Overview

Dataset statistics

Number of variables9
Number of observations75
Missing cells36
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 KiB
Average record size in memory74.8 B

Variable types

Numeric1
Categorical3
Text5

Dataset

Description전라북도 정읍시에 소재한 부동산 중계업소 현황 중(시군구, 중개업소명, 등록번호, 구분, 중개업자명, 전화번호, 사무소도로명주소 ) 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15102223/fileData.do

Alerts

시군구 has constant value ""Constant
데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 연번High correlation
구분 is highly imbalanced (64.7%)Imbalance
전화번호 has 36 (48.0%) missing valuesMissing
연번 has unique valuesUnique
중개업소명 has unique valuesUnique
등록번호 has unique valuesUnique
중개업자명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:53:21.839688
Analysis finished2023-12-12 16:53:22.534145
Duration0.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38
Minimum1
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-13T01:53:22.608092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.7
Q119.5
median38
Q356.5
95-th percentile71.3
Maximum75
Range74
Interquartile range (IQR)37

Descriptive statistics

Standard deviation21.794495
Coefficient of variation (CV)0.57353933
Kurtosis-1.2
Mean38
Median Absolute Deviation (MAD)19
Skewness0
Sum2850
Variance475
MonotonicityStrictly increasing
2023-12-13T01:53:22.752978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.3%
49 1
 
1.3%
56 1
 
1.3%
55 1
 
1.3%
54 1
 
1.3%
53 1
 
1.3%
52 1
 
1.3%
51 1
 
1.3%
50 1
 
1.3%
48 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
1 1
1.3%
2 1
1.3%
3 1
1.3%
4 1
1.3%
5 1
1.3%
6 1
1.3%
7 1
1.3%
8 1
1.3%
9 1
1.3%
10 1
1.3%
ValueCountFrequency (%)
75 1
1.3%
74 1
1.3%
73 1
1.3%
72 1
1.3%
71 1
1.3%
70 1
1.3%
69 1
1.3%
68 1
1.3%
67 1
1.3%
66 1
1.3%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
전라북도 정읍시
75 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라북도 정읍시
2nd row전라북도 정읍시
3rd row전라북도 정읍시
4th row전라북도 정읍시
5th row전라북도 정읍시

Common Values

ValueCountFrequency (%)
전라북도 정읍시 75
100.0%

Length

2023-12-13T01:53:22.907739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:53:23.020864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라북도 75
50.0%
정읍시 75
50.0%

중개업소명
Text

UNIQUE 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size732.0 B
2023-12-13T01:53:23.180238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length10.506667
Min length7

Characters and Unicode

Total characters788
Distinct characters110
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)100.0%

Sample

1st row금강공인중개사사무소
2nd row동신공인중개사사무소
3rd row칠보부동산중개인사무소
4th row개미부동산중개인사무소
5th row홈런부동산중개사무소
ValueCountFrequency (%)
사무소 7
 
8.3%
공인중개사 2
 
2.4%
금강공인중개사사무소 1
 
1.2%
한솔공인중개사사무소 1
 
1.2%
한진공인중개사사무소 1
 
1.2%
금터공인중개사사무소 1
 
1.2%
정원공인중개사사무소 1
 
1.2%
풍전공인중개사사무소 1
 
1.2%
예다음부동산중개사무소 1
 
1.2%
철이아빠공인중개사사무소 1
 
1.2%
Other values (67) 67
79.8%
2023-12-13T01:53:23.570402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 142
18.0%
개 77
9.8%
중 75
9.5%
무 73
9.3%
소 73
9.3%
인 69
 
8.8%
공 64
 
8.1%
동 15
 
1.9%
부 13
 
1.6%
산 13
 
1.6%
Other values (100) 174
22.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 777
98.6%
Space Separator 9
 
1.1%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 142
18.3%
개 77
9.9%
중 75
9.7%
무 73
9.4%
소 73
9.4%
인 69
8.9%
공 64
 
