Overview

Dataset statistics

Number of variables8
Number of observations101
Missing cells101
Missing cells (%)12.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory67.3 B

Variable types

Numeric2
Categorical3
Text3

Dataset

Description서울특별시 중랑구 기계설비 성능점검 대상 건축물 현황입니다. 건축물명, 주소, 연면적, 세대수, 용도 등의 정보를 포함하고 있습니다.
Author서울특별시 중랑구
URLhttps://www.data.go.kr/data/15124631/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
연번 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
세대수 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
용도 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연면적(제곱미터) has 39 (38.6%) missing valuesMissing
세대수 has 62 (61.4%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:10:09.623486
Analysis finished2023-12-12 05:10:11.338599
Duration1.72 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct101
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51
Minimum1
Maximum101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:10:11.444635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q126
median51
Q376
95-th percentile96
Maximum101
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation29.300171
Coefficient of variation (CV)0.57451315
Kurtosis-1.2
Mean51
Median Absolute Deviation (MAD)25
Skewness0
Sum5151
Variance858.5
MonotonicityStrictly increasing
2023-12-12T14:10:11.617788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (91) 91
90.1%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
101 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
서울특별시
101 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 101
100.0%

Length

2023-12-12T14:10:11.757583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:10:11.867385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 101
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
중랑구
101 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중랑구 101
100.0%

Length

2023-12-12T14:10:12.014531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:10:12.227061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중랑구 101
100.0%

건축물명
Text

Distinct97
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2023-12-12T14:10:12.425065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length7.7326733
Min length2

Characters and Unicode

Total characters781
Distinct characters196
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)95.0%

Sample

1st row코스트코상봉점
2nd row금란교회
3rd rowE-MART 상봉점
4th row홈플러스 상봉점
5th row상봉 프레미어스 엠코 비주거동
ValueCountFrequency (%)
신내아파트 5
 
3.8%
신내 3
 
2.3%
상봉 2
 
1.5%
한일써너스빌리젠시 2
 
1.5%
서울특별시 2
 
1.5%
홈플러스 2
 
1.5%
상봉점 2
 
1.5%
서울중랑경찰서 1
 
0.8%
원묵중학교 1
 
0.8%
중랑중학교 1
 
0.8%
Other values (110) 110
84.0%
2023-12-12T14:10:12.886746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 34
 
4.4%
학 33
 
4.2%
신 31
 
4.0%
30
 
3.8%
내 24
 
3.1%
중 23
 
2.9%
등 20
 
2.6%
트 17
 
2.2%
울 16
 
2.0%
스 16
 
2.0%
Other values (186) 537
68.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 688
88.1%
Space Separator 30
 
3.8%
Uppercase Letter 22
 
2.8%
Decimal Number 17
 
2.2%
Open Punctuation 6
 
0.8%
Close Punctuation 6
 
0.8%
Lowercase Letter 6
 
0.8%
Other Punctuation 5
 
0.6%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 34
 
4.9%
학 33
 
4.8%
신 31
 
4.5%
내 24
 
3.5%
중 23
 
3.3%
등 20
 
2.9%
트 17
 
2.5%
울 16
 
2.3%
스 16
 
2.3%
아 15
 
2.2%
Other values (154) 459
66.7%
Uppercase Letter
ValueCountFrequency (%)
T 3
13.6%
L 3
13.6%
E 3
13.6%
S 2
 
9.1%
H 1
 
4.5%
G 1
 
4.5%
V 1
 
4.5%
K 1
 
4.5%
R 1
 
4.5%
A 1
 
4.5%
Other values (5) 5
22.7%
Decimal Number
ValueCountFrequency (%)
1 8
47.1%
2 4
23.5%
4 2
 
11.8%
3 1
 
5.9%
5 1
 
5.9%
9 1
 
5.9%
Lowercase Letter
ValueCountFrequency (%)
e 2
33.3%
c 1
16.7%
r 1
16.7%
t 1
16.7%
n 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
. 1
 
20.0%
Space Separator
ValueCountFrequency (%)
30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 688
88.1%
Common 65
 
