Overview

Dataset statistics

Number of variables7
Number of observations840
Missing cells184
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.1 KiB
Average record size in memory56.2 B

Variable types

Text5
Categorical2

Dataset

Description경상남도 양산시 부동산 등록번호 중개업소명 , 중개업소종별, 중개업자명, 사무실연락처, 소재주소를 확인할 수 있습니다.
Author경상남도 양산시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3065863

Alerts

직위 is highly overall correlated with 중개업소종별High correlation
중개업소종별 is highly overall correlated with 직위High correlation
중개업소종별 is highly imbalanced (94.2%)Imbalance
직위 is highly imbalanced (98.7%)Imbalance
사무소전화번호 has 182 (21.7%) missing valuesMissing

Reproduction

Analysis started2024-04-21 07:07:28.305715
Analysis finished2024-04-21 07:07:30.082290
Duration1.78 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록번호
Text

Distinct837
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
2024-04-21T16:07:30.793947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length13.758333
Min length9

Characters and Unicode

Total characters11557
Distinct characters12
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

Unique836 ?
Unique (%)99.5%

Sample

1st row가4330-201
2nd row가4330-267
3rd row가4330-404
4th row가4330-431
5th row가4330-438
ValueCountFrequency (%)
가4330-2265 4
 
0.5%
48330-2019-00085 1
 
0.1%
48330-2017-00271 1
 
0.1%
48330-2017-00272 1
 
0.1%
48330-2017-00259 1
 
0.1%
48330-2017-00260 1
 
0.1%
48330-2017-00262 1
 
0.1%
48330-2017-00263 1
 
0.1%
48330-2017-00264 1
 
0.1%
48330-2017-00265 1
 
0.1%
Other values (827) 827
98.5%
2024-04-21T16:07:31.948023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3008
26.0%
3 1901
16.4%
- 1372
11.9%
4 1074
 
9.3%
2 1037
 
9.0%
1 959
 
8.3%
8 781
 
6.8%
가 309
 
2.7%
7 296
 
2.6%
6 286
 
2.5%
Other values (2) 534
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9876
85.5%
Dash Punctuation 1372
 
11.9%
Other Letter 309
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3008
30.5%
3 1901
19.2%
4 1074
 
10.9%
2 1037
 
10.5%
1 959
 
9.7%
8 781
 
7.9%
7 296
 
3.0%
6 286
 
2.9%
5 272
 
2.8%
9 262
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 1372
100.0%
Other Letter
ValueCountFrequency (%)
가 309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11248
97.3%
Hangul 309
 
2.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3008
26.7%
3 1901
16.9%
- 1372
12.2%
4 1074
 
9.5%
2 1037
 
9.2%
1 959
 
8.5%
8 781
 
6.9%
7 296
 
2.6%
6 286
 
2.5%
5 272
 
2.4%
Hangul
ValueCountFrequency (%)
가 309
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11248
97.3%
Hangul 309
 
2.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3008
26.7%
3 1901
16.9%
- 1372
12.2%
4 1074
 
9.5%
2 1037
 
9.2%
1 959
 
8.5%
8 781
 
6.9%
7 296
 
2.6%
6 286
 
2.5%
5 272
 
2.4%
Hangul
ValueCountFrequency (%)
가 309
100.0%

사무소명
Text

Distinct832
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
2024-04-21T16:07:32.680920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length19
Mean length11.514286
Min length4

Characters and Unicode

Total characters9672
Distinct characters381
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique826 ?
Unique (%)98.3%

Sample

1st row부성부동산중개사무소
2nd row제일공인중개사사무소
3rd row명보공인중개사사무소
4th row하늘공인중개사사무소
5th row북정부동산공인중개사사무소
ValueCountFrequency (%)
주식회사범어부동산중개 4
 
0.5%
큰길합동공인중개사사무소 2
 
0.2%
좋은사람합동공인중개사사무소 2
 
0.2%
114합동공인중개사사무소 2
 
0.2%
주)타워부동산중개법인 2
 
0.2%
신대동합동공인중개사사무소 2
 
0.2%
sk합동공인중개사사무소 2
 
0.2%
동원가비공인중개사사무소 1
 
0.1%
우리땅공인중개사사무소 1
 
0.1%
양산채움공인중개사사무소 1
 
0.1%
Other values (823) 823
97.7%
2024-04-21T16:07:34.028515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 1611
16.7%
개 845
 
