This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency because existing conditions pose a serious and immediate threat to the health or welfare of the community and other financial resources are not available to meet that need. With respect to activities that principally benefit low- and moderate-income persons, at least 51 percent of the activity's beneficiaries must be low and moderate income. For CDBG, a person is considered to be of low income only if he or she is a member of a household whose income would qualify as "very low income" under the Section 8 Housing Assistance Payments program. Generally, these Section 8 limits are based on 50% of area median. Similarly, CDBG moderate income relies on Section 8 "lower income" limits, which are generally tied to 80% of area median. These data are derived from the 2011-2015 American Community Survey (ACS) and based on Census 2010 geography.

To learn more about the Low to Moderate Income Populations visit: https://www.hudexchange.info/programs/acs-low-mod-summary-data/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low to Moderate Income Populations by Tract

FY2024 full and partial census tracts that qualify as Low-Moderate Income Areas (LMA) where 51% or more of the population are considered as having Low-Moderate Income. The low- and moderate-income summary data (LMISD) is based on the 2016-2020 American Community Survey (ACS). As of August 1, 2024, to qualify any new low- and moderate-income area (LMA) activities, Community Development Block Grant (CDBG) grantees should use this map and data.

For more information about LMA/LMI click the following link to open in new browser tab https://www.hudexchange.info/programs/cdbg/cdbg-low-moderate-income-data/

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015. Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population. The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight. The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income (LMI) persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency. Most activities funded by the CDBG program are designed to benefit low- and moderate-income (LMI) persons. That benefit may take the form of housing, jobs, and services. Additionally, activities may qualify for CDBG assistance if the activity will benefit all the residents of a primarily residential area where at least 51 percent of the residents are low- and moderate-income persons, i.e. area-benefit (LMA). [Certain exception grantees may qualify activities as area-benefit with fewer LMI persons than 51 percent.]The Office of Community Planning and Development (CPD) provides estimates of the number of persons that can be considered Low-, Low- to Moderate-, and Low-, Moderate-, and Medium-income persons based on special tabulations of data from the 2016-2020 ACS 5-Year Estimates and the 2020 Island Areas Census. The Low- and Moderate-Income Summary Data may be used by CDBG grantees to determine whether or not a CDBG-funded activity qualifies as an LMA activity. The LMI percentages are calculated at various principal geographies provided by the U.S. Census Bureau. CPD provides the following datasets:Geographic Summary Level "150": Census Tract-Block Group.The block groups are associated with the HUD Unit-of-Government-Identification-Code for the CDBG grantee jurisdiction by fiscal year that is associated with each block group.Local government jurisdictions include; Summary Level 160: Incorporated Cities and Census-Designated Places, i.e. "Places", Summary Level 170: Consolidated Cities, Summary Level 050: County, and Summary Level 060: County Subdivision geographies.In the data files, these geographies are identified by their Federal Information Processing Standards (FIPS) codes and names for the place, consolidated city, or block group, county subdivision, county, and state.The statistical information used in the calculation of estimates identified in the data sets comes from the 2016-2020 ACS, 2020 Island Areas Census, and the Income Limits for Metropolitan Areas and for Non Metropolitan Counties. The data necessary to determine an LMI percentage for an area is not published in the publicly-available ACS data tables. Therefore, the Bureau of Census matches family size, income, and the income limits in a special tabulation to produce the estimates.Estimates are provided at three income levels: Low Income (up to 50 percent of the Area Median Income (AMI)); Moderate Income (greater than 50 percent AMI and up to 80 percent AMI), and Medium Income (greater than 80 percent AMI and up to 120 AMI). HUD is publishing the margin of error (MOE) data for all block groups and all places in the 2020 ACS LMISD. These data are provided within the LMISD tables.The MOE does not provide an expanded range for compliance. For example, a service area of 50 percent LMI with a 2 percent MOE would still be just 50 percent LMI for compliance purposes. However, the 2 percent MOE would inform the grantee about the accuracy of the ACS data before undergoing the effort and cost of conducting a local income survey, which is the alternative to using the HUD-provided data.CPD Notice 24-04 announced the publication of LMISD based on the 2020 ACS, and updated CPD Notice 19-02 as well as explains policy about the accuracy of surveys conducted pursuant to CPD Notice 14-013.Questions about the calculation of the estimates may be directed to Formula Help Desk.Questions about the use of the data should be directed to the staff of the CPD Field Office.

