# REITs for Everybody Might Now Mean REITs for Nobody

**Timely Portfolio**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

**THIS IS MY OPINION AND ANALYSIS AND IS NOT INVESTMENT ADVICE. YOU ARE RESPONSIBLE FOR YOUR OWN GAINS AND LOSSES.**

I think REITs traditionally attract conservative dividend investors (grandparents), but due to their recent behavior, REITs also attract beta chasers (hedge funds and traders). This additional demand has made REITs overvalued and even more volatile. Both sets of REIT buyers might not want to buy now.

In terms of attractiveness to the traditional conservative dividend buyer, REITs seem unattractive on both an absolute and relative yield basis.

From TimelyPortfolio |

From TimelyPortfolio |

The newly found volatility of REITs for the beta chaser works well in a bullish stock market, but of course works in reverse on the downside. This volatility also could easily scare the conservative long-term dividend buyer already concerned about low yields.

From TimelyPortfolio |

From TimelyPortfolio |

From TimelyPortfolio |

From TimelyPortfolio |

From TimelyPortfolio |

One way to potentially dampen volatility would be to use a momentum type system as suggested by The Aleph Blog on REITs How to Make More Returns on REITs. Results are pretty good. Like the author, I was skeptical about his approach, since we would have no advance knowledge of momentum quintiles in 1971, but interestingly if we use the Dow Jones Industrial Average momentum quintiles 1896-1971, we would get very similar out of sample REIT results.

From TimelyPortfolio |

From TimelyPortfolio |

Please let me know what you think.

