Overview

Dataset statistics

Number of variables23
Number of observations10000
Missing cells26869
Missing cells (%)11.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory206.0 B

Variable types

Categorical8
Numeric12
Text3

Dataset

Description접수연도,자치구코드,자치구명,법정동코드,법정동명,지번구분,지번구분명,본번,부번,층,계약일,전월세 구분,임대면적(㎡),보증금(만원),임대료(만원),건물명,건축년도,건물용도,계약기간,신규갱신여부,계약갱신권사용여부,종전 보증금,종전 임대료
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21276/S/1/datasetView.do

Alerts

접수연도 has constant value ""Constant
지번구분 is highly imbalanced (63.4%)Imbalance
지번구분명 is highly imbalanced (63.4%)Imbalance
계약갱신권사용여부 is highly imbalanced (67.2%)Imbalance
본번 has 2014 (20.1%) missing valuesMissing
부번 has 2014 (20.1%) missing valuesMissing
층 has 2015 (20.2%) missing valuesMissing
건물명 has 2014 (20.1%) missing valuesMissing
건축년도 has 2063 (20.6%) missing valuesMissing
계약기간 has 299 (3.0%) missing valuesMissing
종전 보증금 has 7459 (74.6%) missing valuesMissing
종전 임대료 has 8991 (89.9%) missing valuesMissing
부번 has 3538 (35.4%) zerosZeros
임대료(만원) has 4802 (48.0%) zerosZeros
종전 임대료 has 124 (1.2%) zerosZeros

Reproduction

Analysis started2024-05-04 05:57:20.919569
Analysis finished2024-05-04 05:57:22.515836
Duration1.6 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

접수연도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 10000
100.0%

Length

2024-05-04T05:57:22.627273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:57:22.799069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024 10000
100.0%

자치구코드
Real number (ℝ)

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11476.445
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:57:22.952378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11170
Q111320
median11500
Q311650
95-th percentile11740
Maximum11740
Range630
Interquartile range (IQR)330

Descriptive statistics

Standard deviation180.57667
Coefficient of variation (CV)0.015734547
Kurtosis-1.1433036
Mean11476.445
Median Absolute Deviation (MAD)150
Skewness-0.23830034
Sum1.1476446 × 108
Variance32607.934
MonotonicityNot monotonic
2024-05-04T05:57:23.167921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11710 959
 
9.6%
11500 791
 
7.9%
11680 617
 
6.2%
11440 577
 
5.8%
11560 557
 
5.6%
11740 532
 
5.3%
11620 478
 
4.8%
11260 444
 
4.4%
11215 419
 
4.2%
11650 413
 
4.1%
Other values (15) 4213
42.1%
ValueCountFrequency (%)
11110 127
 
1.3%
11140 96
 
1.0%
11170 294
2.9%
11200 267
2.7%
11215 419
4.2%
11230 322
3.2%
11260 444
4.4%
11290 298
3.0%
11305 193
1.9%
11320 230
2.3%
ValueCountFrequency (%)
11740 532
5.3%
11710 959
9.6%
11680 617
6.2%
11650 413
4.1%
11620 478
4.8%
11590 352
 
3.5%
11560 557
5.6%
11545 267
 
2.7%
11530 388
3.9%
11500 791
7.9%

자치구명
Categorical

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
송파구
959 
강서구
791 
강남구
 
617
마포구
 
577
영등포구
 
557
Other values (20)
6499 

Length

Max length4
Median length3
Mean length3.102
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row은평구
2nd row송파구
3rd row송파구
4th row용산구
5th row동작구

Common Values

ValueCountFrequency (%)
송파구 959
 
9.6%
강서구 791
 
7.9%
강남구 617
 
6.2%
마포구 577
 
5.8%
영등포구 557
 
5.6%
강동구 532
 
5.3%
관악구 478
 
4.8%
중랑구 444
 
4.4%
광진구 419
 
4.2%
서초구 413
 
4.1%
Other values (15) 4213
42.1%

Length

2024-05-04T05:57:23.428659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 959
 
9.6%
강서구 791
 
7.9%
강남구 617
 
6.2%
마포구 577
 
5.8%
영등포구 557
 
5.6%
강동구 532
 
5.3%
관악구 478
 
4.8%
중랑구 444
 
4.4%
광진구 419
 
4.2%
서초구 413
 
4.1%
Other values (15) 4213
42.1%

법정동코드
Real number (ℝ)

Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10917.29
Minimum10100
Maximum18700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:57:23.706660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10100
5-th percentile10100
Q110200
median10600
Q311000
95-th percentile13200
Maximum18700
Range8600
Interquartile range (IQR)800

Descriptive statistics

Standard deviation1157.1128
Coefficient of variation (CV)0.10598901
Kurtosis12.543012
Mean10917.29
Median Absolute Deviation (MAD)400
Skewness3.1135972
Sum1.091729 × 108
Variance1338909.9
MonotonicityNot monotonic
2024-05-04T05:57:23.959404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10100 1371
13.7%
10200 1308
13.1%
10300 1062
10.6%
10500 844
 