8.7%
중 840
 
8.7%
소 823
 
8.5%
무 819
 
8.5%
인 783
 
8.1%
공 768
 
7.9%
산 304
 
3.1%
동 293
 
3.0%
부 235
 
2.4%
Other values (371) 2351
24.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9498
98.2%
Uppercase Letter 66
 
0.7%
Decimal Number 64
 
0.7%
Lowercase Letter 23
 
0.2%
Close Punctuation 7
 
0.1%
Open Punctuation 7
 
0.1%
Space Separator 2
 
< 0.1%
Math Symbol 2
 
< 0.1%
Dash Punctuation 2
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 1611
17.0%
개 845
 
8.9%
중 840
 
8.8%
소 823
 
8.7%
무 819
 
8.6%
인 783
 
8.2%
공 768
 
8.1%
산 304
 
3.2%
동 293
 
3.1%
부 235
 
2.5%
Other values (336) 2177
22.9%
Uppercase Letter
ValueCountFrequency (%)
K 16
24.2%
O 8
12.1%
E 6
 
9.1%
T 5
 
7.6%
N 5
 
7.6%
C 4
 
6.1%
S 4
 
6.1%
G 4
 
6.1%
W 4
 
6.1%
M 2
 
3.0%
Other values (5) 8
12.1%
Decimal Number
ValueCountFrequency (%)
1 15
23.4%
2 12
18.8%
8 9
14.1%
5 8
12.5%
4 7
10.9%
3 6
 
9.4%
6 4
 
6.2%
7 3
 
4.7%
Lowercase Letter
ValueCountFrequency (%)
e 12
52.2%
w 3
 
13.0%
h 3
 
13.0%
c 2
 
8.7%
n 2
 
8.7%
k 1
 
4.3%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9492
98.1%
Latin 89
 
0.9%
Common 85
 
0.9%
Han 6
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 1611
17.0%
개 845
 
8.9%
중 840
 
8.8%
소 823
 
8.7%
무 819
 
8.6%
인 783
 
8.2%
공 768
 
8.1%
산 304
 
3.2%
동 293
 
3.1%
부 235
 
2.5%
Other values (330) 2171
22.9%
Latin
ValueCountFrequency (%)
K 16
18.0%
e 12
13.5%
O 8
 
9.0%
E 6
 
6.7%
T 5
 
5.6%
N 5
 
5.6%
C 4
 
4.5%
S 4
 
4.5%
G 4
 
4.5%
W 4
 
4.5%
Other values (11) 21
23.6%
Common
ValueCountFrequency (%)
1 15
17.6%
2 12
14.1%
8 9
10.6%
5 8
9.4%
) 7
8.2%
( 7
8.2%
4 7
8.2%
3 6
 
7.1%
6 4
 
4.7%
7 3
 
3.5%
Other values (4) 7
8.2%
Han
ValueCountFrequency (%)
信 1
16.7%
新 1
16.7%
上 1
16.7%
世 1
16.7%
眞 1
16.7%
玉 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9492
98.1%
ASCII 174
 
1.8%
CJK 6
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 1611
17.0%
개 845
 
8.9%
중 840
 
8.8%
소 823
 
8.7%
무 819
 
8.6%
인 783
 
8.2%
공 768
 
8.1%
산 304
 
3.2%
동 293
 
3.1%
부 235
 
2.5%
Other values (330) 2171
22.9%
ASCII
ValueCountFrequency (%)
K 16
 
9.2%
1 15
 
8.6%
2 12
 
6.9%
e 12
 
6.9%
8 9
 
5.2%
O 8
 
4.6%
5 8
 
4.6%
) 7
 
4.0%
( 7
 
4.0%
4 7
 
4.0%
Other values (25) 73
42.0%
CJK
ValueCountFrequency (%)
信 1
16.7%
新 1
16.7%
上 1
16.7%
世 1
16.7%
眞 1
16.7%
玉 1
16.7%

