Overview

Real Estate Across the United States (REXUS) is the primary tool used by PBS to track and manage the government's real property assets and to store inventory data, building data, customer data, and lease information. STAR manages aspects of real property space management, including identification of all building space and daily management of 22,000 assignments for all property to its client Federal agencies. This data set contains PBS building inventory that consists of both owned and leased buildings with active and excess status.

https://catalog.data.gov/dataset/real-estate-across-the-united-states-rexus-inventory-building

Explore Structure




Index Type Example Value
0 dict { }
... ... ...
Key Type Example Value Comment
"city" str "HARTFORD"
[Preview ]
"zip" str "61031125"
[Preview ]
"county" str "HARTFORD"
[Preview ]
"state" str "CT"
[Preview ]
"line 1" str "135 HIGH ST"
[Preview ]
"line 2" str " "
[Preview ]
Value Count
"WASHINGTON" 487
"EL PASO" 89
"LAREDO" 81
"LAKEWOOD" 79
"SPRINGFIELD" 79
"ARLINGTON" 78
"SAN DIEGO" 64
"KANSAS CITY" 63
"ATLANTA" 63
"CHICAGO" 62
"HOUSTON" 61
"BROWNSVILLE" 57
"COLUMBUS" 55
"PORTLAND" 51
"MIAMI" 49
"SAN ANTONIO" 49
"LOS ANGELES" 48
"NEW YORK-MANHATTAN" 47
"SAINT LOUIS" 46
"PHILADELPHIA" 46
"SILVER SPRING" 44
"ALEXANDRIA" 44
"FORT WORTH" 42
"ALBUQUERQUE" 41
"ANCHORAGE" 40
"JACKSONVILLE" 39
"OKLAHOMA CITY" 38
"SAN FRANCISCO" 38
"LAS VEGAS" 38
"PHOENIX" 38
"ROCKVILLE" 37
"DETROIT" 36
"BALTIMORE" 36
"BETHESDA" 36
"TAMPA" 35
"DALLAS" 35
"AUSTIN" 34
"NASHVILLE" 33
"INDIANAPOLIS" 33
"BABB" 33
"BATON ROUGE" 33
"CHARLOTTE" 32
"RICHMOND" 31
"TUCSON" 31
"COLUMBIA" 31
"BUFFALO" 31
"DEL RIO" 30
"SEATTLE" 30
"SALT LAKE CITY" 30
"DENVER" 29
"RALEIGH" 28
"GREENVILLE" 28
"MADISON" 28
"FRESNO" 28
"WILMINGTON" 28
"ALBANY" 26
"ORLANDO" 26
"NEWARK" 26
"NEW ORLEANS" 26
"SACRAMENTO" 26
"LITTLE ROCK" 26
"CLEVELAND" 25
"EAGLE PASS" 25
"LAUREL" 25
"COLORADO SPRINGS" 25
"MEMPHIS" 25
"CHARLESTON" 25
"NEW YORK-QUEENS" 24
"LOUISVILLE" 24
"BATTLE CREEK" 24
"JACKSON" 24
"JEFFERSONVILLE" 23
"AUBURN" 22
"WOODLAWN" 21
"PHARR" 21
"NEW HAVEN" 21
"MCALLEN" 21
"GREENSBORO" 21
"OMAHA" 21
"SPOKANE" 20
"BOISE" 20
"MENLO PARK" 20
"MILWAUKEE" 20
"SAVANNAH" 20
"TULSA" 20
"MONTGOMERY" 19
"BIRMINGHAM" 19
"PITTSBURGH" 19
"HUNTSVILLE" 19
"RAPID CITY" 19
"NEW YORK-KINGS" 19
"TALLAHASSEE" 19
"SYRACUSE" 19
"TORNILLO" 19
"BOSTON" 18
"OGDEN" 18
"MOBILE" 18
"LOS INDIOS" 17
"FAYETTEVILLE" 17
"KNOXVILLE" 17
... ...