8.4%
10700 692
 
6.9%
10800 608
 
6.1%
10600 567
 
5.7%
10900 511
 
5.1%
10400 403
 
4.0%
11100 296
 
3.0%
Other values (67) 2338
23.4%
ValueCountFrequency (%)
10100 1371
13.7%
10200 1308
13.1%
10300 1062
10.6%
10400 403
 
4.0%
10500 844
8.4%
10600 567
5.7%
10700 692
6.9%
10800 608
6.1%
10900 511
 
5.1%
11000 185
 
1.8%
ValueCountFrequency (%)
18700 3
 
< 0.1%
18600 5
 
0.1%
18500 1
 
< 0.1%
18400 4
 
< 0.1%
18300 3
 
< 0.1%
18100 5
 
0.1%
17900 2
 
< 0.1%
17700 1
 
< 0.1%
17500 23
0.2%
17400 25
0.2%

법정동명
Text

Distinct341
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T05:57:24.430871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1436
Min length2

Characters and Unicode

Total characters31436
Distinct characters190
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

Unique48 ?
Unique (%)0.5%

Sample

1st row진관동
2nd row장지동
3rd row문정동
4th row효창동
5th row사당동
ValueCountFrequency (%)
화곡동 303
 
3.0%
신림동 233
 
2.3%
봉천동 230
 
2.3%
상계동 158
 
1.6%
목동 155
 
1.6%
면목동 151
 
1.5%
신정동 142
 
1.4%
독산동 134
 
1.3%
역삼동 134
 
1.3%
천호동 133
 
1.3%
Other values (331) 8227
82.3%
2024-05-04T05:57:25.202178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 9933
31.6%
신 942
 
3.0%
가 876
 
2.8%
곡 664
 
2.1%
천 527
 
1.7%
화 515
 
1.6%
산 476
 
1.5%
상 464
 
1.5%
봉 409
 
1.3%
방 403
 
1.3%
Other values (180) 16227
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30825
98.1%
Decimal Number 611
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 9933
32.2%
신 942
 
3.1%
가 876
 
2.8%
곡 664
 
2.2%
천 527
 
1.7%
화 515
 
1.7%
산 476
 
1.5%
상 464
 
1.5%
봉 409
 
1.3%
방 403
 
1.3%
Other values (172) 15616
50.7%
Decimal Number
ValueCountFrequency (%)
1 162
26.5%
2 126
20.6%
3 111
18.2%
4 63
 
10.3%
6 61
 
10.0%
5 46
 
7.5%
7 25
 
4.1%
8 17
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30825
98.1%
Common 611
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 9933
32.2%
신 942
 
3.1%
가 876
 
2.8%
곡 664
 
2.2%
천 527
 
1.7%
화 515
 
1.7%
산 476
 
1.5%
상 464
 
1.5%
봉 409
 
1.3%
방 403
 
1.3%
Other values (172) 15616
50.7%
Common
ValueCountFrequency (%)
1 162
26.5%
2 126
20.6%
3 111
18.2%
4 63
 
10.3%
6 61
 
10.0%
5 46
 
7.5%
7 25
 
4.1%
8 17
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30825
98.1%
ASCII 611
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 9933
32.2%
신 942
 
3.1%
가 876
 
2.8%
곡 664
 
2.2%
천 527
 
1.7%
화 515
 
1.7%
산 476
 
1.5%
상 464
 
1.5%
봉 409
 
1.3%
방 403
 
1.3%
Other values (172) 15616
50.7%
ASCII
ValueCountFrequency (%)
1 162
26.5%
2 126
20.6%
3 111
18.2%
4 63
 
10.3%
6 61
 
10.0%
5 46
 
7.5%
7 25
 
4.1%
8 17
 
2.8%

지번구분
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
7980 
<NA>
2015 
2
 
4
3
 
1

Length

Max length4
Median length1
Mean length1.6045
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7980
79.8%
<NA> 2015
 
20.2%
2 4
 
< 0.1%
3 1
 
< 0.1%

Length

2024-05-04T05:57:25.519823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:57:25.832036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7980
79.8%
na 2015
 
20.2%
2 4
 
< 0.1%
3 1
 
< 0.1%

지번구분명
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대지
7980 
<NA>
2015 
산
 
4
블럭
 
1

Length

Max length4
Median length2
Mean length2.4026
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
대지 7980
79.8%
<NA> 2015
 
20.2%
산 4
 
< 0.1%
블럭 1
 
< 0.1%

Length

2024-05-04T05:57:26.050750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:57:26.231129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대지 7980
79.8%
na 2015
 
20.2%
산 4
 
< 0.1%
블럭 1
 
< 0.1%

본번
Real number (ℝ)

MISSING 

Distinct1300
Distinct (%)16.3%
Missing2014
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean521.26521
Minimum1
Maximum4974
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:57:26.484771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q1145
median404
Q3739
95-th percentile1452
Maximum4974
Range4973
Interquartile range (IQR)594

