There’s more to affordability than how much you pay for housing. Transportation costs are the second-biggest budget item for most families and have an important and robust relationship with the location and cost housing. The Location Affordability Index (LAI) offers a more holistic perspective on affordability by showing users the combined cost of housing and transportation as a percentage of household income.
Despite the simplicity of the concept, the Index is generated using an enormous amount of data and a series of complex analyses. This page walks through in detail what the Index is and how it is produced in general language.
For a more technical description, please view the LAI Data and Methodology - Version 2.
Note: This explanation of the data and methodology behind the LAI is specific to Version 2 of the LAI. While it has value as a lay-language narrative explanation and is broadly applicable to Version 2.1, data users should consult the full Version 2.1 data and methodology documentation.
The goal of the Location Affordability Index (LAI) is to give consumers access to reliable, standardized data on the cost of location to make more informed decisions about where to live and work. There are key four elements—explained in greater detail in the following sections—that must be grasped in order to fully understand what the Index is, how it is produced, and what it tells us. They are:
In all, the Index draws from six different Federal data sources and Illinois state odometer readings:
These data contain information about the characteristics of every Census block group in the Index’s coverage area.
The LAI covers virtually every populated block group in the 50 states and the District of Columbia. Census block groups generally have populations between 600 and 3,000 people. They vary in size depending on an area’s population density, ranging from only a few city blocks to the entirety of some rural counties. Block groups are the smallest geographical unit for which reliable data is available; they can generally be thought of as representing neighborhoods (bearing in mind the different ways people understand the concept of “neighborhood”).
For non-metropolitan areas that account for the balance of the population living in the 50 states and the District of Columbia, the LAI is calculated at the county level. For simplicity, the remainder of this page will refer only to block groups, but all of the data and methodology described applies to non-metropolitan counties as well.
To calculate the housing and transportation costs for a given location, we use data for demographics and features of the built environment that we know influence these costs: income, average household size, average commuters per household, population density, walkability, transit access, and employment access. Using these data and statistical regression – a widely used statistical technique that assesses the relationship between one or more input variables and an output variable – we generate mathematical models for the relationship between all of these data points and housing and transportation costs. By plugging data into these models, we can estimate components of housing and transportation costs - mostly at the Census block-group level - that can then be used to calculate the Index.
For an illustration of how this works, think about the relationship between driving and walkability. However you measure it, the greater a neighborhood’s walkability, the less its residents will drive, all else being equal. In order to use data on walkability to predict driving (and thus transportation costs), a researcher would need to model this relationship. He or she would do this by looking at existing data on vehicle miles traveled (VMT) and walkability for many block groups (there are almost 200,000 covered by the Index). Next, he or she would use statistical regression modeling to come up with the best possible approximation (or model) of that relationship, represented by an equation for a line through the middle of the data points (think of this model as a machine that takes in data on walkability and spits out estimates on VMT, as in Figure 1 below). He or she would use this equation and data on walkability to estimate the VMT for specific block groups, which could then be used to calculate total transportation costs.
For the purposes of the LAI, we are interested in predicting the following outputs as they apply to households:
These are predicted using the input and output variables listed in Table 1.
|Gross Density||# of households (HH) / total acres||Census ACS, TIGER/Line files|
|Block Density||# of blocks / total land area||Census TIGER/Line files|
|Employment Access Index||Number of jobs in area block groups / squared distance of block groups||Census LEHD-LODES|
|Retail Employment Access Index||Number of retail jobs in area block groups / squared distance of block groups||Census LEHD-LODES|
|Median Commute Distance||Calculated from data on spatial distributions of workers' employment and residential locations and the relation between the two at the Census block level||Census LEHD-LODES|
|Job Density||# of jobs / total land area||Census LEHD-LODES|
|Retail Density||# of retail jobs / total land area||Census LEHD-LODES|
|Fraction of Rental Units||Number of rental units as a percentage of total housing units||Census ACS|
|Fraction of Single Family Detached Housing Units||Number of single family detached housing units as a percentage of total housing units||Census ACS|
|Median Rooms/Owner HU||Median number of rooms in owner occupied housing units (HU)||Census ACS|
|Median Rooms/Renter HU||Median number of rooms in renter occupied housing units||Census ACS|
|Area Median Household Income||Determined using County median household income for rural areas or CBSA median household income for Metropolitan and Micropolitan Areas||Census ACS|
|Fraction of Median Income Owners||Median income for owners at the block group level as a percentage of either CBSA or County median income (County for rural areas / CBSA for Metropolitan and Micropolitan Areas)||Census ACS|
|Fraction of Area Median Income Renters||Median income for renters at the block group level as a percentage of either CBSA or County median income (County for rural areas / CBSA for Metropolitan and Micropolitan Areas)||Census ACS|
|Average Household Size: Owners||Calculated from data on Tenure and Total Population in Occupied Housing Units by Tenure||Census ACS|
|Average Household Size: Renters||Calculated from data on Tenure and Total Population in Occupied Housing Units by Tenure||Census ACS|
|Average Commuters per Household Owners||Calculated using the total number of workers 16 years and over who do not work at home||Census ACS|
|Average Commuters per Household Renters||Calculated using the total number of workers 16 years and over who do not work at home||Census ACS|
|Median Selected Monthly Owner Costs||Includes mortgage payments, utilities, fuel, and condominium and mobile home fees where appropriate||Census ACS|
|Median Gross Rent||Includes contract rent as well as utilities and fuel if paid by the renter||Census ACS|
|Autos per Household Owners||Calculated from Aggregate Number of Vehicles Available by Tenure and Occupied Housing Units||Census ACS|
|Autos per Household Renters||Calculated from Aggregate Number of Vehicles Available by Tenure and Occupied Housing Units||Census ACS|
|Percent Transit Journey to Work Owners||Calculated from Means of Transportation to Work by Tenure||Census ACS|
|Percent Transit Journey to Work Renters||Calculated from Means of Transportation to Work by Tenure||Census ACS|
The inputs used to generate these models (Table 1) are calculated from Federal and transit data to approximate key demographic characteristics and features of the built environment: income, average household size, average commuters per household, population density, walkability, transit access, and employment access. To capture walkability, for instance, we calculate a neighborhood’s block density (blocks per square mile) and intersection density (intersections per square mile), both of which are negatively correlated with how much people walk in an area. (For more information about these model inputs, see the detailed methodology documentation.)
