%matplotlib inline
import matplotlib.pyplot as plt
import requests
import pandas as pd
from StringIO import StringIO
import json
import numpy as np
import statsmodels.formula.api as sm
cities = """Atlanta,GA,33.762909,-84.422675
Austin,TX,30.303936,-97.754355
Boston,MA,42.331960,-71.020173
Chicago,IL,41.837551,-87.681844
Cleveland,OH,41.478462,-81.679435
Denver,CO,39.761850,-104.881105
Las Vegas,NV,36.229214,-115.26008
Los Angeles,CA,34.019394,-118.410825
Miami,FL,25.775163,-80.208615
Minneapolis,MN,44.963324,-93.268320
Nashville,TN,36.171800,-86.785002
New Orleans,LA,30.053420,-89.934502
New York,NY,40.663619,-73.938589
Philadelphia,PA,40.009376,-75.133346
Phoenix,AZ,33.572154,-112.090132
Salt Lake City,UT,40.778996,-111.932630
San Francisco,CA,37.727239,-123.032229
Seattle,WA,47.620499,-122.350876
Washington,DC,38.904103,-77.017229"""
citiesDf = pd.read_csv(StringIO(cities), sep=',', header=None)
citiesDf.columns = ['city', 'state', 'latitude', 'longitude']
url = 'http://api.openhouseproject.co/api/property/?offset=0&limit=1500&close_to=({},{},{})'
distance = 100
fields = ['price', 'bathrooms', 'bedrooms', 'building_size']
dfMap = {}
for i in range(citiesDf.shape[0]):
    row = citiesDf.iloc[i]
    city = row.city
    print 'City:', city
    if not(dfMap.has_key(city)):
        lat = row.latitude
        lng = row.longitude
        s = url.format(distance, lat, lng)
        r = requests.get(s)
        o = json.loads(r.content)
        results = o['results']
        properties = []
        for result in results:
            data = {}
            for field in fields:
                data[field] = result[field]
            properties.append(data)
        dfMap[city] = properties
City: Atlanta
City: Austin
City: Boston
City: Chicago
City: Cleveland
City: Denver
City: Las Vegas
City: Los Angeles
City: Miami
City: Minneapolis
City: Nashville
City: New Orleans
City: New York
City: Philadelphia
City: Phoenix
City: Salt Lake City
City: San Francisco
City: Seattle
City: Washington
for city in dfMap.keys():
    df = pd.DataFrame(dfMap[city])
    df['intercept'] = 1
    model = sm.ols(formula="price ~ bedrooms + building_size + intercept", data=df).fit()
    print("CITY: " + city)
    print model.summary()
    print "\n\n\n\n"
CITY: Seattle
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.414
Model:                            OLS   Adj. R-squared:                  0.413
Method:                 Least Squares   F-statistic:                     522.5
Date:                Wed, 02 Nov 2016   Prob (F-statistic):          2.28e-172
Time:                        18:06:08   Log-Likelihood:                -19852.
No. Observations:                1482   AIC:                         3.971e+04
Df Residuals:                    1479   BIC:                         3.972e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept      4.954e+04   6276.742      7.892      0.000      3.72e+04  6.19e+04
bedrooms        2.87e+04   4450.888      6.448      0.000         2e+04  3.74e+04
building_size    97.9198      4.322     22.656      0.000        89.442   106.398
intercept      4.954e+04   6276.742      7.892      0.000      3.72e+04  6.19e+04
==============================================================================
Omnibus:                     1073.381   Durbin-Watson:                   1.339
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            25458.610
Skew:                           3.102   Prob(JB):                         0.00
Kurtosis:                      22.334   Cond. No.                     1.91e+17
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 1.45e-25. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: San Francisco
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.397
Model:                            OLS   Adj. R-squared:                  0.396
Method:                 Least Squares   F-statistic:                     491.0
Date:                Wed, 02 Nov 2016   Prob (F-statistic):          1.46e-164
Time:                        18:06:08   Log-Likelihood:                -22817.