Descriptive statistics

Standard deviation538.06848
Coefficient of variation (CV)1.0322355
Kurtosis23.448095
Mean521.26521
Median Absolute Deviation (MAD)291
Skewness3.5122296
Sum4162824
Variance289517.69
MonotonicityNot monotonic
2024-05-04T05:57:26.889202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 51
 
0.5%
1 44
 
0.4%
22 39
 
0.4%
17 39
 
0.4%
281 36
 
0.4%
46 31
 
0.3%
105 31
 
0.3%
697 31
 
0.3%
593 31
 
0.3%
12 30
 
0.3%
Other values (1290) 7623
76.2%
(Missing) 2014
 
20.1%
ValueCountFrequency (%)
1 44
0.4%
2 27
0.3%
3 19
0.2%
4 17
 
0.2%
5 25
0.2%
6 21
0.2%
7 17
 
0.2%
8 11
 
0.1%
9 23
0.2%
10 23
0.2%
ValueCountFrequency (%)
4974 1
 
< 0.1%
4969 6
0.1%
4967 2
 
< 0.1%
4964 6
0.1%
4958 12
0.1%
4955 2
 
< 0.1%
4945 2
 
< 0.1%
4944 1
 
< 0.1%
4943 1
 
< 0.1%
4934 3
 
< 0.1%

부번
Real number (ℝ)

MISSING  ZEROS 

Distinct284
Distinct (%)3.6%
Missing2014
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean19.000751
Minimum0
Maximum2663
Zeros3538
Zeros (%)35.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:57:27.198343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q313
95-th percentile80
Maximum2663
Range2663
Interquartile range (IQR)13

Descriptive statistics

Standard deviation75.491356
Coefficient of variation (CV)3.9730721
Kurtosis368.88607
Mean19.000751
Median Absolute Deviation (MAD)1
Skewness15.380873
Sum151740
Variance5698.9448
MonotonicityNot monotonic
2024-05-04T05:57:27.459560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3538
35.4%
1 525
 
5.2%
2 318
 
3.2%
3 268
 
2.7%
5 231
 
2.3%
4 209
 
2.1%
6 172
 
1.7%
7 136
 
1.4%
8 128
 
1.3%
9 112
 
1.1%
Other values (274) 2349
23.5%
(Missing) 2014
20.1%
ValueCountFrequency (%)
0 3538
35.4%
1 525
 
5.2%
2 318
 
3.2%
3 268
 
2.7%
4 209
 
2.1%
5 231
 
2.3%
6 172
 
1.7%
7 136
 
1.4%
8 128
 
1.3%
9 112
 
1.1%
ValueCountFrequency (%)
2663 1
< 0.1%
1979 1
< 0.1%
1779 1
< 0.1%
1652 1
< 0.1%
1453 1
< 0.1%
1310 1
< 0.1%
1267 1
< 0.1%
1248 1
< 0.1%
993 1
< 0.1%
866 1
< 0.1%

층
Real number (ℝ)

MISSING 

Distinct43
Distinct (%)0.5%
Missing2015
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean7.1595492
Minimum-1
Maximum59
Zeros0
Zeros (%)0.0%
Negative80
Negative (%)0.8%
Memory size166.0 KiB
2024-05-04T05:57:27.787558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median5
Q310
95-th percentile18
Maximum59
Range60
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.6368555
Coefficient of variation (CV)0.7873199
Kurtosis4.4730484
Mean7.1595492
Median Absolute Deviation (MAD)3
Skewness1.6016189
Sum57169
Variance31.77414
MonotonicityNot monotonic
2024-05-04T05:57:28.116154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 1030
10.3%
2 982
9.8%
4 887
8.9%
5 765
 
7.6%
6 499
 
5.0%
1 398
 
4.0%
8 380
 
3.8%
7 374
 
3.7%
9 341
 
3.4%
11 327
 
3.3%
Other values (33) 2002
20.0%
(Missing) 2015
20.2%
ValueCountFrequency (%)
-1 80
 
0.8%
1 398
 
4.0%
2 982
9.8%
3 1030
10.3%
4 887
8.9%
5 765
7.6%
6 499
5.0%
7 374
 
3.7%
8 380
 
3.8%
9 341
 
3.4%
ValueCountFrequency (%)
59 1
 
< 0.1%
56 1
 
< 0.1%
48 2
 
< 0.1%
46 1
 
< 0.1%
40 1
 
< 0.1%
38 2
 
< 0.1%
37 1
 
< 0.1%
35 2
 
< 0.1%
34 5
0.1%
33 6
0.1%

계약일
Real number (ℝ)

Distinct55
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20240366
Minimum20240309
Maximum20240502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:57:28.369670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20240309
5-th percentile20240312
Q120240319
median20240330
Q320240413
95-th percentile20240427
Maximum20240502
Range193
Interquartile range (IQR)94

Descriptive statistics

Standard deviation50.003806
Coefficient of variation (CV)2.4704991 × 10-6
Kurtosis-1.3088739
Mean20240366
Median Absolute Deviation (MAD)20
Skewness0.29751907
Sum2.0240366 × 1011
Variance2500.3806
MonotonicityNot monotonic
2024-05-04T05:57:28.656990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20240323 356
 