This is a report for all the relevant columns of DCA - Amount Allocated, Obligated, Paid- broken down by program, project, county and municipality.

A Qualified Census Tract (QCT) is any census tract (or equivalent geographic area defined by the Census Bureau) in which at least 50% of households have an income less than 60% of the Area Median Gross Income (AMGI). HUD has defined 60% of AMGI as 120% of HUD's Very Low Income Limits (VLILs), which are based on 50% of area median family income, adjusted for high cost and low income areas.

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income (LMI) persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency. Most activities funded by the CDBG program are designed to benefit low- and moderate-income (LMI) persons. That benefit may take the form of housing, jobs, and services. Additionally, activities may qualify for CDBG assistance if the activity will benefit all the residents of a primarily residential area where at least 51 percent of the residents are low- and moderate-income persons, i.e. area-benefit (LMA). [Certain exception grantees may qualify activities as area-benefit with fewer LMI persons than 51 percent.]The Office of Community Planning and Development (CPD) provides estimates of the number of persons that can be considered Low-, Low- to Moderate-, and Low-, Moderate-, and Medium-income persons based on special tabulations of data from the 2016-2020 ACS 5-Year Estimates and the 2020 Island Areas Census. The Low- and Moderate-Income Summary Data may be used by CDBG grantees to determine whether or not a CDBG-funded activity qualifies as an LMA activity. The LMI percentages are calculated at various principal geographies provided by the U.S. Census Bureau. CPD provides the following datasets:Geographic Summary Level "150": Census Tract-Block Group.The block groups are associated with the HUD Unit-of-Government-Identification-Code for the CDBG grantee jurisdiction by fiscal year that is associated with each block group.Local government jurisdictions include; Summary Level 160: Incorporated Cities and Census-Designated Places, i.e. "Places", Summary Level 170: Consolidated Cities, Summary Level 050: County, and Summary Level 060: County Subdivision geographies.In the data files, these geographies are identified by their Federal Information Processing Standards (FIPS) codes and names for the place, consolidated city, or block group, county subdivision, county, and state.The statistical information used in the calculation of estimates identified in the data sets comes from the 2016-2020 ACS, 2020 Island Areas Census, and the Income Limits for Metropolitan Areas and for Non Metropolitan Counties. The data necessary to determine an LMI percentage for an area is not published in the publicly-available ACS data tables. Therefore, the Bureau of Census matches family size, income, and the income limits in a special tabulation to produce the estimates.Estimates are provided at three income levels: Low Income (up to 50 percent of the Area Median Income (AMI)); Moderate Income (greater than 50 percent AMI and up to 80 percent AMI), and Medium Income (greater than 80 percent AMI and up to 120 AMI). HUD is publishing the margin of error (MOE) data for all block groups and all places in the 2020 ACS LMISD. These data are provided within the LMISD tables.The MOE does not provide an expanded range for compliance. For example, a service area of 50 percent LMI with a 2 percent MOE would still be just 50 percent LMI for compliance purposes. However, the 2 percent MOE would inform the grantee about the accuracy of the ACS data before undergoing the effort and cost of conducting a local income survey, which is the alternative to using the HUD-provided data.CPD Notice 24-04 announced the publication of LMISD based on the 2020 ACS, and updated CPD Notice 19-02 as well as explains policy about the accuracy of surveys conducted pursuant to CPD Notice 14-013.Questions about the calculation of the estimates may be directed to Formula Help Desk.Questions about the use of the data should be directed to the staff of the CPD Field Office.

