Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory966.8 KiB
Average record size in memory99.0 B

Variable types

Categorical7
Text1
Numeric2
DateTime1

Dataset

Description국유부동산 관리·처분에 따른 매각 관련 현황(소재지, 지목, 면적, 입찰종류, 계약일자, 계약금액 등)을 제공합니다.2014년부터 2023년12.31까지 매각 내역을 제공
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15004205/fileData.do

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
재산구분 is highly overall correlated with 공부지목High correlation
공부지목 is highly overall correlated with 재산구분High correlation
지역구분 is highly overall correlated with 부서 and 1 other fieldsHigh correlation
부서 is highly overall correlated with 지역구분 and 1 other fieldsHigh correlation
팀 is highly overall correlated with 지역구분 and 1 other fieldsHigh correlation
재산구분 is highly imbalanced (93.8%)Imbalance
처분구분명 is highly imbalanced (68.9%)Imbalance
대장면적(제곱미터) is highly skewed (γ1 = 20.59996202)Skewed
대장금액(원) is highly skewed (γ1 = 40.18013125)Skewed

Reproduction

Analysis started2024-03-14 23:54:37.771925
Analysis finished2024-03-14 23:54:42.710100
Duration4.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도구분
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2017
2915 
2016
2513 
2014
2249 
2015
2101 
2018
 
222

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2017
3rd row2017
4th row2015
5th row2018

Common Values

ValueCountFrequency (%)
2017 2915
29.1%
2016 2513
25.1%
2014 2249
22.5%
2015 2101
21.0%
2018 222
 
2.2%

Length

2024-03-15T08:54:42.823450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:54:43.093447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 2915
29.1%
2016 2513
25.1%
2014 2249
22.5%
2015 2101
21.0%
2018 222
 
2.2%

지역구분
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도
1782 
경상북도
1029 
경상남도
963 
강원도
942 
전라북도
938 
Other values (12)
4346 

Length

Max length7
Median length5
Mean length3.993
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row서울특별시
3rd row경기도
4th row충청북도
5th row대구광역시

Common Values

ValueCountFrequency (%)
경기도 1782
17.8%
경상북도 1029
10.3%
경상남도 963
9.6%
강원도 942
9.4%
전라북도 938
9.4%
전라남도 917
9.2%
부산광역시 830
8.3%
충청남도 657
 
6.6%
서울특별시 532
 
5.3%
충청북도 466
 
4.7%
Other values (7) 944
9.4%

Length

2024-03-15T08:54:43.511053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 1782
17.8%
경상북도 1029
10.3%
경상남도 963
9.6%
강원도 942
9.4%
전라북도 938
9.4%
전라남도 917
9.2%
부산광역시 830
8.3%
충청남도 657
 
6.6%
서울특별시 532
 
5.3%
충청북도 466
 
4.7%
Other values (7) 944
9.4%

소재지명
Text

Distinct9930
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T08:54:44.803299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length21.472
Min length13

Characters and Unicode

Total characters214720
Distinct characters366
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9885 ?
Unique (%)98.9%

Sample

1st row서울특별시 동작구 흑석동 143-79
2nd row서울특별시 성북구 장위동 276-22
3rd row경기도 가평군 가평읍 승안리 919
4th row충청북도 보은군 탄부면 대양리 129
5th row대구광역시 달성군 화원읍 설화리 507-1
ValueCountFrequency (%)
경기도 1782
 
3.8%
경상북도 1029
 
2.2%
경상남도 963
 
2.0%
강원도 942
 
2.0%
전라북도 938
 
2.0%
전라남도 917
 
1.9%
부산광역시 830
 
1.8%
충청남도 657
 
1.4%
서울특별시 532
 
1.1%
충청북도 466
 
1.0%
Other values (13608) 38078
80.8%
2024-03-15T08:54:46.784455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41044
 
19.1%
- 8822
 
4.1%
1 8589
 
4.0%
도 8479
 
3.9%
시 7180
 
3.3%
리 6201
 
2.9%
2 5634
 
2.6%
동 5252
 
2.4%
3 4773
 
2.2%
면 4351
 
2.0%
Other values (356) 114395
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 120743
56.2%
Decimal Number 44111
 
20.5%
Space Separator 41044
 
19.1%
Dash Punctuation 8822
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 8479
 
