Overview

Dataset statistics

Number of variables8
Number of observations181
Missing cells64
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 KiB
Average record size in memory66.7 B

Variable types

Text4
Numeric2
Categorical2

Dataset

Description1. 중개업을 영위하고자 개업공인중개사가 김천시에 개설등록을 한 중개사무소로서, 중개대상물(토지, 건축물 그 밖의 토지의 정착물, 그 밖에 대통령령으로 정하는 재산권 및 물권)에 대하여 거래당사자간의 매매, 교환, 임대차 그 밖의 권리의 득실변경에 관한 행위를 알선하는 중개사무소입니다.2. 경상북도 김천시에 등록된 공인중개사사무소 현황으로 중개사무소명, 소재지도로명주소, 전화번호, 공제가입유무, 대표자명 등의 정보를 제공합니다.3. 해당 중개사무소가 현재 중개업을 영위하는지 여부를 김천시청 부동산관리팀(054-420-6383)에 확인하시기 바랍니다.
Author경상북도 김천시
URLhttps://www.data.go.kr/data/15127130/fileData.do

Alerts

공제가입유무 has constant value ""Constant
데이터기준일자 has constant value ""Constant
전화번호 has 64 (35.4%) missing valuesMissing
중개사무소명 has unique valuesUnique
대표자명 has unique valuesUnique

Reproduction

Analysis started2024-03-16 04:13:53.834032
Analysis finished2024-03-16 04:13:55.387990
Duration1.55 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

중개사무소명
Text

UNIQUE 

Distinct181
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-03-16T13:13:55.584217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length10.895028
Min length7

Characters and Unicode

Total characters1972
Distinct characters208
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique181 ?
Unique (%)100.0%

Sample

1st row지오공인중개사사무소
2nd row두드림공인중개사사무소
3rd row우제공인중개사사무소
4th row희망공인중개사사무소
5th row원탑공인중개사사무소
ValueCountFrequency (%)
사무소 4
 
2.2%
지오공인중개사사무소 1
 
0.5%
우리공인중개사사무소 1
 
0.5%
혁신1번지공인중개사사무소 1
 
0.5%
반석공인중개사사무소 1
 
0.5%
대한부동산중개사무소 1
 
0.5%
부부공인중개사사무소 1
 
0.5%
터공인중개사사무소 1
 
0.5%
카카오공인중개사사무소 1
 
0.5%
김천한솔부동산공인중개사사무소 1
 
0.5%
Other values (172) 172
93.0%
2024-03-16T13:13:56.053612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 354
18.0%
중 183
 
9.3%
개 182
 
9.2%
소 178
 
9.0%
무 178
 
9.0%
인 176
 
8.9%
공 172
 
8.7%
동 24
 
1.2%
산 20
 
1.0%
부 20
 
1.0%
Other values (198) 485
24.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1947
98.7%
Uppercase Letter 17
 
0.9%
Space Separator 4
 
0.2%
Decimal Number 3
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 354
18.2%
중 183
 
9.4%
개 182
 
9.3%
소 178
 
9.1%
무 178
 
9.1%
인 176
 
9.0%
공 172
 
8.8%
동 24
 
1.2%
산 20
 
1.0%
부 20
 
1.0%
Other values (182) 460
23.6%
Uppercase Letter
ValueCountFrequency (%)
E 3
17.6%
W 3
17.6%
N 2
11.8%
S 1
 
5.9%
C 1
 
5.9%
G 1
 
5.9%
A 1
 
5.9%
B 1
 
5.9%
L 1
 
5.9%
I 1
 
5.9%
Other values (2) 2
11.8%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
4
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1947
98.7%
Latin 17
 
0.9%
Common 8
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 354
18.2%
중 183
 
9.4%
개 182
 
9.3%
소 178
 
9.1%
무 178
 
9.1%
인 176
 
9.0%
공 172
 
8.8%
동 24
 
1.2%
산 20
 
1.0%
부 20
 
1.0%
Other values (182) 460
23.6%
Latin
ValueCountFrequency (%)
E 3
17.6%
W 3
17.6%
N 2
11.8%
S 1
 