8.2%
동 15
 
1.9%
부 13
 
1.7%
산 13
 
1.7%
Other values (97) 163
21.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
O 1
50.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 777
98.6%
Common 9
 
1.1%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 142
18.3%
개 77
9.9%
중 75
9.7%
무 73
9.4%
소 73
9.4%
인 69
8.9%
공 64
 
8.2%
동 15
 
1.9%
부 13
 
1.7%
산 13
 
1.7%
Other values (97) 163
21.0%
Latin
ValueCountFrequency (%)
K 1
50.0%
O 1
50.0%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 777
98.6%
ASCII 11
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 142
18.3%
개 77
9.9%
중 75
9.7%
무 73
9.4%
소 73
9.4%
인 69
8.9%
공 64
 
8.2%
동 15
 
1.9%
부 13
 
1.7%
산 13
 
1.7%
Other values (97) 163
21.0%
ASCII
ValueCountFrequency (%)
9
81.8%
K 1
 
9.1%
O 1
 
9.1%

등록번호
Text

UNIQUE 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size732.0 B
2023-12-13T01:53:23.820009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length13.613333
Min length7

Characters and Unicode

Total characters1021
Distinct characters13
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

Unique75 ?
Unique (%)100.0%

Sample

1st row가1717-11
2nd row가1717-38
3rd row나1717-9
4th row나1717-22
5th row나1717-25
ValueCountFrequency (%)
가1717-11 1
 
1.3%
45180-2017-00009 1
 
1.3%
45180-2019-00011 1
 
1.3%
45180-2019-00007 1
 
1.3%
45180-2019-00006 1
 
1.3%
45180-2019-00005 1
 
1.3%
45180-2019-00004 1
 
1.3%
45180-2019-00003 1
 
1.3%
45180-2019-00002 1
 
1.3%
45180-2019-00001 1
 
1.3%
Other values (65) 65
86.7%
2023-12-13T01:53:24.562299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 311
30.5%
1 173
16.9%
- 127
12.4%
2 92
 
9.0%
8 68
 
6.7%
5 67
 
6.6%
7 63
 
6.2%
4 61
 
6.0%
가 18
 
1.8%
9 17
 
1.7%
Other values (3) 24
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 871
85.3%
Dash Punctuation 127
 
12.4%
Other Letter 23
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 311
35.7%
1 173
19.9%
2 92
 
10.6%
8 68
 
7.8%
5 67
 
7.7%
7 63
 
7.2%
4 61
 
7.0%
9 17
 
2.0%
6 12
 
1.4%
3 7
 
0.8%
Other Letter
ValueCountFrequency (%)
가 18
78.3%
나 5
 
21.7%
Dash Punctuation
ValueCountFrequency (%)
- 127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 998
97.7%
Hangul 23
 
2.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 311
31.2%
1 173
17.3%
- 127
12.7%
2 92
 
9.2%
8 68
 
6.8%
5 67
 
6.7%
7 63
 
6.3%
4 61
 
6.1%
9 17
 
1.7%
6 12
 
1.2%
Hangul
ValueCountFrequency (%)
가 18
78.3%
나 5
 
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 998
97.7%
Hangul 23
 
2.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 311
31.2%
1 173
17.3%
- 127
12.7%
2 92
 
9.2%
8 68
 
6.8%
5 67
 
6.7%
7 63
 
6.3%
4 61
 
6.1%
9 17
 
1.7%
6 12
 
1.2%
Hangul
ValueCountFrequency (%)
가 18
78.3%
나 5
 
21.7%

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size732.0 B
공인중개사
70 
중개인
 
5

Length

Max length5
Median length5
Mean length4.8666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공인중개사
2nd row공인중개사
3rd row중개인
4th row중개인
5th row중개인

Common Values

ValueCountFrequency (%)
공인중개사 70
93.3%
중개인 5
 
6.7%

Length

2023-12-13T01:53:24.744006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:53:24.860620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공인중개사 70
93.3%
중개인 5
 
6.7%

중개업자명
Text

UNIQUE 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size732.0 B
2023-12-13T01:53:25.151086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9866667
Min length2

