Predictors
This is the full list of parcel-level variables used as predictors and sampling filters for our models of fair market value (FMV).
These variables are derived from geospatial parcel boundaries and publicly available geospatal datasets.
In addition to the variables listed here, we obtain some predictors from parcel boundaries (geolocation and size: lat_id, long_id, ha) or transactions (date of sale).
Terrain
- elev
Average elevation of parcel (meters).
- Source:
USGS National Elevation Dataset (NED) 1/3 Arc-Second
- Geoprocessing:
Mean of pixel values within parcel (zonal statistics). Elevation raster exported at 0.00449 degrees resolution from Google Earth Engine (EPSG:4326).
Climate
- clim_ppt_summer
Average precipitation in summer (Jun-Aug) (mm)
- Source:
PRISM monthly climate normals
- Access:
- Accessed:
Aug 10, 2023
- Geoprocessing:
Zonal statistics (mean)
- clim_ppt_winter
Average precipitation in winter (Dec-Feb) (mm)
- Source:
see
clim_ppt_summer
- clim_tmean_summer
Average temperature in summer (Jun-Aug) (C)
- Source:
see
clim_ppt_summer
- clim_tmean_winter
Average temperature in winter (Dec-Feb) (C)
- Source:
see
clim_ppt_summer
Hydrology
- cst_50
Percentage (0-100) of coastal waters within a 50m radius. Used as proxy for beachfront properties and boating access.
- Source:
ESRI North America Water Polygons
- Accessed:
Jun 18, 2019
- cst_2500
Percentage (0-100) of coastal waters within a 2500m radius. Used as proxy for ocean proximity for near-ocean properties. Positively associated with distance to coast as well as with the added value of properties surrounded by coastal waters on several sides, such as islands, peninsulas, etc.
- Source:
see
cst_50
- lake_dist
Distance (m) to nearest large lake (> 4ha).
- Source:
National Hydrographic Database (NHDPlus High Resolution)
- Source:
National Hydrographic Database (NHDPlus High Resolution)
- Access:
https://www.usgs.gov/national-hydrography/nhdplus-high-resolution
- Accessed:
Jun 18, 2019
- lake_frontage
Approximate total lake frontage of the parcel (in meters).
- Source:
see
lake_dist- Geoprocessing:
Area of intersection of parcel polygon with 50-meter-buffers around NHD lake waterbodies, divided by the buffer width (50m).
- river_frontage
Approximate total river frontage of the parcel (in meters). Only waterbody polygons from the NHD are included (no lines).
- Source:
see
lake_dist- Geoprocessing:
see
lake_frontage, but using river waterbodies.
- water_exposure
- Computation:
- p_wet
Percentage (0-100) of parcel area covered by wetland polygons.
- Source:
National Wetlands Inventory (NWI), U.S. Fish & Wildlife Service
- Access:
https://www.fws.gov/program/national-wetlands-inventory/wetlands-data
- Accessed:
Jun 18, 2019
- fld_fr_fath_p100
Flood risk: average meters of inundation depth within the 1% annual exceedance probability floodplain (pluvial floods).
- Source:
Fathom-US Flood Hazard data (Wing et al 2018)
- Access:
https://www.fathom.global/product/flood-hazard-data-maps/fathom-us/ (licensed)
- Accessed:
Mar 26, 2020
- Geoprocessing:
Zonal statistics (mean)
- fld_fr_fath_f100
Flood risk: average meters of inundation depth within the 1% annual exceedance probability floodplain (fluvial floods).
- Source:
see
fld_fr_fath_p100
Soils
- f_soil_<soil_class>
Fraction (0-1) of parcel area covered by soil_class.
Eleven soil class categories are distinguished (e.g. “prime” farmland, “state priority” soil, etc.). See Gold et al (2023) for a description.
- Source:
SSURGO
- Access:
- Accessed:
Aug 11, 2023
- Geoprocessing:
Polygon intersections
Land cover and use
- p_barren
Percentage (0-100) of pixels in parcel that were “barren” in 2011.
- Source:
National Land Cover Database, Year-2011 Land Cover (Edition 2014-10-10)
- Access:
- Accessed:
June 18, 2019
- p_forest
Percentage (0-100) of pixels in parcel that were “forest” (deciduous, evergreen, or mixed) in 2011.
- Source:
see
p_barren
- p_grassland
Percentage (0-100) of pixels in parcel that were “grassland” in 2011.
- Source:
see
p_barren
- irr_2000_2020
Percentage (0-100) of pixels in parcel that were “irrigated” between 2000 and 2020 (averaged across all years)
- Source:
IrrMapper Irrigated Lands, Version 1.2
- Access:
https://developers.google.com/earth-engine/datasets/catalog/UMT_Climate_IrrMapper_RF_v1_2
- Accessed:
April 11, 2022
Buildings
All of the following indicators are derived from Microsoft’s open-source USBuildingFootprints dataset, which contains polygons of 125.2 million buildings inferred from high-resolution satellite imagery with neural networks.
- Access:
- Accessed:
Aug 21, 2023
Microsoft’s building footprints are our preferred open-source metric for the presence of buildings in CONUS, as they are more consistently available across CONUS than other indicators (e.g., tax assessor data). However, building footprints introduce new sources of error. For instance, footprints under trees are often missed.
