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Research Analysis of Housing market

This is the data analysis/ collecting work. Go through the attached documentation for the data collection.

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Once the final analysis document is filled with all the values then feed the document to any visualization tool such as power BI or Tabule to obtain the demarkation of renters Vs Owner.

Also provide the 2nd excel sheet from the

Consolidated Planning/CHAS Data | HUD USER

(You have to create a new excel sheet for this with col names )

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Research Experience I
Abstract
Interest in the global unaffordable housing dilemma is manifest in its growing publications. However,
there is limited systematic review of the literature concerning data visualization and mapping of the
structure of knowledge and trend of publications on housing worldwide. As part of the research
experience, first, we will conduct a literature review analysis on profiling affordable housing residents,
second perform multifaceted linkage between ACS and AHS datasets, and third, we will perform
exploratory clustering analysis to create profiles based on factors stated above and identified from the
literature.
Introduction
Housing is one of the most crucial factors in a country’s socioeconomic development. As a result, an
effective housing supply is one of the government’s policies. These regulations are frequently
implemented to ensure housing affordability for people of all income levels, particularly those in the
middle- and lower-income brackets [1]. Housing affordability refers to getting a particular standard of
housing at a price or rent that does not impose an undue strain on household incomes in the eyes of a third
party (typically the government). Several words have been coined to characterize housing that is
affordable to middle- and low-income earners, as well as the impoverished. Depending on the country,
several of these phrases could refer to various housing tenures. Affordable housing (which is commonly
used in the United States and can refer to both rental and ownership housing); public and social housing
(which are more strongly linked to rental housing in a European context); cooperative housing (which
issued in some European countries and refers to housing where the entire building is owned in common
by a homeowner; association), and so on [3]. Despite the benefits of maintaining housing affordability
and accessibility for socioeconomic growth, the global housing affordability dilemma continues to be
unsolvable. In both rich and developing countries, the housing crisis is a big issue. This is demonstrated
by a lack of housing facilities, which has the consequence of promoting the construction of slums
(overcrowded and dilapidated urban residential facilities with inadequate infrastructure) [4]. Apart from
the housing shortages, several of the existing facilities are insufficient. If left unchecked, the housing
scarcity and its inevitable corollary could get worse, given that the world& population is expected to
expand from 3.6 billion to 6.3 billion by 2050[4]. In response to the global housing problem, international
organizations such as the World Bank and the United Nations (UN) have begun to develop policies to
ensure enough housing [5]. As a result, in addition to assuring housing affordability, achieving social and
environmental sustainability goals for comprehensive sustainable development continues to be a hot topic
in both developed and developing countries. Much empirical research on the various aspects of
sustainable housing has been undertaken to achieve these goals. The abundance of studies broadens the
knowledge foundation on which policymakers can base their decisions. More crucially, a systematic
evaluation of the expanding articles on housing could enhance the effect of existing knowledge for policy
formation.
Research Methodology:
Exploratory research has been conducted based on the data available for the owners Vs renters in the
public databases. The post covid year 2019 is considered for the research because of the data accuracy.
The census (https://www.census.gov/) has the data for the house owners Vs renters based on the number
of housing units. There are certain data sets are collected based on this and Florida housing website
(http://flhousingdata.shimberg.ufl.edu/ ) also provide the information on the data regarding the
homeowners Vs renters but there was no county level data available at the Florida housing data.
Below is the search conducted. And based on the source of the information provided for these data set.
Further research is conducted to explore the source of the Florida housing
Figure 1: Screen shot of the Florida housing website search
Based on the data set some of the fields in the data set has values which determine the renter’s vs owners
And collectively I have selected these because of the clear demarcation of the data. The next data set
selected was from the CHAS (Consolidated planning data set)
https://www.huduser.gov/portal/datasets/cp.html. The search for the dataset
CHAS Data base
The CHAS data’s main purpose is to show the number of households needing housing assistance. This is
estimated by the number of households that have certain housing problems and have income low enough
to qualify for HUD’s programs (primarily 30, 50, and 80 percent of median income). It is also important
to consider the prevalence of housing problems among diverse types of households, such as the elderly,
disabled, minorities, and different household types. The CHAS data provides counts of the numbers of
households that fit these HUD-specified characteristics in HUD-specified geographic areas.
In addition to estimating low-income housing needs, the CHAS data contribute to a more comprehensive
market analysis by documenting issues like lead paint risks, “affordability mismatch,” and the interaction
of affordability with variables like age of homes, number of bedrooms, and type of building.
Data Format
HUD has identified many characteristics of interest to housing planners and policymakers, and as a result,
the CHAS data can be unwieldy. To streamline the data and make it easier to use, HUD has created a
series of “tables,” which are grouped by theme.
Each of these tables contains certain “dimensions” (also referred to as variables). These dimensions can
be combined in many ways, and the data files for each table present every combination. As an example,
consider Table 11. Table 11 contains 3 dimensions: tenure, housing problems, and household income.
Tenure has 2 options: owner-occupied or renter occupied. Housing problems has 3 options: household has
at least one housing problem, household has no housing problems, or household has no income (so cost
burden could not be computed) but no other housing problems. Household income, in this table, has 13
options. Thus Table 11 has 78 buckets (2*3*13=78), and every household belongs in one (and only one)
of those buckets. In the CHAS data, we have counted the number of households in each of those buckets,
for thousands of states, counties, cities, and neighborhoods.
Because CHAS provides count estimates, users interested in percentage estimates (e.g., percentage of
low-income renters with severe housing cost burden) will need to make the calculation themselves. To
make these calculations, users may need to sum count estimates (buckets) in both the numerator and
denominator before dividing to obtain the percentage estimate. Users should be careful to identify the
appropriate total or subtotal that serves as the denominator. In some cases, totals and subtotals are
provided in the CHAS data.
Below are the data sets selected from the CHAS dataset. These have the renters and owners’ demarcation.
Housing Cost Burden Overview 3
Income by Housing Problems (Renters)
Income by Housing Problems (Owners)
Income by Cost Burden (Renters only)
Income by Cost Burden (Owners only)
In the census data base, some of the data sets chosen for this study are as below:
The data set below from the census have the percentage of homeowners Vs renters’ data which is
available and based on the level of income from the CHAS data set the data is planned to be combined.
Based on the level of income between the 2 data sets.
The project plan is to include the
Data Set S2502 – Demographic characteristics for occupied housing unit
AGE OF HOUSEHOLDER -Percentage occupied housing unit Vs Owner Occupied housing unit
Under 35 years
35 to 44 years
45 to 54 years
55 to 64 years
65 to 74 years
75 to 84 years
85 years and over
YEAR HOUSEHOLDER MOVED INTO UNIT- Percentage occupied housing unit Renter Vs Owner
Occupied housing unit
Moved in 2019 or later
Moved in 2015 to 2018
Moved in 2010 to 2014
Moved in 2000 to 2009
Moved in 1990 to 1999
Moved in 1989 or earlier
S2501- Occupancy Characteristics data set – Percentage occupied housing unit Renters Vs Owner
HOUSEHOLD SIZE
1-person household
2-person household
3-person household
4-or-more-person household
OCCUPANTS PER ROOM- Percentage occupied housing unit Renters Vs Owner
1.00 or less occupants per room
1.01 to 1.50 occupants per room
1.51 or more occupants per room
S2403- Financial characteristics
Occupied housing units:
HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2021 INFLATION-ADJUSTED
DOLLARS)- Percentage occupied housing unit Renters Vs Owner
Less than $5,000
$5,000 to $9,999
$10,000 to $14,999
$15,000 to $19,999
$20,000 to $24,999
$25,000 to $34,999
$35,000 to $49,999
$50,000 to $74,999
$75,000 to $99,999
$100,000 to $149,999
$150,000 or more
Median household income (dollars)
S2504PHYSICAL HOUSING CHARACTERISTICS FOR OCCUPIED HOUSING UNITS
Occupied housing unit- Percentage occupied housing unit Vs Owner
YEAR STRUCTURE BUILT
2014 or later
2010 to 2013
2000 to 2009
1980 to 1999
1960 to 1979
1940 to 1959
1939 or earlier
ROOMS
1 room
2 or 3 rooms
4 or 5 rooms
6 or 7 rooms
8 or more rooms
BEDROOMS
No bedroom
1 bedroom
2 or 3 bedrooms
4 or more bedrooms
S2506FINANCIAL CHARACTERISTICS FOR HOUSING UNITS WITH A MORTGAGE
Owner-occupied housing units with a mortgage– Percentage occupied housing unit Vs Owner
VALUE
Less than $50,000
$50,000 to $99,999
$100,000 to $299,999
$300,000 to $499,999
$500,000 to $749,999
$750,000 to $999,999
$1,000,000 or more
HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED
DOLLARS)
Less than $10,000
$10,000 to $24,999
$25,000 to $34,999
$35,000 to $49,999
$50,000 to $74,999
$75,000 to $99,999
$100,000 to $149,999
$150,000 or more
B08137- Means of Transportation to work by tenure
Total:
Householder lived in owner-occupied housing units
Householder lived in renter-occupied housing units
Car, truck, or van – drove alone:
Householder lived in owner-occupied housing units
Householder lived in renter-occupied housing units
Car, truck, or van – carpooled:
Householder lived in owner-occupied housing units
Householder lived in renter-occupied housing units
Public transportation (excluding taxicab):
Householder lived in owner-occupied housing units
Householder lived in renter-occupied housing units
Walked:
Householder lived in owner-occupied housing units
Householder lived in renter-occupied housing units
Taxicab, motorcycle, bicycle, or other means:
Householder lived in owner-occupied housing units
Householder lived in renter-occupied housing units
Worked from home:
Householder lived in owner-occupied housing units
Householder lived in renter-occupied housing units
Plan for the Research II
The next step for the research is to extract the county level data from the census database from the below
files for the year 2019. This research is considering only the data from 2019 as the data obtained for 2019
will be more accurate as there will not be any impact of the covid 19 pandemic.
