S&P/ Case-Shiller U.S. City Price Index as of 02-2021

 

library(ggplot2)

library(Quandl)

library(zoo)

library(quantmod)

library(TTR)

library(forecast)

library(ggfortify)

library(psych)

library(pastecs)

library(xts)

start <- as.Date("1990-01-01")

getSymbols(c(

  

  'NYXRSA',

  'BOXRSA',

  'SDXRSA',

  'CHXRSA',

  'DNXRSA',

  'LVXRSA',

  'DAXRSA',

  'WDXRSA',

  'MIXRSA',

  'ATXRSA',

  'SFXRSA',

  'LXXRSA',

  'SEXRSA',

  'CSUSHPISA'

  

), from=start, src='FRED')

names(NYXRSA)<-"New York"

names(BOXRSA)<-"Boston"

names(SDXRSA)<-"San Diego"

names(CHXRSA)<-"Chicago"

names(DNXRSA)<-"Denver"

names(LVXRSA)<-"Las Vegas"

names(WDXRSA)<-"DC"

names(MIXRSA)<-"Miami"

names(DAXRSA)<-"Dallas"

names(ATXRSA)<-"Atlanta"

names(SFXRSA)<-"San Francisco"

names(LXXRSA)<-"Los Angeles"

names(SEXRSA)<-"Seattle"

names(CSUSHPISA)<-"National" 

Mortgage<-getSymbols('MORTGAGE30US', src='FRED')

Housing_Price=ATXRSA

Compare_Max=Housing_Price/max(Housing_Price)*100

Diff_Housing=Delt(Housing_Price,k=12)*100

Annual_Changes=annualReturn(Housing_Price)*100

par(mfrow=c(2,1))

plot(Diff_Housing)

plot(Annual_Changes)

summary(last(Annual_Changes,'7 years'))

describe(Annual_Changes)

par(mfrow=c(3,1))

plot(ATXRSA, main="ATLANTA")

plot(Delt(ATXRSA,  k=12)*100, main="Percent changes from previous year ", col="red")

barplot(annualReturn(ATXRSA), main="Annual Changes ", col="blue", cex.names=0.8, las=2)

par(mfrow=c(3,1))

plot(BOXRSA, main="BOSTON")

plot(Delt(BOXRSA,  k=12)*100, main="Percent changes from previous year ", col="red")

barplot(annualReturn(BOXRSA), main="Annual Changes ", col="blue", cex.names=0.8, las=2)

par(mfrow=c(3,1))

plot(CHXRSA, main="CHICAGO")

plot(Delt(CHXRSA,  k=12)*100, main="Percent changes from previous year ", col="red")

barplot(annualReturn(CHXRSA), main="Annual Changes ", col="blue", cex.names=0.8, las=2)

par(mfrow=c(3,1))

plot(DAXRSA, main="DALLAS")

plot(Delt(DAXRSA,  k=12)*100, main="Percent changes from previous year ", col="red")

barplot(annualReturn(DAXRSA), main="Annual Changes ", col="blue", cex.names=0.8, las=2)

par(mfrow=c(3,1))

plot(DNXRSA, main="DENVER")

plot(Delt(DNXRSA,  k=12)*100, main="Percent changes from previous year ", col="red")

barplot(annualReturn(DNXRSA), main="Annual Changes ", col="blue", cex.names=0.8, las=2)

par(mfrow=c(3,1))

plot(LVXRSA, main="LAS VEGAS")

plot(Delt(LVXRSA,  k=12)*100, main="Percent changes from previous year ", col="red")

barplot(annualReturn(LVXRSA), main="Annual Changes ", col="blue", cex.names=0.8, las=2)

par(mfrow=c(3,1))

plot(MIXRSA, main="MIAMI")

plot(Delt(MIXRSA,  k=12)*100, main="Percent changes from previous year ", col="red")

barplot(annualReturn(MIXRSA), main="Annual Changes ", col="blue", cex.names=0.8, las=2)

par(mfrow=c(3,1))

plot(NYXRSA, main="NEW YORK")

plot(Delt(NYXRSA,  k=12)*100, main="Percent changes from previous year ", col="red")