Value Count
"200322608" 57
"802250546" 54
"594119700" 33
"200012734" 29
"780409998" 29
"200162705" 27
"631201703" 24
"761153400" 22
"799076656" 22
"785216762" 21
"207082478" 21
"200013024" 21
"785778650" 21
"208920002" 21
"471304229" 20
"798539998" 18
"780437846" 17
"940253561" 17
"785679800" 17
"788405356" 16
"799054204" 15
"853415300" 15
"209930000" 14
"880089801" 14
"921547209" 13
"221501911" 13
"467740000" 13
"490373001" 12
"788525194" 11
"880299800" 11
"212075176" 11
"785204119" 11
"641313009" 10
"945017815" 10
"922319703" 10
"212260000" 10
"463271001" 9
"853498534" 9
"490373085" 9
"798439998" 9
"129194440" 9
"123021462" 9
"780412295" 9
"997809800" 9
"785799998" 8
"209030001" 8
"204070001" 8
"856212430" 8
"200010000" 8
"785729998" 7
"221019998" 7
"853499534" 7
"592509706" 7
"785379998" 7
"785204954" 7
"799013248" 7
"203720001" 7
"701141806" 7
"798459998" 7
"587729998" 6
"798399998" 6
"785033147" 6
"54609901" 6
"200102976" 5
"588449998" 5
"58309998" 5
"921733115" 5
"595379800" 5
"780450000" 5
"981342388" 5
"582719745" 5
"788524860" 5
"49369998" 5
"891197533" 5
"205600001" 5
"208741299" 5
"229034872" 5
"47309998" 5
"200080000" 5
"731297653" 5
"926773400" 4
"708145316" 4
"631146128" 4
"200064922" 4
"856339901" 4
"88444135" 4
"203700001" 4
"136079998" 4
"139011618" 4
"597609601" 4
"46199998" 4
"200240001" 4
"136620000" 4
"856209998" 4
"212441849" 4
"856219998" 4
"54409998" 4
"958251846" 4
"838269998" 4
"599179701" 4
... ...
Value Count
"DISTRICT OF COLUMBIA" 486
"MONTGOMERY" 204
"JEFFERSON" 176
"EL PASO" 134
"LOS ANGELES" 130
"INDEPENDENT CITY" 112
"PRINCE GEORGE'S" 110
"SAN DIEGO" 102
"FAIRFAX" 101
"COOK" 101
"CAMERON" 88
"WEBB" 81
"JACKSON" 81
"ORANGE" 79
"FRANKLIN" 79
"HIDALGO" 75
"WASHINGTON" 75
"CLARK" 75
"KING" 75
"ARLINGTON" 72
"PUERTO RICO" 68
"HARRIS" 67
"MIAMI-DADE" 64
"DALLAS" 60
"WAYNE" 59
"MARICOPA" 56
"MARION" 54
"TARRANT" 54
"PIMA" 53
"ERIE" 53
"BEXAR" 52
"HILLSBOROUGH" 51
"NEW YORK" 47
"BROWARD" 47
"ST. LOUIS - CITY" 46
"ESSEX" 45
"PHILADELPHIA" 45
"MIDDLESEX" 44
"SUFFOLK" 43
"CUMBERLAND" 43
"BALTIMORE" 41
"FULTON" 41
"CUYAHOGA" 41
"BERNALILLO" 41
"SACRAMENTO" 40
"ANCHORAGE" 40
"HAMILTON" 39
"DUVAL" 39
"SAN FRANCISCO" 38
"ORLEANS" 38
"SALT LAKE" 37
"OKLAHOMA" 37
"ALAMEDA" 37
"DEKALB" 37
"BALTIMORE - CITY" 36
"MULTNOMAH" 36
"MADISON" 35
"DAVIDSON" 35
"DOUGLAS" 34
"GLACIER" 34
"EAST BATON ROUGE" 33
"TRAVIS" 33
"MECKLENBURG" 32
"KENT" 32
"WAKE" 31
"PULASKI" 31
"VAL VERDE" 31
"IMPERIAL" 31
"SAN MATEO" 31
"YUMA" 31
"DONA ANA" 31
"RIVERSIDE" 31
"FRESNO" 30
"ALLEGHENY" 29
"LAKE" 29
"CALHOUN" 29
"DENVER" 29
"MONROE" 27
"DANE" 26
"MILWAUKEE" 26
"MAVERICK" 25
"SHELBY" 25
"HENNEPIN" 25
"POLK" 25
"KNOX" 25
"RICHMOND CITY" 25
"SANTA CRUZ" 24
"DUPAGE" 24
"QUEENS" 24
"CHITTENDEN" 24
"ALLEN" 23
"LEE" 23
"AROOSTOOK" 23
"SAN BERNARDINO" 23
"WHATCOM" 23
"ALBANY" 23
"SANGAMON" 23
"ANNE ARUNDEL" 23
"GUILFORD" 22
"MERCER" 22
... ...