S2502 – Demographic characteristics for occupied housing unit
S2501- Occupancy Characteristics data set
S2403- Financial characteristics
S2504-PHYSICAL HOUSING CHARACTERISTICS FOR OCCUPIED HOUSING UNITS
S2506-FINANCIAL CHARACTERISTICS FOR HOUSING UNITS WITH A MORTGAGE
The data for the S2403- Financial characteristics
Occupied housing units:
HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2021 INFLATION-ADJUSTED
DOLLARS)- Percentage occupied housing unit Vs Owner
Less than $5,000
$5,000 to $9,999
$10,000 to $14,999
$15,000 to $19,999
$20,000 to $24,999
The once color coded in GREEN will be Bucket I
$25,000 to $34,999
$35,000 to $49,999
The one color coded in PINK will be Bucket 2
$50,000 to $74,999
$75,000 to $99,999
The one color coded in PINK will be Bucket 3
$100,000 to $149,999
$150,000 or more
The one color coded in PINK will be Bucket 4
The data from this data set will be prepared with this bucket list manually for a single county and later
the same mechanism will extend to all the counties of Florida. Based on the Percentage occupied housing
unit Vs Owner which is the common unit for the all the data sets above. The data will be combined based
on the comparison in unit and percentage for the renters Vs Owners.
Each county level data will be manually downloaded to the local system and all the files will be stored in
the extended hard drive. The naming convention will be based on the county.
Ex: All the below files will be stored in the separate folder named Duval County.
S2502 – Demographic characteristics for occupied housing unit
S2501- Occupancy Characteristics data set
S2403- Financial characteristics
S2504-PHYSICAL HOUSING CHARACTERISTICS FOR OCCUPIED HOUSING UNITS
S2506-FINANCIAL CHARACTERISTICS FOR HOUSING UNITS WITH A MORTGAGE
The dataset will also have a new column named County added to the final data set.
The B08137- Means of Transportation to work by tenure dataset is ignored for now as it does not have
a clear comparison for the renters Vs Owners. Later part of the research we must see if this is usable.
Later the comparison will be done with the data set created from the census data and to the CHAS dataset
with the selection of Income by Cost Burden (Renters only) and Income by Cost Burden (Renters only)
and Percentage occupied housing unit Renters Vs Owner. Using tablue cluster modeling will be created to
show the pictorial images and provide the insight based on the grouping or clustering of the data in the
data. Data comparison will be done with the data analyzed from census and data obtained from the fl
housing for 2019 on county bases.
References
1.Census Bureau Data
2. https://www.huduser.gov/portal/datasets/cp.html.
3. http://flhousingdata.shimberg.ufl.edu/
References:
[1] Adabre, M. A., & Chan, A. P. (2019). Critical success factors (CSFs) for sustainable affordable
housing. Building and Environment, 156, 203–214.
[2] MacLennan, D., & Williams, R. (1990). Affordable housing in Britain and America. York:
Joseph
Rowntree Foundation.
[3] Czischke, D., & van Bortel, G. (2018). An exploration of concepts and polices on ‘affordable
housing’
in England, Italy, Poland and The Netherlands. Journal of Housing and the Built Environment, 1–21.
[4] Golubchikov, O., & Badyina, A. (2012). Sustainable housing for sustainable cities: a policy
framework for developing countries. Nairobi, Kenya: UNHABITAT
Definitions:
HAMFI – HUD Area Median Family Income. This is the median family income calculated by HUD for
each authority, to determine Fair Market Rents (FMRs) and income limits for HUD programs. HAMFI
will not necessarily be the same as other calculations of median incomes (such as a simple Census
number), due to a series of adjustments that are made (For full documentation of these adjustments,
consult the HUD Income Limit Briefing Materials). If you see the terms “area median income” (AMI)
or “median family income” (MFI) used in the CHAS, assume it refers to HAMFI.
Household – All people living in a housing unit. Members of a household can be related (see family) or
unrelated.
Household Income – Adjusted household income, which includes the income of all members of the
household at the time of the survey, adjusted for inflation to reflect the most recent year of the data
release (e.g. 2013 dollars in the 2009-2013 CHAS data).
Family – Related individuals living in the same household. The Census Bureau also tracks subfamilies.
Housing Problems – There are four housing problems in the CHAS data: 1) housing unit lacks complete
kitchen facilities; 2) housing unit lacks complete plumbing facilities; 3) household is overcrowded; and 4)
household is cost burdened. A household is said to have a housing problem if they have any 1 or more of
these 4 problems.
Overcrowding – More than 1 person per room.
Severe overcrowding – More than 1.5 persons per room.
Cost burden – Monthly housing costs (including utilities) exceeding 30% of monthly income.
Severe cost burden – Monthly housing costs (including utilities) exceeding 50% of monthly income.
Elderly – People aged 62 and up. Individuals aged 75 and up are recognized as a population with unique
needs than those 62-74, so the CHAS data separates these groups. “Elderly” refers to individuals 62-74,
while those 75 and up may be referred to as “extra elderly” or “frail elderly”.
Disabled – In 2008, Census modified the ACS questions related to disability. Beginning with the 20082010 and 2008-2012 CHAS data, HUD has separately identified four different physical or cognitive
limitations: hearing or vision impairment, ambulatory limitation, cognitive limitation, and independent
living limitation
Steps :
1. Go to (https://www.census.gov/)
2. Go to Data and Maps then click on GO TO DATA CENSUS.GOV
4. Use the below data set and get the values for these are add it to the Final Analysis excel document
both for the renters and owners.
