Total sales tax on other spending | $53,028,125 | $69,225,000 | $90,968,750 |
| 4C. Tax totals roll-up (no PIT) | Low | Mid | High |
|---|---|---|---|
| NYS total (hotel + other) | $44,025,000 | $56,575,000 | $73,375,000 |
| NYC total (hotel + other) | $75,778,125 | $97,607,813 | $126,789,063 |
| MCTD total (hotel + other) | $3,881,250 | $5,057,813 | $6,632,813 |
| Grand total (no PIT) | $123,684,375 | $159,240,625 | $206,796,875 |
| Rounded grand total (no PIT) | $123,700,000 | $159,200,000 | $206,800,000 |
| 4D. Tax formulas and notes | Low | Mid | High |
|---|---|---|---|
| Hotel revenue formula | room-nights × ADR | room-nights × ADR | room-nights × ADR |
| NYS hotel-room sales tax | Hotel revenue × 4.0% | Hotel revenue × 4.0% | Hotel revenue × 4.0% |
| NYC hotel-room sales tax | Hotel revenue × 4.5% | Hotel revenue × 4.5% | Hotel revenue × 4.5% |
| MCTD hotel-room sales tax | Hotel revenue × 0.375% | Hotel revenue × 0.375% | Hotel revenue × 0.375% |
| NYC hotel occupancy tax | Hotel revenue × 5.875% | Hotel revenue × 5.875% | Hotel revenue × 5.875% |
| NYC per-room hotel tax | $2.00 × room-nights | $2.00 × room-nights | $2.00 × room-nights |
| NYS hotel unit fee | $1.50 × room-nights | $1.50 × room-nights | $1.50 × room-nights |
| NYS sales tax on other spending | Other spending × 4.0% | Other spending × 4.0% | Other spending × 4.0% |
| NYC sales tax on other spending | Other spending × 4.5% | Other spending × 4.5% | Other spending × 4.5% |
| MCTD sales tax on other spending | Other spending × 0.375% | Other spending × 0.375% | Other spending × 0.375% |
| Scope note: PIT excluded to avoid double counting with Trade Mart payroll impacts. | |||
The Trade Mart turns urban innovation into recurring commercial activity. Jobs, payroll, procurement, buyer traffic, events, travel, and tax revenue flow from one engine.
| Trade Mart summary | Mid |
|---|---|
| Direct on-site jobs | 8,500 |
| Direct payroll ($/yr) | $1,427,500,000 |
| Local procurement ($/yr) | $428,250,000 |
| Total direct activity ($/yr) | $1,855,750,000 |
| Note: Direct activity shown here is kept separate from tourism and real-estate modules to reduce double counting. | |
| Trade Mart summary | Low | Mid | High |
|---|---|---|---|
| Direct on-site jobs | 8,500 | 8,500 | 8,500 |
| Direct payroll ($/yr) | $1,140,000,000 | $1,427,500,000 | $1,790,000,000 |
| Total jobs incl. indirect + induced | 12,750 | 15,300 | 17,850 |
| Knock-on jobs | 4,250 | 6,800 | 9,350 |
| Note: Only direct jobs should be treated as additive across modules to avoid double counting. | |||
| 1. Jobs based on area | Area | Density | Direct jobs |
|---|---|---|---|
| Showrooms | 1,000,000 sf | 1,000 sf/job | 1,000 |
| Support offices | 1,500,000 sf | 200 sf/job | 7,500 |
| Trade Mart total | 2,500,000 sf | — | 8,500 |
| Formula: Jobs = Area / Density. Example: 1,500,000 sf / 200 sf per job = 7,500 jobs. | |||
| 2A. Low-case payroll detail | Jobs | Low wage ($/yr) | Payroll ($/yr) |
|---|---|---|---|
| Showrooms | 1,000 | $90,000 | $90,000,000 |
| Support offices | 7,500 | $140,000 | $1,050,000,000 |
| Total for direct | 8,500 | $1,140,000,000 |
| 2B. Mid-case payroll detail | Jobs | Mid wage ($/yr) | Payroll ($/yr) |
|---|---|---|---|
| Showrooms | 1,000 | $115,000 | $115,000,000 |
| Support offices | 7,500 | $175,000 | $1,312,500,000 |
| Total for direct | 8,500 | — | $1,427,500,000 |
| 2C. High-case payroll detail | Jobs | High wage ($/yr) | Payroll ($/yr) |
|---|---|---|---|
| Showrooms | 1,000 | $140,000 | $140,000,000 |
| Support offices | 7,500 | $220,000 | $1,650,000,000 |
| Total for direct | 8,500 | — | $1,790,000,000 |
| 2D. Direct jobs | Jobs | Low wage | Mid wage | High wage |
|---|---|---|---|---|
| Showrooms | 1,000 | $90,000/yr | $115,000/yr | $140,000/yr |
