Overview

Dataset statistics

Number of variables8
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory70.8 B

Variable types

Categorical2
Text3
Numeric3

Dataset

Description한국자산관리공사가 운영 · 관리하는 국유지 개발사업 준공사업장 건물 임대 현황에 대한 자료입니다.(총 임대면적, 임대중 면적, 임대가능 면적 등)
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15045319/fileData.do

Alerts

연도 has constant value ""Constant
총 임대면적(A)(제곱미터) is highly overall correlated with 임대 중인 면적(B)(제곱미터)High correlation
임대 중인 면적(B)(제곱미터) is highly overall correlated with 총 임대면적(A)(제곱미터)High correlation
사업장 has unique valuesUnique
소재지 has unique valuesUnique
총 임대면적(A)(제곱미터) has unique valuesUnique
임대 중인 면적(B)(제곱미터) has 5 (14.7%) zerosZeros
임대가능면적(A-B)(제곱미터) has 15 (44.1%) zerosZeros

Reproduction

Analysis started2024-03-14 10:10:03.162403
Analysis finished2024-03-14 10:10:06.831522
Duration3.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size400.0 B
2023-12-31
34 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-12-31
2nd row2023-12-31
3rd row2023-12-31
4th row2023-12-31
5th row2023-12-31

Common Values

ValueCountFrequency (%)
2023-12-31 34
100.0%

Length

2024-03-14T19:10:07.044668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:10:07.359965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-12-31 34
100.0%

사업장
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size400.0 B
2024-03-14T19:10:08.187803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length6.6764706
Min length4

Characters and Unicode

Total characters227
Distinct characters99
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)100.0%

Sample

1st row저동빌딩
2nd row대전센터
3rd row대학생주택(2건)
4th row삼성동A빌딩
5th row삼성동B빌딩
ValueCountFrequency (%)
저동빌딩 1
 
2.5%
신사동빌딩 1
 
2.5%
부산통합청사 1
 
2.5%
익산통합청사 1
 
2.5%
무안다산마을 1
 
2.5%
광주통합청사 1
 
2.5%
원주통합청사 1
 
2.5%
세종다산마을 1
 
2.5%
대전센터 1
 
2.5%
갈매동청사 1
 
2.5%
Other values (30) 30
75.0%
2024-03-14T19:10:09.502557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 16
 
7.0%
청 13
 
5.7%
합 11
 
4.8%
동 10
 
4.4%
빌 7
 
3.1%
딩 7
 
3.1%
복 7
 
3.1%
6
 
2.6%
주 6
 
2.6%
구 5
 
2.2%
Other values (89) 139
61.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 214
94.3%
Space Separator 6
 
2.6%
Uppercase Letter 4
 
1.8%
Close Punctuation 1
 
0.4%
Decimal Number 1
 
0.4%
Open Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 16
 
7.5%
청 13
 
6.1%
합 11
 
5.1%
동 10
 
4.7%
빌 7
 
3.3%
딩 7
 
3.3%
복 7
 
3.3%
주 6
 
2.8%
구 5
 
2.3%
지 5
 
2.3%
Other values (83) 127
59.3%
Uppercase Letter
ValueCountFrequency (%)
A 2
50.0%
B 2
50.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 214
94.3%
Common 9
 
4.0%
Latin 4
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 16
 
7.5%
청 13
 
6.1%
합 11
 
5.1%
동 10
 
4.7%
빌 7
 
3.3%
딩 7
 
3.3%
복 7
 
3.3%
주 6
 
2.8%
구 5
 
2.3%
지 5
 
2.3%
Other values (83) 127
59.3%
Common
ValueCountFrequency (%)
6
66.7%
) 1
 
11.1%
2 1
 
11.1%
( 1
 
11.1%
Latin
ValueCountFrequency (%)
A 2
50.0%
B 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 214
94.3%
ASCII 13
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 16
 
