The Maryland Department of Housing and Community Development offers multifamily finance programs for the construction and rehabilitation of affordable rental housing units for low to moderate income families, senior citizens and individuals with disabilities. Our multifamily bond programs issues tax-exempt and taxable revenue mortgage bonds to finance the acquisition, preservation and creation of affordable multifamily rental housing units in priority funding areas. By advocating for increased production of rental housing units, we help create much-needed jobs and leverage opportunities to live, work and prosper for hardworking Maryland families, senior citizens, and individuals with disabilities throughout the state.​ DISCLAIMER: Some of the information may be tied to the Department’s bond funded loan programs and should not be relied upon in making an investment decision. The Department provides comprehensive quarterly and annual financial information and operating data regarding its bonds and bond funded loan programs, all of which is posted on the publicly-accessible Electronic Municipal Market Access system website (commonly known as EMMA) that is maintained by the Municipal Securities Rulemaking Board, and on the Department’s website under Investor Information. More information accessible here: http://dhcd.maryland.gov/Investors/Pages/default.aspx

The Public Use Database (PUDB) is released annually to meet FHFA’s requirement under 12 U.S.C. 4543 and 4546(d) to publicly disclose data about the Enterprises’ single-family and multifamily mortgage acquisitions. The datasets supply mortgage lenders, planners, researchers, policymakers, and housing advocates with information concerning the flow of mortgage credit in America’s neighborhoods. Beginning with data for mortgages acquired in 2018, FHFA has ordered that the PUDB be expanded to include additional data that is the same as the data definitions used by the regulations implementing the Home Mortgage Disclosure Act, as required by 12 U.S.C. 4543(a)(2) and 4546(d)(1).The PUDB single-family datasets include loan-level records that include data elements on the income, race, and sex of each borrower as well as the census tract location of the property, loan-to-value (LTV) ratio, age of mortgage note, and affordability of the mortgage. New for 2018 are the inclusion of the borrower’s debt-to-income (DTI) ratio and detailed LTV ratio data at the census tract level. The PUDB multifamily property-level datasets include information on the unpaid principal balance and type of seller/servicer from which the Enterprise acquired the mortgage. New for 2018 is the inclusion of property size data at the census tract level. The multifamily unit-class files also include information on the number and affordability of the units in the property. Both the single-family and multifamily datasets include indicators of whether the purchases are from “underserved” census tracts, as defined in terms of median income and minority percentage of population.Prior to 2010 the single-family PUDB consisted of three files: Census Tract, National A, and National B files. With the 2010 PUDB a fourth file, National C, was added to provide information on high-cost mortgages acquired by the Enterprises. The single-family Census Tract file includes information on the location of the property based on the 2010 Census for acquisition years 2012 through 2021, and the 2020 Census beginning with the 2022 acquisition year. The National files contain other information but lack detailed geographic information in order to protect Enterprise proprietary data. The multifamily datasets also consist of a Census Tract file, and a National file without detailed geographic information.Several dashboards are available to analyze the data:Enterprise Multifamily Public Use Database DashboardThe Enterprise Multifamily Public Use Database (PUDB) Dashboard provides users an interactive way to generate and visualize Enterprise PUDB data of multifamily mortgage acquisitions by Fannie Mae and Freddie Mac. It shows characteristics about multifamily loans, properties and units at the national level, and characteristics about multifamily loans and properties at the state level. It includes key statistics, time series charts, and state maps of multifamily housing characteristics such as median loan amount, number of properties, average number of units per property, and unit affordability. The underlying aggregate statistics presented in the dashboard come from three multifamily data files in the Enterprise PUDB, updated annually since 2008, including two property-level datasets and a data file on the size and affordability of individual units.Enterprise Multifamily Public Use DashboardPress Release - FHFA Releases Data Visualization Dashboard for Enterprises’ Multifamily Mortgage AcquisitionsMortgage Loan and Natural Disaster DashboardFHFA published an interactive Mortgage Loan and Natural Disaster Dashboard that combines FHFA’s PUDB reports on single-family and multifamily acquisitions for the regulated entities, FEMA’s National Risk Index (NRI), and FHFA’s Duty to Serve 2023 High-Needs rural areas. Desired geographies can be exported to .pdf and Excel from the Public Use Database and National Risk Index Dashboard.Mortgage Loan and Natural Disaster DashboardMortgage Loan and Natural Disaster Dashboard FAQs