3.6%
20240330 303
 
3.0%
20240316 293
 
2.9%
20240313 293
 
2.9%
20240318 283
 
2.8%
20240319 279
 
2.8%
20240312 278
 
2.8%
20240320 275
 
2.8%
20240311 273
 
2.7%
20240315 271
 
2.7%
Other values (45) 7096
71.0%
ValueCountFrequency (%)
20240309 149
1.5%
20240310 75
 
0.8%
20240311 273
2.7%
20240312 278
2.8%
20240313 293
2.9%
20240314 260
2.6%
20240315 271
2.7%
20240316 293
2.9%
20240317 69
 
0.7%
20240318 283
2.8%
ValueCountFrequency (%)
20240502 39
 
0.4%
20240501 114
1.1%
20240430 96
1.0%
20240429 129
1.3%
20240428 27
 
0.3%
20240427 139
1.4%
20240426 113
1.1%
20240425 141
1.4%
20240424 112
1.1%
20240423 144
1.4%

전월세 구분
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
월세
5189 
전세
4783 
<NA>
 
28

Length

Max length4
Median length2
Mean length2.0056
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row월세
2nd row전세
3rd row전세
4th row전세
5th row월세

Common Values

ValueCountFrequency (%)
월세 5189
51.9%
전세 4783
47.8%
<NA> 28
 
0.3%

Length

2024-05-04T05:57:29.056568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:57:29.392045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
월세 5189
51.9%
전세 4783
47.8%
na 28
 
0.3%

임대면적(㎡)
Real number (ℝ)

Distinct4228
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.797411
Minimum8.82
Maximum249.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:57:29.675165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.82
5-th percentile16.759
Q128.06
median42.29
Q359.994
95-th percentile99.464
Maximum249.94
Range241.12
Interquartile range (IQR)31.934

Descriptive statistics

Standard deviation29.980696
Coefficient of variation (CV)0.60205332
Kurtosis5.482861
Mean49.797411
Median Absolute Deviation (MAD)17.54
Skewness1.7389468
Sum497974.11
Variance898.84215
MonotonicityNot monotonic
2024-05-04T05:57:29.977979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.0 126
 
1.3%
84.98 107
 
1.1%
59.99 78
 
0.8%
20.0 76
 
0.8%
33.0 76
 
0.8%
84.97 71
 
0.7%
84.99 71
 
0.7%
40.0 69
 
0.7%
59.96 68
 
0.7%
18.0 68
 
0.7%
Other values (4218) 9190
91.9%
ValueCountFrequency (%)
8.82 1
 
< 0.1%
10.0 3
< 0.1%
10.8 1
 
< 0.1%
11.0 1
 
< 0.1%
11.251 1
 
< 0.1%
11.5 1
 
< 0.1%
11.52 2
< 0.1%
11.54 1
 
< 0.1%
11.56 1
 
< 0.1%
11.66 1
 
< 0.1%
ValueCountFrequency (%)
249.94 1
< 0.1%
249.92 1
< 0.1%
249.66 1
< 0.1%
246.66 1
< 0.1%
246.15 1
< 0.1%
245.5 1
< 0.1%
244.66 1
< 0.1%
243.87 1
< 0.1%
243.81 1
< 0.1%
238.07 1
< 0.1%

보증금(만원)
Real number (ℝ)

Distinct1364
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22589.629
Minimum0
Maximum550000
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:57:30.224840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile500
Q13500
median15000
Q330000
95-th percentile70000
Maximum550000
Range550000
Interquartile range (IQR)26500

Descriptive statistics

Standard deviation26133.213
Coefficient of variation (CV)1.1568677
Kurtosis25.568108
Mean22589.629
Median Absolute Deviation (MAD)12935
Skewness3.0786866
Sum2.2589629 × 108
Variance6.8294481 × 108
MonotonicityNot monotonic
2024-05-04T05:57:30.462717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 940
 
9.4%
500 427
 
4.3%
5000 368
 
3.7%
10000 356
 
3.6%
2000 339
 
3.4%
3000 295
 
2.9%
20000 233
 
2.3%
30000 207
 
2.1%
15000 162
 
1.6%
25000 153
 
1.5%
Other values (1354) 6520
65.2%
ValueCountFrequency (%)
0 17
 
0.2%
3 1
 
< 0.1%
20 1
 
< 0.1%
30 1
 
< 0.1%
35 1
 
< 0.1%
50 2
 
< 0.1%
90 1
 
< 0.1%
100 45
0.4%
150 2
 
< 0.1%
170 1
 
< 0.1%
ValueCountFrequency (%)
550000 1
< 0.1%
300000 1
< 0.1%
275000 1
< 0.1%
235000 1
< 0.1%
230000 1
< 0.1%
228800 1
< 0.1%
220000 2
< 0.1%
215000 1
< 0.1%
210000 1
< 0.1%
205000 1
< 0.1%

임대료(만원)
Real number (ℝ)