Value Count
"TX" 886
"CA" 746
"DC" 486
"VA" 440
"FL" 413
"MD" 403
"NY" 367
"PA" 245
"CO" 242
"WA" 242
"IL" 241
"MI" 235
"AZ" 222
"GA" 218
"NC" 212
"MO" 209
"OH" 201
"IN" 157
"TN" 152
"MT" 150
"LA" 149
"NJ" 146
"KY" 137
"NM" 136
"OK" 130
"MA" 127
"AL" 124
"WI" 122
"AK" 110
"WV" 106
"OR" 105
"MN" 98
"ME" 97
"UT" 96
"SC" 95
"MS" 87
"AR" 83
"ND" 80
"VT" 75
"NV" 75
"KS" 74
"IA" 74
"PR" 68
"SD" 66
"ID" 63
"CT" 58
"NE" 51
"WY" 45
"HI" 42
"NH" 39
"DE" 28
"RI" 27
"VI" 15
"GU" 9
"MP" 6
"AS" 5
Value Count
"W 6TH AVE & KIPLING ST" 51
"NEBRASKA AVENUE COMPLEX" 26
"2701 MARTIN LUTHER KING JR. AVE. SE" 25
"4300 GOODFELLOW BLVD" 24
"3300 S. EXPRESSWAY 77/83" 23
"2701 MARTIN LUTHER KING JR AVE SE" 23
"501 W FELIX ST" 22
"797 S ZARAGOZA RD" 22
"9000 ROCKVILLE PK" 20
"7TH & FLA AVE NW" 20
"1201 E 10TH ST" 20
"345 MIDDLEFIELD RD" 17
"715 BOB BULLOCK LOOP" 17
"RIO GRANDE ROAD" 17
"9901 S CAGE BLVD" 17
"US BORDER STATION" 16
"6TH ST NW" 16
"Termination SH-255" 16
"3600 E PAISANO DR" 15
"100 LOS INDIOS BLVD" 15
"INT'L BOUNDARY LINE" 14
"10903 NEW HAMPSHIRE AVE" 14
"104 SANTA TERESA" 14
"TERMINATION I-35" 13
"6999 LOISDALE RD" 13
"1400 Lower Island (Fm 1109) Road" 12
"CURTIS BAY DEPOT" 12
"500 S ADAMS ST" 11
"1500 E BANNISTER RD" 11
"1300 MEXICO BLVD" 10
"207 W DEL MAR BLVD" 9
"50 WASHINGTON AVE N" 9
"3200 SHEFFIELD AVE" 9
"620 CENTRAL AVE" 9
"6401 SECURITY BLVD" 9
"1 AMSTERDAM ROAD" 9
"152 US HIGHWAY 206 SOUTH" 8
"200 N MARIPOSA RD" 8
"1375 SOUTH AVE E" 8
"AMERICAN END INTL BRIDGE" 8
"PALOMAS & 2ND ST" 8
"5911 N STEWART RD" 8
"2701 MARTIN LUTHER KING JR. AVE SE" 7
"1000 S EL PASO ST" 7
"74 WASHINGTON AVE N" 7
"HWY 24 AT CAN BDR" 7
"3819 PATTERSON DR" 6
"1400 Lower Island (FM 1109) Road" 6
"M 1221 8 TENTHS AK HWY" 6
"US HWY 52 AT CAN BDR" 6
"US HWY 281 AND FM 493" 6
"6TH STREET NW" 6
"300 WEST MADRID STREET" 6
"1500 E ELIZABETH ST" 6
"1699 E. CARR RD." 6
"1301 EMMET ST N" 5