require(quantmod) require(PerformanceAnalytics) #get NAREIT data #I like NAREIT since I get back to 1971 #much easier though to get Wilshire REIT from FRED #also it is daily instead of monthly #getSymbols("WILLREITIND",src="FRED") will do this require(gdata) reitURL <- "http://returns.reit.com/returns/MonthlyHistoricalReturns.xls" reitExcel <- read.xls(reitURL,sheet="Data",pattern="All REITs",stringsAsFactors=FALSE) #clean up dates so we can use xts functionality later datetoformat <- reitExcel[,1] datetoformat <- paste(substr(datetoformat,1,3),"-01-",substr(datetoformat,5,6),sep="") datetoformat <- as.Date(datetoformat,format="%b-%d-%y") reitExcel[,1] <- datetoformat #############now start the yield analysis##################### #get REIT yield reitYield <- as.xts(as.numeric(reitExcel[4:NROW(reitExcel),7]), order.by=reitExcel[4:NROW(reitExcel),1]) ######get BAA and 10y from Fed to compare getSymbols("BAA",src="FRED") getSymbols("GS10",src="FRED") ######get SP500 yield from some multpl.com ##fantastic site with easily accessible historical information spYield <- read.csv("http://www.multpl.com/s-p-500-dividend-yield/s-p-500-dividend-yield.csv") spYield <- as.xts(spYield[,2],order.by=as.Date(spYield[,1])) yieldCompare <- na.omit(merge(reitYield,spYield,BAA,GS10)) chart.TimeSeries(yieldCompare, legend.loc = "topleft",cex.legend=1.2,lwd=3, main="Yield Comparison of REITs with S&P500, BAA Yield, and US 10y Yield", colorset = c("cadetblue","darkolivegreen3","goldenrod","gray70")) #get yield spread information yieldSpread <- yieldCompare[,1:3] yieldSpread[,1] <- yieldCompare[,1]-yieldCompare[,2] yieldSpread[,2] <- yieldCompare[,1]-yieldCompare[,3] yieldSpread[,3] <- yieldCompare[,1]-yieldCompare[,4] colnames(yieldSpread) <- c("REIT Yield - S&P500 Yield", "REIT Yield - BAA Yield","REIT Yield - US 10y Yield") chart.TimeSeries(yieldSpread, legend.loc = "topleft",cex.legend=1.2,lwd=3, main="Yield Spreads of REITs with S&P500, BAA Yield, and US 10y Yield", colorset = c("cadetblue","darkolivegreen3","goldenrod")) #############now start the return analysis################### #shift colnames over 1 colnames(reitExcel) <- colnames(reitExcel)[c(1,1:(NCOL(reitExcel)-1))] #get dates and return columns reitData <- reitExcel[,c(3,24,38)] #name columns colnames(reitData) <- c(paste(colnames(reitExcel)[c(3,24,38)],".Total.Return",sep="")) reitData <- reitData[3:NROW(reitData),] #erase commas col2cvt <- 1:NCOL(reitData) reitData[,col2cvt] <- lapply(reitData[,col2cvt],function(x){as.numeric(gsub(",", "", x))}) #create xts reitData <- as.xts(reitData,order.by=reitExcel[3:NROW(reitExcel),1]) #######get sp500 to compare beta and other measures getSymbols("SP500",src="FRED") SP500 <- to.monthly(SP500)[,4] #get 1st of month to align when we merge index(SP500) <- as.Date(index(SP500)) #merge REIT and S&p returnCompare <- na.omit(merge(reitData,SP500)) returnCompare <- ROC(returnCompare,n=1,type="discrete") charts.RollingRegression(returnCompare[, 1:3], returnCompare[,4], width=36,lwd = 3,legend.loc = "topleft",cex.legend=1.2, main="NAREIT REIT Indexes Compared to the S&P 500 36 month Rolling", colorset=c("cadetblue","darkolivegreen3","goldenrod")) chart.RollingPerformance(returnCompare, FUN="Return.annualized",width=36,lwd = 3,legend.loc = "topleft",cex.legend=1.2, main="NAREIT REIT Indexes Compared to the S&P 500 36 month Rolling Return", colorset=c("cadetblue","darkolivegreen3","goldenrod","gray70")) chart.RiskReturnScatter(returnCompare["1971::2003"], lwd = 3,legend.loc = "topleft",cex.legend=1.2, main="NAREIT REIT Indexes Compared to the S&P 500 1971-2003", colorset=c("cadetblue","darkolivegreen3","goldenrod","gray70")) chart.RiskReturnScatter(returnCompare["2004::"], lwd = 3,legend.loc = "topleft",cex.legend=1.2, main="NAREIT REIT Indexes Compared to the S&P 500 Since 2004", colorset=c("cadetblue","darkolivegreen3","goldenrod","gray70")) charts.PerformanceSummary(returnCompare,ylog=TRUE, lwd = 3,legend.loc = "topleft",cex.legend=1.2, main="NAREIT REIT Indexes Compared to the S&P 500", colorset=c("cadetblue","darkolivegreen3","goldenrod","gray70")) #############now start the bucket analysis################### #bucket momentum as described by Aleph Blog #get 10 month moving average #set up avg with same as reitData avg <- reitData[,1:3] avg <- as.data.frame(avg) avg[,1:3] <- lapply(reitData[,1:3],runMean,n=10) avg <- as.xts(avg) #get % above 10 month moving average momscore <- reitData/avg-1 #break into 5 evenly distributed by frequency quintiles #get signal into 3 column xts signal <- momscore for(i in 1:3) { breaks <- quantile(momscore[,i], probs = seq(0, 1, 0.20),na.rm=TRUE) #use default labels=TRUE to see how this works buckets <- cut(momscore[,i], include.lowest=TRUE, breaks=breaks) #store so we can see later ifelse(i==1,bucket_ranges <- names(table(buckets)), bucket_ranges <- rbind(bucket_ranges,names(table(buckets)))) #now use labels=FALSE to return 1-5 based on quintile buckets <- cut(momscore[,i], breaks=breaks, labels=FALSE) signal[,i] <- as.xts(buckets,order.by=index(signal)) #move forward by 1 } #name bucket_ranges with reit column names rownames(bucket_ranges)<-colnames(reitData) signal <- lag(signal,k=1) ret <- signal #showing my R weakness here and had to go back to for..next for(i in 1:3) { ret[,i] <- ifelse(signal[,i] >= 3,1,0) * ROC(reitData[,1],1,type="discrete") } charts.PerformanceSummary(ret,ylog=TRUE,legend.loc = "topleft",cex.legend=1.2, main="NAREIT REIT Index Data with Aleph Blog Momentum", colorset=c("cadetblue","darkolivegreen3","goldenrod")) getSymbols("DJIA",src="FRED") #examine DJIA quantiles prior to 1973 to see if we could #know in advance what possible REIT quantiles would work DJIA <- to.monthly(DJIA)["1896::1971",4] momDJIA <- DJIA/runMean(DJIA,n=10)-1 breaks <- quantile(momDJIA, probs = seq(0, 1, 0.20),na.rm=TRUE) buckets <- cut(momDJIA, breaks=breaks) table(buckets) #what happens if we apply the DJIA prior to 1973 buckets to the REITs ret <- merge(ret,ret) for(i in 1:3) { #if REITs > 3.95% above 10 month moving average then long #3.95% is the lower end of the DJIA 1896-1971 3 momentum quantile ret[,i+3] <- lag(ifelse(momscore[,i] >= 0.0395,1,0),1) * ROC(reitData[,1],1,type="discrete") } colnames(ret)[4:6]<-paste(colnames(reitData[,1:3])," with DJIA buckets",sep="") #much much better than I expected charts.PerformanceSummary(ret,ylog=TRUE,legend.loc = "topleft",cex.legend=1.2, main="NAREIT REIT Index Data with Aleph Blog Momentum but DJIA Momentum Buckets", colorset=c("cadetblue","darkolivegreen3","goldenrod", "coral","darkorchid","darkolivegreen"))

**leave a comment**for the author, please follow the link and comment on their blog:

**Timely Portfolio**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.