Note: This map is not an official zoning map. For precise zoning information, please call or visit the Seattle Municipal Tower, Seattle Department of Construction and InspectionsFor properties subject to Mandatory Housing Affordability, the fee areas map specifies the locational dimension of the MHA requirement. Mandatory Housing Affordability requires new development to contribute to affordable housing by including affordable housing in the development or making a payment to the City’s Office of Housing to support affordable housing. The amount of the MHA contribution varies based on a property’s _location and other factors specified in Seattle Municipal Code Chapters 23.58B and 23.58C. For properties subject to MHA, the fee areas map specifies the locational dimension of the MHA requirement. MHA amounts in Downtown and South Lake Union have specific requirement levels for each zone as listed in SMC 23.58B and 23.58C. For other areas, the relative high, medium or low aspect of the MHA requirement corresponds to market strength area of the city.

This map uses a two-color thematic shading to emphasize where areas experience the least to the most affordable housing across the US. This web map is part of the How Affordable is the American Dream story map.

Esri’s Housing Affordability Index (HAI) is a powerful tool to analyze local real estate markets. Esri’s housing affordability index measures the financial ability of a typical household to purchase an existing home in an area. A HAI of 100 represents an area that on average has sufficient household income to qualify for a loan on a home valued at the median home price. An index greater than 100 suggests homes are easily afforded by the average area resident. A HAI less than 100 suggests that homes are less affordable. The housing affordability index is not applicable in areas with no households or in predominantly rental markets . Esri’s home value estimates cover owner-occupied homes only. For a full demographic analysis of US growth refer to Esri's Trending in 2017: The Selectivity of Growth.

The pop-up is configured to show the following 2017 demographics for each County and ZIP Code:

Total Households 2010-17 Annual Pop Change Median Age Percent Owner-Occupied Housing Units Median Household Income Median Home Value Housing Affordability Index Share of Income to Mortgage

As of November 2023, this map has been updated to use a new format. For details, please see here.
Snapshot of the Mayor’s Office of Housing and Community Development (MOHCD) and the Office of Community Investment and Infrastructure (OCII) affordable housing pipeline projects. The projects listed are in the process of development--or are anticipated to be developed--in partnership with non-profit or for-profit developers and financed through city funding agreements, ground leases, disposition and participation agreements and conduit bond financing. The Affordable Housing Pipeline also includes housing units produced by private developers through the Inclusionary Affordable Housing Program. Data reflects all projects as of June 30, 2023.

This dataset contains information about the percent of income households spend on housingin cities in San Mateo County. This data is for owner occupied housing with or without a mortgage. This data was extracted from the United States Census Bureau's American Community Survey 2014 5 year estimates.

Note: This map is not an official zoning map. For precise zoning information, please call or visit the Seattle Municipal Tower, Seattle Department of Construction and InspectionsZoning areas where Mandatory Housing Affordability requirements may apply.Mandatory Housing Affordability requires new development to contribute to affordable housing by including affordable housing in the development or making a payment to the City’s Office of Housing to support affordable housing. The amount of the MHA contribution varies based on a property’s location and other factors specified in Seattle Municipal Code Chapters 23.58B and 23.58C.

The travel time data on this map is modeled from a 2005 transit network. The home values are as of 2000 and are expressed in year 2000 dollars. The home value estimates were created by the Association of Bay Area Governements by combining ParcelQuest real estate transaction data and real estate tax assessment data. This information can be generated for any address in the region using an interactive mapping tool available under Maps at onebayarea.org/maps.htm (Note - this tool is no longer available).

Esri’s Housing Affordability Index (HAI) measures the financial ability of a typical household to purchase an existing home in an area. A HAI of 100 represents an area that on average has sufficient household income to qualify for a loan on a home valued at the median home price. An index greater than 100 suggests homes are easily afforded by the average area resident. A HAI less than 100 suggests that homes are less affordable. The housing affordability index is not applicable in areas with no households or in predominantly rental markets . Esri’s home value estimates cover owner-occupied homes only.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

First launched by the U.S. Department of Housing and Urban Development (HUD) and Department of Transportation (DOT) in November 2013, the Location Affordability Index (LAI) provides ubiquitous, standardized household housing and transportation cost estimates for all 50 states and the District of Columbia. Because what is affordable is different for everyone, users can choose among eight household profiles—which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.

Version 3 updates the constituent data sets with 2012-2016 American Community Survey data and makes several methodological tweaks, most notably moving to modeling at the Census tract level rather at the block group. As with Version 2, the inputs to the simultaneous equation model (SEM) include six endogenous variables—housing costs, car ownership, and transit usage for both owners and renters—and 18 exogenous variables, with vehicle miles traveled still modeled separately due to data limitations.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 2012-2016 Data Dictionary: DD_Location Affordability Indev v.3.0LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation

The percentage of households that pay more than 30% of their total household income on rent and related expenses out of all households in an area. Source: American Community Survey Years Available: 2006-2010, 2007-2011, 2008-2012, 2009-2013, 2010-2014, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018-2022, 2019-2023Please note: We do not recommend comparing overlapping years of data due to the nature of this dataset. For more information, please visit: https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html

This map shows households that spend more than 30 percent of their income on housing, a threshold widely used by many affordable housing advocates and official government sources including Housing and Urban Development. Census asks about income and housing costs to understand whether housing is affordable in local communities. When housing is not sufficient or not affordable, income data helps communities:

• Enroll eligible households in programs designed to assist them.
• Qualify for grants from the Community Development Block Grant (CDBG), HOME Investment Partnership Program, Emergency Solutions Grants (ESG), Housing Opportunities for Persons with AIDS (HOPWA), and other programs.