barplot(annualReturn(NYXRSA), main="Annual Changes ", col="blue", cex.names=0.8, las=2)

par(mfrow=c(3,1))

plot(SDXRSA, main="SAN DIEGO")

plot(Delt(SDXRSA,  k=12)*100, main="Percent changes from previous year ", col="red")

barplot(annualReturn(SDXRSA), main="Annual Changes ", col="blue", cex.names=0.8, las=2)

par(mfrow=c(3,1))

plot(SEXRSA, main="SEATTLE")

plot(Delt(SEXRSA,  k=12)*100, main="Percent changes from previous year ", col="red")

barplot(annualReturn(SEXRSA), main="Annual Changes ", col="blue", cex.names=0.8, las=2)

par(mfrow=c(3,1))

plot(SFXRSA, main="SAN FRANSICO")

plot(Delt(SFXRSA,  k=12)*100, main="Percent changes from previous year ", col="red")

barplot(annualReturn(SFXRSA), main="Annual Changes ", col="blue", cex.names=0.8, las=2)

par(mfrow=c(3,1))

plot(WDXRSA, main="WASHINGTON DC")

plot(Delt(WDXRSA,  k=12)*100, main="Percent changes from previous year ", col="red")

barplot(annualReturn(WDXRSA), main="Annual Changes ", col="blue", cex.names=0.8, las=2)

par(mfrow=c(3,1))

plot(CSUSHPISA, main="US NATIONAL")

plot(Delt(CSUSHPISA,  k=12)*100, main="Percent changes from previous year ", col="red")

barplot(annualReturn(CSUSHPISA), main="Annual Changes ", col="blue", cex.names=0.8, las=2)

dev.off()

barplot(Annual_Changes, main=" Annual Changes ", col="blue", cex.names=0.8, las=2)

Monthly_Price<-monthlyReturn(Housing_Price)

Monthly_Rate<-MORTGAGE30US

Housing<-na.omit(merge(Monthly_Price,MORTGAGE30US))

head(Housing)

# Scatter chart for Price chagnes

scatter.smooth(last(Housing, "10 years"))

abline(lm(Housing$monthly.returns~Housing$MORTGAGE30US), col="blue")

# Regressiion Model

alli.mod1=lm(Combined$monthly.returns~Combined$MORTGAGE30US, data=Combined)

summary(alli.mod1)

autoplot(MORTGAGE30US)

y<-(Housing_Price)

library(ggfortify)

library(forecast)

autoplot(y)

d.arima<-auto.arima(y)

d.arima

d.forecast<-forecast(d.arima)

d.forecast

autoplot(forecast(d.arima, 36))

fit<-ets(y)

autoplot(stl(y, plot=FALSE))

ggtsdiag(auto.arima(y))

autoplot(forecast(fit,36))

Housing_Prices <- as.xts(data.frame(

  

  NYXRSA,

  BOXRSA,

  SDXRSA,

  CHXRSA,

  DNXRSA,

  LVXRSA,

  WDXRSA,

  MIXRSA,

  LXXRSA,

  SFXRSA

))

Housing_return = apply(Housing_Prices, 1, function(x) {x / Housing_Prices[1,]}) %>% 

  t %>% as.xts

head(Housing_return)

Summary_Stat<-summary(Housing_return)

cor(Housing_return)

write.table(Summary_Stat, "Summary.xls")

plot(as.zoo(Housing_return), screens = 1, lty = 1:10, xlab = "Date", ylab = "Home Price Index")

legend("topleft", c("New York", "Boston", "San Diego","Chicago",

                    "Denver", "Las Vegas","DC",

                    "Miami", "Los Angeles", "San Francisco"), lty = 1:10, cex = 0.5)

plot(Housing_return,  xlab = "Date", ylab = "Index (1987=100)")

legend("right", c("New York", "Boston", "San Diego","Chicago",

                    "Denver", "Las Vegas","DC",

                    "Miami","Los Angeles","San Francisco"), 

       fill=c("blue","red","green","black","sky blue","purple","yellow",

              "white", "black","red"

                                                                   

                    ) )

ggplot(Housing_return, aes( x=))

tail(Housing_return)