| Support offices | 7,500 | $140,000/yr | $175,000/yr | $220,000/yr |
| Total direct jobs | 8,500 | — | — | — |
| Total payroll ($/yr) | 8,500 | $1,140,000,000/yr | $1,427,500,000/yr | $1,790,000,000/yr |
| 3. Local procurement | Low | Mid | High |
|---|---|---|---|
| Procurement assumption (% of payroll) | 20% | 30% | 40% |
| Local procurement ($/yr) | $228,000,000 | $428,250,000 | $716,000,000 |
| Formula: Local procurement = Payroll × Procurement share. | |||
| What it reflects: Tenant and campus operating spend—security, cleaning, repairs, IT/AV, catering, event staffing, printing/signage, and local logistics; excludes landlord building OpEx. | |||
| 4. Direct campus activity (Economic expansion) | Low | Mid | High |
|---|---|---|---|
| Payroll ($/yr) | $1,515,000,000 | $1,902,500,000 | $2,390,000,000 |
| Local procurement ($/yr) | $303,000,000 | $570,750,000 | $956,000,000 |
| Total direct activity ($/yr) | $1,818,000,000 | $2,473,250,000 | $3,346,000,000 |
| Formula: Total direct activity = Payroll + Local procurement. | |||
| 5. Indirect and induced jobs | Low | Mid | High |
|---|---|---|---|
| Direct jobs | 8,500 | 8,500 | 8,500 |
| Total jobs incl. indirect + induced | 12,750 | 15,300 | 17,850 |
| Knock-on jobs | 4,250 | 6,800 | 9,350 |
| Implied total multiplier | 1.50x | 1.80x | 2.10x |
| What this shows: Additional off-site jobs supported through suppliers, vendors, and household spending. | |||
| Definitions: Direct = on-site Trade Mart jobs. Indirect = supplier and vendor jobs supported by Trade Mart spending. Induced = jobs supported by household spending from wages. Knock-on = indirect + induced combined. | |||
| 6. Modeled tax revenues | Low | Mid | High |
|---|---|---|---|
| NYS PIT | $62,700,000 | $85,650,000 | $116,350,000 |
| NYC resident PIT | $34,200,000 | $45,680,000 | $60,860,000 |
| MCTMT | $10,203,000 | $12,774,125 | $16,020,500 |
| Sales tax on employee spending | $17,688,469 | $22,150,602 | $27,779,445 |
| Sales tax on local procurement | $10,117,500 | $18,998,344 | $31,772,500 |
| Total modeled taxes ($/yr) | $134,908,969 | $185,253,070 | $252,782,445 |
| Rounded total modeled taxes ($/yr) | $134,900,000 | $185,300,000 | $252,800,000 |
| NYS stands for New York State, NYC for New York City, PIT for personal income tax, and MCTMT stands for the Metropolitan Commuter Transportation Mobility Tax. | |||
| 6A. Tax assumptions and formulas | Low | Mid | High |
|---|---|---|---|
| NYS PIT effective rate | 5.5% | 6.0% | 6.5% |
| NYC resident PIT rate | 3.0% | 3.2% | 3.4% |
| MCTMT rate | 0.895% | 0.895% | 0.895% |
| Employee spending sales tax assumption | Payroll × 35% local spend × 50% taxable × 8.875% | ||
| Procurement sales tax assumption | Local procurement × 50% taxable × 8.875% | ||
| Procurement assumption | Payroll × 20% | Payroll × 30% | Payroll × 40% |
| NYS PIT formula | NYS PIT = Payroll × NYS PIT rate | ||
| NYC resident PIT formula | NYC PIT = Payroll × NYC resident PIT rate | ||
| MCTMT formula | MCTMT = Payroll × 0.895% | ||
| Employee spending sales tax formula | Payroll × 35% × 50% × 8.875% | ||
| Procurement sales tax formula | Local procurement × 50% × 8.875% | ||
| 6B. Scope note and caveat | Value |
|---|---|
| Scope note | NYC resident PIT assumes employees are NYC residents. If some workers commute from outside NYC, this line should be reduced accordingly. NYS PIT would still apply. |
| Additivity note | To avoid double counting across modules, only direct jobs should be treated as additive; indirect and induced jobs should not be added again in Tourism or other spillover modules. |
Invent City can make underused FiDi space productive again. Filling 3.0M sf can strengthen NOI, property values, refinancing, reinvestment, street life, and public revenue.
