The housing market in Chicago-Naperville-Elgin as of 3/2021

 

The Housing Market in Los Angeles-Long beach-Anaheim, CA as of 03/2021

 

library(quantmod)

library(reshape2)

library(ggplot2)

library(googleVis)

#Importing Housing Data

download.file("https://econdata.s3-us-west-2.amazonaws.com/Reports/Core/RDC_Inventory_Core_Metrics_Metro_History.csv",

              destfile = "Metro_Hist.csv")

Housing_Metro <- read.csv("Metro_Hist.csv")

# Select Housing Prices for top 20 cities

Housing<- subset(Housing_Metro, Housing_Metro$HouseholdRank=='2') 

Housing<-Housing[order(Housing$month_date_yyyymm),]

ratio=Housing$average_listing_price/Housing$median_listing_price

d <- density(ratio)

plot(d, main="Average Prices / Median Prices")

polygon(d, col="red", border="blue")

var0<-Housing$median_listing_price

var1<-Housing$average_listing_price

date <- seq(as.Date("2016-07-01"), by="1 month", length.out=57) 

# Creating Charts

ggplot() + geom_line(aes(x=date,y=var0),color='red') +

  

  geom_line(aes(x=date,y=var1),color='blue') + 

  

  

  ylab('Housing Prices')+xlab('Date')+

  

  labs(title=" Median Listing Prices (in Red) and Average Listing Prices (in Blue)")

# Percent Chagnes

var2<-Housing$median_listing_price_yy

var3<-Housing$average_listing_price_yy

date <- seq(as.Date("2016-07-01"), by="1 month", length.out=57)  

ggplot() + geom_line(aes(x=date,y=var2),color='red') +

  

  geom_line(aes(x=date,y=var3),color='blue') + 

  

  ylab('Housing Prices')+xlab('Date')+

  

  labs(title=" Median Listing Prices (in Red) and Average Listing Prices Y to Y (in Blue)")

barplot(Housing$active_listing_count_yy, main="Active Listing Counting"

        ,

        names.arg = Housing$Month, cex.names = 0.3 )

barplot(Housing$median_days_on_market_yy, main="Days on Market Y to Y"

        ,

        names.arg = Housing$Month, cex.names = 0.3 )

barplot(Housing$pending_listing_count_yy, main="Pending Listing Count Y to Y"

        ,

        names.arg = Housing$Month, cex.names = 0.5 )

barplot(Housing$new_listing_count_yy, main="New Listing Y to Y"

        ,

        names.arg = Housing$Month, cex.names = 0.5 )

# Basic line plot with points

ggplot(data=Housing, aes(x=month_date_yyyymm, y=ratio, group=1)) +

  geom_line()+

  geom_point()+

  labs(title=" Ratio ( Average Price / Median Price) ")

# Basic line plot with points

ggplot(data=Housing, aes(x=month_date_yyyymm, y=active_listing_count, group=1)) +

  geom_line(linetype="dashed")+

  geom_point()+

  labs(title=" Active Listing Count")

The housing market in New York - Newwark-Jersey city, NY-NJ-PA as of 03/2021

 

# Select Housing Prices for top 20 cities

Housing<- subset(Housing_Metro, Housing_Metro$HouseholdRank=='1') 

Housing<-Housing[order(Housing$month_date_yyyymm),]

ratio=Housing$average_listing_price/Housing$median_listing_price

d <- density(ratio)

plot(d, main="Average Prices / Median Prices")

polygon(d, col="red", border="blue")

var0<-Housing$median_listing_price

var1<-Housing$average_listing_price

date <- seq(as.Date("2016-07-01"), by="1 month", length.out=57) 

# Creating Charts

ggplot() + geom_line(aes(x=date,y=var0),color='red') +

  

  geom_line(aes(x=date,y=var1),color='blue') + 

  

  

  ylab('Housing Prices')+xlab('Date')+

  

  labs(title=" Median Listing Prices (in Red) and Average Listing Prices (in Blue)")

# Percent Chagnes

var2<-Housing$median_listing_price_yy

var3<-Housing$average_listing_price_yy

date <- seq(as.Date("2016-07-01"), by="1 month", length.out=57)  

ggplot() + geom_line(aes(x=date,y=var2),color='red') +

  

  geom_line(aes(x=date,y=var3),color='blue') + 

  

  ylab('Housing Prices')+xlab('Date')+

  

  labs(title=" Median Listing Prices (in Red) and Average Listing Prices Y to Y (in Blue)")

barplot(Housing$active_listing_count_yy, main="Active Listing Counting"

        ,

        names.arg = Housing$Month, cex.names = 0.3 )

barplot(Housing$median_days_on_market_yy, main="Days on Market Y to Y"

        ,

        names.arg = Housing$Month, cex.names = 0.3 )

barplot(Housing$pending_listing_count_yy, main="Pending Listing Count Y to Y"

        ,

        names.arg = Housing$Month, cex.names = 0.5 )

barplot(Housing$new_listing_count_yy, main="New Listing Y to Y"

        ,

        names.arg = Housing$Month, cex.names = 0.5 )

# Basic line plot with points

ggplot(data=Housing, aes(x=month_date_yyyymm, y=ratio, group=1)) +

  geom_line()+

  geom_point()+

  labs(title=" Ratio ( Average Price / Median Price) ")

# Basic line plot with points

ggplot(data=Housing, aes(x=month_date_yyyymm, y=active_listing_count, group=1)) +

  geom_line(linetype="dashed")+

  geom_point()+

  labs(title=" Active Listing Count")

Housing Prices in top 20 U.S. Metro cities as of 3/2021

Analyzing GDP by Industry with R

 

library(quantmod)

library(Quandl)

library(xts)

library(zoo)