This is a report for all the relevant columns of DCA - Amount Allocated, Obligated, Paid- broken down by program, project, county and municipality.

The Community Development Block Grant (CDBG) program requires that each CDBG funded activity must either principally benefit low- and moderate-income persons, aid in the prevention or elimination of slums or blight, or meet a community development need having a particular urgency because existing conditions pose a serious and immediate threat to the health or welfare of the community and other financial resources are not available to meet that need. With respect to activities that principally benefit low- and moderate-income persons, at least 51 percent of the activity's beneficiaries must be low and moderate income. For CDBG, a person is considered to be of low income only if he or she is a member of a household whose income would qualify as "very low income" under the Section 8 Housing Assistance Payments program. Generally, these Section 8 limits are based on 50% of area median. Similarly, CDBG moderate income relies on Section 8 "lower income" limits, which are generally tied to 80% of area median. These data are from the 2011-2015 American Community Survey (ACS). To learn more about the Low to Moderate Income Populations visit: https://www.hudexchange.info/programs/acs-low-mod-summary-data/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low to Moderate Income Populations by Block GroupDate of Coverage: ACS 2020-2016

Analysis of ‘NYSERDA Low- to Moderate-Income New York State Census Population Analysis Dataset: Average for 2013-2015’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/8bd0ae94-40d3-4c9b-8a6b-de032e07929f on 12 February 2022.

--- Dataset description provided by original source is as follows ---

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

The Low- to Moderate-Income (LMI) New York State (NYS) Census Population Analysis dataset is resultant from the LMI market database designed by APPRISE as part of the NYSERDA LMI Market Characterization Study (https://www.nyserda.ny.gov/lmi-tool). All data are derived from the U.S. Census Bureau’s American Community Survey (ACS) 1-year Public Use Microdata Sample (PUMS) files for 2013, 2014, and 2015.

Each row in the LMI dataset is an individual record for a household that responded to the survey and each column is a variable of interest for analyzing the low- to moderate-income population.

The LMI dataset includes: county/county group, households with elderly, households with children, economic development region, income groups, percent of poverty level, low- to moderate-income groups, household type, non-elderly disabled indicator, race/ethnicity, linguistic isolation, housing unit type, owner-renter status, main heating fuel type, home energy payment method, housing vintage, LMI study region, LMI population segment, mortgage indicator, time in home, head of household education level, head of household age, and household weight.

The LMI NYS Census Population Analysis dataset is intended for users who want to explore the underlying data that supports the LMI Analysis Tool. The majority of those interested in LMI statistics and generating custom charts should use the interactive LMI Analysis Tool at https://www.nyserda.ny.gov/lmi-tool. This underlying LMI dataset is intended for users with experience working with survey data files and producing weighted survey estimates using statistical software packages (such as SAS, SPSS, or Stata).

--- Original source retains full ownership of the source dataset ---

The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, and energy characteristics. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts. The file below, "1. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "2. Data Dictionary 2018". The Low-Income Energy Affordability Data comes primarily from the 2018 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2018 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit: https://www.energy.gov/eere/slsc/low-income-energy-affordability-data-lead-tool For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), visit: https://betterbuildingsinitiative.energy.gov/accelerators/clean-energy-low-income-communities

This is a report for all the relevant columns of HMFA - The Amount Obligated and Disbursed broken down by federal agency, program, applicant, project, county, and municipality.

