Overview

Dataset statistics

Number of variables7
Number of observations506
Missing cells524
Missing cells (%)14.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.8 KiB
Average record size in memory62.3 B

Variable types

Text1
Numeric6

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 상업용부동산 임대동향조사 중 소규모상가의 분기별 층별임대료 및 층별효용비율 데이터입니다. - (단위:천원/㎡ , %) - 공표시기 : 계간(분기)
Author한국부동산원
URLhttps://www.data.go.kr/data/15069838/fileData.do

Alerts

2022_1분기_지하1층 is highly overall correlated with 2022_1분기_1층 and 4 other fieldsHigh correlation
2022_1분기_1층 is highly overall correlated with 2022_1분기_지하1층 and 4 other fieldsHigh correlation
2022_1분기_2층 is highly overall correlated with 2022_1분기_지하1층 and 4 other fieldsHigh correlation
2022_2분기_지하1층 is highly overall correlated with 2022_1분기_지하1층 and 4 other fieldsHigh correlation
2022_2분기_1층 is highly overall correlated with 2022_1분기_지하1층 and 4 other fieldsHigh correlation
2022_2분기_2층 is highly overall correlated with 2022_1분기_지하1층 and 4 other fieldsHigh correlation
2022_1분기_지하1층 has 250 (49.4%) missing valuesMissing
2022_1분기_2층 has 12 (2.4%) missing valuesMissing
2022_2분기_지하1층 has 250 (49.4%) missing valuesMissing
2022_2분기_2층 has 12 (2.4%) missing valuesMissing
시도 광역상권 하위상권 구분 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:13:40.889495
Analysis finished2023-12-12 18:13:45.988259
Duration5.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도 광역상권 하위상권 구분
Text

UNIQUE 

Distinct506
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2023-12-13T03:13:46.168391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length11.701581
Min length6

Characters and Unicode

Total characters5921
Distinct characters208
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique506 ?
Unique (%)100.0%

Sample

1st row서울 임대료
2nd row서울 효용비율
3rd row서울 도심 임대료
4th row서울 도심 효용비율
5th row서울 도심 광화문임대료
ValueCountFrequency (%)
서울 118
 
11.0%
경기 60
 
5.6%
부산 42
 
3.9%
경남 34
 
3.2%
충남 32
 
3.0%
경북 28
 
2.6%
전남 26
 
2.4%
대구 26
 
2.4%
광주 22
 
2.1%
강원 22
 
2.1%
Other values (478) 660
61.7%
2023-12-13T03:13:46.549155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1012
 
17.1%
대 345
 
5.8%
용 261
 
4.4%
임 253
 
4.3%
료 253
 
4.3%
효 253
 
4.3%
비 253
 
4.3%
율 253
 
4.3%
서 140
 
2.4%
남 140
 
2.4%
Other values (198) 2758
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4867
82.2%
Space Separator 1012
 
17.1%
Other Punctuation 40
 
0.7%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 345
 
7.1%
용 261
 
5.4%
임 253
 
5.2%
료 253
 
5.2%
효 253
 
5.2%
비 253
 
5.2%
율 253
 
5.2%
서 140
 
2.9%
남 140
 
2.9%
경 138
 
2.8%
Other values (195) 2578
53.0%
Space Separator
ValueCountFrequency (%)
1012
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 40
100.0%
Decimal Number
ValueCountFrequency (%)
1 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4867
82.2%
Common 1054
 
17.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 345
 
7.1%
용 261
 
5.4%
임 253
 
5.2%
료 253
 
5.2%
효 253
 
5.2%
비 253
 
5.2%
율 253
 
5.2%
서 140
 
2.9%
남 140
 
2.9%
경 138
 
2.8%
Other values (195) 2578
53.0%
Common
ValueCountFrequency (%)
1012
96.0%
/ 40
 
3.8%
1 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4867
82.2%
ASCII 1054
 
17.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1012
96.0%
/ 40
 
3.8%
1 2
 
0.2%
Hangul
ValueCountFrequency (%)
대 345
 
7.1%
용 261
 
5.4%
임 253
 
5.2%
료 253
 
5.2%
효 253
 
5.2%
비 253
 
5.2%
율 253
 
5.2%
서 140
 
2.9%
남 140
 
2.9%
경 138
 
2.8%
Other values (195) 2578
53.0%

2022_1분기_지하1층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct221
Distinct (%)86.3%
Missing250
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean22.427344
Minimum1.3
Maximum93.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-12-13T03:13:46.687149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile2.7
Q17.325
median18.155
Q332.1375
95-th percentile51.2175
Maximum93.75
Range92.45
Interquartile range (IQR)24.8125

