Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells392
Missing cells (%)0.3%
Duplicate rows265
Duplicate rows (%)2.6%
Total size in memory1.3 MiB
Average record size in memory135.0 B

Variable types

Categorical4
Text3
Numeric7
Unsupported1

Dataset

Description한국토지주택공사에서 위탁관리중인 마이홈포털의 기존 매입임대 주택 정보로, 광역시도,단지명,세대수,주택유형,임대사업자,공급면적등의 정보를 제공합니다.
Author한국토지주택공사
URLhttps://www.data.go.kr/data/15084927/fileData.do

Alerts

임대종류 has constant value ""Constant
Dataset has 265 (2.6%) duplicate rowsDuplicates
임대사업자 is highly overall correlated with 광역시도High correlation
광역시도 is highly overall correlated with 임대사업자High correlation
형명 has 392 (3.9%) missing valuesMissing
주차수 is highly skewed (γ1 = 23.06143075)Skewed
공급면적(공용) is highly skewed (γ1 = 99.96885764)Skewed
전환보증금 is highly skewed (γ1 = 33.42006635)Skewed
형명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
주차수 has 9968 (99.7%) zerosZeros
공급면적(공용) has 3166 (31.7%) zerosZeros
임대보증금 has 148 (1.5%) zerosZeros
월임대료 has 304 (3.0%) zerosZeros
전환보증금 has 9844 (98.4%) zerosZeros

Reproduction

Analysis started2024-03-14 17:54:20.763015
Analysis finished2024-03-14 17:54:36.797556
Duration16.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

임대종류
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
매입임대
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
매입임대 10000
100.0%

Length

2024-03-15T02:54:37.001709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:54:37.278447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
매입임대 10000
100.0%

광역시도
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
서울특별시
5880 
인천광역시
930 
부산광역시
841 
대구광역시
658 
대전광역시
 
552
Other values (4)
1139 

Length

Max length7
Median length5
Mean length4.9446
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row부산광역시
3rd row광주광역시
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
서울특별시 5880
58.8%
인천광역시 930
 
9.3%
부산광역시 841
 
8.4%
대구광역시 658
 
6.6%
대전광역시 552
 
5.5%
광주광역시 517
 
5.2%
울산광역시 335
 
3.4%
경기도 282
 
2.8%
세종특별자치시 5
 
0.1%

Length

2024-03-15T02:54:37.465445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:54:37.774519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 5880
58.8%
인천광역시 930
 
9.3%
부산광역시 841
 
8.4%
대구광역시 658
 
6.6%
대전광역시 552
 
5.5%
광주광역시 517
 
5.2%
울산광역시 335
 
3.4%
경기도 282
 
2.8%
세종특별자치시 5
 
< 0.1%

시군구
Text

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T02:54:38.670816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0384
Min length2

Characters and Unicode

Total characters30384
Distinct characters72
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row강동구
2nd row연제구
3rd row남구
4th row은평구
5th row송파구
ValueCountFrequency (%)
강동구 563
 
5.5%
금천구 557
 
5.4%
구로구 484
 
4.7%
북구 470
 
4.6%
서구 468
 
4.6%
남구 426
 
4.1%
송파구 402
 
3.9%
성북구 375
 
3.6%
미추홀구 341
 
3.3%
도봉구 339
 
3.3%
Other values (48) 5857
57.0%
2024-03-15T02:54:39.960353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 10348
34.1%
동 1637
 
5.4%
서 1234
 
4.1%
강 1171
 
3.9%
북 1125
 
3.7%
천 810
 
2.7%
남 744
 
2.4%
금 684
 
2.3%
성 659
 
2.2%
대 632
 
2.1%
Other values (62) 11340
37.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30102
99.1%
Space Separator 282
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 10348
34.4%
동 1637
 
5.4%
서 1234
 
4.1%
강 1171
 
3.9%
북 1125
 
3.7%
천 810
 
2.7%
남 744
 
2.5%
금 684
 
2.3%
성 659
 
2.2%
대 632
 
2.1%
Other values (61) 11058
36.7%
Space Separator
ValueCountFrequency (%)
282
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30102
99.1%
Common 282
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 10348
34.4%
동 1637
 
5.4%
서 1234
 
4.1%
강 1171
 
3.9%
북 1125
 
3.7%
천 810
 
2.7%
남 744
 
2.5%
금 684
 
2.3%
성 659
 
2.2%
대 632
 
2.1%
Other values (61) 11058
36.7%
Common
ValueCountFrequency (%)
282
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30102
99.1%
ASCII 282
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 10348
34.4%
동 1637
 
