# Cost of Living Projections

## Introduction

I do not like negotiating for salary. Especially, without valid projections to determine a range.

I prepared this report to estimate a salary expectation that will maintain my current standard of living.

I present two Monte Carlo models of Houston and NYC annual living costs. The data is somewhat dated and –particularly in the case of houston– are high level estimates.

In order to produce a better report, I am currently scraping data from the internet for more accurate sample distributions. I will be able to present that soon.

With that said, the model should not deviate by more than about 5-10 percent from what is presented in below.

## Findings

An annual salary of \$90,000 would be sufficient to qualify for rent in Houston and most likely the median level income neighbors of NYC.

I came about this number by quantifying a confidence inverval of annual rent costs in boths cities across a normal distribution. I then simply multiplied that number by 3 in order to meet the lease qualifications of most landlords.

## Limitations of the Model

### Old Nyc Data

The data I am using was sourced from 2018. I will be updating it soon.

### Houston Data

The houston estimate is based an estimate to stay in the property I am currently staying in. The rent is 2400 a month. I estimated that it could raise at maximum to about 2600 in the next year. If I were to move similiar housing goes for around 2200 to about 2600 a month. I used these as the bounds of my estimates

## Houston Cost of Living Expenses

I intend to stay in Houston for the next year. I would like to move to NY eventually to be nearer to a central office, but not in the near future.

``````lower_bound = int(2400)
upper_bound = int(2600)

median = 2500

cap_range = range(lower_bound, upper_bound)

rent_distribution = np.random.normal(loc=median , scale=standard_dev, size=10000)

rent_sample = choice(rent_distribution,12)
``````

### Houston Monthly food costs

``````lower_bound = int(300)
upper_bound = int(500)

median = 400
standard_dev = 50

food_range = range(lower_bound, upper_bound)

food_distribution = np.random.normal(loc=median , scale=standard_dev, size=10000)

food_sample = choice(food_distribution, 12)
``````

### Houston Insurance Costs

``````lower_bound = int(200)
upper_bound = int(300)

median = 250
standard_dev = 25

insurance_range = range(lower_bound, upper_bound)

insurance_distribution = np.random.normal(loc=median , scale=standard_dev, size=10000)
``````

#### The Houston Cost of Living DF

``````cost_of_living_df = pd.DataFrame()
cost_of_living_df['rent']= choice(rent_distribution,12)
cost_of_living_df['food'] = choice(food_distribution, 12)
cost_of_living_df['insurance'] = choice(insurance_distribution, 12)
cost_of_living_df['monthly_cost'] = cost_of_living_df.rent + cost_of_living_df.food + cost_of_living_df.insurance
cost_of_living_df
``````

rent food insurance monthly_cost
0 2472.688851 334.419350 231.162225 3038.270426
1 2399.284893 444.677340 248.645107 3092.607340
2 2684.456976 430.277801 252.578613 3367.313390
3 2478.390464 360.661703 291.989836 3131.042002
4 2513.324309 429.771020 252.866861 3195.962190
5 2501.390892 413.121444 243.717854 3158.230190
6 2554.433859 363.994333 226.672435 3145.100627
7 2530.369935 299.997467 239.663510 3070.030911
8 2635.681318 394.667441 241.502045 3271.850803
9 2596.457738 513.944623 229.362551 3339.764912
10 2455.017883 371.266360 283.637179 3109.921421
11 2427.449703 485.960065 276.488430 3189.898198

## Houston Costs Per Annum Algorithm

The algorithm below calculates the annual cost of rent, food, and insurance to determine total cost per year. Rent, food, and insurance are set by random choice based on the distributions defined in the functions above.