7.5%
청 13
 
6.1%
합 11
 
5.1%
동 10
 
4.7%
빌 7
 
3.3%
딩 7
 
3.3%
복 7
 
3.3%
주 6
 
2.8%
구 5
 
2.3%
지 5
 
2.3%
Other values (83) 127
59.3%
ASCII
ValueCountFrequency (%)
6
46.2%
A 2
 
15.4%
B 2
 
15.4%
) 1
 
7.7%
2 1
 
7.7%
( 1
 
7.7%

소재지
Text

UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size400.0 B
2024-03-14T19:10:10.566868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length21
Mean length17.117647
Min length12

Characters and Unicode

Total characters582
Distinct characters103
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)100.0%

Sample

1st row서울시 중구 삼일대로 340
2nd row대전시 서구 한밭대로 713
3rd row서울시 마포구 성산동 81-15/ 강동구 성내동 466-4
4th row서울시 강남구 테헤란로83길 32
5th row서울시 강남구 테헤란로83길 33
ValueCountFrequency (%)
서울시 18
 
12.5%
강남구 5
 
3.5%
광주시 2
 
1.4%
테헤란로83길 2
 
1.4%
영등포구 2
 
1.4%
경기 2
 
1.4%
경기도 2
 
1.4%
수원시 2
 
1.4%
영통구 2
 
1.4%
법조로 2
 
1.4%
Other values (95) 105
72.9%
2024-03-14T19:10:12.087396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
110
18.9%
시 34
 
5.8%
1 31
 
5.3%
구 30
 
5.2%
서 23
 
4.0%
로 22
 
3.8%
3 22
 
3.8%
동 19
 
3.3%
울 18
 
3.1%
- 12
 
2.1%
Other values (93) 261
44.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 333
57.2%
Decimal Number 125
 
21.5%
Space Separator 110
 
18.9%
Dash Punctuation 12
 
2.1%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 34
 
10.2%
구 30
 
9.0%
서 23
 
6.9%
로 22
 
6.6%
동 19
 
5.7%
울 18
 
5.4%
대 11
 
3.3%
산 8
 
2.4%
강 8
 
2.4%
남 8
 
2.4%
Other values (79) 152
45.6%
Decimal Number
ValueCountFrequency (%)
1 31
24.8%
3 22
17.6%
2 11
 
8.8%
0 10
 
8.0%
5 10
 
8.0%
9 10
 
8.0%
4 10
 
8.0%
7 9
 
7.2%
8 7
 
5.6%
6 5
 
4.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
110
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 333
57.2%
Common 249
42.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 34
 
10.2%
구 30
 
9.0%
서 23
 
6.9%
로 22
 
6.6%
동 19
 
5.7%
울 18
 
5.4%
대 11
 
3.3%
산 8
 
2.4%
강 8
 
2.4%
남 8
 
2.4%
Other values (79) 152
45.6%
Common
ValueCountFrequency (%)
110
44.2%
1 31
 
12.4%
3 22
 
8.8%
- 12
 
4.8%
2 11
 
4.4%
0 10
 
4.0%
5 10
 
4.0%
9 10
 
4.0%
4 10
 
4.0%
7 9
 
3.6%
Other values (4) 14
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 333
57.2%
ASCII 249
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110
44.2%
1 31
 
12.4%
3 22
 
8.8%
- 12
 
4.8%
2 11
 
4.4%
0 10
 
4.0%
5 10
 
4.0%
9 10
 
4.0%
4 10
 
4.0%
7 9
 
3.6%
Other values (4) 14
 
5.6%
Hangul
ValueCountFrequency (%)
시 34
 
10.2%
구 30
 
9.0%
서 23
 
6.9%
로 22
 
6.6%
동 19
 
5.7%
울 18
 
5.4%
대 11
 
3.3%
산 8
 
2.4%
강 8
 
2.4%
남 8
 
2.4%
Other values (79) 152
45.6%

규모
Text

Distinct22
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Memory size400.0 B
2024-03-14T19:10:12.830695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.2647059
Min length4