중개업소종별
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
공인중개사
829 
법인
 
8
중개인
 
2
<NA>
 
1

Length

Max length5
Median length5
Mean length4.9654762
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
공인중개사 829
98.7%
법인 8
 
1.0%
중개인 2
 
0.2%
<NA> 1
 
0.1%

Length

2024-04-21T16:07:34.464710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T16:07:34.809980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공인중개사 829
98.7%
법인 8
 
1.0%
중개인 2
 
0.2%
na 1
 
0.1%

중개업자명
Text

Distinct811
Distinct (%)96.7%
Missing1
Missing (%)0.1%
Memory size6.7 KiB
2024-04-21T16:07:36.122481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9988081
Min length2

Characters and Unicode

Total characters2516
Distinct characters185
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

Unique787 ?
Unique (%)93.8%

Sample

1st row최춘복
2nd row조정인
3rd row윤길원
4th row최동철
5th row문상근
ValueCountFrequency (%)
김경희 3
 
0.4%
김정숙 3
 
0.4%
김정미 3
 
0.4%
김은희 3
 
0.4%
이영희 2
 
0.2%
이미영 2
 
0.2%
차미경 2
 
0.2%
김수경 2
 
0.2%
김경미 2
 
0.2%
김도연 2
 
0.2%
Other values (801) 815
97.1%
2024-04-21T16:07:37.856439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 197
 
7.8%
정 127
 
5.0%
이 115
 
4.6%
영 79
 
3.1%
희 71
 
2.8%
경 63
 
2.5%
숙 63
 
2.5%
미 59
 
2.3%
박 58
 
2.3%
윤 48
 
1.9%
Other values (175) 1636
65.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2516
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 197
 
7.8%
정 127
 
5.0%
이 115
 
4.6%
영 79
 
3.1%
희 71
 
2.8%
경 63
 
2.5%
숙 63
 
2.5%
미 59
 
2.3%
박 58
 
2.3%
윤 48
 
1.9%
Other values (175) 1636
65.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2516
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 197
 
7.8%
정 127
 
5.0%
이 115
 
4.6%
영 79
 
3.1%
희 71
 
2.8%
경 63
 
2.5%
숙 63
 
2.5%
미 59
 
2.3%
박 58
 
2.3%
윤 48
 
1.9%
Other values (175) 1636
65.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2516
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 197
 
7.8%
정 127
 
5.0%
이 115
 
4.6%
영 79
 
3.1%
희 71
 
2.8%
경 63
 
2.5%
숙 63
 
2.5%
미 59
 
2.3%
박 58
 
2.3%
윤 48
 
1.9%
Other values (175) 1636
65.0%

직위
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
대표
839 
<NA>
 
1

Length

Max length4
Median length2
Mean length2.002381
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
대표 839
99.9%
<NA> 1
 
0.1%

Length

2024-04-21T16:07:38.102918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T16:07:38.314300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대표 839
99.9%
na 1
 
0.1%

사무소전화번호
Text

MISSING 

Distinct626
Distinct (%)95.1%
Missing182
Missing (%)21.7%
Memory size6.7 KiB
2024-04-21T16:07:39.051262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters7896
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique597 ?
Unique (%)90.7%

Sample

1st row055-382-6546
2nd row055-374-9797
3rd row055-386-4818
4th row055-367-3000
5th row055-389-2154
ValueCountFrequency (%)
055-363-0001 4
 
0.6%
055-388-5538 3
 
0.5%
055-386-2266 2
 
0.3%
055-363-8949 2
 
0.3%
055-366-1923 2
 
0.3%
055-374-3000 2
 
0.3%
055-366-1177 2
 
0.3%
055-367-0016 2
 
0.3%
055-366-3600 2
 
0.3%
055-372-0033 2
 
0.3%
Other values (616) 635
96.5%
2024-04-21T16:07:40.340152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 1633
20.7%
0 1331
16.9%
- 1314
16.6%
3 970
12.3%
8 601
 
