Take-home Exercise 3

1.0 The Task

In this take-home exercise, we are required to uncover the salient patterns of the resale prices of public housing property by residential towns and estates in Singapore by using appropriate analytical visualisation techniques learned in Lesson 4: Fundamentals of Visual Analytics. Students are encouraged to apply appropriate interactive techniques to enhance user and data discovery experiences.

For the purpose of this study, the focus should be on 3-ROOM, 4-ROOM and 5-ROOM types. You can choose to focus on either one housing type or multiple housing types. The study period should be on 2022.

2.0 The Data

Resale flat princes based on registration date from Jan-2017 onwards should be used to prepare the analytical visualisation. It is available at Data.gov.sg.

3.0 Getting Started

3.1 Packages used

Before we get started, it is important for us to install the necessary R packages into R and launch these R packages into R environment.

The R packages needed for this exercise are as follows:

Show the code 
pacman::p_load(ggstatsplot, ggthemes, plotly, corrplot, lubridate, ggpubr, plotly, gganimate, viridis, ggridges, ggrepel, testthat, hmisc, tidyverse, funModeling, PMCMRplus, gifski, ggplot2,treemap )

4 Data preparation

4.1 Importing Data

Show the code 
resale_prices <- read_csv("data/aspatial/resale-flat-prices-based-on-registration-date-from-jan-2017-onwards.csv")

4.2 Data Wrangling

4.2.1 Get summary of the data

Show the code 
summary(resale_prices)
    month               town            flat_type            block          
 Length:146872      Length:146872      Length:146872      Length:146872     
 Class :character   Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character  
                                                                            
                                                                            
                                                                            
 street_name        storey_range       floor_area_sqm   flat_model       
 Length:146872      Length:146872      Min.   : 31.0   Length:146872     
 Class :character   Class :character   1st Qu.: 82.0   Class :character  
 Mode  :character   Mode  :character   Median : 94.0   Mode  :character  
                                       Mean   : 97.6                     
                                       3rd Qu.:113.0                     
                                       Max.   :249.0                     
 lease_commence_date remaining_lease     resale_price    
 Min.   :1966        Length:146872      Min.   : 140000  
 1st Qu.:1985        Class :character   1st Qu.: 358000  
 Median :1996        Mode  :character   Median : 448000  
 Mean   :1996                           Mean   : 478316  
 3rd Qu.:2007                           3rd Qu.: 565800  
 Max.   :2019                           Max.   :1418000

4.2.2 Check if any missing values

Show the code 
skimr::skim(resale_prices)
Data summary
Name resale_prices
Number of rows 146872
Number of columns 11
_______________________
Column type frequency:
character 8
numeric 3
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
month 0 1 7 7 0 74 0
town 0 1 5 15 0 26 0
flat_type 0 1 6 16 0 7 0
block 0 1 1 4 0 2654 0
street_name 0 1 7 20 0 564 0
storey_range 0 1 8 8 0 17 0
flat_model 0 1 4 22 0 21 0
remaining_lease 0 1 8 18 0 659 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
floor_area_sqm 0 1 97.60 24.09 31 82 94 113 249 ▃▇▃▁▁
lease_commence_date 0 1 1995.52 13.73 1966 1985 1996 2007 2019 ▂▇▅▆▇
resale_price 0 1 478315.95 165533.82 140000 358000 448000 565800 1418000 ▆▇▂▁▁

4.2.3 EDA

town

Show the code 
freq(data=resale_prices, 
     input = 'town')

flat_type

Show the code 
freq(data=resale_prices, 
     input = 'flat_type')

storey_range

Show the code 
freq(data=resale_prices, 
     input = 'storey_range')

floor_area_sqm

Show the code 
freq(data=resale_prices, 
     input = 'floor_area_sqm')

lease_commence_date

Show the code 
freq(data=resale_prices, 
     input = 'lease_commence_date')
Show the code 
unique(resale_prices$remaining_lease)

resale_price

Show the code 
gghistostats(
  data = resale_prices,
  x = resale_price,
  type = "bayes",
  test.value = 60,
  xlab = "Resale price"
) +
  theme_minimal()

4.2.4 Derive new variables

Separate the years and months.

Show the code 
resale_prices_1 <- resale_prices %>% 
  separate(month, c("Year", "Month"), sep = "-")

Convert string to integer.

Show the code 
resale_prices_1$Year <- strtoi(resale_prices_1$Year)
resale_prices_1$Month <- strtoi(resale_prices_1$Month)

convert reaming_lease to reaming_lease_year.

