The Department of Housing Preservation and Development (HPD) reports on projects, buildings, and units that began after January 1, 2014, and are counted towards either the Housing New York plan (1/1/2014 – 12/31/2021) or the Housing Our Neighbors: A Blueprint for Housing & Homelessness plan (1/1/2022 – present).

The Department of Housing Preservation and Development (HPD) reports on projects, buildings, and units that began after January 1, 2014, and are counted towards either the Housing New York plan (1/1/2014 – 12/31/2021) or the Housing Our Neighbors: A Blueprint for Housing & Homelessness plan (1/1/2022 – present).

Statistics on youth in foster care reported in compliance with Local Law 145 amended by City Council.

Cells with one to five youth are not shown to protect anonymity.

A list of contractors and subcontractors who are ineligible for inclusion on any HPD prequalified list(s) for housing development projects due to a failure to provide the wage reporting information required by Local Law 44 of 2012 (LL44). Information about LL44 is available on HPD’s LL44 webpage: http://www1.nyc.gov/site/hpd/developers/transparency-ll44.page

The Department of Housing Preservation and Development (HPD) receives a sub-allocation of 9% Low Income Housing Tax Credits and allocated its credits through one competitive round each calendar year. It is also charged with allocating 4% Low Income Housing Tax Credits to projects receiving tax exempt bonds through New York City Housing Development Corporation.
Each entry represents an allocation to a low income housing development project with households at or below 60% of Area Median Income.

For the Low Income Housing Tax Credits Awarded by HPD: Building-Level (9% Awards) dataset, please follow this link

Number of units from projected future housing growth in the City as used in enrollment projection.

The Department of Housing Preservation and Development (HPD) receives a sub-allocation of 9% Low Income Housing Tax Credits and allocated its credits through one competitive round each calendar year. It is also charged with allocating 4% Low Income Housing Tax Credits to projects receiving tax exempt bonds through New York City Housing Development Corporation.
Each entry represents an allocation to a low income housing development project with households at or below 60% of Area Median Income.

For the Low Income Housing Tax Credits Awarded by HPD: Project-Level (4% Awards) dataset, please follow this link

This chart shows the percentage of homes where mice were observed or reported at the initial visit and revisit, by county for the 2009-2014 funding cycle. The chart gives an indication of the ability of the HNP environmental intervention to decrease the prevalence of mice in the home. The HNP pest control intervention involves providing products (such as traps and cleaning supplies) and educating residents about removing harborage and food for pests by storing garbage appropriately, cleaning, and repairing holes that give mice entry into the home. In some cases, the HNP contacts local code enforcement or the landlord.
We can see that the initial visit percentages vary substantially across the across counties, which could reflect differing geography, urban versus rural target areas, and the quality of the housing stock. Except for one county, the homes that were selected for a revisit have a lower percentage with mice homes that had an initial visit, indicating an overall reduction in the percentage of homes with mice after the HNP intervention, although mice were not eradicated from all the homes. Oneida and Cortland Counties show no mice in the revisited homes, which could be an indication of success of the intervention and/or the selection of homes to revisit. For Westchester County, it is possible that the homes that the revisited homes were the worst and, therefore, had a higher percentage. These three counties demonstrate why the comparisons need to be interpreted with caution. Because revisits are a subset of the initial visits, the comparison has limitations. Please read the overview document under the “About” tab for more information on the limitations.

The Department of Housing Preservation and Development (HPD) receives a sub-allocation of 9% Low Income Housing Tax Credits and allocated its credits through one competitive round each calendar year. It is also charged with allocating 4% Low Income Housing Tax Credits to projects receiving tax exempt bonds through New York City Housing Development Corporation.
Each entry represents an allocation to a low income housing development project with households at or below 60% of Area Median Income.

For the Low Income Housing Tax Credits Awarded by HPD: Building-Level (4% Awards) dataset, please follow this link

The Department of Housing Preservation and Development (HPD) receives a sub-allocation of 9% Low Income Housing Tax Credits and allocated its credits through one competitive round each calendar year. It is also charged with allocating 4% Low Income Housing Tax Credits to projects receiving tax exempt bonds through New York City Housing Development Corporation.
Each entry represents an allocation to a low income housing development project with households at or below 60% of Area Median Income.

For the Low Income Housing Tax Credits Awarded by HPD: Project-Level (9% Awards) dataset, please follow this link

The NYS Healthy Neighborhoods Program (HNP) is a healthy homes program that seeks to reduce the burden of housing-related illness and injury. The dataset includes information about building characteristics, primary respondent demographics and the presence/absence of 34 specific housing conditions for dwellings visited by each county program.