I run the simulation 10,000 times which in theory corresponds to 10,000 random samples of annual costs. The point in doing this is to create a random normal distribution to define convidence intervals of my total annual costs.

``````
years = 10000
year_counter = 0
#carbon_total_millions_metric_tons = 300000000
#total_tons_shipped = 0
total_price = 0
cycle_price_samples = np.zeros(shape=years)
cycle_rent_samples = np.zeros(shape=years)
cycle_food_samples = np.zeros(shape=years)
cycle_insurance_samples = np.zeros(shape=years)
annual_cost = 0

for year in range(years):
# Define a New DataFrame. It should fall out of scope with each iteration
cost_of_living_df = pd.DataFrame()
#random choice of rent
cost_of_living_df['rent']= choice(rent_distribution,12)
#random choice of food
cost_of_living_df['food'] = choice(food_distribution, 12)
#random Choice of Insurance
cost_of_living_df['insurance'] = choice(insurance_distribution, 12)
#Random Choice of total annual cost
cost_of_living_df['monthly_cost'] = cost_of_living_df.rent + cost_of_living_df.food + cost_of_living_df.insurance
# must use apply to account for multiple 0 conditions.  If i simply vectorized the function across the dataframe in a single call i would assign the the same values each day
#calculate cost per day for fun...
# query all that are = o.  Summate the capacities deduct the total
annual_cost = cost_of_living_df['monthly_cost'].sum()
annual_rent = cost_of_living_df.rent.sum()
annual_food = cost_of_living_df.food.sum()
annual_insurance = cost_of_living_df.insurance.sum()
cycle_price_samples[year] = annual_cost
cycle_food_samples[year] = annual_food
cycle_insurance_samples[year] = annual_insurance
cycle_rent_samples[year] = annual_rent
#print(carbon_total_millions_metric_tons)
year_counter = year_counter+1
``````

### Houston Prediction Df

``````prediction_df = pd.DataFrame()
prediction_df['rent'] = cycle_rent_samples
prediction_df['food'] = cycle_food_samples
prediction_df['insurance'] = cycle_insurance_samples
prediction_df['total'] = cycle_price_samples
``````
``````prediction_df.describe()
``````

rent food insurance total
count 10000.000000 10000.000000 10000.000000 10000.000000
mean 30003.016272 4800.864106 2997.910667 37801.791045
std 344.473477 171.736899 86.991071 394.976839
min 28586.298471 4159.970425 2699.038887 36163.596078
25% 29771.562236 4683.226307 2940.117598 37537.005225
50% 30003.442289 4800.664909 2997.584664 37797.598919
75% 30234.927776 4915.307716 3056.853675 38072.961560
max 31370.239418 5495.020896 3314.016695 39469.935965

### Houston Annual Cost Histogram

``````prediction_df.total.plot.hist(grid=True, bins=20, rwidth=0.9,
color='#607c8e')
plt.xlabel('Annual Total Costs Price USD')
plt.ylabel('Frequency')
plt.grid(axis='y', alpha=0.75)
`````` ### Houston: Calculating the Confidence Interval For Total Costs

The data is nearly normal. Greater samples sizes would produce a graph of nearly perfect normality

``````
st.norm.interval(alpha=0.90, loc=np.mean(prediction_df.total), scale=st.sem(prediction_df.total))
``````
``````(37795.2942543157, 37808.287836034055)
``````

### Houston Annual Rent Histogram

``````### Annual Cost Histogram Histogram
prediction_df.rent.plot.hist(grid=True, bins=20, rwidth=0.9,
color='#607c8e')
plt.title('Annual Rent Cost Distribution ')
plt.xlabel('Annual Rent Costs Price USD')
plt.ylabel('Frequency')
plt.grid(axis='y', alpha=0.75)
`````` ### Houston: Calculating the Confidence Interval For Annual Rent

The data is nearly normal. Greater samples sizes would produce a graph of nearly perfect normality

``````
st.norm.interval(alpha=0.95, loc=np.mean(prediction_df.rent), scale=st.sem(prediction_df.rent))
``````
``````(29996.264715447538, 30009.767827637417)
``````

## New York Cost of Living Expenses

For the sake of comparison, the New York Expense distributions are calculated below. I assume that everything but rent will be equivalent to Houston. A more accurate model would account for insurance, food, and incidental differences.

I am assuming the rent of a two bedroom apartment.