| Real estate summary | Low | Mid | High |
|---|---|---|---|
| Stabilized leased area (sf) | 3,000,000 | 3,000,000 | 3,000,000 |
| Annual rent ($/yr) | $165,000,000 | $165,000,000 | $165,000,000 |
| NOI ($/yr) | $111,750,000 | $106,860,000 | $102,000,000 |
| Illustrative implied value at 6.0% cap ($) | $1,862,500,000 | $1,781,000,000 | $1,700,000,000 |
| Modeled recurring NYC revenue capacity ($/yr) | $69,704,500 | $70,675,890 | $71,313,250 |
| Rounded recurring NYC revenue capacity ($/yr) | $69,700,000 | $70,700,000 | $71,300,000 |
| Note: This module is kept separate to avoid double counting with jobs, tourism, and construction modules. | |||
| 1. Leasing assumptions | Area | Asking rent | Annual rent |
|---|---|---|---|
| Trade Mart - Showrooms | 1,000,000 sf | $40/sf/yr | $40,000,000/yr |
| Trade Mart - Support offices | 1,500,000 sf | $60/sf/yr | $90,000,000/yr |
| Trade Mart - Total | 2,500,000 sf | — | $130,000,000/yr |
| Additional offices (separate) | 500,000 sf | $70/sf/yr | $35,000,000/yr |
| IC rent total (all space) | 3,000,000 sf | — | $165,000,000/yr |
| 1A. Average gross rent across all space | Value |
|---|---|
| Total area (sf) | 3,000,000 |
| Total annual rent ($/yr) | $165,000,000 |
| Average gross rent ($/sf/yr) | $55.00/sf/yr |
| 1B. Operating expense assumptions | OpEx ($/sf/yr) |
|---|---|
| Low | $17.75 |
| Mid | $19.38 |
| High | $21.00 |
| 1C. Cap-rate assumptions | Cap rate |
|---|---|
| Low cap | 5.5% |
| Base cap | 6.0% |
| Higher cap | 7.0% |
| High cap | 8.0% |
| 1D. Property-tax uplift assumptions | Value |
|---|---|
| NYC Class 4 assessment ratio | 45% |
| NYC Class 4 tax rate | 10.848% |
| Illustrative phase-in | 50% / 75% / 100% |
| 1E. CRT assumptions | Value |
|---|---|
| Rent base proxy ($/yr) | $165,000,000 |
| Effective CRT rate | 3.9% |
| Coverage factor | 70% / 85% / 95% |
| CRT gross upper bound ($/yr) | $6,435,000 |
| 1F. Vacancy context | Value |
|---|---|
| FiDi Financial East office vacancy (a) | 26.0% |
| FiDi Insurance office vacancy (a) | 29.5% |
| Retail storefront vacancy in FiDi/BPC (Q3 2024) (b) | 24% |
| Source note | (a) Cushman & Wakefield, Q4 2025; (b) Small Business Services |
| 2. Real-estate logic | Definition |
|---|---|
| Gross rent | Total annual rent collected |
| OpEx | Building operating expenses |
| NOI | Gross rent minus OpEx |
| Implied value | NOI divided by cap rate |
| 2A. NOI per square foot | Gross rent ($/sf/yr) | OpEx ($/sf/yr) | NOI ($/sf/yr) |
|---|---|---|---|
| Low expense | $55.00 | $17.75 | $37.25 |
| Mid expense | $55.00 | $19.38 | $35.62 |
| High expense | $55.00 | $21.00 | $34.00 |
| 2B. Total NOI on 3.0M sf | NOI ($/sf/yr) | Area (sf) | Total NOI ($/yr) |
|---|---|---|---|
| Low expense | $37.25 | 3,000,000 | $111,750,000 |
| Mid expense | $35.62 | 3,000,000 | $106,860,000 |
| High expense | $34.00 | 3,000,000 | $102,000,000 |
| 2C. Implied value from capitalized NOI | Cap rate | Implied value ($) | Value per sf |
|---|---|---|---|
| Low expense | 5.5% | $2,031,818,182 | $677.27/sf |
| Low expense | 6.0% | $1,862,500,000 | $620.83/sf |
| Low expense | 7.0% | $1,596,428,571 | $532.14/sf |
| Low expense | 8.0% | $1,396,875,000 | $465.63/sf |
| Mid expense | 5.5% | $1,942,909,091 | $647.64/sf |
| Mid expense | 6.0% | $1,781,000,000 | $593.67/sf |
| Mid expense | 7.0% | $1,526,571,429 | $508.86/sf |
| Mid expense | 8.0% | $1,335,750,000 | $445.25/sf |
| High expense | 5.5% | $1,854,545,455 | $618.18/sf |
| High expense | 6.0% | $1,700,000,000 | $566.67/sf |
| High expense | 7.0% | $1,457,142,857 | $485.71/sf |
| High expense | 8.0% | $1,275,000,000 | $425.00/sf |
| 2D. Plain-English economic benefits | Value |
|---|---|
| Benefit 1 | Fills vacant office and retail space |
| Benefit 2 | Creates steady rental income |
| Benefit 3 | Improves NOI |
| Benefit 4 | Supports stronger building values |
| Benefit 5 | Helps owners refinance, reinvest, and stabilize assets |
| Benefit 6 | Can improve confidence in the broader FiDi market |
| 3. Job-counting treatment | Value |
|---|---|
| Approach | This module does not claim incremental job creation, to avoid double counting with separate Trade Mart operations, tourism, and construction modules. |
| Why | It shows how filling vacant space can improve building income, support asset value, and expand recurring city revenue. |