"110 IRVING ST NW" 5
"4735 E MARGINAL WAY S" 5
"202 STATE HIGHWAY 255" 5
"198 WEST SERVICE ROAD" 5
"HC 82 BOX 8250" 5
"6092 Arnold Trail" 5
"6TH STREET N W" 5
"160 E GARRISON ST" 5
"TERMINATION OF FM 1088" 5
"2800 S EASTERN AVE" 5
"11000 S CAGE ST" 4
"2301 S MAIN ST" 4
"3 CUSTOMS STREET" 4
"24000 AVILA RD" 4
"1655 WOODSON ROAD" 4
"INT-81" 4
"8395 Highway 93 North" 4
"1151 HOYT AVE" 4
"Interstate 15 N at Canadian border" 4
"2800 COTTAGE WAY" 4
"HWY ???? AT CAN BDR" 4
"8999 Bennett Creek Blvd." 4
"QUINCE ORCHARD RD" 4
"Highway 67" 4
"700 QUALIA DR" 4
"33643 US HIGHWAY 97" 3
"1500 W ELIZABETH ST" 3
"RTE 118" 3
"9700 PAGE BLVD" 3
"9777 VIA DE LA AMISTAD" 3
"M STREET SE" 3
"2810 W FORT ST" 3
"MILE 42 HAINES HIGHWAY" 3
"PAN AMERICAN AVE" 3
"400 EDWARDS AVENUE" 3
"100 W MAIN ST" 3
"TO BE CONSTRUCTED" 3
"HWY ??? AT CAN BDR" 3
"795 E SAN YSIDRO BLVD" 3
"BORDER STATION" 3
"10710 N NEWPORT HWY" 3
"717 MADISON PL NW" 3
"24000 AVILA ROAD" 3
"1125 CHAPLINE ST" 3
... ...
Value Count
" " 8526
"3801 NEBRASKA AVENUE" 26
"US HWY 89 AT CANADIAN BORDER" 16
"SUITE 100" 14
"US HWY 89 @ CANADIAN BORDER" 12
"SUITE 200" 12
"SUITE A" 10
"2ND FLOOR" 8
"SUITE 300" 7
"1375 SOUTH AVE E" 7
"SUITE 301" 5
"SUITE B" 5
"SUITE 201" 5
"SUITE 120" 4
"ANACOSTIA NAVAL STATION" 4
"Suite 200" 4
"SUITE 150" 4
"MAIN PORT BLDG" 4
"TERMINAL BUILDING" 4
"4TH FLOOR" 4
"HWY 191" 4
"SUITE C" 4
"1st Floor" 4
"Suite 100" 3
"STATE HWY 256 @ CANADIAN BORDER" 3
"SUITE 1" 3
"SUITE 2" 3
"SUITE 103" 3
"SUITE 102" 3
"-" 3
"SUITE 210" 3
"10TH FLOOR" 3
"6TH FLOOR" 3
"SUITE 110" 3
"GSA CENTER" 3
"SUITE 800" 3
"BLDG C" 3
"3RD FLOOR" 3
"Suite 5" 2
"Suite 1" 2
"Suite 2" 2
"Suite 3" 2
"NA" 2
"SUITE 408" 2
"Suite B" 2
"Suite C" 2
"BUILDING B" 2
"Suite 320" 2
"Suite 500" 2
"TOWER 1" 2
"SUITE 500" 2
"BORDER STATION" 2
"SUITE 5" 2
"SUITE 7" 2
"1ST FLOOR" 2
"Suite 206" 2
"SUITE F" 2
"ST HWY 17 @ CANADIAN BORDER" 2