The data i am using was scraped from craigslist in 2018. I will redo it later for 2022 data to get a better model.

``````nyc_df = pd.read_csv("/Users/jnapolitano/Projects/cost-of-living-projections/nyc-housing.csv", encoding="unicode-escape")
``````
``````#assuiming a two bedroom
nyc_df = nyc_df[nyc_df['Bedrooms']== '2br']
``````
``````nyc_df.describe()
``````

Zipcode Price
count 2626.000000 2625.000000
mean 10845.203351 2755.018286
std 556.758722 7465.827048
min 10001.000000 16.000000
25% 10065.000000 1950.000000
50% 11210.000000 2330.000000
75% 11231.000000 2922.000000
max 11697.000000 378888.000000

The price is about 2800 with a std of 7,465. Which is absurd. To do a better analysis, I need to clean the data.

``````
idx = (nyc_df.Price > 500) & (nyc_df.Price < 4500)
nyc_df = nyc_df[idx]
``````
``````nyc_df.describe()
``````

Zipcode Price
count 2441.000000 2441.00000
mean 10881.331422 2435.25891
std 541.102216 728.96291
min 10001.000000 600.00000
25% 10302.000000 1950.00000
50% 11211.000000 2300.00000
75% 11233.000000 2750.00000
max 11697.000000 4495.00000

When accounting for outliers the data is far more managable. I’m surprised by the mean price. Again this data is old, but it is also does not accout for neighborhoods. I will redo the analysis at a later data filtered by neighborhoods.

### Creating the NYC Distributions

``````lower_bound = int(600)
upper_bound = int(4500)

median = 2435
standard_dev = 729

cap_range = range(lower_bound, upper_bound)

rent_distribution = np.random.normal(loc=median , scale=standard_dev, size=10000)

rent_sample = choice(rent_distribution,12)
``````

### NYC Monthly food costs

``````lower_bound = int(300)
upper_bound = int(500)

median = 400
standard_dev = 50

food_range = range(lower_bound, upper_bound)

food_distribution = np.random.normal(loc=median , scale=standard_dev, size=10000)

food_sample = choice(food_distribution, 12)
``````

### NYC Insurance Costs

``````lower_bound = int(200)
upper_bound = int(300)

median = 250
standard_dev = 25

insurance_range = range(lower_bound, upper_bound)

insurance_distribution = np.random.normal(loc=median , scale=standard_dev, size=10000)
``````

#### NYC Cost of Living Distribution

``````cost_of_living_df = pd.DataFrame()
cost_of_living_df['rent']= choice(rent_distribution,12)
cost_of_living_df['food'] = choice(food_distribution, 12)
cost_of_living_df['insurance'] = choice(insurance_distribution, 12)
cost_of_living_df['monthly_cost'] = cost_of_living_df.rent + cost_of_living_df.food + cost_of_living_df.insurance
cost_of_living_df
``````

rent food insurance monthly_cost
0 2440.594149 404.104193 263.802114 3108.500457
1 3509.157666 399.234822 206.641152 4115.033640
2 3351.649621 297.314475 284.177204 3933.141300
3 1977.607960 359.872656 255.831381 2593.311996
4 2169.224724 386.271512 244.469415 2799.965652
5 2661.843885 356.660878 218.425732 3236.930495
6 3595.833071 385.012912 273.882653 4254.728637
7 1765.419028 404.770447 236.665360 2406.854835
8 1708.955308 348.178355 231.690103 2288.823766
9 3227.258413 392.787025 252.315570 3872.361007
10 1941.492537 404.384587 247.628257 2593.505381
11 2081.218740 416.678465 213.204362 2711.101567

## NYC Costs Per Annum Algorithm

The algorithm below calculates the annual cost of rent, food, and insurance to determine total cost per year. Rent, food, and insurance are set by random choice based on the distributions defined in the functions above.