5.9%
C 1
 
5.9%
G 1
 
5.9%
A 1
 
5.9%
B 1
 
5.9%
L 1
 
5.9%
I 1
 
5.9%
Other values (2) 2
11.8%
Common
ValueCountFrequency (%)
4
50.0%
1 2
25.0%
/ 1
 
12.5%
2 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1947
98.7%
ASCII 25
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 354
18.2%
중 183
 
9.4%
개 182
 
9.3%
소 178
 
9.1%
무 178
 
9.1%
인 176
 
9.0%
공 172
 
8.8%
동 24
 
1.2%
산 20
 
1.0%
부 20
 
1.0%
Other values (182) 460
23.6%
ASCII
ValueCountFrequency (%)
4
16.0%
E 3
12.0%
W 3
12.0%
1 2
 
8.0%
N 2
 
8.0%
/ 1
 
4.0%
S 1
 
4.0%
C 1
 
4.0%
2 1
 
4.0%
G 1
 
4.0%
Other values (6) 6
24.0%

소재지도로명주소
Text

Distinct175
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-03-16T13:13:56.508994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length40
Mean length24.370166
Min length16

Characters and Unicode

Total characters4411
Distinct characters174
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)93.9%

Sample

1st row경상북도 김천시 신기길 118-12
2nd row경상북도 김천시 혁신4로 75 103호
3rd row경상북도 김천시 아포읍 한지2길 56 2층
4th row경상북도 김천시 시청1길 93 상가2동 107호
5th row경상북도 김천시 송설로 142 상가 1-2호
ValueCountFrequency (%)
경상북도 181
 
19.1%
김천시 181
 
19.1%
율곡동 14
 
1.5%
영남대로 14
 
1.5%
아포읍 11
 
1.2%
상가동 10
 
1.1%
시청로 10
 
1.1%
신음동 9
 
1.0%
혁신4로 9
 
1.0%
1층 9
 
1.0%
Other values (284) 499
52.7%
2024-03-16T13:13:57.377317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
839
19.0%
1 232
 
5.3%
상 214
 
4.9%
시 202
 
4.6%
김 190
 
4.3%
천 190
 
4.3%
도 183
 
4.1%
경 182
 
4.1%
북 181
 
4.1%
로 124
 
2.8%
Other values (164) 1874
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2584
58.6%
Space Separator 839
 
19.0%
Decimal Number 791
 
17.9%
Open Punctuation 66
 
1.5%
Close Punctuation 66
 
1.5%
Other Punctuation 32
 
0.7%
Dash Punctuation 29
 
0.7%
Lowercase Letter 3
 
0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 214
 
8.3%
시 202
 
7.8%
김 190
 
7.4%
천 190
 
7.4%
도 183
 
7.1%
경 182
 
7.0%
북 181
 
7.0%
로 124
 
4.8%
동 95
 
3.7%
호 68
 
2.6%
Other values (145) 955
37.0%
Decimal Number
ValueCountFrequency (%)
1 232
29.3%
0 103
13.0%
2 97
12.3%
4 79
 
10.0%
3 69
 
8.7%
6 52
 
6.6%
5 49
 
6.2%
8 46
 
5.8%
7 39
 
4.9%
9 25
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 31
96.9%
. 1
 
3.1%
Lowercase Letter
ValueCountFrequency (%)
c 2
66.7%
k 1
33.3%
Space Separator
ValueCountFrequency (%)
839
100.0%
Open Punctuation
ValueCountFrequency (%)
( 66
100.0%
Close Punctuation
ValueCountFrequency (%)
) 66
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2584
58.6%
Common 1823
41.3%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 214
 
8.3%
시 202
 
7.8%
김 190
 
7.4%
천 190
 
7.4%
도 183
 
7.1%
경 182
 
7.0%
북 181
 
7.0%
로 124
 
4.8%
동 95
 
3.7%
호 68
 
2.6%
Other values (145) 955
37.0%
Common
ValueCountFrequency (%)
839
46.0%
1 232
 
12.7%
0 103
 
5.7%
2 97
 
5.3%
4 79
 
4.3%
3 69
 
3.8%
( 66
 
3.6%
) 66
 
3.6%
6 52
 
2.9%
5 49
 
2.7%
Other values (6) 171
 
9.4%
Latin
ValueCountFrequency (%)
c 2
50.0%
k 1
25.0%
A 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2584
58.6%
ASCII 1827
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
839
45.9%
1 232
 
