Overview

Dataset statistics

Number of variables10
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory93.3 B

Variable types

Categorical4
Numeric6

Dataset

Description충청남도개발공사에서 추진하는 행복주택(임대주택) 사업에 대한 도민들의 알권리 충족을 위해 공급세대별 청약현황을 공개하여 향후 추진되는 행복주택 사업 지원을 위한 정보제공
URLhttps://www.data.go.kr/data/15106737/fileData.do

Alerts

공급구분 is highly overall correlated with 세부구분 and 1 other fieldsHigh correlation
세부구분 is highly overall correlated with 59(A) and 3 other fieldsHigh correlation
44제곱미터 is highly overall correlated with 59(A) and 4 other fieldsHigh correlation
59(A) is highly overall correlated with 44제곱미터 and 5 other fieldsHigh correlation
59(B) is highly overall correlated with 44제곱미터 and 4 other fieldsHigh correlation
59(C) is highly overall correlated with 44제곱미터 and 4 other fieldsHigh correlation
59(D) is highly overall correlated with 44제곱미터 and 5 other fieldsHigh correlation
59(E) is highly overall correlated with 44제곱미터 and 5 other fieldsHigh correlation
36제곱미터 is highly overall correlated with 59(D) and 2 other fieldsHigh correlation
44제곱미터 has 7 (33.3%) zerosZeros
59(A) has 5 (23.8%) zerosZeros
59(B) has 1 (4.8%) zerosZeros
59(C) has 10 (47.6%) zerosZeros
59(D) has 10 (47.6%) zerosZeros
59(E) has 2 (9.5%) zerosZeros

Reproduction

Analysis started2023-12-12 02:29:50.306236
Analysis finished2023-12-12 02:29:54.498195
Duration4.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구 분
Categorical

Distinct5
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
일반공급
5 
우선공급
4 
일반공급1순위
4 
일반공급2순위
4 
일반공급3순위
4 

Length

Max length7
Median length7
Mean length5.7142857
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row우선공급
2nd row우선공급
3rd row우선공급
4th row우선공급
5th row일반공급

Common Values

ValueCountFrequency (%)
일반공급 5
23.8%
우선공급 4
19.0%
일반공급1순위 4
19.0%
일반공급2순위 4
19.0%
일반공급3순위 4
19.0%

Length

2023-12-12T11:29:54.590955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:29:54.741035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반공급 5
23.8%
우선공급 4
19.0%
일반공급1순위 4
19.0%
일반공급2순위 4
19.0%
일반공급3순위 4
19.0%

공급구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
신혼부부
13 
한부모가족
4 
주거급여
3 
기 타
 
1

Length

Max length6
Median length4
Mean length4.2857143
Min length4

Unique

Unique1 ?
Unique (%)4.8%

Sample

1st row신혼부부
2nd row신혼부부
3rd row주거급여
4th row기 타
5th row신혼부부

Common Values

ValueCountFrequency (%)
신혼부부 13
61.9%
한부모가족 4
 
19.0%
주거급여 3
 
14.3%
기 타 1
 
4.8%

Length

2023-12-12T11:29:54.895668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:29:55.042510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신혼부부 13
59.1%
한부모가족 4
 
18.2%
주거급여 3
 
13.6%
기 1
 
4.5%
타 1
 
4.5%

세부구분
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
예비
4 
7년
4 
자녀
4 
한부모가족
4 
주거급여
3 
Other values (2)
2 

Length

Max length6
Median length2
Mean length3.047619
Min length2

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row예비
2nd row2년
3rd row주거급여
4th row기 타
5th row예비

Common Values

ValueCountFrequency (%)
예비 4
19.0%
7년 4
19.0%
자녀 4
19.0%
한부모가족 4
19.0%
주거급여 3
14.3%
2년 1
 
4.8%
기 타 1
 
4.8%

Length

2023-12-12T11:29:55.176932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:29:55.303903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
예비 4
18.2%
7년 4
18.2%
자녀 4
18.2%
한부모가족 4
18.2%
주거급여 3
13.6%
2년 1
 