7.0%
시 7180
 
5.9%
리 6201
 
5.1%
동 5252
 
4.3%
면 4351
 
3.6%
경 4125
 
3.4%
산 3611
 
3.0%
남 3604
 
3.0%
군 3350
 
2.8%
구 3230
 
2.7%
Other values (344) 71360
59.1%
Decimal Number
ValueCountFrequency (%)
1 8589
19.5%
2 5634
12.8%
3 4773
10.8%
4 4337
9.8%
5 4109
9.3%
6 3846
8.7%
7 3558
8.1%
8 3286
 
7.4%
9 3035
 
6.9%
0 2944
 
6.7%
Space Separator
ValueCountFrequency (%)
41044
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8822
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 120743
56.2%
Common 93977
43.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 8479
 
7.0%
시 7180
 
5.9%
리 6201
 
5.1%
동 5252
 
4.3%
면 4351
 
3.6%
경 4125
 
3.4%
산 3611
 
3.0%
남 3604
 
3.0%
군 3350
 
2.8%
구 3230
 
2.7%
Other values (344) 71360
59.1%
Common
ValueCountFrequency (%)
41044
43.7%
- 8822
 
9.4%
1 8589
 
9.1%
2 5634
 
6.0%
3 4773
 
5.1%
4 4337
 
4.6%
5 4109
 
4.4%
6 3846
 
4.1%
7 3558
 
3.8%
8 3286
 
3.5%
Other values (2) 5979
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 120743
56.2%
ASCII 93977
43.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41044
43.7%
- 8822
 
9.4%
1 8589
 
9.1%
2 5634
 
6.0%
3 4773
 
5.1%
4 4337
 
4.6%
5 4109
 
4.4%
6 3846
 
4.1%
7 3558
 
3.8%
8 3286
 
3.5%
Other values (2) 5979
 
6.4%
Hangul
ValueCountFrequency (%)
도 8479
 
7.0%
시 7180
 
5.9%
리 6201
 
5.1%
동 5252
 
4.3%
면 4351
 
3.6%
경 4125
 
3.4%
산 3611
 
3.0%
남 3604
 
3.0%
군 3350
 
2.8%
구 3230
 
2.7%
Other values (344) 71360
59.1%

재산구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
토지
9927 
건물
 
73

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row토지
2nd row토지
3rd row토지
4th row토지
5th row토지

Common Values

ValueCountFrequency (%)
토지 9927
99.3%
건물 73
 
0.7%

Length

2024-03-15T08:54:47.013788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:54:47.181140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
토지 9927
99.3%
건물 73
 
0.7%

공부지목
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대
2829 
전
2054 
답
1352 
도로
827 
임야
765 
Other values (21)
2173 

Length

Max length5
Median length1
Mean length1.5485
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도로
2nd row구거
3rd row도로
4th row대
5th row대

Common Values

ValueCountFrequency (%)
대 2829
28.3%
전 2054
20.5%
답 1352
13.5%
도로 827
 
8.3%
임야 765
 
7.6%
잡종지 597
 
6.0%
구거 569
 
5.7%
하천 196
 
2.0%
공장용지 161
 
1.6%
묘지 117
 
1.2%
Other values (16) 533
 
5.3%

Length

2024-03-15T08:54:47.383271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대 2829
28.3%
전 2054
20.5%
답 1352
13.5%
도로 827
 
8.3%
임야 765
 
7.6%
잡종지 597
 
6.0%
구거 569
 
5.7%
하천 196
 
2.0%
공장용지 161
 
1.6%
묘지 117
 
1.2%
Other values (16) 533
 
5.3%

대장면적(제곱미터)
Real number (ℝ)

SKEWED 

Distinct1709
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean505.5212
Minimum0
Maximum80649
Zeros20
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T08:54:47.766360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q133
median113
Q3397
95-th percentile1703.15
Maximum80649
Range80649
Interquartile range (IQR)364

Descriptive statistics

Standard deviation2132.4042
Coefficient of variation (CV)4.218229
Kurtosis603.32195
Mean505.5212
Median Absolute Deviation (MAD)100
Skewness20.599962
Sum5055212
Variance4547147.6
MonotonicityNot monotonic
2024-03-15T08:54:48.230027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 142
 