U.S. Consumer Price Index (CPI) Analysis as of 04-2021

 

library(Quandl)

library(ggplot2)

library(tseries);library(timeseries);library(xts);library(forecast)

library (quantmod)

library(psych)

library(plotly) #install.package(plotly)

#Food and Beverages (CPIFABSL)

#Housing (CPIHOSNS)

#Apparel (CPIAPPSL)

#Transportation (CPITRNSL)

#Medical Care (CPIMEDSL)

#Recreation (CPIRECSL)

#Education and Communication (CPIEDUSL)

# Other Goods and Services (CPIOGSSL)

#Commodities (CUSR0000SAC)

# Services (CUSR0000SAS)

start <- as.Date("1990-01-01")

getSymbols(c('CPIFABSL','CPIHOSNS','CPIAPPSL','CPITRNSL','CPIMEDSL','CPIRECSL',

             

             'CPIEDUSL','CPIOGSSL','CUSR0000SAC','CUSR0000SAS',"CPIAUCSL","CPILFESL"), from=start, src='FRED')

CPI<-merge (CPIFABSL,CPIHOSNS,CPIAPPSL,CPITRNSL,CPIMEDSL,CPIRECSL,

             

           CPIEDUSL,CPIOGSSL,CUSR0000SAC,CUSR0000SAS,CPIAUCSL,CPILFESL)

Diff_CPI <- (CPI/lag(CPI)-1)*100  

Diff_CPI[1,] <- 0

Diff_CPIAUCSL=Delt(CPIAUCSL,k=12)*100

Diff_CPIFESL=Delt(CPILFESL,k=12)*100

Diff_CPIFABSL=Delt(CPI$CPIFABSL,k=12)*100

Diff_CPIHOSNS=Delt(CPI$CPIHOSNS,k=12)*100

Diff_CPIAPPSL=Delt(CPI$CPIAPPSL,k=12)*100

Diff_CPITRNSL=Delt(CPI$CPITRNSL,k=12)*100

Diff_CPIMEDSL=Delt(CPI$CPIMEDSL,k=12)*100

Diff_CPIRECSL=Delt(CPI$CPIRECSL,k=12)*100

Diff_CPIEDUSL=Delt(CPI$CPIEDUSL,k=12)*100

Diff_CPIOGSSL=Delt(CPI$CPIOGSSL,k=12)*100

Diff_CUSR0000SAC=Delt(CPI$CUSR0000SAC,k=12)*100

Diff_CUSR0000SAS=Delt(CPI$CUSR0000SAS,k=12)*100

Diff_12<-merge(Diff_CPIFABSL, Diff_CPIHOSNS, Diff_CPIAPPSL, Diff_CPITRNSL, 

               Diff_CPIMEDSL, Diff_CPIRECSL, Diff_CPIEDUSL, Diff_CPIOGSSL,

               Diff_CUSR0000SAC, Diff_CUSR0000SAS)

Diff_12=window(Diff_12,start=as.Date("2001-01-01"), end=as.Date("2020-12-31"))

cor.distance <- cor(Diff_12)

corrplot::corrplot(cor.distance)

plot(Diff_CPIAPPSL)

plot(Diff_12)

plot(Diff_CPIAUCSL, main='Changes from previous year -CPI for All',las=2, subset='2000-01-01/')

plot(Diff_CPIFESL, main='Changes from previous year - Core CPI',las=2,subset='2000-01-01/')

plot(Diff_CPIFABSL, main='Changes from previous year -Food and Beverage',las=2, subset='2000-01-01/')

plot(Diff_CPIHOSNS, main='Changes from previous year -Housing',las=2,subset='2000-01-01/')

plot(Diff_CPIAPPSL, main='Changes from previous year -APPAREL', las=2,subset='2000-01-01/')

plot(Diff_CPITRNSL, main='Changes from previous year -Transportation',las=2,subset='2000-01-01/')

plot(Diff_CPIMEDSL, main='Changes from previous year -Medical Care',las=2,subset='2000-01-01/')

plot(Diff_CPIRECSL, main='Changes from previous year -Recreation',las=2,subset='2000-01-01/')

plot(Diff_CPIEDUSL, main='Changes from previous year -Education and Communication',las=2,subset='2000-01-01/')

plot(Diff_CPIOGSSL, main='Changes from previous year -Other Goods and Services',las=2,subset='2000-01-01/')

plot(Diff_CUSR0000SAC, main='Changes from previous year -Commodities',las=2,subset='2000-01-01/')

plot(Diff_CUSR0000SAS, main='Changes from previous year -Services',las=2,subset='2000-01-01/')