5.4%
서 1234
 
4.1%
강 1171
 
3.9%
북 1125
 
3.7%
천 810
 
2.7%
남 744
 
2.5%
금 684
 
2.3%
성 659
 
2.2%
대 632
 
2.1%
Other values (61) 11058
36.7%
ASCII
ValueCountFrequency (%)
282
100.0%

도로명주소
Text

Distinct5825
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T02:54:41.488606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length20.3007
Min length14

Characters and Unicode

Total characters203007
Distinct characters363
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

Unique3579 ?
Unique (%)35.8%

Sample

1st row서울특별시 강동구 양재대로95길 27-6
2nd row부산광역시 연제구 해맞이로109번길 25
3rd row광주광역시 남구 진다리로39번길 12
4th row서울특별시 은평구 연서로37가길 10-10
5th row서울특별시 송파구 동남로28길 28
ValueCountFrequency (%)
서울특별시 5880
 
14.6%
인천광역시 930
 
2.3%
부산광역시 841
 
2.1%
대구광역시 658
 
1.6%
강동구 563
 
1.4%
금천구 557
 
1.4%
대전광역시 552
 
1.4%
광주광역시 517
 
1.3%
구로구 484
 
1.2%
북구 470
 
1.2%
Other values (5702) 28957
71.7%
2024-03-15T02:54:43.681283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30409
 
15.0%
구 11234
 
5.5%
시 10262
 
5.1%
로 9843
 
4.8%
1 9024
 
4.4%
길 8692
 
4.3%
서 7546
 
3.7%
2 6313
 
3.1%
울 6268
 
3.1%
별 5885
 
2.9%
Other values (353) 97531
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 126546
62.3%
Decimal Number 42291
 
20.8%
Space Separator 30409
 
15.0%
Dash Punctuation 3756
 
1.9%
Other Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 11234
 
8.9%
시 10262
 
8.1%
로 9843
 
7.8%
길 8692
 
6.9%
서 7546
 
6.0%
울 6268
 
5.0%
별 5885
 
4.7%
특 5885
 
4.7%
광 4639
 
3.7%
역 3854
 
3.0%
Other values (340) 52438
41.4%
Decimal Number
ValueCountFrequency (%)
1 9024
21.3%
2 6313
14.9%
3 5058
12.0%
4 3880
9.2%
5 3744
8.9%
6 3390
 
8.0%
7 3027
 
7.2%
8 2747
 
6.5%
0 2555
 
6.0%
9 2553
 
6.0%
Space Separator
ValueCountFrequency (%)
30409
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3756
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 126546
62.3%
Common 76461
37.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 11234
 
8.9%
시 10262
 
8.1%
로 9843
 
7.8%
길 8692
 
6.9%
서 7546
 
6.0%
울 6268
 
5.0%
별 5885
 
4.7%
특 5885
 
4.7%
광 4639
 
3.7%
역 3854
 
3.0%
Other values (340) 52438
41.4%
Common
ValueCountFrequency (%)
30409
39.8%
1 9024
 
11.8%
2 6313
 
8.3%
3 5058
 
6.6%
4 3880
 
5.1%
- 3756
 
4.9%
5 3744
 
4.9%
6 3390
 
4.4%
7 3027
 
4.0%
8 2747
 
3.6%
Other values (3) 5113
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 126546
62.3%
ASCII 76461
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30409
39.8%
1 9024
 
11.8%
2 6313
 
8.3%
3 5058
 
6.6%
4 3880
 
5.1%
- 3756
 
4.9%
5 3744
 
4.9%
6 3390
 
4.4%
7 3027
 
4.0%
8 2747
 
3.6%
Other values (3) 5113
 
6.7%
Hangul
ValueCountFrequency (%)
구 11234
 
8.9%
시 10262
 
8.1%
로 9843
 
7.8%
길 8692
 
6.9%
서 7546
 
6.0%
울 6268
 
5.0%
별 5885
 
4.7%
특 5885
 
4.7%
광 4639
 
3.7%
역 3854
 
3.0%
Other values (340) 52438
41.4%

단지명
Text

Distinct2081
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T02:54:44.818576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length19
Mean length7.771
Min length2

Characters and Unicode

Total characters77710
Distinct characters476
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique895 ?
Unique (%)8.9%

Sample

1st row서울특별시 강동구
2nd row부산광역시 연제구
3rd row진다리로39번길12
4th row서울특별시 은평구
5th row더존에이스빌
ValueCountFrequency (%)
서울특별시 1610
 
