The housing market in San Francisco-Oakland-Hayward, CA as of 03/2021

 

The housing market in Atlanta-Sandy Springs-Roswell, GA 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=='9') 

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 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