Each dwelling visited by the HNP is classified into one of three periods based on the year it was built: before 1950 (“pre-1950”), built 1950-1978, or built after 1978. These periods relate to the general age of the housing. Older housing can present health and safety hazards to their residents. In addition, these groupings coincide with the presence of lead in paint, a health hazard for young children.
For dwellings visited by the Clinton County HNP, this chart displays the percent of homes built in each period by funding cycle. For example, the first set of bars shows the percent of dwellings that were built before 1950 during each of the 3 funding cycles. We can see that Clinton County has increased the percent of the oldest homes from about 10% in the first funding cycle to almost 30% during the current funding cycle. Looking at the orange bars, we see that during the current funding cycle, Clinton County’s HNP has visited about the same percentage of homes built in each time period.
The counties participating in the Healthy Neighborhoods Program can create this type of chart to view trends in how they are targeting their visits. For example, if a county wants to reach older homes, it may want to refocus its efforts on communities with older housing stock. The "About" tab contains additional details concerning this dataset.

Local law 159 of 2019 allows HPD to be reimbursed for charges incurred for the relocation of occupants as a result of vacate orders that were issued by HPD, the Fire Department, the Department of Buildings, and other NYC agencies. Such charges, if not paid by the owner, become a tax lien against the property, and are charged to the owner through the Department of Finance statement of account for taxes. The Local Law 159 of 2019: Vacate Relocation Charges By Building dataset shows all aggregate building payments made by HPD in connection with charges incurred for the relocation of occupants as a result of vacate orders. Please see the data dictionary for more details about this dataset.

This chart shows the percentage of homes with at least one smoker at the initial visit and revisit, by county for the 2009-2014 funding cycle. The chart gives an indication of the ability of the HNP’s smoking intervention (referrals and education) to decrease the prevalence of smoking in the home. The initial visit percentages range across counties from about 8% to almost 60%. There was a substantial reduction in Albany County, but most counties are essentially unchanged which may indicate the difficulty in changing people’s smoking habits. Three counties show no homes with smokers at the revisit, but these are newly funded counties that have conducted very few revisits to date. Because revisits are a subset of the initial visits, closer examination of the dataset is necessary to confirm the number of initial visits and revisits that were conducted before drawing conclusions. Please read the overview document under the “About” tab for more information on the limitations.

This filtered view of the dataset is a list of the county health departments that were granted funds to implement the Healthy Neighborhoods Program (HNP) during each of three funding cycles (2006-2009, 2009-2014, and 2014-2019). The list of county health departments differs across funding cycles. For example, Albany County received funding from 2006-2014, whereas Clinton County has received funding continuously since 2006. It is important to be aware of the funding history for each county when looking at trends in housing demographics and conditions. The "About" tab contains additional details concerning this dataset.

The Mandatory Inclusionary Housing program was adopted in 2016 to encourage the creation of housing for families at a range of incomes in areas planned for housing growth. When actions of the Commission significantly increase residential density, the City Planning Commission and the City Council will apply one or more options to require affordable housing as part of new residential construction.

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

"Neighborhood Financial Health (NFH) Digital Mapping and Data Tool provides neighborhood financial health indicator data for every neighborhood in New York City. DCWP's Office of Financial Empowerment (OFE) also developed NFH Indexes to present patterns in the data within and across neighborhoods. NFH Index scores describe relative differences between neighborhoods across the same indicators; they do not evaluate neighborhoods against fixed standards. OFE intends for the NFH Indexes to provide an easy reference tool for comparing neighborhoods, and to establish patterns in the relationship of NFH indicators to economic and demographic factors, such as race and income. Understanding these connections is potentially useful for uncovering systems that perpetuate the racial wealth gap, an issue with direct implications for OFE’s mission to expand asset building opportunities for New Yorkers with low and moderate incomes. This data tool was borne out of the Collaborative for Neighborhood Financial Health, a community-led initiative designed to better understand how neighborhoods influence the financial health of their residents.

The Department of City Planning aggregates information about 30,000+ facilities and program sites that are owned, operated, funded, licensed, or certified by a City, State, or Federal agency in the City of New York into a central database called the City Planning Facilities Database (FacDB). These facilities generally help to shape quality of life in the city’s neighborhoods, and this dataset is the basis for a series of planning activities. This public data resource allows all New Yorkers to understand the breadth of government resources in their neighborhoods. The data is also complemented with a new interactive web map that enables users to easily filter the data for their needs. Users are strongly encouraged to read the database documentation, particularly with regard to analytical limitations.

Questions about this database can be directed to dcpopendata@planning.nyc.gov

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

The dataset is underlying data of the EquityNYC website (https://equity.nyc.gov), which was developed by the Mayor's Office for Economic Opportunity to explore the economic, social, environmental, and physical health of New York City across race/ethnicity, gender, neighborhood, and income.