Housing Affordability Reports describes the extent and general nature of local housing needs by: South Australia, Metropolitan Adelaide, Greater Adelaide and Local Government Areas. Reports from …Show full descriptionHousing Affordability Reports describes the extent and general nature of local housing needs by: South Australia, Metropolitan Adelaide, Greater Adelaide and Local Government Areas. Reports from 2018 and 2013 are available.

This dataset contains Housing Affordability Supply and Demand Data broken down by very low, low and moderate income brackets.

This dataset relates to section 4, Housing Stress, of the Affordability master reports produced by the SA Housing Authority. Each master report covers one Local Government Area and is entitled Housing Affordability Demand and Supply by Local Government Area.

Explanatory Notes: Data sourced from the Australian Bureau of Statistics (ABS), Census for Population and Housing and it is updated every 5 years in line with the ABS Census.

The nature of the income imputation means that the reported proportion may significantly overstate the true proportion. Census housing stress data is best used in comparing results over Censuses (ie did it increase or decrease in an area) rather than using it to ascertain what proportion of households were in rental stress.

Income bands are based on household income.

High income households can also experience rental stress. These households are included in the total but not identified separately. Data is representative of households in very low, low and moderate income brackets.

Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.

Field Definitions: LGA Name: 2011 Local Government Areas are an ABS approximation of officially gazetted LGAs as defined by each State and Territory Local Government Department. The boundaries produced for LGAs are constructed from allocations of whole Mesh Blocks and reviewed annually.

Tenure Type: This is a consolidation of the census tenure and landlord types. The following definitions have been used: Rented: Private and not stated, this is comprised of rented dwellings (excluding rent free) where the Landlord type is a Real Estate Agent, Person not in the same household or where the Landlord type is not stated Rented: Other, this is comprised of rented dwellings (excluding rent free) where the Landlord type is Employer (Govt or other), Housing cooperative,community,church group, or Residential park (incl caravan parks and marinas) Rented: TOTAL, this is comprised of the sum of Rented: Public, Rented: Private and not stated, and Rented: Other landlord. Please note that this field should be excluded when summing the total households Other tenure types: this is comprised of dwellings that are owned outright, occupied rent free, occupied under a life tenure scheme, other tenure types and tenure type not stated. Total Households: this is comprised of the sum of Being purchased (incl rent,buy), Rented: TOTAL and Other tenure types.

Total - Includes all South Australian households.

Source: The data was downloaded from data.sa.gov.au and spatialised by the Adelaide Data Hub using the ABS 2011 Local Government Areas dataset.

Housing Affordability Supply and Demand Data. Number of South Australian households paying more than 30% of their household income on housing (rent or mortgage) broken down by very low, low and moderate income brackets. This dataset relates to section 4, Housing Stress, of the Affordability master reports produced by the SA Housing Authority. Each master report covers one Local Government Area and is entitled ‘Housing Affordability – Demand and Supply by Local Government Area’. The Demand for Supply for LGA reports are available online at: https://data.sa.gov.au/data/dataset/housing-affordability-demand-and-supply-by-local-government-area Explanatory Notes: Data sourced from the Australian Bureau of Statistics (ABS), Census for Population and Housing and it is updated every 5 years in line with the ABS Census. The nature of the income imputation means that the reported proportion may significantly overstate the true proportion. Census housing stress data is best used in comparing results over Censuses (ie did it increase or decrease in an area) rather than using it to ascertain what proportion of households were in rental stress. Income bands are based on household income. High income households can also experience rental stress. These households are included in the total but not identified separately. Data is representative of households in very low, low and moderate income brackets. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.

Housing Affordability Supply and Demand Data. Number of South Australian households paying more than 50% of their household income on housing (rent or mortgage) broken down by very low, low and moderate income brackets. This dataset relates to section 4, Housing Stress, of the Affordability master reports produced by the SA Housing Authority. Each master report covers one Local Government Area and is entitled ‘Housing Affordability – Demand and Supply by Local Government Area’. The Demand for Supply for LGA reports are available online at: https://data.sa.gov.au/data/dataset/housing-affordability-demand-and-supply-by-local-government-area Explanatory Notes: Data sourced from the Australian Bureau of Statistics (ABS), Census for Population and Housing and it is updated every 5 years in line with the ABS Census. The nature of the income imputation means that the reported proportion may significantly overstate the true proportion. Census housing stress data is best used in comparing results over Censuses (ie did it increase or decrease in an area) rather than using it to ascertain what proportion of households were in rental stress. Income bands are based on household income. High income households can also experience rental stress. These households are included in the total but not identified separately. Data is representative of households in very low, low and moderate income brackets. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.