Show the code 
resale_prices_2 <- bind_cols(resale_prices_1,
                              (str_split_fixed(resale_prices_1$remaining_lease, 
                                           " ", 
                                           4) %>% 
                             data.frame() %>% 
                             rename(year_lease = X1, 
                                    omit1 = X2, 
                                    month_lease = X3, 
                                    omit2 = X4) %>%
                             select (-c(omit1, omit2)) %>%
                             mutate(month_lease = 
                                      ifelse(month_lease == "", 0, 
                                             month_lease)) %>% 
                             mutate_if(is.character, as.numeric))
                          ) %>%
  mutate (remaining_lease_years = round(year_lease + month_lease/12,2))

derive new variables price_psm,price_thousand,property_age.

Show the code 
resale_prices_3 <- resale_prices_2 %>%
  mutate(price_psm = round(resale_price / floor_area_sqm)) %>%
  mutate(price_thousand = round(resale_price / 1000))

Filter by year 2022

Show the code 
resale_prices_2022 <- resale_prices_3 %>%
  filter(Year == "2022", flat_type %in% c("3 ROOM", "4 ROOM", "5 ROOM"))

4.2.5 Rview output

Show the code 
unique(resale_prices_2022$Year)
[1] 2022
Show the code 
unique(resale_prices_2022$flat_type)
[1] "3 ROOM" "4 ROOM" "5 ROOM"
Show the code 
summary(resale_prices_2022)
      Year          Month            town            flat_type        
 Min.   :2022   Min.   : 1.000   Length:24372       Length:24372      
 1st Qu.:2022   1st Qu.: 3.000   Class :character   Class :character  
 Median :2022   Median : 5.000   Mode  :character   Mode  :character  
 Mean   :2022   Mean   : 6.047                                        
 3rd Qu.:2022   3rd Qu.:10.000                                        
 Max.   :2022   Max.   :12.000                                        
                NA's   :4450                                          
    block           street_name        storey_range       floor_area_sqm  
 Length:24372       Length:24372       Length:24372       Min.   : 51.00  
 Class :character   Class :character   Class :character   1st Qu.: 81.00  
 Mode  :character   Mode  :character   Mode  :character   Median : 93.00  
                                                          Mean   : 94.08  
                                                          3rd Qu.:110.00  
                                                          Max.   :159.00  
                                                                          
  flat_model        lease_commence_date remaining_lease     resale_price    
 Length:24372       Min.   :1967        Length:24372       Min.   : 200000  
 Class :character   1st Qu.:1985        Class :character   1st Qu.: 428000  
 Mode  :character   Median :1998        Mode  :character   Median : 515000  
                    Mean   :1997                           Mean   : 536394  
                    3rd Qu.:2014                           3rd Qu.: 610000  
                    Max.   :2019                           Max.   :1418000  
                                                                            
   year_lease     month_lease     remaining_lease_years   price_psm    
 Min.   :43.00   Min.   : 0.000   Min.   :43.08         Min.   : 3333  
 1st Qu.:61.00   1st Qu.: 3.000   1st Qu.:61.75         1st Qu.: 4838  
 Median :74.00   Median : 6.000   Median :74.58         Median : 5368  
 Mean   :74.06   Mean   : 5.557   Mean   :74.52         Mean   : 5736  
 3rd Qu.:91.00   3rd Qu.: 9.000   3rd Qu.:91.42         3rd Qu.: 6176  
 Max.   :96.00   Max.   :11.000   Max.   :96.42         Max.   :14731  
                                                                       
 price_thousand  
 Min.   : 200.0  
 1st Qu.: 428.0  
 Median : 515.0  
 Mean   : 536.4  
 3rd Qu.: 610.0  
 Max.   :1418.0

5.0 Data Visualisation

5.1 Correlation analysis

Show the code 
resale_prices_2022_cor <- resale_prices_2022%>%
  select (1:2, 8, 10, 15:17)
Show the code 
cluster_vars.cor = cor(resale_prices_2022_cor[,3:6])
corrplot.mixed(cluster_vars.cor,
         lower = "ellipse", 
               upper = "number",
               tl.pos = "lt",
               diag = "l",
               tl.col = "black")

There is no strong correlation amongst the above variables.

5.2 Normality analysis

5.2.1 Overall check Prices by flat_type

Show the code 
set.seed(1234)

resale_prices_2022_3room <- resale_prices_2022 %>%
  filter(flat_type == "3 ROOM")
resale_prices_2022_4room <- resale_prices_2022 %>%
  filter(flat_type == "4 ROOM")
resale_prices_2022_5room <- resale_prices_2022 %>%
  filter(flat_type == "5 ROOM")

p1 <- gghistostats(
  data = resale_prices_2022_3room,
  x = price_thousand,
  type = "bayes",
  test.value = 60,
  xlab = "3 room Price in Thousands") +
  
  theme_minimal() 
        
p2 <- gghistostats(
  data = resale_prices_2022_4room,
  x = price_thousand,
  type = "bayes",
  test.value = 60,
  xlab = "4 room Price in Thousands") +
  
  theme_minimal() 

p3 <- gghistostats(
  data = resale_prices_2022_5room,
  x = price_thousand,
  type = "bayes",
  test.value = 60,
  xlab = "5 room Price in Thousands") +
  
  theme_minimal() 
ggarrange(p1,p2,p3, ncol = 3, nrow = 1)

The chart shows, overall, 3 Room price mean is about 350k, 4 Room price mean is about 464K, 5 Room price mean is about 555k.

