This article provides detailed data dictionaries and previews for every dataset in the vehicletrends package.
vmt_age
Cumulative odometer mileage quantiles for used vehicles at 3-month age bins, computed from vehicle listings data. Only used vehicles with valid mileage and age up to 15 years (180 months) are included. Quantiles are provided at the 25th, 50th, and 75th percentiles across various groupings of powertrain and vehicle type.
| Variable | Description |
|---|---|
age_bin |
Midpoint of 3-month age bin in months (e.g., 1.5, 4.5, …, up to 180) |
powertrain |
Powertrain category: “All”, “Gasoline”, “Battery Electric (BEV)”, “BEV (Tesla)”, “BEV (Non-Tesla)”, “Hybrid Electric (HEV)”, “Plug-In Hybrid Electric (PHEV)”, “Diesel”, “Flex Fuel (E85)” |
vehicle_type |
Vehicle type: “All”, “Car”, “CUV”, “SUV”, “Pickup”, “Minivan” |
quantile |
Quantile level (25, 50, or 75) |
miles |
Total odometer miles at the given quantile |
head(vmt_age, 10)
#> age_bin powertrain vehicle_type quantile miles
#> 1 1.5 All All 25 7.487188
#> 2 1.5 All All 50 127.162159
#> 3 1.5 All All 75 1422.437125
#> 4 4.5 All All 25 19.418350
#> 5 4.5 All All 50 803.475768
#> 6 4.5 All All 75 3162.262634
#> 7 7.5 All All 25 300.481568
#> 8 7.5 All All 50 2326.992600
#> 9 7.5 All All 75 4963.841562
#> 10 10.5 All All 25 1306.355234
vmt_daily
Daily VMT quantiles for used vehicles, computed as total odometer miles divided by vehicle age in days. Only used vehicles with valid mileage and age up to 15 years are included. Percentile quantiles from the 1st through 99th are provided across various groupings of powertrain and vehicle type.
| Variable | Description |
|---|---|
powertrain |
Powertrain category: “All”, “Gasoline”, “Battery Electric (BEV)”, “BEV (Tesla)”, “BEV (Non-Tesla)”, “Hybrid Electric (HEV)”, “Plug-In Hybrid Electric (PHEV)”, “Diesel”, “Flex Fuel (E85)” |
vehicle_type |
Vehicle type: “All”, “Car”, “CUV”, “SUV”, “Pickup”, “Minivan” |
quantile |
Quantile level (1 through 99) |
miles |
Daily vehicle miles traveled at the given quantile |
head(vmt_daily, 10)
#> powertrain vehicle_type quantile miles
#> 1 All All 1 2.719209
#> 2 All All 2 4.637514
#> 3 All All 3 6.048788
#> 4 All All 4 7.249902
#> 5 All All 5 8.233122
#> 6 All All 6 9.068620
#> 7 All All 7 9.872219
#> 8 All All 8 10.630848
#> 9 All All 9 11.298499
#> 10 All All 10 11.929582
vmt_annual_type
Estimated annual vehicle miles traveled for each combination of powertrain and vehicle type. Computed by fitting OLS regressions of total odometer miles on vehicle age (in years) for used vehicle listings; the slope coefficient represents the estimated annual VMT.
| Variable | Description |
|---|---|
vehicle_type |
Vehicle type: “Car”, “CUV”, “SUV”, “Pickup”, “Minivan” |
powertrain |
Powertrain category: “Gasoline”, “Battery Electric (BEV)”, “Hybrid Electric (HEV)”, “Plug-In Hybrid Electric (PHEV)”, “Diesel”, “Flex Fuel (E85)” |
vmt_annual |
Estimated annual vehicle miles traveled |
head(vmt_annual_type, 10)
#> powertrain vehicle_type vmt_annual
#> 1 Battery Electric (BEV) CUV 9805.068
#> 2 Battery Electric (BEV) Car 6710.569
#> 3 Battery Electric (BEV) Minivan 5523.487
#> 4 Battery Electric (BEV) Pickup 9538.308
#> 5 Battery Electric (BEV) SUV 7433.105
#> 6 Diesel CUV 10124.972
#> 7 Diesel Car 10112.002
#> 8 Diesel Minivan 4841.334
#> 9 Diesel Pickup 12466.379
#> 10 Diesel SUV 10947.811
vmt_annual_model
Estimated annual vehicle miles traveled for individual vehicle make/model combinations. Computed by fitting OLS regressions of total odometer miles on vehicle age (in years) for used vehicle listings; the slope coefficient represents the estimated annual VMT. Only make/model combinations with at least 100 listings are included, and results are filtered to positive annual VMT values only.