No. Observations:                1495   AIC:                         4.564e+04
Df Residuals:                    1492   BIC:                         4.566e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept      9.646e+04   3.32e+04      2.910      0.004      3.14e+04  1.61e+05
bedrooms      -4.246e+04    2.4e+04     -1.772      0.077     -8.95e+04  4539.866
building_size   779.2938     29.164     26.721      0.000       722.088   836.500
intercept      9.646e+04   3.32e+04      2.910      0.004      3.14e+04  1.61e+05
==============================================================================
Omnibus:                     1492.900   Durbin-Watson:                   1.061
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            97357.034
Skew:                           4.673   Prob(JB):                         0.00
Kurtosis:                      41.413   Cond. No.                     3.66e+17
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 4.78e-26. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: Phoenix
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.392
Model:                            OLS   Adj. R-squared:                  0.391
Method:                 Least Squares   F-statistic:                     424.1
Date:                Wed, 02 Nov 2016   Prob (F-statistic):          7.89e-143
Time:                        18:06:08   Log-Likelihood:                -17061.
No. Observations:                1317   AIC:                         3.413e+04
Df Residuals:                    1314   BIC:                         3.414e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept      5091.6859   4660.862      1.092      0.275     -4051.859  1.42e+04
bedrooms       4235.3360   2329.635      1.818      0.069      -334.874  8805.546
building_size   101.1079      3.826     26.425      0.000        93.602   108.614
intercept      5091.6859   4660.862      1.092      0.275     -4051.859  1.42e+04
==============================================================================
Omnibus:                      856.770   Durbin-Watson:                   1.039
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            26640.247
Skew:                           2.526   Prob(JB):                         0.00
Kurtosis:                      24.446   Cond. No.                     7.41e+17
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 1.14e-26. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: Chicago
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.070
Model:                            OLS   Adj. R-squared:                  0.067
Method:                 Least Squares   F-statistic:                     26.10
Date:                Wed, 02 Nov 2016   Prob (F-statistic):           1.18e-11
Time:                        18:06:08   Log-Likelihood:                -9803.0
No. Observations:                 695   AIC:                         1.961e+04
Df Residuals:                     692   BIC:                         1.963e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept      2.557e+04   2.23e+04      1.148      0.251     -1.82e+04  6.93e+04
bedrooms       4.166e+04   1.34e+04      3.109      0.002      1.54e+04   6.8e+04
building_size    57.5367     10.844      5.306      0.000        36.246    78.827
intercept      2.557e+04   2.23e+04      1.148      0.251     -1.82e+04  6.93e+04
==============================================================================
Omnibus:                      581.727   Durbin-Watson:                   0.633
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            13384.562
Skew:                           3.699   Prob(JB):                         0.00
Kurtosis:                      23.186   Cond. No.                     6.70e+17
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 5.84e-27. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: Miami
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.749
Model:                            OLS   Adj. R-squared:                  0.749
Method:                 Least Squares   F-statistic:                     2092.
Date:                Wed, 02 Nov 2016   Prob (F-statistic):               0.00
Time:                        18:06:08   Log-Likelihood:                -20766.
No. Observations:                1403   AIC:                         4.154e+04
Df Residuals:                    1400   BIC:                         4.155e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept     -3.795e+05   1.36e+04    -27.888      0.000     -4.06e+05 -3.53e+05
bedrooms       3.203e+05   5515.840     58.062      0.000      3.09e+05  3.31e+05
building_size   198.8681     12.313     16.150      0.000       174.713   223.023
intercept     -3.795e+05   1.36e+04    -27.888      0.000     -4.06e+05 -3.53e+05
==============================================================================
Omnibus:                     1479.035   Durbin-Watson:                   1.672
Prob(Omnibus):                  0.000   Jarque-Bera (JB):           126993.425
Skew:                           4.995   Prob(JB):                         0.00
Kurtosis:                      48.525   Cond. No.                     2.92e+17
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 6.68e-26. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: Boston
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.019
Model:                            OLS   Adj. R-squared:                  0.017
Method:                 Least Squares   F-statistic:                     14.17
Date:                Wed, 02 Nov 2016   Prob (F-statistic):           8.01e-07
Time:                        18:06:08   Log-Likelihood:                -22039.