9.7%
인천광역시 791
 
4.8%
부산광역시 668
 
4.0%
대구광역시 600
 
3.6%
대전광역시 466
 
2.8%
광주광역시 441
 
2.7%
남구 402
 
2.4%
서구 393
 
2.4%
북구 391
 
2.4%
울산광역시 333
 
2.0%
Other values (1927) 10445
63.1%
2024-03-15T02:54:46.279036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6540
 
8.4%
구 5633
 
7.2%
시 5301
 
6.8%
광 3989
 
5.1%
역 3308
 
4.3%
서 2564
 
3.3%
울 2053
 
2.6%
동 2015
 
2.6%
빌 1878
 
2.4%
별 1639
 
2.1%
Other values (466) 42790
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64893
83.5%
Space Separator 6540
 
8.4%
Decimal Number 4820
 
6.2%
Uppercase Letter 614
 
0.8%
Dash Punctuation 335
 
0.4%
Close Punctuation 147
 
0.2%
Open Punctuation 147
 
0.2%
Lowercase Letter 124
 
0.2%
Letter Number 49
 
0.1%
Other Punctuation 41
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 5633
 
8.7%
시 5301
 
8.2%
광 3989
 
6.1%
역 3308
 
5.1%
서 2564
 
4.0%
울 2053
 
3.2%
동 2015
 
3.1%
빌 1878
 
2.9%
별 1639
 
2.5%
특 1615
 
2.5%
Other values (410) 34898
53.8%
Uppercase Letter
ValueCountFrequency (%)
A 112
18.2%
B 104
16.9%
I 92
15.0%
T 38
 
6.2%
S 38
 
6.2%
K 35
 
5.7%
H 26
 
4.2%
C 22
 
3.6%
D 22
 
3.6%
Y 18
 
2.9%
Other values (11) 107
17.4%
Decimal Number
ValueCountFrequency (%)
1 1456
30.2%
2 782
16.2%
3 528
 
11.0%
0 472
 
9.8%
4 358
 
7.4%
6 293
 
6.1%
5 253
 
5.2%
7 246
 
5.1%
9 220
 
4.6%
8 212
 
4.4%
Lowercase Letter
ValueCountFrequency (%)
l 43
34.7%
s 22
17.7%
e 19
15.3%
w 14
 
11.3%
c 6
 
4.8%
o 5
 
4.0%
h 5
 
4.0%
u 5
 
4.0%
a 3
 
2.4%
b 2
 
1.6%
Letter Number
ValueCountFrequency (%)
Ⅱ 22
44.9%
Ⅲ 11
22.4%
Ⅳ 9
18.4%
ⅴ 4
 
8.2%
Ⅰ 3
 
6.1%
Other Punctuation
ValueCountFrequency (%)
＆ 24
58.5%
. 11
26.8%
# 5
 
12.2%
' 1
 
2.4%
Close Punctuation
ValueCountFrequency (%)
) 141
95.9%
] 6
 
4.1%
Open Punctuation
ValueCountFrequency (%)
( 141
95.9%
[ 6
 
4.1%
Space Separator
ValueCountFrequency (%)
6540
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 335
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64893
83.5%
Common 12030
 
15.5%
Latin 787
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 5633
 
8.7%
시 5301
 
8.2%
광 3989
 
6.1%
역 3308
 
5.1%
서 2564
 
4.0%
울 2053
 
3.2%
동 2015
 
3.1%
빌 1878
 
2.9%
별 1639
 
2.5%
특 1615
 
2.5%
Other values (410) 34898
53.8%
Latin
ValueCountFrequency (%)
A 112
14.2%
B 104
13.2%
I 92
 
11.7%
l 43
 
5.5%
T 38
 
4.8%
S 38
 
4.8%
K 35
 
4.4%
H 26
 
3.3%
C 22
 
2.8%
D 22
 
2.8%
Other values (26) 255
32.4%
Common
ValueCountFrequency (%)
6540
54.4%
1 1456
 
12.1%
2 782
 
6.5%
3 528
 
4.4%
0 472
 
3.9%
4 358
 
3.0%
- 335
 
2.8%
6 293
 
2.4%
5 253
 
2.1%
7 246
 
2.0%
Other values (10) 767
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64893
83.5%
ASCII 12744
 
16.4%
Number Forms 49
 
0.1%
None 24
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6540
51.3%
1 1456
 
