Overview

Dataset statistics

Number of variables19
Number of observations79
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.4 KiB
Average record size in memory160.6 B

Variable types

Categorical12
Numeric7

Dataset

Description한국토지주택공사에서 위탁 관리 중인 마이홈포털의 기존 5년임대 주택 정보로, 광역시도,단지명,세대수,주택유형,임대사업자,공급면적 등의 정보를 제공합니다.
Author한국토지주택공사
URLhttps://www.data.go.kr/data/15084922/fileData.do

Alerts

임대종류 has constant value ""Constant
주택유형 has constant value ""Constant
시군구 is highly overall correlated with 세대수 and 11 other fieldsHigh correlation
준공일자 is highly overall correlated with 세대수 and 12 other fieldsHigh correlation
도로명주소 is highly overall correlated with 세대수 and 12 other fieldsHigh correlation
난방방식 is highly overall correlated with 주차수 and 11 other fieldsHigh correlation
단지명 is highly overall correlated with 세대수 and 12 other fieldsHigh correlation
건물형태 is highly overall correlated with 세대수 and 12 other fieldsHigh correlation
임대사업자 is highly overall correlated with 세대수 and 11 other fieldsHigh correlation
광역시도 is highly overall correlated with 세대수 and 10 other fieldsHigh correlation
승강기설치여부 is highly overall correlated with 세대수 and 13 other fieldsHigh correlation
세대수 is highly overall correlated with 주차수 and 8 other fieldsHigh correlation
주차수 is highly overall correlated with 세대수 and 10 other fieldsHigh correlation
공급면적(전용) is highly overall correlated with 공급면적(공용) and 3 other fieldsHigh correlation
공급면적(공용) is highly overall correlated with 공급면적(전용) and 2 other fieldsHigh correlation
임대보증금 is highly overall correlated with 월임대료 and 4 other fieldsHigh correlation
월임대료 is highly overall correlated with 임대보증금 and 8 other fieldsHigh correlation
전환보증금 is highly overall correlated with 광역시도 and 6 other fieldsHigh correlation
형명 is highly overall correlated with 주차수 and 4 other fieldsHigh correlation
주차수 has 34 (43.0%) zerosZeros
임대보증금 has 7 (8.9%) zerosZeros
월임대료 has 7 (8.9%) zerosZeros
전환보증금 has 37 (46.8%) zerosZeros

Reproduction

Analysis started2024-03-14 16:35:10.620766
Analysis finished2024-03-14 16:35:26.161501
Duration15.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

임대종류
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size760.0 B
5년임대
79 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5년임대
2nd row5년임대
3rd row5년임대
4th row5년임대
5th row5년임대

Common Values

ValueCountFrequency (%)
5년임대 79
100.0%

Length

2024-03-15T01:35:26.346400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:35:26.633084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5년임대 79
100.0%

광역시도
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size760.0 B
경기도
29 
대구광역시
28 
광주광역시
6 
전라북도
5 
부산광역시
3 
Other values (4)
8 

Length

Max length7
Median length5
Mean length4.2025316
Min length3

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st row부산광역시
2nd row부산광역시
3rd row부산광역시
4th row대구광역시
5th row대구광역시

Common Values

ValueCountFrequency (%)
경기도 29
36.7%
대구광역시 28
35.4%
광주광역시 6
 
7.6%
전라북도 5
 
6.3%
부산광역시 3
 
3.8%
대전광역시 3
 
3.8%
인천광역시 2
 
2.5%
경상북도 2
 
2.5%
강원특별자치도 1
 
1.3%

Length

2024-03-15T01:35:26.825050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:35:27.200706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 29
36.7%
대구광역시 28
35.4%
광주광역시 6
 
7.6%
전라북도 5
 
6.3%
부산광역시 3
 
3.8%
대전광역시 3
 
3.8%
인천광역시 2
 
2.5%
경상북도 2
 
2.5%
강원특별자치도 1
 
1.3%

시군구
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size760.0 B
달성군
15 
동구
10 
수원시 팔달구
10 
성남시 중원구
9 
광산구
6 
Other values (10)
29 

Length

Max length7
Median length4
Mean length4.2911392
Min length2

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st row기장군
2nd row기장군
3rd row기장군
4th row중구
5th row중구

Common Values

ValueCountFrequency (%)
달성군 15
19.0%
동구 10
12.7%
수원시 팔달구 10
12.7%
성남시 중원구 9
11.4%
광산구 6
 
7.6%
성남시 수정구 5
 
6.3%
완주군 5
 
6.3%
북구 4
 
5.1%
기장군 3
 
3.8%
안양시 만안구 3
 
3.8%
Other values (5) 9
11.4%

Length

2024-03-15T01:35:27.545557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
달성군 15
13.9%
성남시 14
13.0%
수원시 12
11.1%
동구 10
9.3%
팔달구 10
9.3%
중원구 9
8.3%
광산구 6
 