The Low-Income Energy Affordability Data (LEAD) Tool was created by the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA) to help state and local partners understand housing and energy characteristics for the low- and moderate-income (LMI) communities they serve. The LEAD Tool provides estimated LMI household energy data based on income, energy expenditures, fuel type, housing type, and geography, which stakeholders can use to make data-driven decisions when planning for their energy goals. From the LEAD Tool website, users can also create and download customized heat-maps and charts for various geographies, housing, energy characteristics, and population demographics and educational attainment. Datasets are available for 50 states plus Puerto Rico and Washington D.C., along with their cities, counties, and census tracts, as well as tribal areas. The file below, "01. Description of Files," provides a list of all files included in this dataset. A description of the abbreviations and units used in the LEAD Tool data can be found in the file below titled "02. Data Dictionary 2022". A list of geographic regions used in the LEAD Tool can be found in files 04-11. The Low-Income Energy Affordability Data comes primarily from the 2022 U.S. Census American Community Survey 5-Year Public Use Microdata Samples and is calibrated to 2022 U.S. Energy Information Administration electric utility (Survey Form-861) and natural gas utility (Survey Form-176) data. The methodology for the LEAD Tool can viewed below (3. Methodology Document). For more information, and to access the interactive LEAD Tool platform, please visit the "10. LEAD Tool Platform" resource link below. For more information on the Better Building's Clean Energy for Low Income Communities Accelerator (CELICA), please visit the "11. CELICA Website" resource below.

This is a report for all the relevant columns of HMFA - The Amount Obligated and Disbursed broken down by federal agency, program, applicant, project, county, and municipality.

Each year, the City of Rochester receives funds from HUD for housing and community development activities to address priority needs locally identified by the City. To receive these federal funds, the City must submit a strategic plan - the Consolidated Plan - every five years that identifies local needs and how these needs will be addressed.The purpose of the Consolidated Plan (Con Plan) is to guide funding decisions over the next five years for specific federal funds. The Con Plan supports three overarching goals applied according to the City’s needs:To provide decent housing by preserving the affordable housing stock, increasing the availability of affordable housing, reducing discriminatory barriers, increasing the supply of supportive housing for those with special needs, and transitioning persons and families experiencing homelessness into housing.To provide a quality living environment through safer, more livable and accessible neighborhoods, greater supports and opportunities for low- and moderate-income (LMI) residents throughout the City, improved public infrastructure and facilities, increased housing choices, and neighborhood reinvestment. To expand economic opportunities through job creation, homeownership opportunities, façade improvement, development activities that promote long-term community viability and the empowerment of low- and moderate-income persons to achieve self-sufficiencyIn summary, the five-year 2020-2024 Consolidated Plan and the first year Annual Action Plan for 2020 have been developed with community input and support the implementation of Rochester 2034. It is expected that the City will continue to fulfill the intent of the CDBG, HOME ESG and HOPWA programs by facilitating the: affordability of safe, decent housing; availability, accessibility, and sustainability of suitable living environments; accessibility of economic opportunities; provision of housing and services for those experiencing homelessness; and meeting the housing and services needs of persons with HIV/AIDS and their families.

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.

This dataset provides access to Qualified Census Tracts (QCTs) in Connecticut to assist in administration of American Rescue Plan (ARP) funds. The Secretary of HUD must designate QCTs, which are areas where either 50 percent or more of the households have an income less than 60 percent of the AMGI for such year or have a poverty rate of at least 25 percent. HUD designates QCTs based on new income and poverty data released in the American Community Survey (ACS). Specifically, HUD relies on the most recent three sets of ACS data to ensure that anomalous estimates, due to sampling, do not affect the QCT status of tracts. QCTs are identified for the purpose of Low-Income Housing Credits under IRC Section 42, with the purpose of increasing the availability of low-income rental housing by providing an income tax credit to certain owners of newly constructed or substantially rehabilitated low-income rental housing projects. Also included are the number of households from the 2010 census (the “p0150001” variable), the average poverty rate using the 2014-2018 ACS data (the “pov_rate_18” variable), and the ratio of Tract Average Household Size Adjusted Income Limit to Tract Median Household Income using the 2014-2018 ACS data (the “inc_factor_18” variable). For the last variable mentioned in the previous paragraph, the income limit is the limit for being considered a very low income household (size-adjusted and based on Area Mean Gross Income). This value is divided by the median household income for the given tract, to get a sense of how the limit and median incomes compare. For example, if ratio>1, it implies that the tract is very low income because the limit income is greater than the median income. This ratio is a compact way to include the separate variables for the household income limit and median household income for each tract.