| Variable | Description |
|---|---|
make |
Vehicle manufacturer (e.g., “Toyota”, “BMW”, “Mercedes-Benz”) |
model |
Vehicle model name (e.g., “Camry”, “RAV4”, “Model 3”) |
vehicle_type |
Vehicle type: “Car”, “CUV”, “SUV”, “Pickup”, “Minivan” |
powertrain |
Powertrain category: “Gasoline”, “Battery Electric (BEV)”, “Hybrid Electric (HEV)”, “Plug-In Hybrid Electric (PHEV)”, “Diesel”, “Flex Fuel (E85)” |
vmt_annual |
Estimated annual vehicle miles traveled |
head(vmt_annual_model, 10)
#> make model powertrain vehicle_type
#> 1 Acura MDX Gasoline CUV
#> 2 Acura RDX Gasoline CUV
#> 3 Acura TL Gasoline Car
#> 4 Acura TLX Gasoline Car
#> 5 Alfa Romeo Tonale Plug-In Hybrid Electric (PHEV) CUV
#> 6 Audi A3 Diesel Car
#> 7 Audi A3 Sedan Diesel Car
#> 8 Audi A3 Sportback e-tron Plug-In Hybrid Electric (PHEV) Car
#> 9 Audi A4 Flex Fuel (E85) Car
#> 10 Audi A4 Gasoline Car
#> vmt_annual
#> 1 11334.421
#> 2 10640.250
#> 3 8155.315
#> 4 11107.978
#> 5 7538.852
#> 6 8582.209
#> 7 10542.387
#> 8 8538.215
#> 9 9182.230
#> 10 9070.911
depreciation
Retention rate (price / MSRP) quantiles for used vehicles, computed at 3-month age bins from vehicle listings data. Only used vehicles with valid price and MSRP values are included, with ages ranging from 12 to 180 months (15 years). Quantiles are provided at the 25th, 50th, and 75th percentiles across various groupings of powertrain and vehicle type.
| Variable | Description |
|---|---|
age_bin |
Midpoint of 3-month age bin in months (e.g., 13.5, 16.5, …, up to 180) |
powertrain |
Powertrain category: “All”, “Gasoline”, “Battery Electric (BEV)”, “BEV (Tesla)”, “BEV (Non-Tesla)”, “Hybrid Electric (HEV)”, “Plug-In Hybrid Electric (PHEV)”, “Diesel”, “Flex Fuel (E85)” |
vehicle_type |
Vehicle type: “All”, “Car”, “CUV”, “SUV”, “Pickup”, “Minivan” |
quantile |
Quantile level (25, 50, or 75) |
rr |
Retention rate (price / MSRP) at the given quantile |
head(depreciation, 10)
#> age_bin powertrain vehicle_type quantile rr
#> 1 13.5 All All 25 0.8077305
#> 2 13.5 All All 50 0.9425665
#> 3 13.5 All All 75 1.0857731
#> 4 16.5 All All 25 0.7602528
#> 5 16.5 All All 50 0.9009337
#> 6 16.5 All All 75 1.0469076
#> 7 19.5 All All 25 0.7075987
#> 8 19.5 All All 50 0.8456087
#> 9 19.5 All All 75 0.9995060
#> 10 22.5 All All 25 0.7011685
dep_annual_type
Estimated annual depreciation rate for each combination of powertrain
and vehicle type. Computed by fitting log-linear regressions of
retention rate (price / MSRP) on vehicle age (in years) for used vehicle
listings; the annual depreciation rate is 1 - exp(b) where
b is the age coefficient. Only used vehicles aged 1–10
years with valid price and MSRP values are included.