5.6%
수정구 5
 
4.6%
완주군 5
 
4.6%
북구 4
 
3.7%
Other values (8) 18
16.7%

도로명주소
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size760.0 B
경기도 수원시 팔달구 고등로 15
10 
대구광역시 달성군 구지면 과학마을로2길 6
8 
경기도 성남시 중원구 금광로 39
6 
광주광역시 광산구 목련로 15
6 
대구광역시 동구 송라로14길 132
5 
Other values (17)
44 

Length

Max length23
Median length22
Mean length19.367089
Min length15

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st row부산광역시 기장군 정관읍 정관5로 144
2nd row부산광역시 기장군 정관읍 정관5로 144
3rd row부산광역시 기장군 정관읍 정관5로 144
4th row대구광역시 중구 달구벌대로447길 77
5th row대구광역시 중구 달구벌대로447길 77

Common Values

ValueCountFrequency (%)
경기도 수원시 팔달구 고등로 15 10
 
12.7%
대구광역시 달성군 구지면 과학마을로2길 6 8
 
10.1%
경기도 성남시 중원구 금광로 39 6
 
7.6%
광주광역시 광산구 목련로 15 6
 
7.6%
대구광역시 동구 송라로14길 132 5
 
6.3%
경기도 성남시 수정구 희망로 533 5
 
6.3%
대구광역시 달성군 다사읍 대실역남로 35 4
 
5.1%
경기도 성남시 중원구 광명로 202 3
 
3.8%
대구광역시 달성군 다사읍 대실역남로 33 3
 
3.8%
대전광역시 동구 동대전로46번길 120 3
 
3.8%
Other values (12) 26
32.9%

Length

2024-03-15T01:35:27.931319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 29
 
7.9%
대구광역시 28
 
7.6%
15 16
 
4.3%
달성군 15
 
4.1%
성남시 14
 
3.8%
수원시 12
 
3.3%
39 12
 
3.3%
팔달구 10
 
2.7%
고등로 10
 
2.7%
동구 10
 
2.7%
Other values (60) 212
57.6%

단지명
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size760.0 B
수원역푸르지오자이
10 
달성2차청아람
8 
성남금광1 B블록(5년공임-E편한세상 금빛 그랑메종)
6 
다사로움2단지
6 
신천청아람(임대)
5 
Other values (17)
44 

Length

Max length29
Median length14
Mean length11.64557
Min length5

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st row정관풀리페
2nd row정관풀리페
3rd row정관풀리페
4th row삼덕청아람리슈빌
5th row삼덕청아람리슈빌

Common Values

ValueCountFrequency (%)
수원역푸르지오자이 10
 
12.7%
달성2차청아람 8
 
10.1%
성남금광1 B블록(5년공임-E편한세상 금빛 그랑메종) 6
 
7.6%
다사로움2단지 6
 
7.6%
신천청아람(임대) 5
 
6.3%
성남신흥2 B블록(5년공임-산성역자이푸르지오) 5
 
6.3%
죽곡청아람4단지 4
 
5.1%
성남중1 B블록(5년공임-신흥역하늘채랜더스원) 3
 
3.8%
죽곡청아람3단지(임대) 3
 
3.8%
이스트시티 3
 
3.8%
Other values (12) 26
32.9%

Length

2024-03-15T01:35:28.407688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원역푸르지오자이 10
 
8.3%
달성2차청아람 8
 
6.6%
성남금광1 6
 
5.0%
b블록(5년공임-e편한세상 6
 
5.0%
금빛 6
 
5.0%
그랑메종 6
 
5.0%
다사로움2단지 6
 
5.0%
신천청아람(임대 5
 
4.1%
성남신흥2 5
 
4.1%
b블록(5년공임-산성역자이푸르지오 5
 
4.1%
Other values (23) 58
47.9%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean483.46835
Minimum1
Maximum1228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T01:35:28.766268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.4
Q1208
median412
Q3812
95-th percentile1228
Maximum1228
Range1227
Interquartile range (IQR)604

Descriptive statistics

Standard deviation376.1005
Coefficient of variation (CV)0.77792165
Kurtosis-0.66811602
Mean483.46835
Median Absolute Deviation (MAD)278
Skewness0.55381824
Sum38194
Variance141451.59
MonotonicityNot monotonic
2024-03-15T01:35:29.022674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1228 8
 