Map opens in Atlanta. Use the bookmarks or search bar to view other cities. Data is symbolized to show the relationship between burdensome housing costs for owner households with a mortgage and renter households:

This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

Displacement risk indicator showing how many households within the specified groups are facing either housing cost burden (contributing more than 30% of monthly income toward housing costs) or severe housing cost burden (contributing more than 50% of monthly income toward housing costs).

Affordable Housing Development.

Affordable housing production and preservation projects are managed by the Department of Housing and Community Development (DHCD), the Deputy Mayor for Planning and Economic Development (DMPED), the DC Housing Authority, the DC Housing Finance Agency and DC's Inclusionary Zoning program. This dataset comprehensively covers affordable housing projects which started (i.e. reached financial closing and/or started construction) or completed since January of 2015. The data includes affordable housing projects (production and preservation, rental and for-sale) which were subsidized by DMPED, DHCD, DCHFA, or DCHA, and those which were produced as a result of Planned Unit Development (PUD) proffers or Inclusionary Zoning (IZ) requirements.

Note: This map is not an official zoning map. For precise zoning information, please call or visit the Seattle Municipal Tower, Seattle Department of Construction and Inspections

For properties subject to Mandatory Housing Affordability, the fee areas map specifies the locational dimension of the MHA requirement.

Mandatory Housing Affordability requires new development to contribute to affordable housing by including affordable housing in the development or making a payment to the City’s Office of Housing to support affordable housing. The amount of the MHA contribution varies based on a property’s location and other factors specified in Seattle Municipal Code Chapters 23.58B and 23.58C. For properties subject to MHA, the fee areas map specifies the locational dimension of the MHA requirement. MHA amounts in Downtown and South Lake Union have specific requirement levels for each zone as listed in SMC 23.58B and 23.58C. For other areas, the relative high, medium or low aspect of the MHA requirement corresponds to market strength area of the city.

Public Parcels - Metro CTUsThis web map was created by Metro Transit's Transit Oriented Development (TOD) Office to showcase the newly expanded public parcel data in relation to existing and planned transit facilities across the Twin Cities Metropolitan Area. As of August, 2019, the parcels can also be viewed in relation to Federally approved Opportunity Zones. More information on the new US Department of Treasury Opportunity Zone Program can be found here. The purpose of the public parcel data is to increase awareness of the location and quantity of publicly owned lands at all levels of government. The Q-1 2020 dataset now includes more than 35,000 parcels from across 128 cities, townships, and unorganized territories (CTUs). These parcels are further classified and displayed by eight broad ownership or administrative categories. Users can view, analyze, share, and research publicly-owned lands that may be good candidates for TOD or some other higher/better use.The purpose of the original pilot project was to increase awareness of publicly owned parcel locations relative to Metro-area transit facilities and facilitate TOD analyses. While the current geographic extent of the data has been greatly expanded, the purpose remains the same; to raise awareness of publicly owned land for the highest & best use.For those with desktop GIS software, the Public Parcel shapefile and/or geodatabase can be downloaded here: https://gisdata.mn.gov/dataset/us-mn-state-metc-plan-public-parcels-metro-ctus

Pathways to Removing Obstacles to Housing (PRO Housing) Pathways to Removing Obstacles to Housing, or PRO Housing, is a competitive grant program being administered by HUD. PRO Housing seeks to identify and remove barriers to affordable housing production and preservation.

Under the Need rating factor, applicants will be awarded ten (10) points if their application primarily serves a ‘priority geography’. Priority geography means a geography that has an affordable housing need greater than a threshold calculation for one of three measures. The threshold calculation is determined by the need of the 90th-percentile jurisdiction (top 10%) for each factor as computed comparing only jurisdictions with greater than 50,000 population. Threshold calculations are done at the county and place level and applied respectively to county and place applicants. An application can also quality as a priority geography if it serves a geography that scores in the top 5% of its State for the same three measures. The measures are as follows:

Affordable housing not keeping pace, measured as (change in population 2019-2009 divided by 2009 population) – (change in number of units affordable and available to households at 80% HUD Area Median Family Income (HAMFI) 2019-2009 divided by units affordable and available at 80% HAMFI 2009). Insufficient affordable housing, measured as number of households at 80% HAMFI divided by number of affordable and available units for households at 80% HAMFI. Widespread housing cost burden or substandard housing, measured as number of households with housing problems at 100% HAMFI divided by number of households at 100% HAMFI. Housing problems is defined as: cost burden of at least 50%, overcrowding, or substandard housing.

For more information on Pro Housing, please visit: https://www.hud.gov/program_offices/comm_planning/pro_housing

These data were developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. .
For a deep dive into the data model including every specific metric, see the ACS 2018-2022 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2018-2022). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

Analysis of ‘Mandatory Housing Affordability (MHA) Zones’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/de9e62d8-b451-4615-b5b8-34bd4ccac801 on 27 January 2022.

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

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