week3_final_assignment
Jagdish Chhabria
January 7, 2019
- Data Exploration: This should include summary statistics, means, medians, quartiles, or any other relevant information about the data set. Please include some conclusions in the R Markdown text.
The question for analysis is whether there is a correlation between the Case-Shiller (CS) Home Price Index (National) and 3 popular Exchange-Traded-Funds (symbols: ITB, XHB, REZ) related to housing, home building and leasing residential properties. The CS index is a 3-month average of home prices across 20 US cities, available with a 2-month lag. The ETF prices are intended to reflect the market’s expectation of the future returns for the referenced assets. While ETF prices are likley to be impacted by overall market sentiment, a preliminary hypothesis could expect both the index and the ETF closing prices to be positively correlated, assuming that both are jointly dependent on the housing market. The associated data-set includes monthly values for the index and the ETF closing prices between the years 2014 to 2018, obtained from the Internet (S&P website, Yahoo Finance website.
# require(RCurl)
#sf_houses<-read.csv(text=getURL("https://raw.githubusercontent.com/Jagdish16/jagdish_r_repo/master/case_shiller_index_home_etfs.csv"), header=T)
theURL<-"https://raw.githubusercontent.com/Jagdish16/jagdish_r_repo/master/case_shiller_index_home_etfs.csv"
housing<-read.table(file=theURL, header=TRUE, fill = TRUE, sep=",",quote="\"")
print(housing)## Date Case_Shiller_Index ITB_Close XHB_Close REZ_Close
## 1 2/1/2014 162.575 25.78047 32.74688 41.36633
## 2 3/1/2014 163.138 23.75059 31.32268 41.27561
## 3 4/1/2014 163.441 22.95388 29.86284 43.49641
## 4 5/1/2014 163.696 23.58195 30.34450 44.39548
## 5 6/1/2014 164.067 24.33760 31.54864 44.39548
## 6 7/1/2014 164.540 21.81393 28.42053 45.07732
## 7 8/1/2014 165.221 23.64076 30.43679 46.58885
## 8 9/1/2014 165.919 22.08894 28.54595 42.93599
## 9 10/1/2014 166.666 23.66217 30.08561 48.43508
## 10 11/1/2014 167.367 25.51031 32.34710 49.57014
## 11 12/1/2014 168.090 25.44150 32.97529 49.90051
## 12 1/1/2015 168.701 24.93919 33.26696 54.40937
## 13 2/1/2015 169.224 27.20281 35.25580 52.30915
## 14 3/1/2015 169.889 27.78348 35.76028 53.06045
## 15 4/1/2015 170.380 25.53012 33.60630 50.69436
## 16 5/1/2015 170.940 26.21959 34.90782 50.78040
## 17 6/1/2015 171.471 27.03711 35.56828 48.20739
## 18 7/1/2015 172.035 28.02564 36.53482 52.09622
## 19 8/1/2015 172.947 27.34546 35.22237 49.51746
## 20 9/1/2015 173.860 25.72878 33.27799 51.47975
## 21 10/1/2015 174.847 26.82299 34.77804 52.81220
## 22 11/1/2015 175.834 28.17450 35.37162 53.90716
## 23 12/1/2015 176.648 26.73421 33.26002 55.58025
## 24 1/1/2016 177.405 24.02615 29.75089 54.23069
## 25 2/1/2016 177.817 24.18415 30.09207 53.34803
## 26 3/1/2016 178.315 26.74180 32.98722 58.23798
## 27 4/1/2016 178.907 26.29164 32.86353 56.19393
## 28 5/1/2016 179.530 27.28042 33.37123 58.27419
## 29 6/1/2016 180.093 27.33975 32.74637 60.52482
## 30 7/1/2016 180.683 28.65432 35.28005 62.09016
## 31 8/1/2016 181.881 28.74340 35.33872 59.87428
## 32 9/1/2016 182.887 27.26862 33.18750 59.25925
## 33 10/1/2016 183.847 25.53981 30.96495 57.03197
## 34 11/1/2016 184.924 27.11500 33.14874 54.32354
## 35 12/1/2016 185.905 27.22397 33.14874 56.54865
## 36 1/1/2017 187.040 28.68217 33.85790 56.75985
## 37 2/1/2017 187.643 30.21942 35.34961 60.18798
## 38 3/1/2017 188.328 31.71700 36.53709 58.82791
## 39 4/1/2017 189.057 32.09753 37.17158 59.77786
## 40 5/1/2017 189.859 32.17695 36.85698 60.63304
## 41 6/1/2017 190.577 33.69595 37.87942 61.14991
## 42 7/1/2017 191.242 33.65738 37.77567 61.75650
## 43 8/1/2017 192.730 33.87600 37.60822 61.59532
## 44 9/1/2017 193.879 36.31062 39.22366 60.28700
## 45 10/1/2017 194.969 39.54638 40.81405 60.17881
## 46 11/1/2017 196.218 42.61978 43.25145 60.87690
## 47 12/1/2017 197.449 43.48511 43.67577 59.51898
## 48 1/1/2018 198.678 42.64963 44.26730 56.78278
## 49 2/1/2018 199.733 38.10418 40.10272 52.83980
## 50 3/1/2018 200.579 39.27784 40.35002 55.57771
## 51 4/1/2018 201.330 38.26920 38.61639 56.53675
## 52 5/1/2018 201.942 38.31900 38.93357 58.99063
## 53 6/1/2018 202.465 37.99038 39.20119 61.39584
## 54 7/1/2018 202.729 37.75783 39.36177 61.77779
## 55 8/1/2018 203.835 37.59830 39.75917 64.13648
## 56 9/1/2018 204.612 35.23532 38.18946 61.80728
## 57 10/1/2018 205.622 31.11254 33.84528 62.16099#summary(housing)- Data wrangling: Please perform some basic transformations. They will need to make sense but could include column renaming, creating a subset of the data, replacing values, or creating new columns with derived data (for example - if it makes sense you could sum two columns together)
Calculate the month-over-month percent changes for all 4 variables.