Industry_Data <- read.csv("Industry Data.csv")

View(Industry_Data)

GDP<-ts(Industry_Data, start=c(2005,1), end=c(2020,2), frequency=4)

data<-GDP

data

# Percent changes from previous

percent_GDP<-diff(data, lag=4)/lag(data, k=-4)*100

Hw# Percentage of each industry in total industry

pct_GDP<-(data/data[,1])*100

summary(percent_GDP)

plot(pct_GDP)

par(mfrow=c(2,1))

plot(percent_GDP[,2], main="Private Industry")

plot(pct_GDP[,2])

par(mfrow=c(2,1))

plot(percent_GDP[,3], main="  Agriculture, forestry, fishing, and hunting")

plot(pct_GDP[,3])

par(mfrow=c(2,1))

plot(percent_GDP[,4], main="      Farms")

plot(pct_GDP[,4])

par(mfrow=c(2,1))

plot(percent_GDP[,6], main="       Mining")

plot(pct_GDP[,6])

par(mfrow=c(2,1))

plot(percent_GDP[,7], main="        Oil and gas extraction")

plot(pct_GDP[,7])

par(mfrow=c(2,1))

plot(percent_GDP[,11], main=" Construction")

plot(pct_GDP[,11])

par(mfrow=c(2,1))

plot(percent_GDP[,12], main="   Manufacturing")

plot(pct_GDP[,12])

par(mfrow=c(2,1))

plot(percent_GDP[,13], main="     Durable goods")

plot(pct_GDP[,13])

par(mfrow=c(2,1))

plot(percent_GDP[,19], main="Computer and electronic products")

plot(pct_GDP[,19])

par(mfrow=c(2,1))

plot(percent_GDP[,20], main="Electrical equipment, appliances, and components")

plot(pct_GDP[,20])

par(mfrow=c(2,1))

plot(percent_GDP[,21], main="Motor vehicles, bodies and trailers, and parts")

plot(pct_GDP[,21])

par(mfrow=c(2,1))

plot(percent_GDP[,27], main="    Textile mills and textile product mills")

plot(pct_GDP[,27])

par(mfrow=c(2,1))

plot(percent_GDP[,28], main="         Apparel and leather and allied products")

plot(pct_GDP[,28])

par(mfrow=c(2,1))

plot(percent_GDP[,34], main="    Wholesale trade")

plot(pct_GDP[,34])

par(mfrow=c(2,1))

plot(percent_GDP[,35], main="     Retail trade")

plot(pct_GDP[,35])

par(mfrow=c(2,1))

plot(percent_GDP[,40], main="   Transportation and warehousing")

plot(pct_GDP[,40])

par(mfrow=c(2,1))

plot(percent_GDP[,41], main="      Air transportation")

plot(pct_GDP[,41])

par(mfrow=c(2,1))

plot(percent_GDP[,42], main=" Rail transportation")

plot(pct_GDP[,42])

par(mfrow=c(2,1))

plot(percent_GDP[,49], main="Information")

plot(pct_GDP[,49])

par(mfrow=c(2,1))

plot(percent_GDP[,55], main="    Finance and insurance")

plot(pct_GDP[,55])

par(mfrow=c(2,1))

plot(percent_GDP[,60], main=" Real estate")

plot(pct_GDP[,60])

par(mfrow=c(2,1))

plot(percent_GDP[,62], main=" Housing")

plot(pct_GDP[,62])

Analyzing Gross Domestic Product (GDP) as of 2020-07-01

 

library(Quandl)

library(ggplot2)

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

library (quantmod)

library(psych)

library(plotly) #install.package(plotly)

getSymbols(c('GDPDEF','GDP','PCEC','GPDI','NETEXP','GCE'), src='FRED')

ratio_PCEC=PCEC/GDP*100

ratio_GPDI=GPDI/GDP*100

ratio_NETEXP=NETEXP/GDP*100

ratio_GCE=GCE/GDP*100

basket<-cbind(ratio_PCEC, ratio_GPDI, ratio_NETEXP, ratio_GCE)

summary(basket)

tail(basket)

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

plot(x = basket, xlab = "Year", ylab = "Percent",

       main = "Percent in GDP", col = myColors, screens = 1, subset = "1980-01-04/")

legend(x = "topleft", legend = c("PCEC", "GDPI", "NETEXP", "GCE"),

       lty = 1, col = myColors)

describe(ratio_GPDI)

plot(ratio_PCEC, main="% of Personal Consumption Expenditure in GDP", ylab="Percent")

lines(mean_PCEC, col='red')

plot(ratio_GPDI, main="% of Gross Private Domestic Investment in GDP")

plot(ratio_NETEXP, main="% of Net Export in GDP")

plot(ratio_GCE, main="% of Government Consumption Expenditure in GDP")

Diff_GDP=Delt(GDP, k=4)*100

Diff_PCEC=Delt(PCEC, k=4)*100

Diff_GPDI=Delt(GPDI, k=4)*100

Diff_NETEXP=Delt(NETEXP, k=4)*100

Diff_GCE=Delt(GCE, k=4)*100

Diff_basket<-cbind(Diff_GDP, Diff_PCEC, Diff_GPDI, Diff_GCE)

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

plot(x = Diff_basket, xlab = "Year", ylab = "Percent",

     main = "Percent in GDP", col = myColors, screens = 1, subset = "1980-01-04/")

legend(x = "topleft", legend = c("GDP", "PCEC", "GDPI",  "GCE"),

       lty = 1, col = myColors)

Diff_GDPDEF=Delt(GDPDEF, k=4)*100

Real_GDP=Diff_GDP-Diff_GDPDEF

plot(Real_GDP)