8.3%
Latin 28
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 34
 
4.9%
학 33
 
4.8%
신 31
 
4.5%
내 24
 
3.5%
중 23
 
3.3%
등 20
 
2.9%
트 17
 
2.5%
울 16
 
2.3%
스 16
 
2.3%
아 15
 
2.2%
Other values (154) 459
66.7%
Latin
ValueCountFrequency (%)
T 3
 
10.7%
L 3
 
10.7%
E 3
 
10.7%
S 2
 
7.1%
e 2
 
7.1%
H 1
 
3.6%
G 1
 
3.6%
c 1
 
3.6%
r 1
 
3.6%
t 1
 
3.6%
Other values (10) 10
35.7%
Common
ValueCountFrequency (%)
30
46.2%
1 8
 
12.3%
( 6
 
9.2%
) 6
 
9.2%
2 4
 
6.2%
, 4
 
6.2%
4 2
 
3.1%
3 1
 
1.5%
. 1
 
1.5%
5 1
 
1.5%
Other values (2) 2
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 687
88.0%
ASCII 93
 
11.9%
Compat Jamo 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 34
 
4.9%
학 33
 
4.8%
신 31
 
4.5%
내 24
 
3.5%
중 23
 
3.3%
등 20
 
2.9%
트 17
 
2.5%
울 16
 
2.3%
스 16
 
2.3%
아 15
 
2.2%
Other values (153) 458
66.7%
ASCII
ValueCountFrequency (%)
30
32.3%
1 8
 
8.6%
( 6
 
6.5%
) 6
 
6.5%
2 4
 
4.3%
, 4
 
4.3%
T 3
 
3.2%
L 3
 
3.2%
E 3
 
3.2%
S 2
 
2.2%
Other values (22) 24
25.8%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

주소
Text

Distinct99
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2023-12-12T14:10:13.283046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length18.80198
Min length11

Characters and Unicode

Total characters1899
Distinct characters59
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)96.0%

Sample

1st row서울특별시 중랑구 망우로 336
2nd row서울특별시 중랑구 망우로 455
3rd row서울특별시 중랑구 상봉로 118
4th row서울특별시 중랑구 망우로 353
5th row서울특별시 중랑구 망우로 353
ValueCountFrequency (%)
중랑구 102
24.6%
서울특별시 88
21.2%
신내로 11
 
2.7%
봉화산로 9
 
2.2%
양원역로 7
 
1.7%
용마산로 7
 
1.7%
동일로 6
 
1.4%
망우로 6
 
1.4%
상봉로 4
 
1.0%
면목동 4
 
1.0%
Other values (144) 171
41.2%
2023-12-12T14:10:13.861990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
318
16.7%
중 107
 
5.6%
랑 103
 
5.4%
구 102
 
5.4%
로 101
 
5.3%
서 88
 
4.6%
특 88
 
4.6%
별 88
 
4.6%
시 88
 
4.6%
울 88
 
4.6%
Other values (49) 728
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1197
63.0%
Decimal Number 352
 
18.5%
Space Separator 318
 
16.7%
Open Punctuation 13
 
0.7%
Close Punctuation 13
 
0.7%
Dash Punctuation 5
 
0.3%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 107
 
8.9%
랑 103
 
8.6%
구 102
 
8.5%
로 101
 
8.4%
서 88
 
7.4%
특 88
 
7.4%
별 88
 
7.4%
시 88
 
7.4%
울 88
 
7.4%
길 44
 
3.7%
Other values (34) 300
25.1%
Decimal Number
ValueCountFrequency (%)
1 73
20.7%
3 43
12.2%
2 42
11.9%
5 36
10.2%
6 34
9.7%
7 29
 
8.2%
0 27
 
7.7%
4 26
 
7.4%
9 23
 
6.5%
8 19
 
5.4%
Space Separator
ValueCountFrequency (%)
318
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1197
63.0%
Common 702
37.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 107
 