The Austin City Council approved the Energy Conservation Audit and Disclosure ordinance in 2008 and revised the initiative in April 2011 to improve the energy efficiency of homes and buildings that receive electricity from Austin Energy. Multifamily properties older than 10 years are required to perform an audit and report the results to the City of Austin and all residents living in those communities. * The affected area within the City of Austin that is served by Austin Energy

Active loan characteristics in USDA RD Section 538 Multifamily Guaranteed Loan program, including loan, property, and community characteristics. Loan characteristics include obligation fiscal year, lender, borrower, loan closing date, loan amount, total development cost, loan to cost ratio, and federal LIHTC tax credit indicator. Property characteristics include _location and address, colonias or tribal _location indicator, EZ/EC _location indicator, project size, project type, construction type, number of units by bedroom size, and average contract rent by bedroom size. Community characteristics include the area population and median household income at time of obligation.

VITAL SIGNS INDICATOR Housing Production (LU4)

FULL MEASURE NAME Produced housing units by unit type

LAST UPDATED October 2019

DESCRIPTION Housing production is measured in terms of the number of units that local jurisdictions produces throughout a given year. The annual production count captures housing units added by new construction and annexations, subtracts demolitions and destruction from natural disasters, and adjusts for units lost or gained by conversions.

DATA SOURCE California Department of Finance Form E-8 1990-2010 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-8/

California Department of Finance Form E-5 2011-2018 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-5/

U.S. Census Bureau Population Estimates 2000-2018 https://www.census.gov/programs-surveys/popest.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Single-family housing units include single detached units and single attached units. Multi-family housing includes two to four units and five plus or apartment units.

Housing production data for metropolitan areas for each year is the difference of annual housing unit estimates from the Census Bureau’s Population Estimates Program. Housing production data for the region, counties, and cities for each year is the difference of annual housing unit estimates from the California Department of Finance. Department of Finance data uses an annual cycle between January 1 and December 31, whereas U.S. Census Bureau data uses an annual cycle from April 1 to March 31 of the following year.

Housing production data shows how many housing units have been produced over time. Like housing permit statistics, housing production numbers are an indicator of where the region is growing. However, since permitted units are sometimes not constructed or there can be a long lag time between permit approval and the start of construction, production data also reflects the effects of barriers to housing production. These range from a lack of builder confidence to high construction costs and limited financing. Data also differentiates the trends in multi-family, single-family and mobile home production.

This report is the result of the Austin City Code 6-7’s Energy Conservation Audit and Disclosure Ordinance approved in November 2008 (amended in April 2011) to improve the energy efficiency of homes and buildings that receive electricity from Austin Energy. The ordinance meets one of the goals of the Austin Climate Protection Plan, which is to offset 800 megawatts of peak energy demand by 2020. In addition, this report contains information on multi-family properties older than 10 years that are required to perform an energy audit and report the results to the City of Austin and all residents living in those communities. The Austin Energy report quantifies the 2015 energy efficiency findings and the progress towards meeting City Council goals of Resolution 20081106-048.