Data Set S2502 – Demographic characteristics for occupied housing unit
AGE OF HOUSEHOLDER -Percentage occupied housing unit Vs Owner Occupied housing unit
Under 35 years
35 to 44 years
45 to 54 years
55 to 64 years
65 to 74 years
75 to 84 years
85 years and over
YEAR HOUSEHOLDER MOVED INTO UNIT- Percentage occupied housing unit Renter Vs Owner
Occupied housing unit
Moved in 2019 or later
Moved in 2015 to 2018
Moved in 2010 to 2014
Moved in 2000 to 2009
Moved in 1990 to 1999
Moved in 1989 or earlier
S2501- Occupancy Characteristics data set – Percentage occupied housing unit Renters Vs Owner
HOUSEHOLD SIZE
1-person household
2-person household
3-person household
4-or-more-person household
OCCUPANTS PER ROOM- Percentage occupied housing unit Renters Vs Owner
1.00 or less occupants per room
1.01 to 1.50 occupants per room
1.51 or more occupants per room
S2403- Financial characteristics
Occupied housing units:
HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2021 INFLATION-ADJUSTED
DOLLARS)- Percentage occupied housing unit Renters Vs Owner
Less than $5,000
$5,000 to $9,999
$10,000 to $14,999
$15,000 to $19,999
$20,000 to $24,999
$25,000 to $34,999
$35,000 to $49,999
$50,000 to $74,999
$75,000 to $99,999
$100,000 to $149,999
$150,000 or more
Median household income (dollars)
S2504PHYSICAL HOUSING CHARACTERISTICS FOR OCCUPIED HOUSING UNITS
Occupied housing unit- Percentage occupied housing unit Vs Owner
YEAR STRUCTURE BUILT
2014 or later
2010 to 2013
2000 to 2009
1980 to 1999
1960 to 1979
1940 to 1959
1939 or earlier
ROOMS
1 room
2 or 3 rooms
4 or 5 rooms
6 or 7 rooms
8 or more rooms
BEDROOMS
No bedroom
1 bedroom
2 or 3 bedrooms
4 or more bedrooms
S2506FINANCIAL CHARACTERISTICS FOR HOUSING UNITS WITH A MORTGAGE
Owner-occupied housing units with a mortgage– Percentage occupied housing unit Vs Owner
VALUE
Less than $50,000
$50,000 to $99,999
$100,000 to $299,999
$300,000 to $499,999
$500,000 to $749,999
$750,000 to $999,999
$1,000,000 or more
HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2019 INFLATION-ADJUSTED
DOLLARS)
Less than $10,000
$10,000 to $24,999
$25,000 to $34,999
$35,000 to $49,999
$50,000 to $74,999
$75,000 to $99,999
$100,000 to $149,999
$150,000 or more
B08137- Means of Transportation to work by tenure
Total:
Householder lived in owner-occupied housing units
Householder lived in renter-occupied housing units
Car, truck, or van – drove alone:
Householder lived in owner-occupied housing units
Householder lived in renter-occupied housing units
Car, truck, or van – carpooled:
Householder lived in owner-occupied housing units
Householder lived in renter-occupied housing units
Public transportation (excluding taxicab):
Householder lived in owner-occupied housing units
Householder lived in renter-occupied housing units
Walked:
Householder lived in owner-occupied housing units
Householder lived in renter-occupied housing units
Taxicab, motorcycle, bicycle, or other means:
Householder lived in owner-occupied housing units
Householder lived in renter-occupied housing units
Worked from home:
Householder lived in owner-occupied housing units
Householder lived in renter-occupied housing units
The next step for the research is to extract the county level data from the census database from the below
files for the year 2019. This research is considering only the data from 2019 as the data obtained for 2019
will be more accurate as there will not be any impact of the covid 19 pandemic.
S2502 – Demographic characteristics for occupied housing unit
S2501- Occupancy Characteristics data set
S2403- Financial characteristics
S2504-PHYSICAL HOUSING CHARACTERISTICS FOR OCCUPIED HOUSING UNITS
S2506-FINANCIAL CHARACTERISTICS FOR HOUSING UNITS WITH A MORTGAGE
The data for the S2403- Financial characteristics
Occupied housing units:
HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2021 INFLATION-ADJUSTED
DOLLARS)- Percentage occupied housing unit Vs Owner
Less than $5,000
$5,000 to $9,999
$10,000 to $14,999
$15,000 to $19,999
$20,000 to $24,999
The once color coded in GREEN will be Bucket I
$25,000 to $34,999
$35,000 to $49,999
The one color coded in PINK will be Bucket 2
$50,000 to $74,999
$75,000 to $99,999
The one color coded in PINK will be Bucket 3
$100,000 to $149,999
$150,000 or more
The one color coded in PINK will be Bucket 4
5. Then go to Consolidated Planning/CHAS Data | HUD USER
6. Check for only these header level columns in the downloaded data set
Below are the data sets selected from the CHAS dataset. These have the renters and owners’ demarcation.
Housing Cost Burden Overview 3
Income by Housing Problems (Renters)
Income by Housing Problems (Owners)
Income by Cost Burden (Renters only)
Income by Cost Burden (Owners only)
7.Based on the county name download the excel sheet and prepare comparison between the 2 data sets
and show the comparison in excel pie chart or pie chart.
Date
1/1/2016
1/2/2016
1/3/2016
1/4/2016
1/5/2016
1/6/2016
1/7/2016
1/8/2016
1/9/2016
1/10/2016
1/11/2016
1/12/2016
1/13/2016
1/14/2016
1/15/2016
1/16/2016
1/17/2016
1/18/2016
1/19/2016
1/20/2016
1/21/2016
1/22/2016
1/23/2016
1/24/2016
1/25/2016
1/26/2016
1/27/2016
1/28/2016
1/29/2016
1/30/2016
1/31/2016
2/1/2016
2/2/2016
2/3/2016
2/4/2016
2/5/2016
2/6/2016
2/7/2016
2/8/2016
2/9/2016
2/10/2016
2/11/2016
Average of total_lmp_rt
Coal
15.995
19.107
16.542
21.201
49.236
37.679
24.910
22.549
20.751
17.847
26.083
25.555
33.486
24.968
23.001