7.6%
6 554
 
7.0%
7 332
 
4.2%
2 317
 
4.0%
1 295
 
3.7%
4 292
 
3.7%
Other values (2) 257
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6580
83.3%
Dash Punctuation 1314
 
16.6%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1633
24.8%
0 1331
20.2%
3 970
14.7%
8 601
 
9.1%
6 554
 
8.4%
7 332
 
5.0%
2 317
 
4.8%
1 295
 
4.5%
4 292
 
4.4%
9 255
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 1314
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7896
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1633
20.7%
0 1331
16.9%
- 1314
16.6%
3 970
12.3%
8 601
 
7.6%
6 554
 
7.0%
7 332
 
4.2%
2 317
 
4.0%
1 295
 
3.7%
4 292
 
3.7%
Other values (2) 257
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7896
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 1633
20.7%
0 1331
16.9%
- 1314
16.6%
3 970
12.3%
8 601
 
7.6%
6 554
 
7.0%
7 332
 
4.2%
2 317
 
4.0%
1 295
 
3.7%
4 292
 
3.7%
Other values (2) 257
 
3.3%

소재지주소
Text

Distinct556
Distinct (%)66.3%
Missing1
Missing (%)0.1%
Memory size6.7 KiB
2024-04-21T16:07:41.637991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length24
Mean length19.222884
Min length14

Characters and Unicode

Total characters16128
Distinct characters132
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique437 ?
Unique (%)52.1%

Sample

1st row경상남도 양산시 하북면 신평로 43
2nd row경상남도 양산시 상북면 상북중앙로 302
3rd row경상남도 양산시 삼호동부로 150(삼호동)
4th row경상남도 양산시 웅상대로 812
5th row경상남도 양산시 고향의봄10길 15
ValueCountFrequency (%)
경상남도 838
21.7%
양산시 838
21.7%
물금읍 344
 
8.9%
동면 106
 
2.7%
물금로 39
 
1.0%
백호로 37
 
1.0%
상북면 33
 
0.9%
양주로 32
 
0.8%
야리로 30
 
0.8%
11 26
 
0.7%
Other values (565) 1547
40.0%
2024-04-21T16:07:43.461163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3031
18.8%
산 967
 
6.0%
양 903
 
5.6%
상 896
 
5.6%
남 865
 
5.4%
도 845
 
5.2%
시 839
 
5.2%
경 838
 
5.2%
1 643
 
4.0%
로 554
 
3.4%
Other values (122) 5747
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10483
65.0%
Space Separator 3031
 
18.8%
Decimal Number 2286
 
14.2%
Open Punctuation 127
 
0.8%
Close Punctuation 124
 
0.8%
Dash Punctuation 77
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 967
 
9.2%
양 903
 
8.6%
상 896
 
8.5%
남 865
 
8.3%
도 845
 
8.1%
시 839
 
8.0%
경 838
 
8.0%
로 554
 
5.3%
금 465
 
4.4%
물 396
 
3.8%
Other values (108) 2915
27.8%
Decimal Number
ValueCountFrequency (%)
1 643
28.1%
5 250
 
10.9%
3 250
 
10.9%
2 240
 
10.5%
6 181
 
7.9%
4 168
 
7.3%
9 145
 
6.3%
7 145
 
6.3%
0 139
 
6.1%
8 125
 
5.5%
Space Separator
ValueCountFrequency (%)
3031
100.0%
Open Punctuation
ValueCountFrequency (%)
( 127
100.0%
Close Punctuation
ValueCountFrequency (%)
) 124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10483
65.0%
Common 5645
35.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 967
 
9.2%
양 903
 
8.6%
상 896
 
8.5%
남 865
 
8.3%
도 845
 
8.1%
시 839
 
8.0%
경 838
 
8.0%
로 554
 
5.3%
금 465
 
4.4%
물 396
 
3.8%
Other values (108) 2915
27.8%
Common
ValueCountFrequency (%)
3031
53.7%
1 643
 
11.4%
5 250
 
4.4%
3 250
 
4.4%
2 240
 
4.3%
6 181
 
3.2%
4 168
 
3.0%
9 145
 
2.6%
7 145
 
2.6%
0 139
 
2.5%
Other values (4) 453
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10483
65.0%
ASCII 5645
35.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3031
53.7%
1 643
 
