American Housing Survey (AHS)

The American Housing Survey tracks housing structures across the United States.

A collection of tables, most with one row per housing unit.
A complex sample survey designed to generalize to both occupied and vacant housing units across the United States and also for about twenty-five metropolitan areas.
Released more or less biennially since 1973.
Sponsored by the Department of Housing and Urban Development (HUD) and conducted by the U.S. Census Bureau.

Simplified Download and Importation

The R lodown package easily downloads and imports all available AHS microdata by simply specifying "ahs" with an output_dir = parameter in the lodown() function. Depending on your internet connection and computer processing speed, you might prefer to run this step overnight.

library(lodown)
lodown( "ahs" , output_dir = file.path( path.expand( "~" ) , "AHS" ) )

lodown also provides a catalog of available microdata extracts with the get_catalog() function. After requesting the AHS catalog, you could pass a subsetted catalog through the lodown() function in order to download and import specific extracts (rather than all available extracts).

library(lodown)
# examine all available AHS microdata files
ahs_cat <-
    get_catalog( "ahs" ,
        output_dir = file.path( path.expand( "~" ) , "AHS" ) )

# 2013 only
ahs_cat <- subset( ahs_cat , year == 2013 )
# download the microdata to your local computer
ahs_cat <- lodown( "ahs" , ahs_cat )

Analysis Examples with the `survey` library

Construct a complex sample survey design:

library(survey)

ahs_df <- 
    readRDS( 
        file.path( path.expand( "~" ) , "AHS" , 
            "2013/national_v1.2/newhouse_repwgt.rds" 
        ) 
    )

ahs_design <- 
    svrepdesign(
        weights = ~ wgt90geo ,
        repweights = "repwgt[1-9]" ,
        type = "Fay" ,
        rho = ( 1 - 1 / sqrt( 4 ) ) ,
        mse = TRUE ,
        data = ahs_df
    )

Variable Recoding

Add new columns to the data set:

ahs_design <- 
    update( 
        ahs_design , 

        tenure = 
            factor( 
                ifelse( is.na( tenure ) , 4 , tenure ) , 
                levels = 1:4 , 
                labels = 
                    c( 'Owned or being bought' ,
                    'Rented for cash rent' ,
                    'Occupied without payment of cash rent' ,
                    'Not occupied' )
            ) ,
            
            
        lotsize =
            factor( 
                1 + findInterval( lot ,
                    c( 5500 , 11000 , 22000 , 
                    44000 , 220000 , 440000 ) ) , 
                levels = 1:7 ,
                labels = c( "Less then 1/8 acre" , 
                "1/8 up to 1/4 acre" , "1/4 up to 1/2 acre" ,
                "1/2 up to 1 acre" , "1 up to 5 acres" , 
                "5 up to 10 acres" , "10 acres or more" ) ) ,
                
                
        below_poverty = as.numeric( poor < 100 )
                
    )

Unweighted Counts

Count the unweighted number of records in the survey sample, overall and by groups:

sum( weights( ahs_design , "sampling" ) != 0 )

svyby( ~ one , ~ tenure , ahs_design , unwtd.count )

Weighted Counts

Count the weighted size of the generalizable population, overall and by groups:

svytotal( ~ one , ahs_design )

svyby( ~ one , ~ tenure , ahs_design , svytotal )

Descriptive Statistics

Calculate the mean (average) of a linear variable, overall and by groups:

svymean( ~ rooms , ahs_design , na.rm = TRUE )

svyby( ~ rooms , ~ tenure , ahs_design , svymean , na.rm = TRUE )

Calculate the distribution of a categorical variable, overall and by groups:

svymean( ~ lotsize , ahs_design , na.rm = TRUE )

svyby( ~ lotsize , ~ tenure , ahs_design , svymean , na.rm = TRUE )

Calculate the sum of a linear variable, overall and by groups:

svytotal( ~ rooms , ahs_design , na.rm = TRUE )

svyby( ~ rooms , ~ tenure , ahs_design , svytotal , na.rm = TRUE )

Calculate the weighted sum of a categorical variable, overall and by groups:

svytotal( ~ lotsize , ahs_design , na.rm = TRUE )

svyby( ~ lotsize , ~ tenure , ahs_design , svytotal , na.rm = TRUE )

Calculate the median (50th percentile) of a linear variable, overall and by groups:

svyquantile( ~ rooms , ahs_design , 0.5 , na.rm = TRUE )

svyby( 
    ~ rooms , 
    ~ tenure , 
    ahs_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE ,
    keep.var = TRUE ,
    na.rm = TRUE
)

Estimate a ratio:

svyratio( 
    numerator = ~ rooms , 
    denominator = ~ rent , 
    ahs_design ,
    na.rm = TRUE
)

Subsetting

Restrict the survey design to homes with a garage or carport:

sub_ahs_design <- subset( ahs_design , garage == 1 )

Calculate the mean (average) of this subset:

svymean( ~ rooms , sub_ahs_design , na.rm = TRUE )

Measures of Uncertainty

Extract the coefficient, standard error, confidence interval, and coefficient of variation from any descriptive statistics function result, overall and by groups:

this_result <- svymean( ~ rooms , ahs_design , na.rm = TRUE )

coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )

grouped_result <-
    svyby( 
        ~ rooms , 
        ~ tenure , 
        ahs_design , 
        svymean ,
        na.rm = TRUE 
    )
    
coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )

Calculate the degrees of freedom of any survey design object:

degf( ahs_design )

Calculate the complex sample survey-adjusted variance of any statistic:

svyvar( ~ rooms , ahs_design , na.rm = TRUE )

Include the complex sample design effect in the result for a specific statistic:

# SRS without replacement
svymean( ~ rooms , ahs_design , na.rm = TRUE , deff = TRUE )

# SRS with replacement
svymean( ~ rooms , ahs_design , na.rm = TRUE , deff = "replace" )

Compute confidence intervals for proportions using methods that may be more accurate near 0 and 1. See ?svyciprop for alternatives:

svyciprop( ~ below_poverty , ahs_design ,
    method = "likelihood" , na.rm = TRUE )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( rooms ~ below_poverty , ahs_design )

Perform a chi-squared test of association for survey data:

svychisq( 
    ~ below_poverty + lotsize , 
    ahs_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        rooms ~ below_poverty + lotsize , 
        ahs_design 
    )

summary( glm_result )

Analysis Examples with `srvyr`

The R srvyr library calculates summary statistics from survey data, such as the mean, total or quantile using dplyr-like syntax. srvyr allows for the use of many verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions, the tidyverse style of non-standard evaluation and more consistent return types than the survey package. This vignette details the available features. As a starting point for AHS users, this code replicates previously-presented examples:

library(srvyr)
ahs_srvyr_design <- as_survey( ahs_design )