21.858
24.329
44.580
55.058
51.039
30.057
39.034
43.233
28.411
27.269
21.884
23.192
29.255
25.810
24.954
18.405
21.060
21.294
19.243
21.193
27.645
24.644
21.031
21.448
25.441
26.722
39.480
Gas
0.23
0.26
0.27
0.32
0.36
0.35
0.34
0.32
0.30
0.32
0.37
0.36
0.38
0.36
0.34
0.36
0.38
0.42
0.43
0.42
0.41
0.40
0.40
0.39
0.39
0.37
0.36
0.36
0.35
0.35
0.31
0.33
0.34
0.32
0.30
0.30
0.30
0.29
0.37
0.37
0.42
0.45
Hydro
0.04
0.04
0.04
0.06
0.06
0.06
0.05
0.04
0.04
0.03
0.04
0.04
0.04
0.04
0.03
0.03
0.03
0.03
0.05
0.05
0.03
0.03
0.03
0.03
0.04
0.03
0.03
0.04
0.04
0.03
0.03
0.03
0.25
0.24
0.03
0.04
0.04
0.03
0.24
0.24
0.22
0.23
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.02
0.01
0.01
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.02
0.01
0.05
0.03
0.02
0.02
0.01
0.01
0.05
0.03
0.04
0.04
2/12/2016
2/13/2016
2/14/2016
2/15/2016
2/16/2016
2/17/2016
2/18/2016
2/19/2016
2/20/2016
2/21/2016
2/22/2016
2/23/2016
2/24/2016
2/25/2016
2/26/2016
2/27/2016
2/28/2016
2/29/2016
3/1/2016
3/2/2016
3/3/2016
3/4/2016
3/5/2016
3/6/2016
3/7/2016
3/8/2016
3/9/2016
3/10/2016
3/11/2016
3/12/2016
3/13/2016
3/14/2016
3/15/2016
3/16/2016
3/17/2016
3/18/2016
3/19/2016
3/20/2016
3/21/2016
3/22/2016
3/23/2016
3/24/2016
3/25/2016
38.682
28.599
35.589
52.456
37.436
22.989
21.994
28.758
19.431
15.795
20.424
20.204
21.095
27.042
23.889
21.895
15.372
18.377
22.455
20.756
26.720
28.050
26.856
22.291
21.774
17.096
17.246
19.920
18.834
22.724
18.607
24.336
21.677
17.423
18.655
18.786
20.275
21.298
23.421
25.028
20.866
20.255
18.284
0.46
0.42
0.45
0.47
0.46
0.42
0.40
0.38
0.33
0.31
0.35
0.33
0.33
0.32
0.34
0.32
0.28
0.30
0.31
0.29
0.31
0.32
0.30
0.31
0.27
0.27
0.27
0.29
0.29
0.27
0.25
0.25
0.26
0.26
0.26
0.27
0.28
0.28
0.32
0.33
0.30
0.29
0.27
0.23
0.23
0.23
0.23
0.22
0.25
0.25
0.24
0.23
0.25
0.27
0.26
0.25
0.26
0.27
0.27
0.24
0.26
0.27
0.31
0.31
0.32
0.31
0.28
0.29
0.28
0.28
0.28
0.28
0.28
0.31
0.32
0.30
0.28
0.29
0.30
0.30
0.29
0.30
0.29
0.28
0.29
0.30
0.05
0.04
0.04
0.04
0.03
0.04
0.05
0.05
0.04
0.04
0.06
0.05
0.04
0.05
0.05
0.05
0.04
0.06
0.05
0.05
0.05
0.05
0.03
0.04
0.05
0.05
0.05
0.05
0.04
0.03
0.03
0.06
0.06
0.06
0.05
0.04
0.06
0.06
0.06
0.05
0.04
0.04
0.04
3/26/2016
3/27/2016
3/28/2016
3/29/2016
3/30/2016
3/31/2016
4/1/2016
4/2/2016
4/3/2016
4/4/2016
4/5/2016
4/6/2016
4/7/2016
4/8/2016
4/9/2016
4/10/2016
4/11/2016
4/12/2016
4/13/2016
4/14/2016
4/15/2016
4/16/2016
4/17/2016
4/18/2016
4/19/2016
4/20/2016
4/21/2016
4/22/2016
4/23/2016
4/24/2016
4/25/2016
4/26/2016
4/27/2016
4/28/2016
4/29/2016
4/30/2016
5/1/2016
5/2/2016
5/3/2016
5/4/2016
5/5/2016
5/6/2016
5/7/2016
17.709
17.329
28.011
25.340
23.341
21.469
23.825
20.071
21.911
28.503
27.294
33.798
27.388
29.542
29.639
30.290
25.977
25.952
29.948
33.042
30.801
23.769
22.546
39.070
30.324
32.681
31.684
30.867
22.248
20.681
20.132
22.882
22.247
27.195
28.258
24.458
24.910
27.893
25.471
22.708
24.597
22.897
19.750
0.28
0.27
0.27
0.30
0.29
0.29
0.31
0.30
0.32
0.36
0.38
0.38
0.38
0.37
0.37
0.35
0.32
0.32
0.33
0.33
0.32
0.32
0.32
0.32
0.32
0.32
0.34
0.35
0.34
0.35
0.34
0.38
0.37
0.37
0.38
0.37
0.34
0.34
0.36
0.32
0.33
0.32
0.29
0.31
0.30
0.30
0.30
0.28
0.27
0.28
0.26
0.25
0.25
0.25
0.22
0.22
0.23
0.27
0.26
0.24
0.26
0.26
0.25
0.25
0.24
0.23
0.28
0.28
0.25
0.24
0.26
0.25
0.21
0.23
0.22
0.22
0.24
0.23
0.22
0.22
0.24
0.24
0.23
0.23
0.25
0.24
0.03
0.03
0.04
0.05
0.05
0.04
0.04
0.03
0.04
0.05
0.05
0.06
0.04
0.05
0.04
0.05
0.04
0.04
0.05
0.05
0.04
0.04
0.04
0.05
0.04
0.04
0.04
0.04
0.02
0.03
0.05
0.03
0.03
0.03
0.04
0.03
0.03
0.04
0.04
0.04
0.05
0.04
0.03
5/8/2016
5/9/2016
5/10/2016
5/11/2016
5/12/2016
5/13/2016
5/14/2016
5/15/2016
5/16/2016
5/17/2016
5/18/2016
5/19/2016
5/20/2016
5/21/2016
5/22/2016
5/23/2016
5/24/2016
5/25/2016
5/26/2016
5/27/2016
5/28/2016
5/29/2016
5/30/2016
5/31/2016
6/1/2016
6/2/2016
6/3/2016
6/4/2016
6/5/2016
6/6/2016
6/7/2016
6/8/2016
6/9/2016
6/10/2016
6/11/2016
6/12/2016
6/13/2016
6/14/2016
6/15/2016
6/16/2016
6/17/2016
6/18/2016
6/19/2016
17.310
20.092
21.987
26.691
23.775
21.879
19.391
17.762
20.867
24.064
19.148
20.305
20.459
19.535
16.554
18.273
23.697
24.868
34.998
32.760
22.907
22.522
21.855
34.284
42.927
39.691
33.235
23.665
21.318
26.587
25.679
18.952
17.574
20.353
30.255
24.017
18.092
21.987
28.923
27.114
28.664
24.647
23.292
0.28
0.31
0.34
0.33
0.33
0.31
0.30
0.29
0.29
0.33
0.31
0.31
0.30
0.27
0.27
0.32
0.31
0.31
0.31
0.32
0.31
0.30
0.32
0.32
0.36
0.38
0.35
0.36
0.35
0.34
0.34
0.33
0.35
0.34
0.34
0.38
0.37
0.40
0.40
0.40
0.39
0.40
0.40
0.23
0.24
0.23
0.24
0.24
0.24
0.24
0.22
0.24
0.24
0.23
0.25
0.24
0.24
0.24
0.26
0.27
0.28
0.31
0.30
0.27
0.27
0.28
0.32
0.31
0.29
0.27
0.27
0.28
0.30
0.30
0.29
0.29
0.30
0.30
0.30
0.30
0.29
0.28
0.29
0.29
0.27
0.29
0.03
0.05
0.05
0.05
0.05
0.04
0.03
0.03
0.04
0.03
0.03
0.03
0.03
0.04
0.03
0.04
0.03
0.04
0.04
0.04
0.04
0.04
0.04
0.05
0.05
0.05
0.04
0.04
0.04
0.05
0.05
0.05
0.05
0.05
0.05
0.04
0.05
0.04
0.04
0.05
0.05
0.04
0.04
6/20/2016
6/21/2016
6/22/2016
6/23/2016
6/24/2016
6/25/2016
6/26/2016
6/27/2016
6/28/2016
6/29/2016
6/30/2016
7/1/2016
7/2/2016
7/3/2016
7/4/2016
7/5/2016
7/6/2016
7/7/2016
7/8/2016
7/9/2016
7/10/2016
7/11/2016
7/12/2016
7/13/2016
7/14/2016
7/15/2016
7/16/2016
7/17/2016
7/18/2016
7/19/2016
7/20/2016
7/21/2016
7/22/2016
7/23/2016
7/24/2016
7/25/2016
7/26/2016
7/27/2016
7/28/2016
7/29/2016
7/30/2016
7/31/2016
8/1/2016
26.856
25.468
23.920
25.332
30.216
28.870
27.694
27.823
26.094
26.757
23.735
22.642
17.453
13.821
11.274