10.1%
497 6
 
7.6%
908 6
 
7.6%
812 5
 
6.3%
215 5
 
6.3%
564 5
 
6.3%
304 4
 
5.1%
830 3
 
3.8%
606 3
 
3.8%
279 3
 
3.8%
Other values (14) 31
39.2%
ValueCountFrequency (%)
1 2
2.5%
2 2
2.5%
8 2
2.5%
14 3
3.8%
40 1
 
1.3%
48 3
3.8%
59 2
2.5%
126 2
2.5%
134 2
2.5%
208 2
2.5%
ValueCountFrequency (%)
1228 8
10.1%
908 6
7.6%
830 3
 
3.8%
812 5
6.3%
606 3
 
3.8%
599 2
 
2.5%
564 5
6.3%
497 6
7.6%
412 3
 
3.8%
406 3
 
3.8%

주택유형
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size760.0 B
아파트
79 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아파트 79
100.0%

Length

2024-03-15T01:35:29.245947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:35:29.424340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 79
100.0%

임대사업자
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size760.0 B
LH경기남부
29 
대구도시공사
24 
LH대구경북
6 
광주광역시도시공사
6 
전북개발공사
5 
Other values (4)
9 

Length

Max length9
Median length6
Mean length6.1392405
Min length3

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st row부산도시공사
2nd row부산도시공사
3rd row부산도시공사
4th row대구도시공사
5th row대구도시공사

Common Values

ValueCountFrequency (%)
LH경기남부 29
36.7%
대구도시공사 24
30.4%
LH대구경북 6
 
7.6%
광주광역시도시공사 6
 
7.6%
전북개발공사 5
 
6.3%
부산도시공사 3
 
3.8%
LH대전충남 3
 
3.8%
LH인천 2
 
2.5%
태백시 1
 
1.3%

Length

2024-03-15T01:35:29.691950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:35:29.946349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
lh경기남부 29
36.7%
대구도시공사 24
30.4%
lh대구경북 6
 
7.6%
광주광역시도시공사 6
 
7.6%
전북개발공사 5
 
6.3%
부산도시공사 3
 
3.8%
lh대전충남 3
 
3.8%
lh인천 2
 
2.5%
태백시 1
 
1.3%

준공일자
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size760.0 B
2021-01-29
10 
2010-11-04
8 
2022-11-23
6 
2009-09-30
6 
2010-02-03
5 
Other values (17)
44 

Length

Max length10
Median length10
Mean length9.8481013
Min length4

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st row2016-05-16
2nd row2016-05-16
3rd row2016-05-16
4th row2013-04-07
5th row2013-04-07

Common Values

ValueCountFrequency (%)
2021-01-29 10
 
12.7%
2010-11-04 8
 
10.1%
2022-11-23 6
 
7.6%
2009-09-30 6
 
7.6%
2010-02-03 5
 
6.3%
2023-10-31 5
 
6.3%
2011-10-12 4
 
5.1%
2022-09-29 3
 
3.8%
2012-02-09 3
 
3.8%
2018-10-11 3
 
3.8%
Other values (12) 26
32.9%

Length

2024-03-15T01:35:30.220860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-01-29 10
 
12.7%
2010-11-04 8
 
10.1%
2022-11-23 6
 
7.6%
2009-09-30 6
 
7.6%
2010-02-03 5
 
6.3%
2023-10-31 5
 
6.3%
2011-10-12 4
 
5.1%
2016-10-28 3
 
3.8%
2016-05-16 3
 
3.8%
2015-06-29 3
 
3.8%
Other values (12) 26
32.9%

건물형태
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size760.0 B
계단식
39 
복도식
14 
<NA>
13 
혼합식
13 

Length

Max length4
Median length3
Mean length3.164557
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
계단식 39
49.4%
복도식 14
 
17.7%
<NA> 13
 
16.5%
혼합식 13
 
16.5%

Length

2024-03-15T01:35:30.623055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:35:30.946891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
계단식 39
49.4%
복도식 14
 
17.7%
na 13
 
16.5%
혼합식 13
 
16.5%

난방방식
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size760.0 B
지역가스난방
30 
지역난방
27 
<NA>
14 
개별가스난방
5 
개별난방
 
2

Length

Max length6
Median length4
Mean length4.9113924
Min length4

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st row지역난방
2nd row지역난방
3rd row지역난방
4th row지역가스난방
5th row지역가스난방

Common Values

ValueCountFrequency (%)
지역가스난방 30
38.0%
지역난방 27
34.2%
<NA> 14
17.7%
개별가스난방 5
 
6.3%
개별난방 2
 
2.5%
개별유류난방 1
 
1.3%

Length

2024-03-15T01:35:31.289735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:35:31.541280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지역가스난방 30
38.0%
지역난방 27
34.2%
na 14
17.7%
개별가스난방 5
 