| Variable | Description |
|---|---|
vehicle_type |
Vehicle type: “Car”, “CUV”, “SUV”, “Pickup”, “Minivan” |
powertrain |
Powertrain category: “Gasoline”, “Battery Electric (BEV)”, “Hybrid Electric (HEV)”, “Plug-In Hybrid Electric (PHEV)”, “Diesel”, “Flex Fuel (E85)” |
dep_annual |
Estimated annual depreciation rate (proportion, 0–1) |
head(dep_annual_type, 10)
#> powertrain vehicle_type dep_annual
#> 1 Battery Electric (BEV) CUV 0.10402026
#> 2 Battery Electric (BEV) Car 0.10233225
#> 3 Battery Electric (BEV) Minivan 0.06215965
#> 4 Battery Electric (BEV) Pickup 0.05573484
#> 5 Battery Electric (BEV) SUV 0.02428713
#> 6 Flex Fuel (E85) CUV 0.06586182
#> 7 Flex Fuel (E85) Car 0.05712966
#> 8 Flex Fuel (E85) Minivan 0.07188022
#> 9 Flex Fuel (E85) Pickup 0.04602709
#> 10 Flex Fuel (E85) SUV 0.09711303
dep_annual_model
Estimated annual depreciation rate for individual vehicle make/model
combinations. Computed by fitting log-linear regressions of retention
rate (price / MSRP) on vehicle age (in years) for used vehicle listings;
the annual depreciation rate is 1 - exp(b) where
b is the age coefficient. Only used vehicles aged 1–10
years with valid price and MSRP values are included. Only make/model
combinations with at least 100 listings are included, and results are
filtered to positive depreciation rates only.
| Variable | Description |
|---|---|
make |
Vehicle manufacturer (e.g., “Toyota”, “BMW”, “Mercedes-Benz”) |
model |
Vehicle model name (e.g., “Camry”, “RAV4”, “Model 3”) |
vehicle_type |
Vehicle type: “Car”, “CUV”, “SUV”, “Pickup”, “Minivan” |
powertrain |
Powertrain category: “Gasoline”, “Battery Electric (BEV)”, “Hybrid Electric (HEV)”, “Plug-In Hybrid Electric (PHEV)”, “Diesel”, “Flex Fuel (E85)” |
dep_annual |
Estimated annual depreciation rate (proportion, 0–1) |
head(dep_annual_model, 10)
#> make model powertrain vehicle_type dep_annual
#> 1 Acura MDX Gasoline CUV 0.11083871
#> 2 Acura RDX Gasoline CUV 0.09383736
#> 3 Acura TL Gasoline Car 0.07885553
#> 4 Acura TLX Gasoline Car 0.07452962
#> 5 Audi A4 Flex Fuel (E85) Car 0.07030819
#> 6 Audi A4 Gasoline Car 0.09650968
#> 7 Audi A5 Flex Fuel (E85) Car 0.05352090
#> 8 Audi A5 Cabriolet Flex Fuel (E85) Car 0.07388719
#> 9 Audi A5 Coupe Flex Fuel (E85) Car 0.07335039
#> 10 Audi Allroad Flex Fuel (E85) Car 0.08773891
percent_listings
Share of vehicle listings for combinations of grouping variables, computed across all listing years and inventory types. Includes six pairwise combinations (powertrain by vehicle type, powertrain by price bin, vehicle type by powertrain, vehicle type by price bin, price bin by powertrain, and price bin by vehicle type) plus three singular time trends for powertrain, vehicle type, and price bin individually.
