Ratio = Average Listing Price/Median Listing Price for top 20 metro cities

아래 차트는 평균가를 중간가격를 나눈지수입니다. 이지수가 1보다크면 평균가격이 중간가보다 크며 이지수가 크면 클수로 평균주택가격이 높습니다. 즉 주택가격이높은 주택이 아주많이 있습니다. 가장높은 도시는 마이애미 이고 뉴욕/뉴저지 입니다. 가장 낮은 도시는 덴버입니다. 즉 덴버의 중간가격과 평균가격이 비슷합니다.

The chart below shows the average listing price divided by the median listing price.

If the number is greater than 1, the average price is higher than the middle price.

If the ratio is higher, the average listing price is higher. There are many houses with high housing prices in a city.

The cities with highest ratio are Miami and New York / New Jersey. The city with lowest ratio is Denver.

That is, the average price and the average price of Denver are similar.


시애틀, 뉴욕 그리고 아틀란타는 중간가격 상승이 평균가격 상승보다 높습니다.

반면에 마이애미 평균가격상승이 중간가격상승보다 높습니다. 즉 고가의 주택가격이 더많이 올랐습니다.

In Seattle, New York and Atlanta, the median home price increases are higher than average home price increases.

In Miami, the average house prices increase is higher than median house prices increase.

In other words, the prices of expensive housings have risen more than median housing prices.

Days on Market before the houses were sold in Top 20 Metro Areas

The following chart shows the changes of days on the market before the houses were sold in major top 20 metro areas.

미국 20대 도시들중에 주택매매가 가장 빨리 일어나는곳은 시애틀, 샌프란시스코, 그리고 덴버입니다.

샌프란시스코에서의 주택매매는 작년대비 빨라졌는데 집이 팔리는기간이 25일 걸립니다.

시애틀은 작년보다 조금더 오래걸리지만 그래도 24일 밖에 안걸립니다.

주택매매가 가장오래걸리는 도시는 마이애미입니다.

Seattle, San Francisco, and Denver are among the fastest-selling cities among the top 20 cities in the United States.

Home sales in San Francisco are faster than last year, and the house takes 25 days to sell.

In Seattle, it takes a little longer than last year, but it only takes 24 days to sell a house.

Miami is the city that takes the longest time to buy and sell homes.


Obs CBSATitle

Days_on_Market

Days_on_Market_M_M

Days_on_Market_Y_Y

1 New York-Newark-Jersey City, NY-NJ-PA

55

0.028

-0.0833

2 Los Angeles-Long Beach-Anaheim, CA

37.5

0.1364

-0.026

3 Chicago-Naperville-Elgin, IL-IN-WI

44

0.0115

-0.0833

4 Dallas-Fort Worth-Arlington, TX

38

0.0411

0

5 Houston-The Woodlands-Sugar Land, TX

46

0.0455

-0.0891

6 Philadelphia-Camden-Wilmington, PA-NJ-DE-MD

55

0.028

-0.1129

7 Washington-Arlington-Alexandria, DC-VA-MD-WV

37.5

0.1029

-0.026

8 Miami-Fort Lauderdale-West Palm Beach, FL

89

0.0349

0

9 Atlanta-Sandy Springs-Roswell, GA

45

0.0345

-0.0426

10 Boston-Cambridge-Newton, MA-NH

32.5

0.1818

-0.1447

11 San Francisco-Oakland-Hayward, CA

25

0.1236

-0.1525

12 Detroit-Warren-Dearborn, MI

37

0

-0.0133

13 Phoenix-Mesa-Scottsdale, AZ

44

0.0353

-0.1111

14 Seattle-Tacoma-Bellevue, WA

24

0.0667

0.0213

15 Minneapolis-St. Paul-Bloomington, MN-WI

34

0.0303

-0.1169

16 Riverside-San Bernardino-Ontario, CA

44

0.0115

-0.033

17 Tampa-St. Petersburg-Clearwater, FL

54.5

0.0283

-0.0763

18 San Diego-Carlsbad, CA

34

0.0462

-0.0811

19 St. Louis, MO-IL

48

-0.0204

-0.068

20 Denver-Aurora-Lakewood, CO

31

0

0.0333

The Median Housing Prices in Top 20 Metros as of June 2018

The following chart and table show the median housing price changes from the previous year in the top 20 cities. The biggest increase cities are Seattle, New York, and Atlanta while the median housing prices in Denver and Washington DC declined from the previous year.