4.5%
기 1
 
4.5%
타 1
 
4.5%

36제곱미터
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
0
15 
1
2 
44
2 
2
 
1
56
 
1

Length

Max length2
Median length1
Mean length1.1428571
Min length1

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row0
2nd row2
3rd row56
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 15
71.4%
1 2
 
9.5%
44 2
 
9.5%
2 1
 
4.8%
56 1
 
4.8%

Length

2023-12-12T11:29:55.453365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:29:55.575495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15
71.4%
1 2
 
9.5%
44 2
 
9.5%
2 1
 
4.8%
56 1
 
4.8%

44제곱미터
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8571429
Minimum0
Maximum38
Zeros7
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T11:29:55.692667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile7
Maximum38
Range38
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.1565049
Coefficient of variation (CV)2.1146494
Kurtosis17.259254
Mean3.8571429
Median Absolute Deviation (MAD)2
Skewness4.0118606
Sum81
Variance66.528571
MonotonicityNot monotonic
2023-12-12T11:29:55.843261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 7
33.3%
2 5
23.8%
7 2
 
9.5%
3 2
 
9.5%
1 2
 
9.5%
38 1
 
4.8%
5 1
 
4.8%
6 1
 
4.8%
ValueCountFrequency (%)
0 7
33.3%
1 2
 
9.5%
2 5
23.8%
3 2
 
9.5%
5 1
 
4.8%
6 1
 
4.8%
7 2
 
9.5%
38 1
 
4.8%
ValueCountFrequency (%)
38 1
 
4.8%
7 2
 
9.5%
6 1
 
4.8%
5 1
 
4.8%
3 2
 
9.5%
2 5
23.8%
1 2
 
9.5%
0 7
33.3%

59(A)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.095238
Minimum0
Maximum187
Zeros5
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T11:29:56.001360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q328
95-th percentile112
Maximum187
Range187
Interquartile range (IQR)27

Descriptive statistics

Standard deviation48.97847
Coefficient of variation (CV)1.8769122
Kurtosis5.2654542
Mean26.095238
Median Absolute Deviation (MAD)3
Skewness2.3084514
Sum548
Variance2398.8905
MonotonicityNot monotonic
2023-12-12T11:29:56.145753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 5
23.8%
2 3
14.3%
3 2
 
9.5%
6 2
 
9.5%
1 2
 
9.5%
187 1
 
4.8%
112 1
 
4.8%
4 1
 
4.8%
81 1
 
4.8%
30 1
 
4.8%
Other values (2) 2
 
9.5%
ValueCountFrequency (%)
0 5
23.8%
1 2
 
9.5%
2 3
14.3%
3 2
 
9.5%
4 1
 
4.8%
6 2
 
9.5%
28 1
 
4.8%
30 1
 
4.8%
80 1
 
4.8%
81 1
 
4.8%
ValueCountFrequency (%)
187 1
 
4.8%
112 1
 
4.8%
81 1
 
4.8%
80 1
 
4.8%
30 1
 
4.8%
28 1
 
4.8%
6 2
9.5%
4 1
 
4.8%
3 2
9.5%
2 3
14.3%

59(B)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.380952
Minimum0
Maximum208
Zeros1
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T11:29:56.278581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median8
Q3104
95-th percentile125
Maximum208
Range208
Interquartile range (IQR)100

Descriptive statistics

Standard deviation61.110945
Coefficient of variation (CV)1.4767892
Kurtosis1.1130306
Mean41.380952
Median Absolute Deviation (MAD)6
Skewness1.4490368
Sum869
Variance3734.5476
MonotonicityNot monotonic
2023-12-12T11:29:56.420553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4 4
19.0%
8 2
 