32.160
36.581
32.198
27.906
21.799
25.488
31.329
29.386
30.890
34.796
28.919
26.083
26.480
32.811
32.417
33.910
29.322
33.108
30.773
26.091
38.263
33.194
44.368
39.879
32.628
28.650
35.687
33.718
0.40
0.40
0.40
0.41
0.41
0.43
0.42
0.43
0.42
0.40
0.39
0.39
0.36
0.33
0.35
0.39
0.41
0.41
0.40
0.40
0.40
0.40
0.41
0.41
0.41
0.39
0.40
0.39
0.40
0.41
0.40
0.40
0.41
0.42
0.41
0.42
0.41
0.42
0.42
0.42
0.41
0.41
0.42
0.31
0.30
0.27
0.27
0.29
0.29
0.29
0.30
0.29
0.30
0.28
0.28
0.28
0.28
0.28
0.30
0.33
0.32
0.32
0.29
0.28
0.31
0.32
0.32
0.34
0.32
0.29
0.29
0.34
0.34
0.34
0.33
0.33
0.33
0.32
0.34
0.34
0.33
0.33
0.32
0.31
0.29
0.32
0.04
0.04
0.04
0.05
0.05
0.04
0.04
0.04
0.04
0.05
0.05
0.05
0.04
0.03
0.04
0.05
0.04
0.03
0.03
0.04
0.04
0.04
0.04
0.04
0.03
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.03
0.04
0.03
0.03
0.03
0.04
0.03
0.04
0.04
0.04
0.04
8/2/2016
8/3/2016
8/4/2016
8/5/2016
8/6/2016
8/7/2016
8/8/2016
8/9/2016
8/10/2016
8/11/2016
8/12/2016
8/13/2016
8/14/2016
8/15/2016
8/16/2016
8/17/2016
8/18/2016
8/19/2016
8/20/2016
8/21/2016
8/22/2016
8/23/2016
8/24/2016
8/25/2016
8/26/2016
8/27/2016
8/28/2016
8/29/2016
8/30/2016
8/31/2016
9/1/2016
9/2/2016
9/3/2016
9/4/2016
9/5/2016
9/6/2016
9/7/2016
9/8/2016
9/9/2016
9/10/2016
9/11/2016
9/12/2016
9/13/2016
29.048
26.843
30.981
25.716
25.264
25.188
27.983
30.351
48.982
64.169
52.586
43.611
33.023
28.703
32.999
36.047
37.327
29.533
27.934
22.759
22.531
16.707
26.685
49.871
39.606
23.844
26.250
29.814
28.611
30.947
26.773
20.385
16.759
17.419
17.845
36.323
32.052
36.979
32.698
35.406
26.710
32.196
33.735
0.41
0.41
0.39
0.39
0.40
0.41
0.40
0.40
0.40
0.42
0.41
0.40
0.40
0.40
0.38
0.39
0.40
0.38
0.38
0.37
0.37
0.39
0.39
0.47
0.49
0.40
0.42
0.42
0.41
0.42
0.40
0.38
0.34
0.36
0.36
0.37
0.38
0.40
0.41
0.42
0.40
0.38
0.38
0.32
0.32
0.35
0.34
0.30
0.28
0.30
0.33
0.34
0.35
0.34
0.34
0.31
0.32
0.33
0.33
0.33
0.33
0.30
0.29
0.29
0.29
0.30
0.36
0.36
0.31
0.28
0.32
0.30
0.31
0.29
0.29
0.29
0.28
0.29
0.34
0.34
0.32
0.31
0.29
0.28
0.29
0.32
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.03
0.03
0.03
0.04
0.04
0.05
0.04
0.04
0.04
0.03
0.04
0.05
0.05
0.04
0.04
0.03
0.04
0.03
0.04
0.04
0.02
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.03
0.04
0.05
9/14/2016
9/15/2016
9/16/2016
9/17/2016
9/18/2016
9/19/2016
9/20/2016
9/21/2016
9/22/2016
9/23/2016
9/24/2016
9/25/2016
9/26/2016
9/27/2016
9/28/2016
9/29/2016
9/30/2016
10/1/2016
10/2/2016
10/3/2016
10/4/2016
10/5/2016
10/6/2016
10/7/2016
10/8/2016
10/9/2016
10/10/2016
10/11/2016
10/12/2016
10/13/2016
10/14/2016
10/15/2016
10/16/2016
10/17/2016
10/18/2016
10/19/2016
10/20/2016
10/21/2016
10/22/2016
10/23/2016
10/24/2016
10/25/2016
10/26/2016
33.999
25.563
32.061
22.978
36.211
35.989
28.829
25.445
33.310
30.902
24.980
24.709
20.526
21.031
20.182
19.309
19.854
22.142
19.582
26.501
26.959
27.159
28.923
25.320
23.419
19.894
23.500
27.253
26.595
25.821
23.738
24.966
23.728
31.711
27.362
31.586
30.634
26.930
23.002
21.008
26.523
24.876
26.747
0.38
0.39
0.39
0.39
0.38
0.38
0.40
0.41
0.41
0.42
0.41
0.38
0.39
0.37
0.37
0.36
0.35
0.35
0.35
0.36
0.36
0.35
0.33
0.33
0.34
0.33
0.36
0.35
0.36
0.36
0.37
0.34
0.36
0.35
0.36
0.39
0.39
0.37
0.36
0.34
0.36
0.36
0.35
0.32
0.29
0.28
0.27
0.30
0.31
0.30
0.29
0.30
0.31
0.28
0.24
0.26
0.25
0.25
0.24
0.25
0.26
0.27
0.29
0.28
0.29
0.31
0.31
0.28
0.26
0.27
0.26
0.25
0.26
0.26
0.26
0.24
0.28
0.29
0.28
0.27
0.26
0.24
0.22
0.25
0.26
0.26
0.03
0.03
0.05
0.03
0.03
0.03
0.03
0.03
0.04
0.04
0.04
0.03
0.03
0.03
0.03
0.04
0.04
0.03
0.03
0.05
0.04
0.04
0.05
0.04
0.03
0.03
0.04
0.05
0.04
0.04
0.03
0.03
0.04
0.04
0.03
0.03
0.03
0.02
0.02
0.04
0.05
0.05
0.04
10/27/2016
10/28/2016
10/29/2016
10/30/2016
10/31/2016
11/1/2016
11/2/2016
11/3/2016
11/4/2016
11/5/2016
11/6/2016
11/7/2016
11/8/2016
11/9/2016
11/10/2016
11/11/2016
11/12/2016
11/13/2016
11/14/2016
11/15/2016
11/16/2016
11/17/2016
11/18/2016
11/19/2016
11/20/2016
11/21/2016
11/22/2016
11/23/2016
11/24/2016
11/25/2016
11/26/2016
11/27/2016
11/28/2016
11/29/2016
11/30/2016
12/1/2016
12/2/2016
12/3/2016
12/4/2016
12/5/2016
12/6/2016
12/7/2016
12/8/2016
28.877
24.392
26.982
21.953
27.574
31.456
26.735
25.103
24.535
22.686
21.147
23.497
23.392
24.660
24.821
23.721
23.795
21.502
27.347
27.901
26.041
22.970
25.038
20.323
21.721
26.941
28.098
25.933
21.578
22.617
21.842
23.139
28.033
29.457
26.042
24.277
33.323
30.481
27.037
28.691
33.627
27.869
28.955
0.34
0.32
0.34
0.35
0.35
0.35
0.35
0.36
0.36
0.34
0.33
0.36
0.34
0.35
0.35
0.33
0.33
0.32
0.32
0.31
0.32
0.32
0.29
0.30
0.33
0.35
0.35
0.35
0.35
0.32
0.31
0.31
0.31
0.32
0.32
0.32
0.32
0.34
0.36
0.36
0.38
0.38
0.39
0.29
0.29
0.24
0.25
0.24
0.26
0.27
0.27
0.27
0.27
0.27
0.28
0.28
0.27
0.27
0.28
0.28
0.27
0.30
0.30
0.29
0.26
0.27
0.26
0.25
0.27
0.27
0.26
0.26
0.26
0.26
0.26
0.26
0.27
0.27
0.27
0.28
0.29
0.27
0.26
0.26
0.25
0.23
0.04
0.03
0.02
0.04
0.05
0.05
0.05
0.04
0.03
0.02
0.02
0.04
0.04
0.04
0.04
0.04
0.02
0.02
0.02
0.03
0.04
0.04
0.03
0.02
0.04
0.04
0.04
0.04
0.02
0.02
0.02
0.04
0.04
0.04
0.04
0.04
0.05
0.04
0.04
0.04
0.04
0.05
0.04
12/9/2016
12/10/2016
12/11/2016
12/12/2016
12/13/2016
12/14/2016
12/15/2016
12/16/2016
12/17/2016
12/18/2016
12/19/2016
12/20/2016
12/21/2016
12/22/2016
12/23/2016
12/24/2016
12/25/2016
12/26/2016
12/27/2016
12/28/2016
12/29/2016
12/30/2016
12/31/2016
1/1/2017
1/2/2017
1/3/2017
1/4/2017
1/5/2017
1/6/2017
1/7/2017
1/8/2017
1/9/2017
1/10/2017
1/11/2017
1/12/2017
1/13/2017
1/14/2017
1/15/2017