"SUITE 101" 2
"SUITE 107" 2
"SUITE 350" 2
"ST ELIZABETHS WEST CAMPUS" 2
"UNIT J" 2
"2nd Floor" 2
"I-29 @ CANADIAN BORDER" 2
"BUILDING 3" 2
"BUILDING 2" 2
"SUITE 10" 2
"BUILDING D" 2
"OLD SAN JUAN" 2
"Suite 400" 2
"5TH FLOOR" 2
"NEW CONSTRUCTION" 2
"SUITE 205" 2
"2365 Lincoln Avenue" 2
"STE 400" 2
"Building C-1" 1
"1732 N FIRST ST,SUITE 600" 1
"Suite F" 1
"WEST" 1
"SUITE C,REAR" 1
"UNIT 2J" 1
"#12" 1
"590 E. WESTERN RESERVE ROAD" 1
"SUITE 255A" 1
"Suite 220" 1
"Suite 221" 1
"HANGARS Q 9 & 10" 1
"P.O. BOX 8279" 1
"186 EXCHANGE STREET" 1
"SUITE 101/102" 1
"4114 LEGATO ROAD" 1
"STE. 180" 1
"MAKUSHIN APARTMENTS" 1
"1375 SOUTH AVE EAST" 1
"7th Floor" 1
"BAY 2" 1
"11TH FLOOR" 1
"BISHOP INTERNATIONAL AIRPORT" 1
"BEHIND WHITE FLINT NORTH ONE" 1
... ...
Key Type Example Value Comment
"data" dict { }
"location" dict { }
Key Type Example Value Comment
"ansi usable" str "97884"
[Preview ]
"ADA Accessible" str "Will Conform"
[Preview ]
Value Count
"0" 879
"3200" 23
"5000" 19
"1500" 15
"7000" 14
"1000" 14
"4000" 14
"8000" 13
"6000" 12
"10000" 12
"4200" 11
"2000" 10
"800" 10
"1058" 10
"2400" 10
"4800" 10
"600" 9
"15000" 9
"3000" 9
"12000" 8
"3600" 8
"1250" 7
"2600" 7
"4900" 7
"1800" 7
"1436" 7
"2500" 7
"1050" 7
"1200" 7
"11000" 7
"7800" 7
"2700" 7
"2100" 6
"10800" 6
"4500" 6
"5400" 6
"900" 6
"7200" 6
"3500" 6
"1600" 6
"9000" 6
"1150" 6
"3700" 5
"6500" 5
"2593" 5
"5900" 5
"9600" 5
"9999" 5
"2200" 5
"5500" 5
"6300" 5
"4400" 5
"6400" 5
"10500" 5
"6800" 5
"6600" 5
"660" 5
"5100" 5
"1056" 5
"3100" 5
"7500" 5
"1400" 5
"64" 5
"5800" 5
"8500" 5
"2300" 5
"1560" 4
"25000" 4
"714" 4
"1860" 4
"4300" 4
"1020" 4
"500" 4
"17500" 4
"1230" 4
"2850" 4
"540" 4
"5440" 4
"400" 4
"1350" 4
"947" 4
"4463" 4
"5580" 4
"8300" 4
"3900" 4
"4764" 4
"1145" 4
"3698" 4
"18000" 4
"6882" 4
"450" 4
"440" 4
"5269" 4
"1740" 3
"736" 3
"1503" 3
"656" 3
"8800" 3
"5775" 3
"5940" 3
... ...