9.5%
2 2
 
9.5%
118 1
 
4.8%
0 1
 
4.8%
3 1
 
4.8%
11 1
 
4.8%
104 1
 
4.8%
208 1
 
4.8%
119 1
 
4.8%
Other values (6) 6
28.6%
ValueCountFrequency (%)
0 1
 
4.8%
1 1
 
4.8%
2 2
9.5%
3 1
 
4.8%
4 4
19.0%
6 1
 
4.8%
8 2
9.5%
11 1
 
4.8%
13 1
 
4.8%
15 1
 
4.8%
ValueCountFrequency (%)
208 1
4.8%
125 1
4.8%
119 1
4.8%
118 1
4.8%
110 1
4.8%
104 1
4.8%
15 1
4.8%
13 1
4.8%
11 1
4.8%
8 2
9.5%

59(C)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8571429
Minimum0
Maximum7
Zeros10
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T11:29:56.547493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4142434
Coefficient of variation (CV)1.2999772
Kurtosis0.15381862
Mean1.8571429
Median Absolute Deviation (MAD)1
Skewness1.1507749
Sum39
Variance5.8285714
MonotonicityNot monotonic
2023-12-12T11:29:56.672416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 10
47.6%
3 4
 
19.0%
1 3
 
14.3%
7 2
 
9.5%
4 1
 
4.8%
6 1
 
4.8%
ValueCountFrequency (%)
0 10
47.6%
1 3
 
14.3%
3 4
 
19.0%
4 1
 
4.8%
6 1
 
4.8%
7 2
 
9.5%
ValueCountFrequency (%)
7 2
 
9.5%
6 1
 
4.8%
4 1
 
4.8%
3 4
 
19.0%
1 3
 
14.3%
0 10
47.6%

59(D)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0952381
Minimum0
Maximum7
Zeros10
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T11:29:56.798081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7000882
Coefficient of variation (CV)1.2886785
Kurtosis-0.88363582
Mean2.0952381
Median Absolute Deviation (MAD)1
Skewness0.91611122
Sum44
Variance7.2904762
MonotonicityNot monotonic
2023-12-12T11:29:57.275404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 10
47.6%
6 3
 
14.3%
1 3
 
14.3%
7 2
 
9.5%
4 1
 
4.8%
3 1
 
4.8%
2 1
 
4.8%
ValueCountFrequency (%)
0 10
47.6%
1 3
 
14.3%
2 1
 
4.8%
3 1
 
4.8%
4 1
 
4.8%
6 3
 
14.3%
7 2
 
9.5%
ValueCountFrequency (%)
7 2
 
9.5%
6 3
 
14.3%
4 1
 
4.8%
3 1
 
4.8%
2 1
 
4.8%
1 3
 
14.3%
0 10
47.6%

59(E)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.904762
Minimum0
Maximum82
Zeros2
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T11:29:57.403292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q353
95-th percentile72
Maximum82
Range82
Interquartile range (IQR)52

Descriptive statistics

Standard deviation29.473556
Coefficient of variation (CV)1.4807289
Kurtosis-0.44454292
Mean19.904762
Median Absolute Deviation (MAD)2
Skewness1.162548
Sum418
Variance868.69048
MonotonicityNot monotonic
2023-12-12T11:29:57.543707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 7
33.3%
6 3
14.3%
53 2
 