Affordable Housing Market Analysis The global affordable housing market is projected to reach $1,983.52 billion by 2033, exhibiting a CAGR of 4.71% from 2025 to 2033. The rising population, urbanization, affordability crisis, and supportive government policies are the primary drivers fueling market growth. The increasing demand for affordable single-family homes, multi-family units, and townhouses, coupled with the adoption of innovative construction methods like prefabrication, 3D printing, and sustainable construction, are key trends shaping the market. The market faces restraints such as escalating land and construction costs, regulatory challenges, and the shortage of skilled labor. Nevertheless, the emergence of crowdfunding platforms and non-profit organizations providing financial assistance, as well as government subsidies and tax incentives, are expected to mitigate these constraints. The market is segmented based on housing type, funding source, construction method, and target demographics. D.R. Horton, Taylor Morrison, PulteGroup, Zillow, Hovnanian Enterprises, and Lennar Corporation are notable companies in the global affordable housing market, with operations in key regions like North America, Europe, and Asia Pacific. Recent developments include: Recent developments in the Affordable Housing Market have highlighted the urgent need for innovative housing solutions as governments and organizations strive to address the growing housing crisis exacerbated by economic challenges and population growth. Various nations are prioritizing policies that encourage public-private partnerships to stimulate investment in affordable housing initiatives. Additionally, the integration of sustainable building practices and smart technologies is gaining traction as stakeholders aim to improve energy efficiency while reducing construction costs. Recent collaborations among international entities and local governments focus on leveraging funding for housing projects, particularly in urban areas where demand is surging. Moreover, rising material costs and labor shortages are prompting stakeholders to explore alternative building materials and methods, including modular construction and 3D printing, to streamline processes. These trends underscore a collective commitment to creating equitable housing opportunities while navigating the complexities of market dynamics, aiming for significant progress by 2032. Overall, this evolving landscape reflects a concerted effort to promote affordability, sustainability, and accessibility in housing worldwide.. Key drivers for this market are: Green building technologies adoption Public-private partnerships expansion Innovative financing solutions development Urban regeneration projects implementation Digital platforms for housing access. Potential restraints include: rising urbanization, government initiatives; increasing housing demand; socioeconomic disparities; affordable financing options.

Affordable Housing Market Outlook

According to our latest research, the affordable housing market size reached USD 69.2 billion globally in 2024, driven by rapid urbanization, supportive government policies, and rising demand for cost-effective housing solutions. The market is projected to expand at a robust CAGR of 6.1% from 2025 to 2033, reaching an estimated USD 117.4 billion by the end of the forecast period. The growth is primarily attributed to increasing urban migration, widening income disparities, and a surge in public and private investments aimed at addressing the global housing deficit. As per our latest research, the affordable housing sector is undergoing significant transformation as stakeholders focus on innovative construction methods, sustainable materials, and digital technologies to streamline project delivery and reduce costs.

One of the primary growth drivers for the affordable housing market is the escalating rate of urbanization, particularly in emerging economies. Urban populations are swelling at an unprecedented pace, with millions migrating to cities in search of better employment opportunities and improved living standards. This mass migration has led to a surge in demand for affordable, quality housing, placing immense pressure on urban infrastructure and local governments. Consequently, both public and private sector players are ramping up investments in affordable housing projects, leveraging innovative financing models and partnerships to bridge the housing gap. Furthermore, the emergence of smart city initiatives and sustainable urban planning is fostering the development of integrated, affordable housing solutions that cater to the diverse needs of low- and middle-income populations.

Another significant factor propelling the affordable housing market is the increasing involvement of governments and international organizations in addressing the global housing crisis. Numerous policy interventions, such as subsidies, tax incentives, and relaxed regulatory frameworks, are being introduced to stimulate the supply of affordable homes. Governments are also collaborating with private developers through public-private partnerships (PPPs) to expedite project execution and ensure long-term sustainability. Additionally, multilateral agencies and non-governmental organizations are providing technical and financial assistance to support large-scale affordable housing initiatives, particularly in regions with acute housing shortages. These concerted efforts are not only enhancing access to affordable housing but also fostering socio-economic development and reducing urban poverty.

