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Housing Index Zillow.R
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# Zillow Housing Index From 2010
### Changing the Working Directory
setwd('./Kaggle/Zillow')
### Loading the Libraries
library(forecast)
library(zoo)
library(ggplot2)
library(ggthemes)
library(tidyr)
library(dplyr)
library(readr)
### Reading the Datasets
rent <- read.csv('./price.csv')
sqft <- read.csv('./pricepersqft.csv')
### Looking at the Top 10
values=head(rent,10)
values=data.frame(t(as.matrix(values[,7:81])))
colnames(values)=rent[1:10,2]
### Monthly Percentage Change (Seattle)
suppressMessages(library(quantmod))
pct_change <- function(rent) {
nc <- ncol(rent)
ln <- colnames(rent)
meta <- rent[c(1:6)]
data <- rent[c(7:nc)]
data <- t(apply(data, 1, Delt))
rv <- cbind(meta, data)
colnames(rv) <- ln
rv[-7]
}
# Select data for the Seattle, WA metro region.
# Total of 98 places.
pc <- subset(rent, rent$Metro == 'Seattle')
pc <- pct_change(pc)
last = ncol(pc)
pc <- pc[order(pc[last], decreasing = TRUE),]
pc <- cbind(pc[c(2,5)], round(pc[(last-3):last], 3))
# Top 10 places in the Seattle region with the
# highest most recent monthly percentage change.
head(pc, n=10)
### Monthly Percentage Change (San Francisco)
pct_change <- function(rent) {
nc <- ncol(rent)
ln <- colnames(rent)
meta <- rent[c(1:6)]
data <- rent[c(7:nc)]
data <- t(apply(data, 1, Delt))
rv <- cbind(meta, data)
colnames(rv) <- ln
rv[-7]
}
# Select data for the San Francisco, CA metro region.
pc <- subset(rent, rent$Metro == 'San Francisco')
pc <- pct_change(pc)
last = ncol(pc)
pc <- pc[order(pc[last], decreasing = TRUE),]
pc <- cbind(pc[c(2,5)], round(pc[(last-3):last], 3))
# Top 10 places in the San Francisco region with the
# highest most recent monthly percentage change.
head(pc, n=10)
### Yearly Percentage Change (Sacramento)
get_range <- function(rent) {
last = ncol(rent)
n <- colnames(rent)
val <- length(n[7:last])
val <- round(val/12)
rv <- seq(last - val * 12, last, 12)
rv
}
# Select data for the Sacramento, CA metro region.
# Total of 55 places.
pc <- subset(rent, rent$Metro == 'Sacramento')
years <- get_range(pc)
pc <- cbind(pc[1:6], pc[years])
# use function defined above
pc <- pct_change(pc)
last = ncol(pc)
pc <- pc[order(pc[last], decreasing = TRUE),]
pc <- cbind(pc[c(2,5)], round(pc[(last-3):last], 2))
# Top 10 places in the Sacramento region with the
# highest most recent yearly percentage change.
head(pc, n=10)
### Yearly Percentage Change (San Francisco)
get_range <- function(rent) {
last = ncol(rent)
n <- colnames(rent)
val <- length(n[7:last])
val <- round(val/12)
rv <- seq(last - val * 12, last, 12)
rv
}
# Select data for the San Francisco, CA metro region.
pc <- subset(rent, rent$Metro == 'San Francisco')
years <- get_range(pc)
pc <- cbind(pc[1:6], pc[years])
# use function defined above
pc <- pct_change(pc)
last = ncol(pc)
pc <- pc[order(pc[last], decreasing = TRUE),]
pc <- cbind(pc[c(2,5)], round(pc[(last-3):last], 2))
# Top 10 places in the San Francisco region with the
# highest most recent yearly percentage change.
head(pc, n=10)
### Index Numbers (Los Angeles)
index_base_100 <- function(rent) {
nc <- ncol(rent)
ln <- colnames(rent)
meta <- rent[, c(1:6)]
data <- rent[7:nc]
base <- rent[7]
index <- function(x) {
x / base
}
data <- apply(data, 2, index)
data <- data.frame(data)
data <- data * 100
data <- round(data)
rv <- cbind(meta, data)
colnames(rv) <- ln
rv
}
# Select data for the Los Angeles, CA metro region.
# Total of 148 places.
# Base: November 2010 = 100
idx <- subset(rent, rent$Metro == 'Los Angeles')
idx <- index_base_100(idx)
last = ncol(idx)
s <- seq(last-36, last, 12)
idx <- idx[order(idx[last], decreasing = TRUE),]
idx <- cbind(idx[c(2,5)], idx[s])
# The top 10 places in the Los Angeles metro region
# with the largest index change over the base period.
head(idx, n=10)
#### San Francisco Region for Index Numbers
index_base_100 <- function(rent) {
nc <- ncol(rent)
ln <- colnames(rent)
meta <- rent[, c(1:6)]
data <- rent[7:nc]
base <- rent[7]
index <- function(x) {
x / base
}
data <- apply(data, 2, index)
data <- data.frame(data)
data <- data * 100
data <- round(data)
rv <- cbind(meta, data)
colnames(rv) <- ln
rv
}
# Select data for the San Francisco, CA metro region.
# Base: November 2010 = 100
idx <- subset(rent, rent$Metro == 'San Francisco')
idx <- index_base_100(idx)
last = ncol(idx)
s <- seq(last-36, last, 12)
idx <- idx[order(idx[last], decreasing = TRUE),]
idx <- cbind(idx[c(2,5)], idx[s])
# The top 10 places in the San Francisco metro region
# with the largest index change over the base period.
head(idx, n=10)
### Top 10 Cities By Population Using Time-Series Analysis
date <- seq(as.Date("2010/11/01"), as.Date("2017/01/31"),"month")
date <- as.yearmon(date)
ts=zoo(values,order.by = date)
values=fortify(ts)
values$Index=as.Date(values$Index)
autoplot(ts,facet=NULL)+
theme_minimal()+
labs(x="Time",y="Price")
forecasts=matrix(,ncol=10,nrow=11)
for(i in 1:10){
forecasts[,i]=forecast(auto.arima(ts[,i],lambda = 0,stepwise = F),h=11)$mean
}
colnames(forecasts) = rent[1:10,2]
results=rbind(values[,2:11],forecasts)
date_2 <- seq(as.Date("2010/11/01"), as.Date("2017/12/31"),"month")
date_2 <- as.yearmon(date_2)
results=zoo(results,order.by = date_2)
autoplot(results,facet=NULL)+
theme_minimal()+
labs(x="Time",y="Price")+
geom_vline(aes(xintercept=2017),size=0.2)