No. Observations:                1497   AIC:                         4.408e+04
Df Residuals:                    1494   BIC:                         4.410e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept      2.389e+05   1.67e+04     14.326      0.000      2.06e+05  2.72e+05
bedrooms       2.277e+04   1.04e+04      2.193      0.028      2401.960  4.31e+04
building_size    23.7034      6.049      3.919      0.000        11.838    35.568
intercept      2.389e+05   1.67e+04     14.326      0.000      2.06e+05  2.72e+05
==============================================================================
Omnibus:                     1396.564   Durbin-Watson:                   1.411
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            64403.125
Skew:                           4.305   Prob(JB):                         0.00
Kurtosis:                      33.958   Cond. No.                     3.92e+17
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 9.71e-26. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: Nashville
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.598
Model:                            OLS   Adj. R-squared:                  0.596
Method:                 Least Squares   F-statistic:                     312.9
Date:                Wed, 02 Nov 2016   Prob (F-statistic):           5.28e-84
Time:                        18:06:08   Log-Likelihood:                -6182.1
No. Observations:                 424   AIC:                         1.237e+04
Df Residuals:                     421   BIC:                         1.238e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept     -1.195e+05      4e+04     -2.989      0.003     -1.98e+05 -4.09e+04
bedrooms       7.913e+04   2.58e+04      3.062      0.002      2.83e+04   1.3e+05
building_size   247.8825     14.882     16.657      0.000       218.631   277.134
intercept     -1.195e+05      4e+04     -2.989      0.003     -1.98e+05 -4.09e+04
==============================================================================
Omnibus:                      238.081   Durbin-Watson:                   1.467
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2218.749
Skew:                           2.246   Prob(JB):                         0.00
Kurtosis:                      13.267   Cond. No.                     4.09e+18
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 4.45e-28. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: Washington
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.243
Model:                            OLS   Adj. R-squared:                  0.242
Method:                 Least Squares   F-statistic:                     189.4
Date:                Wed, 02 Nov 2016   Prob (F-statistic):           4.50e-72
Time:                        18:06:08   Log-Likelihood:                -16288.
No. Observations:                1183   AIC:                         3.258e+04
Df Residuals:                    1180   BIC:                         3.260e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept     -9520.0959    1.2e+04     -0.794      0.427      -3.3e+04   1.4e+04
bedrooms       1.114e+04   8401.710      1.326      0.185     -5345.097  2.76e+04
building_size   138.5637      8.895     15.577      0.000       121.111   156.016
intercept     -9520.0959    1.2e+04     -0.794      0.427      -3.3e+04   1.4e+04
==============================================================================
Omnibus:                     1973.903   Durbin-Watson:                   0.720
Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1282925.457
Skew:                          10.688   Prob(JB):                         0.00
Kurtosis:                     162.907   Cond. No.                     4.77e+17
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 1.96e-26. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: Philadelphia
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.043
Model:                            OLS   Adj. R-squared:                  0.042
Method:                 Least Squares   F-statistic:                     28.06
Date:                Wed, 02 Nov 2016   Prob (F-statistic):           1.20e-12
Time:                        18:06:08   Log-Likelihood:                -18058.