| Variable | Description |
|---|---|
listing_year |
Year of the vehicle listing (2018–2024) |
inventory_type |
Inventory type: “New” or “Used” |
group_var |
Name of the grouping variable: “powertrain”, “vehicle_type”, or “price_bin” |
group_level |
Level of the grouping variable (e.g., “Gasoline”, “Car”, “$30k-$40k”) |
category_var |
Name of the category variable: “powertrain”,
“vehicle_type”, or “price_bin”; NA for singular time
trends |
category_level |
Level of the category variable; NA for
singular time trends |
n |
Number of listings in this group-category combination |
p |
Proportion of listings within the group (sums to 1 within each listing year, inventory type, and group level) |
head(percent_listings, 10)
#> listing_year inventory_type group_var group_level category_var
#> 1 2018 New powertrain Battery Electric (BEV) vehicle_type
#> 2 2025 New powertrain Battery Electric (BEV) vehicle_type
#> 3 2024 New powertrain Battery Electric (BEV) vehicle_type
#> 4 2024 New powertrain Battery Electric (BEV) vehicle_type
#> 5 2025 New powertrain Battery Electric (BEV) vehicle_type
#> 6 2023 New powertrain Battery Electric (BEV) vehicle_type
#> 7 2022 New powertrain Battery Electric (BEV) vehicle_type
#> 8 2023 New powertrain Battery Electric (BEV) vehicle_type
#> 9 2024 New powertrain Battery Electric (BEV) vehicle_type
#> 10 2025 New powertrain Battery Electric (BEV) vehicle_type
#> category_level n p
#> 1 Minivan 31 0.001001163
#> 2 Minivan 6115 0.011988104
#> 3 Minivan 933 0.001800251
#> 4 SUV 10858 0.020950834
#> 5 SUV 29138 0.057123365
#> 6 SUV 1341 0.003776146
#> 7 Pickup 7685 0.043135384
#> 8 Pickup 19497 0.054901950
#> 9 Pickup 38112 0.073538237
#> 10 Pickup 51014 0.100009998
percent_dealers
Percentage of dealers that have at least one listing for each combination of two grouping variables, computed across all listing years and inventory types. Three variable-pair combinations are included: powertrain by vehicle type, powertrain by price bin, and vehicle type by price bin.
| Variable | Description |
|---|---|
listing_year |
Year of the vehicle listing (2018–2024) |
inventory_type |
Inventory type: “New” or “Used” |
group_var |
Name of the grouping variable: “powertrain”, “vehicle_type”, or “price_bin” |
group_level |
Level of the grouping variable (e.g., “Gasoline”, “Car”, “$30k-$40k”) |
category_var |
Name of the category variable: “powertrain”, “vehicle_type”, or “price_bin” |
category_level |
Level of the category variable |
p |
Proportion of dealers with at least one listing in this group-category combination |
head(percent_dealers, 10)
#> listing_year inventory_type group_var group_level category_var
#> 1 2019 New powertrain Battery Electric (BEV) vehicle_type
#> 2 2020 New powertrain Battery Electric (BEV) vehicle_type
#> 3 2021 New powertrain Battery Electric (BEV) vehicle_type
#> 4 2018 New powertrain Battery Electric (BEV) vehicle_type
#> 5 2018 New powertrain Battery Electric (BEV) vehicle_type
#> 6 2021 New powertrain Battery Electric (BEV) vehicle_type
#> 7 2020 New powertrain Battery Electric (BEV) vehicle_type
#> 8 2019 New powertrain Battery Electric (BEV) vehicle_type
#> 9 2022 New powertrain Battery Electric (BEV) vehicle_type
#> 10 2024 New powertrain Battery Electric (BEV) vehicle_type
#> category_level p
#> 1 Car 0.146704506
#> 2 Car 0.158801921
#> 3 Car 0.191919192
#> 4 Car 0.151886245
#> 5 Minivan 0.001160766
#> 6 CUV 0.318823595
#> 7 CUV 0.064255439
#> 8 CUV 0.054558091
#> 9 Pickup 0.110011107
#> 10 Pickup 0.215214411
hhi_local_summary
Herfindahl-Hirschman Index (HHI) summary statistics across US census tracts, measuring market concentration for different vehicle market dimensions. HHI values are computed per census tract based on dealers reachable within a 60-minute drive time isochrone, then summarized across tracts. Higher HHI values indicate greater market concentration (less diversity). Nine grouping-variable combinations are included: for each grouping variable (powertrain, vehicle type, price bin), HHI is computed over the other three variables (make, and the two remaining grouping variables). Census-tract-level HHI values (before summarization) are available as parquet files: hhi_pb_60.parquet, hhi_pt_60.parquet, hhi_vt_60.parquet.