11.4%
5 250
 
4.4%
3 250
 
4.4%
2 240
 
4.3%
6 181
 
3.2%
4 168
 
3.0%
9 145
 
2.6%
7 145
 
2.6%
0 139
 
2.5%
Other values (4) 453
 
8.0%
Hangul
ValueCountFrequency (%)
산 967
 
9.2%
양 903
 
8.6%
상 896
 
8.5%
남 865
 
8.3%
도 845
 
8.1%
시 839
 
8.0%
경 838
 
8.0%
로 554
 
5.3%
금 465
 
4.4%
물 396
 
3.8%
Other values (108) 2915
27.8%

Correlations

2024-04-21T16:07:43.649149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중개업소종별
중개업소종별1.000
2024-04-21T16:07:43.794479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
직위중개업소종별
직위1.0001.000
중개업소종별1.0001.000
2024-04-21T16:07:43.950285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중개업소종별직위
중개업소종별1.0001.000
직위1.0001.000

Missing values

2024-04-21T16:07:29.167910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T16:07:29.570869image/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.
2024-04-21T16:07:29.901293image/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

등록번호사무소명중개업소종별중개업자명직위사무소전화번호소재지주소
0가4330-201부성부동산중개사무소중개인최춘복대표055-382-6546경상남도 양산시 하북면 신평로 43
1가4330-267제일공인중개사사무소공인중개사조정인대표055-374-9797경상남도 양산시 상북면 상북중앙로 302
2가4330-404명보공인중개사사무소공인중개사윤길원대표055-386-4818경상남도 양산시 삼호동부로 150(삼호동)
3가4330-431하늘공인중개사사무소공인중개사최동철대표055-367-3000경상남도 양산시 웅상대로 812
4가4330-438북정부동산공인중개사사무소공인중개사문상근대표055-389-2154경상남도 양산시 고향의봄10길 15
5가4330-467양산지금부동산공인중개사사무소공인중개사서성희대표055-363-6336경상남도 양산시 상북면 양산대로 1557
6가4330-484하북면공인중개사사무소공인중개사박헌생대표055-384-8900경상남도 양산시 하북면 통도사로 17
7가4330-524경남공인중개사사무소공인중개사이호문대표055-389-0021경상남도 양산시 동면 계석로 10
8가4330-532재일공인중개사사무소공인중개사김재일대표055-387-6406경상남도 양산시 물금읍 오봉로 195(물금읍)
9가4330-543손만희공인중개사사무소공인중개사손만희대표055-362-4989경상남도 양산시 서창서4길 2(삼호동)
등록번호사무소명중개업소종별중개업자명직위사무소전화번호소재지주소
83048330-2020-00084수암부동산중개공인중개사변종도대표<NA>경상남도 양산시 물금읍 백호로 155
83148330-2020-0008633공인중개사사무소공인중개사하다교대표<NA>경상남도 양산시 물금읍 황산로 767
83248330-2020-00087백상공인중개사사무소공인중개사김민대표<NA>경상남도 양산시 물금읍 가촌서로 13
83348330-2020-00088금선공인중개사사무소공인중개사박설희대표055-912-0057경상남도 양산시 중앙로 122
83448330-2020-00089이예솔공인중개사사무소공인중개사이예솔대표<NA>경상남도 양산시 물금읍 가촌서로 13
83548330-2020-00090e-스마트부동산중개공인중개사노삼용대표<NA>경상남도 양산시 동면 내송로 11
83648330-2020-00091내송공인중개사사무소공인중개사김영수대표<NA>경상남도 양산시 동면 내송로 43
83748330-2020-00092사송더샵봄봄공인중개사사무소공인중개사허영주대표055-323-3300경상남도 양산시 동면 내송로 11
83848330-2020-00093나무부동산중개사무소공인중개사조용석대표055-366-1923경상남도 양산시 물금읍 황산로 453
83948330-2020-00094로또공인중개사사무소공인중개사양현주대표<NA>경상남도 양산시 하북면 충렬로 1355