| Caveat | Stabilizing vacant space can still support employment indirectly by making buildings more viable and attracting more tenants, activity, and investment. |
| 4A. Commercial Rent Tax (CRT) | Low | Mid | High |
|---|---|---|---|
| CRT gross upper bound ($/yr) | $6,435,000 | $6,435,000 | $6,435,000 |
| Coverage factor | 70% | 85% | 95% |
| CRT modeled ($/yr) | $4,504,500 | $5,469,750 | $6,113,250 |
| Rounded CRT modeled ($/yr) | $4,500,000 | $5,500,000 | $6,100,000 |
| 4B. Property-tax capacity uplift (mid-case illustration) | Value |
|---|---|
| Mid-case implied value ($) | $1,781,000,000 |
| Assessment ratio | 45% |
| Class 4 tax rate | 10.848% |
| Phase-in | 75% |
| Property-tax capacity uplift ($/yr) | $65,206,140 |
| Rounded property-tax capacity uplift ($/yr) | $65,200,000 |
| Formula: Property-tax capacity uplift ≈ market value × 45% × 10.848% × phase-in | |
| 4C. Total modeled recurring NYC revenue capacity | Low | Mid | High |
|---|---|---|---|
| Property-tax capacity uplift ($/yr) | $65,200,000 | $65,206,140 | $65,200,000 |
| CRT modeled ($/yr) | $4,504,500 | $5,469,750 | $6,113,250 |
| Total recurring NYC revenue capacity ($/yr) | $69,704,500 | $70,675,890 | $71,313,250 |
| Rounded total recurring NYC revenue capacity ($/yr) | $69,700,000 | $70,700,000 | $71,300,000 |
| 4D. Plain-English tax benefits | Value |
|---|---|
| Benefit 1 | More leased space can support higher building income |
| Benefit 2 | Higher income can support higher property value |
| Benefit 3 | Higher value can support higher NYC property-tax revenue capacity |
| Benefit 4 | Leased commercial space can also generate CRT revenue |
| 4E. Scope note |
|---|
| This section models recurring NYC revenue capacity from stabilized leasing and value. It excludes one-time transaction taxes such as RPTT, RETT, and mortgage recording tax, and keeps jobs separate to avoid double counting. |
© Invent.City
AI and Robotics
AI and robotics will change how cities work. They can predict failures, automate routine work, manage buildings, optimize streets, improve logistics, monitor infrastructure, and speed public services.
- City operations: faster sensing, maintenance, dispatch, inspection, and response.
- Urban systems: smarter energy, mobility, water, waste, safety, and building performance.
- Economic upside: new companies, new jobs, new services, and a productivity layer for existing industries.
- Public risk: bias, surveillance, job disruption, cybersecurity, and accountability must be tested before scale.
Climate Risk
Climate risk is now an economic issue for cities. Heat, flooding, sea-level rise, water stress, insurance pressure, infrastructure damage, and energy demand are forcing cities to adapt and decarbonize.
- Risk creates demand. Cities need tools that reduce damage, cost, downtime, and exposure.
- Adaptation is a market. Buildings, streets, waterfronts, power, water, and public spaces all need upgrades.
- Proof matters. Buyers need to see performance before they commit capital.
- FiDi is a test case. Waterfront risk, dense buildings, transit, and public space make the district a strong proving ground.
Demographic Change
Demographic change is reshaping urban demand. Aging cities need safer, more accessible systems. Fast-growing cities need housing, mobility, energy, water, waste, health, food, and services at scale.
- Aging creates new needs: care, mobility, housing, health, safety, and service delivery.
- Growth creates pressure: infrastructure, affordability, congestion, sanitation, and power demand.
- Both create markets: cities must keep upgrading the systems residents depend on.
- Invent City can help: show what works, compare options, and move solutions faster into deployment.
Economics Drives Action
Cost and benefit move decisions. Cities, investors, property owners, companies, agencies, and residents act faster when the economic case is clear, measurable, and urgent.
The cost of inaction is rising. Climate damage could cost the global economy up to $38T annually by 2050, according to the Potsdam Institute for Climate Impact Research.
The upside of action is massive. New urban-solution markets could exceed $43T by 2050 in corporate revenues across buildings, energy, mobility, water, waste, resilience, AI, and city operations.
Invent City turns urgency into opportunity. It frames urban challenges as investable markets, helps companies prove value, helps buyers compare solutions, and helps cities move faster.
