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Land Asset Ratio

Source: R/get-land-assets-gross-ratio.R
get_land_assets_gross_ratio.Rd

Share of total assets invested in land, buildings, and equipment.

Formula:

lar = land_buildings_equipment / total_assets

Definitional Range

Usually in [0, 1], but can exceed 1 when accumulated depreciation is large relative to other assets. The distribution is highly right-skewed: most nonprofits with no owned real estate show values near zero, while capital-intensive organizations (healthcare, housing, higher education) may show values above 0.50.

Benchmarks and rules of thumb

  • Most informative for within-subsector comparisons.

  • High values indicate illiquidity risk: if the majority of assets are fixed property, the organization has limited ability to quickly convert assets to cash.

Calculated For: 990 filers only.

Usage

get_land_assets_gross_ratio( df,
  land_buildings = "F9_10_ASSET_LAND_BLDG",
  total_assets   = "F9_10_ASSET_TOT_EOY",
  winsorize = 0.98 ,
  range     = "zp",
  sanitize  = TRUE,
  summarize = FALSE )

Arguments

df

A data.frame containing the fields required for computing the metric.

land_buildings

Land, buildings, and equipment at cost or other basis, before accumulated depreciation (Part X line 10a).

total_assets

Total assets, EOY.

winsorize

The winsorization value (between 0 and 1), defaults to 0.98, which winsorizes at the 1st and 99th percentiles.

range

Character string specifying the theoretical range of the ratio, used to set winsorization bounds. Default "zp". Options: "np" (negative to positive), "zp" (zero to positive), "zo" (zero to one), "nz" (negative to zero), or a custom "lo;hi" pair (e.g. "0;10").

sanitize

Logical (default TRUE). If TRUE, NA values in the financial input columns are imputed to zero before the ratio is computed, respecting form scope: Part X and VIII/IX fields (990 only) are imputed only for 990 filers; Part I summary fields (990 + 990EZ) are imputed for all filers. The returned dataframe always contains the original unmodified input columns.

summarize

Logical. If TRUE, prints a summary() of the results and plots density curves for all four output columns (raw, winsorized, z-score, percentile). Defaults to FALSE.

Value

Object of class data.frame: the original dataframe appended with four new columns:

- `land_assets_gross`   - land asset ratio (raw)
- `land_assets_gross_w` - winsorized version
- `land_assets_gross_z` - standardized z-score (based on winsorized values)
- `land_assets_gross_p` - percentile rank (1-100)

Details

Primary uses and key insights

The land, buildings, and equipment to assets ratio (gross version) measures what share of total assets is represented by the gross book value of fixed physical assets before accumulated depreciation is netted out. A high ratio indicates a capital-intensive organization with a large physical footprint; a low ratio indicates a lean service organization whose assets are primarily financial.

The gross version uses the cost or other basis of land, buildings, and equipment (Part X line 10a), before accumulated depreciation (line 10b) is netted out. This is distinct from the net version (get_land_assets_net_ratio()), which uses the net book value after depreciation (Part X line 10c). The gross version gives a better picture of the original investment in fixed assets; the net version better reflects current book value.

Formula variations and their sources

F9_10_ASSET_LAND_BLDG (cost or other basis, line 10a) / total assets. Because total assets carry fixed assets at net book value, the ratio can exceed 1 for heavily depreciated organizations. Earlier versions used accumulated depreciation (F9_10_ASSET_LAND_BLDG_DEPREC, line 10b), which is not the gross cost.

Canonical citations

  • Frumkin, P. & Keating, E.K. (2001). The price of doing good. Policy and Society, 20(4), 94-112. - Asset composition analysis for nonprofits.

Variables used:

  • F9_10_ASSET_LAND_BLDG: Land, buildings, and equipment, cost or other basis, line 10a (land_buildings)

  • F9_10_ASSET_TOT_EOY: Total assets, EOY (total_assets)

Examples

library( fiscal )
data( dat10k )

d <- get_land_assets_gross_ratio( df = dat10k )
#>    :: Total assets equal to zero :: 346 case(s) replaced with NaN
head( d[ , c( "land_assets_gross", "land_assets_gross_w", "land_assets_gross_z", "land_assets_gross_p" ) ] )
#>   land_assets_gross land_assets_gross_w land_assets_gross_z land_assets_gross_p
#> 1                NA                  NA                  NA                  NA
#> 2         0.9728302           0.9728302           0.5983719                  84
#> 3                NA                  NA                  NA                  NA
#> 4         1.4272793           1.4272793           0.9847328                  93
#> 5                NA                  NA                  NA                  NA
#> 6         0.0000000           0.0000000          -0.9649662                   1