Descriptive statistics

Standard deviation18.49908
Coefficient of variation (CV)0.82484489
Kurtosis2.5406745
Mean22.427344
Median Absolute Deviation (MAD)11.76
Skewness1.4030564
Sum5741.4
Variance342.21596
MonotonicityNot monotonic
2023-12-13T03:13:46.829696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7 5
 
1.0%
7.5 4
 
0.8%
5.7 3
 
0.6%
3.4 3
 
0.6%
2.4 3
 
0.6%
18.4 3
 
0.6%
1.3 2
 
0.4%
2.3 2
 
0.4%
16.1 2
 
0.4%
5.3 2
 
0.4%
Other values (211) 227
44.9%
(Missing) 250
49.4%
ValueCountFrequency (%)
1.3 2
 
0.4%
2.0 2
 
0.4%
2.3 2
 
0.4%
2.4 3
0.6%
2.6 2
 
0.4%
2.7 5
1.0%
2.8 1
 
0.2%
2.9 2
 
0.4%
3.0 1
 
0.2%
3.1 1
 
0.2%
ValueCountFrequency (%)
93.75 1
0.2%
92.49 1
0.2%
90.96 1
0.2%
88.8 1
0.2%
87.44 1
0.2%
87.29 1
0.2%
73.65 1
0.2%
69.96 1
0.2%
56.53 1
0.2%
56.26 1
0.2%

2022_1분기_1층
Real number (ℝ)

HIGH CORRELATION 

Distinct187
Distinct (%)37.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.664427
Minimum6.4
Maximum137.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-12-13T03:13:46.979729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.4
5-th percentile10.225
Q118.925
median100
Q3100
95-th percentile100
Maximum137.9
Range131.5
Interquartile range (IQR)81.075

Descriptive statistics

Standard deviation39.446852
Coefficient of variation (CV)0.62949354
Kurtosis-1.8109273
Mean62.664427
Median Absolute Deviation (MAD)1.4
Skewness-0.19300776
Sum31708.2
Variance1556.0541
MonotonicityNot monotonic
2023-12-13T03:13:47.136844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 253
50.0%
12.7 5
 
1.0%
12.9 4
 
0.8%
14.1 4
 
0.8%
14.7 3
 
0.6%
11.2 3
 
0.6%
10.1 3
 
0.6%
9.3 3
 
0.6%
13.5 3
 
0.6%
16.6 3
 
0.6%
Other values (177) 222
43.9%
ValueCountFrequency (%)
6.4 1
 
0.2%
6.8 1
 
0.2%
7.3 1
 
0.2%
7.4 1
 
0.2%
7.5 1
 
0.2%
7.9 3
0.6%
8.4 1
 
0.2%
8.5 1
 
0.2%
9.0 1
 
0.2%
9.1 1
 
0.2%
ValueCountFrequency (%)
137.9 1
 
0.2%
102.8 1
 
0.2%
100.0 253
50.0%
82.9 1
 
0.2%
81.4 1
 
0.2%
70.8 1
 
0.2%
69.5 1
 
0.2%
68.9 1
 
0.2%
65.9 1
 
0.2%
65.8 1
 
0.2%

2022_1분기_2층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct303
Distinct (%)61.3%
Missing12
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean29.783806
Minimum2.7
Maximum126.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-12-13T03:13:47.287714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile4.565
Q18.6
median31.95
Q346.7
95-th percentile59.735
Maximum126.7
Range124
Interquartile range (IQR)38.1

Descriptive statistics

Standard deviation20.468559
Coefficient of variation (CV)0.68723785
Kurtosis-0.37218566
Mean29.783806
Median Absolute Deviation (MAD)19.35
Skewness0.36162539
Sum14713.2
Variance418.96189
MonotonicityNot monotonic
2023-12-13T03:13:47.497176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.3 7
 
1.4%
8.6 7
 
1.4%
6.8 6
 
1.2%
10.1 6
 
1.2%
5.9 6
 
1.2%
45.4 5
 
1.0%
4.0 5
 
1.0%
6.4 4
 
0.8%
7.0 4
 
0.8%
5.6 4
 
0.8%
Other values (293) 440
87.0%
(Missing) 12
 
2.4%
ValueCountFrequency (%)
2.7 1
 
0.2%
3.0 1
 
0.2%
3.3 1
 
0.2%
3.4 1
 
0.2%
3.5 2
 
0.4%
3.6 1
 
0.2%
3.7 1
 
0.2%
3.8 2
 
0.4%
4.0 5
1.0%
4.2 3
0.6%
ValueCountFrequency (%)
126.7 1
0.2%
86.1 1
0.2%
79.2 1
0.2%
77.1 1
0.2%
75.6 1
0.2%
74.2 1
0.2%
74.1 1
0.2%
69.1 1
0.2%
68.8 1
0.2%
67.3 1
0.2%