11.4%
2 782
 
6.1%
3 528
 
4.1%
0 472
 
3.7%
4 358
 
2.8%
- 335
 
2.6%
6 293
 
2.3%
5 253
 
2.0%
7 246
 
1.9%
Other values (40) 1481
 
11.6%
Hangul
ValueCountFrequency (%)
구 5633
 
8.7%
시 5301
 
8.2%
광 3989
 
6.1%
역 3308
 
5.1%
서 2564
 
4.0%
울 2053
 
3.2%
동 2015
 
3.1%
빌 1878
 
2.9%
별 1639
 
2.5%
특 1615
 
2.5%
Other values (410) 34898
53.8%
None
ValueCountFrequency (%)
＆ 24
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 22
44.9%
Ⅲ 11
22.4%
Ⅳ 9
18.4%
ⅴ 4
 
8.2%
Ⅰ 3
 
6.1%

세대수
Real number (ℝ)

Distinct107
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.4152
Minimum1
Maximum349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:54:46.748397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median11
Q315
95-th percentile51
Maximum349
Range348
Interquartile range (IQR)7

Descriptive statistics

Standard deviation31.197031
Coefficient of variation (CV)1.7913679
Kurtosis57.895569
Mean17.4152
Median Absolute Deviation (MAD)3
Skewness6.7917818
Sum174152
Variance973.25473
MonotonicityNot monotonic
2024-03-15T02:54:47.139048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 1276
 
12.8%
8 1136
 
11.4%
12 969
 
9.7%
11 580
 
5.8%
5 443
 
4.4%
16 437
 
4.4%
7 431
 
4.3%
6 400
 
4.0%
1 389
 
3.9%
9 371
 
3.7%
Other values (97) 3568
35.7%
ValueCountFrequency (%)
1 389
 
3.9%
2 61
 
0.6%
3 269
 
2.7%
4 209
 
2.1%
5 443
 
4.4%
6 400
 
4.0%
7 431
 
4.3%
8 1136
11.4%
9 371
 
3.7%
10 1276
12.8%
ValueCountFrequency (%)
349 41
0.4%
240 1
 
< 0.1%
200 2
 
< 0.1%
181 3
 
< 0.1%
175 28
0.3%
174 18
0.2%
173 22
0.2%
168 29
0.3%
153 1
 
< 0.1%
149 15
 
0.1%

주택유형
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
다세대주택
5053 
다가구주택
3214 
아파트
 
481
<NA>
 
470
오피스텔
 
458
Other values (2)
 
324

Length

Max length5
Median length5
Mean length4.7786
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row다가구주택
2nd row<NA>
3rd row다가구주택
4th row다가구주택
5th row다세대주택

Common Values

ValueCountFrequency (%)
다세대주택 5053
50.5%
다가구주택 3214
32.1%
아파트 481
 
4.8%
<NA> 470
 
4.7%
오피스텔 458
 
4.6%
연립주택 228
 
2.3%
단독주택 96
 
1.0%

Length

2024-03-15T02:54:47.594804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:54:47.919227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
다세대주택 5053
50.5%
다가구주택 3214
32.1%
아파트 481
 
4.8%
na 470
 
4.7%
오피스텔 458
 
4.6%
연립주택 228
 
2.3%
단독주택 96
 
1.0%

임대사업자
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
SH공사
4246 
LH서울
1634 
LH부산울산
1028 
LH인천
799 
LH대구경북
635 
Other values (10)
1658 

Length

Max length9
Median length4
Mean length4.6747
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLH서울
2nd rowLH부산울산
3rd row광주광역시도시공사
4th rowLH서울
5th rowSH공사

Common Values

ValueCountFrequency (%)
SH공사 4246
42.5%
LH서울 1634
 
16.3%
LH부산울산 1028
 
10.3%
LH인천 799
 
8.0%
LH대구경북 635
 
6.3%
LH대전충남 501
 
5.0%
LH광주전남 480
 
4.8%
LH경기남부 280
 
2.8%
부산도시공사 148
 
1.5%
인천도시공사 131
 
1.3%
Other values (5) 118
 
1.2%

Length

2024-03-15T02:54:48.327162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sh공사 4246
42.5%
lh서울 1634
 
16.3%
lh부산울산 1028
 
10.3%
lh인천 799
 
8.0%
lh대구경북 635
 
6.3%
lh대전충남 501
 
5.0%
lh광주전남 480
 
4.8%
lh경기남부 280
 
2.8%
부산도시공사 148
 
1.5%
인천도시공사 131
 
1.3%
Other values (5) 118
 
1.2%

주차수
Real number (ℝ)

SKEWED  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0356
Minimum0
Maximum24
Zeros9968
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:54:48.676255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70369181
Coefficient of variation (CV)19.766624
Kurtosis588.64598
Mean0.0356
Median Absolute Deviation (MAD)0
Skewness23.061431
Sum356
Variance0.49518216
MonotonicityNot monotonic
2024-03-15T02:54:49.050138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 9968
99.7%
16 6
 