미국 20대 주택가격 (2018년 6월기준)

전년대비 주택가격상승 가장높은 도시들: 시애틀, 뉴욕 그리고 아틀란타
전년대비 주택가격 하락한 도시들: 덴버와 와싱톤디시

Obs

CBSATitle

Median_Listing_Price

Median_Listing_Price_Y_Y

1

New York-Newark-Jersey City, NY-NJ-PA

$542,550

0.132

2

Los Angeles-Long Beach-Anaheim, CA

$762,550

0.052

3

Chicago-Naperville-Elgin, IL-IN-WI

$300,000

0.038

4

Dallas-Fort Worth-Arlington, TX

$356,398

0.011

5

Houston-The Woodlands-Sugar Land, TX

$328,891

0.030

6

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD

$269,950

0.080

7

Washington-Arlington-Alexandria, DC-VA-MD-WV

$455,050

-.018

8

Miami-Fort Lauderdale-West Palm Beach, FL

$395,050

0.042

9

Atlanta-Sandy Springs-Roswell, GA

$338,011

0.127

10

Boston-Cambridge-Newton, MA-NH

$529,050

0.060

11

San Francisco-Oakland-Hayward, CA

$993,250

0.095

12

Detroit-Warren-Dearborn, MI

$259,950

0.085

13

Phoenix-Mesa-Scottsdale, AZ

$342,045

0.053

14

Seattle-Tacoma-Bellevue, WA

$582,370

0.165

15

Minneapolis-St. Paul-Bloomington, MN-WI

$349,950

0.077

16

Riverside-San Bernardino-Ontario, CA

$395,050

0.055

17

Tampa-St. Petersburg-Clearwater, FL

$275,045

0.025

18

San Diego-Carlsbad, CA

$699,045

0.033

19

St. Louis, MO-IL

$209,950

0.050

20

Denver-Aurora-Lakewood, CO

$467,550

-.098

Enter a caption

Continue reading “The Median Housing Prices in Top 20 Metros as of June 2018”

Top 100 cities with most annual median housing prices increase as of June 2018

미국에는 500 개의 도시들이 있습니다. 아래 도표는 작년대비 주택가격이 제일 많이 상승한 100개의 도시들입니다. (2018년 6월기준). 제일 많이 주택가격이 오른 도시는 Oddess (Texas) 입니다. 자금만치 44.3% 상승했습니다. 대체로 작은도시들의 주택가격이 많이 상승했는데 20대 대도시중에는 시애틀 (16.5%) 뉴욕/뉴저지 (13.2%) 그리고 아틀란타(12.7%) 이 100등에 들어갔습니다.

There are about 500 cities in the United States. The chart below shows the cities with the most increase in housing prices compared to last year as of June 2018. The city in Texas, Oddess is the city with the biggest increase in housing prices. The housing price increased by 44.3% from last year. Housing prices in many smaller cities have risen sharply and there are three largest metro cities: Seattle (16.5 percent), New York (13.2 percent) and Atlanta (12.7 percent) are ranked in top 100 cities with the most increase in housing prices increase compared to last year.