12.7%
0 103
 
5.6%
2 97
 
5.3%
4 79
 
4.3%
3 69
 
3.8%
( 66
 
3.6%
) 66
 
3.6%
6 52
 
2.8%
5 49
 
2.7%
Other values (9) 175
 
9.6%
Hangul
ValueCountFrequency (%)
상 214
 
8.3%
시 202
 
7.8%
김 190
 
7.4%
천 190
 
7.4%
도 183
 
7.1%
경 182
 
7.0%
북 181
 
7.0%
로 124
 
4.8%
동 95
 
3.7%
호 68
 
2.6%
Other values (145) 955
37.0%

전화번호
Text

MISSING 

Distinct117
Distinct (%)100.0%
Missing64
Missing (%)35.4%
Memory size1.5 KiB
2024-03-16T13:13:57.966352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1404
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

Unique117 ?
Unique (%)100.0%

Sample

1st row054-434-0779
2nd row054-435-7508
3rd row054-439-7900
4th row054-437-9100
5th row054-911-1324
ValueCountFrequency (%)
054-434-2223 1
 
0.9%
054-432-1113 1
 
0.9%
054-435-5949 1
 
0.9%
054-434-5757 1
 
0.9%
054-435-5086 1
 
0.9%
054-432-4959 1
 
0.9%
054-435-1114 1
 
0.9%
054-436-6789 1
 
0.9%
054-437-6780 1
 
0.9%
054-436-5285 1
 
0.9%
Other values (107) 107
91.5%
2024-03-16T13:13:58.642021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 296
21.1%
- 234
16.7%
0 187
13.3%
5 179
12.7%
3 170
12.1%
8 65
 
4.6%
6 60
 
4.3%
7 58
 
4.1%
9 53
 
3.8%
2 52
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1170
83.3%
Dash Punctuation 234
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 296
25.3%
0 187
16.0%
5 179
15.3%
3 170
14.5%
8 65
 
5.6%
6 60
 
5.1%
7 58
 
5.0%
9 53
 
4.5%
2 52
 
4.4%
1 50
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 234
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1404
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 296
21.1%
- 234
16.7%
0 187
13.3%
5 179
12.7%
3 170
12.1%
8 65
 
4.6%
6 60
 
4.3%
7 58
 
4.1%
9 53
 
3.8%
2 52
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1404
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 296
21.1%
- 234
16.7%
0 187
13.3%
5 179
12.7%
3 170
12.1%
8 65
 
4.6%
6 60
 
4.3%
7 58
 
4.1%
9 53
 
3.8%
2 52
 
3.7%

위도
Real number (ℝ)

Distinct154
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.124527
Minimum35.980224
Maximum36.180571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-16T13:13:58.897550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.980224
5-th percentile36.114755
Q136.118736
median36.123628
Q336.132282
95-th percentile36.158287
Maximum36.180571
Range0.20034751
Interquartile range (IQR)0.01354619

Descriptive statistics

Standard deviation0.020231715
Coefficient of variation (CV)0.00056005479
Kurtosis27.644155
Mean36.124527
Median Absolute Deviation (MAD)0.0049237
Skewness-3.7935782
Sum6538.5395
Variance0.00040932228
MonotonicityNot monotonic
2024-03-16T13:13:59.197687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.11870383 5
 
2.8%
36.11873582 4
 
2.2%
36.11751562 4
 
2.2%
36.1223717 4
 
2.2%
36.12092394 3
 
1.7%
36.12084502 3
 
1.7%
36.13185772 2
 
1.1%
36.13271651 2
 
1.1%
36.13411199 2
 
1.1%
36.13586396 2
 
1.1%
Other values (144) 150
82.9%
ValueCountFrequency (%)
35.98022391 1
0.6%
35.98180093 1
0.6%
36.06756914 1
0.6%
36.06995548 1
0.6%
36.10246467 1
0.6%
36.10257264 1
0.6%
36.1109101 1
0.6%
36.11413598 1
0.6%
36.11457924 1
0.6%
36.11475478 1
0.6%
ValueCountFrequency (%)
36.18057142 1
0.6%
36.16066332 1
0.6%
36.15982814 1
0.6%
36.1596152 1
0.6%
36.15953414 1
0.6%
36.15949487 1
0.6%
36.15916668 1
0.6%
36.1585491 1
0.6%
36.15847641 1
0.6%
36.15828726 1
0.6%