Characters and Unicode

Total characters224
Distinct characters88
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

Unique75 ?
Unique (%)100.0%

Sample

1st row유선홍
2nd row홍성옥
3rd row왕기남
4th row이희수
5th row이동준
ValueCountFrequency (%)
유선홍 1
 
1.3%
정민지 1
 
1.3%
신은진 1
 
1.3%
김광호 1
 
1.3%
채규선 1
 
1.3%
송희정 1
 
1.3%
이수경 1
 
1.3%
신현중 1
 
1.3%
신근희 1
 
1.3%
홍범표 1
 
1.3%
Other values (65) 65
86.7%
2023-12-13T01:53:25.615649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 21
 
9.4%
이 12
 
5.4%
수 8
 
3.6%
영 8
 
3.6%
홍 7
 
3.1%
희 7
 
3.1%
유 6
 
2.7%
현 6
 
2.7%
성 5
 
2.2%
정 5
 
2.2%
Other values (78) 139
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 224
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 21
 
9.4%
이 12
 
5.4%
수 8
 
3.6%
영 8
 
3.6%
홍 7
 
3.1%
희 7
 
3.1%
유 6
 
2.7%
현 6
 
2.7%
성 5
 
2.2%
정 5
 
2.2%
Other values (78) 139
62.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 224
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 21
 
9.4%
이 12
 
5.4%
수 8
 
3.6%
영 8
 
3.6%
홍 7
 
3.1%
희 7
 
3.1%
유 6
 
2.7%
현 6
 
2.7%
성 5
 
2.2%
정 5
 
2.2%
Other values (78) 139
62.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 224
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 21
 
9.4%
이 12
 
5.4%
수 8
 
3.6%
영 8
 
3.6%
홍 7
 
3.1%
희 7
 
3.1%
유 6
 
2.7%
현 6
 
2.7%
성 5
 
2.2%
정 5
 
2.2%
Other values (78) 139
62.1%

전화번호
Text

MISSING 

Distinct39
Distinct (%)100.0%
Missing36
Missing (%)48.0%
Memory size732.0 B
2023-12-13T01:53:25.892982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters468
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st row063-535-5243
2nd row063-536-6000
3rd row063-536-2500
4th row063-536-2759
5th row063-571-8600
ValueCountFrequency (%)
063-535-5243 1
 
2.6%
063-538-8007 1
 
2.6%
063-537-6541 1
 
2.6%
063-536-3455 1
 
2.6%
063-244-2244 1
 
2.6%
063-533-7770 1
 
2.6%
063-535-1888 1
 
2.6%
063-532-0089 1
 
2.6%
063-536-0913 1
 
2.6%
063-537-8945 1
 
2.6%
Other values (29) 29
74.4%
2023-12-13T01:53:26.314691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 88
18.8%
- 78
16.7%
5 65
13.9%
0 63
13.5%
6 56
12.0%
7 24
 
5.1%
8 22
 
4.7%
4 21
 
4.5%
1 20
 
4.3%
2 18
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 390
83.3%
Dash Punctuation 78
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 88
22.6%
5 65
16.7%
0 63
16.2%
6 56
14.4%
7 24
 
6.2%
8 22
 
5.6%
4 21
 
5.4%
1 20
 
5.1%
2 18
 
4.6%
9 13
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 468
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 88
18.8%
- 78
16.7%
5 65
13.9%
0 63
13.5%
6 56
12.0%
7 24
 
5.1%
8 22
 
4.7%
4 21
 
4.5%
1 20
 
4.3%
2 18
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 468
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 88
18.8%
- 78
16.7%
5 65
13.9%
0 63
13.5%
6 56
12.0%
7 24
 
5.1%
8 22
 
4.7%
4 21
 
4.5%
1 20
 
4.3%
2 18
 
3.8%

사무소도로명주소
Text

Distinct73
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
2023-12-13T01:53:26.694815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length30
Mean length18.866667
Min length15