8.9%
랑 103
 
8.6%
구 102
 
8.5%
로 101
 
8.4%
서 88
 
7.4%
특 88
 
7.4%
별 88
 
7.4%
시 88
 
7.4%
울 88
 
7.4%
길 44
 
3.7%
Other values (34) 300
25.1%
Common
ValueCountFrequency (%)
318
45.3%
1 73
 
10.4%
3 43
 
6.1%
2 42
 
6.0%
5 36
 
5.1%
6 34
 
4.8%
7 29
 
4.1%
0 27
 
3.8%
4 26
 
3.7%
9 23
 
3.3%
Other values (5) 51
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1197
63.0%
ASCII 702
37.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
318
45.3%
1 73
 
10.4%
3 43
 
6.1%
2 42
 
6.0%
5 36
 
5.1%
6 34
 
4.8%
7 29
 
4.1%
0 27
 
3.8%
4 26
 
3.7%
9 23
 
3.3%
Other values (5) 51
 
7.3%
Hangul
ValueCountFrequency (%)
중 107
 
8.9%
랑 103
 
8.6%
구 102
 
8.5%
로 101
 
8.4%
서 88
 
7.4%
특 88
 
7.4%
별 88
 
7.4%
시 88
 
7.4%
울 88
 
7.4%
길 44
 
3.7%
Other values (34) 300
25.1%

연면적(제곱미터)
Text

MISSING 

Distinct62
Distinct (%)100.0%
Missing39
Missing (%)38.6%
Memory size940.0 B
2023-12-12T14:10:14.224077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length7.7580645
Min length5

Characters and Unicode

Total characters481
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)100.0%

Sample

1st row33995.44
2nd row41295.56
3rd row39537.67
4th row60964
5th row65064.8
ValueCountFrequency (%)
15637.67 1
 
1.6%
13930.14 1
 
1.6%
21530 1
 
1.6%
15352.59 1
 
1.6%
17155.99 1
 
1.6%
16165.28 1
 
1.6%
11314.26 1
 
1.6%
12937.6 1
 
1.6%
11891.22 1
 
1.6%
11929.01 1
 
1.6%
Other values (52) 52
83.9%
2023-12-12T14:10:14.754651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 90
18.7%
. 59
12.3%
2 48
10.0%
3 46
9.6%
9 43
8.9%
4 40
8.3%
0 37
7.7%
5 33
 
6.9%
6 31
 
6.4%
7 28
 
5.8%
Other values (2) 26
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 421
87.5%
Other Punctuation 59
 
12.3%
Space Separator 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 90
21.4%
2 48
11.4%
3 46
10.9%
9 43
10.2%
4 40
9.5%
0 37
8.8%
5 33
 
7.8%
6 31
 
7.4%
7 28
 
6.7%
8 25
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 59
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 481
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 90
18.7%
. 59
12.3%
2 48
10.0%
3 46
9.6%
9 43
8.9%
4 40
8.3%
0 37
7.7%
5 33
 
6.9%
6 31
 
6.4%
7 28
 
5.8%
Other values (2) 26
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 481
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 90
18.7%
. 59
12.3%
2 48
10.0%
3 46
9.6%
9 43
8.9%
4 40
8.3%
0 37
7.7%
5 33
 
6.9%
6 31
 
6.4%
7 28
 
5.8%
Other values (2) 26
 
5.4%

세대수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)100.0%
Missing62
Missing (%)61.4%
Infinite0
Infinite (%)0.0%
Mean901.94872
Minimum331
Maximum1896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T14:10:14.903172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum331
5-th percentile396.2
Q1577.5
median804
Q31230
95-th percentile1550.5
Maximum1896
Range1565
Interquartile range (IQR)652.5

Descriptive statistics

Standard deviation400.51834
Coefficient of variation (CV)0.44405889
Kurtosis-0.54749673
Mean901.94872
Median Absolute Deviation (MAD)266
Skewness0.6119225
Sum35176
Variance160414.94
MonotonicityNot monotonic
2023-12-12T14:10:15.056828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1113 1
 