1.4%
3 130
 
1.3%
10 119
 
1.2%
1 118
 
1.2%
13 112
 
1.1%
7 107
 
1.1%
4 103
 
1.0%
17 101
 
1.0%
9 94
 
0.9%
6 90
 
0.9%
Other values (1699) 8884
88.8%
ValueCountFrequency (%)
0 20
 
0.2%
1 118
1.2%
2 142
1.4%
3 130
1.3%
4 103
1.0%
5 76
0.8%
6 90
0.9%
7 107
1.1%
8 81
0.8%
9 94
0.9%
ValueCountFrequency (%)
80649 1
< 0.1%
78446 1
< 0.1%
70766 1
< 0.1%
49075 1
< 0.1%
47197 1
< 0.1%
41046 1
< 0.1%
36955 1
< 0.1%
35853 1
< 0.1%
34685 1
< 0.1%
33521 1
< 0.1%

대장금액(원)
Real number (ℝ)

SKEWED 

Distinct9378
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43249045
Minimum0
Maximum2.190961 × 1010
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T08:54:48.675372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile181975
Q11512355.8
median5434950
Q319222955
95-th percentile1.3249745 × 108
Maximum2.190961 × 1010
Range2.190961 × 1010
Interquartile range (IQR)17710599

Descriptive statistics

Standard deviation4.0877061 × 108
Coefficient of variation (CV)9.4515522
Kurtosis1928.1852
Mean43249045
Median Absolute Deviation (MAD)4818350
Skewness40.180131
Sum4.3249045 × 1011
Variance1.6709341 × 1017
MonotonicityNot monotonic
2024-03-15T08:54:49.159845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1008000 6
 
0.1%
1530000 6
 
0.1%
100 5
 
0.1%
17340 5
 
0.1%
3003000 5
 
0.1%
8400000 4
 
< 0.1%
4290000 4
 
< 0.1%
429000 4
 
< 0.1%
528000 4
 
< 0.1%
156000 4
 
< 0.1%
Other values (9368) 9953
99.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
17 1
 
< 0.1%
20 1
 
< 0.1%
23 1
 
< 0.1%
30 1
 
< 0.1%
52 1
 
< 0.1%
70 1
 
< 0.1%
100 5
0.1%
140 1
 
< 0.1%
ValueCountFrequency (%)
21909610000 1
< 0.1%
21433031920 1
< 0.1%
17173587000 1
< 0.1%
8871567149 1
< 0.1%
7636237000 1
< 0.1%
7451200000 1
< 0.1%
5718490190 1
< 0.1%
4256760000 1
< 0.1%
3656190000 1
< 0.1%
3459600000 1
< 0.1%

처분구분명
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
처분(매각수의)
8389 
처분(매각입찰)
925 
처분(무상관리전환)
 
538
처분(사용승인)
 
65
처분(무상양여)
 
48
Other values (2)
 
35

Length

Max length10
Median length8
Mean length8.1092
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row처분(매각수의)
2nd row처분(매각수의)
3rd row처분(매각수의)
4th row처분(매각입찰)
5th row처분(매각수의)

Common Values

ValueCountFrequency (%)
처분(매각수의) 8389
83.9%
처분(매각입찰) 925
 
9.2%
처분(무상관리전환) 538
 
5.4%
처분(사용승인) 65
 
0.7%
처분(무상양여) 48
 
0.5%
처분(무상귀속) 27
 
0.3%
처분(유상관리전환) 8
 
0.1%

Length

2024-03-15T08:54:49.600285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:54:49.956910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
처분(매각수의 8389
83.9%
처분(매각입찰 925
 
9.2%
처분(무상관리전환 538
 
5.4%
처분(사용승인 65
 
0.7%
처분(무상양여 48
 
0.5%
처분(무상귀속 27
 
0.3%
처분(유상관리전환 8
 
0.1%

계약일자
Date

Distinct1144
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2014-01-01 00:00:00
Maximum2018-12-24 00:00:00
2024-03-15T08:54:50.344502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:54:50.803787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

부서
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대구경북지역본부
1222 
광주전남지역본부
1164 
부산지역본부
1085 
경기지역본부
1050 
강원지역본부
942 
Other values (7)
4537 

Length

Max length8
Median length6
Mean length6.843
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울서부지역본부
2nd row서울동부지역본부
3rd row서울동부지역본부
4th row충북지역본부
5th row대구경북지역본부