1/16/2017
1/17/2017
1/18/2017
1/19/2017
1/20/2017
43.155
34.626
30.168
31.359
33.842
28.032
43.592
67.314
33.187
25.835
37.455
36.857
30.348
28.091
27.971
26.402
22.686
23.973
23.746
27.987
27.844
27.170
28.670
24.529
28.476
31.176
27.470
37.134
31.942
44.031
59.096
68.773
53.357
27.410
27.333
25.002
30.702
26.668
32.366
35.684
33.331
30.752
28.502
0.42
0.42
0.42
0.42
0.41
0.42
0.45
0.46
0.46
0.44
0.44
0.42
0.42
0.39
0.40
0.43
0.41
0.37
0.39
0.39
0.39
0.39
0.40
0.42
0.41
0.42
0.39
0.4
0.41
0.44
0.44
0.42
0.41
0.38
0.37
0.37
0.35
0.34
0.34
0.33
0.32
0.33
0.31
0.24
0.24
0.25
0.23
0.24
0.24
0.27
0.27
0.24
0.23
0.28
0.28
0.25
0.23
0.23
0.21
0.17
0.16
0.19
0.21
0.21
0.21
0.21
0.18
0.2
0.2
0.23
0.26
0.26
0.25
0.23
0.26
0.23
0.22
0.22
0.23
0.27
0.26
0.26
0.27
0.27
0.29
0.28
0.04
0.04
0.03
0.04
0.04
0.03
0.04
0.05
0.02
0.02
0.04
0.04
0.04
0.04
0.04
0.02
0.02
0.04
0.05
0.05
0.05
0.05
0.05
0.03
0.05
0.05
0.05
0.04
0.05
0.04
0.03
0.04
0.04
0.03
0.04
0.05
0.05
0.04
0.06
0.04
0.05
0.06
0.05
1/21/2017
1/22/2017
1/23/2017
1/24/2017
1/25/2017
1/26/2017
1/27/2017
1/28/2017
1/29/2017
1/30/2017
1/31/2017
2/1/2017
2/2/2017
2/3/2017
2/4/2017
2/5/2017
2/6/2017
2/7/2017
2/8/2017
2/9/2017
2/10/2017
2/11/2017
2/12/2017
2/13/2017
2/14/2017
2/15/2017
2/16/2017
2/17/2017
2/18/2017
2/19/2017
2/20/2017
2/21/2017
2/22/2017
2/23/2017
2/24/2017
2/25/2017
2/26/2017
2/27/2017
2/28/2017
3/1/2017
3/2/2017
3/3/2017
3/4/2017
22.156
21.481
22.725
28.581
24.252
24.181
26.503
24.638
23.399
25.512
26.252
24.764
23.925
27.190
31.900
23.654
25.506
25.140
23.704
27.271
28.892
23.820
22.642
29.319
28.745
27.797
28.892
26.611
25.613
21.630
23.848
26.604
24.478
22.741
21.746
20.287
20.048
24.963
22.842
21.643
24.843
26.967
32.187
0.31
0.32
0.33
0.36
0.35
0.34
0.35
0.34
0.35
0.38
0.37
0.37
0.36
0.37
0.37
0.35
0.36
0.36
0.37
0.37
0.38
0.36
0.34
0.35
0.34
0.33
0.35
0.35
0.32
0.31
0.3
0.29
0.29
0.29
0.29
0.27
0.28
0.31
0.31
0.3
0.32
0.32
0.34
0.27
0.23
0.26
0.27
0.26
0.23
0.23
0.24
0.22
0.24
0.23
0.23
0.23
0.24
0.25
0.25
0.23
0.23
0.24
0.26
0.27
0.25
0.23
0.28
0.28
0.29
0.29
0.28
0.26
0.26
0.27
0.29
0.3
0.28
0.28
0.27
0.29
0.31
0.3
0.28
0.29
0.3
0.32
0.03
0.03
0.06
0.05
0.04
0.05
0.05
0.04
0.05
0.05
0.05
0.03
0.04
0.06
0.04
0.02
0.05
0.03
0.05
0.06
0.05
0.02
0.02
0.06
0.05
0.03
0.06
0.06
0.02
0.02
0.07
0.06
0.06
0.05
0.04
0.04
0.05
0.06
0.04
0.02
0.06
0.06
0.05
3/5/2017
3/6/2017
3/7/2017
3/8/2017
3/9/2017
3/10/2017
3/11/2017
3/12/2017
3/13/2017
3/14/2017
3/15/2017
3/16/2017
3/17/2017
3/18/2017
3/19/2017
3/20/2017
3/21/2017
3/22/2017
3/23/2017
3/24/2017
3/25/2017
3/26/2017
3/27/2017
3/28/2017
3/29/2017
3/30/2017
3/31/2017
4/1/2017
4/2/2017
4/3/2017
4/4/2017
4/5/2017
4/6/2017
4/7/2017
4/8/2017
4/9/2017
4/10/2017
4/11/2017
4/12/2017
4/13/2017
4/14/2017
4/15/2017
4/16/2017
29.434
27.009
25.630
26.178
26.468
29.024
31.614
31.488
44.979
41.023
48.572
42.400
36.978
33.276
34.511
36.878
30.890
26.864
34.865
27.836
26.108
24.716
33.677
33.278
25.709
27.378
31.453
33.698
28.609
33.617
31.295
29.574
28.425
34.716
33.410
24.286
26.331
28.722
28.393
27.105
21.841
22.263
19.598
0.34
0.34
0.34
0.34
0.34
0.34
0.37
0.38
0.39
0.4
0.41
0.42
0.4
0.36
0.33
0.33
0.33
0.32
0.33
0.32
0.32
0.31
0.32
0.31
0.32
0.28
0.27
0.26
0.26
0.26
0.26
0.27
0.25
0.28
0.27
0.28
0.29
0.3
0.29
0.3
0.32
0.31
0.3
0.34
0.29
0.27
0.26
0.31
0.31
0.29
0.03
0.05
0.02
0.02
0.04
0.03
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.02
0.04
0.04
0.04
0.04
0.04
0.03
0.02
0.03
0.05
0.05
0.04
0.32
0.32
0.31
0.31
0.31
0.32
0.33
0.32
0.3
0.32
0.33
0.33
0.33
0.33
0.32
0.31
0.28
0.26
0.26
0.25
0.25
0.24
0.25
0.23
0.2
0.22
0.24
0.25
0.25
0.21
0.21
0.2
0.02
0.04
0.05
0.05
0.05
0.04
0.05
0.04
0.04
0.05
0.05
0.04
0.05
0.04
0.04
0.04
4/17/2017
4/18/2017
4/19/2017
4/20/2017
4/21/2017
4/22/2017
4/23/2017
4/24/2017
4/25/2017
4/26/2017
4/27/2017
4/28/2017
4/29/2017
4/30/2017
5/1/2017
5/2/2017
5/3/2017
5/4/2017
5/5/2017
5/6/2017
5/7/2017
5/8/2017
5/9/2017
5/10/2017
5/11/2017
5/12/2017
5/13/2017
5/14/2017
5/15/2017
5/16/2017
5/17/2017
5/18/2017
5/19/2017
5/20/2017
5/21/2017
5/22/2017
5/23/2017
5/24/2017
5/25/2017
5/26/2017
5/27/2017
5/28/2017
5/29/2017
26.105
26.221
30.023
27.750
25.968
24.136
22.833
27.688
27.017
34.054
27.944
28.447
27.058
26.625
30.585
29.442
30.333
25.426
31.601
25.951
23.525
30.007
31.449
30.824
30.354
30.940
31.101
23.677
30.222
33.129
36.553
49.786
57.506
30.767
24.138
27.437
27.824
27.802
24.872
24.389
24.069
18.272
19.168
0.36
0.35
0.34
0.33
0.34
0.34
0.34
0.33
0.33
0.33
0.32
0.33
0.33
0.33
0.32
0.32
0.34
0.35
0.32
0.33
0.33
0.33
0.33
0.34
0.34
0.34
0.34
0.34
0.36
0.36
0.34
0.37
0.36
0.37
0.38
0.36
0.35
0.35
0.35
0.34
0.35
0.34
0.32
0.23
0.23
0.25
0.25
0.24
0.22
0.22
0.26
0.25
0.3
0.3
0.28
0.28
0.27
0.27
0.26
0.26
0.26
0.26
0.24
0.22
0.26
0.27
0.3
0.3
0.26
0.26
0.24
0.26
0.3
0.32
0.34
0.32
0.29
0.24
0.27
0.26
0.26
0.25
0.27
0.27
0.23
0.24
0.05
0.04
0.03
0.04
0.03
0.03
0.03
0.05
0.04
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.03
0.05
0.05
0.05
0.04
0.06
0.03
0.04
0.06
0.05
0.05
0.05
0.05
0.03
0.02
0.05
0.04
0.04
0.04
0.05
0.05
0.04
0.05
5/30/2017
5/31/2017
6/1/2017
6/2/2017
6/3/2017
6/4/2017
6/5/2017
6/6/2017
6/7/2017
6/8/2017
6/9/2017
6/10/2017
6/11/2017
6/12/2017
6/13/2017
6/14/2017
6/15/2017
6/16/2017
6/17/2017
6/18/2017
6/19/2017
6/20/2017
6/21/2017
6/22/2017
6/23/2017
6/24/2017
6/25/2017
6/26/2017
6/27/2017
6/28/2017
6/29/2017
6/30/2017
7/1/2017
7/2/2017
7/3/2017
7/4/2017
7/5/2017
7/6/2017
7/7/2017