ZEROS 

Distinct216
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.0832
Minimum0
Maximum1180
Zeros4802
Zeros (%)48.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:57:30.807665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q350
95-th percentile130
Maximum1180
Range1180
Interquartile range (IQR)50

Descriptive statistics

Standard deviation60.174958
Coefficient of variation (CV)1.7655313
Kurtosis55.543831
Mean34.0832
Median Absolute Deviation (MAD)8
Skewness5.3165857
Sum340832
Variance3621.0256
MonotonicityNot monotonic
2024-05-04T05:57:31.349843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4802
48.0%
50 282
 
2.8%
60 264
 
2.6%
40 248
 
2.5%
30 237
 
2.4%
20 198
 
2.0%
10 191
 
1.9%
70 178
 
1.8%
80 175
 
1.8%
45 165
 
1.7%
Other values (206) 3260
32.6%
ValueCountFrequency (%)
0 4802
48.0%
1 4
 
< 0.1%
2 14
 
0.1%
3 14
 
0.1%
4 15
 
0.1%
5 60
 
0.6%
6 26
 
0.3%
7 45
 
0.4%
8 35
 
0.4%
9 39
 
0.4%
ValueCountFrequency (%)
1180 1
< 0.1%
1053 1
< 0.1%
1000 1
< 0.1%
900 1
< 0.1%
850 2
< 0.1%
780 1
< 0.1%
700 1
< 0.1%
590 1
< 0.1%
580 2
< 0.1%
570 2
< 0.1%

건물명
Text

MISSING 

Distinct4739
Distinct (%)59.3%
Missing2014
Missing (%)20.1%
Memory size156.2 KiB
2024-05-04T05:57:31.990379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length7.2111195
Min length1

Characters and Unicode

Total characters57588
Distinct characters619
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3462 ?
Unique (%)43.4%

Sample

1st row은평뉴타운 꿈에그린
2nd row송파더센트레(위례22단지)
3rd row(67-3)
4th row효창신비빌A
5th row사당우성2
ValueCountFrequency (%)
오피스텔 91
 
1.0%
송파시그니처롯데캐슬 29
 
0.3%
강남 29
 
0.3%
천왕연지타운2단지 29
 
0.3%
헬리오시티 28
 
0.3%
구파발10단지어울림(1020~1028동)임대bl3-5 27
 
0.3%
상암 26
 
0.3%
푸르지오시티 26
 
0.3%
파크리오 26
 
0.3%
신동아 24
 
0.3%
Other values (4896) 8469
96.2%
2024-05-04T05:57:32.976877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1767
 
3.1%
스 1752
 
3.0%
) 1370
 
2.4%
( 1369
 
2.4%
빌 1278
 
2.2%
이 1214
 
2.1%
지 1211
 
2.1%
2 1190
 
2.1%
트 1098
 
1.9%
동 1042
 
1.8%
Other values (609) 44297
76.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43753
76.0%
Decimal Number 7220
 
12.5%
Uppercase Letter 1432
 
2.5%
Close Punctuation 1370
 
2.4%
Open Punctuation 1369
 
2.4%
Dash Punctuation 988
 
1.7%
Space Separator 825
 
1.4%
Lowercase Letter 372
 
0.6%
Math Symbol 107
 
0.2%
Other Punctuation 107
 
0.2%
Other values (2) 45
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 1752
 
4.0%
빌 1278
 
2.9%
이 1214
 
2.8%
지 1211
 
2.8%
트 1098
 
2.5%
동 1042
 
2.4%
파 1028
 
2.3%
아 910
 
2.1%
리 884
 
2.0%
라 785
 
1.8%
Other values (532) 32551
74.4%
Uppercase Letter
ValueCountFrequency (%)
L 132
 
9.2%
B 124
 
8.7%
A 121
 
8.4%
C 102
 
7.1%
I 100
 
7.0%
S 96
 
6.7%
E 85
 
5.9%
M 84
 
5.9%
D 80
 
5.6%
R 56
 
3.9%
Other values (15) 452
31.6%
Lowercase Letter
ValueCountFrequency (%)
e 124
33.3%
l 39
 
10.5%
i 27
 
7.3%
s 24
 
6.5%
a 23
 
6.2%
o 18
 
4.8%
n 17
 
4.6%
u 16
 
4.3%
t 15
 
4.0%
r 10
 
2.7%
Other values (13) 59
15.9%
Decimal Number
ValueCountFrequency (%)
1 1767
24.5%
2 1190
16.5%
3 758
10.5%
0 658
 
9.1%
4 609
 
8.4%
5 532
 
7.4%
6 471
 
6.5%
7 441
 
6.1%
8 411
 
5.7%
9 383
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 88
82.2%
& 5
 
4.7%
？ 4
 
3.7%
. 4
 
3.7%
: 2
 
1.9%
' 2
 
1.9%
/ 1
 
0.9%
# 1
 
0.9%
Letter Number
ValueCountFrequency (%)
Ⅱ 25
56.8%
Ⅰ 14
31.8%
Ⅲ 2
 
4.5%
Ⅳ 2
 
4.5%
Ⅴ 1
 
2.3%
Close Punctuation
ValueCountFrequency (%)
) 1370
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1369
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 988
100.0%
Space Separator
ValueCountFrequency (%)
825
100.0%
Math Symbol
ValueCountFrequency (%)
~ 107
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43744
76.0%
Common 11987
 