library(knitr)## Warning: package 'knitr' was built under R version 3.5.2opts_chunk$set(echo = FALSE, message = FALSE, results = 'asis')
housing$Case_Shiller_Diff = ((housing$Case_Shiller_Index - lag(housing$Case_Shiller_Index))*100/ housing$Case_Shiller_Index)
housing$Case_Shiller_Diff<-round(housing$Case_Shiller_Diff,2)
housing$ITB_Diff = ((housing$ITB_Close - lag(housing$ITB_Close))*100/ housing$ITB_Close)
housing$ITB_Diff<-round(housing$ITB_Diff,2)
housing$XHB_Diff = ((housing$XHB_Close - lag(housing$XHB_Close))*100/ housing$XHB_Close)
housing$XHB_Diff<-round(housing$XHB_Diff,2)
housing$REZ_Diff = ((housing$REZ_Close - lag(housing$REZ_Close))*100/ housing$REZ_Close)
housing$REZ_Diff<-round(housing$REZ_Diff,2)
housing$Date<-as.Date(housing$Date, format = "%m/%d/%Y")
#kable(head(housing), digits = 2, align = c(rep("l", 4), rep("c", 4), rep("r", 4)))
housing## Date Case_Shiller_Index ITB_Close XHB_Close REZ_Close
## 1 2014-02-01 162.575 25.78047 32.74688 41.36633
## 2 2014-03-01 163.138 23.75059 31.32268 41.27561
## 3 2014-04-01 163.441 22.95388 29.86284 43.49641
## 4 2014-05-01 163.696 23.58195 30.34450 44.39548
## 5 2014-06-01 164.067 24.33760 31.54864 44.39548
## 6 2014-07-01 164.540 21.81393 28.42053 45.07732
## 7 2014-08-01 165.221 23.64076 30.43679 46.58885
## 8 2014-09-01 165.919 22.08894 28.54595 42.93599
## 9 2014-10-01 166.666 23.66217 30.08561 48.43508
## 10 2014-11-01 167.367 25.51031 32.34710 49.57014
## 11 2014-12-01 168.090 25.44150 32.97529 49.90051
## 12 2015-01-01 168.701 24.93919 33.26696 54.40937
## 13 2015-02-01 169.224 27.20281 35.25580 52.30915
## 14 2015-03-01 169.889 27.78348 35.76028 53.06045
## 15 2015-04-01 170.380 25.53012 33.60630 50.69436
## 16 2015-05-01 170.940 26.21959 34.90782 50.78040
## 17 2015-06-01 171.471 27.03711 35.56828 48.20739
## 18 2015-07-01 172.035 28.02564 36.53482 52.09622
## 19 2015-08-01 172.947 27.34546 35.22237 49.51746
## 20 2015-09-01 173.860 25.72878 33.27799 51.47975
## 21 2015-10-01 174.847 26.82299 34.77804 52.81220
## 22 2015-11-01 175.834 28.17450 35.37162 53.90716
## 23 2015-12-01 176.648 26.73421 33.26002 55.58025
## 24 2016-01-01 177.405 24.02615 29.75089 54.23069
## 25 2016-02-01 177.817 24.18415 30.09207 53.34803
## 26 2016-03-01 178.315 26.74180 32.98722 58.23798
## 27 2016-04-01 178.907 26.29164 32.86353 56.19393
## 28 2016-05-01 179.530 27.28042 33.37123 58.27419
## 29 2016-06-01 180.093 27.33975 32.74637 60.52482
## 30 2016-07-01 180.683 28.65432 35.28005 62.09016
## 31 2016-08-01 181.881 28.74340 35.33872 59.87428
## 32 2016-09-01 182.887 27.26862 33.18750 59.25925
## 33 2016-10-01 183.847 25.53981 30.96495 57.03197
## 34 2016-11-01 184.924 27.11500 33.14874 54.32354
## 35 2016-12-01 185.905 27.22397 33.14874 56.54865
## 36 2017-01-01 187.040 28.68217 33.85790 56.75985
## 37 2017-02-01 187.643 30.21942 35.34961 60.18798
## 38 2017-03-01 188.328 31.71700 36.53709 58.82791
## 39 2017-04-01 189.057 32.09753 37.17158 59.77786
## 40 2017-05-01 189.859 32.17695 36.85698 