0.1%
8 6
 
0.1%
7 5
 
0.1%
15 4
 
< 0.1%
6 3
 
< 0.1%
24 2
 
< 0.1%
3 2
 
< 0.1%
12 1
 
< 0.1%
4 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 9968
99.7%
3 2
 
< 0.1%
4 1
 
< 0.1%
6 3
 
< 0.1%
7 5
 
0.1%
8 6
 
0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
15 4
 
< 0.1%
16 6
 
0.1%
ValueCountFrequency (%)
24 2
 
< 0.1%
19 1
 
< 0.1%
16 6
0.1%
15 4
< 0.1%
12 1
 
< 0.1%
10 1
 
< 0.1%
8 6
0.1%
7 5
0.1%
6 3
< 0.1%
4 1
 
< 0.1%

형명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing392
Missing (%)3.9%
Memory size156.2 KiB

공급면적(전용)
Real number (ℝ)

Distinct4907
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.925588
Minimum0
Maximum216.48
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:54:49.400251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20.2865
Q129.98
median43.88
Q355.65
95-th percentile76.505
Maximum216.48
Range216.48
Interquartile range (IQR)25.67

Descriptive statistics

Standard deviation17.894543
Coefficient of variation (CV)0.39831515
Kurtosis3.2981615
Mean44.925588
Median Absolute Deviation (MAD)12.96
Skewness1.074584
Sum449255.88
Variance320.21466
MonotonicityNot monotonic
2024-03-15T02:54:49.772436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.99 40
 
0.4%
30.455 30
 
0.3%
29.51 23
 
0.2%
26.52 22
 
0.2%
29.98 21
 
0.2%
29.69 21
 
0.2%
29.92 19
 
0.2%
29.76 19
 
0.2%
42.19 19
 
0.2%
26.23 18
 
0.2%
Other values (4897) 9768
97.7%
ValueCountFrequency (%)
0.0 1
< 0.1%
5.0 1
< 0.1%
6.03 1
< 0.1%
6.21 1
< 0.1%
6.402 1
< 0.1%
8.1 1
< 0.1%
8.32 1
< 0.1%
8.4 1
< 0.1%
8.64 1
< 0.1%
8.75 1
< 0.1%
ValueCountFrequency (%)
216.48 1
< 0.1%
173.39 1
< 0.1%
162.26 1
< 0.1%
152.73 1
< 0.1%
151.51 1
< 0.1%
150.9 1
< 0.1%
148.3 1
< 0.1%
147.91 1
< 0.1%
146.86 1
< 0.1%
146.08 1
< 0.1%

공급면적(공용)
Real number (ℝ)

SKEWED  ZEROS 

Distinct3197
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.13146
Minimum0
Maximum65539
Zeros3166
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:54:50.222283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.86
Q310.177575
95-th percentile25.212
Maximum65539
Range65539
Interquartile range (IQR)10.177575

Descriptive statistics

Standard deviation655.38226
Coefficient of variation (CV)46.377533
Kurtosis9995.8464
Mean14.13146
Median Absolute Deviation (MAD)5.86
Skewness99.968858
Sum141314.6
Variance429525.91
MonotonicityNot monotonic
2024-03-15T02:54:50.585968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3166
31.7%
0.01 167
 
1.7%
0.1 38
 
0.4%
35.4341 30
 
0.3%
13.61 17
 
0.2%
22.06 17
 
0.2%
5.1 14
 
0.1%
23.15 13
 
0.1%
5.63 13
 
0.1%
9.29 13
 
0.1%
Other values (3187) 6512
65.1%
ValueCountFrequency (%)
0.0 3166
31.7%
0.001 1
 
< 0.1%
0.01 167
 
1.7%
0.1 38
 
0.4%
0.11 1
 
< 0.1%
0.22 1
 
< 0.1%
0.24 1
 
< 0.1%
0.25 1
 
< 0.1%
0.26 2
 
< 0.1%
0.28 1
 
< 0.1%
ValueCountFrequency (%)
65539.0 1
 
< 0.1%
82.85 1
 
< 0.1%
82.49 1
 
< 0.1%
80.73 1
 
< 0.1%
79.2847 4
< 0.1%
79.124 1
 
< 0.1%
77.4254 3
< 0.1%
73.9966 1
 
< 0.1%
73.2514 1
 
< 0.1%
72.52 1
 
< 0.1%

임대보증금
Real number (ℝ)