No. Observations:                1247   AIC:                         3.612e+04
Df Residuals:                    1244   BIC:                         3.614e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept      5103.7935   2.06e+04      0.248      0.804     -3.52e+04  4.55e+04
bedrooms       5.661e+04   1.23e+04      4.597      0.000      3.25e+04  8.08e+04
building_size    57.0294     12.614      4.521      0.000        32.282    81.777
intercept      5103.7935   2.06e+04      0.248      0.804     -3.52e+04  4.55e+04
==============================================================================
Omnibus:                     2006.943   Durbin-Watson:                   1.215
Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1345255.345
Skew:                           9.904   Prob(JB):                         0.00
Kurtosis:                     162.683   Cond. No.                     7.51e+17
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 7.02e-27. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: Denver
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.141
Model:                            OLS   Adj. R-squared:                  0.140
Method:                 Least Squares   F-statistic:                     121.7
Date:                Wed, 02 Nov 2016   Prob (F-statistic):           1.11e-49
Time:                        18:06:08   Log-Likelihood:                -22102.
No. Observations:                1485   AIC:                         4.421e+04
Df Residuals:                    1482   BIC:                         4.423e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept      1.067e+05    2.5e+04      4.272      0.000      5.77e+04  1.56e+05
bedrooms       2.319e+04   1.84e+04      1.259      0.208     -1.29e+04  5.93e+04
building_size   151.1385     13.440     11.245      0.000       124.775   177.502
intercept      1.067e+05    2.5e+04      4.272      0.000      5.77e+04  1.56e+05
==============================================================================
Omnibus:                     2996.628   Durbin-Watson:                   1.562
Prob(Omnibus):                  0.000   Jarque-Bera (JB):          8917349.471
Skew:                          15.727   Prob(JB):                         0.00
Kurtosis:                     381.325   Cond. No.                     5.28e+17
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 4.21e-26. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: Las Vegas
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                        -inf
Model:                            OLS   Adj. R-squared:                    nan
Method:                 Least Squares   F-statistic:                       nan
Date:                Wed, 02 Nov 2016   Prob (F-statistic):                nan
Time:                        18:06:08   Log-Likelihood:                 20.069
No. Observations:                   1   AIC:                            -38.14
Df Residuals:                       0   BIC:                            -40.14
Df Model:                           0                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept         0.3258        inf          0        nan           nan       nan
bedrooms          1.3033        inf          0        nan           nan       nan
building_size   902.5250        inf          0        nan           nan       nan
intercept         0.3258        inf          0        nan           nan       nan
==============================================================================
Omnibus:                          nan   Durbin-Watson:                   0.000
Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.375
Skew:                           0.000   Prob(JB):                        0.829
Kurtosis:                       0.000   Cond. No.                         1.00
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The input rank is higher than the number of observations.





CITY: Salt Lake City
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.565
Model:                            OLS   Adj. R-squared:                  0.562
Method:                 Least Squares   F-statistic:                     181.8
Date:                Wed, 02 Nov 2016   Prob (F-statistic):           2.50e-51
Time:                        18:06:08   Log-Likelihood:                -3699.8
No. Observations:                 283   AIC:                             7406.
Df Residuals:                     280   BIC:                             7416.
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept      5890.6616   1.25e+04      0.473      0.637     -1.86e+04  3.04e+04
bedrooms       9816.9175   6620.289      1.483      0.139     -3214.940  2.28e+04
building_size   168.0871     10.134     16.586      0.000       148.138   188.036
intercept      5890.6616   1.25e+04      0.473      0.637     -1.86e+04  3.04e+04
==============================================================================
Omnibus:                      242.379   Durbin-Watson:                   1.378
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             9003.112
Skew:                           3.125   Prob(JB):                         0.00
Kurtosis:                      29.916   Cond. No.                     8.32e+17
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 1.41e-27. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: Minneapolis
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.242
Model:                            OLS   Adj. R-squared:                  0.241
Method:                 Least Squares   F-statistic:                     232.4
Date:                Wed, 02 Nov 2016   Prob (F-statistic):           2.43e-88
Time:                        18:06:08   Log-Likelihood:                -19274.