1.0%
719 1
 
1.0%
704 1
 
1.0%
686 1
 
1.0%
626 1
 
1.0%
601 1
 
1.0%
587 1
 
1.0%
582 1
 
1.0%
573 1
 
1.0%
565 1
 
1.0%
Other values (29) 29
28.7%
(Missing) 62
61.4%
ValueCountFrequency (%)
331 1
1.0%
335 1
1.0%
403 1
1.0%
478 1
1.0%
490 1
1.0%
504 1
1.0%
532 1
1.0%
555 1
1.0%
565 1
1.0%
573 1
1.0%
ValueCountFrequency (%)
1896 1
1.0%
1609 1
1.0%
1544 1
1.0%
1505 1
1.0%
1432 1
1.0%
1402 1
1.0%
1362 1
1.0%
1326 1
1.0%
1315 1
1.0%
1244 1
1.0%

용도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
건축물
62 
공동주택
39 

Length

Max length4
Median length3
Mean length3.3861386
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건축물 62
61.4%
공동주택 39
38.6%

Length

2023-12-12T14:10:15.227875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:10:15.676468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건축물 62
61.4%
공동주택 39
38.6%

Interactions

2023-12-12T14:10:10.793918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:10.602318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:10.904769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:10:10.690959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:10:15.773947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번건축물명주소연면적(제곱미터)세대수용도
연번1.0000.9491.0001.0000.8640.951
건축물명0.9491.0000.9921.0000.0001.000
주소1.0000.9921.0001.0001.0001.000
연면적(제곱미터)1.0001.0001.0001.000NaNNaN
세대수0.8640.0001.000NaN1.000NaN
용도0.9511.0001.000NaNNaN1.000
2023-12-12T14:10:15.945970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세대수용도
연번1.000-0.9230.814
세대수-0.9231.0001.000
용도0.8141.0001.000

Missing values

2023-12-12T14:10:11.034022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:10:11.164409image/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.
2023-12-12T14:10:11.275213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번시도명시군구명건축물명주소연면적(제곱미터)세대수용도
01서울특별시중랑구코스트코상봉점서울특별시 중랑구 망우로 33633995.44<NA>건축물
12서울특별시중랑구금란교회서울특별시 중랑구 망우로 45541295.56<NA>건축물
23서울특별시중랑구E-MART 상봉점서울특별시 중랑구 상봉로 11839537.67<NA>건축물
34서울특별시중랑구홈플러스 상봉점서울특별시 중랑구 망우로 35360964<NA>건축물
45서울특별시중랑구상봉 프레미어스 엠코 비주거동서울특별시 중랑구 망우로 35365064.8<NA>건축물
56서울특별시중랑구신아타운서울특별시 중랑구 봉화산로 19431128.63<NA>건축물
67서울특별시중랑구신내 데시앙플렉스 지식산업센터서울특별시 중랑구 신내역로3길 40-3683043.47<NA>건축물
78서울특별시중랑구신내 SK V1 center서울특별시 중랑구 신내역로 11199871.25<NA>건축물
89서울특별시중랑구상봉 듀오트리스서울특별시 중랑구 상봉로 13199839.19<NA>건축물
910서울특별시중랑구중곡초등학교서울특별시 중랑구 면목로23 2034500.47<NA>건축물
연번시도명시군구명건축물명주소연면적(제곱미터)세대수용도
9192서울특별시중랑구늘푸른동아서울특별시 중랑구 동일로 476<NA>573공동주택
9293서울특별시중랑구용마한신1차서울특별시 중랑구 사가정로 71길 19<NA>565공동주택
9394서울특별시중랑구면목두산(4.5단지)서울특별시 중랑구 사가정로 42길 90<NA>555공동주택
9495서울특별시중랑구신내새한서울특별시 중랑구 용마산로 616<NA>532공동주택
9596서울특별시중랑구한일써너스빌서울특별시 중랑구 망우로 346<NA>504공동주택
9697서울특별시중랑구신내역 금강펜테리움 센트럴파크서울특별시 중랑구 용마산로136길 160<NA>490공동주택
9798서울특별시중랑구신내동성4차서울특별시 중랑구 봉화산로 216<NA>478공동주택
9899서울특별시중랑구중랑숲 시티프라디움(엘에이치 엘스타시온)서울특별시 중랑구 양원역로10길 47<NA>403공동주택
99100서울특별시중랑구신내9단지서울특별시 중랑구 신내로 135<NA>335공동주택
100101서울특별시중랑구양원역금호어울림포레스트서울특별시 중랑구 양원역로10길 75<NA>331공동주택