20.8%
Latin 1848
 
3.2%
Han 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 1752
 
4.0%
빌 1278
 
2.9%
이 1214
 
2.8%
지 1211
 
2.8%
트 1098
 
2.5%
동 1042
 
2.4%
파 1028
 
2.4%
아 910
 
2.1%
리 884
 
2.0%
라 785
 
1.8%
Other values (526) 32542
74.4%
Latin
ValueCountFrequency (%)
L 132
 
7.1%
B 124
 
6.7%
e 124
 
6.7%
A 121
 
6.5%
C 102
 
5.5%
I 100
 
5.4%
S 96
 
5.2%
E 85
 
4.6%
M 84
 
4.5%
D 80
 
4.3%
Other values (43) 800
43.3%
Common
ValueCountFrequency (%)
1 1767
14.7%
) 1370
11.4%
( 1369
11.4%
2 1190
9.9%
- 988
8.2%
825
6.9%
3 758
 
6.3%
0 658
 
5.5%
4 609
 
5.1%
5 532
 
4.4%
Other values (14) 1921
16.0%
Han
ValueCountFrequency (%)
家 4
44.4%
人 1
 
11.1%
康 1
 
11.1%
笑 1
 
11.1%
南 1
 
11.1%
美 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43744
76.0%
ASCII 13787
 
23.9%
Number Forms 44
 
0.1%
CJK 9
 
< 0.1%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1767
12.8%
) 1370
 
9.9%
( 1369
 
9.9%
2 1190
 
8.6%
- 988
 
7.2%
825
 
6.0%
3 758
 
5.5%
0 658
 
4.8%
4 609
 
4.4%
5 532
 
3.9%
Other values (61) 3721
27.0%
Hangul
ValueCountFrequency (%)
스 1752
 
4.0%
빌 1278
 
2.9%
이 1214
 
2.8%
지 1211
 
2.8%
트 1098
 
2.5%
동 1042
 
2.4%
파 1028
 
2.4%
아 910
 
2.1%
리 884
 
2.0%
라 785
 
1.8%
Other values (526) 32542
74.4%
Number Forms
ValueCountFrequency (%)
Ⅱ 25
56.8%
Ⅰ 14
31.8%
Ⅲ 2
 
4.5%
Ⅳ 2
 
4.5%
Ⅴ 1
 
2.3%
CJK
ValueCountFrequency (%)
家 4
44.4%
人 1
 
11.1%
康 1
 
11.1%
笑 1
 
11.1%
南 1
 
11.1%
美 1
 
11.1%
None
ValueCountFrequency (%)
？ 4
100.0%

건축년도
Real number (ℝ)

MISSING 

Distinct57
Distinct (%)0.7%
Missing2063
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean2007.852
Minimum1965
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:57:33.379831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1965
5-th percentile1986
Q12000
median2011
Q32018
95-th percentile2022
Maximum2024
Range59
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.724498
Coefficient of variation (CV)0.0058393237
Kurtosis-0.53358818
Mean2007.852
Median Absolute Deviation (MAD)9
Skewness-0.61641719
Sum15936321
Variance137.46384
MonotonicityNot monotonic
2024-05-04T05:57:33.846040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2020 418
 
4.2%
2021 372
 
3.7%
2019 361
 
3.6%
2022 348
 
3.5%
2018 343
 
3.4%
2017 323
 
3.2%
2016 310
 
3.1%
2002 308
 
3.1%
2003 279
 
2.8%
2014 252
 
2.5%
Other values (47) 4623
46.2%
(Missing) 2063
20.6%
ValueCountFrequency (%)
1965 1
 
< 0.1%
1968 1
 
< 0.1%
1969 1
 
< 0.1%
1970 1
 
< 0.1%
1971 12
0.1%
1972 1
 
< 0.1%
1974 1
 
< 0.1%
1975 10
0.1%
1976 18
0.2%
1977 12
0.1%
ValueCountFrequency (%)
2024 97
 
1.0%
2023 238
2.4%
2022 348
3.5%
2021 372
3.7%
2020 418
4.2%
2019 361
3.6%
2018 343
3.4%
2017 323
3.2%
2016 310
3.1%
2015 226
2.3%

건물용도
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
아파트
3958 
연립다세대
2602 
단독다가구
2015 
오피스텔
1425 

Length

Max length5
Median length4
Mean length4.0659
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아파트 3958
39.6%
연립다세대 2602
26.0%
단독다가구 2015
20.2%
오피스텔 1425
 
14.2%

Length

2024-05-04T05:57:34.319713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:57:34.671106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트 3958
39.6%
연립다세대 2602
26.0%
단독다가구 2015
20.2%
오피스텔 1425
 
14.2%

계약기간
Text

MISSING 

Distinct183
Distinct (%)1.9%
Missing299
Missing (%)3.0%
Memory size156.2 KiB
2024-05-04T05:57:35.249542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.797134
Min length7