This paper uses data on residential buildings from the nationwide vector database. Attribute information on buildings (location, volume, function, etc.) provides opportunities to estimate the number of residents. The recalculation of the population from the urban units into new spatial units was based on the area-weighted aggregation method. The location of buildings constituted a limiting variable, while the total square meterage (calculated as the area of the buildings and the number of their floors) constituted the binding variable. The introduction of additional binding variables related to the type of building and its location, as well as various methods of determining the square meterage per building type, resulted in the creation of a total of 19 maps of Cracow’s population. The results of the recalculation of population were related to demographic data compiled by the organisation Statistics Poland (GUS) relating to the 1x1 km grid. Comparison of the results with demographic data relating to other reference units allowed the reduction of subjective interpretation and the refining of input data conversion methods. As a result, correct methods for segmenting buildings were identified, useful optimisation criteria were selected, and the accuracy of population maps developed based on the database was calculated. For the input data, based solely on the amount of population in urban units, the calculated value of the mean absolute percentage error (MAPE) in the 1x1 km grid was 310.8%, and for the root mean square error (RMSE) was 1476 people. In the dasymetric method, directly associating the population with the volume of buildings, the errors fell to 21.9% and 632 people, respectively. Among the remaining 18 variants introducing the segmentation of buildings from the database, the best result was obtained for the variant based on minimizing the RMSE, associating the number of residents to single-family buildings (2.88 people/building) and associating the number of residents to the square footage in multi-family buildings (37.1m2/person) (MAPE=19.2%, RMSE=556 people).

The following information will allow you to understand the intent of data provided. This report is in conjunction with Austin City Code 6-7’s Energy Conservation Audit and Disclosure Ordinance approved in November 2008 (amended in April 2011) to improve the energy efficiency of homes and buildings that receive electricity from Austin Energy. The ordinance meets one of the primary goals of the Austin Climate Protection Plan which is to offset 800 megawatts of peak energy demand by 2020 to help reduce Austin's carbon footprint. In addition, this report contains information on multi-family properties older than 10 years that are required to perform an energy audit and report the results to the City of Austin and all residents living in those communities. The Austin Energy report quantifies the 2013 energy efficiency findings and the progress towards meeting City Council goals of Resolution 20081106-048.

This dataset features three gridded population dadasets of Germany on a 10m grid. The units are people per grid cell.

Datasets

DE_POP_VOLADJ16: This dataset was produced by disaggregating national census counts to 10m grid cells based on a weighted dasymetric mapping approach. A building density, building height and building type dataset were used as underlying covariates, with an adjusted volume for multi-family residential buildings.

DE_POP_TDBP: This dataset is considered a best product, based on a dasymetric mapping approach that disaggregated municipal census counts to 10m grid cells using the same three underyling covariate layers.

DE_POP_BU: This dataset is based on a bottom-up gridded population estimate. A building density, building height and building type layer were used to compute a living floor area dataset in a 10m grid. Using federal statistics on the average living floor are per capita, this bottom-up estimate was created.

Please refer to the related publication for details.

Temporal extent

The building density layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: http://doi.org/10.1594/PANGAEA.920894)

The building height layer is representative for ca. 2015 (doi: 10.5281/zenodo.4066295)

The building types layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: 10.5281/zenodo.4601219)

The underlying census data is from 2018.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems.

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).
A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

Census data were provided by the German Federal Statistical Offices.

Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

As the renewable energy transition accelerates, housing, due to its high energy demand, can play a critical role in the clean energy shift. Specifically, multifamily housing provides a unique opportunity for solar photovoltaic (PV) system adoption, given the existing competing interests between landlords and tenants which has historically slowed this transition. To address this transition gap, this project identified and ranked Metropolitan Statistical Areas (MSAs) in the United States for ZNE Capital (the client) to acquire multifamily housing to install solar PV systems. The group identified seven criteria to determine favorable markets for rooftop solar PV on multifamily housing: landlord policy favorability, real estate market potential, CO2 abatement potential, electricity generation potential, solar installation internal rate of return, climate risk avoidance, and health costs associated with primary air pollutants. A total investment favorability score is calculated based on criteria importance assigned by the user. Investment favorability scores were investigated for different preferences to demonstrate the robustness and generalizability of the framework. The data analysis and criteria calculations were conducted using RStudio, ultimately to provide reproducible code to be used for future projects. The results are presented in a ranked list from best to worst metro areas to invest in. Future studies can utilize the reproducible code to inform decisions on where to invest in solar PV on multifamily housing anywhere in the United States by changing weights within the model depending on preferences. Methods

Collecting real estate and landlord data for metropolitan statistical areas (MSAs) from federal agency databases.

