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”)

The existing housing prices in New York-Newark-Jersey City from 5/2012 to 2/1/2019

동부를 대표하는 뉴욕주택시장은 2018년에 들어와서 쿨다운한것을 볼수가 있습니다. 중간가격은 $50만불이 넘고 평균가격이 $100만불이 넘습니다. 평균가격의 중택가격상승률이 중간가격상승률 보다 하락폭이 더크고 이것은 고급주택시장이 쿨다운 했다는것을 의미합니다. 아무래도 지난 감세정책으로 모게지비용에 대한 세금혜택을 $100만불에서 $75만불로 하향한것과 그리고 재산세 해택도 만불로 줄인것이 고급주택에 대한 수요를 줄였습니다.

2018년 후반에는 가격이 하락했습니다. 주택재고량이 몇달째 늘어나고 있고 주택판매기간이 올해 2019년에는 늘었습니다. 기간이 늘어난것이 지속적으로 될것인지 지켜보아야할것 같습니다.

The following charts shows the existing housing prices in New York-Newark-Jersey City from 5/2012 to 2/1/2019.

The average prices of New York is above 1 million dollars and the median prices is above $0.5 million dollars. The average prices is twice more than the median prices. There are many expensive houses in the New York metro.

아래 차트는 평균가격과 중간가격의 비율입니다. 한때 2.25 이상 높았다가 2배로 하락했습니다. 전국 평균은 1.33 입니다.

The following chart shows the year to year prices changes in median and average housing prices. The year to year prices changes in average housing prices declined significantly in 2018. It becomes more difficult to sell more expensive houses.

The following chart shows the annual changes in days on Market. The trend had been downward, but it reversed in January, 2019. It shows that it took longer to sell a house in January 2019 and We need to know whether the trend is only a temporary or continue.

The following chart shows that the total listing has been increasing. More houses on sales joins the market and it takes longer to sell.