6.3%
개별난방 2
 
2.5%
개별유류난방 1
 
1.3%

승강기설치여부
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size760.0 B
<NA>
40 
전체동 설치
38 
미설치
 
1

Length

Max length6
Median length4
Mean length4.9493671
Min length3

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st row전체동 설치
2nd row전체동 설치
3rd row전체동 설치
4th row전체동 설치
5th row전체동 설치

Common Values

ValueCountFrequency (%)
<NA> 40
50.6%
전체동 설치 38
48.1%
미설치 1
 
1.3%

Length

2024-03-15T01:35:31.766609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:35:32.045213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 40
34.2%
전체동 38
32.5%
설치 38
32.5%
미설치 1
 
0.9%

주차수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean359.67089
Minimum0
Maximum1228
Zeros34
Zeros (%)43.0%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T01:35:32.285476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median215
Q3728.5
95-th percentile1228
Maximum1228
Range1228
Interquartile range (IQR)728.5

Descriptive statistics

Standard deviation424.84388
Coefficient of variation (CV)1.1812018
Kurtosis-0.58968316
Mean359.67089
Median Absolute Deviation (MAD)215
Skewness0.88776061
Sum28414
Variance180492.33
MonotonicityNot monotonic
2024-03-15T01:35:32.975121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 34
43.0%
1228 8
 
10.1%
481 6
 
7.6%
914 6
 
7.6%
215 5
 
6.3%
304 4
 
5.1%
833 3
 
3.8%
279 3
 
3.8%
731 3
 
3.8%
335 2
 
2.5%
Other values (3) 5
 
6.3%
ValueCountFrequency (%)
0 34
43.0%
10 1
 
1.3%
134 2
 
2.5%
215 5
 
6.3%
279 3
 
3.8%
304 4
 
5.1%
335 2
 
2.5%
481 6
 
7.6%
726 2
 
2.5%
731 3
 
3.8%
ValueCountFrequency (%)
1228 8
10.1%
914 6
7.6%
833 3
 
3.8%
731 3
 
3.8%
726 2
 
2.5%
481 6
7.6%
335 2
 
2.5%
304 4
5.1%
279 3
 
3.8%
215 5
6.3%

형명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Memory size760.0 B
39
14 
51
11 
59
10 
24
4 
46
4 
Other values (26)
36 

Length

Max length9
Median length2
Mean length2.6075949
Min length2

Unique

Unique19 ?
Unique (%)24.1%

Sample

1st row59A
2nd row59B
3rd row59C
4th row39
5th row59

Common Values

ValueCountFrequency (%)
39 14
17.7%
51 11
13.9%
59 10
12.7%
24 4
 
5.1%
46 4
 
5.1%
84 3
 
3.8%
32 3
 
3.8%
40 3
 
3.8%
59A 2
 
2.5%
36 2
 
2.5%
Other values (21) 23
29.1%

Length

2024-03-15T01:35:33.402361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
39 14
17.3%
51 11
13.6%
59 10
 
12.3%
24 4
 
4.9%
46 4
 
4.9%
84 3
 
3.7%
32 3
 
3.7%
40 3
 
3.7%
44 2
 
2.5%
84㎡ 2
 
2.5%
Other values (22) 25
30.9%

공급면적(전용)
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.810518
Minimum36.47
Maximum84.9934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T01:35:33.642798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.47
5-th percentile39.492
Q144.3
median59.6724
Q359.99
95-th percentile84.93575
Maximum84.9934
Range48.5234
Interquartile range (IQR)15.69

Descriptive statistics

Standard deviation15.029616
Coefficient of variation (CV)0.26455693
Kurtosis-0.40915782
Mean56.810518
Median Absolute Deviation (MAD)10.0424
Skewness0.70950819
Sum4488.0309
Variance225.88937
MonotonicityNot monotonic
2024-03-15T01:35:33.891490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.99 6
 
7.6%
40.6 3
 
3.8%
59.7456 3
 
3.8%
59.95 2
 
2.5%
51.99 2
 
2.5%
36.47 2
 
2.5%
44.3 2
 
2.5%
84.9934 2
 
2.5%
84.6695 2
 
2.5%
39.5 2
 
2.5%
Other values (53) 53
67.1%
ValueCountFrequency (%)
36.47 2
2.5%
39.31 1
1.3%
39.42 1
1.3%
39.5 2
2.5%
39.6 1
1.3%
39.6472 1
1.3%
39.73 1
1.3%
39.9 1
1.3%
39.901 1
1.3%
39.91 1
1.3%
ValueCountFrequency (%)
84.9934 2
2.5%
84.9837 1
1.3%
84.9695 1
1.3%
84.932 1
1.3%
84.8978 1
1.3%
84.896 1
1.3%
84.853 1
1.3%
84.812 1
1.3%
84.778 1
1.3%
84.6695 2
2.5%

공급면적(공용)
Real number (ℝ)

HIGH CORRELATION 

Distinct74
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.953148
Minimum7.005
Maximum28.1614
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T01:35:34.135100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.005
5-th percentile14.78046
Q118.83595
median21.438
Q323.46695
95-th percentile25.39087
Maximum28.1614
Range21.1564
Interquartile range (IQR)4.631

Descriptive statistics

Standard deviation3.6475917
Coefficient of variation (CV)0.17408323
Kurtosis1.6756411
Mean20.953148
Median Absolute Deviation (MAD)2.4003
Skewness-0.88969138
Sum1655.2987
Variance13.304926
MonotonicityNot monotonic
2024-03-15T01:35:34.476134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.1393 3
 
3.8%
21.5448 3
 
3.8%
17.6125 2
 
2.5%
23.1653 1
 
1.3%
14.8517 1
 
1.3%
15.4757 1
 
1.3%
20.503 1
 
1.3%
18.0329 1
 
1.3%
18.0093 1
 
1.3%
14.8887 1
 
1.3%
Other values (64) 64
81.0%
ValueCountFrequency (%)
7.005 1
 
1.3%
14.1393 3
3.8%
14.8517 1
 
1.3%
14.8887 1
 
1.3%
14.9814 1
 
1.3%
15.0077 1
 
1.3%
15.4757 1
 
1.3%
16.7355 1
 
1.3%
17.4725 1
 
1.3%
17.6125 2
2.5%
ValueCountFrequency (%)
28.1614 1
1.3%
27.9722 1
1.3%
27.122 1
1.3%
25.4968 1
1.3%
25.3791 1
1.3%
25.1452 1
1.3%
24.8666 1
1.3%
24.7474 1
1.3%
24.7352 1
1.3%
24.6 1
1.3%

임대보증금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52804291
Minimum0
Maximum1.18424 × 108
Zeros7
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T01:35:34.801527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q125912500
median49296000
Q378165000
95-th percentile1.18424 × 108
Maximum1.18424 × 108
Range1.18424 × 108
Interquartile range (IQR)52252500

Descriptive statistics

Standard deviation32368349
Coefficient of variation (CV)0.61298709
Kurtosis-0.72155943
Mean52804291
Median Absolute Deviation (MAD)24429000
Skewness0.2312494
Sum4.171539 × 109
Variance1.04771 × 1015
MonotonicityNot monotonic
2024-03-15T01:35:35.255848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 7
 
8.9%
24867000 6
 
7.6%
118424000 5
 
6.3%
49296000 4
 
5.1%
85016000 3
 
3.8%
69796000 3
 
3.8%
40408000 3
 
3.8%
30829000 3
 
3.8%
80479000 2
 
2.5%
53403000 2
 
2.5%
Other values (39) 41
51.9%
ValueCountFrequency (%)
0 7
8.9%
13681000 2
 
2.5%
14500000 1
 
1.3%
21000000 1
 
1.3%
23943000 1
 
1.3%
24104000 1
 
1.3%
24867000 6
7.6%
25800000 1
 
1.3%
26025000 1
 
1.3%
27248000 1
 
1.3%
ValueCountFrequency (%)
118424000 5
6.3%
103521000 1
 
1.3%
95263000 2
 
2.5%
87840000 1
 
1.3%
87596000 1
 
1.3%
87248000 1
 
1.3%
87183000 1
 
1.3%
85969000 1
 
1.3%
85016000 3
3.8%
80479000 2
 
2.5%

월임대료
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean323186.84
Minimum0
Maximum510090
Zeros7
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T01:35:35.615017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1297270
median347000
Q3398760
95-th percentile468450
Maximum510090
Range510090
Interquartile range (IQR)101490

Descriptive statistics

Standard deviation129194.97
Coefficient of variation (CV)0.39975321
Kurtosis1.3295817
Mean323186.84
Median Absolute Deviation (MAD)52280
Skewness-1.3157171
Sum25531760
Variance1.6691341 × 1010
MonotonicityNot monotonic
2024-03-15T01:35:36.021077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 7
 
8.9%
276320 6
 
7.6%
468450 5
 
6.3%
324660 4
 
5.1%
342690 3
 
3.8%
347000 3
 
3.8%
319930 3
 
3.8%
186410 3
 
3.8%
449790 3
 
3.8%
175370 2
 
2.5%
Other values (36) 40
50.6%
ValueCountFrequency (%)
0 7
8.9%
73500 1
 
1.3%
175370 2
 
2.5%
186410 3
3.8%
276320 6
7.6%
290000 1
 
1.3%
304540 2
 
2.5%
315060 2
 
2.5%
317730 1
 
1.3%
319930 3
3.8%
ValueCountFrequency (%)
510090 1
 
1.3%
503040 1
 
1.3%
480850 1
 
1.3%
468450 5
6.3%
466940 1
 
1.3%
449790 3
3.8%
439640 1
 
1.3%
424680 1
 
1.3%
424250 2
 
2.5%
422700 1
 
1.3%

전환보증금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25479038
Minimum0
Maximum73000000
Zeros37
Zeros (%)46.8%
Negative0
Negative (%)0.0%
Memory size839.0 B
2024-03-15T01:35:36.397318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median34000000
Q348999000
95-th percentile56100000
Maximum73000000
Range73000000
Interquartile range (IQR)48999000

Descriptive statistics

Standard deviation24914889
Coefficient of variation (CV)0.97785832
Kurtosis-1.7254792
Mean25479038
Median Absolute Deviation (MAD)24000000
Skewness0.085610489
Sum2.012844 × 109
Variance6.2075171 × 1014
MonotonicityNot monotonic
2024-03-15T01:35:36.795614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 37
46.8%
56000000 7
 
8.9%
48999000 6
 
7.6%
43000000 3
 
3.8%
50000000 3
 
3.8%
45000000 3
 
3.8%
41000000 3
 
3.8%
47000000 3
 
3.8%
40000000 2
 
2.5%
34070000 1
 
1.3%
Other values (11) 11
 
13.9%
ValueCountFrequency (%)
0 37
46.8%
33370000 1
 
1.3%
33410000 1
 
1.3%
34000000 1
 
1.3%
34070000 1
 
1.3%
38000000 1
 
1.3%
39000000 1
 
1.3%
40000000 2
 
2.5%
41000000 3
 
3.8%
43000000 3
 
3.8%
ValueCountFrequency (%)
73000000 1
 
1.3%
72000000 1
 
1.3%
58000000 1
 
1.3%
57000000 1
 
1.3%
56000000 7
8.9%
51000000 1
 
1.3%
50000000 3
3.8%
48999000 6
7.6%
47000000 3
3.8%
46000000 1
 
1.3%

Interactions

2024-03-15T01:35:23.367682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:13.052155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:14.814712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:16.675731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:18.730369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:20.519346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:21.829413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:23.555706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:13.311120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:15.066079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:16.958243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:18.978938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:20.675117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:22.006658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:23.816957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:13.558921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:15.332304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:17.236623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:19.220823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:20.867772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:22.258753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:24.061725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:13.800977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:15.484717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:17.535731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:19.449239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:21.174193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:22.501254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:24.251070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:14.032761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:15.718359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:17.911721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:19.671368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:21.360730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:22.662050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:24.503595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:14.245522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:16.029038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:18.225423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:19.921297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:21.515170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:22.914240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:24.758970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:14.486428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:16.324765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:18.469876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:20.159836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:21.663569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:35:23.154114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T01:35:37.085774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
광역시도시군구도로명주소단지명세대수임대사업자준공일자건물형태난방방식승강기설치여부주차수형명공급면적(전용)공급면적(공용)임대보증금월임대료전환보증금
광역시도1.0000.9901.0001.0000.7760.9991.0000.7800.8811.0000.8890.8440.5300.7270.6230.7540.822
시군구0.9901.0001.0001.0000.9350.9901.0000.9460.9451.0000.9620.8360.7400.7730.8280.8880.849
도로명주소1.0001.0001.0001.0000.9941.0001.0001.0001.0001.0001.0000.9000.7220.8240.9010.9410.870
단지명1.0001.0001.0001.0000.9941.0001.0001.0001.0001.0001.0000.9000.7220.8240.9010.9410.870
세대수0.7760.9350.9940.9941.0000.7920.9920.8020.6231.0000.9810.7610.5030.6290.6520.8670.544
임대사업자0.9990.9901.0001.0000.7921.0001.0000.7800.9171.0000.9020.8680.5880.7250.6410.7690.819
준공일자1.0001.0001.0001.0000.9921.0001.0001.0001.0001.0001.0000.9100.7770.8250.9040.9390.879
건물형태0.7800.9461.0001.0000.8020.7801.0001.0000.644NaN0.8130.8010.6280.5710.8720.5590.931
난방방식0.8810.9451.0001.0000.6230.9171.0000.6441.0001.0000.7640.9780.5210.7150.6510.7400.553
승강기설치여부1.0001.0001.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.0001.0000.4081.0000.000
주차수0.8890.9621.0001.0000.9810.9021.0000.8130.7641.0001.0000.9360.6120.6230.6640.8800.743
형명0.8440.8360.9000.9000.7610.8680.9100.8010.9781.0000.9361.0001.0000.8490.8720.8940.410
공급면적(전용)0.5300.7400.7220.7220.5030.5880.7770.6280.5211.0000.6121.0001.0000.5660.7230.5790.412
공급면적(공용)0.7270.7730.8240.8240.6290.7250.8250.5710.7151.0000.6230.8490.5661.0000.6730.8110.608
임대보증금0.6230.8280.9010.9010.6520.6410.9040.8720.6510.4080.6640.8720.7230.6731.0000.8180.721
월임대료0.7540.8880.9410.9410.8670.7690.9390.5590.7401.0000.8800.8940.5790.8110.8181.0000.642
전환보증금0.8220.8490.8700.8700.5440.8190.8790.9310.5530.0000.7430.4100.4120.6080.7210.6421.000
2024-03-15T01:35:37.415326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구준공일자도로명주소난방방식단지명건물형태형명임대사업자광역시도승강기설치여부
시군구1.0000.9500.9440.8220.9440.8820.3540.8970.8980.930
준공일자0.9501.0001.0000.9041.0000.8910.4440.9070.9070.870
도로명주소0.9441.0001.0000.9041.0000.8910.4230.9020.9020.870
난방방식0.8220.9040.9041.0000.9040.5980.6250.8620.8030.959
단지명0.9441.0001.0000.9041.0000.8910.4230.9020.9020.870
건물형태0.8820.8910.8910.5980.8911.0000.4060.7000.7001.000
형명0.3540.4440.4230.6250.4230.4061.0000.4570.4220.615
임대사업자0.8970.9070.9020.8620.9020.7000.4571.0000.9490.959
광역시도0.8980.9070.9020.8030.9020.7000.4220.9491.0000.959
승강기설치여부0.9300.8700.8700.9590.8701.0000.6150.9590.9591.000
2024-03-15T01:35:37.656166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수주차수공급면적(전용)공급면적(공용)임대보증금월임대료전환보증금광역시도시군구도로명주소단지명임대사업자준공일자건물형태난방방식승강기설치여부형명
세대수1.0000.6940.1710.1490.2230.0770.0390.5210.7230.8670.8670.5430.8700.6970.4330.9440.338
주차수0.6941.0000.4020.319-0.072-0.229-0.4100.6990.8030.8960.8960.7250.9010.7140.5920.9300.591
공급면적(전용)0.1710.4021.0000.6290.2870.279-0.2150.3080.4230.3610.3610.3550.3660.5080.3600.9590.816
공급면적(공용)0.1490.3190.6291.000-0.261-0.013-0.1180.4640.4440.4630.4630.4610.4720.4180.5310.9440.437
임대보증금0.223-0.0720.287-0.2611.0000.7550.2720.3380.4670.5650.5650.3540.5720.5650.4320.4030.446
월임대료0.077-0.2290.279-0.0130.7551.0000.4990.4940.6180.6790.6790.5130.6850.4330.5870.9300.506
전환보증금0.039-0.410-0.215-0.1180.2720.4991.0000.5690.5630.5590.5590.5650.5650.6650.4100.0000.140
광역시도0.5210.6990.3080.4640.3380.4940.5691.0000.8980.9020.9020.9490.9070.7000.8030.9590.422
시군구0.7230.8030.4230.4440.4670.6180.5630.8981.0000.9440.9440.8970.9500.8820.8220.9300.354
도로명주소0.8670.8960.3610.4630.5650.6790.5590.9020.9441.0001.0000.9021.0000.8910.9040.8700.423
단지명0.8670.8960.3610.4630.5650.6790.5590.9020.9441.0001.0000.9021.0000.8910.9040.8700.423
임대사업자0.5430.7250.3550.4610.3540.5130.5650.9490.8970.9020.9021.0000.9070.7000.8620.9590.457
준공일자0.8700.9010.3660.4720.5720.6850.5650.9070.9501.0001.0000.9071.0000.8910.9040.8700.444
건물형태0.6970.7140.5080.4180.5650.4330.6650.7000.8820.8910.8910.7000.8911.0000.5981.0000.406
난방방식0.4330.5920.3600.5310.4320.5870.4100.8030.8220.9040.9040.8620.9040.5981.0000.9590.625
승강기설치여부0.9440.9300.9590.9440.4030.9300.0000.9590.9300.8700.8700.9590.8701.0000.9591.0000.615
형명0.3380.5910.8160.4370.4460.5060.1400.4220.3540.4230.4230.4570.4440.4060.6250.6151.000

Missing values

2024-03-15T01:35:25.166494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T01:35:25.870764image/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