No. Observations:                1460   AIC:                         3.855e+04
Df Residuals:                    1457   BIC:                         3.857e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept      9070.9843   6044.126      1.501      0.134     -2785.133  2.09e+04
bedrooms       6.666e+04   3507.031     19.007      0.000      5.98e+04  7.35e+04
building_size   174.5718     17.783      9.817      0.000       139.689   209.454
intercept      9070.9843   6044.126      1.501      0.134     -2785.133  2.09e+04
==============================================================================
Omnibus:                      741.784   Durbin-Watson:                   1.259
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            10954.992
Skew:                           2.003   Prob(JB):                         0.00
Kurtosis:                      15.808   Cond. No.                     2.35e+16
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 9.97e-26. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: Los Angeles
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.419
Model:                            OLS   Adj. R-squared:                  0.418
Method:                 Least Squares   F-statistic:                     528.0
Date:                Wed, 02 Nov 2016   Prob (F-statistic):          2.20e-173
Time:                        18:06:08   Log-Likelihood:                -21369.
No. Observations:                1467   AIC:                         4.274e+04
Df Residuals:                    1464   BIC:                         4.276e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept     -1.119e+05   2.15e+04     -5.193      0.000     -1.54e+05 -6.96e+04
bedrooms       1.489e+05   1.27e+04     11.752      0.000      1.24e+05  1.74e+05
building_size   259.3579     10.626     24.407      0.000       238.514   280.202
intercept     -1.119e+05   2.15e+04     -5.193      0.000     -1.54e+05 -6.96e+04
==============================================================================
Omnibus:                     1462.600   Durbin-Watson:                   1.551
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            94763.364
Skew:                           4.658   Prob(JB):                         0.00
Kurtosis:                      41.256   Cond. No.                     2.99e+17
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 7.15e-26. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: Cleveland
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.249
Model:                            OLS   Adj. R-squared:                  0.248
Method:                 Least Squares   F-statistic:                     247.2
Date:                Wed, 02 Nov 2016   Prob (F-statistic):           1.76e-93
Time:                        18:06:08   Log-Likelihood:                -19076.
No. Observations:                1498   AIC:                         3.816e+04
Df Residuals:                    1495   BIC:                         3.817e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept      9369.5884   4172.535      2.246      0.025      1184.944  1.76e+04
bedrooms       1.147e+04   2797.122      4.102      0.000      5986.303   1.7e+04
building_size    47.7234      2.472     19.307      0.000        42.875    52.572
intercept      9369.5884   4172.535      2.246      0.025      1184.944  1.76e+04
==============================================================================
Omnibus:                     1324.877   Durbin-Watson:                   1.062
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            91897.289
Skew:                           3.769   Prob(JB):                         0.00
Kurtosis:                      40.623   Cond. No.                     2.44e+17
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 5.91e-26. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: New York
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.223
Model:                            OLS   Adj. R-squared:                  0.219
Method:                 Least Squares   F-statistic:                     52.37
Date:                Wed, 02 Nov 2016   Prob (F-statistic):           1.03e-20
Time:                        18:06:08   Log-Likelihood:                -5570.8
No. Observations:                 367   AIC:                         1.115e+04
Df Residuals:                     364   BIC:                         1.116e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept      4.302e+04   5.58e+04      0.771      0.441     -6.67e+04  1.53e+05
bedrooms       5.004e+04   3.07e+04      1.632      0.103     -1.02e+04   1.1e+05
building_size   416.3654     42.984      9.686      0.000       331.837   500.894
intercept      4.302e+04   5.58e+04      0.771      0.441     -6.67e+04  1.53e+05
==============================================================================
Omnibus:                      613.196   Durbin-Watson:                   1.788
Prob(Omnibus):                  0.000   Jarque-Bera (JB):           257324.727
Skew:                           9.287   Prob(JB):                         0.00
Kurtosis:                     131.385   Cond. No.                     2.74e+18
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 1.05e-28. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: Atlanta
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.258
Model:                            OLS   Adj. R-squared:                  0.256
Method:                 Least Squares   F-statistic:                     230.3
Date:                Wed, 02 Nov 2016   Prob (F-statistic):           1.37e-86
Time:                        18:06:08   Log-Likelihood:                -17684.
