The housing market in Los Angeles-Long Beach-Anaheim from 05-01-2012 to 02-01-2019

미서부 로스앤잴래스 주택시장 동향를 보면 중간가격은 $70만불인데 평균가격은 $150만불 가까이 나갑니다. 평균가격이 중간가격의 두배입니다. 확연히 이지역의 주택시장은 2018년 여름이후에 확연히 식어가고있는것을 볼수가 있습니다. 주태가격이 하락한것을 볼수가 있습니다. 또한 재고량이 늘어나고 주택판매기간이 늘어나기 시작했습니다. 확실히 주택시장이 한풀 꺾인것을 볼수가 있습니다.

According to the housing market in Los Angeles-Long Beach-Anaheim, the median price is $ 700,000 and the average price is close to $ 1.5 million. The average price is twice the median price. It is evident that the housing market in this area is clearly cooling down after the summer of 2018. We can see that the prices have fallen while inventory levels have increased and time to sell a house has begun to increase. It is certain that the housing market has been cooling.

The following charts shows the housing market in Los Angeles-Long Beach-Anaheim from 05-01-2012 to 02-01-2019. The average housing prices is approximately $1.45 million while the median housing prices is approximately $700,000.

The housing market in Los Angeles-Long Beach – Anaheim is cooling since Summer 2018. The following chart shows that the housing prices in both median and average are falling in late of 2018. It is taking longer to sell houses and the inventory of houses increase also.

The following chart shows the ratio of average price over median price in LA metro area. It is above 2 and the national average is 1.33.

R-Codes:

library(quantmod)

library(Hmisc)

library(reshape2)

library(ggplot2)

library(googleVis)

#Importing Housing Data

Housing_Metro <- read.csv("~/R programs/Housing/RDC_InventoryCoreMetrics_Metro_Hist.csv")

# Select Housing Prices for top 20 cities

Housing<- subset(Housing_Metro, Housing_Metro$Nielsen.Rank==2)

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

var0<-Housing$Median.Listing.Price

var1<-Housing$Avg.Listing.Price

date <- seq(as.Date("2012-05-01"), by="1 month", length.out=82)

# 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)”)

var2<-Housing$Median.Listing.Price.Y.Y

var3<-Housing$Avg.Listing.Price.Y.Y

date <- seq(as.Date("2012-05-01"), by="1 month", length.out=82)

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$Median.Listing.Price, main=”Median Listing Price”

, col=’blue’,names.arg = Housing$Month, cex.names = 0.3 )

barplot(Housing$Median.Listing.Price.Y.Y, main=”Median Listing Price Y to Y”

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

barplot(Housing$Days.on.Market, main=”Days on Market”

,

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

barplot(Housing$Days.on.Market.Y.Y, main=”Days on Market Y to Y”

,

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

barplot(Housing$Price.Increase.Count.Y.Y, main=”Price Increase Y to Y”

,

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

barplot(Housing$Price.Decrease.Count.Y.Y, main=”Price Decrease Y to Y”

,

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

barplot(Housing$Total.Listing.Count.Y.Y, main=”Total Listing Y to Y”

,

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

barplot(Housing$Pending.Listing.Count.Y.Y, main=”Pending Listing Y to Y”

,

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

# Basic line plot with points

ggplot(data=Housing, aes(x=Housing$Month, y=Housing$Pending.Ratio, group=1)) +

geom_line()+

geom_point()+

labs(title=” Active Listing Count Y to Y”)

# Basic line plot with points

ggplot(data=Housing, aes(x=Housing$Month, y=Housing$Active.Listing.Count.Y.Y, group=1)) +

geom_line(linetype=”dashed”)+

geom_point()+

labs(title=” Active Listing Count”)

#Histogram Chart

x <- na.exclude(Housing_Metro$Median.Listing.Price.Y.Y)

h<-hist(x, breaks=10, col="blue", xlab="Median Housing Prices",

main=”Histogram with Normal Curve”)

d<-density(x)

plot(d, col=”red”, border=”blue”)

#boxplot

boxplot(x,

col=(c(“blue”)),

main=”Median Listing Price Y/Y”)

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