Mortgage Backed Security Market Outlook

The global mortgage-backed security (MBS) market size was valued at approximately $2.1 trillion in 2023 and is projected to reach $3.5 trillion by 2032, growing at a CAGR of 5.5%. A key driver of this growth is the increasing demand for mortgage-backed securities due to their ability to provide liquidity and diversify investment portfolios. The growth is further stimulated by favorable government policies and increased homeownership rates, which collectively bolster the issuance of new MBS.

One of the primary growth factors for the MBS market is the low-interest-rate environment, which has persisted over recent years. This scenario has encouraged borrowing and refinancing activities, leading to a higher number of mortgages that can be securitized. Moreover, the stability and relatively lower risk associated with MBS compared to other investment vehicles make them an attractive option for institutional investors. Additionally, advancements in financial technology have streamlined the process of bundling and selling these securities, increasing market efficiency.

Another significant factor contributing to the expansion of the MBS market is the role of government-sponsored enterprises (GSEs) such as Fannie Mae, Freddie Mac, and Ginnie Mae. These GSEs guarantee a significant portion of the residential MBS, providing a safety net that minimizes risk for investors. The support from these entities ensures a continuous and reliable flow of investment into the housing sector, which in turn stimulates further securitization of mortgages. Moreover, government policies aimed at bolstering housing finance systems in emerging markets are expected to create additional opportunities for growth.

The diversification of mortgage products, including the rise in demand for commercial mortgage-backed securities (CMBS), is another driving force for the market. Commercial real estate has shown robust growth, and investors are increasingly looking towards CMBS as a way to gain exposure to this sector. The structured nature of these securities, offering tranches with varying risk and return profiles, allows investors to tailor their investment strategies according to their risk tolerance.

In the context of the MBS market, Lenders Mortgage Insurance (LMI) plays a crucial role in facilitating homeownership, especially for borrowers who are unable to provide a substantial down payment. LMI is a type of insurance that protects lenders against the risk of borrower default, allowing them to offer loans with lower down payment requirements. This insurance is particularly significant in markets where home prices are high, and saving for a large deposit is challenging for many potential homeowners. By mitigating the risk for lenders, LMI enables more individuals to enter the housing market, thereby supporting the overall growth of mortgage-backed securities. As a result, LMI not only aids in increasing homeownership rates but also contributes to the liquidity and stability of the housing finance system.

Type Analysis

The mortgage-backed security market is bifurcated into Residential MBS and Commercial MBS. Residential MBS (RMBS) dominate the market due to the larger volume of residential mortgages compared to commercial ones. RMBS are typically backed by residential loans, including home mortgages, and are considered less risky. They offer a steady income stream to investors through mortgage payments made by homeowners. The demand for RMBS is bolstered by the high rate of homeownership and the continuous flow of new mortgages.

On the other hand, Commercial MBS (CMBS) are seeing increased traction due to their attractive yields and the growth of the commercial real estate sector. CMBS are backed by loans on commercial properties such as office buildings, retail centers, and hotels. They offer investors exposure to the commercial property market, which is often less correlated with the residential real estate market, providing an additional layer of diversification. The complexity and higher risk associated with CMBS attract sophisticated investors looking for higher returns.

Within RMBS, the market is further segmented into agency RMBS and non-agency RMBS. Agency RMBS are guaranteed by GSEs, making them more secure and attractive to risk-averse investors. Non-agency RMBS, though not backed by GSEs, offer higher yields and are appealing to investors with a higher risk appetite. The interplay betw

Single-family houses and apartments in large residential buildings with **** or more units were the most popular structure type for American renters in 2023. About ** percent of the population who lived in rental accommodation occupied an apartment in a multifamily building. The share of households renting such apartments was even higher, at about ** percent. In 2023, the average asking rent for an unfurnished apartment in the U.S. declined slightly, after surging for three years in a row.