Obs CBSATitle

Nielsen

Rank

Median

Listing

Price

Annual

Median

Listing

Price changes

1 Odessa, TX

292

$288,525

.443

2 Palatka, FL

474

$244,950

.384

3 Lima, OH

383

$124,500

.366

4 Lufkin, TX

443

$224,750

.363

5 Mansfield, OH

331

$133,125

.325

6 Indianapolis-Carmel-Anderson, IN

32

$279,550

.325

7 Kokomo, IN

420

$121,750

.269

8 Midland, TX

260

$350,000

.263

9 Shelby, NC

395

$177,043

.246

10 Jamestown-Dunkirk-Fredonia, NY

301

$149,275

.245

11 San Jose-Sunnyvale-Santa Clara, CA

36

$1,240,300

.241

12 Pocatello, ID

455

$214,750

.235

13 Muskegon, MI

243

$185,000

.234

14 Sheboygan, WI

335

$212,550

.221

15 Dubuque, IA

391

$244,775

.221

16 Jefferson City, MO

281

$184,550

.218

17 Forest City, NC

483

$283,525

.210

18 Branson, MO

414

$217,500

.209

19 Frankfort, KY

465

$214,225

.207

20 Port Angeles, WA

434

$400,025

.205

21 Clarksburg, WV

392

$166,300

.204

22 Yakima, WA

209

$273,830

.199

23 LaGrange, GA

493

$213,325

.198

24 Rome, GA

413

$199,950

.197

25 Burlington, NC

262

$237,984

.190

26 Youngstown-Warren-Boardman, OH-PA

94

$119,000

.189

27 Muncie, IN

341

$84,475

.185

28 State College, PA

270

$307,500

.183

29 Springfield, MO

110

$212,450

.180

30 North Wilkesboro, NC

476

$223,750

.178

31 Bowling Green, KY

249

$258,725

.176

32 Seneca, SC

454

$299,050

.174

33 Mount Vernon-Anacortes, WA

329

$482,300

.174

34 Casper, WY

428

$270,050

.174

35 Johnson City, TN

204

$222,275

.170

36 Battle Creek, MI

298

$144,950

.169

37 Aberdeen, WA

477

$221,875

.169

38 Clearlake, CA

489

$335,050

.167

39 Seattle-Tacoma-Bellevue, WA

14

$582,370

.165

40 Florence-Muscle Shoals, AL

263

$189,375

.164

41 Warner Robins, GA

229

$212,450

.164

42 Danville, IL

449

$79,550

.161

43 Salem, OR

139

$356,450

.159

44 Centralia, WA

461

$289,500

.158

45 Hermiston-Pendleton, OR

451

$240,550

.153

46 Racine, WI

220

$248,750

.152

47 Klamath Falls, OR

480

$239,950

.151

48 Rockford, IL

154

$149,950

.149

49 Wausau, WI

295

$194,975

.147

50 Joplin, MO

241

$154,950

.147

51 Boise City, ID

83

$337,050

.145

52 Huntsville, TX

479

$208,650

.143

53 Columbia, SC

67

$226,879

.140

54 La Crosse-Onalaska, WI-MN

293

$259,725

.139

55 St. Cloud, MN

223

$244,750

.138

56 Ogdensburg-Massena, NY

370

$127,050

.138

57 Charleston-Mattoon, IL

500

$96,700

.138

58 Manitowoc, WI

427

$129,950

.138

59 Ann Arbor, MI

142

$372,500

.137

60 Tullahoma-Manchester, TN

382

$255,050

.137

61 Greenwood, SC

401

$197,050

.136

62 Springfield, OH

297

$113,475

.135

63 San Angelo, TX

340

$225,365

.135

64 Show Low, AZ

404

$311,800

.134

65 Akron, OH

74

$169,950

.133

66 Olympia-Tumwater, WA

168

$379,549

.133

67 Dayton, OH

64

$150,050

.133

68 Rapid City, SD

265

$281,450

.132

69 Mankato-North Mankato, MN

397

$254,675

.132

70 New York-Newark-Jersey City, NY-NJ-PA

1

$542,550

.132

71 Evansville, IN-KY

156

$171,450

.132

72 Holland, MI

353

$299,950

.132

73 Elizabethtown-Fort Knox, KY

283

$192,275

.131

74 Lansing-East Lansing, MI

108

$172,450

.131

75 Greeneville, TN

478

$189,950

.130

76 Cincinnati, OH-KY-IN

28

$259,950

.130

77 Cedar Rapids, IA

169

$212,650

.128

78 Atlanta-Sandy Springs-Roswell, GA

9

$338,011

.127

79 Bloomington, IN

244

$272,450

.127

80 Green Bay, WI

157

$225,000

.125

81 Kalamazoo-Portage, MI

149

$275,050

.121

82 Asheville, NC

106

$379,675

.120

83 Sierra Vista-Douglas, AZ

319

$186,500

.120

84 Grand Rapids-Wyoming, MI

53

$289,500

.119

85 Lake Havasu City-Kingman, AZ

207

$284,975

.118

86 Hattiesburg, MS

280

$195,550

.117

87 Bellingham, WA

203

$444,300

.116

88 Wooster, OH

356

$165,925

.116

89 Erie, PA

167

$145,275

.115

90 Fort Wayne, IN

119

$197,711

.114

91 Killeen-Temple, TX

127

$202,500

.114

92 Clarksville, TN-KY

174

$210,550

.114

93 Shawnee, OK

481

$155,025

.114

94 Spokane-Spokane Valley, WA

98

$289,475

.114

95 Appleton, WI

194

$210,000

.114

96 Rochester, MN

202

$299,950

.113

97 Jacksonville, NC

239

$194,700

.113

98 Chattanooga, TN-GA

99

$272,450

.112

99 Elkhart-Goshen, IN

228

$199,950

.112

100 Winston-Salem, NC

80

$214,000

.112

Banks Assests and Liabilities

Before the financial crisis, banks generated many real estate loans to make easy money. It once accounted for more than 60% of the total loan amount. However, after the financial crisis, real estate loans accounted for less than 55%. The rate of increase in business lending has decreased and it rises again this year. This means that the US economy is good, but real estate loans and personal consumption loans are showing a gradual decline. This is evidence that real estate is slowly cooling down

The following chart displays the percentage of each loan in total loans which is combination of business loans, real estate loans and consumer loans.

