Objective - to determine which variables are likely to classify property into PROFIT or LOSS segment.
Dataset from https://www.edgeprop.sg/pullout/the-edge-property. I took the profit and loss transactions of property from the period of 3 weeks of October and 2 weeks of September.
setwd("E:\\interim\\property")
data <- read.csv("profit_loss.csv", header = T, stringsAsFactors = F)
#CONVERT into correct class formats
data$DISTRICT <- as.factor(data$DISTRICT)
data$MAKE <- as.factor(data$MAKE)
data$AREA <- as.numeric(data$AREA)
data$SALE_PSF <- as.numeric(data$SALE_PSF)
data$PURCHASE_PSF <- as.numeric(data$PURCHASE_PSF)
data$PROFIT_LOSS <- as.numeric(data$PROFIT_LOSS)
head(data)## PROJECT DISTRICT AREA SALE_PSF PURCHASE_PSF HOLD_YEARS MAKE
## 1 Beverly Hill 10 3778 1747 709 14.4 P
## 2 Orchard Bel Air 10 3229 1208 483 12.2 P
## 3 The Waterside 15 2433 1554 848 17.6 P
## 4 Bayshore Park 16 3531 765 331 11.6 P
## 5 Newton Suites 11 1238 1793 798 12.4 P
## 6 Mount Faber Lodge 4 2594 964 524 22.2 P
## PROFIT_LOSS ANNUALISED
## 1 3920000 6
## 2 2340000 8
## 3 1717000 4
## 4 1531112 7
## 5 1232000 7
## 6 1140000 3str(data)## 'data.frame': 100 obs. of 9 variables:
## $ PROJECT : chr "Beverly Hill" "Orchard Bel Air" "The Waterside" "Bayshore Park" ...
## $ DISTRICT : Factor w/ 18 levels "1","3","4","5",..: 7 7 11 12 8 3 16 8 16 16 ...
## $ AREA : num 3778 3229 2433 3531 1238 ...
## $ SALE_PSF : num 1747 1208 1554 765 1793 ...
## $ PURCHASE_PSF: num 709 483 848 331 798 524 637 803 924 301 ...
## $ HOLD_YEARS : num 14.4 12.2 17.6 11.6 12.4 22.2 11 15.3 8.2 16.3 ...
## $ MAKE : Factor w/ 2 levels "L","P": 2 2 2 2 2 2 2 2 2 2 ...
## $ PROFIT_LOSS : num 3920000 2340000 1717000 1531112 1232000 ...
## $ ANNUALISED : num 6 8 4 7 7 3 8 5 6 6 ...Some explanations of variables listed
PROJECT - is the name of condo.
AREA - is the square feet.
SALES_PSF - sold in 2017 psf.
PURCHASE_PSF - bought in some years back psf.
HOLD_YEARS - number of years property held before SOLD.
PROFIT_LOSS - absolute number in dollars.
Annualised - profit or loss annualised % over the years. Negative means loss and vice versa.
MAKE - this is a factor which i indicate Profit (P) or Loss (L). This is used for classification of a likely PROFIT or LOSS of Property based on other independent variables.
Which variables are CONTROLLABLE by the BUYER ?
I would say AREA, HOLDING YEARS AND PURCHASE PSF. Why ? This is because property buyer can decide how long to HOLD a property, how BIG an area to buy and at what price buyer would PURCHASE at that point in time.
Which variables are useless in this analytics ?
Condo name is useless because it cannot be used to determine profit or loss. SALES PSF can be a useful variable, but seller usually goes with market price and seller cannot dictate the price. Profit and Loss and Annualised % are DERIVED values which is useless.
Prepare Data
#create a subset of Profit and LOSS
profit <- subset(data, data$MAKE =="P")
loss <- subset(data, data$MAKE =="L")Visualization between AREA AND HOLDING YEARS
plot(profit$HOLD_YEARS, profit$AREA, pch = 20, col=2, xlim=c(1,20), ylim=c(500, 5000), main="Holding Years vs Area", xlab="Holding Years", ylab="Area")
points(loss$HOLD_YEARS, loss$AREA, col=3, pch=15)
legend("topright", legend =c("Profit", "Loss"), pch=c(20,15), col=c(2:3))
Can we use the 2 variables to classify into PROFIT and LOSS classification?
## Call:
## lda(data$MAKE ~ data$HOLD_YEARS + data$AREA, na.action = "na.omit")
##
## Prior probabilities of groups:
## L P
## 0.5 0.5
##
## Group means:
## data$HOLD_YEARS data$AREA
## L 6.666 1574.56
## P 14.394 1987.86
##
## Coefficients of linear discriminants:
## LD1
## data$HOLD_YEARS 0.2877120804
## data$AREA 0.0002344837##
## L P
## L 48 2
## P 8 42