5.3 Proportion and numbers of flat types in Singapore

5.3.1 Derive new varibles

Show the code 
flat_type_proportion<- resale_prices_2022 %>% 
  group_by(town, flat_type) %>% 
  summarise(
  
    n=n())%>% 
  mutate(pct_flat = round(n/sum(n)*100)) 
head(flat_type_proportion)
# A tibble: 6 × 4
# Groups:   town [2]
  town       flat_type     n pct_flat
  <chr>      <chr>     <int>    <dbl>
1 ANG MO KIO 3 ROOM      528       53
2 ANG MO KIO 4 ROOM      305       31
3 ANG MO KIO 5 ROOM      154       16
4 BEDOK      3 ROOM      555       44
5 BEDOK      4 ROOM      462       36
6 BEDOK      5 ROOM      253       20

5.3.2 Proportion of flat types in Singapore

Show the code 
plot_ly(flat_type_proportion, labels = ~flat_type, values = ~pct_flat, type = 'pie') %>% layout(title = 'Pie chart by flat types in Singapore in 2022',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

The above chart shows, the top 1 proportion flat type is 4 Room, followed by 3 Room and 5 Room.

5.3.4 Derive new varibles

Show the code 
flat_type_number<- resale_prices_2022 %>% 
  group_by(town, flat_type) %>% 
  summarise(
  
    n=n())

head(flat_type_number)
# A tibble: 6 × 3
# Groups:   town [2]
  town       flat_type     n
  <chr>      <chr>     <int>
1 ANG MO KIO 3 ROOM      528
2 ANG MO KIO 4 ROOM      305
3 ANG MO KIO 5 ROOM      154
4 BEDOK      3 ROOM      555
5 BEDOK      4 ROOM      462
6 BEDOK      5 ROOM      253

5.3.5 Numbers of flat types by planning areas in Singapore

Show the code 
ggplot(flat_type_number,aes(y = reorder(town,n),x = n,fill = flat_type))+
geom_bar(stat = 'identity')+
coord_cartesian(xlim = c(0,3000))+
scale_fill_brewer(palette = "RdBu")+
labs(title = "Number of flats by flat type and by planning area",
       x = "Number of flats",
       y = "Planning areas in Singapore",
       fill = "flat_type")

From the above chart, we can see the top 3 areas which have most flats are SENGKANG, PUNGGOL and YISHUN.

5.4 resale prices by planning areas

5.4.1 Overview of resale prices by planning areas by months

Show the code 
ggplot(data = resale_prices_2022, aes(x = price_thousand, y = town, fill = after_stat(x))) +
  
  geom_density_ridges_gradient(scale = 3, rel_min_height = 0.01) +
  
  theme_minimal() +
  
  labs(title = 'Resale Prices by Planning Area in 2022, Month: {frame_time}') +
  
  theme(legend.position="none",
 
  plot.title = element_text(face = "bold", size = 12),
  
  axis.title.x = element_text(size = 10, hjust = 1),
  axis.title.y = element_text(size = 10, angle = 360),
  axis.text = element_text(size = 8)) +
  
  scale_fill_viridis(name = "price_thousand", option = "D") +
  
  transition_time(resale_prices_2022$Month) +
  ease_aes('linear')

5.4.2 Average resale prices by room type by planning areas

Visualizing the uncertainty of point estimates

Show the code 
prices_mean_by_town <- resale_prices_2022 %>% 
  group_by(town) %>% 
  summarise(
    flat_type,
    n=n(),
    mean = round(mean(price_psm)),
    sd=sd(price_psm))%>%
 
  mutate(se=sd/sqrt(n-1))
Show the code 
knitr::kable(head(prices_mean_by_town), format = 'html')
town flat_type n mean sd se
ANG MO KIO 3 ROOM 987 5940 1510.833 48.11473
ANG MO KIO 3 ROOM 987 5940 1510.833 48.11473
ANG MO KIO 3 ROOM 987 5940 1510.833 48.11473
ANG MO KIO 3 ROOM 987 5940 1510.833 48.11473
ANG MO KIO 3 ROOM 987 5940 1510.833 48.11473
ANG MO KIO 3 ROOM 987 5940 1510.833 48.11473
Show the code 
type <- '3 ROOM'
prices_by_town <- resale_prices_2022 %>% filter(flat_type==type) %>% group_by(town)  
ggplot(prices_by_town, aes(x=reorder(town, price_psm), y=price_psm, color = flat_type)) +
  geom_boxplot() +
  labs(title="Flat Resale Price (psm) by Town for 3 Room ", 
       x="Town", 
       y="Resale Price ($ psm)") +
  theme_classic() +
  theme(plot.title = element_text(size=16, hjust=0.5),
        axis.title.x = element_text(size=15),
        axis.text.x = element_text(angle=45, hjust=1),
        axis.title.y = element_text(size=15))