60.63304
## 41 2017-06-01 190.577 33.69595 37.87942 61.14991
## 42 2017-07-01 191.242 33.65738 37.77567 61.75650
## 43 2017-08-01 192.730 33.87600 37.60822 61.59532
## 44 2017-09-01 193.879 36.31062 39.22366 60.28700
## 45 2017-10-01 194.969 39.54638 40.81405 60.17881
## 46 2017-11-01 196.218 42.61978 43.25145 60.87690
## 47 2017-12-01 197.449 43.48511 43.67577 59.51898
## 48 2018-01-01 198.678 42.64963 44.26730 56.78278
## 49 2018-02-01 199.733 38.10418 40.10272 52.83980
## 50 2018-03-01 200.579 39.27784 40.35002 55.57771
## 51 2018-04-01 201.330 38.26920 38.61639 56.53675
## 52 2018-05-01 201.942 38.31900 38.93357 58.99063
## 53 2018-06-01 202.465 37.99038 39.20119 61.39584
## 54 2018-07-01 202.729 37.75783 39.36177 61.77779
## 55 2018-08-01 203.835 37.59830 39.75917 64.13648
## 56 2018-09-01 204.612 35.23532 38.18946 61.80728
## 57 2018-10-01 205.622 31.11254 33.84528 62.16099
## Case_Shiller_Diff ITB_Diff XHB_Diff REZ_Diff
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 0 0 0
## 15 0 0 0 0
## 16 0 0 0 0
## 17 0 0 0 0
## 18 0 0 0 0
## 19 0 0 0 0
## 20 0 0 0 0
## 21 0 0 0 0
## 22 0 0 0 0
## 23 0 0 0 0
## 24 0 0 0 0
## 25 0 0 0 0
## 26 0 0 0 0
## 27 0 0 0 0
## 28 0 0 0 0
## 29 0 0 0 0
## 30 0 0 0 0
## 31 0 0 0 0
## 32 0 0 0 0
## 33 0 0 0 0
## 34 0 0 0 0
## 35 0 0 0 0
## 36 0 0 0 0
## 37 0 0 0 0
## 38 0 0 0 0
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## 42 0 0 0 0
## 43 0 0 0 0
## 44 0 0 0 0
## 45 0 0 0 0
## 46 0 0 0 0
## 47 0 0 0 0
## 48 0 0 0 0
## 49 0 0 0 0
## 50 0 0 0 0
## 51 0 0 0 0
## 52 0 0 0 0
## 53 0 0 0 0
## 54 0 0 0 0
## 55 0 0 0 0
## 56 0 0 0 0
## 57 0 0 0 0- Graphics: Please make sure to display at least one scatter plot, box plot and histogram. Don’t be limited to this. Please explore the many other options in R packages such as ggplot2.
- Meaningful question for analysis: Please state at the beginning a meaningful question for analysis. Use the first three steps and anything else that would be helpful to answer the question you are posing from the data set you chose. Please write a brief conclusion paragraph in R markdown at the end.
The scatterplot between the percentage changes in Case Shiller Index and ITB does not show any patterns. However the scatterplot between the percentafe changes in ITB and XHB does show a positive correlation which would be expected since they’re both ETFs associated with housing assets. However the same cannot be said of ITB and REZ, and XHB and REZ which is a little unexpected. This would indicate that the underlying assets for these ETFs are not similar.
The initial conclusion would be that a deeper analysis is required into: 1) Difference in type of underlying assets for the 3 ETFs. 2) Construction methodlogy for the Case Shiller Index. 3) Additional independent variables that both the index and ETFs depend on.
- BONUS - place the original .csv in a github file and have R read from the link. This will be a very useful skill as you progress in your data science education and career.