ZEROS 

Distinct4723
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21415995
Minimum0
Maximum4.64184 × 108
Zeros148
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:54:50.864228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2000000
Q15004000
median10010000
Q322390000
95-th percentile71016400
Maximum4.64184 × 108
Range4.64184 × 108
Interquartile range (IQR)17386000

Descriptive statistics

Standard deviation40315305
Coefficient of variation (CV)1.8824857
Kurtosis35.019273
Mean21415995
Median Absolute Deviation (MAD)6693000
Skewness5.3231635
Sum2.1415995 × 1011
Variance1.6253238 × 1015
MonotonicityNot monotonic
2024-03-15T02:54:51.117697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000000 345
 
3.5%
0 148
 
1.5%
19930000 24
 
0.2%
600000 22
 
0.2%
24660000 19
 
0.2%
18800000 19
 
0.2%
24380000 18
 
0.2%
3150000 17
 
0.2%
18990000 17
 
0.2%
19180000 17
 
0.2%
Other values (4713) 9354
93.5%
ValueCountFrequency (%)
0 148
1.5%
600000 22
 
0.2%
1000000 7
 
0.1%
1112000 2
 
< 0.1%
1253000 1
 
< 0.1%
1283000 1
 
< 0.1%
1315000 2
 
< 0.1%
1379000 1
 
< 0.1%
1417000 1
 
< 0.1%
1452000 4
 
< 0.1%
ValueCountFrequency (%)
464184000 1
< 0.1%
462840000 1
< 0.1%
445788000 1
< 0.1%
438400000 1
< 0.1%
408240000 1
< 0.1%
399000000 1
< 0.1%
397140000 1
< 0.1%
393600000 1
< 0.1%
390400000 2
< 0.1%
388080000 1
< 0.1%

월임대료
Real number (ℝ)

ZEROS 

Distinct6699
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218378.93
Minimum0
Maximum1020830
Zeros304
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:54:51.374838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35809.5
Q1119937.5
median198650
Q3290300
95-th percentile465405
Maximum1020830
Range1020830
Interquartile range (IQR)170362.5

Descriptive statistics

Standard deviation139531.41
Coefficient of variation (CV)0.63894172
Kurtosis2.376447
Mean218378.93
Median Absolute Deviation (MAD)84650
Skewness1.1641153
Sum2.1837893 × 109
Variance1.9469015 × 1010
MonotonicityNot monotonic
2024-03-15T02:54:51.839894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 304
 
3.0%
205400 16
 
0.2%
284300 15
 
0.1%
223900 14
 
0.1%
251100 14
 
0.1%
254100 14
 
0.1%
151800 13
 
0.1%
212900 13
 
0.1%
252200 13
 
0.1%
193700 13
 
0.1%
Other values (6689) 9571
95.7%
ValueCountFrequency (%)
0 304
3.0%
7700 1
 
< 0.1%
7800 1
 
< 0.1%
8200 1
 
< 0.1%
9500 1
 
< 0.1%
9730 1
 
< 0.1%
10300 1
 
< 0.1%
11050 1
 
< 0.1%
11670 1
 
< 0.1%
11870 1
 
< 0.1%
ValueCountFrequency (%)
1020830 1
< 0.1%
1016020 1
< 0.1%
1012710 1
< 0.1%
1007280 1
< 0.1%
998130 1
< 0.1%
998090 1
< 0.1%
953550 1
< 0.1%
925820 1
< 0.1%
891100 1
< 0.1%
889880 1
< 0.1%

전환보증금
Real number (ℝ)

SKEWED  ZEROS 

Distinct141
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean242120.69
Minimum0
Maximum2.0275 × 108
Zeros9844
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:54:52.261649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2.0275 × 108
Range2.0275 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4048908.6
Coefficient of variation (CV)16.722688
Kurtosis1305.6068
Mean242120.69
Median Absolute Deviation (MAD)0
Skewness33.420066
Sum2.4212069 × 109
Variance1.6393661 × 1013
MonotonicityNot monotonic
2024-03-15T02:54:52.733971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9844
98.4%
20154000 3
 
< 0.1%
3480000 2
 
< 0.1%
10000000 2
 
< 0.1%
4550000 2
 
< 0.1%
6260000 2
 
< 0.1%
30170000 2
 
< 0.1%
12400000 2
 
< 0.1%
6200000 2
 
< 0.1%
5020000 2
 
< 0.1%
Other values (131) 137
 
1.4%
ValueCountFrequency (%)
0 9844
98.4%
1770000 1
 
< 0.1%
1830000 1
 
< 0.1%
1890000 1
 
< 0.1%
2420000 1
 
< 0.1%
2660000 1
 
< 0.1%
2700000 1
 
< 0.1%
2740000 1
 
< 0.1%
2780000 1
 
< 0.1%
2891760 1
 
< 0.1%
ValueCountFrequency (%)
202750000 1
< 0.1%
148500000 1
< 0.1%
142000000 1
< 0.1%
138000000 1
< 0.1%
135400000 1
< 0.1%
119077000 1
< 0.1%
78150000 1
< 0.1%
48720000 1
< 0.1%
37140000 1
< 0.1%
31600000 1
< 0.1%