경도
Real number (ℝ)

Distinct154
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.14658
Minimum128.02639
Maximum128.27934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-16T13:13:59.493516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.02639
5-th percentile128.08608
Q1128.11376
median128.12862
Q3128.18303
95-th percentile128.25985
Maximum128.27934
Range0.2529509
Interquartile range (IQR)0.0692759

Descriptive statistics

Standard deviation0.048958593
Coefficient of variation (CV)0.0003820515
Kurtosis0.20745899
Mean128.14658
Median Absolute Deviation (MAD)0.0320368
Skewness0.62756655
Sum23194.531
Variance0.0023969439
MonotonicityNot monotonic
2024-03-16T13:13:59.809462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.1790271 5
 
2.8%
128.1875141 4
 
2.2%
128.1839454 4
 
2.2%
128.1732254 4
 
2.2%
128.1830314 3
 
1.7%
128.1874081 3
 
1.7%
128.1183272 2
 
1.1%
128.1157397 2
 
1.1%
128.1172973 2
 
1.1%
128.1137555 2
 
1.1%
Other values (144) 150
82.9%
ValueCountFrequency (%)
128.02639 1
0.6%
128.0276215 1
0.6%
128.0750132 1
0.6%
128.0774129 1
0.6%
128.0775718 1
0.6%
128.0776015 1
0.6%
128.0779575 1
0.6%
128.0825305 1
0.6%
128.0858099 1
0.6%
128.0860756 1
0.6%
ValueCountFrequency (%)
128.2793409 1
0.6%
128.2675884 1
0.6%
128.2644992 1
0.6%
128.2631168 1
0.6%
128.2625794 1
0.6%
128.262486 1
0.6%
128.2624298 1
0.6%
128.2622195 1
0.6%
128.2600765 1
0.6%
128.2598481 1
0.6%

공제가입유무
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
o
181 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
o 181
100.0%

Length

2024-03-16T13:14:00.076147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:14:00.216359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
o 181
100.0%

대표자명
Text

UNIQUE 

Distinct181
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-03-16T13:14:00.693727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9944751
Min length2

Characters and Unicode

Total characters542
Distinct characters133
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

Unique181 ?
Unique (%)100.0%

Sample

1st row남가연
2nd row백진숙
3rd row박규야
4th row조영숙
5th row김규엽
ValueCountFrequency (%)
남가연 1
 
0.6%
문혜정 1
 
0.6%
이대희 1
 
0.6%
권용현 1
 
0.6%
한효경 1
 
0.6%
박서우 1
 
0.6%
유현주 1
 
0.6%
문인환 1
 
0.6%
이재기 1
 
0.6%
김혜옥 1
 
0.6%
Other values (171) 171
94.5%
2024-03-16T13:14:01.563486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 44
 
8.1%
이 24
 
4.4%
박 23
 
4.2%
정 17
 
3.1%
희 15
 
2.8%
영 14
 
2.6%
미 12
 
2.2%
현 11
 
2.0%
경 11
 
2.0%
숙 11
 
2.0%
Other values (123) 360
66.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 542
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 44
 
8.1%
이 24
 
4.4%
박 23
 
4.2%
정 17
 
3.1%
희 15
 
2.8%
영 14
 
2.6%
미 12
 
2.2%
현 11
 
2.0%
경 11
 
2.0%
숙 11
 
2.0%
Other values (123) 360
66.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 542
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 44
 
8.1%
이 24
 
4.4%
박 23
 
4.2%
정 17
 
3.1%
희 15
 
2.8%
영 14
 
2.6%
미 12
 
2.2%
현 11
 
2.0%
경 11
 
2.0%
숙 11
 
2.0%
Other values (123) 360
66.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 542
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 44
 