Characters and Unicode

Total characters104743
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

Unique78 ?
Unique (%)0.8%

Sample

1st row24.05~25.05
2nd row24.06~26.06
3rd row24.03~26.03
4th row24.06~26.06
5th row24.05~26.05
ValueCountFrequency (%)
24.04~26.04 3061
31.6%
24.05~26.05 2287
23.6%
24.06~26.06 958
 
9.9%
24.03~26.03 905
 
9.3%
24.04~25.04 547
 
5.6%
24.03~25.03 285
 
2.9%
26.03 261
 
2.7%
24.07~26.07 235
 
2.4%
24.05~25.05 159
 
1.6%
26.04 127
 
1.3%
Other values (173) 876
 
9.0%
2024-05-04T05:57:36.261739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 19402
18.5%
2 19038
18.2%
0 18855
18.0%
4 17053
16.3%
6 10536
10.1%
~ 9701
9.3%
5 6379
 
6.1%
3 2905
 
2.8%
7 572
 
0.5%
1 166
 
0.2%
Other values (2) 136
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 75640
72.2%
Other Punctuation 19402
 
18.5%
Math Symbol 9701
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 19038
25.2%
0 18855
24.9%
4 17053
22.5%
6 10536
13.9%
5 6379
 
8.4%
3 2905
 
3.8%
7 572
 
0.8%
1 166
 
0.2%
8 101
 
0.1%
9 35
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 19402
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9701
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 104743
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 19402
18.5%
2 19038
18.2%
0 18855
18.0%
4 17053
16.3%
6 10536
10.1%
~ 9701
9.3%
5 6379
 
6.1%
3 2905
 
2.8%
7 572
 
0.5%
1 166
 
0.2%
Other values (2) 136
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104743
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 19402
18.5%
2 19038
18.2%
0 18855
18.0%
4 17053
16.3%
6 10536
10.1%
~ 9701
9.3%
5 6379
 
6.1%
3 2905
 
2.8%
7 572
 
0.5%
1 166
 
0.2%
Other values (2) 136
 
0.1%

신규갱신여부
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
신규
7156 
갱신
2541 
<NA>
 
303

Length

Max length4
Median length2
Mean length2.0606
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row갱신
2nd row갱신
3rd row갱신
4th row신규
5th row신규

Common Values

ValueCountFrequency (%)
신규 7156
71.6%
갱신 2541
 
25.4%
<NA> 303
 
3.0%

Length

2024-05-04T05:57:36.690951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:57:37.033624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규 7156
71.6%
갱신 2541
 
25.4%
na 303
 
3.0%

계약갱신권사용여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9398 
○
 
602

Length

Max length4
Median length4
Mean length3.8194
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9398
94.0%
○ 602
 
6.0%

Length

2024-05-04T05:57:37.376699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T05:57:37.742401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9398
94.0%
○ 602
 
6.0%

종전 보증금
Real number (ℝ)

MISSING 

Distinct579
Distinct (%)22.8%
Missing7459
Missing (%)74.6%
Infinite0
Infinite (%)0.0%
Mean30516.994
Minimum0
Maximum367500
Zeros16
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:57:38.314362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1000
Q110000
median23000
Q342300
95-th percentile81900
Maximum367500
Range367500
Interquartile range (IQR)32300

Descriptive statistics

Standard deviation29484.697
Coefficient of variation (CV)0.96617305
Kurtosis17.00319
Mean30516.994
Median Absolute Deviation (MAD)14600
Skewness2.7513121
Sum77543682
Variance8.6934737 × 108
MonotonicityNot monotonic
2024-05-04T05:57:38.790810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 106
 
1.1%
10000 85
 
0.9%
3000 54
 
0.5%
20000 52
 
0.5%
5000 52
 
0.5%
25000 51
 
0.5%
30000 47
 
0.5%
2000 44
 
0.4%
40000 39
 
0.4%
15000 36
 
0.4%
Other values (569) 1975
 
19.8%
(Missing) 7459
74.6%
ValueCountFrequency (%)
0 16
0.2%
100 6
 
0.1%
200 3
 
< 0.1%
250 1
 
< 0.1%
300 9
 
0.1%
500 32
0.3%
600 1
 
< 0.1%
700 2
 
< 0.1%
800 1
 
< 0.1%
831 1
 
< 0.1%
ValueCountFrequency (%)
367500 1
< 0.1%
350000 1
< 0.1%
250000 1
< 0.1%
235000 1
< 0.1%
220000 1
< 0.1%
195000 1
< 0.1%
190000 2
< 0.1%
180000 2
< 0.1%
160000 2
< 0.1%
157500 1
< 0.1%

종전 임대료
Real number (ℝ)

MISSING  ZEROS 

Distinct161
Distinct (%)16.0%
Missing8991
Missing (%)89.9%
Infinite0
Infinite (%)0.0%
Mean65.809713
Minimum0
Maximum1120
Zeros124
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T05:57:39.330035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median50
Q379
95-th percentile176.8
Maximum1120
Range1120
Interquartile range (IQR)59