The share of real estate loans in total loans exceeded more than 60% during the housing boom before the financial crisis, but declined to 55%. The banks generated more the real estate loans than business loans during the housing boom for easy money. However, that the lending pattern has been changing since the financial crisis in 2008. The share of business loans in total loans is still below the level before the real estate boom which began in 2001.

The following chart shows the monthly changes of each loans in last five years. The monthly growth rate of real estate loans is gradually slowing.

The following chart shows the annual changes for three loans: Business loans, Real Estate loans, and Consumer loans. The growth rate of business loans increases while real estate and consumer loans decreases in 2018.

The following chart shows the business loans.

The following chart shows the real estate loans.

The following chart shows the consumer loans.

R-Codes:

library(Quandl)

library(ggplot2)

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

library (quantmod)

library(psych)

library(plotly) #install.package(plotly)

getSymbols(c(‘TOTLL’,’TOTBKCR’,’BUSLOANS’,’REALLN’,’CONSUMER’), src=’FRED’)

Loans<-merge(BUSLOANS,REALLN,CONSUMER)

names(Loans)<-c(‘Business Loans’,’Real Estate Loans’,’Consumer Loans’)

Tot_Loans=BUSLOANS+REALLN+CONSUMER

Per_BUSLOANS=BUSLOANS/Tot_Loans

Per_REALLN=REALLN/Tot_Loans

Per_CONSUMER=CONSUMER/Tot_Loans

Per_Loans<-merge(Per_BUSLOANS,Per_REALLN, Per_CONSUMER)

summary(Per_Loans)

tail(Per_Loans)

dev.off()

Monthly_Loans<-merge(monthlyReturn(BUSLOANS), monthlyReturn(REALLN), monthlyReturn(CONSUMER))

names(Monthly_Loans)<-c(‘Business Loans’,’Real Estate Loans’,’Consumer Loans’)

summary(Monthly_Loans)

tail(Monthly_Loans)

plot(x = last(Monthly_Loans, “5 years”), xlab = “Year”, ylab = “Index”,

main = “Dollar”, col=myColors, screens = 1)

legend(x = “topleft”, legend = c(‘Business Loans’,’Real Estate Loans’,’Consumer Loans’),

lty = 1, col=myColors)

Annual_Loans<-merge(annualReturn(BUSLOANS), annualReturn(REALLN), annualReturn(CONSUMER))

names(Annual_Loans)<-c(‘Business Loans’,’Real Estate Loans’,’Consumer Loans’)

plot(x = last(Annual_Loans, “30 years”), xlab = “Year”, ylab = “Index”,

main = “Dollar”, col=myColors, screens = 1)

legend(x = “topleft”, legend = c(‘Business Loans’,’Real Estate Loans’,’Consumer Loans’),

lty = 1, col=myColors)

plot(x = last(Per_Loans, “30 years”), xlab = “Year”, ylab = “Index”,

main = “Dollar”, col=myColors, screens = 1)

legend(x = “topleft”, legend = c(‘Business Loans’,’Real Estate Loans’,’Consumer Loans’),

lty = 1, col=myColors)

myColors <- c(“red”, “blue”,”black”)

plot(x = last(Loans, “30 years”), xlab = “Year”, ylab = “Index”,

main = “Dollar”, col=myColors, screens = 1)

legend(x = “topleft”, legend = c(‘Business Loans’,’Real Estate Loans’,’Consumer Loans’),

lty = 1, col=myColors)

plot(x = last(Per_Loans, “30 years”), xlab = “Year”, ylab = “Index”,

main = “Dollar”, col=myColors, screens = 1)

legend(x = “topleft”, legend = c(‘Business Loans’,’Real Estate Loans’,’Consumer Loans’),

lty = 1, col=myColors)