2022_2분기_지하1층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct217
Distinct (%)84.8%
Missing250
Missing (%)49.4%
Infinite0
Infinite (%)0.0%
Mean22.151523
Minimum0
Maximum94.33
Zeros2
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-12-13T03:13:47.694828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.675
Q17.075
median18.08
Q332.595
95-th percentile50.5775
Maximum94.33
Range94.33
Interquartile range (IQR)25.52

Descriptive statistics

Standard deviation18.36582
Coefficient of variation (CV)0.82909964
Kurtosis2.4992589
Mean22.151523
Median Absolute Deviation (MAD)11.73
Skewness1.3834039
Sum5670.79
Variance337.30335
MonotonicityNot monotonic
2023-12-13T03:13:47.876128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7 5
 
1.0%
7.5 3
 
0.6%
18.4 3
 
0.6%
16.1 3
 
0.6%
5.7 3
 
0.6%
3.4 3
 
0.6%
2.4 3
 
0.6%
21.13 2
 
0.4%
1.3 2
 
0.4%
6.1 2
 
0.4%
Other values (207) 227
44.9%
(Missing) 250
49.4%
ValueCountFrequency (%)
0.0 2
 
0.4%
1.3 2
 
0.4%
2.0 2
 
0.4%
2.3 2
 
0.4%
2.4 3
0.6%
2.6 2
 
0.4%
2.7 5
1.0%
2.8 1
 
0.2%
2.9 2
 
0.4%
3.0 1
 
0.2%
ValueCountFrequency (%)
94.33 1
0.2%
92.96 1
0.2%
90.77 1
0.2%
88.78 1
0.2%
87.32 1
0.2%
77.0 1
0.2%
73.64 1
0.2%
70.42 1
0.2%
56.58 1
0.2%
53.5 1
0.2%

2022_2분기_1층
Real number (ℝ)

HIGH CORRELATION 

Distinct190
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.689921
Minimum6.4
Maximum137.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-12-13T03:13:48.085971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.4
5-th percentile10.3
Q119
median100
Q3100
95-th percentile100
Maximum137.9
Range131.5
Interquartile range (IQR)81

Descriptive statistics

Standard deviation39.4399
Coefficient of variation (CV)0.62912665
Kurtosis-1.8094371
Mean62.689921
Median Absolute Deviation (MAD)1.55
Skewness-0.19477434
Sum31721.1
Variance1555.5057
MonotonicityNot monotonic
2023-12-13T03:13:48.280289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 253
50.0%
14.7 5
 
1.0%
19.3 4
 
0.8%
12.7 4
 
0.8%
13.3 3
 
0.6%
20.0 3
 
0.6%
22.8 3
 
0.6%
18.5 3
 
0.6%
11.3 3
 
0.6%
16.6 3
 
0.6%
Other values (180) 222
43.9%
ValueCountFrequency (%)
6.4 1
 
0.2%
6.8 1
 
0.2%
7.3 2
0.4%
7.5 1
 
0.2%
7.9 3
0.6%
8.3 1
 
0.2%
8.5 1
 
0.2%
9.0 1
 
0.2%
9.1 1
 
0.2%
9.2 2
0.4%
ValueCountFrequency (%)
137.9 1
 
0.2%
103.1 1
 
0.2%
100.0 253
50.0%
82.9 1
 
0.2%
81.5 1
 
0.2%
70.8 1
 
0.2%
69.7 1
 
0.2%
69.0 1
 
0.2%
65.9 2
 
0.4%
63.9 1
 
0.2%

2022_2분기_2층
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct300
Distinct (%)60.7%
Missing12
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean29.783401
Minimum2.7
Maximum125.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-12-13T03:13:48.853472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile4.565
Q18.6
median32.35
Q346.7
95-th percentile59.735
Maximum125.8
Range123.1
Interquartile range (IQR)38.1

Descriptive statistics

Standard deviation20.435365
Coefficient of variation (CV)0.6861327
Kurtosis-0.40477469
Mean29.783401
Median Absolute Deviation (MAD)19.15
Skewness0.35242796
Sum14713
Variance417.60415
MonotonicityNot monotonic
2023-12-13T03:13:49.006802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.6 6
 