Interactions

2024-03-15T02:54:34.063571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:22.964310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:24.433816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:26.714547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:28.936259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:30.978317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:32.319393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:34.296973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:23.305992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:24.688231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:27.080608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:29.215908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:31.150447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:32.594763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:34.477500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:23.527003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:25.009982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:27.421925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:29.498747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:31.360255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:32.865942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:34.655022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:23.712380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:25.287680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:27.741347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:29.755239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:31.511537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:33.123505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:34.888671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:23.896135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:25.574779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:28.018576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:30.023463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:31.731902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:33.407114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:35.159528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:24.067845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:25.868577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:28.382987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:30.284959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:31.911984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:33.577140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:35.445472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:24.244030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:26.341482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:28.651560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:30.576698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:32.073387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:54:33.757296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T02:54:53.033006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
광역시도시군구세대수주택유형임대사업자주차수공급면적(전용)공급면적(공용)임대보증금월임대료전환보증금
광역시도1.0000.9780.1080.5880.9900.1590.3000.0170.2410.2760.101
시군구0.9781.0000.6910.7270.9420.1590.4110.1510.4150.4780.362
세대수0.1080.6911.0000.4830.1500.0000.1100.0000.1890.2540.000
주택유형0.5880.7270.4831.0000.7310.0000.2920.0530.5520.4220.096
임대사업자0.9900.9420.1500.7311.0000.1230.3180.0000.2980.3920.300
주차수0.1590.1590.0000.0000.1231.0000.0450.0000.1450.0130.000
공급면적(전용)0.3000.4110.1100.2920.3180.0451.0000.0150.3080.3350.000
공급면적(공용)0.0170.1510.0000.0530.0000.0000.0151.0000.0830.0000.000
임대보증금0.2410.4150.1890.5520.2980.1450.3080.0831.0000.4720.131
월임대료0.2760.4780.2540.4220.3920.0130.3350.0000.4721.0000.000
전환보증금0.1010.3620.0000.0960.3000.0000.0000.0000.1310.0001.000
2024-03-15T02:54:53.412470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
임대사업자광역시도주택유형
임대사업자1.0000.9390.448
광역시도0.9391.0000.339
주택유형0.4480.3391.000
2024-03-15T02:54:53.659218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수주차수공급면적(전용)공급면적(공용)임대보증금월임대료전환보증금광역시도주택유형임대사업자
세대수1.0000.003-0.352-0.0580.1560.231-0.0920.0530.2930.065
주차수0.0031.000-0.0010.0650.050-0.075-0.0070.0560.0000.055
공급면적(전용)-0.352-0.0011.0000.3190.2890.274-0.0060.1410.1660.124
공급면적(공용)-0.0580.0650.3191.000-0.0620.1830.0090.0170.0380.000
임대보증금0.1560.0500.289-0.0621.0000.433-0.1340.1110.3300.116
월임대료0.231-0.0750.2740.1830.4331.000-0.1440.1290.2380.157
전환보증금-0.092-0.007-0.0060.009-0.134-0.1441.0000.0500.0530.133
광역시도0.0530.0560.1410.0170.1110.1290.0501.0000.3390.939
주택유형0.2930.0000.1660.0380.3300.2380.0530.3391.0000.448
임대사업자0.0650.0550.1240.0000.1160.1570.1330.9390.4481.000

Missing values

2024-03-15T02:54:35.869598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:54:36.632807image/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