8.1%
이 24
 
4.4%
박 23
 
4.2%
정 17
 
3.1%
희 15
 
2.8%
영 14
 
2.6%
미 12
 
2.2%
현 11
 
2.0%
경 11
 
2.0%
숙 11
 
2.0%
Other values (123) 360
66.4%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-03-11
181 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-11
2nd row2024-03-11
3rd row2024-03-11
4th row2024-03-11
5th row2024-03-11

Common Values

ValueCountFrequency (%)
2024-03-11 181
100.0%

Length

2024-03-16T13:14:01.807781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:14:01.970808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-11 181
100.0%

Interactions

2024-03-16T13:13:54.591838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:54.290344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:54.770151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:54.446902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T13:14:02.119526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.0000.809
경도0.8091.000
2024-03-16T13:14:02.249069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.000-0.301
경도-0.3011.000

Missing values

2024-03-16T13:13:54.975285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T13:13:55.214204image/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

중개사무소명소재지도로명주소전화번호위도경도공제가입유무대표자명데이터기준일자
0지오공인중개사사무소경상북도 김천시 신기길 118-12<NA>36.137718128.116558o남가연2024-03-11
1두드림공인중개사사무소경상북도 김천시 혁신4로 75 103호<NA>36.121463128.189798o백진숙2024-03-11
2우제공인중개사사무소경상북도 김천시 아포읍 한지2길 56 2층<NA>36.158549128.262219o박규야2024-03-11
3희망공인중개사사무소경상북도 김천시 시청1길 93 상가2동 107호<NA>36.14255128.11571o조영숙2024-03-11
4원탑공인중개사사무소경상북도 김천시 송설로 142 상가 1-2호<NA>36.123628128.100273o김규엽2024-03-11
5뉴코아루공인중개사사무소경상북도 김천시 무실7길 45 401동 107호054-434-077936.11091128.158205o박중근2024-03-11
6박현주공인중개사사무소경상북도 김천시 용전3로 7 102호054-435-750836.121379128.191709o박현주2024-03-11
7효천공인중개사사무소경상북도 김천시 김천로 27-1 2층<NA>36.12602128.106409o이효천2024-03-11
8봄봄공인중개사사무소경상북도 김천시 혁신4로 54 114동 109호054-439-790036.120845128.187408o채교웅2024-03-11
9한신더휴공인중개사사무소경상북도 김천시 혁신4로 54 114동 109호<NA>36.120845128.187408o엄영란2024-03-11
중개사무소명소재지도로명주소전화번호위도경도공제가입유무대표자명데이터기준일자
171대로공인중개사사무소경상북도 김천시 영남대로 2024<NA>36.117852128.149988o이응원2024-03-11
172다복공인중개사사무소경상북도 김천시 용전4로2길 35 101호(율곡동)054-436-369036.121067128.195074o박옥진2024-03-11
173일송공인중개사사무소경상북도 김천시 자산로 106 1층054-435-001236.120733128.120632o이한상2024-03-11
174천지공인중개사경상북도 김천시 영남대로 2089 (덕곡동)054-436-888336.115049128.156725o신효숙2024-03-11
175동양공인중개사사무소경상북도 김천시 시민로 31 (부곡동)054-433-820836.126717128.10241o차동수2024-03-11
176엣지공인중개사사무소경상북도 김천시 용전1로 8 상가동 1층 104호(율곡동, 사랑으로 부영1단지)<NA>36.118149128.188068o이인옥2024-03-11
177정성부동산중개사사무소경상북도 김천시 혁신1로 75 제2층 223호<NA>36.115243128.179197o최상중2024-03-11
178천상부동산중개경상북도 김천시 혁신로 185 113호(율곡동, 메타폴리스)<NA>36.117516128.183945o이영자2024-03-11
179태양공인중개사사무소경상북도 김천시 구농고길 21054-436-634336.126228128.105287o장예림2024-03-11
180성광공인중개사사무소경상북도 김천시 김천로 35054-434-530036.12637128.107143o박성규2024-03-11