U.S. Employment Analysis by Sectors as of 04-2021

 

library(Quandl)

library(ggplot2)

library(tseries);library(timeseries);library(xts);library(forecast)

library (quantmod)

library(psych)

library(plotly) #install.package(plotly)

#Service-Providing Industries (SRVPRD)

#USGOOD -  All Employees: Goods-Producing Industries

#Private Service-Providing (CES0800000001)

#Mining and Logging: Oil and Gas Extraction (CES1021100001)

#Construction (USCONS)

#Manufacturing (MANEMP)

#Durable Goods (DMANEMP)

#Nondurable goods (NDMANEMP)

#Trade, Transportation and Utilities (USTPU)

#Wholesale Trade (USWTRADE)

#Transportation and Warehousing (CES4300000001)

#Retail Trade (USTRADE)

#Utilities (CES4422000001)

#Information Services (USINFO)

#Financial Activities (USFIRE)

#Professional and Business Services (USPBS)

#Education and Health Services (USEHS)

#Leisure and Hospitality (USLAH)

#Other Services (USSERV)

#Government (USGOVT)

getSymbols(c('PAYEMS','USGOOD','SRVPRD','CES0800000001','CES1021100001','USCONS','MANEMP',

             'DMANEMP','NDMANEMP','USTPU','USWTRADE','USTRADE','CES4300000001','CES4422000001',

            'USINFO','USFIRE','USPBS','USEHS','USLAH','USSERV','USGOVT'), from="1990-01-01",src='FRED')

EMP<-merge (PAYEMS,USGOOD,SRVPRD,CES0800000001,CES1021100001,USCONS,MANEMP,

            DMANEMP,NDMANEMP,USTPU,USWTRADE,USTRADE,CES4300000001,CES4422000001,

            USINFO,USFIRE,USPBS,USEHS,USLAH,USSERV,USGOVT)

names(EMP)<-c('PAYEMS','USGOOD','SRVPRD','CES0800000001','CES1021100001',

              'USCONS','MANEMP','DMANEMP','NDMANEMP','USTPU',

              'USWTRADE','USTRADE','CES4300000001','CES4422000001',

              'USINFO','USFIRE','USPBS','USEHS','USLAH',

              'USSERV','USGOVT')

# % in Total Nonfarm

PER_Good=USGOOD/PAYEMS*100

PER_Service=SRVPRD/PAYEMS*100

PER_CES0800000001=CES0800000001/PAYEMS*100

PER_CES1021100001=CES1021100001/PAYEMS*100

PER_USCONS=USCONS/PAYEMS*100

PER_MANEMP=MANEMP/PAYEMS*100

PER_DMANEMP=DMANEMP/MANEMP*100

PER_NDMANEMP=NDMANEMP/MANEMP*100

PER_USTPU=USTPU/PAYEMS*100

PER_USTRADE=USTRADE/PAYEMS*100

PER_USINFO=USINFO/PAYEMS*100

PER_USPBS=USPBS/PAYEMS*100

PER_USEHS=USEHS/PAYEMS*100

PER_USWTRADE=USWTRADE/PAYEMS*100

PER_USFIRE=USFIRE/PAYEMS*100

PER_USLAH=USLAH/PAYEMS*100

PER_USSERV=USSERV/PAYEMS*100

PER_USGOVT=USGOVT/PAYEMS*100

plot(PER_CES1021100001)

PER_USPBS

summary(PER_USCONS)

hist(PER_USCONS)

