51 attribute fields, profiled from the October 2022 vintage.
Columns in Gooding County
| Field | Type | Filled | Distinct | Example values |
|---|---|---|---|---|
| PARCEL_ID | OFTString | 9.9k | RP07S13E141478, RPW2000032003A, RP00100000001D | |
| LABEL | OFTString | 2.0k | 1478, 003A, 001D | |
| OWNERSHIP | OFTString | 31 | PRIVATE, PRIVATE, PRIVATE | |
| DESCRIPTIO | OFTString | 9.2k | 10027, 10089, 2810 | |
| Area | OFTString | 8.9k | 2.34227874426e+05, 9.16191531978e+03, 4.36157224622e+04 | |
| Acres | OFTString | 8.9k | 5.37713210345e+00, 2.10328634522e-01, 1.00127921171e+00 | |
| link | OFTString | 9.6k | http://idahoparcels.us/county/photos/gooding/ITD HWY 30.jpg, http://idahoparcels.us/county/photos/gooding/ .jpg, http://idahoparcels.us/county/photos/gooding/ .jpg | |
| Shape_Leng | OFTString | 8.7k | 1.92265010930e+03, 3.96585178015e+02, 1.11599999881e+03 | |
| Shape_Area | OFTString | 9.0k | 2.34227872342e+05, 9.16152003562e+03, 4.36157224622e+04 | |
| pcparc00_O | OFTString | 9.8k | 8963, 3810, 4936 | |
| PM_PAR_14 | OFTString | 9.8k | RP07S13E141478, RPW2000032003A, RP00100000001D | |
| PM_PAR_15 | OFTString | 9.8k | RP07S13E141478A, RPW2000032003AA, RP00100000001DA | |
| PM_PAR_TYP | OFTString | 1 | RP, RP, RP | |
| PM_PAR_NUM | OFTString | 9.8k | 07S13E141478, W2000032003A, 00100000001D | |
| PM_PAR_STS | OFTString | 2 | A, A, A | |
| PM_IAEXIST | OFTString | 1 | I, I, I | |
| PM_CARE_OF | OFTString | 2 | V, V, V | |
| PM_MAIL_NM | OFTString | 5.8k | JONES, JOHN W JR TRUSTEE, DEELSTRA, JOB STEVEN, BURCH, ROY | |
| PM_MAIL_A1 | OFTString | 5.2k | P O BOX 265, 582 4TH AVE WEST, 1703 SOUTH 2200 EAST MPO | |
| PM_MAIL_A2 | OFTString | 189 | 1834 EAST 1600 SOUTH, 1505 MADRONA ST NORTH #800, 1505 MADRONA ST NORTH #800 | |
| PM_MAIL_CT | OFTString | 276 | HAGERMAN, WENDELL, GOODING | |
| PM_MAIL_ST | OFTString | 35 | ID, ID, ID | |
| PM_MAIL_ZP | OFTString | 304 | 83332, 83355, 83330 | |
| PM_PROP_AD | OFTString | 5.8k | 18048 U S HWY 30, 582 4TH AVE W, 1920 E 1700 S | |
| PM_PROP_ZP | OFTString | 10 | 83332, 83355, 83330 | |
| PM_EFF_DAT | OFTString | 191 | 20190501, 20220501, 20210501 | |
| PM_EXP_DAT | OFTString | 2 | 0, 0, 0 | |
| PM_LOCN_CD | OFTString | 17 | 7000, 2111, 5101 | |
| PM_PARC_CD | OFTString | 97 | C4, C4, C3 | |
| PM_ZONING | OFTString | — | ||
| PM_DEEDCDT | OFTString | 3.5k | 20150413, 20190808, 20160120 | |
| PM_DEEDRF1 | OFTString | 6.9k | 251245/6TD, 265759 QD, 253803 | |
| PM_DEEDRF2 | OFTString | 6.2k | 150313, 261833 QD, 251500 | |
| PM_DEEDRF3 | OFTString | 5.3k | 258709, 251359, 154208 | |
| PM_DEEDRF4 | OFTString | 4.1k | 246856, 208146, 151010 | |
| PM_DEEDRF5 | OFTString | 3.1k | 246256, 204698/699, 155807/8 | |
| PM_LANDREC | OFTString | 12 | 2, 1, 1 | |
| PM_IMPRECS | OFTString | 7 | 1, 1, 1 | |
| PM_PI_YEAR | OFTString | 27 | 2019, 2022, 2021 | |
| PM_TAXAREA | OFTString | 49 | 70000, 20000, 50000 | |
| PM_TAX_KEY | OFTString | 8.9k | RP07S13E141478T, RPW2000032003AT, RP00100000001DT | |
| PM_TAXYEAR | OFTString | 1 | 2021, 2021, 2021 | |
| PM_TAX_AMT | OFTString | 8.0k | 1468.86, 1349.84, 597.82 | |
| PM_PV_AREA | OFTString | 1 | 0, 0, 0 | |
| PM_PV_ACRE | OFTString | 3.7k | 5.395, 0.2, 1 | |
| PM_TOT_VAL | OFTString | 8.0k | 390337, 244035, 176101 | |
| PM_IMP_VAL | OFTString | 5.4k | 312646, 189507, 106096 | |
| PM_EX_VAL | OFTString | 5.7k | 77691, 54528, 70005 | |
| PM_NET_VAL | OFTString | 2.0k | 125000, 122018, 88051 | |
| PM_LND_VAL | OFTString | 8.1k | 265337, 122017, 88050 | |
| PM_CATS | OFTString | 451 | |02|10|31|, |20|41|, |15|37| |
Why this differs from the next county over
There is no national parcel schema. Every assessor's office built its own, usually decades ago, usually inside whatever CAMA system it bought at the time. One county's owner column is OWNER_NAME, the next one's is own1, and a third splits it across four fields. Shapefile's ten-character DBF limit truncated a generation of them into things like TOTLNDVAL.
Nothing here is renamed or remapped. The columns above are exactly what is in the file, because a normalisation layer that quietly guesses wrong is worse than no normalisation at all — and every guess it makes is one you cannot audit.
The Filled column is the share of rows with a real value. Placeholder text such as n/a counts as empty; a column that is 100% present but 90% placeholder is a column that will disappoint you, and the profiler treats it accordingly.