A novel index for estimating the expense of housing and transportation in Canada has been developed, based on the Center for Neighborhood Technology’s Housing + Transportation (H+T®) Affordability Index. By combining census data and data from other statistical programs we obtain a composite index (the H+T Index) for all aggregate dissemination areas of Canada, except for territories and First Nations.

The Location Affordability Index is an indicator of housing and transportation costs at the neighborhood level. It gives the percentage of a given family's income estimated to be spent on housing and transportation costs in a given location for eight different household profiles. It is calculated using actual and modeled data for Census block groups in all 942 Combined Base Statistical Areas, which cover 94% of the U.S. population.

The Low Transportation Cost Index is based on estimates of transportation expenses for a 3-person single-parent family earning 50% of the median income for renters in the surrounding region (i.e. Core-Based Statistical Area). The estimates come from the Location Affordability Index (LAI). The data correspond to those for household type 6 as noted in the LAI data dictionary. More specifically, among this household type, we model transportation costs as a percent of income for renters. Neighborhoods are defined as census tracts. The LAI data do not contain transportation cost information for Puerto Rico.

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

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —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.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Also available is a gallery of 8 web maps (one for each household profile) all symbolized the same way for easy comparison: Median-Income Family, Very Low-Income Individual, Working Individual, Single Professional, Retired Couple, Single-Parent Family, Moderate-Income Family, and Dual-Professional Family.An accompanying story map provides side-by-side comparisons and additional context.--Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:"Manhattan, San Francisco, and downtown Boston are some of the most expensive places to live in the country, yet the LAI shows them as affordable for the typical regional household. Why?" These areas have some of the lowest transportation costs in the country, which helps offset the high cost of housing. The area median income (AMI) in these regions is also high, so when costs are shown as a percent of income for the typical regional household these neighborhoods appear affordable; however, they are generally unaffordable to households earning less than the AMI.Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 4/18/19Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation_**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**

Title: Location Affordability Index - NMCDC Copy

Summary: This layer contains the Location Affordability Index from U.S. Dept. of Housing and Urban Development (HUD) - standardized household, housing, and transportation cost estimates by census tract for 8 household profiles.

Notes: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas.

Prepared by: dianaclavery_uo, copied by EMcRae_NMCDC

Source: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas. Check the source documentation or other details above for more information about data sources.

Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=447a461f048845979f30a2478b9e65bb

UID: 73

Data Requested: Family income spent on basic need

Method of Acquisition: Search for Location Affordability Index in the Living Atlas. Make a copy of most recent map available. To update this map, copy the most recent map available. In a new tab, open the AGOL Assistant Portal tool and use the functions in the portal to copy the new maps JSON, and paste it over the old map (this map with item id

Date Acquired: Map copied on May 10, 2022

Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6

Tags: PENDING

The Location Affordability Index is an indicator of housing and transportation costs at the neighborhood level. It gives the percentage of a given family's income estimated to be spent on housing and transportation costs in a given location for eight different household profiles. It is calculated using actual and modeled data for Census block groups in all 942 Combined Base Statistical Areas, which cover 94% of the U.S. population.

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 at the Census block-group level for the majority of the populated area of the United States. 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 neighborhood location while holding household demographics constant.

In Version 1, these estimates were originally generated with data from several federal sources and vehicle miles traveled (VMT) data from Illinois EPA using separate OLS regression models for household housing costs, VMT, car ownership, and transit usage. Version 2, in addition to updating all the constituent data sources, represents a significant a methodological and technical advance from Version 1, modelling auto ownership, housing costs, and transit usage for both homeowners and renters are concurrently using simultaneous equation modeling (SEM) to capture the interrelationship of these factors. The inputs to the SEM include these six endogenous variables and 18 exogenous variables, with VMT still modeled separately due to data limitations.

To learn more about the Location Affordability Index (v.2.0) 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. Data Dictionary: DD_Location Affordability Indev v.2.0 Date of Coverage: 2008-2012

A novel index for estimating the expense of housing and transportation in Canada has been developed, based on the Center for Neighborhood Technology’s Housing + Transportation (H+T®) Affordability Index. By combining Census data and data from other statistical programs we obtain a composite index (the H+T Index) for all Aggregate Dissemination Areas of Canada, except for Territories and First Nations. This table uses the methodology of table 46100090, calculated on Aggregate Dissemination Areas.

The Location Affordability Index (LAI) helps to better understand the combined cost of housing and transportation. First launched by the U.S. Department of Housing and Urban Development (HUD) and Department of Transportation (DOT) in November 2013, the 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.

The Location Affordability Index (LAI) helps to better understand the combined cost of housing and transportation. The LAI estimates the percentage of a family’s income dedicated to the combined cost of housing and transportation in a given _location. Because what is “affordable” is different for everyone, users can choose among a diverse set of family profiles—which vary by household income, size, and number of commuters—and see the affordability landscape for each in a given neighborhood, city, or region.

Affordability index by census tract. The affordability index is defined as the housing plus transportation costs divided by the typical regional income. Data is provided by CNT. For more information please visit https://htaindex.cnt.org/map/.