The goal is simple: make action easier, faster, and more profitable than delay.
| Benchmark | 2050 Value |
|---|---|
| Urban Market Size | $43.4T |
| Cost of Climate Crisis | $38T |
| California GDP | $10.8T |
| Texas GDP | $6.9T |
| Metro NYC GDP | $5.8T |
| Florida GDP | $4.3T |
Cities are a Growth Market
Cities are one of the world’s biggest growth markets. More than 4B people live in urban areas today. More than 6B could by 2100. That creates massive demand for better buildings, energy, mobility, water, waste, logistics, safety, public space, AI, and city services.
For companies, cities are customers with urgent needs. Older cities must modernize. Fast-growing cities must build. Climate risk, aging infrastructure, population growth, electrification, AI, and rising public expectations are forcing a long upgrade cycle.
For investors, cities create recurring demand. Cities do not buy once. They retrofit, maintain, replace, expand, finance, regulate, and improve year after year. A solution that proves value in one city can scale to many others.
- Buildings need upgrades. Energy, cooling, electrification, retrofits, security, elevators, facades, roofs, and operations.
- Streets need systems. Mobility, curb space, delivery, safety, lighting, sensors, vegetation, waste, and public space.
- Infrastructure needs reinvestment. Power, water, wastewater, stormwater, resilience, logistics, emergency response, and digital systems.
- Operations need intelligence. AI, robotics, permitting, inspections, procurement, routing, service delivery, and public communication.
Invent City turns that demand into a market. It gives companies a place to demonstrate solutions, gives buyers a place to compare them, and gives investors a clearer path from proof to scale.
Stakeholders
Stakeholders are the real market. Urban solutions scale only when the people who fund, approve, buy, operate, visit, work in, and live with change see clear value.
- Budget holders decide what gets funded.
- Agencies and regulators decide what gets approved.
- Operators and owners decide what gets implemented.
- Residents, workers, and visitors decide whether change earns trust.
- Companies and investors need proof, demand signals, partners, and a path to scale.
Invent City keeps stakeholders close. That makes product-market fit clearer, pilots stronger, and adoption faster.
The Scale of the Urban Market
The urban-solutions market is large enough to anchor a major economic cluster. It is not one narrow sector. It cuts across buildings, energy, mobility, water, waste, logistics, AI, robotics, safety, food, health, and services.
The economic logic is direct. Climate damage is becoming more expensive. Urban upgrades are becoming more urgent. Companies that solve real city problems can reach very large, recurring markets.
- Risk creates urgency. Heat, flooding, energy stress, aging infrastructure, and service pressure increase demand.
- Scale creates opportunity. Cities buy systems, not just products.
- Proof creates adoption. Buyers need evidence before spending public or private capital.
- Invent City creates proof. FiDi can become a visible marketplace for solutions that cities need worldwide.
Innovation District
An innovation district is a concentrated growth zone. It puts companies, startups, investors, agencies, universities, talent, suppliers, buyers, and civic partners in one connected place.
Proximity creates speed. Ideas move faster. Partnerships form faster. Buyers are easier to reach. Talent is easier to find. Capital sees the market more clearly.
The model is proven. Kendall Square shows the power of universities, biotech, tech, capital, and talent. 22@Barcelona shows how old industrial land can become an innovation economy. EUREF-Campus Berlin shows how energy, mobility, climate, and urban systems can be tested together.
Invent City applies the model to the future of cities. FiDi can concentrate companies solving urgent urban needs: AI, climate, mobility, energy, buildings, water, waste, logistics, resilience, robotics, and public services.
- Speed learning. Ideas, data, talent, and opportunities move faster when the market is close.
- Lower friction. Buyers, partners, suppliers, investors, and agencies become easier to reach.
- Build trust. Companies can demonstrate solutions in real urban conditions.
- Grow demand. A visible cluster attracts firms, capital, media, visitors, and public-sector attention.
- Create value. Underused space becomes productive economic infrastructure.
Innovation District Examples
The strongest innovation districts show how place can accelerate growth. They cluster talent, research, companies, capital, customers, culture, and infrastructure so ideas move faster from concept to market.
- Kendall Square — Cambridge: MIT, biotech, tech, venture capital, research, and talent in one dense ecosystem.
- 22@Barcelona — Spain: former industrial land turned into a technology, design, media, and knowledge district.
- EUREF-Campus — Berlin: a living lab for climate, energy, mobility, buildings, and urban systems.
- Zhangjiang Science City — Shanghai: a major platform for AI, semiconductors, life sciences, research, and global companies.
- Dubai Internet City — UAE: a tech district that concentrates global firms, startups, talent, and digital infrastructure.
- MaRS Discovery District — Toronto: startups, research, investors, corporations, and public partners in health, climate, fintech, and advanced technology.