Characters and Unicode

Total characters247
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)41.2%

Sample

1st row지하4/지상15
2nd row지하1/지상15
3rd row지상4층
4th row지하2/지상6
5th row지하2/지상4
ValueCountFrequency (%)
지하1/지상5 4
 
11.8%
지하2/지상6 3
 
8.8%
지하1/지상4 3
 
8.8%
지하1/지상3 2
 
5.9%
지하2/지상12 2
 
5.9%
지하2/지상20 2
 
5.9%
지하1/지상15 2
 
5.9%
지하2/지상8 2
 
5.9%
지하3/지상13 1
 
2.9%
지하4/지상15 1
 
2.9%
Other values (12) 12
35.3%
2024-03-14T19:10:14.003896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 67
27.1%
상 34
13.8%
하 33
13.4%
/ 33
13.4%
1 21
 
8.5%
2 16
 
6.5%
5 9
 
3.6%
3 9
 
3.6%
4 7
 
2.8%
6 5
 
2.0%
Other values (5) 13
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 135
54.7%
Decimal Number 79
32.0%
Other Punctuation 33
 
13.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 21
26.6%
2 16
20.3%
5 9
11.4%
3 9
11.4%
4 7
 
8.9%
6 5
 
6.3%
0 4
 
5.1%
7 4
 
5.1%
8 3
 
3.8%
9 1
 
1.3%
Other Letter
ValueCountFrequency (%)
지 67
49.6%
상 34
25.2%
하 33
24.4%
층 1
 
0.7%
Other Punctuation
ValueCountFrequency (%)
/ 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 135
54.7%
Common 112
45.3%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 33
29.5%
1 21
18.8%
2 16
14.3%
5 9
 
8.0%
3 9
 
8.0%
4 7
 
6.2%
6 5
 
4.5%
0 4
 
3.6%
7 4
 
3.6%
8 3
 
2.7%
Hangul
ValueCountFrequency (%)
지 67
49.6%
상 34
25.2%
하 33
24.4%
층 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 135
54.7%
ASCII 112
45.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 67
49.6%
상 34
25.2%
하 33
24.4%
층 1
 
0.7%
ASCII
ValueCountFrequency (%)
/ 33
29.5%
1 21
18.8%
2 16
14.3%
5 9
 
8.0%
3 9
 
8.0%
4 7
 
6.2%
6 5
 
4.5%
0 4
 
3.6%
7 4
 
3.6%
8 3
 
2.7%

총 임대면적(A)(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22168.676
Minimum1432
Maximum128728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size434.0 B
2024-03-14T19:10:14.395558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1432
5-th percentile1910.35
Q16020.75
median12401.5
Q326141.75
95-th percentile76179.05
Maximum128728
Range127296
Interquartile range (IQR)20121

Descriptive statistics

Standard deviation27376.72
Coefficient of variation (CV)1.234928
Kurtosis6.8450851
Mean22168.676
Median Absolute Deviation (MAD)6616
Skewness2.4847489
Sum753735
Variance7.4948478 × 108
MonotonicityNot monotonic
2024-03-14T19:10:14.807848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
26938 1
 
2.9%
12545 1
 
2.9%
5705 1
 
2.9%
2050 1
 
2.9%
40266 1
 
2.9%
5866 1
 
2.9%
5996 1
 
2.9%
10398 1
 
2.9%
23753 1
 
2.9%
52253 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
1432 1
2.9%
1651 1
2.9%
2050 1
2.9%
2924 1
2.9%
4246 1
2.9%
4298 1
2.9%
5705 1
2.9%
5866 1
2.9%
5996 1
2.9%
6095 1
2.9%
ValueCountFrequency (%)
128728 1
2.9%
89441 1
2.9%
69038 1
2.9%
52253 1
2.9%
40592 1
2.9%
40266 1
2.9%
36628 1
2.9%
31885 1
2.9%
26938 1
2.9%
23753 1
2.9%