1.2%
5.9 6
 
1.2%
10.1 6
 
1.2%
5.3 5
 
1.0%
38.2 5
 
1.0%
4.0 5
 
1.0%
6.8 5
 
1.0%
8.0 4
 
0.8%
5.6 4
 
0.8%
6.2 4
 
0.8%
Other values (290) 444
87.7%
(Missing) 12
 
2.4%
ValueCountFrequency (%)
2.7 1
 
0.2%
3.0 1
 
0.2%
3.3 1
 
0.2%
3.4 2
 
0.4%
3.6 1
 
0.2%
3.7 2
 
0.4%
3.8 1
 
0.2%
4.0 5
1.0%
4.2 4
0.8%
4.3 2
 
0.4%
ValueCountFrequency (%)
125.8 1
0.2%
86.3 1
0.2%
79.2 1
0.2%
77.0 1
0.2%
76.9 1
0.2%
74.3 1
0.2%
74.0 1
0.2%
68.1 1
0.2%
67.1 1
0.2%
67.0 1
0.2%

Interactions

2023-12-13T03:13:44.864520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:41.259662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:41.861777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:42.897758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:43.634765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:44.190701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:44.952873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:41.349897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:41.956492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:43.002215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:43.728939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:44.298992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:45.080772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:41.465451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:42.075841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:43.114491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:43.827869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:44.423593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:45.233505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:41.570938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:42.490561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:43.255986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:43.909162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:44.532781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:45.362017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:41.665742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:42.602315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:43.376497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:43.990306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:44.619722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:45.511605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:41.761895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:42.762126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:43.523372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:44.085096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:13:44.752918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:13:49.111591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2022_1분기_지하1층2022_1분기_1층2022_1분기_2층2022_2분기_지하1층2022_2분기_1층2022_2분기_2층
2022_1분기_지하1층1.0000.5740.8520.9790.5750.813
2022_1분기_1층0.5741.0000.8510.5891.0000.853
2022_1분기_2층0.8520.8511.0000.7130.8510.999
2022_2분기_지하1층0.9790.5890.7131.0000.5900.666
2022_2분기_1층0.5751.0000.8510.5901.0000.853
2022_2분기_2층0.8130.8530.9990.6660.8531.000
2023-12-13T03:13:49.233567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2022_1분기_지하1층2022_1분기_1층2022_1분기_2층2022_2분기_지하1층2022_2분기_1층2022_2분기_2층
2022_1분기_지하1층1.0000.8230.7940.9900.8240.796
2022_1분기_1층0.8231.0000.9120.8111.0000.912
2022_1분기_2층0.7940.9121.0000.7790.9121.000
2022_2분기_지하1층0.9900.8110.7791.0000.8110.780
2022_2분기_1층0.8241.0000.9120.8111.0000.912
2022_2분기_2층0.7960.9121.0000.7800.9121.000

Missing values

2023-12-13T03:13:45.677334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:13:45.798206image/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.
2023-12-13T03:13:45.918926image/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

시도 광역상권 하위상권 구분2022_1분기_지하1층2022_1분기_1층2022_1분기_2층2022_2분기_지하1층2022_2분기_1층2022_2분기_2층
0서울 임대료23.548.725.423.448.825.4
1서울 효용비율48.31100.052.147.9100.052.1
2서울 도심 임대료15.065.931.915.065.932.0
3서울 도심 효용비율22.76100.048.522.73100.048.5
4서울 도심 광화문임대료<NA>81.442.9<NA>81.542.9
5서울 도심 광화문효용비율<NA>100.052.7<NA>100.052.7
6서울 도심 남대문임대료<NA>62.423.9<NA>62.623.9
7서울 도심 남대문효용비율<NA>100.038.4<NA>100.038.3
8서울 도심 동대문임대료8.547.521.18.547.621.0
9서울 도심 동대문효용비율17.8100.044.417.77100.044.2
시도 광역상권 하위상권 구분2022_1분기_지하1층2022_1분기_1층2022_1분기_2층2022_2분기_지하1층2022_2분기_1층2022_2분기_2층
496제주 임대료3.514.17.33.514.27.3
497제주 효용비율24.52100.051.724.47100.051.6
498제주 광양사거리임대료2.612.39.12.612.39.1
499제주 광양사거리효용비율21.14100.074.121.13100.074.0
500제주 노형오거리임대료<NA>20.28.6<NA>20.28.6
501제주 노형오거리효용비율<NA>100.042.7<NA>100.042.6
502제주 서귀포도심임대료9.016.96.99.017.06.9
503제주 서귀포도심효용비율53.53100.040.953.29100.040.7
504제주 중앙사거리임대료<NA>11.25.8<NA>11.25.8
505제주 중앙사거리효용비율<NA>100.051.6<NA>100.051.5