7/8/2017
7/9/2017
7/10/2017
7/11/2017
23.798
26.200
27.267
26.700
21.914
21.066
23.042
21.209
21.356
23.086
25.732
23.372
19.495
32.786
36.446
33.527
28.647
30.218
25.798
32.647
41.115
23.543
25.339
28.547
30.295
26.017
21.516
20.766
21.482
17.684
26.161
28.912
29.605
30.124
30.530
28.225
28.907
28.179
26.903
26.772
22.156
27.883
27.917
0.33
0.33
0.32
0.33
0.32
0.33
0.33
0.31
0.3
0.32
0.32
0.32
0.34
0.36
0.39
0.39
0.37
0.36
0.36
0.35
0.35
0.36
0.37
0.37
0.36
0.35
0.33
0.34
0.37
0.36
0.35
0.36
0.36
0.35
0.35
0.35
0.37
0.38
0.37
0.37
0.37
0.37
0.37
0.26
0.27
0.29
0.3
0.29
0.26
0.28
0.28
0.28
0.3
0.29
0.29
0.29
0.34
0.34
0.32
0.33
0.32
0.31
0.31
0.33
0.29
0.28
0.31
0.3
0.29
0.29
0.29
0.29
0.28
0.31
0.32
0.29
0.31
0.34
0.3
0.32
0.3
0.29
0.29
0.28
0.3
0.3
0.05
0.05
0.06
0.06
0.06
0.06
0.06
0.06
0.03
0.04
0.06
0.05
0.05
0.05
0.04
0.05
0.05
0.05
0.04
0.05
0.06
0.05
0.05
0.05
0.06
0.05
0.04
0.04
0.03
0.05
0.05
0.05
0.04
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
7/12/2017
7/13/2017
7/14/2017
7/15/2017
7/16/2017
7/17/2017
7/18/2017
7/19/2017
7/20/2017
7/21/2017
7/22/2017
7/23/2017
7/24/2017
7/25/2017
7/26/2017
7/27/2017
7/28/2017
7/29/2017
7/30/2017
7/31/2017
8/1/2017
8/2/2017
8/3/2017
8/4/2017
8/5/2017
8/6/2017
8/7/2017
8/8/2017
8/9/2017
8/10/2017
8/11/2017
8/12/2017
8/13/2017
8/14/2017
8/15/2017
8/16/2017
8/17/2017
8/18/2017
8/19/2017
8/20/2017
8/21/2017
8/22/2017
8/23/2017
28.652
33.446
29.293
25.666
26.210
34.586
40.505
47.982
45.707
37.431
34.320
27.874
26.240
24.979
28.729
32.095
28.280
21.880
22.838
31.776
29.407
27.436
29.884
27.409
21.759
17.977
20.888
22.786
26.787
25.010
25.243
29.239
25.739
25.044
29.208
29.465
34.180
39.517
31.125
27.207
29.584
31.566
27.356
0.39
0.39
0.39
0.39
0.38
0.38
0.38
0.38
0.38
0.38
0.38
0.37
0.36
0.36
0.36
0.36
0.36
0.33
0.31
0.32
0.34
0.36
0.37
0.35
0.33
0.34
0.34
0.35
0.36
0.36
0.36
0.35
0.35
0.36
0.37
0.38
0.38
0.37
0.37
0.37
0.37
0.39
0.39
0.33
0.33
0.32
0.29
0.3
0.35
0.38
0.4
0.39
0.38
0.35
0.32
0.31
0.3
0.34
0.33
0.33
0.31
0.33
0.37
0.37
0.36
0.34
0.31
0.32
0.28
0.3
0.3
0.33
0.33
0.32
0.3
0.29
0.31
0.32
0.33
0.33
0.35
0.34
0.34
0.36
0.32
0.3
0.04
0.04
0.04
0.04
0.05
0.04
0.05
0.05
0.04
0.04
0.04
0.04
0.05
0.05
0.05
0.05
0.05
0.04
0.05
0.05
0.05
0.04
0.05
0.05
0.04
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.05
0.04
0.04
0.04
0.04
0.05
8/24/2017
8/25/2017
8/26/2017
8/27/2017
8/28/2017
8/29/2017
8/30/2017
8/31/2017
9/1/2017
9/2/2017
9/3/2017
9/4/2017
9/5/2017
9/6/2017
9/7/2017
9/8/2017
9/9/2017
9/10/2017
9/11/2017
9/12/2017
9/13/2017
9/14/2017
9/15/2017
9/16/2017
9/17/2017
9/18/2017
9/19/2017
9/20/2017
9/21/2017
9/22/2017
9/23/2017
9/24/2017
9/25/2017
9/26/2017
9/27/2017
9/28/2017
9/29/2017
9/30/2017
10/1/2017
10/2/2017
10/3/2017
10/4/2017
10/5/2017
23.013
23.816
19.758
18.100
20.732
19.844
23.266
24.172
19.365
14.118
15.751
7.776
22.564
20.736
19.630
20.459
17.961
3.480
17.424
26.264
26.330
28.853
29.814
32.055
30.061
31.382
35.564
52.310
90.570
32.956
30.253
33.362
45.879
43.203
49.068
28.267
18.992
18.599
17.695
21.964
29.763
26.341
30.079
0.38
0.34
0.31
0.31
0.31
0.33
0.34
0.33
0.32
0.28
0.29
0.3
0.3
0.32
0.31
0.31
0.3
0.28
0.28
0.27
0.28
0.3
0.3
0.3
0.3
0.31
0.32
0.33
0.34
0.35
0.33
0.33
0.35
0.36
0.37
0.35
0.35
0.3
0.3
0.31
0.31
0.31
0.32
0.31
0.31
0.31
0.3
0.31
0.3
0.31
0.31
0.3
0.3
0.3
0.29
0.33
0.32
0.29
0.29
0.29
0.29
0.33
0.34
0.36
0.37
0.37
0.37
0.36
0.36
0.36
0.39
0.39
0.41
0.4
0.4
0.4
0.39
0.38
0.36
0.3
0.29
0.26
0.3
0.31
0.35
0.36
0.05
0.05
0.03
0.05
0.05
0.03
0.05
0.05
0.02
0.03
0.04
0.05
0.05
0.03
0.03
0.04
0.02
0.04
0.05
0.05
0.05
0.05
0.05
0.05
0.04
0.05
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.04
0.02
0.04
0.04
0.04
0.03
0.04
10/6/2017
10/7/2017
10/8/2017
10/9/2017
10/10/2017
10/11/2017
10/12/2017
10/13/2017
10/14/2017
10/15/2017
10/16/2017
10/17/2017
10/18/2017
10/19/2017
10/20/2017
10/21/2017
10/22/2017
10/23/2017
10/24/2017
10/25/2017
10/26/2017
10/27/2017
10/28/2017
10/29/2017
10/30/2017
10/31/2017
11/1/2017
11/2/2017
11/3/2017
11/4/2017
11/5/2017
11/6/2017
11/7/2017
11/8/2017
11/9/2017
11/10/2017
11/11/2017
11/12/2017
11/13/2017
11/14/2017
11/15/2017
11/16/2017
11/17/2017
33.988
26.299
26.793
46.523
35.371
29.322
26.972
23.458
26.082
25.289
23.710
24.290
24.767
23.800
23.486
22.184
21.206
27.453
29.402
27.016
26.022
25.104
24.926
25.894
26.221
29.962
36.589
29.963
28.176
21.039
21.900
29.416
33.723
40.018
33.504
29.352
39.935
35.273
34.715
29.034
32.358
26.393
27.275
0.3
0.29
0.31
0.32
0.33
0.33
0.34
0.34
0.33
0.33
0.32
0.31
0.31
0.32
0.31
0.29
0.28
0.28
0.27
0.29
0.3
0.28
0.29
0.31
0.29
0.31
0.31
0.31
0.3
0.3
0.31
0.3
0.29
0.32
0.33
0.32
0.34
0.33
0.33
0.32
0.3
0.31
0.32
0.37
0.34
0.33
0.37
0.37
0.35
0.33
0.3
0.28
0.27
0.3
0.29
0.31
0.3
0.3
0.27
0.28
0.33
0.31
0.32
0.31
0.3
0.29
0.29
0.29
0.29
0.31
0.3
0.3
0.28
0.28
0.32
0.35
0.35
0.34
0.32
0.31
0.3
0.32
0.31
0.31
0.3
0.3
0.04
0.04
0.03
0.03
0.04
0.03
0.03
0.02
0.04
0.04
0.03
0.03
0.03
0.03
0.03
0.02
0.03
0.04
0.04
0.04
0.04
0.05
0.04
0.04
0.06
0.06
0.06
0.05
0.04
0.05
0.04
0.05
0.04
0.04
0.05
0.05
0.05
0.04
0.04
0.05
0.05
0.05
0.06
11/18/2017
11/19/2017
11/20/2017
11/21/2017
11/22/2017
11/23/2017
11/24/2017
11/25/2017
11/26/2017
11/27/2017
11/28/2017
11/29/2017
11/30/2017
12/1/2017
12/2/2017
12/3/2017
12/4/2017
12/5/2017
12/6/2017
12/7/2017
12/8/2017
12/9/2017
12/10/2017
12/11/2017
12/12/2017
12/13/2017
12/14/2017
12/15/2017
12/16/2017
12/17/2017
12/18/2017
12/19/2017