Real estate metrics: Six indicator metrics were selected to represent areas with growing housing demands. The metrics included were population growth, employment growth, average annual occupancy, annual rent change, the ratios of median annual rent to median income, and median income to median home price. The population estimates and median income data was downloaded from the Census Bureau. Median rent data was downloaded from HUDuser. Median home price data was downloaded from National Association of REALTORS®. Students were provided temporary memberships to Yardi Systems Matrix to obtain multifamily occupancy rates, and this data will not be redistributed. All the real estate metrics were combined into a single dataset using CBSA codes, which each MSA has a unique 5-digit identifier. Income-to-home price and rent-to-income ratios were calculated in R Studio.

Landlord data: the minimum security deposit and eviction notice data was collected for each state and manually compiled into an Excel. Security deposit information was provided as the number of months of rent. States with no maximum deposit limit received a score of 1.0, meaning it was the most favorable. Two month's rent was scored as 0.5, and one month's rent was given a score of 0.

Using NREL's REopt web tool to 1) model solar PV system on multifamily buildings in various cities and 2) obtain data to represent energy generation, CO2 abatement potential, avoided health costs from emissions, and solar project financial criteria.

An anchor city was identified within each MSA as the city with the highest population to input into the REopt tool. Default inputs were changed based on information provided by industry experts and changes in federal funding programs. Detailed instructions of inputs were created to ensure consistency when running the model for each city. The four outputs collected from the tool include: annual energy generation from renewables (%), lifecycle total CO2 emissions, health costs associated with primary air pollutants, and internal rate of return(%). The group divided up a list of cities, input the respective data for each one, obtained the outputs, then compiled it into a Google sheet. Outputs were checked by other members to ensure accuracy.

Collecting climate risk data from FEMA's National Risk Index Map.

Climate risk data was downloaded as a CSV file. The risk score was used to represent impacts of climate variability on long-term real estate investments. Risk scores were provided at the county level. The group identified the county each city resided in, to associate the correct score to each city in R Studio

Normalizing the data

Metrics were normalized by subtracting the minimum value for the metric from each value and dividing by the difference between the maximum and minimum values. This resulted in scores between 0 and 1 that were relative to the MSAs included in the analysis.

Weighing the data

Real Estate and Landlord Criteria metrics: these two criteria contained more than one metric, so the metrics within these criteria were weighted to produce real estate and landlord scores. Weights for each criterion sum to 1, in which higher weights indicate greater importance for multifamily real estate investments. Each weight was multiplied by the respective metric, then all weighted metrics within each criterion were summed to produce the criteria score. Investment Favorability Score: seven criteria were multiplied by respective weights based on the stakeholder's preferences. Weights sum to 1 to ensure consistency throughout the project. The sum of the seven weighted criteria is the investment favorability score.

This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Prince Edward Island; Nova Scotia; Newfoundland and Labrador ...), Housing estimates (3 items: Housing starts; Housing under construction; Housing completions ...), Type of unit (6 items: Total units; Semi-detached; Single-detached; Multiples ...).

This feature service denotes the locations of HUD assisted Multi-Family properties that primarily serve disabled residents. In addition, each property illustrated through this service has at least one active Service Coordinator contract or grant, Section 8 New Construction contract, Section 811 Project Assistance Contracts (PAC) contract, or Section 811 Project Rental Assistance Contract (PRAC). Please note that the data provided through this service only includes location data and attributes for those addresses that can be geocoded to an interpolated point along a street segment, or to a ZIP+4 centroid location. While not all records are able to be geocoded and mapped, we are continuously working to improve the address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD.

To learn more about the Section 811 Program visit: https://www.hud.gov/program_offices/housing/mfh/progdesc/disab811, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Multifamily Properties Date of Coverage: 03/2020

Analysis of ‘2013: ECAD Multi-Family Energy Audit and EUI Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b491875e-d9b8-49e5-a1f0-9f2b2486246a on 26 January 2022.