임대 중인 면적(B)(제곱미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19481.559
Minimum0
Maximum128573
Zeros5
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size434.0 B
2024-03-14T19:10:15.194256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12986
median10876
Q321612
95-th percentile76179.05
Maximum128573
Range128573
Interquartile range (IQR)18626

Descriptive statistics

Standard deviation27829.655
Coefficient of variation (CV)1.4285127
Kurtosis7.2489146
Mean19481.559
Median Absolute Deviation (MAD)8568
Skewness2.5719109
Sum662373
Variance7.744897 × 108
MonotonicityNot monotonic
2024-03-14T19:10:15.605538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 5
 
14.7%
26938 1
 
2.9%
89441 1
 
2.9%
11605 1
 
2.9%
36628 1
 
2.9%
9004 1
 
2.9%
14398 1
 
2.9%
16510 1
 
2.9%
16785 1
 
2.9%
23221 1
 
2.9%
Other values (20) 20
58.8%
ValueCountFrequency (%)
0 5
14.7%
960 1
 
2.9%
1651 1
 
2.9%
2050 1
 
2.9%
2566 1
 
2.9%
4246 1
 
2.9%
4298 1
 
2.9%
5482 1
 
2.9%
5491 1
 
2.9%
7870 1
 
2.9%
ValueCountFrequency (%)
128573 1
2.9%
89441 1
2.9%
69038 1
2.9%
49785 1
2.9%
36628 1
2.9%
32210 1
2.9%
31885 1
2.9%
26938 1
2.9%
23221 1
2.9%
16785 1
2.9%

임대가능면적(A-B)(제곱미터)
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2687.1176
Minimum0
Maximum40266
Zeros15
Zeros (%)44.1%
Negative0
Negative (%)0.0%
Memory size434.0 B
2024-03-14T19:10:15.977480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median189
Q3664.75
95-th percentile11149.45
Maximum40266
Range40266
Interquartile range (IQR)664.75

Descriptive statistics

Standard deviation7367.8381
Coefficient of variation (CV)2.7419113
Kurtosis21.496178
Mean2687.1176
Median Absolute Deviation (MAD)189
Skewness4.3716946
Sum91362
Variance54285039
MonotonicityNot monotonic
2024-03-14T19:10:16.376940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 15
44.1%
223 1
 
2.9%
1148 1
 
2.9%
98 1
 
2.9%
532 1
 
2.9%
12545 1
 
2.9%
10398 1
 
2.9%
5996 1
 
2.9%
5866 1
 
2.9%
40266 1
 
2.9%
Other values (10) 10
29.4%
ValueCountFrequency (%)
0 15
44.1%
98 1
 
2.9%
155 1
 
2.9%
223 1
 
2.9%
271 1
 
2.9%
358 1
 
2.9%
391 1
 
2.9%
472 1
 
2.9%
504 1
 
2.9%
532 1
 
2.9%
ValueCountFrequency (%)
40266 1
2.9%
12545 1
2.9%
10398 1
2.9%
8382 1
2.9%
5996 1
2.9%
5866 1
2.9%
2468 1
2.9%
1148 1
2.9%
685 1
2.9%
604 1
2.9%

담당부서
Categorical

Distinct4
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size400.0 B
수도권동부개발처
15 
중부개발처
8 
수도권서부개발처
7 
남부개발처
4 

Length

Max length8
Median length8
Mean length6.9411765
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수도권서부개발처
2nd row중부개발처
3rd row수도권동부개발처
4th row수도권동부개발처
5th row수도권동부개발처

Common Values

ValueCountFrequency (%)
수도권동부개발처 15
44.1%
중부개발처 8
23.5%
수도권서부개발처 7
20.6%
남부개발처 4
 
11.8%

Length

2024-03-14T19:10:16.802342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:10:17.135325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수도권동부개발처 15
44.1%
중부개발처 8
23.5%
수도권서부개발처 7
20.6%
남부개발처 4
 