Technological advancements in construction methods and materials are further accelerating the growth of the affordable housing market. The adoption of modular and prefabricated construction techniques is enabling developers to deliver high-quality housing units at lower costs and within shorter timeframes. These innovative approaches are also contributing to improved energy efficiency, reduced environmental impact, and enhanced structural durability. Moreover, the integration of digital technologies, such as Building Information Modeling (BIM) and project management software, is streamlining the design, planning, and execution of affordable housing projects. As a result, stakeholders are increasingly embracing technology-driven solutions to optimize resource utilization, minimize risks, and ensure compliance with stringent regulatory standards.

From a regional perspective, Asia Pacific continues to dominate the affordable housing market, accounting for the largest share in 2024, followed by North America and Europe. The region's rapid urbanization, burgeoning population, and proactive government policies are driving significant investments in affordable housing infrastructure. Countries such as China, India, and Indonesia are at the forefront, implementing ambitious housing schemes and leveraging innovative construction technologies to address the growing demand. Meanwhile, developed regions like North America and Europe are witnessing renewed interest in affordable housing, fueled by rising property prices, income inequality, and shifting demographic trends. Latin America and the Middle East & Africa are also emerging as promising markets, supported by favorable regulatory environments and increased foreign direct investments.

Number of affordable housing completions (seasonally adjusted)

How the figure is calculated:

Total reported numbers of completions under the relevant programmes within the reporting period. Because delivery is seasonal and reflects funding profiles, with more starts and completions being reported in the second six months than are reported in the first six months, the current figures are compared back to the equivalent period of the year before rather than the preceding six months.

Why is this indicator in the business plan?

These are the most timely indicators on affordable housing delivery. Increasing the supply of affordable housing is a key part of DCLG policy.

How often is it updated?

Bi-annually, approximately June and November.

Where does the data come from?

Homes and Communities Agency (HCA) - Investment Management System and other programme information. Published figures are at http://www.homesandcommunities.co.uk/housing-statistics.

Greater London Authority (GLA) - Investment Management System and other programme information. Published figures are at http://www.london.gov.uk/priorities/housing-land/increasing-housing-supply/gla-affordable-housing-statistics.

What area does the headline figure cover?

England

Are further breakdowns of the data available?

Yes, can be split by type (social rent, affordable rent, intermediate rent, Low Cost Home Ownership) and by local authority area.

What does a change in this indicator show?

An increase in this indicator is good and shows more new affordable houses are being completed through the HCA and GLA.

Time Lag

Published within two months of the end of the reporting period.

Next available update

June 2015.

Type of Data

Official Statistics.

Robustness and data limitations

• Does not include all affordable housing starts and completions because some will be delivered outside the HCA and GLA roles.
• Delivery is seasonal and reflects funding profiles. Delivery tends to be lower in the first six months than the last six months of the year and therefore comparisons with the previous six-monthly period are not usually appropriate.
• A small number of the affordable housing starts reported by the HCA over this period are actually located in London (the Get Britain Building programme in London is administered by the HCA on behalf of the GLA).

Links to Further Information

http://www.homesandcommunities.co.uk/housing-statistics

http://www.london.gov.uk/priorities/housing-land/increasing-housing-supply/gla-affordable-housing-statistics

Contact Details

CorporatePerformance@communities.gsi.gov.uk

Number of affordable housing starts (seasonally adjusted)

How the figure is calculated:

Total reported numbers of starts under the relevant programmes within the reporting period. Because delivery is seasonal and reflects funding profiles, with more starts and completions being reported in the second six months than are reported in the first six months, the current figures are compared back to the equivalent period of the year before rather than the preceding six months.

Why is this indicator in the business plan?

These are the most timely indicators on affordable housing delivery. Increasing the supply of affordable housing is a key part of DCLG policy.

How often is it updated?

Bi-annually, approximately June and November.

Where does the data come from?

Homes and Communities Agency (HCA) - Investment Management System and other programme information. Published figures are at http://www.homesandcommunities.co.uk/housing-statistics.

Greater London Authority (GLA) - Investment Management System and other programme information. Published figures are at http://www.london.gov.uk/priorities/housing-land/increasing-housing-supply/gla-affordable-housing-statistics.

What area does the headline figure cover?

England

Are further breakdowns of the data available?

Yes, can be split by type (social rent, affordable rent, intermediate rent, Low Cost Home Ownership) and by local authority area.