# Business Loans

par(mfrow=c(3,1))

plot(last(BUSLOANS, “30 years”), main=”Business Loans”, col=”black”)

barplot(last(monthlyReturn(BUSLOANS), “30 years”), main=”Monthly Changes”, col=”red”)

barplot(last(annualReturn(BUSLOANS), “30 years”), main=” Annual Changes “, col=”blue”)

# Real Estate Loans

par(mfrow=c(3,1))

plot(last(REALLN, “30 years”), main=”Real Estate Loans”, col=”black”)

barplot(last(monthlyReturn(REALLN), “30 years”), main=”Monthly Changes”, col=”red”)

barplot(last(annualReturn(REALLN), “30 years”), main=” Annual Changes “, col=”blue”)

# Consumer Loans

par(mfrow=c(3,1))

plot(last(CONSUMER, “30 years”), main=”Consumer Loans”, col=”black”)

barplot(last(monthlyReturn(CONSUMER), “30 years”), main=”Monthly Changes”, col=”red”)

barplot(last(annualReturn(CONSUMER), “30 years”), main=” Annual Changes “, col=”blue”)

Analyzing of Assets in 2018 as of 7-16-2018

올해 오일과 나스닥이 가장높은 이익률을 보이고 있습니다 그리고 비트코인이 가장낮은 이익률을 보였습니다.

올해 최고의 투자처는 오일과 나스닥(기술주) 입니다.

비트코인은 거의 다른자산들과 연관성이 없습니다. 그래서 기관 투자자들이 관심가지고 있는 이유입니다.

달러자산은 다른자산들과는 반대로움직입니다. 연준의 금리인상으로 유동성이 적어짐으로 다른자신들의 가치가 낮아집니다.

금은 달러와 반대로 움직이지만 주식시장과는 낮은 긍정적인 연관성이 있습니다.

주식시장은 달러와 반대로 움직이지만 오일과 금과는 낮은 긍정적인 연관성이 있습니다.

The following chart shows the cumulative returns of the assets: Bitcoin, Gold, Dollar, Oil, S&P500, and NASDAQ.

Oil and Nasdaq have been providing the highest returns while Bitcoin provides the lowest return.

Bitcoin is almost never associated with any other asset. Dollar asset moves against other assets. Gold moves against the dollar but has a low correlation with the stock market. The stock market moves against the dollar, but it has a low positive correlation with oil and gold.

The following chart shows the correlation matrix of all assets.

The Bitcoin has no correlation with other assets in 2018,

The Dollar is negatively correlated with other assets in 2018.

The Gold is negatively correlated with the dollar, but has positive correlation with SP500 in 2018.

The SP500 and NASDAQ have negative correlation with the dollar while have low positive correlation with oil and gold in 2018.

 

 

R codes to generate:

 

require(IKTrading)
require(quantmod)
require(PerformanceAnalytics)
library(fPortfolio)

getSymbols(c(
“CBBTCUSD”, #Bitcoin
“GOLDAMGBD228NLBM”, #Gold
“DTWEXB”, #Exchange Rate
“DCOILWTICO”, #WTI Oil
“SP500”, #S&P 500
“NASDAQCOM” #NASDAQ

),

from=”2015-01-01″, src=’FRED’)
Assets=na.omit(merge(CBBTCUSD,GOLDAMGBD228NLBM,DTWEXB,DCOILWTICO,SP500,NASDAQCOM))

names(Assets)<-c(‘Bitcoin’,’Gold’,’Dollar’,’Oil’,’SP500′,’NASDAQ’)
Assets_changes<-Assets/lag(Assets)-1

Assets2018=window(Assets_changes,start=as.Date(“2018-01-01”), end=as.Date(“2018-12-31”))
#Chart for Performance Summary
charts.PerformanceSummary(Assets2018, main=’Assets Changes in 2018′,
wealth.index = TRUE)
cor.distance <- cor(Assets2018)
corrplot::corrplot(cor.distance)

cor.distance

table.AnnualizedReturns(Assets2018, scale=252, Rf=0.005/252)
myColors <- c(“red”, “darkblue”,”brown”,”green”,”black”,”blue”)
plot(x = last(Assets_changes, “1 years”), xlab = “Year”, ylab = “Index”,
main = “Dollar”, col=myColors, screens = 1)
legend(x = “topright”, legend = c(‘Bitcoin’,’Gold’,’Dollar’,’Oil’,’SP500′,’NASDAQ’),
lty = 1, col=myColors)