| Variable | Description |
|---|---|
group_var |
Grouping variable: “powertrain”, “vehicle_type”, or “price_bin” |
group_level |
Level of the grouping variable (e.g., “Gasoline”, “Car”, “$30k-$40k”) |
hhi_var |
Variable over which HHI is computed: “make”, “powertrain”, “vehicle_type”, or “price_bin” |
listing_year |
Year of the vehicle listing |
mean |
Mean HHI across census tracts |
median |
Median HHI across census tracts |
q25 |
25th percentile HHI across census tracts |
q75 |
75th percentile HHI across census tracts |
IQR |
Interquartile range of HHI across census tracts |
upper |
Upper whisker bound (q75 + 1.5 * IQR) |
lower |
Lower whisker bound (q25 - 1.5 * IQR) |
head(hhi_local_summary, 10)
#> group_var group_level hhi_var listing_year mean median
#> 1 powertrain Battery Electric (BEV) make 2025 0.2281757 0.1922108
#> 2 powertrain Battery Electric (BEV) make 2024 0.2290159 0.1958011
#> 3 powertrain Battery Electric (BEV) make 2021 0.2991893 0.2577515
#> 4 powertrain Battery Electric (BEV) make 2023 0.2503708 0.2178611
#> 5 powertrain Battery Electric (BEV) make 2022 0.3367828 0.3089308
#> 6 powertrain Diesel make 2023 0.2252425 0.2148836
#> 7 powertrain Diesel make 2022 0.2284639 0.2197895
#> 8 powertrain Diesel make 2025 0.2340765 0.2228044
#> 9 powertrain Diesel make 2024 0.2305378 0.2193800
#> 10 powertrain Flex Fuel (E85) make 2022 0.4768817 0.3200439
#> q25 q75 IQR upper lower
#> 1 0.1364988 0.2650060 0.12850720 0.4577668 -0.05626202
#> 2 0.1277967 0.2726680 0.14487134 0.4899751 -0.08951030
#> 3 0.1943984 0.3456040 0.15120567 0.5724125 -0.03241015
#> 4 0.1347314 0.2948265 0.16009508 0.5349691 -0.10541120
#> 5 0.1661640 0.4424988 0.27633481 0.8570010 -0.24833819
#> 6 0.2071404 0.2250711 0.01793070 0.2519672 0.18024436
#> 7 0.2090337 0.2306076 0.02157394 0.2629685 0.17667277
#> 8 0.2175441 0.2327736 0.01522954 0.2556179 0.19469977
#> 9 0.2138155 0.2302501 0.01643458 0.2549020 0.18916363
#> 10 0.2128875 0.7386170 0.52572951 1.5272113 -0.57570677
p_local_summary
Local market share summary statistics across US census tracts,
measuring the distribution of the share of vehicle listings
(p) for different vehicle market segments. Values are
computed per census tract based on dealers reachable within a 60-minute
drive time isochrone, then summarized across tracts. Three grouping
variables are included: powertrain, vehicle type, and price bin.
Census-tract-level values (before summarization) are available as
parquet files: p_pb_60.parquet,
p_pt_60.parquet,
p_vt_60.parquet.