Characters and Unicode

Total characters1415
Distinct characters87
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

Unique71 ?
Unique (%)94.7%

Sample

1st row전라북도 정읍시 충정로 136
2nd row전라북도 정읍시 중앙로 259
3rd row전라북도 정읍시 충정로 253-4
4th row전라북도 정읍시 충정로 212
5th row전라북도 정읍시 학산로 112-1, 홈런부동산중개사무소
ValueCountFrequency (%)
전라북도 75
22.6%
정읍시 75
22.6%
충정로 14
 
4.2%
중앙로 9
 
2.7%
샘골로 8
 
2.4%
학산로 6
 
1.8%
상가동 5
 
1.5%
102호 3
 
0.9%
수성택지4길 3
 
0.9%
106호 3
 
0.9%
Other values (108) 131
39.5%
2023-12-13T01:53:27.232623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
257
18.2%
정 96
 
6.8%
읍 82
 
5.8%
북 78
 
5.5%
도 77
 
5.4%
시 77
 
5.4%
라 76
 
5.4%
전 75
 
5.3%
1 72
 
5.1%
로 59
 
4.2%
Other values (77) 466
32.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 876
61.9%
Space Separator 257
 
18.2%
Decimal Number 247
 
17.5%
Dash Punctuation 17
 
1.2%
Other Punctuation 14
 
1.0%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 96
11.0%
읍 82
 
9.4%
북 78
 
8.9%
도 77
 
8.8%
시 77
 
8.8%
라 76
 
8.7%
전 75
 
8.6%
로 59
 
6.7%
길 16
 
1.8%
중 15
 
1.7%
Other values (62) 225
25.7%
Decimal Number
ValueCountFrequency (%)
1 72
29.1%
2 35
14.2%
3 19
 
7.7%
9 19
 
7.7%
6 19
 
7.7%
5 19
 
7.7%
0 18
 
7.3%
8 17
 
6.9%
4 17
 
6.9%
7 12
 
4.9%
Space Separator
ValueCountFrequency (%)
257
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 876
61.9%
Common 539
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 96
11.0%
읍 82
 
9.4%
북 78
 
8.9%
도 77
 
8.8%
시 77
 
8.8%
라 76
 
8.7%
전 75
 
8.6%
로 59
 
6.7%
길 16
 
1.8%
중 15
 
1.7%
Other values (62) 225
25.7%
Common
ValueCountFrequency (%)
257
47.7%
1 72
 
13.4%
2 35
 
6.5%
3 19
 
3.5%
9 19
 
3.5%
6 19
 
3.5%
5 19
 
3.5%
0 18
 
3.3%
8 17
 
3.2%
4 17
 
3.2%
Other values (5) 47
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 876
61.9%
ASCII 539
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
257
47.7%
1 72
 
13.4%
2 35
 
6.5%
3 19
 
3.5%
9 19
 
3.5%
6 19
 
3.5%
5 19
 
3.5%
0 18
 
3.3%
8 17
 
3.2%
4 17
 
3.2%
Other values (5) 47
 
8.7%
Hangul
ValueCountFrequency (%)
정 96
11.0%
읍 82
 
9.4%
북 78
 
8.9%
도 77
 
8.8%
시 77
 
8.8%
라 76
 
8.7%
전 75
 
8.6%
로 59
 
6.7%
길 16
 
1.8%
중 15
 
1.7%
Other values (62) 225
25.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
2022-12-31
75 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-31
2nd row2022-12-31
3rd row2022-12-31
4th row2022-12-31
5th row2022-12-31

Common Values

ValueCountFrequency (%)
2022-12-31 75
100.0%

Length

2023-12-13T01:53:27.412844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:53:27.535545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-31 75
100.0%