# Chart for Total Nonfarm

myColors <- c("red", "darkblue")

plot(x = EMP_PER, xlab = "Year", ylab = "% in Total Nonfarm",

     main = "% in Total Nonfarm Employment",col=myColors,   screens = 1)

legend(x = "bottom", legend = c('USGOOD','SRVPRD'),

       lty = 1, col=myColors)

plot(x = EMP_MAN, xlab = "Year", ylab = "% in Manufacturing",

     main = "% in Total Manufacturing Employment",col=myColors,   screens = 1)

legend(x = "bottom", legend = c('Durable','NonDurable'),

       lty = 1, col=myColors)

write.table(EMP, file='EMP.xls')

myColors <- c("red", "darkblue","brown","yellow","darkred","blue","pink","green","violet","black")

plot(x = last(EMP, "30 years"), xlab = "Year", ylab = "Index",

     main = "CPI", col=myColors,  screens = 1)

legend(x = "topleft", legend = c('PAYEMS','USGOOD','SRVPRD','CES0800000001','CES1021100001',

                                 'USCONS','MANEMP','DMANEMP','NDMANEMP','USTPU',

                                 'USWTRADE','USTRADE','CES4300000001','CES4422000001',

                                 'USINFO','USFIRE','USPBS','USEHS','USLAH',

                                 'USSERV','USGOVT'),

       lty = 1, col=myColors)

Diff_EMP <- (EMP/lag(EMP)-1)  

Diff_EMP[1,] <- 0

Diff_EMP12<-diff(EMP,lag =12)

write.table(Diff_EMP, file='Diff_EMP.xls')

tail(Diff_EMP12)

# Retail Trade

par(mfrow=c(3,1))

plot(USTRADE, main="Retail Trade", col="black")

barplot(Diff_EMP12$USTRADE, main="Changes from previous year ", col="red")

barplot(annualReturn(USTRADE), main=" Annual Changes ", col="blue")

# Financial Activities

par(mfrow=c(3,1))

plot(USFIRE, main="Financial Activities", col="black")

barplot(Diff_EMP12$USFIRE, main="Changes from previous year ", col="red")

barplot(annualReturn(USFIRE), main=" Annual Changes ", col="blue")

# Professional and Business Services

par(mfrow=c(3,1))

plot(USPBS, main="Professional and Business Services", col="black")

barplot(Diff_EMP12$USPBS, main="Changes from previous year ", col="red")

barplot(annualReturn(USPBS), main=" Annual Changes ", col="blue")

# Education and Health Services

par(mfrow=c(3,1))

plot(USEHS, main="Education and Health Services", col="black")

barplot(Diff_EMP12$USEHS, main="Changes from previous year ", col="red")

barplot(annualReturn(USEHS), main=" Annual Changes ", col="blue")

# Leisure and Hospitality

par(mfrow=c(3,1))

plot(USLAH, main="Leisure and Hospitality", col="black")

barplot(Diff_EMP12$USLAH, main="Changes from previous year ", col="red")

barplot(annualReturn(USLAH), main=" Annual Changes ", col="blue")

# Other Services

par(mfrow=c(3,1))

plot(USSERV, main="Other Services", col="black")

barplot(Diff_EMP12$USSERV, main="Changes from previous year ", col="red")

barplot(annualReturn(USSERV), main=" Annual Changes ", col="blue")

# US Government

par(mfrow=c(3,1))

plot(USGOVT, main="Government", col="black")

barplot(Diff_EMP12$USGOVT, main="Changes from previous year ", col="red")

barplot(annualReturn(USGOVT), main=" Annual Changes ", col="blue")

# Wholesale Trade

par(mfrow=c(3,1))

plot(USWTRADE, main="Wholesale Trade", col="black")

barplot(Diff_EMP12$USWTRADE, main="Changes from previous year ", col="red")

barplot(annualReturn(USWTRADE), main=" Annual Changes ", col="blue")

# Mining and Logging: Oil and Gas Extraction

par(mfrow=c(3,1))

plot(CES1021100001, main="Mining and Logging: Oil and Gas Extraction", col="black")

barplot(Diff_EMP12$CES1021100001, main="Changes from previous year ", col="red")

barplot(annualReturn(CES1021100001), main=" Annual Changes ", col="blue")