Location Affordability Index v 1.0 [United States]
Description:
The Location Affordability Index (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.
The Location Affordability Index (LAI) estimates three dependent variables of
transportation behavior (auto ownership, auto use, and transit use) as functions of 14 independent
variables (median income, per capita income, average household size, average commuters per household,
residential density, gross density, block density, intersection density, transit connectivity, transit
frequency of service, transit access shed, employment access, job diversity, and average commute
distance).
To hone in on the built environment’s influence on transportation costs, the independent
household variables (income, household size, and commuters per household) are set at fixed values to
control for any variation they might cause.
The LAI also estimates two dependent variables of housing costs (Selected Monthly
Owner Costs and Gross Rent) as functions of 16 independent variables: regional median selected monthly
owner costs and regional median gross rent in addition to the 14 variables used in the transportation
model.
To learn more about the Location Affordability Index (v.1.0) visit: https://www.locationaffordability.info/LAPMethods.pdf.
Data Dictionary: DD_Location Affordability Indev v.1.0.
Date of Coverage: 2005-2009
https://www.locationaffordability.info/LAPMethodsV2.pdf
Creator:
United States. Department of Housing and Urban Development
HUD and the dataset and metadata authors assume no responsibility for the use or misuse of the dataset. No warranty, expressed or implied is made with regard to the accuracy of the spatial accuracy, and no liability is assumed by the U.S. Government in general, the dataset creators or the U.S. Department of Housing and Urban Development specifically, as to the spatial or attribute accuracy of the data.