Alternative measures of building presence are available in tax assessor and parcel boundary datasets. However, their availability and quality varies across states and counties. For a comparison of ZTRAX-based and remote-sensing based building variables see Nolte et al. (2023) Land Economics (Appendix Figures A14-16)
- n_bld_fp
Count of building footprints on the parcel.
- Geoprocessing:
Polygon intersections.
- m2_bld_fp
Area of building footprints on the parcel (square meters)
- Geoprocessing:
Polygon intersections.
- p_bld_fp
Percentage (0-100) of the area of the parcel that is covered by footprints.
- Geoprocessing:
Polygon intersections.
- p_bld_fp_*
Percentage of area within the given radius (
*, integer, in meters) that is covered by building footprints. An indicator of nearby building density.- Geoprocessing:
rasterization of building footprints, pixel-based computation of average building footprint presence within circular neighborhood (2D convolution with moving-window kernel), averaged across all pixels within each parcel (zonal statistics).
- p_bld_fp_500
% building footprints within 500m
See
p_bld_fp_*
- p_bld_fp_5000
% building footprints within 5000m
See
p_bld_fp_*
Demographics
- hh_inc_med_bg_2012_2016
Median household income at the census block-group level (2012-2016)
- Source:
American Community Survey, via the National Historical Geographic Information System (NHGIS)
- Access:
- Geoprocessing:
spatial joins of parcel centroids with reference units.
- p_asian_bg_2012_2016
% population in block group identifying as “Asian” on American Community Survey.
- Source:
see
hh_inc_med_bg_2012_2016
- p_black_bg_2021_2016
% population in block group identifying as “Black or African-American” on American Community Survey.
- Source:
see
hh_inc_med_bg_2012_2016
- p_hispanic_bg_2021_2016
% population in block group identifying as “Hispanic” on American Community Survey.
(Note: overlaps with ‘race’ categories, such as white, black, asian, etc.)
- Source:
see
hh_inc_med_bg_2012_2016
- p_mixed_bg_2021_2016
% population in block group identifying as “Mixed” on American Community Survey.
- Source:
see
hh_inc_med_bg_2012_2016
- p_native_bg_2021_2016
% population in block group identifying as “American Indian or Alaska Native” on American Community Survey.
- Source:
see
hh_inc_med_bg_2012_2016
- p_pacific_bg_2021_2016
% population in block group identifying as “Native Hawaiian or Other Pacific Islander” on American Community Survey.
- Source:
see
hh_inc_med_bg_2012_2016
- p_white_bg_2021_2016
% population in block group identifying as “White” on American Community Survey.
- Source:
see
hh_inc_med_bg_2012_2016
- bld_pop_exp_c4
Population gravity (experimental).
A spatial measure of residential population, attributed to building footprints.
- Geoprocessing:
Zonal statistics
Find out more:
Infrastructure
- rd_dst_pvd+
Distance to nearest paved road, including highways (meters).
- Source:
TIGER/Line shapefiles from the U.S. Census Bureau for the year 2019
- Access:
https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html
- Accessed:
Sept 10, 2020
Only computed up to 3km.
- travel
Travel time to major cities (minutes), ca. 2000
- Source:
European Commission & World Bank (Nelson 2007)
- Access:
This dataset was computed with different specifications than
travel_weiss. The two are not intercomparable. Differences do not necessarily reflect change over time.
- travel_weiss
Travel time to major cities (minutes), ca. 2015
- Source:
Weiss et al. 2017 Nature
- Access:
Land protection
- p_prot_2010_5000
See
p_prot_*_*
- p_prot_*_*
Percentage of area within a given <radius> (in meters) that is protected by fee or conservation easement in a given <year>.
- Sources:
Protected Area Database of the United States (PAD-US 2.0)
National Conservation Easement Database (NCED)
New England Protected Open Space (NEPOS) database
Colorado Ownership, Management, and Protection (COMaP) database.
- Geoprocessing:
Rasterization of protection polygons, pixel-based computation of average protection within circular neighborhood (2D convolution with moving-window kernel), averaged across all pixels within each parcel (zonal statistics).
Note
Data for Colorado is licensed from COMaP and cannot be shared.
- p_e
Percentage (0-100) of parcel overlapping with a conservation easement.
- Sources:
see
p_prot_*_*
- ct_p
Percentage (0-100) of parcel overlapping with a public land acquisition.
- Source:
Conservation Almanac (Trust for Public Land)
- Access:
- Accessed:
Sep 15, 2019
Spatial units
Spatial reference units, ordered from those with few units (U.S. states) to those with many (census block groups).
- division
U.S. census division (groups of state)
- state
U.S. state, identified by its two-letter Alpha code (e.g.
CAfor California)- Source:
Census Bureau, via the National Historical Geographic Information System (NHGIS)
- Access:
- Geoprocessing:
Spatial intersection with parcel centroids
- region_id
Region identifier.
Core-based regions are an experimental geographic identifier developed at the PLACES lab.
Regions divide the contiguous U.S. into less than 1000 spatial units that are identified by their high-value “core” (city centers, resorts).
We prefer modeling at the level of regions rather than counties or states, as the latter vary substantially in size and number across the U.S. geography.
- Geoprocessing:
Spatial intersection with parcel centroids
Learn more: