Overview

Dataset statistics

Number of variables16
Number of observations23
Missing cells29
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory138.7 B

Variable types

Numeric5
Text8
Categorical2
DateTime1

Dataset

Description한국주택금융공사 채권관리부 업무 관련 공개 데이터 (해당 부서의 업무와 관련된 데이터베이스에서 공개 가능한 원천 데이터) 주택ID,법정동코드,담보소재지우편번호주소,번지수,번지구분코드,본번주소,부번주소,건물명,설정일자,우편번호,우편번호주소,시도,시군구,읍면,도로명칭,건물거리이름 정보가 포함된 데이터 입니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073025/fileData.do

Alerts

번지구분코드 has constant value ""Constant
주택ID is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
법정동코드 is highly overall correlated with 주택ID and 2 other fieldsHigh correlation
본번주소 is highly overall correlated with 법정동코드 and 2 other fieldsHigh correlation
부번주소 is highly overall correlated with 본번주소High correlation
우편번호 is highly overall correlated with 시도High correlation
시도 is highly overall correlated with 주택ID and 3 other fieldsHigh correlation
부번주소 has 13 (56.5%) missing valuesMissing
우편번호 has 2 (8.7%) missing valuesMissing
우편번호주소 has 2 (8.7%) missing valuesMissing
시군구 has 2 (8.7%) missing valuesMissing
읍면 has 4 (17.4%) missing valuesMissing
도로명칭 has 2 (8.7%) missing valuesMissing
건물거리이름 has 4 (17.4%) missing valuesMissing
주택ID has unique valuesUnique
번지수 has unique valuesUnique
건물명 has unique valuesUnique
설정일자 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:40:54.506958
Analysis finished2023-12-12 20:40:59.350002
Duration4.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

주택ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266092
Minimum110001
Maximum450003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T05:40:59.437573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110001
5-th percentile110002.1
Q1110006.5
median280001
Q3410006.5
95-th percentile450001.9
Maximum450003
Range340002
Interquartile range (IQR)300000

Descriptive statistics

Standard deviation155992.69
Coefficient of variation (CV)0.58623593
Kurtosis-2.1059561
Mean266092
Median Absolute Deviation (MAD)169992
Skewness0.0089038852
Sum6120116
Variance2.433372 × 1010
MonotonicityNot monotonic
2023-12-13T05:40:59.584304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
410009 1
 
4.3%
410008 1
 
4.3%
410004 1
 
4.3%
110009 1
 
4.3%
110001 1
 
4.3%
110002 1
 
4.3%
110003 1
 
4.3%
110004 1
 
4.3%
110005 1
 
4.3%
110006 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
110001 1
4.3%
110002 1
4.3%
110003 1
4.3%
110004 1
4.3%
110005 1
4.3%
110006 1
4.3%
110007 1
4.3%
110008 1
4.3%
110009 1
4.3%
110010 1
4.3%
ValueCountFrequency (%)
450003 1
4.3%
450002 1
4.3%
450001 1
4.3%
410009 1
4.3%
410008 1
4.3%
410007 1
4.3%
410006 1
4.3%
410005 1
4.3%
410004 1
4.3%
410003 1
4.3%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6953341 × 109
Minimum1.1110187 × 109
Maximum4.579025 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T05:40:59.730388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110187 × 109
5-th percentile1.1155164 × 109
Q11.1470114 × 109
median2.8260113 × 109
Q34.129261 × 109
95-th percentile4.5173214 × 109
Maximum4.579025 × 109
Range3.4680063 × 109
Interquartile range (IQR)2.9822496 × 109

Descriptive statistics

Standard deviation1.5536256 × 109
Coefficient of variation (CV)0.57641301
Kurtosis-2.1022988
Mean2.6953341 × 109
Median Absolute Deviation (MAD)1.6760011 × 109
Skewness0.011168248
Sum6.1992685 × 1010
Variance2.4137526 × 1018
MonotonicityNot monotonic
2023-12-13T05:40:59.869404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1150010200 3
 
13.0%
1129013500 2
 
8.7%
4146311600 1
 
4.3%
4113511400 1
 
4.3%
4113511100 1
 
4.3%
1174010600 1
 
4.3%
1138010200 1
 
4.3%
1111018700 1
 
4.3%
1114016700 1
 
4.3%
2826011300 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
1111018700 1
 
4.3%
1114016700 1
 
4.3%
1129013500 2
8.7%
1138010200 1
 
4.3%
1144012700 1
 
4.3%
1150010200 3
13.0%
1168011200 1
 
4.3%
1174010600 1
 
4.3%
2826011300 1
 
4.3%
4111710200 1
 
4.3%
ValueCountFrequency (%)
4579025025 1
4.3%
4518011200 1
4.3%
4511113500 1
4.3%
4146311600 1
4.3%
4146110400 1
4.3%
4145010600 1
4.3%
4113511400 1
4.3%
4113511100 1
4.3%
4113510300 1
4.3%
4111710300 1
4.3%

담보소재지우편번호주소
Text

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-13T05:41:00.123591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length13.521739
Min length11

Characters and Unicode

Total characters311
Distinct characters66
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

Unique18 ?
Unique (%)78.3%

Sample

1st row경기도 용인시 기흥구 중동
2nd row경기도 수원시 영통구 원천동
3rd row전라북도 고창군 고창읍 석정리
4th row경기도 수원시 영통구 이의동
5th row서울특별시 강남구 자곡동
ValueCountFrequency (%)
서울특별시 11
 
14.1%
경기도 8
 
10.3%
등촌동 3
 
3.8%
강서구 3
 
3.8%
분당구 3
 
3.8%
전라북도 3
 
3.8%
성남시 3
 
3.8%
영통구 2
 
2.6%
수원시 2
 
2.6%
용인시 2
 
2.6%
Other values (36) 38
48.7%
2023-12-13T05:41:00.517212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55
17.7%
동 23
 
7.4%
시 22
 
7.1%
구 21
 
6.8%
서 15
 
4.8%
도 11
 
3.5%
특 11
 
3.5%
별 11
 
3.5%
울 11
 
3.5%
기 9
 
2.9%
Other values (56) 122
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 256
82.3%
Space Separator 55
 
17.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
 
9.0%
시 22
 
8.6%
구 21
 
8.2%
서 15
 
5.9%
도 11
 
4.3%
특 11
 
4.3%
별 11
 
4.3%
울 11
 
4.3%
기 9
 
3.5%
경 8
 
3.1%
Other values (55) 114
44.5%
Space Separator
ValueCountFrequency (%)
55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 256
82.3%
Common 55
 
17.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
 
9.0%
시 22
 
8.6%
구 21
 
8.2%
서 15
 
5.9%
도 11
 
4.3%
특 11
 
4.3%
별 11
 
4.3%
울 11
 
4.3%
기 9
 
3.5%
경 8
 
3.1%
Other values (55) 114
44.5%
Common
ValueCountFrequency (%)
55
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 256
82.3%
ASCII 55
 
17.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
55
100.0%
Hangul
ValueCountFrequency (%)
동 23
 
9.0%
시 22
 
8.6%
구 21
 
8.2%
서 15
 
5.9%
도 11
 
4.3%
특 11
 
4.3%
별 11
 
4.3%
울 11
 
4.3%
기 9
 
3.5%
경 8
 
3.1%
Other values (55) 114
44.5%

번지수
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-13T05:41:00.762924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.1304348
Min length5

Characters and Unicode

Total characters141
Distinct characters18
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

Unique23 ?
Unique (%)100.0%

Sample

1st row1147외1필
2nd row556번지
3rd row830번지
4th row1358번지
5th row631번지
ValueCountFrequency (%)
1147외1필 1
 
4.3%
당하지구49 1
 
4.3%
3-1507번지 1
 
4.3%
79-23번지 1
 
4.3%
669-1번지 1
 
4.3%
717-번지 1
 
4.3%
637-번지 1
 
4.3%
3-91번지 1
 
4.3%
91-7번지 1
 
4.3%
66-3번지 1
 
4.3%
Other values (13) 13
56.5%
2023-12-13T05:41:01.208224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 22
15.6%
번 21
14.9%
1 16
11.3%
- 13
9.2%
6 11
7.8%
3 10
7.1%
7 9
6.4%
9 8
 
5.7%
5 8
 
5.7%
0 6
 
4.3%
Other values (8) 17
12.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
56.7%
Other Letter 48
34.0%
Dash Punctuation 13
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16
20.0%
6 11
13.8%
3 10
12.5%
7 9
11.2%
9 8
10.0%
5 8
10.0%
0 6
 
7.5%
2 5
 
6.2%
8 4
 
5.0%
4 3
 
3.8%
Other Letter
ValueCountFrequency (%)
지 22
45.8%
번 21
43.8%
필 1
 
2.1%
외 1
 
2.1%
당 1
 
2.1%
하 1
 
2.1%
구 1
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 93
66.0%
Hangul 48
34.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16
17.2%
- 13
14.0%
6 11
11.8%
3 10
10.8%
7 9
9.7%
9 8
8.6%
5 8
8.6%
0 6
 
6.5%
2 5
 
5.4%
8 4
 
4.3%
Hangul
ValueCountFrequency (%)
지 22
45.8%
번 21
43.8%
필 1
 
2.1%
외 1
 
2.1%
당 1
 
2.1%
하 1
 
2.1%
구 1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93
66.0%
Hangul 48
34.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 22
45.8%
번 21
43.8%
필 1
 
2.1%
외 1
 
2.1%
당 1
 
2.1%
하 1
 
2.1%
구 1
 
2.1%
ASCII
ValueCountFrequency (%)
1 16
17.2%
- 13
14.0%
6 11
11.8%
3 10
10.8%
7 9
9.7%
9 8
8.6%
5 8
8.6%
0 6
 
6.5%
2 5
 
5.4%
8 4
 
4.3%

번지구분코드
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
일반번지
23 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반번지 23
100.0%

Length

2023-12-13T05:41:01.395580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:41:01.525067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반번지 23
100.0%

본번주소
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean561.69565
Minimum3
Maximum1641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T05:41:01.664492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4.2
Q185
median556
Q3773.5
95-th percentile1584.8
Maximum1641
Range1638
Interquartile range (IQR)688.5

Descriptive statistics

Standard deviation505.70719
Coefficient of variation (CV)0.90032243
Kurtosis-0.12692939
Mean561.69565
Median Absolute Deviation (MAD)350
Skewness0.81441731
Sum12919
Variance255739.77
MonotonicityNot monotonic
2023-12-13T05:41:01.835854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 2
 
8.7%
1147 1
 
4.3%
49 1
 
4.3%
305 1
 
4.3%
79 1
 
4.3%
669 1
 
4.3%
717 1
 
4.3%
637 1
 
4.3%
91 1
 
4.3%
66 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
3 2
8.7%
15 1
4.3%
49 1
4.3%
66 1
4.3%
79 1
4.3%
91 1
4.3%
209 1
4.3%
297 1
4.3%
305 1
4.3%
517 1
4.3%
ValueCountFrequency (%)
1641 1
4.3%
1610 1
4.3%
1358 1
4.3%
1147 1
4.3%
906 1
4.3%
830 1
4.3%
717 1
4.3%
669 1
4.3%
637 1
4.3%
631 1
4.3%

부번주소
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)90.0%
Missing13
Missing (%)56.5%
Infinite0
Infinite (%)0.0%
Mean165
Minimum1
Maximum1507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T05:41:01.972760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.45
Q12.25
median6.5
Q319.25
95-th percentile869.8
Maximum1507
Range1506
Interquartile range (IQR)17

Descriptive statistics

Standard deviation472.32004
Coefficient of variation (CV)2.8625457
Kurtosis9.9073765
Mean165
Median Absolute Deviation (MAD)4.5
Skewness3.1430389
Sum1650
Variance223086.22
MonotonicityNot monotonic
2023-12-13T05:41:02.092002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 2
 
8.7%
8 1
 
4.3%
6 1
 
4.3%
3 1
 
4.3%
7 1
 
4.3%
91 1
 
4.3%
1 1
 
4.3%
23 1
 
4.3%
1507 1
 
4.3%
(Missing) 13
56.5%
ValueCountFrequency (%)
1 1
4.3%
2 2
8.7%
3 1
4.3%
6 1
4.3%
7 1
4.3%
8 1
4.3%
23 1
4.3%
91 1
4.3%
1507 1
4.3%
ValueCountFrequency (%)
1507 1
4.3%
91 1
4.3%
23 1
4.3%
8 1
4.3%
7 1
4.3%
6 1
4.3%
3 1
4.3%
2 2
8.7%
1 1
4.3%

건물명
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-13T05:41:02.317098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length8.4782609
Min length5

Characters and Unicode

Total characters195
Distinct characters96
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

Unique23 ?
Unique (%)100.0%

Sample

1st row스프링카운티자이
2nd row광교 두산위브
3rd row서울시니어스고창타워
4th row광교아르데코
5th row서울시니어스강남타워
ValueCountFrequency (%)
시니어스 2
 
6.1%
더 2
 
6.1%
스프링카운티자이 1
 
3.0%
노블레스타워2 1
 
3.0%
둔촌동후성누리움 1
 
3.0%
서울시니어스강서타워 1
 
3.0%
그레이스힐 1
 
3.0%
가양타워 1
 
3.0%
서울 1
 
3.0%
노인복지주택 1
 
3.0%
Other values (21) 21
63.6%
2023-12-13T05:41:02.650042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 12
 
6.2%
10
 
5.1%
시 8
 
4.1%
워 7
 
3.6%
타 7
 
3.6%
어 7
 
3.6%
니 7
 
3.6%
서 6
 
3.1%
울 5
 
2.6%
리 5
 
2.6%
Other values (86) 121
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 184
94.4%
Space Separator 10
 
5.1%
Decimal Number 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 12
 
6.5%
시 8
 
4.3%
워 7
 
3.8%
타 7
 
3.8%
어 7
 
3.8%
니 7
 
3.8%
서 6
 
3.3%
울 5
 
2.7%
리 5
 
2.7%
블 3
 
1.6%
Other values (84) 117
63.6%
Space Separator
ValueCountFrequency (%)
10
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 184
94.4%
Common 11
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 12
 
6.5%
시 8
 
4.3%
워 7
 
3.8%
타 7
 
3.8%
어 7
 
3.8%
니 7
 
3.8%
서 6
 
3.3%
울 5
 
2.7%
리 5
 
2.7%
블 3
 
1.6%
Other values (84) 117
63.6%
Common
ValueCountFrequency (%)
10
90.9%
2 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 184
94.4%
ASCII 11
 
5.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 12
 
6.5%
시 8
 
4.3%
워 7
 
3.8%
타 7
 
3.8%
어 7
 
3.8%
니 7
 
3.8%
서 6
 
3.3%
울 5
 
2.7%
리 5
 
2.7%
블 3
 
1.6%
Other values (84) 117
63.6%
ASCII
ValueCountFrequency (%)
10
90.9%
2 1
 
9.1%

설정일자
Date

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
Minimum2003-03-28 00:00:00
Maximum2019-10-10 00:00:00
2023-12-13T05:41:02.780805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:41:02.931429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)

우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)90.5%
Missing2
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean238547.33
Minimum16495
Maximum580190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T05:41:03.042989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16495
5-th percentile16500
Q1121904
median136858
Q3463815
95-th percentile560295
Maximum580190
Range563695
Interquartile range (IQR)341911

Descriptive statistics

Standard deviation190314.17
Coefficient of variation (CV)0.79780465
Kurtosis-1.2312672
Mean238547.33
Median Absolute Deviation (MAD)36738
Skewness0.69640065
Sum5009494
Variance3.6219484 × 1010
MonotonicityNot monotonic
2023-12-13T05:41:03.168681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
157930 2
 
8.7%
136858 2
 
8.7%
56451 1
 
4.3%
463869 1
 
4.3%
134817 1
 
4.3%
157839 1
 
4.3%
122829 1
 
4.3%
110814 1
 
4.3%
100120 1
 
4.3%
16500 1
 
4.3%
Other values (9) 9
39.1%
(Missing) 2
 
8.7%
ValueCountFrequency (%)
16495 1
4.3%
16500 1
4.3%
56451 1
4.3%
100120 1
4.3%
110814 1
4.3%
121904 1
4.3%
122829 1
4.3%
134817 1
4.3%
135200 1
4.3%
136858 2
8.7%
ValueCountFrequency (%)
580190 1
4.3%
560295 1
4.3%
465810 1
4.3%
463940 1
4.3%
463869 1
4.3%
463815 1
4.3%
449030 1
4.3%
157930 2
8.7%
157839 1
4.3%
136858 2
8.7%

우편번호주소
Text

MISSING 

Distinct20
Distinct (%)95.2%
Missing2
Missing (%)8.7%
Memory size316.0 B
2023-12-13T05:41:03.426093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length19.571429
Min length16

Characters and Unicode

Total characters411
Distinct characters80
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

Unique19 ?
Unique (%)90.5%

Sample

1st row경기도 수원시 영통구 광교중앙로 55
2nd row전라북도 고창군 고창읍 석정2로 140
3rd row경기도 수원시 영통구 광교로42번길 80
4th row서울특별시 강남구 자곡로 100-2
5th row경기도 성남시 분당구 불정로 112
ValueCountFrequency (%)
서울특별시 11
 
12.0%
경기도 7
 
7.6%
전라북도 3
 
3.3%
분당구 3
 
3.3%
성남시 3
 
3.3%
강서구 3
 
3.3%
성북구 2
 
2.2%
영통구 2
 
2.2%
수원시 2
 
2.2%
90 2
 
2.2%
Other values (53) 54
58.7%
2023-12-13T05:41:03.809828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
71
 
17.3%
1 20
 
4.9%
시 20
 
4.9%
로 19
 
4.6%
구 19
 
4.6%
서 14
 
3.4%
0 12
 
2.9%
별 11
 
2.7%
울 11
 
2.7%
특 11
 
2.7%
Other values (70) 203
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 255
62.0%
Decimal Number 79
 
19.2%
Space Separator 71
 
17.3%
Dash Punctuation 6
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 20
 
7.8%
로 19
 
7.5%
구 19
 
7.5%
서 14
 
5.5%
별 11
 
4.3%
울 11
 
4.3%
특 11
 
4.3%
도 10
 
3.9%
길 10
 
3.9%
경 7
 
2.7%
Other values (58) 123
48.2%
Decimal Number
ValueCountFrequency (%)
1 20
25.3%
0 12
15.2%
7 8
 
10.1%
2 8
 
10.1%
3 7
 
8.9%
5 7
 
8.9%
4 7
 
8.9%
6 4
 
5.1%
9 4
 
5.1%
8 2
 
2.5%
Space Separator
ValueCountFrequency (%)
71
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 255
62.0%
Common 156
38.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 20
 
7.8%
로 19
 
7.5%
구 19
 
7.5%
서 14
 
5.5%
별 11
 
4.3%
울 11
 
4.3%
특 11
 
4.3%
도 10
 
3.9%
길 10
 
3.9%
경 7
 
2.7%
Other values (58) 123
48.2%
Common
ValueCountFrequency (%)
71
45.5%
1 20
 
12.8%
0 12
 
7.7%
7 8
 
5.1%
2 8
 
5.1%
3 7
 
4.5%
5 7
 
4.5%
4 7
 
4.5%
- 6
 
3.8%
6 4
 
2.6%
Other values (2) 6
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 255
62.0%
ASCII 156
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
71
45.5%
1 20
 
12.8%
0 12
 
7.7%
7 8
 
5.1%
2 8
 
5.1%
3 7
 
4.5%
5 7
 
4.5%
4 7
 
4.5%
- 6
 
3.8%
6 4
 
2.6%
Other values (2) 6
 
3.8%
Hangul
ValueCountFrequency (%)
시 20
 
7.8%
로 19
 
7.5%
구 19
 
7.5%
서 14
 
5.5%
별 11
 
4.3%
울 11
 
4.3%
특 11
 
4.3%
도 10
 
3.9%
길 10
 
3.9%
경 7
 
2.7%
Other values (58) 123
48.2%

시도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size316.0 B
서울특별시
11 
경기도
7 
전라북도
3 
<NA>
2 

Length

Max length5
Median length4
Mean length4.173913
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row경기도
3rd row전라북도
4th row경기도
5th row서울특별시

Common Values

ValueCountFrequency (%)
서울특별시 11
47.8%
경기도 7
30.4%
전라북도 3
 
13.0%
<NA> 2
 
8.7%

Length

2023-12-13T05:41:03.949328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:41:04.090956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 11
47.8%
경기도 7
30.4%
전라북도 3
 
13.0%
na 2
 
8.7%

시군구
Text

MISSING 

Distinct15
Distinct (%)71.4%
Missing2
Missing (%)8.7%
Memory size316.0 B
2023-12-13T05:41:04.246465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4.2857143
Min length2

Characters and Unicode

Total characters90
Distinct characters35
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

Unique11 ?
Unique (%)52.4%

Sample

1st row수원시 영통구
2nd row고창군
3rd row수원시 영통구
4th row강남구
5th row성남시 분당구
ValueCountFrequency (%)
성남시 3
 
10.7%
강서구 3
 
10.7%
분당구 3
 
10.7%
수원시 2
 
7.1%
영통구 2
 
7.1%
성북구 2
 
7.1%
하남시 1
 
3.6%
은평구 1
 
3.6%
종로구 1
 
3.6%
중구 1
 
3.6%
Other values (9) 9
32.1%
2023-12-13T05:41:04.824847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 18
20.0%
시 9
 
10.0%
7
 
7.8%
성 5
 
5.6%
강 5
 
5.6%
남 5
 
5.6%
분 3
 
3.3%
당 3
 
3.3%
서 3
 
3.3%
북 2
 
2.2%
Other values (25) 30
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83
92.2%
Space Separator 7
 
7.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 18
21.7%
시 9
 
10.8%
성 5
 
6.0%
강 5
 
6.0%
남 5
 
6.0%
분 3
 
3.6%
당 3
 
3.6%
서 3
 
3.6%
북 2
 
2.4%
인 2
 
2.4%
Other values (24) 28
33.7%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83
92.2%
Common 7
 
7.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 18
21.7%
시 9
 
10.8%
성 5
 
6.0%
강 5
 
6.0%
남 5
 
6.0%
분 3
 
3.6%
당 3
 
3.6%
서 3
 
3.6%
북 2
 
2.4%
인 2
 
2.4%
Other values (24) 28
33.7%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83
92.2%
ASCII 7
 
7.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 18
21.7%
시 9
 
10.8%
성 5
 
6.0%
강 5
 
6.0%
남 5
 
6.0%
분 3
 
3.6%
당 3
 
3.6%
서 3
 
3.6%
북 2
 
2.4%
인 2
 
2.4%
Other values (24) 28
33.7%
ASCII
ValueCountFrequency (%)
7
100.0%

읍면
Text

MISSING 

Distinct16
Distinct (%)84.2%
Missing4
Missing (%)17.4%
Memory size316.0 B
2023-12-13T05:41:04.982219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8947368
Min length2

Characters and Unicode

Total characters55
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)73.7%

Sample

1st row원천동
2nd row고창읍
3rd row이의동
4th row자곡동
5th row금붕동
ValueCountFrequency (%)
등촌동 3
15.8%
종암동 2
 
10.5%
신장동 1
 
5.3%
고창읍 1
 
5.3%
이의동 1
 
5.3%
자곡동 1
 
5.3%
금붕동 1
 
5.3%
상암동 1
 
5.3%
남동 1
 
5.3%
원천동 1
 
5.3%
Other values (6) 6
31.6%
2023-12-13T05:41:05.274052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 18
32.7%
촌 4
 
7.3%
등 3
 
5.5%
암 3
 
5.5%
곡 2
 
3.6%
금 2
 
3.6%
종 2
 
3.6%
장 1
 
1.8%
원 1
 
1.8%
구 1
 
1.8%
Other values (18) 18
32.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 55
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 18
32.7%
촌 4
 
7.3%
등 3
 
5.5%
암 3
 
5.5%
곡 2
 
3.6%
금 2
 
3.6%
종 2
 
3.6%
장 1
 
1.8%
원 1
 
1.8%
구 1
 
1.8%
Other values (18) 18
32.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 55
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 18
32.7%
촌 4
 
7.3%
등 3
 
5.5%
암 3
 
5.5%
곡 2
 
3.6%
금 2
 
3.6%
종 2
 
3.6%
장 1
 
1.8%
원 1
 
1.8%
구 1
 
1.8%
Other values (18) 18
32.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 55
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 18
32.7%
촌 4
 
7.3%
등 3
 
5.5%
암 3
 
5.5%
곡 2
 
3.6%
금 2
 
3.6%
종 2
 
3.6%
장 1
 
1.8%
원 1
 
1.8%
구 1
 
1.8%
Other values (18) 18
32.7%

도로명칭
Text

MISSING 

Distinct20
Distinct (%)95.2%
Missing2
Missing (%)8.7%
Memory size316.0 B
2023-12-13T05:41:05.445095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.7619048
Min length3

Characters and Unicode

Total characters100
Distinct characters47
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

Unique19 ?
Unique (%)90.5%

Sample

1st row광교중앙로
2nd row석정2로
3rd row광교로42번길
4th row자곡로
5th row불정로
ValueCountFrequency (%)
종암로 2
 
9.5%
광교중앙로 1
 
4.8%
구미로173번길 1
 
4.8%
명일로 1
 
4.8%
공항대로 1
 
4.8%
양천로 1
 
4.8%
화곡로68길 1
 
4.8%
은평로21길 1
 
4.8%
통일로16길 1
 
4.8%
정동길 1
 
4.8%
Other values (10) 10
47.6%
2023-12-13T05:41:05.739908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 18
 
18.0%
길 10
 
10.0%
1 7
 
7.0%
6 3
 
3.0%
교 3
 
3.0%
대 3
 
3.0%
정 3
 
3.0%
2 3
 
3.0%
번 3
 
3.0%
곡 2
 
2.0%
Other values (37) 45
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 81
81.0%
Decimal Number 19
 
19.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 18
22.2%
길 10
 
12.3%
교 3
 
3.7%
대 3
 
3.7%
정 3
 
3.7%
번 3
 
3.7%
곡 2
 
2.5%
일 2
 
2.5%
명 2
 
2.5%
암 2
 
2.5%
Other values (30) 33
40.7%
Decimal Number
ValueCountFrequency (%)
1 7
36.8%
6 3
15.8%
2 3
15.8%
7 2
 
10.5%
4 2
 
10.5%
8 1
 
5.3%
3 1
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 81
81.0%
Common 19
 
19.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 18
22.2%
길 10
 
12.3%
교 3
 
3.7%
대 3
 
3.7%
정 3
 
3.7%
번 3
 
3.7%
곡 2
 
2.5%
일 2
 
2.5%
명 2
 
2.5%
암 2
 
2.5%
Other values (30) 33
40.7%
Common
ValueCountFrequency (%)
1 7
36.8%
6 3
15.8%
2 3
15.8%
7 2
 
10.5%
4 2
 
10.5%
8 1
 
5.3%
3 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 81
81.0%
ASCII 19
 
19.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 18
22.2%
길 10
 
12.3%
교 3
 
3.7%
대 3
 
3.7%
정 3
 
3.7%
번 3
 
3.7%
곡 2
 
2.5%
일 2
 
2.5%
명 2
 
2.5%
암 2
 
2.5%
Other values (30) 33
40.7%
ASCII
ValueCountFrequency (%)
1 7
36.8%
6 3
15.8%
2 3
15.8%
7 2
 
10.5%
4 2
 
10.5%
8 1
 
5.3%
3 1
 
5.3%

건물거리이름
Text

MISSING 

Distinct18
Distinct (%)94.7%
Missing4
Missing (%)17.4%
Memory size316.0 B
2023-12-13T05:41:05.947752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length9.0526316
Min length5

Characters and Unicode

Total characters172
Distinct characters77
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

Unique17 ?
Unique (%)89.5%

Sample

1st row서울시니어스고창타워
2nd row광교아르데코
3rd row서울시니어스강남타워
4th row정원속궁전
5th row옥성골든카운티
ValueCountFrequency (%)
노블레스타워 2
 
6.7%
더 2
 
6.7%
노인복지주택 2
 
6.7%
하우징 1
 
3.3%
정동상림원 1
 
3.3%
둔촌동후성누리움 1
 
3.3%
서울시니어스강서타워 1
 
3.3%
그레이스힐 1
 
3.3%
가양타워 1
 
3.3%
시니어스 1
 
3.3%
Other values (17) 17
56.7%
2023-12-13T05:41:06.271626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11
 
6.4%
스 10
 
5.8%
시 8
 
4.7%
타 7
 
4.1%
워 7
 
4.1%
어 7
 
4.1%
니 7
 
4.1%
서 6
 
3.5%
울 5
 
2.9%
리 4
 
2.3%
Other values (67) 100
58.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 161
93.6%
Space Separator 11
 
6.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 10
 
6.2%
시 8
 
5.0%
타 7
 
4.3%
워 7
 
4.3%
어 7
 
4.3%
니 7
 
4.3%
서 6
 
3.7%
울 5
 
3.1%
리 4
 
2.5%
노 4
 
2.5%
Other values (66) 96
59.6%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 161
93.6%
Common 11
 
6.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 10
 
6.2%
시 8
 
5.0%
타 7
 
4.3%
워 7
 
4.3%
어 7
 
4.3%
니 7
 
4.3%
서 6
 
3.7%
울 5
 
3.1%
리 4
 
2.5%
노 4
 
2.5%
Other values (66) 96
59.6%
Common
ValueCountFrequency (%)
11
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 161
93.6%
ASCII 11
 
6.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11
100.0%
Hangul
ValueCountFrequency (%)
스 10
 
6.2%
시 8
 
5.0%
타 7
 
4.3%
워 7
 
4.3%
어 7
 
4.3%
니 7
 
4.3%
서 6
 
3.7%
울 5
 
3.1%
리 4
 
2.5%
노 4
 
2.5%
Other values (66) 96
59.6%

Interactions

2023-12-13T05:40:58.112422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:55.259419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:55.911692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:56.905486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:57.543834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:58.226663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:55.415672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:56.398399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:57.048269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:57.660640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:58.353072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:55.585240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:56.528342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:57.174991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:57.773149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:58.491999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:55.721638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:56.664387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:57.293197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:57.889633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:58.619802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:55.820550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:56.781776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:57.415696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:40:58.004963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:41:06.372749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택ID법정동코드담보소재지우편번호주소번지수본번주소부번주소건물명설정일자우편번호우편번호주소시도시군구읍면도로명칭건물거리이름
주택ID1.0001.0001.0001.0000.8750.0001.0001.0000.8731.0001.0001.0001.0001.0001.000
법정동코드1.0001.0001.0001.0000.8750.0001.0001.0000.8731.0001.0001.0001.0001.0001.000
담보소재지우편번호주소1.0001.0001.0001.0000.9550.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
번지수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
본번주소0.8750.8750.9551.0001.0000.0001.0001.0000.6321.0000.8390.8940.9361.0001.000
부번주소0.0000.0000.0001.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.000
건물명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
설정일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
우편번호0.8730.8731.0001.0000.6320.0001.0001.0001.0001.0000.8741.0001.0001.0001.000
우편번호주소1.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시도1.0001.0001.0001.0000.8390.0001.0001.0000.8741.0001.0001.0001.0001.0001.000
시군구1.0001.0001.0001.0000.8940.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
읍면1.0001.0001.0001.0000.9360.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명칭1.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
건물거리이름1.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-13T05:41:06.518055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택ID법정동코드본번주소부번주소우편번호시도
주택ID1.0000.8000.450-0.1090.1331.000
법정동코드0.8001.0000.537-0.2130.4771.000
본번주소0.4500.5371.000-0.6160.1080.710
부번주소-0.109-0.213-0.6161.000-0.1890.000
우편번호0.1330.4770.108-0.1891.0000.844
시도1.0001.0000.7100.0000.8441.000

Missing values

2023-12-13T05:40:58.797261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:40:59.034919image/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.
2023-12-13T05:40:59.219006image/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

주택ID법정동코드담보소재지우편번호주소번지수번지구분코드본번주소부번주소건물명설정일자우편번호우편번호주소시도시군구읍면도로명칭건물거리이름
04100094146311600경기도 용인시 기흥구 중동1147외1필일반번지1147<NA>스프링카운티자이2019-10-10<NA><NA><NA><NA><NA><NA><NA>
14100084111710200경기도 수원시 영통구 원천동556번지일반번지556<NA>광교 두산위브2018-05-2816500경기도 수원시 영통구 광교중앙로 55경기도수원시 영통구원천동광교중앙로<NA>
24500034579025025전라북도 고창군 고창읍 석정리830번지일반번지830<NA>서울시니어스고창타워2017-10-1956451전라북도 고창군 고창읍 석정2로 140전라북도고창군고창읍석정2로서울시니어스고창타워
34100074111710300경기도 수원시 영통구 이의동1358번지일반번지1358<NA>광교아르데코2017-08-2916495경기도 수원시 영통구 광교로42번길 80경기도수원시 영통구이의동광교로42번길광교아르데코
41100111168011200서울특별시 강남구 자곡동631번지일반번지631<NA>서울시니어스강남타워2015-04-28135200서울특별시 강남구 자곡로 100-2서울특별시강남구자곡동자곡로서울시니어스강남타워
54100064113510300경기도 성남시 분당구 정자동209번지일반번지209<NA>정원속궁전2014-02-12463815경기도 성남시 분당구 불정로 112경기도성남시 분당구<NA>불정로정원속궁전
64500024511113500전라북도 전주시 완산구 중인동1610번지일반번지1610<NA>옥성골든카운티2013-05-30560295전라북도 전주시 완산구 중인1길 136-20전라북도전주시 완산구<NA>중인1길옥성골든카운티
74500014518011200전라북도 정읍시 금붕동906-8번지일반번지9068내장산실버아파트2011-11-16580190전라북도 정읍시 금붕1길 190전라북도정읍시금붕동금붕1길<NA>
81100101144012700서울특별시 마포구 상암동1641번지일반번지1641<NA>상암카이져펠리스클래식2011-01-19121904서울특별시 마포구 월드컵북로47길 37서울특별시마포구상암동월드컵북로47길상암 카이저팰리스 클래식
94100054145010600경기도 하남시 신장동517번지일반번지517<NA>블루밍 더클래식2010-08-02465810경기도 하남시 하남대로 770경기도하남시신장동하남대로블루밍더클래식
주택ID법정동코드담보소재지우편번호주소번지수번지구분코드본번주소부번주소건물명설정일자우편번호우편번호주소시도시군구읍면도로명칭건물거리이름
131100081114016700서울특별시 중구 정동15-6번지일반번지156정동상림원2008-11-10100120서울특별시 중구 정동길 21-31서울특별시중구정동정동길정동상림원
141100071111018700서울특별시 종로구 무악동66-3번지일반번지663시니어스 하우징 더 골든팰리스2008-01-01110814서울특별시 종로구 통일로16길 4-1서울특별시종로구무악동통일로16길시니어즈 하우징 더 골든팰리스
151100061138010200서울특별시 은평구 녹번동91-7번지일반번지917시니어캐슬 클라시온2007-09-28122829서울특별시 은평구 은평로21길 34-5서울특별시은평구녹번동은평로21길시니어캐슬 클라시온
161100051129013500서울특별시 성북구 종암동3-91번지일반번지391노블레스타워 노인복지주택2008-04-14136858서울특별시 성북구 종암로 90서울특별시성북구종암동종암로노블레스타워 노인복지주택
171100041150010200서울특별시 강서구 등촌동637-번지일반번지637<NA>서울 시니어스 가양타워2008-01-30157839서울특별시 강서구 화곡로68길 102서울특별시강서구등촌동화곡로68길서울 시니어스 가양타워
181100031150010200서울특별시 강서구 등촌동717-번지일반번지717<NA>그레이스힐2007-03-22157930서울특별시 강서구 양천로 470서울특별시강서구등촌동양천로그레이스힐
191100021150010200서울특별시 강서구 등촌동669-1번지일반번지6691서울시니어스강서타워2003-03-28157930서울특별시 강서구 공항대로 315서울특별시강서구등촌동공항대로서울시니어스강서타워
201100011174010600서울특별시 강동구 둔촌동79-23번지일반번지7923둔촌동후성누리움2007-04-26134817서울특별시 강동구 명일로 135서울특별시강동구둔촌동명일로둔촌동후성누리움
211100091129013500서울특별시 성북구 종암동3-1507번지일반번지31507노블레스타워22010-04-07136858서울특별시 성북구 종암로 90서울특별시성북구종암동종암로노블레스타워 노인복지주택
224100044113511100경기도 성남시 분당구 금곡동305-2번지일반번지3052더 헤리티지2009-09-22463869경기도 성남시 분당구 대왕판교로 155경기도성남시 분당구금곡동대왕판교로더 헤리티지