임대종류광역시도시군구도로명주소단지명세대수주택유형임대사업자준공일자건물형태난방방식승강기설치여부주차수형명공급면적(전용)공급면적(공용)임대보증금월임대료전환보증금
05년임대부산광역시기장군부산광역시 기장군 정관읍 정관5로 144정관풀리페830아파트부산도시공사2016-05-16계단식지역난방전체동 설치83359A59.898623.16533407000038850034070000
15년임대부산광역시기장군부산광역시 기장군 정관읍 정관5로 144정관풀리페830아파트부산도시공사2016-05-16계단식지역난방전체동 설치83359B59.952723.59913340000038520033410000
25년임대부산광역시기장군부산광역시 기장군 정관읍 정관5로 144정관풀리페830아파트부산도시공사2016-05-16계단식지역난방전체동 설치83359C59.998323.3693373000038710033370000
35년임대대구광역시중구대구광역시 중구 달구벌대로447길 77삼덕청아람리슈빌335아파트대구도시공사2013-04-07계단식지역가스난방전체동 설치3353939.647221.3144335220003280900
45년임대대구광역시중구대구광역시 중구 달구벌대로447길 77삼덕청아람리슈빌335아파트대구도시공사2013-04-07계단식지역가스난방전체동 설치3355959.98921.3252604040004396400
55년임대대구광역시동구대구광역시 동구 송라로14길 132신천청아람(임대)215아파트대구도시공사2010-02-03계단식지역가스난방전체동 설치2152459.514522.4095492960003246600
65년임대대구광역시동구대구광역시 동구 송라로14길 132신천청아람(임대)215아파트대구도시공사2010-02-03계단식지역가스난방전체동 설치2152459.672423.2627492960003246600
75년임대대구광역시동구대구광역시 동구 송라로14길 132신천청아람(임대)215아파트대구도시공사2010-02-03계단식지역가스난방전체동 설치2152459.739822.2488492960003246600
85년임대대구광역시동구대구광역시 동구 송라로14길 132신천청아람(임대)215아파트대구도시공사2010-02-03계단식지역가스난방전체동 설치2152459.745620.6101492960003246600
95년임대대구광역시동구대구광역시 동구 송라로14길 132신천청아람(임대)215아파트대구도시공사2010-02-03계단식지역가스난방전체동 설치2152974.422223.5159548210004808500
임대종류광역시도시군구도로명주소단지명세대수주택유형임대사업자준공일자건물형태난방방식승강기설치여부주차수형명공급면적(전용)공급면적(공용)임대보증금월임대료전환보증금
695년임대경기도안양시 만안구경기도 안양시 만안구 안양천서로 177안양덕천 5년 공공임대주택14아파트LH경기남부2016-10-28<NA><NA><NA>03939.7320.3166000
705년임대경기도안양시 만안구경기도 안양시 만안구 안양천서로 177안양덕천 5년 공공임대주택14아파트LH경기남부2016-10-28<NA><NA><NA>04949.6325.3791000
715년임대전라북도완주군전라북도 완주군 이서면 반교로 51혁신도시 에코르 3단지606아파트전북개발공사2015-06-29계단식개별가스난방전체동 설치731084.8120C84.81224.27764940003470000
725년임대전라북도완주군전라북도 완주군 이서면 반교로 51혁신도시 에코르 3단지606아파트전북개발공사2015-06-29계단식개별가스난방전체동 설치731084.8960B84.89623.341730790003470000
735년임대전라북도완주군전라북도 완주군 이서면 반교로 51혁신도시 에코르 3단지606아파트전북개발공사2015-06-29계단식개별가스난방전체동 설치731084.9320A84.93223.418730190003470000
745년임대전라북도완주군전라북도 완주군 이서면 출판로 25혁신에코르 1단지599아파트전북개발공사2014-06-20계단식개별난방전체동 설치72684㎡ A84.77822.096675750003580000
755년임대전라북도완주군전라북도 완주군 이서면 출판로 25혁신에코르 1단지599아파트전북개발공사2014-06-20계단식개별난방전체동 설치72684㎡ B84.85322.094685240003590000
765년임대경상북도영천시경상북도 영천시 충효로 39영천문외 센트럴타운126아파트LH대구경북2019-12-31<NA><NA><NA>03939.9918.76511450000029000034000000
775년임대경상북도영천시경상북도 영천시 충효로 39영천문외 센트럴타운126아파트LH대구경북2019-12-31<NA><NA><NA>05151.5424.18492580000034000040000000
785년임대강원특별자치도태백시강원특별자치도 태백시 동태백로 465육성2차아파트40아파트태백시1999-04-16계단식개별유류난방미설치101749.827.00521000000735000