Common Values

ValueCountFrequency (%)
대구경북지역본부 1222
12.2%
광주전남지역본부 1164
11.6%
부산지역본부 1085
10.8%
경기지역본부 1050
10.5%
강원지역본부 942
9.4%
전북지역본부 938
9.4%
경남지역본부 908
9.1%
대전충남지역본부 789
7.9%
서울서부지역본부 611
6.1%
충북지역본부 466
 
4.7%
Other values (2) 825
8.2%

Length

2024-03-15T08:54:51.164365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대구경북지역본부 1222
12.2%
광주전남지역본부 1164
11.6%
부산지역본부 1085
10.8%
경기지역본부 1050
10.5%
강원지역본부 942
9.4%
전북지역본부 938
9.4%
경남지역본부 908
9.1%
대전충남지역본부 789
7.9%
서울서부지역본부 611
6.1%
충북지역본부 466
 
4.7%
Other values (2) 825
8.2%

팀
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
국유재산관리2팀
1186 
국유재산관리3팀
1157 
국유재산관리1팀
839 
국유재산3팀
717 
국유재산2팀
648 
Other values (21)
5453 

Length

Max length8
Median length6
Mean length6.174
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row국유재산2팀
2nd row국유재산2팀
3rd row국유재산3팀
4th row국유재산관리2팀
5th row국유재산관리3팀

Common Values

ValueCountFrequency (%)
국유재산관리2팀 1186
 
11.9%
국유재산관리3팀 1157
 
11.6%
국유재산관리1팀 839
 
8.4%
국유재산3팀 717
 
7.2%
국유재산2팀 648
 
6.5%
국유재산관리4팀 581
 
5.8%
국유재산1팀 474
 
4.7%
평택지사 424
 
4.2%
원주지사 307
 
3.1%
안동지사 296
 
3.0%
Other values (16) 3371
33.7%

Length

2024-03-15T08:54:51.648709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
국유재산관리2팀 1186
 
11.9%
국유재산관리3팀 1157
 
11.6%
국유재산관리1팀 839
 
8.4%
국유재산3팀 717
 
7.2%
국유재산2팀 648
 
6.5%
국유재산관리4팀 581
 
5.8%
국유재산1팀 474
 
4.7%
평택지사 424
 
4.2%
원주지사 307
 
3.1%
안동지사 296
 
3.0%
Other values (16) 3371
33.7%

Interactions

2024-03-15T08:54:41.652259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:54:41.091855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:54:41.904185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:54:41.379356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:54:51.948896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분지역구분재산구분공부지목대장면적(제곱미터)대장금액(원)처분구분명부서팀
연도구분1.0000.3360.0140.2020.0260.0000.1800.2310.341
지역구분0.3361.0000.1110.4820.0610.1440.2260.9800.945
재산구분0.0140.1111.0001.0000.0000.0590.1520.1260.077
공부지목0.2020.4821.0001.0000.0920.0000.3570.3550.427
대장면적(제곱미터)0.0260.0610.0000.0921.0000.4740.0890.0200.065
대장금액(원)0.0000.1440.0590.0000.4741.0000.0590.0350.000
처분구분명0.1800.2260.1520.3570.0890.0591.0000.2490.306
부서0.2310.9800.1260.3550.0200.0350.2491.0000.942
팀0.3410.9450.0770.4270.0650.0000.3060.9421.000
2024-03-15T08:54:52.262803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
팀부서재산구분지역구분공부지목연도구분처분구분명
팀1.0000.6920.0670.6490.1190.1540.135
부서0.6921.0000.0980.8890.1260.1290.123
재산구분0.0670.0981.0000.1000.9990.0170.163
지역구분0.6490.8890.1001.0000.1580.1800.104
공부지목0.1190.1260.9990.1581.0000.0980.160
연도구분0.1540.1290.0170.1800.0981.0000.115
처분구분명0.1350.1230.1630.1040.1600.1151.000
2024-03-15T08:54:52.561514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대장면적(제곱미터)대장금액(원)연도구분지역구분재산구분공부지목처분구분명부서팀
대장면적(제곱미터)1.0000.4410.0160.0250.0000.0370.0470.0090.026
대장금액(원)0.4411.0000.0000.0650.0640.0000.0210.0170.000
연도구분0.0160.0001.0000.1800.0170.0980.1150.1290.154
지역구분0.0250.0650.1801.0000.1000.1580.1040.8890.649
재산구분0.0000.0640.0170.1001.0000.9990.1630.0980.067
공부지목0.0370.0000.0980.1580.9991.0000.1600.1260.119
처분구분명0.0470.0210.1150.1040.1630.1601.0000.1230.135
부서0.0090.0170.1290.8890.0980.1260.1231.0000.692
팀0.0260.0000.1540.6490.0670.1190.1350.6921.000

