S&P/Case-Shiller U.S. National Home Price Index as 06-2023

As you can see, the housing market is starting to cool down across the United States. The national home price index declined by 0.02% in the latest month, and the 10-city and 20-city composite indices both declined by 0.47% and 1.17%, respectively.

The biggest declines were in the western United States, where home prices in Los Angeles, San Diego, and Las Vegas all declined by more than 8%. The only cities that saw an increase in home prices were Chicago and New York.

There are a number of factors that are contributing to the cooling housing market, including rising interest rates, inflation, and concerns about a potential recession. Rising interest rates make it more expensive to borrow money, which can cool demand for housing. Inflation is also making it more expensive to buy a home, as the cost of everything from materials to labor is rising. And concerns about a potential recession could lead some buyers to delay their purchase or even sell their homes.

It is still too early to say whether the cooling housing market is the start of a longer-term trend. However, the recent data suggests that the market is starting to adjust to higher interest rates and inflation. This could be good news for buyers, as it could lead to more affordable home prices in the future.

S&P/ Case-Shiller U.S. Home Price Index as of 02/2023

The chart displays the S&P/Case-Shiller U.S. Home Price index for major cities as of February 2023. The first chart illustrates the latest percent changes for these cities. Among the cities on the West Coast, San Francisco, Seattle, San Diego, Denver, Las Vegas, and Los Angeles experienced a decline in home prices. In contrast, cities in the South such as Dallas and Atlanta saw the highest increase in home prices. Other cities that experienced an increase in home prices include Atlanta, New York, Boston, Chicago, Washington DC, and Miami.

The following charts depict the percentage changes from the previous year in major cities across the United States. While some cities have experienced a decline in home prices, others continue to show positive growth. However, it is noteworthy that all cities have been experiencing an overall downward trend in home prices.

Among the major cities in the United States, Seattle and San Francisco have witnessed the most significant declines in home prices. These cities have experienced substantial decreases in housing prices compared to other cities.

Despite the overall downward trend in home prices, Dallas and Atlanta have managed to maintain positive growth rates in their housing markets. These cities continue to experience increases in home prices, albeit at a slower pace compared to previous periods.

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")