| Variable | Description |
|---|---|
group_var |
Grouping variable: “powertrain”, “vehicle_type”, or “price_bin” |
group_level |
Level of the grouping variable (e.g., “Gasoline”, “Car”, “$30k-$40k”) |
inventory_type |
Inventory type: “New” or “Used” |
listing_year |
Year of the vehicle listing |
mean |
Mean local market share across census tracts |
median |
Median local market share across census tracts |
q25 |
25th percentile local market share across census tracts |
q75 |
75th percentile local market share across census tracts |
IQR |
Interquartile range of local market share across census tracts |
upper |
Upper whisker bound (q75 + 1.5 * IQR) |
lower |
Lower whisker bound (q25 - 1.5 * IQR) |
head(p_local_summary, 10)
#> group_var group_level inventory_type listing_year mean
#> 1 powertrain Battery Electric (BEV) New 2025 0.042667113
#> 2 powertrain Battery Electric (BEV) New 2024 0.046262512
#> 3 powertrain Battery Electric (BEV) Used 2021 0.005406185
#> 4 powertrain Battery Electric (BEV) Used 2024 0.020051480
#> 5 powertrain Battery Electric (BEV) Used 2023 0.013141464
#> 6 powertrain Battery Electric (BEV) Used 2022 0.008715149
#> 7 powertrain Battery Electric (BEV) Used 2025 0.026557616
#> 8 powertrain Diesel Used 2023 0.032129920
#> 9 powertrain Diesel Used 2022 0.031783710
#> 10 powertrain Diesel Used 2025 0.034581686
#> median q25 q75 IQR upper lower
#> 1 0.041085481 0.034037533 0.048416693 0.014379160 0.06998543 0.0124687935
#> 2 0.044353099 0.037626658 0.051068453 0.013441795 0.07123115 0.0174639654
#> 3 0.004586378 0.003447483 0.006252421 0.002804938 0.01045983 -0.0007599233
#> 4 0.015735710 0.012542852 0.020185771 0.007642919 0.03165015 0.0010784724
#> 5 0.011312013 0.008820033 0.014142383 0.005322350 0.02212591 0.0008365081
#> 6 0.007060302 0.005785052 0.009307720 0.003522668 0.01459172 0.0005010506
#> 7 0.022417082 0.017587623 0.028387948 0.010800325 0.04458844 0.0013871352
#> 8 0.031125883 0.028097768 0.034741198 0.006643430 0.04470634 0.0181326232
#> 9 0.031086034 0.027303897 0.034463712 0.007159815 0.04520343 0.0165641749
#> 10 0.033097019 0.029682575 0.037742402 0.008059828 0.04983214 0.0175928328Large Raw Data Files
The following large files are hosted on Cloudflare R2 and can be
downloaded directly. The hhi_* and p_* parquet
files are the census-tract-level source data used to compute the
hhi_local_summary and p_local_summary package
datasets. File name suffixes indicate the grouping variable
(pb = price bin, pt = powertrain,
vt = vehicle type) and isochrone radius (60 =
60-minute drive time).
| File | Description |
|---|---|
| hhi_pb_60.parquet | HHI per census tract, grouped by price bin (60-min isochrone) |
| hhi_pt_60.parquet | HHI per census tract, grouped by powertrain (60-min isochrone) |
| hhi_vt_60.parquet | HHI per census tract, grouped by vehicle type (60-min isochrone) |
| p_pb_60.parquet | Market share (p) per census tract, grouped by price bin (60-min isochrone) |
| p_pt_60.parquet | Market share (p) per census tract, grouped by powertrain (60-min isochrone) |
| p_vt_60.parquet | Market share (p) per census tract, grouped by vehicle type (60-min isochrone) |
| tracts.rds | US census tract geometries (full resolution) |
| tracts_simplified.rds | US census tract geometries (simplified for faster rendering) |
| hhi_tracts.pmtiles | HHI per census tract as PMTiles for interactive map visualization |
price_trends_new
Average prices and listing counts for new vehicle inventory by listing year, state, model year, and vehicle type. Includes the number of unique make-model-year combinations available in each state.