Value Count
"Yes" 4898
"Will Conform" 4180
"No" 237
Key Type Example Value Comment
"status" str "National Register Listed"
[Preview ]
"type" str "Field Not In Use"
[Preview ]
Value Count
"Not Evaluated" 8154
"Evaluated - Not Historic" 452
"National Register Listed" 405
"National Register Eligible" 147
"National Historic Landmark" 81
"Non-Contributing Element" 75
" " 1
Value Count
"Field Not In Use" 9279
"None" 35
"District Only" 1
Key Type Example Value Comment
"status" str "ACTIVE"
[Preview ]
"disabilities" dict { }
"parking spaces" int 29
"owned or leased" str "OWNED"
[Preview ]
"date" str "1-Jan-33"
[Preview ]
"type" str "BUILDING"
[Preview ]
"history" dict { }
Value Count
"ACTIVE" 9236
"EXCESS" 70
"DECOMMISSIONED" 9
Value Count
"LEASED" 7162
"OWNED" 2153
Value Count
"0" 5043
"1-Jan-42" 72
"1-Jan-85" 52
"1-Jan-91" 50
"1-Jan-69" 47
"1-Jan-10" 47
"1-Jan-96" 47
"1-Jan-02" 46
"1-Jan-72" 45
"1-Jan-67" 45
"1-Jan-33" 45
"1-Jan-65" 42
"1-Jan-99" 42
"1-Jan-89" 40
"1-Jan-73" 40
"1-Jan-00" 40
"1-Jan-32" 39
"1-Jan-66" 35
"1-Jan-41" 35
"1-Jan-82" 35
"1-Jan-74" 34
"1-Jan-71" 34
"1-Jan-83" 34
"1-Jan-06" 33
"1-Jan-03" 32
"1-Jan-87" 32
"1-Jan-76" 31
"1-Jan-70" 30
"1-Jan-97" 30
"1-Jan-01" 30
"1-Jan-92" 29
"1-Jan-05" 29
"1-Jan-04" 29
"1-Jan-88" 28
"1-Jan-98" 27
"1-Jan-34" 27
"1-Jan-11" 26
"1-Jan-90" 26
"1-Jan-81" 26
"1-Jan-80" 26
"1-Jan-37" 26
"1-Jan-08" 25
"1-Jan-07" 25
"1-Jan-84" 25
"1-Jan-44" 25
"1-Jan-86" 25
"1-Jan-64" 24
"1-Jan-77" 24
"1-Jan-75" 23
"1-Jan-52" 23
"1-Jan-60" 21
"1-Jan-12" 20
"1-Jan-43" 20
"1-Jan-68" 19
"1-Nov-14" 18
"1-Jan-63" 18
"1-Jan-53" 18
"1-Jan-79" 17
"1-Oct-09" 17
"1-Jan-61" 17
"1-Jan-94" 17
"1-Jan-78" 16
"1-Jan-95" 16
"1-Jan-59" 16
"1-Jan-93" 16
"1-Aug-10" 16
"1-Jan-31" 16
"1-Dec-08" 15
"10-Apr-00" 15
"1-Jan-09" 15
"1-Jan-36" 15
"1-Dec-09" 14
"1-Aug-09" 14
"1-Jun-08" 14
"1-Jan-35" 14
"1-Sep-00" 13
"1-Oct-08" 13
"1-Mar-08" 13
"1-Jan-18" 13
"1-Jun-09" 13
"1-Jun-07" 13
"1-Oct-07" 12
"1-Sep-09" 12
"1-Oct-53" 12
"1-Mar-09" 12
"1-Jan-62" 12
"1-May-09" 12
"1-Aug-08" 12
"1-Nov-07" 12
"1-Jun-00" 12
"1-Jan-39" 12
"1-Oct-02" 11
"1-Oct-96" 11
"1-May-08" 11
"1-Mar-10" 11
"1-Apr-09" 11
"1-Nov-08" 11
"1-Jan-40" 11
"1-Jan-17" 10
"1-Mar-04" 10
... ...