What does a change in this indicator show?

An increase in this indicator is good and shows more new affordable houses are being started through the HCA and GLA.

Time Lag

Published within two months of the end of the reporting period.

Next available update

June 2015.

Type of Data

Official Statistics.

Robustness and data limitations

• Does not include all affordable housing starts and completions because some will be delivered outside the HCA and GLA roles.
• Delivery is seasonal and reflects funding profiles. Delivery tends to be lower in the first six months than the last six months of the year and therefore comparisons with the previous six-monthly period are not usually appropriate.
• A small number of the affordable housing starts reported by the HCA over this period are actually located in London (the Get Britain Building programme in London is administered by the HCA on behalf of the GLA).

With effect from 1 April 2014, affordable housing starts on site include the starts on site for new build homes purchased at completion. These have not been reported historically

Links to Further Information

http://www.homesandcommunities.co.uk/housing-statistics

http://www.london.gov.uk/priorities/housing-land/increasing-housing-supply/gla-affordable-housing-statistics

Contact Details

CorporatePerformance@communities.gsi.gov.uk

This dataset contains Housing Affordability Supply and Demand Data represented as tenure mix and dwelling type for each local government area.

This dataset relates to section 8 of the Affordability master reports produced by the SA Housing Authority. Each master report covers one Local Government Area and is entitled Housing Affordability Demand and Supply by Local Government Area.

Explanatory Notes: Data sourced from the Australian Bureau of Statistics (ABS), Census for Population and Housing and it is updated every 5 years in line with the ABS Census.

Source: The data was downloaded from data.sa.gov.au and spatialised by the Adelaide Data Hub using the ABS 2011 Local Government Areas dataset. AURIN has archived this dataset as there have been no updates since 2011.

This is the median housing affordability ratio (residence-based) and is calculated by dividing house prices by gross annual earnings, based on the median of both house prices and earnings.

This measure of affordability shows what the people who live in a given area earn in relation to that area's house prices, even if they work elsewhere. This measure does not consider that people may be getting higher earnings from working in other areas.

A higher ratio indicates that on average, it is less affordable for a resident to purchase a house. Conversely, a lower ratio indicates higher affordability in a local authority.

The earnings data are from the Annual Survey of Hours and Earnings which provides a snapshot of earnings at April in each year. Earnings relate to gross full-time individual earnings on a place of work basis. The house price statistics come from the House Price Statistics for Small Areas, which report the median and lower quartile price paid for residential property and refer to a 12-month period with April in the middle (year ending September).

Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

China Local Government Debt: Invest To: Affordable Housing data was reported at 1,094,783.000 RMB mn in Jun 2013. China Local Government Debt: Invest To: Affordable Housing data is updated semiannually, averaging 1,094,783.000 RMB mn from Jun 2013 (Median) to Jun 2013, with 1 observations. China Local Government Debt: Invest To: Affordable Housing data remains active status in CEIC and is reported by National Audit Office. The data is categorized under China Premium Database’s Government and Public Finance – Table CN.FA: Government Debt: National Audit Office.

The Rental Report time series dataset provides detailed time-series statistics for some key Rental Report data from the March quarter of 2000 to the December quarter of 2017. This specific dataset presents statistics on affordable 4 bedroom rental properties by the 2016 Local Government Areas geographic level. Affordable rental properties are those within 30 per cent of gross income for low-income households. The rental thresholds are taken from the household incomes for whom that number of bedrooms is a minimum: For one-bedroom properties, we have taken the income of singles on Newstart allowance; For two-bedroom properties, we have taken a single parent pensioner with one child aged under 5;

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The 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 2015-2019). 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: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

China Local Government Debt: Invest To: Education, Science, Culture, Health and Affordable Housing data was reported at 916,902.000 RMB mn in 2010. China Local Government Debt: Invest To: Education, Science, Culture, Health and Affordable Housing data is updated yearly, averaging 916,902.000 RMB mn from Dec 2010 (Median) to 2010, with 1 observations. The data reached an all-time high of 916,902.000 RMB mn in 2010 and a record low of 916,902.000 RMB mn in 2010. China Local Government Debt: Invest To: Education, Science, Culture, Health and Affordable Housing data remains active status in CEIC and is reported by National Audit Office. The data is categorized under China Premium Database’s Government and Public Finance – Table CN.FA: Government Debt: National Audit Office.