No. Observations:                1331   AIC:                         3.537e+04
Df Residuals:                    1328   BIC:                         3.539e+04
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept     -4.466e+04   7051.345     -6.334      0.000     -5.85e+04 -3.08e+04
bedrooms       7.369e+04   4236.452     17.395      0.000      6.54e+04   8.2e+04
building_size    20.1355      3.292      6.117      0.000        13.677    26.593
intercept     -4.466e+04   7051.345     -6.334      0.000     -5.85e+04 -3.08e+04
==============================================================================
Omnibus:                     1959.891   Durbin-Watson:                   1.379
Prob(Omnibus):                  0.000   Jarque-Bera (JB):           840536.473
Skew:                           8.445   Prob(JB):                         0.00
Kurtosis:                     124.946   Cond. No.                     1.76e+17
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is  1e-25. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: New Orleans
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.154
Model:                            OLS   Adj. R-squared:                  0.149
Method:                 Least Squares   F-statistic:                     27.11
Date:                Wed, 02 Nov 2016   Prob (F-statistic):           1.53e-11
Time:                        18:06:08   Log-Likelihood:                -4079.0
No. Observations:                 300   AIC:                             8164.
Df Residuals:                     297   BIC:                             8175.
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept      8.078e+04   1.82e+04      4.450      0.000      4.51e+04  1.16e+05
bedrooms      -5.855e+04   1.46e+04     -4.011      0.000     -8.73e+04 -2.98e+04
building_size   139.0080     19.311      7.198      0.000       101.005   177.011
intercept      8.078e+04   1.82e+04      4.450      0.000      4.51e+04  1.16e+05
==============================================================================
Omnibus:                      353.767   Durbin-Watson:                   0.492
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            15540.756
Skew:                           5.365   Prob(JB):                         0.00
Kurtosis:                      36.588   Cond. No.                     1.29e+19
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 7.49e-30. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





CITY: Austin
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.766
Model:                            OLS   Adj. R-squared:                  0.757
Method:                 Least Squares   F-statistic:                     81.79
Date:                Wed, 02 Nov 2016   Prob (F-statistic):           1.72e-16
Time:                        18:06:08   Log-Likelihood:                -693.24
No. Observations:                  53   AIC:                             1392.
Df Residuals:                      50   BIC:                             1398.
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [95.0% Conf. Int.]
---------------------------------------------------------------------------------
Intercept     -1.426e+04    4.2e+04     -0.339      0.736     -9.87e+04  7.01e+04
bedrooms      -6.356e+04   3.27e+04     -1.947      0.057     -1.29e+05  2026.475
building_size   255.4130     25.686      9.944      0.000       203.821   307.005
intercept     -1.426e+04    4.2e+04     -0.339      0.736     -9.87e+04  7.01e+04
==============================================================================
Omnibus:                        7.991   Durbin-Watson:                   2.194
Prob(Omnibus):                  0.018   Jarque-Bera (JB):                7.117
Skew:                           0.828   Prob(JB):                       0.0285
Kurtosis:                       3.692   Cond. No.                     2.81e+19
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 5.97e-31. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.





As you can see above, the fits need more work to remove multi-colinearity and address cases where some coefficients have low confidence. But for a quick analysis, let\'s continue.

params = []
for city in dfMap.keys():
    df = pd.DataFrame(dfMap[city])
    df['intercept'] = 1
    model = sm.ols(formula="price ~ bedrooms + building_size + intercept", data=df).fit()
    d = dict(model.params)
    d['rsquared'] = model.rsquared
    d['city'] = city
    params.append(d)
res = pd.DataFrame(params)
res.sort('building_size', inplace=True)
res.index = np.arange(res.shape[0])
/usr/local/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)
  from ipykernel import kernelapp as app
plt.figure(figsize=(4,6))
plt.barh(res.index, res['building_size'])
plt.yticks(res.index + 0.4, res['city'])
plt.xlabel('Coeficient of building_size')
plt.plot()
Out[124]:
[]