52 attribute fields, profiled from the June 2022 vintage.
Columns in Contra Costa County
| Field | Type | Filled | Distinct | Example values |
|---|---|---|---|---|
| PARCEL_ID | OFTString | 50.0k | 085204009, 085205006, 085205007 | |
| SITUS | OFTString | 50.0k | 134 E 10TH ST, 222 E 10TH ST, 242 E 10TH ST | |
| ADDR_NUMBE | OFTString | 7.5k | 134, 222, 242 | |
| STREET_PRE | OFTString | 13 | E, E, E | |
| STREET_NAM | OFTString | 7.8k | 10TH, 10TH, 10TH | |
| STREET_SUF | OFTString | — | ||
| STREET_TYP | OFTString | 36 | St, St, St | |
| SITUS_CITY | OFTString | 24 | PITTSBURG, PITTSBURG, PITTSBURG | |
| SITUS_ZIP | OFTString | 33 | 94565, 94565, 94565 | |
| OWNER | OFTString | 50.0k | WONG TONY C & YVONNE, ALFARO MARGARITA DUENAS TRE, SALUS LLC | |
| BLDG_SQFT | OFTInteger64 | 5.2k | 1085, 1006, 740 | |
| STORY_HEIG | OFTReal | 18 | 1.0, 1.0, 1.0 | |
| LAND_USE_C | OFTString | 77 | 31, 31, 14 | |
| LAND_US_01 | OFTString | 5 | Industrial, Industrial, Residential | |
| LAND_COVER | OFTString | 50.0k | {"23": 104, "24": 236}, {"23": 287}, {"23": 30, "24": 145} | |
| CROP_COVER | OFTString | 31.9k | {"123": 0.08, "124": 0.18}, {"123": 0.1}, {"123": 0.12, "124": 0.0} | |
| MUNI_ID | OFTInteger64 | 3 | 1935012, 1935012, 1935012 | |
| SCHOOL_DIS | OFTInteger64 | 11 | 630600, 630600, 630600 | |
| ACREAGE | OFTReal | 4.2k | 0.0, 0.0, 0.0 | |
| MKT_VAL_LA | OFTReal | 50.0k | 101903.0, 20469.0, 84896.0 | |
| MKT_VAL_BL | OFTReal | 50.0k | 199974.0, 77066.0, 134772.0 | |
| ZIP_CODE | OFTString | 35 | 94565, 94565, 94565 | |
| CONDITION | OFTString | 6 | A, E, A | |
| BUILDINGS | OFTInteger64 | 28 | 0, 0, 0 | |
| POOL | OFTString | 2 | N, N, N | |
| TOTAL_BATH | OFTReal | 13 | 0.0, 0.0, 0.0 | |
| BEDROOMS | OFTInteger64 | 12 | 3, 3, 2 | |
| TOTAL_ROOM | OFTInteger64 | 28 | 7, 7, 6 | |
| YEAR_BUILT | OFTInteger64 | 143 | 1955, 1918, 1918 | |
| LEGAL_DESC | OFTString | 50.0k | PITTS CITY LOTS 17 & 18 POR 16 & 19 BLK 82, CITY OF PITTS POR LOTS 1.2 & 3 BLK 81, CITY OF PITTS POR LOTS 1.2.3 & 8 BLK 81 | |
| PLSS_SECTI | OFTString | 37 | 00, 00, 00 | |
| PLSS_TOWNS | OFTString | 5 | 2N, 2N, 2N | |
| PLSS_RANGE | OFTString | 4 | 1E, 1E, 1E | |
| MAIL_ADDRE | OFTString | 50.0k | 3734 38TH AVE, 823 MICKELSEN CT, 87 PANORAMIC WAY | |
| MAIL_AD_01 | OFTString | 3.3k | OAKLAND CA 94619, BRENTWOOD CA 94513, WALNUT CREEK CA 94595 | |
| OWNER_OCCU | OFTString | 3 | F, F, F | |
| USPS_RESID | OFTString | 3 | F, F, T | |
| CENSUS_TRA | OFTInteger64 | 130 | 310000, 310000, 310000 | |
| CENSUS_BLK | OFTInteger | 7 | 2, 2, 2 | |
| CENSUS_BLO | OFTInteger | 572 | 2012, 2011, 2011 | |
| AREA_METER | OFTReal | 50.0k | 1027.6029755542, 408.017772348995, 486.536303823058 | |
| FLD_ZONE | OFTString | 57 | {X}, {X}, {X} | |
| ZONE_SUBTY | OFTString | 24 | {"0.2 PCT ANNUAL CHANCE FLOOD HAZARD"}, {"0.2 PCT ANNUAL CHANCE FLOOD HAZARD"}, {"0.2 PCT ANNUAL CHANCE FLOOD HAZARD"} | |
| PLACE_GNIS | OFTInteger64 | 36 | 2411430, 2411430, 2411430 | |
| ALT_ID_1 | OFTString | 50.0k | 085-204-009, 085-205-006, 085-205-007 | |
| ELEVATION | OFTString | 50.0k | {"max": 0.0, "min": 0.0, "mean": 0.0}, {"max": 0.0, "min": 0.0, "mean": 0.0}, {"max": 0.0, "min": 0.0, "mean": 0.0} | |
| ROBUST_ID | OFTString | 50.0k | AAAXfc5GhpKBx0SZ, AAAXfdw09IqgAFGD, AAAXfY7x1Lx2b893 | |
| CALC_ACREA | OFTReal | 50.0k | 0.253930971289198, 0.10082527172516, 0.120227986037716 | |
| COUNTY_NAM | OFTString | 1 | Contra Costa, Contra Costa, Contra Costa | |
| COUNTY_FIP | OFTInteger64 | 1 | 6013, 6013, 6013 | |
| STATE_ABBR | OFTString | 1 | CA, CA, CA | |
| CTY_ROW_ID | OFTInteger64 | 50.0k | 374954, 375263, 375862 |
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.