This feature layer visualizes the 2018 overall SVI for U.S. counties and tractsSocial Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. county and tract15 social factors grouped into four major themesIndex value calculated for each county for the 15 social factors, four major themes, and the overall rankWhat is CDC Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and Transportation VariablesFor a detailed description of variable uses, please refer to the full SVI 2018 documentation.RankingsWe ranked counties and tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of counties in multiple states, or across the U.S. as a whole. Rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each county and tract, we generated its percentile rank among all counties and tracts for 1) the fifteen individual variables, 2) the four themes, and 3) its overall position. Overall Rankings:We totaled the sums for each theme, ordered the counties, and then calculated overall percentile rankings. Please note: taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic theme - RPL_THEME1Housing Composition and Disability - RPL_THEME2Minority Status & Language - RPL_THEME3Housing & Transportation - RPL_THEME4FlagsCounties in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Counties below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each county as the total number of all variable flags. SVI Informational VideosIntroduction to CDC Social Vulnerability Index (SVI)Methods for CDC Social Vulnerability Index (SVI)More Questions?CDC SVI 2018 Full DocumentationSVI Home PageContact the SVI Coordinator

Analysis of ‘Housing type and Transportation data set from the CDC Social Vulnerability Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c5aab180-36b5-43ab-b049-2bd4d2a451f5 on 12 February 2022.

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

This dataset contains the columns for housing type and transportation from the ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created Centers for Disease Control and Prevention Social Vulnerability Index (CDC SVI or simply SVI, hereafter) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.

SVI indicates the relative vulnerability of every U.S. Census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 15 social factors, including unemployment, minority status, and disability, and further groups them into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes, as well as an overall ranking.

In addition to tract-level rankings, SVI also has corresponding rankings at the county level. Notes below that describe “tract” methods also refer to county methods. The following counties in this dataset are : Bastrop, Blanco, Burnet, Caldwell, Fayette, Hays Lee, Travis, Williamson for the following years 2014, 2016,2018.

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

Graph and download economic data for Producer Price Index by Commodity: Transportation Equipment: Motor Homes, Built on Purchased Chassis (WPU1415) from Jun 1984 to Jun 2025 about purchase, transportation, equipment, commodities, housing, PPI, inflation, price index, indexes, price, and USA.

sde_grasp_svi_2018.sde.SVI2018_US_tract

Explore the Consumer Price Index dataset for United Arab Emirates, covering various categories such as Food and Beverages, Transportation, Housing, and more. Stay informed about the cost of living trends with this valuable resource.

Consumer price index, Recreational and culture, Food and Beverages, Restaurants and Hotels, Education, Transportation, Communications, Medical care, Miscellaneous goods and services, Textiles, clothing and footwear, Furniture, household goods, Tobacco, Housing, Water, Electricity, Gas, CPI, Cost of living, Household, Food, Transportation, Price

United Arab EmiratesFollow data.kapsarc.org for timely data to advance energy economics research..(2014=100)

Unit of measurement: 2015=100. United Kingdom, 12.5.4 Transport insurance. CPIH is the most comprehensive measure of inflation. It extends CPI to include a measure of the costs associated with owning, maintaining and living in one's own home, known as owner occupiers' housing costs (OOH), along with council tax. This dataset provides CPIH time series (2005 to latest published month), allowing users to customise their own selection, view or download.

Cost of living data based on food, housing, utilities, transportation, healthcare, and consumer discretionary spending in the United States.

This dataset contains the consumer price index (CPI) over time for the U.S. and various geographic areas. The CPI is given for all goods and services combined as well for individual classes such as energy, housing, transportation and food. The 2-month change and year-over-year change in CPI is also included.

Abstract

The purpose of the SEPHER data set is to allow for testing, assessing and generating new analysis and metrics that can address inequalities and climate injustice. The data set was created by Tedesco, M., C. Hultquist, S. E. Char, C. Constantinides, T. Galjanic, and A. D. Sinha.

Methodology

SEPHER draws upon four major source datasets: CDC Social Vulnerability Index, FEMA National Risk Index, Home Mortgage Disclosure Act, and Evictions datasets. The data from these source datasets have been merged, cleaned, and standardized and all of the variables documented in the data dictionary.

CDC Social Vulnerability Index

CDC Social Vulnerability Index (SVI) dataset is a dataset prepared for the Centers for Disease Control and Prevention for the purpose of assessing the degree of social vulnerability of American communities to natural hazards and anthropogenic events. It contains data on 15 social factors taken or derived from Census reports as well as rankings of each tract based on these individual factors, groups of factors corresponding to four related themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) and overall. The data is available for the years 2000, 2010, 2014, 2016, and 2018.

FEMA National Risk Index

The National Risk Index (NRI) dataset compiled by the Federal Emergency Management Agency (FEMA) consists of historic natural disaster data from across the United States at a tract-level. The dataset includes information about 18 natural disasters including earthquakes, tsunamis, wildfires, volcanic activity and many others. Each disaster is detailed out in terms of its frequency, historic impact, potential exposure, expected annual loss and associated risk. The dataset also includes some summary variables for each tract including the total expected loss in terms of building loss, human loss and agricultural loss, the population of the tract, and the area covered by the tract. It finally includes a few more features to characterize the population such as social vulnerability rating and community resilience.

Home Mortgage Disclosure Act

The Home Mortgage Disclosure Act (HMDA) dataset contains loan-level data for home mortgages including information on applications, denials, approvals, and institution purchases. It is managed and expanded annually by the Consumer Financial Protection Bureau based on the data collected from financial institutions. The dataset is used by public officials to make decisions and policies, uncover lending patterns and discrimination among mortgage applicants, and investigate if lenders are serving the housing needs of the communities. It covers the period from 2007 to 2017.

Evictions

The Evictions dataset is compiled and managed by the Eviction Lab at Princeton University and consists of court records related to eviction cases in the United States between 2000 and 2016. Its purpose is to estimate the prevalence of court-ordered evictions and compare eviction rates among states, counties, cities, and neighborhoods. Besides information on eviction filings and judgments, the dataset includes socioeconomic and real estate data for each tract including race/ethnic origin, household income, poverty rate, property value, median gross rent, rent burden, and others.