| Variable | Description |
|---|---|
listing_year |
Calendar year when listing was scraped (2018–2025) |
state |
US state abbreviation (e.g., “CA”, “TX”, “NY”) |
year |
Model year of the vehicle |
vehicle_type |
Vehicle type: “car”, “cuv”, “suv”, “pickup”, “minivan” |
avg_price |
Average listing price (USD) |
n_listings |
Number of listings |
n_make_model_year |
Number of unique make-model-year combinations in this state |
head(price_trends_new, 10)
#> listing_year state year vehicle_type avg_price n_listings n_make_model_year
#> 1 2018 AK 2016 pickup 44915.88 1 407
#> 2 2018 AK 2016 car 31867.35 6 407
#> 3 2018 AK 2016 suv 57233.70 1 407
#> 4 2018 AK 2017 car 35670.13 359 407
#> 5 2018 AK 2017 cuv 42770.38 230 407
#> 6 2018 AK 2017 minivan 40072.08 25 407
#> 7 2018 AK 2017 pickup 52027.46 368 407
#> 8 2018 AK 2017 suv 70851.14 97 407
#> 9 2018 AK 2018 cuv 41896.27 3048 407
#> 10 2018 AK 2018 minivan 45911.83 145 407
price_trends_used
Average prices, listing counts, and depreciation rates for used vehicle inventory by listing year, state, model year, and vehicle type. Includes annual depreciation rates calculated using exponential decay modeling on retention rates (price/MSRP) and the number of unique make-model-year combinations available in each state.
| Variable | Description |
|---|---|
listing_year |
Calendar year when listing was scraped (2018–2025) |
state |
US state abbreviation (e.g., “CA”, “TX”, “NY”) |
year |
Model year of the vehicle |
vehicle_type |
Vehicle type: “car”, “cuv”, “suv”, “pickup”, “minivan” |
avg_price |
Average listing price (USD) |
n_listings |
Number of listings |
dep_annual_rate |
Annual depreciation rate (proportion, 0–1) for this state and listing year |
n_make_model_year |
Number of unique make-model-year combinations in this state |
head(price_trends_used, 10)
#> listing_year state year vehicle_type avg_price n_listings dep_annual_rate
#> 1 2018 AK 2004 cuv 9562.734 8 0.06493018
#> 2 2018 AK 2004 pickup 14244.578 31 0.06493018
#> 3 2018 AK 2004 minivan 9605.733 1 0.06493018
#> 4 2018 AK 2004 car 9885.443 7 0.06493018
#> 5 2018 AK 2004 suv 10516.263 13 0.06493018
#> 6 2018 AK 2005 cuv 9595.951 19 0.06493018
#> 7 2018 AK 2005 minivan 7737.759 6 0.06493018
#> 8 2018 AK 2005 car 8607.808 27 0.06493018
#> 9 2018 AK 2005 pickup 18431.482 69 0.06493018
#> 10 2018 AK 2005 suv 14101.120 39 0.06493018
#> n_make_model_year
#> 1 1561
#> 2 1561
#> 3 1561
#> 4 1561
#> 5 1561
#> 6 1561
#> 7 1561
#> 8 1561
#> 9 1561
#> 10 1561
registrations
Annual vehicle registration counts by US state and powertrain type, scraped from the Alternative Fuels Data Center (AFDC) for years 2016 through 2024.
| Variable | Description |
|---|---|
year |
Registration year (2016–2024) |
state |
US state name (e.g., “Alabama”, “California”) |
powertrain |
Fuel/powertrain type (e.g., “Battery Electric (BEV)”, “Diesel”, “Gasoline”, “Hybrid Electric (HEV)”, “Plug-In Hybrid Electric (PHEV)”, “Biodiesel”, “Compressed Natural Gas (CNG)”, “Ethanol/Flex Fuel (E85)”, “Fuel Cell”, “Hydrogen”, “Methanol”, “Propane”) |
count |
Number of registered vehicles |
head(registrations, 10)
#> year state powertrain count
#> 1 2016 Alabama Battery Electric (BEV) 500
#> 2 2016 Alabama Biodiesel 0
#> 3 2016 Alabama Compressed Natural Gas (CNG) 20100
#> 4 2016 Alabama Diesel 126500
#> 5 2016 Alabama Flex Fuel (E85) 428300
#> 6 2016 Alabama Fuel Cell 0
#> 7 2016 Alabama Gasoline 3777300
#> 8 2016 Alabama Hybrid Electric (HEV) 29100
#> 9 2016 Alabama Methanol 0
#> 10 2016 Alabama Plug-In Hybrid Electric (PHEV) 900