Using quandl zillow api for Chicago and Evanston Home Sale Prices using ARIMA and EWMAs

This post includes code adapted from python for finance and trading algorithms udemy course and python for finance and trading algorithms udemy course notebooks.

Find the quandl api documentation here -

from sklearn.datasets import fetch_california_housing

california = fetch_california_housing()

X = california.data
y = california.target * 100000

print(f'Data shape is {X.shape}')
print(f'Target shape is {y.shape}')

Data shape is (20640, 8)
Target shape is (20640,)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import quandl

quandl.ApiConfig.api_key = ''
%matplotlib inline

quandl_call = (
    "ZILLOW/{category}{code}_{indicator}"
)


def download_data(category, code, indicator):
    """
    Reads in a single dataset from Zillow Quandl API
    
    Parameters
    ----------
    category : "Chicago_Area" or "Evanston"
    
    code : "Evanston" or "Chicago"
    
    indicator : "Sales_Price" or "other"

    
    Returns
    -------
    DataFrame
    """
    AREA_CATEGORY_dict = {"Evanston": "C", "Chicago_Area": "C"}
    AREA_CODE_dict = {"Evanston": "64604", "Chicago": "36156"}
    INDICATOR_CODE_dict = {"Sales_Price": "SP"}
    
    
    
    category = AREA_CATEGORY_dict[category]
    code = AREA_CODE_dict[code]
    indicator = INDICATOR_CODE_dict[indicator]

    
    
    return quandl.get(quandl_call.format(category=category, code=code, indicator=indicator))

# data = quandl.get_table("ZILLOW/REGIONS", paginate=True) 

# col = 'region'
# mask = np.column_stack([data[col].str.contains(r"Boston", na=False) for col in data])
# data.loc[mask.any(axis=1)]

# col = 'region'
# mask = np.column_stack([data[col].str.contains(r"Evanston", na=False) for col in data])
# df=data.loc[mask.any(axis=1)]
#df['region']

Chicago and Evanston Home Sale Prices

EV_SP = download_data('Chicago_Area', 'Evanston', 'Sales_Price')
CH_SP = download_data('Chicago_Area', 'Chicago', 'Sales_Price')

CH_SP.query("Value > 270000")

	Value
Date
2008-03-31	325100.0
2008-04-30	314800.0
2008-05-31	286900.0
2008-06-30	274600.0
2019-03-31	290800.0
2019-04-30	292000.0
2019-05-31	276000.0
2019-06-30	271500.0
2020-01-31	281400.0
2020-02-29	302900.0
2020-03-31	309200.0

from pandasql import sqldf
pysqldf = lambda q: sqldf(q, globals())

q = """SELECT date
        ,Value 
       FROM CH_SP
       WHERE Value > 270000
       LIMIT 10;"""

values = pysqldf(q)
values

	Date	Value
0	2008-03-31 00:00:00.000000	325100.0
1	2008-04-30 00:00:00.000000	314800.0
2	2008-05-31 00:00:00.000000	286900.0
3	2008-06-30 00:00:00.000000	274600.0
4	2019-03-31 00:00:00.000000	290800.0
5	2019-04-30 00:00:00.000000	292000.0
6	2019-05-31 00:00:00.000000	276000.0
7	2019-06-30 00:00:00.000000	271500.0
8	2020-01-31 00:00:00.000000	281400.0
9	2020-02-29 00:00:00.000000	302900.0

fig = plt.figure(figsize=(12, 6))
plt.title('Value')

EV_SP['Value'].plot(label='Evanston')
CH_SP['Value'].plot(label='Chicago')

plt.legend()

<matplotlib.legend.Legend at 0x7f3bf2e42c18>

import seaborn as sns
from scipy.stats import norm

sns.distplot(CH_SP['Value'], fit=norm);

sns.distplot(EV_SP['Value'], fit=norm);