Interactions

2023-12-13T01:53:22.230064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:53:27.615881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번중개업소명등록번호구분중개업자명전화번호사무소도로명주소
연번1.0001.0001.0000.7011.0001.0000.865
중개업소명1.0001.0001.0001.0001.0001.0001.000
등록번호1.0001.0001.0001.0001.0001.0001.000
구분0.7011.0001.0001.0001.0001.0001.000
중개업자명1.0001.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.000
사무소도로명주소0.8651.0001.0001.0001.0001.0001.000
2023-12-13T01:53:27.744967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분
연번1.0000.516
구분0.5161.000

Missing values

2023-12-13T01:53:22.352933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:53:22.483476image/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

연번시군구중개업소명등록번호구분중개업자명전화번호사무소도로명주소데이터기준일자
01전라북도 정읍시금강공인중개사사무소가1717-11공인중개사유선홍<NA>전라북도 정읍시 충정로 1362022-12-31
12전라북도 정읍시동신공인중개사사무소가1717-38공인중개사홍성옥063-535-5243전라북도 정읍시 중앙로 2592022-12-31
23전라북도 정읍시칠보부동산중개인사무소나1717-9중개인왕기남<NA>전라북도 정읍시 충정로 253-42022-12-31
34전라북도 정읍시개미부동산중개인사무소나1717-22중개인이희수063-536-6000전라북도 정읍시 충정로 2122022-12-31
45전라북도 정읍시홈런부동산중개사무소나1717-25중개인이동준063-536-2500전라북도 정읍시 학산로 112-1, 홈런부동산중개사무소2022-12-31
56전라북도 정읍시영원부동산중개인사무소나1717-41중개인김석홍063-536-2759전라북도 정읍시 영원면 영원로 10912022-12-31
67전라북도 정읍시신태인공인중개사사무소가1717-56공인중개사강용원063-571-8600전라북도 정읍시 신태인읍 연정길 252022-12-31
78전라북도 정읍시대한공인중개사사무소가1717-72공인중개사김인옥063-536-8589전라북도 정읍시 수성2로 152022-12-31
89전라북도 정읍시비전공인중개사 사무소가1717-77공인중개사양갑섭<NA>전라북도 정읍시 정인1길 23(수성동)2022-12-31
910전라북도 정읍시참사랑공인중개사사무소가1717-79공인중개사이석영<NA>전라북도 정읍시 충정로 1552022-12-31
연번시군구중개업소명등록번호구분중개업자명전화번호사무소도로명주소데이터기준일자
6566전라북도 정읍시우리공인중개사사무소45180-2021-00006공인중개사이명신<NA>전라북도 정읍시 태인면 태인로 262022-12-31
6667전라북도 정읍시팔도부동산중개사무소45180-2021-00007공인중개사장현민<NA>전라북도 정읍시 상신경1길 13-42022-12-31
6768전라북도 정읍시정읍사랑부동산중개사사무소45180-2021-00008공인중개사김운한<NA>전라북도 정읍시 중앙로 612022-12-31
6869전라북도 정읍시경진공인중개사사무소45180-2022-00001공인중개사유지수<NA>전라북도 정읍시 조곡천1길 6, 3층2022-12-31
6970전라북도 정읍시고려공인중개사사무소45180-2022-00002공인중개사김영모<NA>전라북도 정읍시 중앙1길 922022-12-31
7071전라북도 정읍시정신공인중개사사무소45180-2022-00003공인중개사최보기<NA>전라북도 정읍시 신태인읍 신태인중앙로 292022-12-31
7172전라북도 정읍시수목토공인중개사사무소45180-2022-00004공인중개사유영애063-537-5208전라북도 정읍시 학산로 89-25, 상가동 102호2022-12-31
7273전라북도 정읍시매일공인중개사사무소45180-2022-00005공인중개사조유진<NA>전라북도 정읍시 조곡천1길 82022-12-31
7374전라북도 정읍시이지공인중개사사무소45180-2022-00006공인중개사김은경<NA>전라북도 정읍시 중앙로 55, 정읍연지영무예다음주상복합 108호2022-12-31
7475전라북도 정읍시수공인중개사사무소45180-2022-00007공인중개사이희영<NA>전라북도 정읍시 수성택지4길 282022-12-31