11.8%

Interactions

2024-03-14T19:10:05.135208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:10:03.558930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:10:04.356344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:10:05.396799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:10:03.835879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:10:04.610690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:10:05.870101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:10:04.090201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:10:04.866173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T19:10:17.366912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업장소재지규모총 임대면적(A)(제곱미터)임대 중인 면적(B)(제곱미터)임대가능면적(A-B)(제곱미터)담당부서
사업장1.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0001.000
규모1.0001.0001.0000.7830.3860.5800.705
총 임대면적(A)(제곱미터)1.0001.0000.7831.0000.9970.0000.412
임대 중인 면적(B)(제곱미터)1.0001.0000.3860.9971.0000.0000.381
임대가능면적(A-B)(제곱미터)1.0001.0000.5800.0000.0001.0000.000
담당부서1.0001.0000.7050.4120.3810.0001.000
2024-03-14T19:10:17.653471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총 임대면적(A)(제곱미터)임대 중인 면적(B)(제곱미터)임대가능면적(A-B)(제곱미터)담당부서
총 임대면적(A)(제곱미터)1.0000.7910.0690.282
임대 중인 면적(B)(제곱미터)0.7911.000-0.3370.246
임대가능면적(A-B)(제곱미터)0.069-0.3371.0000.000
담당부서0.2820.2460.0001.000

Missing values

2024-03-14T19:10:06.244216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T19:10:06.662720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

연도사업장소재지규모총 임대면적(A)(제곱미터)임대 중인 면적(B)(제곱미터)임대가능면적(A-B)(제곱미터)담당부서
02023-12-31저동빌딩서울시 중구 삼일대로 340지하4/지상1526938269380수도권서부개발처
12023-12-31대전센터대전시 서구 한밭대로 713지하1/지상1531885318850중부개발처
22023-12-31대학생주택(2건)서울시 마포구 성산동 81-15/ 강동구 성내동 466-4지상4층1432960472수도권동부개발처
32023-12-31삼성동A빌딩서울시 강남구 테헤란로83길 32지하2/지상6424642460수도권동부개발처
42023-12-31삼성동B빌딩서울시 강남구 테헤란로83길 33지하2/지상4165116510수도권동부개발처
52023-12-31세종국책연구단지세종시 시청대로 370지하2/지상12128728128573155중부개발처
62023-12-31서대문세무서서울시 서대문구 세무서길 11지하3/지상7101009829271수도권서부개발처
72023-12-31여의도빌딩서울시 영등포구 여의도동 55-2지하6/지상2340592322108382수도권서부개발처
82023-12-31중부세무서서울시 중구 퇴계로 170지하5/지상61053810147391수도권서부개발처
92023-12-31신사동빌딩서울시 강남구 논현로 803지하2/지상7429842980수도권동부개발처
연도사업장소재지규모총 임대면적(A)(제곱미터)임대 중인 면적(B)(제곱미터)임대가능면적(A-B)(제곱미터)담당부서
242023-12-31영등포복합청사서울시 영등포구 당산동 121-103지하2/지상12599605996수도권서부개발처
252023-12-31남양주복합청사경기 남양주시 다산동 672지하1/지상510398010398수도권동부개발처
262023-12-31갈매동청사경기 구리시 갈매동 583-1지하2/지상612545012545수도권동부개발처
272023-12-31세종다산마을세종시 한누리대로 511지하1/지상152375323221532중부개발처
282023-12-31원주통합청사강원도 원주시 입춘로 50지하1/지상4168831678598중부개발처
292023-12-31광주통합청사광주시 서구 천변우하로 391지하1/지상516510165100중부개발처
302023-12-31무안다산마을전라남도 무안군 삼향읍 남악리 2599지하1/지상815546143981148중부개발처
312023-12-31익산통합청사전라북도 익산시 영등동 191-3, 191-48지하1/지상5900490040중부개발처
322023-12-31부산통합청사부산시 연제구 거제대로 214지하4/지상1036628366280남부개발처
332023-12-31대구통합청사대구시 동구 신서동 1152-1지하1/지상511605116050남부개발처