Descriptive statistics

Standard deviation90.0188
Coefficient of variation (CV)1.367865
Kurtosis43.953027
Mean65.809713
Median Absolute Deviation (MAD)30
Skewness5.3947042
Sum66402
Variance8103.3844
MonotonicityNot monotonic
2024-05-04T05:57:39.868977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 124
 
1.2%
50 44
 
0.4%
60 34
 
0.3%
30 32
 
0.3%
40 31
 
0.3%
55 26
 
0.3%
70 25
 
0.2%
80 22
 
0.2%
10 20
 
0.2%
20 20
 
0.2%
Other values (151) 631
 
6.3%
(Missing) 8991
89.9%
ValueCountFrequency (%)
0 124
1.2%
2 5
 
0.1%
3 8
 
0.1%
4 7
 
0.1%
5 6
 
0.1%
6 2
 
< 0.1%
7 3
 
< 0.1%
8 6
 
0.1%
9 10
 
0.1%
10 20
 
0.2%
ValueCountFrequency (%)
1120 1
< 0.1%
1000 1
< 0.1%
850 1
< 0.1%
750 1
< 0.1%
600 1
< 0.1%
590 1
< 0.1%
550 1
< 0.1%
540 1
< 0.1%
530 1
< 0.1%
500 1
< 0.1%

Sample

접수연도자치구코드자치구명법정동코드법정동명지번구분지번구분명본번부번층계약일전월세 구분임대면적(㎡)보증금(만원)임대료(만원)건물명건축년도건물용도계약기간신규갱신여부계약갱신권사용여부종전 보증금종전 임대료
10446202411380은평구11400진관동1대지660320240418월세19.84120005은평뉴타운 꿈에그린2018오피스텔24.05~25.05갱신<NA>126000
10931202411710송파구10900장지동1대지87901220240417전세59.73600000송파더센트레(위례22단지)2013아파트24.06~26.06갱신<NA>50000<NA>
54321202411710송파구10800문정동1대지673420240313전세45.41230000(67-3)2002연립다세대24.03~26.03갱신<NA>22000<NA>
2325202411170용산구11900효창동1대지241320240428전세29.83280000효창신비빌A2019연립다세대24.06~26.06신규<NA><NA><NA>
14903202411590동작구10700사당동1대지10501320240413월세84.6610000200사당우성21993아파트24.05~26.05신규<NA><NA><NA>
14656202411560영등포구11800도림동1대지22933620240413월세38.862500020그린아델리움2024오피스텔24.06~26.06신규<NA><NA><NA>
294202411470양천구10100신정동1대지128301220240501전세59.99380000푸른마을32001아파트24.05~25.05신규<NA><NA><NA>
23691202411740강동구10900천호동1대지191220240405전세51.44250000우성1985아파트24.06~26.06신규<NA><NA><NA>
34233202411710송파구10400송파동1대지381220240327전세15.57140000숨결2011연립다세대24.05~26.05신규<NA><NA><NA>
6431202411710송파구10900장지동1대지90101520240423전세51.77503000위례24단지(꿈에그린)2013아파트24.05~26.05신규<NA><NA><NA>
접수연도자치구코드자치구명법정동코드법정동명지번구분지번구분명본번부번층계약일전월세 구분임대면적(㎡)보증금(만원)임대료(만원)건물명건축년도건물용도계약기간신규갱신여부계약갱신권사용여부종전 보증금종전 임대료
37977202411380은평구10800역촌동1대지5937320240324월세34.531450015만민하늘애(59-37)2012연립다세대24.05~26.05신규<NA><NA><NA>
54444202411710송파구10200신천동1대지170720240313전세59.95870000파크리오2008아파트24.04~26.04신규<NA><NA><NA>
34833202411680강남구11000압구정동1대지48101220240326월세107.6420000240현대8차(성수현대:91~95동)1980아파트24.05~26.05신규<NA><NA><NA>
42953202411440마포구12700상암동1대지17520320240320전세84.89378000상암월드컵파크9단지2010아파트24.03~26.03갱신<NA>36000<NA>
13470202411410서대문구11900북가좌동1대지4810320240415월세39.96293819DMC래미안e편한세상2012아파트.~26.04신규<NA><NA><NA>
19305202411530구로구11100천왕동1대지28101020240409전세59.98155790천왕연지타운2단지2014아파트.~26.04신규<NA><NA><NA>
30645202411620관악구10100봉천동1대지19695420240330월세34.742000025(196-95)2007연립다세대24.05~26.05신규<NA><NA><NA>
15064202411650서초구10800서초동1대지16096620240413전세84.96750000두진한아름1995아파트24.06~26.06신규<NA><NA><NA>
12647202411560영등포구11800도림동<NA><NA><NA><NA><NA>20240416전세45.9660000<NA><NA>단독다가구24.04~26.04신규<NA><NA><NA>
8868202411290성북구13800장위동1대지32301320240419월세59.495000180꿈의숲아이파크2022아파트24.06~26.06신규<NA><NA><NA>