Är hyran för hög?

Analys av amerikansk folkräkningsdata i Python

Lee Hachadoorian

Asst. Professor of Instruction, Temple University

Definitioner

Olika sätt att beräkna hyra:

Kontraktshyra: Hyra enligt hyresavtal
Bruttohyra: Hyra plus driftskostnader; dessa kan ingå i kontraktshyran eller betalas separat av hyresgästen

Hyrebörda:

Hyresbörda: Att betala 30 % eller mer av hushållsinkomsten i hyra
Allvarlig hyresbörda: Att betala 50 % eller mer av hushållsinkomsten i hyra

Tabell B25074: Hushållsinkomst efter bruttohyra som andel av hushållsinkomst de senaste 12 månaderna

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

USA:s hyreandel av inkomst, 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...

Beräkna hyresbörda

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')

Beräkna hyresbörda

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"]
    )

Beräkna hyresbörda

print(rent["inc_10k_to_20k_rent_burden"])

0    87.008024
Name: inc_10k_to_20k_rent_burden, dtype: float64

Beräkna hyresbörda i en loop

# 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"]

Beräkna hyresbörda i en loop

# 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"])

USA:s hyresbörda per inkomstkategori

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

Ett stapeldiagram som visar andelen hushåll med hyresbörda för 7 inkomstkategorier. Staplarna minskar från 92 % till 3 % när inkomsten ökar.

Nu kör vi en övning!

Analys av amerikansk folkräkningsdata i Python