임대종류광역시도시군구도로명주소단지명세대수주택유형임대사업자주차수형명공급면적(전용)공급면적(공용)임대보증금월임대료전환보증금
42705매입임대서울특별시강동구서울특별시 강동구 양재대로95길 27-6서울특별시 강동구10다가구주택LH서울02121.962.231529000600100
49344매입임대부산광역시연제구부산광역시 연제구 해맞이로109번길 25부산광역시 연제구12<NA>LH부산울산03232.0311.8575340002564600
64470매입임대광주광역시남구광주광역시 남구 진다리로39번길 12진다리로39번길1212다가구주택광주광역시도시공사0NaN45.35.8740000001330500
16187매입임대서울특별시은평구서울특별시 은평구 연서로37가길 10-10서울특별시 은평구5다가구주택LH서울05050.478.863820001648500
37333매입임대서울특별시송파구서울특별시 송파구 동남로28길 28더존에이스빌12다세대주택SH공사050124.980.0164400002142000
10575매입임대서울특별시강북구서울특별시 강북구 인수봉로81길 28서울특별시 강북구8다세대주택LH서울05757.0410.8261790005693300
37575매입임대서울특별시송파구서울특별시 송파구 마천로27길 5반야빌3차18다세대주택SH공사030539.650.0284400003705000
2601매입임대서울특별시동대문구서울특별시 동대문구 답십리로65길 49그레이스빌15다세대주택SH공사040128.560.0123700001612000
75569매입임대경기도수원시 권선구경기도 수원시 권선구 세지로66번길 25-18경기도 수원시10다세대주택LH경기남부049.049.239.0758880003200400
2434매입임대서울특별시동대문구서울특별시 동대문구 경동시장로10길 6주함해븐빌 101동10다세대주택SH공사050256.170.0357300002976000
임대종류광역시도시군구도로명주소단지명세대수주택유형임대사업자주차수형명공급면적(전용)공급면적(공용)임대보증금월임대료전환보증금
15209매입임대서울특별시은평구서울특별시 은평구 갈현로33가길 5-13현영4차10다세대주택SH공사030244.360.0139000001158000
26774매입임대서울특별시구로구서울특별시 구로구 오리로22나길 16-24서울특별시 구로구12다세대주택LH서울07070.318.4278750004165600
18117매입임대서울특별시서대문구서울특별시 서대문구 증가로12가길 66엠제이빌10다세대주택SH공사030257.50.0231600001929000
35039매입임대서울특별시서초구서울특별시 서초구 강남대로95길 19-16서울특별시 서초구10다가구주택LH서울02121.064.1124680001870600
41072매입임대서울특별시강동구서울특별시 강동구 명일로 232(길동 99)16다세대주택SH공사050135.970.0403200002251000
32498매입임대서울특별시동작구서울특별시 동작구 대방동길 33더완우빌20다세대주택SH공사050424.665.29250400002579000
24772매입임대서울특별시구로구서울특별시 구로구 경인로33나길 15-9경인로33나길15-912다세대주택SH공사040247.30811.708323800003336000
31333매입임대서울특별시영등포구서울특별시 영등포구 대림로27나길 17아이유하임A12다세대주택SH공사050125.767.45213500002200000
72052매입임대울산광역시남구울산광역시 남구 돋질로198번길 9-1울산광역시 남구84다가구주택LH부산울산037.037.7611.799751020001607700
49296매입임대부산광역시연제구부산광역시 연제구 토곡남로20번길 17부산광역시 연제구3다가구주택LH부산울산03939.660.04090000423000

Duplicate rows

Most frequently occurring

임대종류광역시도시군구도로명주소단지명세대수주택유형임대사업자주차수공급면적(전용)공급면적(공용)임대보증금월임대료전환보증금# duplicates
122매입임대서울특별시금천구서울특별시 금천구 시흥대로57길 5웨스트밸리175아파트SH공사026.210.014740000151800012
82매입임대서울특별시금천구서울특별시 금천구 가산로9길 109가산로9길 10964오피스텔SH공사042.190.068600000706900011
103매입임대서울특별시금천구서울특별시 금천구 범안로11길 60신영오피스텔168오피스텔SH공사026.5222.061927000019860009
200매입임대서울특별시성동구서울특별시 성동구 자동차시장3길 93와이하우스349오피스텔SH공사030.45535.43412466000025410008
97매입임대서울특별시금천구서울특별시 금천구 두산로9길 23아이유하임133다세대주택SH공사026.230.01918000019760007
203매입임대서울특별시성동구서울특별시 성동구 자동차시장3길 93와이하우스349오피스텔SH공사030.45535.43412551000026290006
33매입임대서울특별시강서구서울특별시 강서구 공항대로75길 47염창동도시형생활주택주건축물1동60다세대주택SH공사016.970.0174500009760005
52매입임대서울특별시구로구서울특별시 구로구 경인로 70계림웨스트밸리149다세대주택SH공사049.9736.142759000028430005
110매입임대서울특별시금천구서울특별시 금천구 범안로21길 28독산헤리츠타워126오피스텔SH공사046.540.06652000068550005
241매입임대서울특별시영등포구서울특별시 영등포구 영등포로 244로프트시티173오피스텔SH공사020.0713.611946000020050005