12/20/2017
12/21/2017
12/22/2017
12/23/2017
12/24/2017
12/25/2017
12/26/2017
12/27/2017
12/28/2017
12/29/2017
12/30/2017
27.181
22.566
25.322
27.990
24.250
23.831
22.032
20.316
22.604
26.156
26.392
24.387
25.052
27.847
28.298
26.008
26.706
24.873
29.306
30.963
35.545
33.188
28.839
28.833
28.264
40.927
35.005
37.872
38.940
29.337
24.646
22.920
24.024
29.227
28.716
23.661
22.373
21.666
35.990
60.035
110.237
94.640
90.205
0.32
0.29
0.31
0.3
0.32
0.33
0.31
0.29
0.31
0.31
0.29
0.31
0.31
0.31
0.32
0.32
0.3
0.29
0.31
0.33
0.35
0.34
0.36
0.37
0.37
0.39
0.38
0.39
0.36
0.35
0.32
0.31
0.34
0.33
0.34
0.32
0.32
0.31
0.36
0.39
0.42
0.42
0.44
0.28
0.26
0.28
0.28
0.29
0.29
0.25
0.25
0.27
0.29
0.29
0.28
0.28
0.29
0.28
0.27
0.26
0.27
0.3
0.31
0.29
0.28
0.28
0.28
0.27
0.29
0.29
0.29
0.29
0.31
0.29
0.27
0.29
0.3
0.3
0.29
0.27
0.27
0.3
0.29
0.28
0.29
0.25
0.03
0.05
0.05
0.04
0.06
0.05
0.02
0.03
0.04
0.05
0.04
0.05
0.05
0.05
0.04
0.02
0.04
0.05
0.04
0.05
0.05
0.04
0.03
0.04
0.04
0.04
0.03
0.04
0.02
0.02
0.04
0.04
0.05
0.05
0.04
0.02
0.02
0.03
0.05
0.05
0.05
0.03
0.02
12/31/2017
98.277
0.44
0.21
0.01
Multiple FuelsNuclear
Oil
0.01
0.48
0.01
0.46
0.01
0.45
0.01
0.44
0.02
0.37
0.01
0.37
0.02
0.41
0.01
0.44
0.01
0.46
0.01
0.48
0.01
0.42
0.02
0.39
0.01
0.38
0.01
0.39
0.01
0.43
0.01
0.46
0.01
0.45
0.01
0.39
0.02
0.33
0.02
0.34
0.01
0.37
0.01
0.37
0.02
0.37
0.02
0.38
0.01
0.36
0.01
0.40
0.01
0.41
0.01
0.38
0.01
0.41
0.01
0.41
0.01
0.44
0.01
0.46
0.01
0.44
0.01
0.39
0.01
0.46
0.02
0.40
0.01
0.41
0.01
0.41
0.01
0.43
0.01
0.39
0.01
0.36
0.01
0.34
Other
0.00
0.00
0.00
0.00
0.02
0.02
0.01
0.00
0.00
0.00
0.00
0.01
0.02
0.01
0.00
0.00
0.00
0.01
0.03
0.02
0.01
0.01
0.02
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
Other Renewables
Solar
0.27
0.01
0.29
0.01
0.27
0.01
0.26
0.01
0.26
0.00
0.25
0.00
0.26
0.00
0.26
0.00
0.24
0.01
0.23
0.01
0.24
0.00
0.24
0.00
0.24
0.00
0.24
0.00
0.23
0.01
0.24
0.01
0.20
0.01
0.24
0.00
0.23
0.00
0.24
0.00
0.25
0.00
0.25
0.00
0.23
0.00
0.22
0.00
0.24
0.00
0.26
0.00
0.25
0.00
0.27
0.00
0.25
0.00
0.26
0.00
0.25
0.00
0.28
0.00
0.25
0.01
0.29
0.01
0.31
0.01
0.31
0.01
0.31
0.00
0.28
0.00
0.27
0.01
0.26
0.01
0.26
0.00
0.00
0.00
Storage
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.01
0.01
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.00
0.01
0.01
0.01
0.02
0.02
0.01
0.01
0.02
0.02
0.03
0.02
0.02
0.02
0.02
0.01
0.02
0.02
0.02
0.02
0.02
0.03
0.03
0.02
0.02
0.02
0.01
0.01
0.02
0.03
0.02
0.02
0.02
0.33
0.36
0.34
0.31
0.35
0.38
0.37
0.37
0.44
0.46
0.44
0.43
0.42
0.42
0.40
0.42
0.44
0.45
0.42
0.44
0.39
0.40
0.40
0.43
0.41
0.47
0.47
0.47
0.48
0.47
0.50
0.48
0.47
0.47
0.46
0.46
0.47
0.44
0.39
0.38
0.43
0.45
0.45
0.01
0.01
0.02
0.02
0.02
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.01
0.00
0.00
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.02
0.03
0.03
0.03
0.02
0.02
0.01
0.01
0.02
0.03
0.03
0.01
0.02
0.02
0.02
0.02
0.02
0.03
0.04
0.03
0.03
0.03
0.04
0.04
0.04
0.03
0.03
0.03
0.02
0.02
0.03
0.03
0.04
0.04
0.04
0.03
0.04
0.04
0.04
0.04
0.03
0.03
0.44
0.46
0.46
0.43
0.41
0.43
0.44
0.44
0.42
0.42
0.39
0.36
0.41
0.40
0.39
0.39
0.43
0.44
0.42
0.42
0.42
0.44
0.47
0.46
0.47
0.47
0.48
0.46
0.48
0.48
0.48
0.46
0.43
0.45
0.44
0.44
0.49
0.48
0.47
0.48
0.47
0.47
0.48
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
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0.00
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0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
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0.00
0.00
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0.00
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0.00
0.00
0.00
0.00
0.00
0.00
0.00
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0.00
0.00
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0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.02
0.02
0.02
0.03
0.03
0.04
0.03
0.02
0.03
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.03
0.04
0.04
0.04
0.04
0.03
0.03
0.04
0.04
0.04
0.05
0.05
0.02
0.03
0.03
0.02
0.02
0.02
0.04
0.03
0.02
0.02
0.04
0.03
0.03
0.02
0.02
0.51
0.49
0.48
0.48
0.49
0.47
0.48
0.49
0.48
0.49
0.51
0.52
0.53
0.51
0.55
0.53
0.51
0.49
0.44
0.42
0.43
0.44
0.46
0.43
0.44
0.44
0.44
0.44
0.46
0.43
0.40
0.45
0.47
0.44
0.43
0.40
0.42
0.41
0.41
0.37
0.40
0.42
0.44
0.00
0.00
0.00
0.00
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0.00
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0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.03
0.06
0.06
0.06
0.06
0.06
0.05
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
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0.00
0.00
0.00
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
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0.01
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0.00
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0.00
0.00
0.00
0.00
0.03
0.03
0.03
0.02
0.03
0.03
0.04
0.04
0.02
0.03
0.02
0.02
0.02
0.02
0.02
0.03
0.04
0.04
0.03
0.02
0.02
0.03
0.04
0.03
0.03
0.03
0.03
0.03
0.04
0.04
0.04
0.05
0.04
0.04
0.03
0.04
0.04
0.04
0.04
0.04
0.03
0.03
0.03
0.40
0.36
0.39
0.37
0.37
0.38
0.39
0.36
0.37
0.39
0.40
0.40
0.42
0.44
0.46
0.40
0.35
0.34
0.34
0.36
0.40
0.38
0.36
0.35
0.36
0.35
0.38
0.41
0.35
0.36
0.37
0.37
0.35
0.35
0.38
0.33
0.33
0.34
0.33
0.34
0.37
0.38
0.36
0.01
0.00
0.00
0.00
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0.00
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