Invent City uses the same principle. Concentrate the market. Reduce friction. Accelerate adoption.
Economic Benefits
NYC grows by building economic engines. Finance made Wall Street global. Real estate funds the city. Tourism fills hotels, restaurants, retail, culture, and transit. Media, advertising, fashion, universities, healthcare, tech, logistics, and culture keep NYC visible and competitive.
Invent City can become the next engine. It targets the industries that cities must buy, finance, regulate, and deploy: AI, climate, mobility, energy, buildings, water, waste, safety, food, health, logistics, robotics, and public services.
Innovation districts turn concentration into growth. They put companies, buyers, investors, agencies, talent, institutions, and real estate into one market. That lowers friction, speeds partnerships, and makes adoption easier.
- For companies: faster market access, stronger visibility, better partnerships, and lower go-to-market friction.
- For investors: better access to companies, customers, data, demand signals, pilots, and deployment opportunities.
- For property owners: productive reuse of vacant space, stronger tenant demand, higher activity, and more valuable buildings.
- For NYC: jobs, visitor spending, business formation, real estate recovery, tax revenue, and a global platform for urban solutions.
The opportunity is direct: turn FiDi’s underused space into a market for the future of cities.
A Proven Platform
Trade marts work because they concentrate markets. They bring sellers, buyers, showrooms, events, media, and deal-making into one place. Invent City applies that proven model to urban solutions.
Chicago Merchandise Mart
A large-scale showroom marketplace that proves how one building can anchor an industry.
Atlanta Market
A permanent marketplace that attracts buyers, sellers, product launches, events, and recurring business activity.
Pacific Design Center
A showroom destination where architecture, events, and market identity reinforce each other.
Chelsea Harbour
A design-focused marketplace where buyers can see products, compare options, and meet brands in one place.
Tokyo Model
A dense urban market where product display, transit access, and city activity strengthen commercial reach.
Vienna Model
A place-based model showing how design, infrastructure, and civic value can support long-term economic identity.
Buy / Sell Platform
The basic logic: sellers gain visibility, buyers compare options, and deals move faster.
World Trade Mart Models
A global pattern: concentrated marketplaces turn fragmented industries into more efficient commercial ecosystems.
Creating Value
For Sellers
The Trade Mart creates value by concentrating demand. Sellers get customers, partners, investors, agencies, project teams, delegations, and media in one market.
- Demo better. Show complex systems in a real city context.
- Sell faster. Meet qualified buyers and project teams more efficiently.
- Build credibility. Use FiDi as proof, not just promotion.
- Scale relationships. Turn visits into pilots, financing, partnerships, and deployment.
For Buyers
The Trade Mart creates value by making choices easier. Buyers can find, compare, test, question, finance, and deploy solutions with less friction.
- Compare faster. See multiple solutions side by side.
- Reduce risk. Test performance before committing capital.
- Understand ROI. See cost, savings, operations, and impact more clearly.
- Move to action. Go from interest to pilots, procurement, financing, and deployment.
Living Lab
FiDi makes the Trade Mart more powerful because it is not a showroom in isolation. It is a real district with real streets, real buildings, real energy demand, real waterfront risk, real public space, and real operating problems to solve.
Open space can become a testing ground. Streets, plazas, POPS, parks, sidewalks, rooftops, and waterfront areas can demonstrate mobility, shade, lighting, water, public safety, waste, logistics, sensors, vegetation, and public-realm technology.
Buildings can become proof points. FiDi’s towers, lobbies, retail spaces, mechanical systems, rooftops, facades, basements, and loading areas can show solutions for retrofits, energy efficiency, electrification, cooling, resilience, security, and operations.
Energy needs can drive innovation. Dense buildings, rising cooling loads, electrification, backup power, EV charging, batteries, microgrids, solar, demand management, and smart controls can all be tested in a high-value urban environment.
- Find solutions. Discover vendors across buildings, streets, energy, mobility, water, waste, logistics, AI, and resilience.
- Compare options. Evaluate performance, cost, risk, fit, savings, and ROI in context.
- Test before scaling. Use FiDi as a practical proving ground before citywide or global deployment.
- Move to deployment. Connect solutions to financing, approvals, partners, project teams, and procurement.
Buyer–Seller Engine
The Trade Mart connects supply and demand. Sellers need qualified customers. Buyers need trusted solutions. Invent City puts both sides in a permanent marketplace built around urgent urban needs.
- Sellers gain access. Customers, partners, agencies, investors, project teams, media, and delegations are easier to reach.
- Buyers gain clarity. Solutions can be seen, compared, questioned, tested, financed, and deployed.
- Deals gain momentum. Showrooms, events, data, pilots, and follow-up tools shorten the path from interest to action.