Many advances in statistics have enabled the creation of more nuanced models for explaining complex phenomena like impact of demographics and the built environment on housing and transportation costs. One approach that has proved useful in urban planning studies is simultaneous (or structural) equation modeling (SEM). SEM allows set of regression models that are theoretically related to interact directly and influence each others’ outputs.
Consider, for instance, the autos per household for homeowners can be predicted using the following input variables:
Notice that Selected Monthly Ownership costs (SMOC) are included here but are also, like autos per household, one of the quantities that need to be predicted using regression modeling. Using SEM, we can model all of our outputs simultaneously, which allows outputs to directly influence each other (in addition to all of the relevant inputs for each output). The resulting model looks like this (for simplicity, only four out of 15 input variables are shown):
Due to limitations with the data for VMT, it was not included in the SEM; it continues to be modeled using standard regression.
Once these models have been developed, we can use them to estimate average autos per household and vehicle miles traveled and the percent of commuters using transit for all 198,373 Census block groups covered by the Index. This is accomplished by plugging data for each of the 15 predictor variables for each block group into both the SEM and the VMT model.
Most of the input variables come from data that describe features of a neighborhood that are common to everyone who lives there: population density, walkability, transit access and quality, and employment access (these are all features of the built environment). For inputs that identify characteristics the residents themselves--household size, income, and number of commuters--using actual data for each block group wouldn’t produce a very useful Index. Since people tend to live in places they can afford, using actual demographic data would produce a map where the majority of neighborhoods look more or less affordable. Instead, we have chosen eight household profiles—characterized by the number of family members, income, and number of commuters—that represent a wide range of American families, providing useful insight on affordability for a variety of different users, including consumers, planning agencies, real estate professionals, and housing counselors.
|Household Profile||Income||Size||# of Commuters|
|Very low-income individual||National poverty line||1||1|
|Working individual||50% of MHHI||1||1|
|Single professional||135% of MHHI||1||1|
|Retired couple||80% of MHHI||2||0|
|Single-parent family||50% of MHHI||3||1|
|Moderate-income family||80% of MHHI||3||1|
|Dual-professional family||150% of MHHI||4||2|
MHHI = Median household income for a given CBSA
Each CBSA and rural county has a unique set of household profiles. We use these regional profiles in combination with the model’s block-group-level input variables to estimate household housing and transportation costs. Figures 3 and 4 illustrate how this is done for homeowners and renters, respectively, using the SEM, which estimates the number of autos per household, the percentage of commuters using transit, and housing costs for each Census block group and non-metropolitan county. This results in 16 sets of estimates for these 3 variables: a set of homeowners and renters for each of the eight household profiles.
Figure 5 illustrates how this works using the VMT Model, generating VMT estimates for the eight profiles.
Once the average transportation usage for each block group is estimated for each demographic profile, we can use those estimates to calculate total annual transportation costs.
The regression models we use estimate transportation usage, not total transportation costs. In order to calculate total automobile-related transportation costs, we multiply the estimated transportation usage (i.e. car ownership and vehicle miles traveled) by the cost per use (Figure 4). Ownership costs include all expenses, from the time of purchase on, that are required to keep the car roadworthy: purchase costs (spread over length of car ownership) or car payments, insurance, license and registration fees, taxes, and routine repairs and maintenance. Driving costs include the cost of gas and maintenance due to wear-and-tear.
The car ownership cost and car use cost components of the Index are generated using the Consumer Expenditure Survey (CES), from US Bureau of Labor Statistics. New research undertaken for the development of this site represents a significant advance over previous measures that focused primarily on autos less than five years old and used a single cost multiplier for all vehicle owners, regardless of income. View the summary of the analysis.
There is no existing data on the average number of transit trips or expenditures per commuter or per household at the block-group level. Using the SEM, we estimate the percentage of workers for each household profile in each block group commuting by transit. We then use these estimates along with data from the ACS, the National Transit Database (maintained by the Federal Transit Administration), and our household profiles to calculate estimated annual transit trips and expenditures per household using the following steps:
This calculation relies on the assumption that the average transit trips and fares per household in a given block group are proportional to the percentage of commuters using transit for their journey to work in that block group, relative to the other block groups in the same metro area. This is a reasonable assumption given what we know about the proportion of transit trips that are work-related: the 2009 National Household Travel survey puts the percentage of transit trips related to work at 33% (versus 20% of car trips; see Table 9), and an analysis of 150 separate on-board passenger surveys by the American Public Transit Association found that 59.2% of transit trips are work-related. Nevertheless, as with the autos per household and annual vehicle miles travelled figures that appear in the Index, these numbers are averages and do not attempt to represent the exact transit expenses for any specific household.
After going through the steps described above, we have all the elements necessary for the Location Affordability Index: housing costs for renters and owners, transportation costs, and income for eight different household profiles for each block group covered by the Index. Selecting from the Household Profile menu in the upper left allows the user to pull up the map for each profile.