Value Count
"BUILDING" 8850
"STRUCTURE" 344
"LAND" 121
Key Type Example Value Comment
"region id" str "1"
[Preview ]
"address" dict { }
"id" str "CT0013"
[Preview ]
"congressional district" str "1"
[Preview ]
Value Count
"4" 1438
"7" 1384
"9" 1105
"5" 1054
"11" 973
"3" 808
"8" 679
"2" 523
"10" 520
"1" 423
"6" 408
Value Count
"IL2475" 1
"IL2474" 1
"IL2477" 1
"IL2476" 1
"IL2470" 1
"IL2473" 1
"IL2479" 1
"NY6257" 1
"WA7748" 1
"AL2132" 1
"CA6937" 1
"CA7512" 1
"CA7041" 1
"CA7042" 1
"CA7517" 1
"CA7048" 1
"CA7518" 1
"CO1837" 1
"AL2287" 1
"AL2285" 1
"AL2281" 1
"CO0659" 1
"TX2451" 1
"CO2045" 1
"OK1394" 1
"PA0610" 1
"GA0044" 1
"HI8091" 1
"CA7394" 1
"FL3007" 1
"TX2453" 1
"MD0055" 1
"MD0056" 1
"MD0053" 1
"MO2006" 1
"MO2005" 1
"MO2000" 1
"DC1391" 1
"DC1390" 1
"MO2009" 1
"MO2008" 1
"SC2338" 1
"MD0231" 1
"MD0232" 1
"MD0236" 1
"MD0239" 1
"MD0238" 1
"LA1498" 1
"CO0009" 1
"CO0006" 1
"LA1499" 1
"TX2007" 1
"TX2009" 1
"LA1262" 1
"TX0894" 1
"TX0892" 1
"TX0893" 1
"TX0891" 1
"CA8129" 1
"WA7879" 1
"WA7878" 1
"MI1868" 1
"MI1869" 1
"WA7874" 1
"WA7871" 1
"CA8127" 1
"MA5673" 1
"MA5672" 1
"MA5671" 1
"TX2143" 1
"TX2140" 1
"MA5679" 1
"MA5678" 1
"TX2148" 1
"TX2149" 1
"MD1726" 1
"KS1578" 1
"KS1579" 1
"MD1722" 1
"MD1723" 1
"KS1575" 1
"KS1576" 1
"KS1577" 1
"KS1570" 1
"NV8000" 1
"KS1572" 1
"WI1701" 1
"KY0088" 1
"WI1703" 1
"WI1702" 1
"WI1705" 1
"WI1704" 1
"WI1707" 1
"WI1706" 1
"WI1709" 1
"CA8181" 1
"KY0085" 1
"KY0086" 1
"OH2115" 1
"GU7473" 1
... ...
Value Count
"1" 982
"2" 952
"3" 815
"5" 603
"0" 547
"98" 546
"4" 541
"8" 494
"7" 468
"6" 415
"9" 236
"10" 195
"23" 194
"11" 187
"12" 184
"14" 153
"13" 146
"16" 133
"34" 125
"18" 113
"28" 105
"24" 103
"15" 90
"20" 83
" " 82
"17" 78
"26" 73
"21" 71
"51" 66
"25" 50
"22" 48
"19" 47
"35" 44
"52" 40
"27" 40
"33" 39
"30" 30
"36" 20
"46" 19
"29" 17
"32" 15
"41" 15
"43" 14
"31" 13
"47" 13
"53" 11
"49" 11
"38" 9
"48" 8
"45" 8
"37" 7
"50" 6
"39" 4
"40" 3
"44" 3
"42" 1

Downloads

Download all of the following files.

Usage

This library has 1 function you can use.
import real_estate
list_of_building = real_estate.get_buildings()
Additionally, some of the functions can return a sample of the Big Data using an extra argument. If you use this sampled Big Data, it may be much faster. When you are sure your code is correct, you can remove the argument to use the full dataset.
import real_estate
# These may be slow!
list_of_building = real_estate.get_buildings(test=True)

Documentation

 real_estate.get_buildings(test=False)

Returns a list of the buildings in the database.