Showrooms
Showrooms make urban solutions easier to buy. They turn complex products, systems, data, and services into experiences that customers can see, compare, and trust.
- For companies: a permanent place to explain, demonstrate, and sell.
- For buyers: a faster way to understand value, cost, risk, and fit.
- For investors: a clearer view of demand, traction, and deployment potential.
- For NYC: a visitor draw, tenant driver, and commercial engine for the district.
Showrooms turn attention into action. They help products move from pitch to proof to purchase.
Immersive Showrooms
Immersive showrooms turn sales pitches into proof. Buyers do not just hear the story. They walk into it.
They make value visible. Future streets, buildings, parks, rooftops, waterfronts, mobility systems, and city operations can be experienced before a buyer invests.
- See the problem. Show heat, flooding, congestion, waste, risk, cost, or operational pain.
- See the solution. Demonstrate performance, savings, resilience, comfort, safety, and ROI.
- Reduce doubt. Buyers understand faster and trust sooner.
- Extend the sale. Roundtables and digital platforms take the experience global before and after each visit.
NYC
NYC gives Invent City instant global credibility. It concentrates capital, media, talent, tourism, culture, real estate, universities, NGOs, advertising, agencies, employers, and decision-makers.
- Capital: Wall Street, banks, private equity, venture capital, family offices, and institutional investors.
- Media: global news, business press, streaming, publishing, advertising, podcasts, and social reach.
- Talent: finance, tech, design, engineering, real estate, policy, operations, and creative industries.
- Institutions: universities, hospitals, foundations, NGOs, agencies, and civic networks.
- Tourism: a built-in global audience for events, delegations, showcases, and public experiences.
What works in New York travels. NYC turns Invent City into a global signal for the future of cities.
FiDi
FiDi gives Invent City leverage no other district can match. Wall Street capital. Global visibility. Deep transit. Airport access. Waterfront risk. Resilience projects. Vacant space. Hotels. Ferries. Blue Highway potential. City Hall nearby.
- Transit: subway, bus, ferry, bike, pedestrian, and airport connections.
- Capital: Wall Street, financial firms, investors, insurers, and corporate decision-makers.
- Risk: waterfront exposure makes resilience tangible, urgent, and visible.
- Space: vacant offices, retail, POPS, streets, rooftops, and public spaces can be activated quickly.
- Audience: workers, residents, tourists, delegations, agencies, media, and civic leaders are already nearby.
FiDi has the assets, urgency, and audience to make Invent City real fast.
IDCNY Comparison
IDCNY shows how showroom commerce can create a destination. Invent City applies that logic to the future of cities: showrooms, trade activity, events, technology, buyers, and market identity.
- Permanent marketplace: buyers can return year-round, not just during one event.
- Cluster identity: a place becomes known for a category.
- Tenant logic: companies locate where customers, partners, and peers concentrate.
- Invent City upgrade: apply the model to urban systems, climate, AI, robotics, buildings, energy, mobility, and resilience.
Vacant Space
Vacant space is FiDi’s pain point — and Invent City’s opening. Empty offices, retail, POPS, streets, parks, and public-realm assets give Invent City a faster, cheaper launch path than new construction.
- Office space: convert floors into showrooms, labs, offices, and trade-mart operations.
- Retail space: create street-level demos, pop-ins, pilots, exhibits, and visitor-facing showcases.
- POPS and streets: test mobility, shade, seating, lighting, sensors, waste, logistics, and public-realm technology.
- Public value: owners gain tenants, streets gain life, and NYC gains jobs, activity, and recurring revenue.
Urban Hubs
Urban hubs can put essential street services in one place. They can reduce clutter, support workers, improve logistics, and make streets cleaner, safer, and more useful.
- Logistics: package transfer, Blue Highway freight, micro-fulfillment, storage, and last-mile routing.
- Mobility: bike share, scooters, charging, battery exchange, parking, and repair.
- Public needs: bathrooms, shade, seating, water, information, wayfinding, and emergency support.
- Cleaner operations: waste consolidation, recycling, sanitation support, and reduced truck conflicts.
Pedestrianization
Pedestrianization can turn streets into economic and civic assets. Invent City does not depend on it, but better public space can support visitors, events, retail, safety, mobility, cleaner streets, and a stronger district identity.
- Times Square — New York: pedestrianization helped create safer, more active, more valuable public space.
- Strøget — Copenhagen: an early pedestrian-street model that helped prove car-free central streets can support commerce and urban life.
- Fort Street — Auckland: shared-street improvements were associated with higher pedestrian volumes and increased consumer spending.
- Gotham Park — FiDi: underused space near the Brooklyn Bridge can support programming, public life, and district activation.
Utopian AI
AI can help cities become smarter, faster, cleaner, safer, and more humane. It can cut energy waste, predict failures, improve services, manage traffic, speed inspections, simplify permits, support healthcare, strengthen resilience, and help agencies do more with less.