Missing values

2024-03-15T08:54:42.120871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:54:42.477053image/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

연도구분지역구분소재지명재산구분공부지목대장면적(제곱미터)대장금액(원)처분구분명계약일자부서팀
182142015서울특별시서울특별시 동작구 흑석동 143-79토지도로55550000처분(매각수의)2015-05-08서울서부지역본부국유재산2팀
544692017서울특별시서울특별시 성북구 장위동 276-22토지구거53580000처분(매각수의)2017-08-01서울동부지역본부국유재산2팀
631732017경기도경기도 가평군 가평읍 승안리 919토지도로41310205230처분(매각수의)2017-06-27서울동부지역본부국유재산3팀
258522015충청북도충청북도 보은군 탄부면 대양리 129토지대7319064400처분(매각입찰)2015-10-07충북지역본부국유재산관리2팀
791492018대구광역시대구광역시 달성군 화원읍 설화리 507-1토지대74019400처분(매각수의)2018-03-22대구경북지역본부국유재산관리3팀
778082018서울특별시서울특별시 동작구 흑석동 79-131토지종교용지77112000처분(매각수의)2018-05-29서울서부지역본부국유재산2팀
507462016경상북도경상북도 영천시 화산면 삼부리 461-5토지대23109940처분(매각수의)2016-06-16대구경북지역본부국유재산관리3팀
237012015강원도강원도 춘천시 동산면 조양리 1391토지도로55918447000처분(매각수의)2015-02-17강원지역본부춘천지사
756442017경상남도경상남도 김해시 내동 1051-16토지대291724630처분(매각수의)2017-01-12경남지역본부국유재산관리2팀
642092017강원도강원도 홍천군 북방면 원소리 493-2토지도로3344245140처분(매각수의)2017-04-10강원지역본부원주지사
연도구분지역구분소재지명재산구분공부지목대장면적(제곱미터)대장금액(원)처분구분명계약일자부서팀
329382015경상남도경상남도 김해시 내덕동 670-31토지대261374179처분(매각수의)2015-04-01경남지역본부국유재산관리2팀
680712017전라북도전라북도 전주시 덕진구 송천동2가 산 189-50토지임야29812814000처분(매각수의)2017-11-27전북지역본부국유재산관리3팀
153882014경상남도경상남도 통영시 서호동 262-10토지대152745000처분(매각수의)2014-12-31경남지역본부국유통영지사
345422016서울특별시서울특별시 동대문구 전농동 129-90토지대25040000처분(매각수의)2016-11-15서울동부지역본부국유재산3팀
519362016경상남도경상남도 창원시 진해구 경화동 1766-1토지구거241929600처분(매각수의)2016-04-05경남지역본부국유재산관리3팀
405942016경기도경기도 화성시 기안동 1-1토지하천19614500080처분(매각수의)2016-11-14경기지역본부국유재산2팀
490342016전라남도전라남도 완도군 완도읍 대야리 637-10토지잡종지791666900처분(매각수의)2016-09-30광주전남지역본부국유재산관리1팀
227052015경기도경기도 화성시 향남읍 상신리 963-1토지전33243824000처분(매각수의)2015-07-22경기지역본부국유재산2팀
229122015경기도경기도 화성시 장안면 사랑리 1-1토지전63543370500처분(매각수의)2015-04-21경기지역본부국유재산2팀
391942016경기도경기도 고양시 덕양구 덕은동 산 112-9토지임야1587868400처분(매각수의)2016-08-04인천지역본부국유재산2팀

Duplicate rows

Most frequently occurring

연도구분지역구분소재지명재산구분공부지목대장면적(제곱미터)대장금액(원)처분구분명계약일자부서팀# duplicates
02015서울특별시서울특별시 동작구 흑석동 143-79토지도로22220000처분(매각수의)2015-05-08서울서부지역본부국유재산2팀2