家賃は高すぎる？

Pythonで学ぶ米国センサスデータ分析

Lee Hachadoorian

Asst. Professor of Instruction, Temple University

定義

家賃の算出方法:

契約家賃: リースで支払う家賃
総家賃: 家賃＋光熱費。光熱費は契約家賃に含まれる場合も、別払いの場合もある

家賃負担:

家賃負担: 世帯収入の30%以上を家賃に支出
深刻な家賃負担: 世帯収入の50%以上を家賃に支出

表 B25074: 過去12か月の世帯収入に占める総家賃の割合別の世帯収入

Total
    Less than $10,000
        Less than 20.0 percent
        20.0 to 24.9 percent
        25.0 to 29.9 percent
        30.0 to 34.9 percent
        35.0 to 39.9 percent
        40.0 to 49.9 percent
        50.0 percent or more
        Not computed
    $10,000 to $19,999
    $20,000 to $34,999
    $35,000 to $49,999
    $50,000 to $74,999
    $75,000 to $99,999
    $100,000 or more

米国の収入に占める家賃の割合, ACS 2012-2016

total                                     42835169
inc_under_10k                              5558843
inc_under_10k_rent_under_20_pct              57052
inc_under_10k_rent_20_to_25_pct              58042
inc_under_10k_rent_25_to_30_pct             208806
inc_under_10k_rent_30_to_35_pct             177709
inc_under_10k_rent_35_to_40_pct             102565
inc_under_10k_rent_40_to_50_pct             150153
inc_under_10k_rent_over_50_pct             3381537
inc_under_10k_rent_not_computed            1422979
inc_10k_to_20k                             7027373
inc_10k_to_20k_rent_under_20_pct            213000
etc...

家賃負担の計算

print(rent.columns[10:19])

Index(['inc_10k_to_20k', 'inc_10k_to_20k_rent_under_20_pct',
   'inc_10k_to_20k_rent_20_to_25_pct', 'inc_10k_to_20k_rent_25_to_30_pct',
   'inc_10k_to_20k_rent_30_to_35_pct', 'inc_10k_to_20k_rent_35_to_40_pct',
   'inc_10k_to_20k_rent_40_to_50_pct', 'inc_10k_to_20k_rent_over_50_pct',
   'inc_10k_to_20k_rent_not_computed'],
  dtype='object')

家賃負担の計算

rent["inc_10k_to_20k_rent_burden"] = 100 * (

    rent["inc_10k_to_20k_rent_30_to_35_pct"] + 
    rent["inc_10k_to_20k_rent_35_to_40_pct"] + 
    rent["inc_10k_to_20k_rent_40_to_50_pct"] + 
    rent["inc_10k_to_20k_rent_over_50_pct"]

    ) / (
    rent["inc_10k_to_20k"] - 
    rent["inc_10k_to_20k_rent_not_computed"]
    )

家賃負担の計算

print(rent["inc_10k_to_20k_rent_burden"])

0    87.008024
Name: inc_10k_to_20k_rent_burden, dtype: float64

ループで家賃負担を計算

# Create list with income category part of column names
incomes = ["inc_under_10k", "inc_10k_to_20k", "inc_20k_to_35k", 
           "inc_35k_to_50k", "inc_50k_to_75k", "inc_75k_to_100k", 
           "inc_over_100k"]

ループで家賃負担を計算

# Create new DataFrame with just the geography name
rent_burden = rent["name"]

# Loop over the list of income categories
for income in incomes:

    # Construct column names
    rent_burden[income] =

        100 * (rent[income + "_rent_30_to_35_pct"] + 
        rent[income + "_rent_35_to_40_pct"] + 
        rent[income + "_rent_40_to_50_pct"] + 
        rent[income + "_rent_over_50_pct"]) / (
        rent[income] - rent[income + "_rent_not_computed"])

米国の所得別家賃負担

print(rent_burden.squeeze())

name               United States
inc_under_10k            92.1685
inc_10k_to_20k            87.008
inc_20k_to_35k           74.7448
inc_35k_to_50k           43.0434
inc_50k_to_75k           21.0937
inc_75k_to_100k          9.11853
inc_over_100k            3.14882
Name: 0, dtype: object

7つの所得区分ごとの家賃負担世帯の割合を示す棒グラフ。所得が上がるほど、バーは92%から3%へと低下する。

練習しましょう！

Pythonで学ぶ米国センサスデータ分析