I run the simulation 10,000 times which in theory corresponds to 10,000 random samples of annual costs. The point in doing this is to create a random normal distribution to define convidence intervals of my total annual costs.

``````
years = 10000
year_counter = 0
#carbon_total_millions_metric_tons = 300000000
#total_tons_shipped = 0
total_price = 0
cycle_price_samples = np.zeros(shape=years)
cycle_rent_samples = np.zeros(shape=years)
cycle_food_samples = np.zeros(shape=years)
cycle_insurance_samples = np.zeros(shape=years)
annual_cost = 0

for year in range(years):
# Define a New DataFrame. It should fall out of scope with each iteration
cost_of_living_df = pd.DataFrame()
#random choice of rent
cost_of_living_df['rent']= choice(rent_distribution,12)
#random choice of food
cost_of_living_df['food'] = choice(food_distribution, 12)
#random Choice of Insurance
cost_of_living_df['insurance'] = choice(insurance_distribution, 12)
#Random Choice of total annual cost
cost_of_living_df['monthly_cost'] = cost_of_living_df.rent + cost_of_living_df.food + cost_of_living_df.insurance
# must use apply to account for multiple 0 conditions.  If i simply vectorized the function across the dataframe in a single call i would assign the the same values each day
#calculate cost per day for fun...
# query all that are = o.  Summate the capacities deduct the total
annual_cost = cost_of_living_df['monthly_cost'].sum()
annual_rent = cost_of_living_df.rent.sum()
annual_food = cost_of_living_df.food.sum()
annual_insurance = cost_of_living_df.insurance.sum()
cycle_price_samples[year] = annual_cost
cycle_food_samples[year] = annual_food
cycle_insurance_samples[year] = annual_insurance
cycle_rent_samples[year] = annual_rent
#print(carbon_total_millions_metric_tons)
year_counter = year_counter+1
``````

### NYC Prediction Df

``````prediction_df = pd.DataFrame()
prediction_df['rent'] = cycle_rent_samples
prediction_df['food'] = cycle_food_samples
prediction_df['insurance'] = cycle_insurance_samples
prediction_df['total'] = cycle_price_samples
``````
``````prediction_df.describe()
``````

rent food insurance total
count 10000.000000 10000.000000 10000.000000 10000.000000
mean 29219.509691 4797.809482 3004.224643 37021.543816
std 2532.300418 172.673041 87.221734 2542.267617
min 18744.517281 4116.639699 2574.323735 26447.949901
25% 27545.387716 4678.877662 2945.270499 35351.052672
50% 29244.878069 4797.251203 3005.337764 37034.425389
75% 30915.545611 4915.266687 3062.210984 38722.269645
max 38516.336096 5429.519670 3327.233629 46383.324453

### NYC Annual Cost Histogram

``````prediction_df.total.plot.hist(grid=True, bins=20, rwidth=0.9,
color='#607c8e')
plt.xlabel('Annual Total Costs Price USD')
plt.ylabel('Frequency')
plt.grid(axis='y', alpha=0.75)
`````` ### NYC: Calculating the Confidence Interval For Total Costs

The data is nearly normal. Greater samples sizes would produce a graph of nearly perfect normality

``````
st.norm.interval(alpha=0.90, loc=np.mean(prediction_df.total), scale=st.sem(prediction_df.total))
``````
``````(36979.727235126586, 37063.36039733022)
``````

### NYC Annual Rent Histogram

``````### Annual Cost Histogram Histogram
prediction_df.rent.plot.hist(grid=True, bins=20, rwidth=0.9,
color='#607c8e')
plt.title('Annual Rent Cost Distribution ')
plt.xlabel('Annual Rent Costs Price USD')
plt.ylabel('Frequency')
plt.grid(axis='y', alpha=0.75)
`````` ### Calculating the Confidence Interval For Annual Rent

The data is nearly normal. Greater samples sizes would produce a graph of nearly perfect normality

``````
st.norm.interval(alpha=0.95, loc=np.mean(prediction_df.rent), scale=st.sem(prediction_df.rent))
``````
``````(29169.877514702926, 29269.14186706609)
``````

## NYC Closing Remarks

The rent distribution in NYC with 2018 data is actually nearly comparible to my houston estimate. An annual salary of 90,000 would permit me to live at about the median level in the city. I will be redoing this report soon as the data is old. I am currently scraping data in houston and nyc to produce a better analysis.

## Imports

``````import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as st
from shapely.geometry import Point
from numpy.random import choice
import warnings

warnings.filterwarnings('ignore')
``````