Different tools can solve different city problems. [LLMs] can explain rules, permits, contracts, service requests, and city data. [Agentic AI] can coordinate work across agencies, buildings, vendors, and field teams. Predictive AI can flag risks before they become failures.
- Better buildings. Lower energy use, predict maintenance, manage occupancy, and improve safety.
- Better streets. Improve traffic, curb space, deliveries, lighting, and pedestrian safety.
- Better services. Speed permitting, inspections, routing, reporting, and customer support.
- Better resilience. Forecast flooding, heat, energy demand, infrastructure stress, and emergency needs.
Dystopian AI
AI can also make cities colder, unfairer, and harder to govern. It can automate bias, expand surveillance, displace workers, deny services, expose private data, hide accountability, and shift public decisions into systems people cannot see or challenge.
The danger is not just bad technology. It is bad deployment. Cities can lose control over data, decisions, cybersecurity, labor impacts, procurement, public trust, and critical infrastructure if AI systems scale without clear standards.
- Bias risk. Bad data can produce unfair outcomes in housing, lending, hiring, policing, and services.
- Surveillance risk. Cameras, sensors, and data platforms can track people without clear limits.
- Labor risk. Automation can eliminate jobs or degrade work without shared benefits.
- Governance risk. Opaque systems can weaken accountability, security, transparency, and public trust.
Urban Robotics
Robotics brings AI into the physical city. AI can plan, sense, learn, and decide. Robots can act. Together, they can help cities move goods, inspect assets, clean streets, maintain buildings, support workers, and respond faster.
Urban robotics can solve real operating problems. Delivery robots can reduce curb pressure. Inspection robots can check tunnels, roofs, facades, elevators, and utilities. Cleaning robots can support public spaces. Service robots can help in hotels, hospitals, offices, and transit hubs.
But robotics must earn public trust. Cities need to test safety, accessibility, labor impact, liability, cybersecurity, privacy, sidewalk conflicts, emergency behavior, and human oversight before robots scale in public space.
- Logistics: delivery robots, loading support, warehouse automation, curb management, and Blue Highway connections.
- Buildings: inspection robots, cleaning systems, maintenance tools, security support, elevators, roofs, basements, and mechanical rooms.
- Public realm: sidewalk robots, waste collection, sanitation, lighting checks, park maintenance, and street-condition monitoring.
- Emergency response: robots and drones for flooding, fire, search, damage assessment, hazardous areas, and rapid situational awareness.
- Governance: clear rules for safety, labor, accessibility, privacy, liability, cybersecurity, and public acceptance.
Living Laboratory
Invent City can become a real-world lab for urban AI. Companies can test tools in actual city conditions before cities buy, regulate, finance, or scale them.
Different forms of AI can solve different city problems. LLMs can explain rules, permits, contracts, service requests, and city data. Agentic AI can coordinate tasks across systems. Computer vision can monitor streets, buildings, safety, traffic, waste, and flooding. Predictive AI can forecast demand, failure, risk, cost, and maintenance needs.
FiDi gives AI a real test environment. It has office towers, older buildings, dense streets, transit, ferries, waterfront risk, vacant space, public plazas, hotels, visitors, workers, residents, security needs, logistics pressure, and energy demand.
- Buildings: energy use, cooling loads, maintenance, occupancy, retrofits, security, elevators, inspections, and tenant services.
- Streets: traffic flow, curb management, delivery routing, pedestrian safety, micromobility, sensors, lighting, and public-space operations.
- Waterfront: flood prediction, storm response, resilience planning, emergency alerts, public-realm protection, and insurance-risk modeling.
- Energy: demand management, electrification, batteries, microgrids, EV charging, solar, backup power, and building-to-grid coordination.
- Governance: privacy, bias, cybersecurity, accountability, transparency, labor impact, and public trust.
AI Showrooms
AI showrooms make a complex market easier to understand. Buyers, investors, agencies, operators, and delegations can compare tools, run simulations, review data, test ROI, and move faster.
- Show the system. Make invisible algorithms, data flows, assumptions, and outputs understandable.
- Compare alternatives. Evaluate tools side by side for cost, risk, performance, governance, and fit.
- Build confidence. Show limits, safeguards, accountability, and public value before purchase.
- Move faster. Turn demos into pilots, pilots into procurement, and procurement into deployment.
24/7 Global Reach
Invent City should work beyond FiDi and beyond event dates. Digital channels, media, data, virtual showrooms, and AI tools can keep the platform active across time zones.
- Before a visit: help buyers discover companies, compare solutions, and plan meetings.
- During a visit: guide delegations through showrooms, demos, events, pilots, and data.
- After a visit: keep relationships moving toward pilots, procurement, financing, and deployment.
- Every day: turn global attention into leads, trust, partnerships, and sales.