The Rental Report time series dataset provides detailed time-series statistics for some key Rental Report data from the March quarter of 2000 to the December quarter of 2017. This specific dataset presents statistics on affordable 2 bedroom rental properties by the 2016 Local Government Areas geographic level. Affordable rental properties are those within 30 per cent of gross income for low-income households. The rental thresholds are taken from the household incomes for whom that number of bedrooms is a minimum: For one-bedroom properties, we have taken the income of singles on Newstart allowance; For two-bedroom properties, we have taken a single parent pensioner with one child aged under 5; For three-bedroom properties, we have taken a couple on Newstart with two children; For four-bedroom properties, we have taken a couple on Newstart with four children. The Rental Report provides the most accurate information on the private rental market in Victoria. The data come from records kept by the Residential Tenancies Bond Authority (RTBA). The RTBA is responsible for receiving, registering and refunding all bonds associated with private residential leases in Victoria. For more information please visit the Department of Health and Human Services.

The Rental Report time series dataset provides detailed time-series statistics for some key Rental Report data from the March quarter of 2000 to the December quarter of 2017. This specific dataset presents statistics on affordable 3 bedroom rental properties by the 2016 Local Government Areas geographic level. Affordable rental properties are those within 30 per cent of gross income for low-income households. The rental thresholds are taken from the household incomes for whom that number of bedrooms is a minimum: For one-bedroom properties, we have taken the income of singles on Newstart allowance; For two-bedroom properties, we have taken a single parent pensioner with one child aged under 5; For three-bedroom properties, we have taken a couple on Newstart with two children; For four-bedroom properties, we have taken a couple on Newstart with four children. The Rental Report provides the most accurate information on the private rental market in Victoria. The data come from records kept by the Residential Tenancies Bond Authority (RTBA). The RTBA is responsible for receiving, registering and refunding all bonds associated with private residential leases in Victoria. For more information please visit the Department of Health and Human Services.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show comparison of housing ownership costs and rental costs to income by Atlanta City Council Districts in the Atlanta region.

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 2013-2017). 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.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

HUM_SMOCAPI_e

# Housing units with a mortgage, costs as a percentage of income computed, 2017

HUM_SMOCAPI_m

# Housing units with a mortgage, costs as a percentage of income computed, 2017 (MOE)

MSMOCAPI30PctPlus_e

# Housing units with a mortgage, costs 30.0 percent of income or more, 2017

MSMOCAPI30PctPlus_m

# Housing units with a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

pMSMOCAPI30PctPlus_e

% Housing units with a mortgage, costs 30.0 percent of income or more, 2017

pMSMOCAPI30PctPlus_m

% Housing units with a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

HUNM_SMOCAPI_e

# Housing units without a mortgage, costs as a percentage of income computed, 2017

HUNM_SMOCAPI_m

# Housing units without a mortgage, costs as a percentage of income computed, 2017 (MOE)

NMSMOCAPI30PctPlus_e

# Housing units without a mortgage, costs 30.0 percent of income or more, 2017

NMSMOCAPI30PctPlus_m

# Housing units without a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

pNMSMOCAPI30PctPlus_e

% Housing units without a mortgage, costs 30.0 percent of income or more, 2017

pNMSMOCAPI30PctPlus_m

% Housing units without a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

OccGRAPI_e

# Occupied units for which rent as a percentage of income can be computed, 2017

OccGRAPI_m

# Occupied units for which rent as a percentage of income can be computed, 2017 (MOE)

GRAPI30PctPlus_e

# Gross rent 30.0 percent of income or greater, 2017

GRAPI30PctPlus_m

# Gross rent 30.0 percent of income or greater, 2017 (MOE)

pGRAPI30PctPlus_e

% Gross rent 30.0 percent of income or greater, 2017

pGRAPI30PctPlus_m

% Gross rent 30.0 percent of income or greater, 2017 (MOE)

HousingCost30PctPlus_e

# All occupied units for which costs exceed 30 percent of income, 2017

HousingCost30PctPlus_m

# All occupied units for which costs exceed 30 percent of income, 2017 (MOE)

PayingForHousing_e

# Total households paying for housing (rent or owner costs), 2017

PayingForHousing_m

# Total households paying for housing (rent or owner costs), 2017 (MOE)

pHousingCost30PctPlus_e

% Occupied units for which costs exceed 30 percent of income, 2017

pHousingCost30PctPlus_m

% Occupied units for which costs exceed 30 percent of income, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.