9.5%
2 2
 
9.5%
0 2
 
9.5%
82 1
 
4.8%
3 1
 
4.8%
72 1
 
4.8%
56 1
 
4.8%
70 1
 
4.8%
ValueCountFrequency (%)
0 2
 
9.5%
1 7
33.3%
2 2
 
9.5%
3 1
 
4.8%
6 3
14.3%
53 2
 
9.5%
56 1
 
4.8%
70 1
 
4.8%
72 1
 
4.8%
82 1
 
4.8%
ValueCountFrequency (%)
82 1
 
4.8%
72 1
 
4.8%
70 1
 
4.8%
56 1
 
4.8%
53 2
 
9.5%
6 3
14.3%
3 1
 
4.8%
2 2
 
9.5%
1 7
33.3%
0 2
 
9.5%

Interactions

2023-12-12T11:29:53.675188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:50.755049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:51.379846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:52.008891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:52.550177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:53.097144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:53.760317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:50.840555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:51.475249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:52.116830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:52.659653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:53.198978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:53.862654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:50.956213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:51.585279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:52.206320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:52.742795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:53.305749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:53.937624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:51.042060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:51.693384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:52.286996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:52.821948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:53.387635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:54.035324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:51.139675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:51.814572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:52.379320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:52.915354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:53.477981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:54.138729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:51.281665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:51.914086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:52.477130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:53.003361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:29:53.582621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:29:57.656977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분공급구분세부구분36제곱미터44제곱미터59(A)59(B)59(C)59(D)59(E)
구 분1.0000.0000.0000.0000.2950.0000.1310.1900.0000.000
공급구분0.0001.0001.0000.6690.0000.0000.0000.0000.3920.000
세부구분0.0001.0001.0000.7940.3820.7170.1470.6120.8340.758
36제곱미터0.0000.6690.7941.0000.3160.6980.0000.4440.7400.544
44제곱미터0.2950.0000.3820.3161.0000.8630.7540.9080.5780.626
59(A)0.0000.0000.7170.6980.8631.0000.8730.8450.8781.000
59(B)0.1310.0000.1470.0000.7540.8731.0000.8020.6830.934
59(C)0.1900.0000.6120.4440.9080.8450.8021.0000.8320.914
59(D)0.0000.3920.8340.7400.5780.8780.6830.8321.0000.902
59(E)0.0000.0000.7580.5440.6261.0000.9340.9140.9021.000
2023-12-12T11:29:57.841616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구 분공급구분36제곱미터세부구분
구 분1.0000.0000.0000.000
공급구분0.0001.0000.5770.907
36제곱미터0.0000.5771.0000.621
세부구분0.0000.9070.6211.000
2023-12-12T11:29:57.987828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
44제곱미터59(A)59(B)59(C)59(D)59(E)구 분공급구분세부구분36제곱미터
44제곱미터1.0000.8370.8750.6410.6530.7420.1950.0000.2140.215
59(A)0.8371.0000.9090.8530.7750.9430.0000.0000.5200.312
59(B)0.8750.9091.0000.7930.7900.8570.0000.0000.0000.000
59(C)0.6410.8530.7931.0000.7630.8140.0320.0000.3890.288
59(D)0.6530.7750.7900.7631.0000.7050.0000.2210.4160.549
59(E)0.7420.9430.8570.8140.7051.0000.0000.0000.5720.448
구 분0.1950.0000.0000.0320.0000.0001.0000.0000.0000.000
공급구분0.0000.0000.0000.0000.2210.0000.0001.0000.9070.577
세부구분0.2140.5200.0000.3890.4160.5720.0000.9071.0000.621
36제곱미터0.2150.3120.0000.2880.5490.4480.0000.5770.6211.000

Missing values

2023-12-12T11:29:54.289563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:29:54.436720image/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

구 분공급구분세부구분36제곱미터44제곱미터59(A)59(B)59(C)59(D)59(E)
0우선공급신혼부부예비0381872087653
1우선공급신혼부부2년251121194482
2우선공급주거급여주거급여566313006
3우선공급기 타기 타0001001
4일반공급신혼부부예비0246103
5일반공급신혼부부7년07811257672
6일반공급신혼부부자녀02301103756
7일반공급한부모가족한부모가족12615336
8일반공급주거급여주거급여44328011
9일반공급1순위신혼부부7년07801186670
구 분공급구분세부구분36제곱미터44제곱미터59(A)59(B)59(C)59(D)59(E)
11일반공급1순위한부모가족한부모가족02611326
12일반공급1순위주거급여주거급여44328011
13일반공급2순위신혼부부예비0112101
14일반공급2순위신혼부부7년0003001
15일반공급2순위신혼부부자녀0022002
16일반공급2순위한부모가족한부모가족0000010
17일반공급3순위신혼부부예비0134002
18일반공급3순위신혼부부7년0014101
19일반공급3순위신혼부부자녀0004001
20일반공급3순위한부모가족한부모가족1004000