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

The following information will allow you to understand the intent of data provided. This report is in conjunction with Austin City Code 6-7’s Energy Conservation Audit and Disclosure Ordinance approved in November 2008 (amended in April 2011) to improve the energy efficiency of homes and buildings that receive electricity from Austin Energy. The ordinance meets one of the primary goals of the Austin Climate Protection Plan which is to offset 800 megawatts of peak energy demand by 2020 to help reduce Austin's carbon footprint. In addition, this report contains information on multi-family properties older than 10 years that are required to perform an energy audit and report the results to the City of Austin and all residents living in those communities. The Austin Energy report quantifies the 2013 energy efficiency findings and the progress towards meeting City Council goals of Resolution 20081106-048.

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

Public Works Eco-stations are huge roll-off bins, strategically placed in city-owned parks and near clusters of multi-family housing complexes. Phoenix residents and businesses are encouraged to use the eco-stations to place their recyclables at any time.

This dataset features a map of building types for Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. A random forest classification was used to map the predominant type of buildings within a pixel. We distinguish single-family residential buildings, multi-family residential buildings, commercial and industrial buildings and lightweight structures. Building types were predicted for all pixels where building density > 25 %. Please refer to the publication for details.

Temporal extent

Sentinel-2 time series data are from 2018. Sentinel-1 time series data are from 2017.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building type values are categorical, according to the following scheme:

0 - No building

1 - Commercial and industrial buildings

2 - Single-family residential buildings

3 - Lightweight structures

4 - Multi-family residential buildings

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).
A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

The dataset was generated by FORCE v. 3.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission.

Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

This feature service denotes the locations of HUD assisted Multi-Family properties that primarily serve elderly residents. In addition, each property illustrated through this service has at least one active Service Coordinator contract or grant, Section 236 loan, Section 8 202 contract, Section 8 Farmers Home Administration (FMHA) 515 contract, Section 8 New Construction contract, Section 202 Project Assistance Contracts (PAC) contract, and Section 202 Project Rental Assistance Contract (PRAC). Please note that the data provided through this service only includes location data and attributes for those addresses that can be geocoded to an interpolated point along a street segment, or to a ZIP+4 centroid location. While not all records are able to be geocoded and mapped, we are continuously working to improve the address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD.

To learn more about the Section 202 Program visit: https://www.hud.gov/program_offices/housing/mfh/progdesc/eld202, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Multifamily Properties Date of Coverage: 12/2023

Analysis of ‘Austin Energy Single Family Audits’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9f0b90ee-60ba-46ce-bcee-8b09242e96bf on 26 January 2022.

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

The Austin City Council approved the Energy Conservation Audit and Disclosure ordinance in 2008 and revised the initiative in April 2011 to improve the energy efficiency of homes and buildings that receive electricity from Austin Energy. Single-family homeowners must have energy audits performed on their properties prior to a sale of their home. Multifamily properties older than 10 years are required to perform an audit and report the results to the City of Austin and all residents living in those communities. Commercial building owners participated in a phased-in reporting since 2012, for buildings 75,000 square feet and larger.

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

The Austin City Council approved the Energy Conservation Audit and Disclosure ordinance in 2008 and revised the initiative in April 2011 to improve the energy efficiency of homes and buildings that receive electricity from Austin Energy. Single-family homeowners must have energy audits performed on their properties prior to a sale of their home. Multifamily properties older than 10 years are required to perform an audit and report the results to the City of Austin and all residents living in those communities. Commercial building owners participated in a phased-in reporting since 2012, for buildings 75,000 square feet and larger.

On-street food scrap collection bin locations within Arlington County. The food scrap bins are located outside select multifamily properties in Arlington County and provide a place for residents to drop off food scraps. For further information about this program refer to the Food Scraps Collection page on the Arlington County web site.Contact: Department of Environmental ServicesData Accessibility: Publicly AvailableUpdate Frequency: As NeededLast Revision Date: 8/14/2024Creation Date: 8/14/2024Feature Dataset Name: DES_SWBLayer Name: Food_Scrap_Bins_pnt