Show the code 
prices_mean_by_town%>% 
  filter(flat_type==type) 
# A tibble: 6,345 × 6
# Groups:   town [26]
   town       flat_type     n  mean    sd    se
   <chr>      <chr>     <int> <dbl> <dbl> <dbl>
 1 ANG MO KIO 3 ROOM      987  5940 1511.  48.1
 2 ANG MO KIO 3 ROOM      987  5940 1511.  48.1
 3 ANG MO KIO 3 ROOM      987  5940 1511.  48.1
 4 ANG MO KIO 3 ROOM      987  5940 1511.  48.1
 5 ANG MO KIO 3 ROOM      987  5940 1511.  48.1
 6 ANG MO KIO 3 ROOM      987  5940 1511.  48.1
 7 ANG MO KIO 3 ROOM      987  5940 1511.  48.1
 8 ANG MO KIO 3 ROOM      987  5940 1511.  48.1
 9 ANG MO KIO 3 ROOM      987  5940 1511.  48.1
10 ANG MO KIO 3 ROOM      987  5940 1511.  48.1
# … with 6,335 more rows
Show the code 
ggplot(prices_mean_by_town) +
  geom_errorbar(
    aes(x=reorder(town,mean), 
        ymin=mean-se, 
        ymax=mean+se), 
    width=0.2, 
    colour="black", 
    alpha=0.9, 
    size=1.5) +
  geom_point(aes
           (x=town, 
            y=mean), 
           stat="identity", 
           color="red",
           size = 1.5,
           alpha=1) +

  theme_classic() +
  theme(plot.title = element_text(size=16, hjust=0.5),
        axis.title.x = element_text(size=12),
        axis.text.x = element_text(angle=60, hjust=1),
        axis.title.y = element_text(size=12)) + 
  ggtitle("Standard error of 3 Room prices_mean_by_town")

Show the code 
ggarrange(prices_by_town, prices_mean_by_town,ncol = 1, nrow =2 )

The above charts are showing CENTRAL AREA, QUEENSTOWN and KALANG are the top 3 areas which have the higest mean price with 90% of confidence interval.

Show the code 
type <- '4 ROOM'
prices_by_town <- resale_prices_2022 %>% filter(flat_type==type) %>% group_by(town)  
ggplot(prices_by_town, aes(x=reorder(town, price_psm), y=price_psm, color = flat_type)) +
  geom_boxplot() +
  labs(title="Flat Resale Price (psm) by Town for 4 Room ", 
       x="Town", 
       y="Resale Price ($ psm)") +
  theme_classic() +
  theme(plot.title = element_text(size=16, hjust=0.5),
        axis.title.x = element_text(size=15),
        axis.text.x = element_text(angle=45, hjust=1),
        axis.title.y = element_text(size=15))

Show the code 
prices_mean_by_town%>% 
  filter(flat_type==type) 
# A tibble: 11,311 × 6
# Groups:   town [26]
   town       flat_type     n  mean    sd    se
   <chr>      <chr>     <int> <dbl> <dbl> <dbl>
 1 ANG MO KIO 4 ROOM      987  5940 1511.  48.1
 2 ANG MO KIO 4 ROOM      987  5940 1511.  48.1
 3 ANG MO KIO 4 ROOM      987  5940 1511.  48.1
 4 ANG MO KIO 4 ROOM      987  5940 1511.  48.1
 5 ANG MO KIO 4 ROOM      987  5940 1511.  48.1
 6 ANG MO KIO 4 ROOM      987  5940 1511.  48.1
 7 ANG MO KIO 4 ROOM      987  5940 1511.  48.1
 8 ANG MO KIO 4 ROOM      987  5940 1511.  48.1
 9 ANG MO KIO 4 ROOM      987  5940 1511.  48.1
10 ANG MO KIO 4 ROOM      987  5940 1511.  48.1
# … with 11,301 more rows
Show the code 
ggplot(prices_mean_by_town) +
  geom_errorbar(
    aes(x=reorder(town,mean), 
        ymin=mean-se, 
        ymax=mean+se), 
    width=0.2, 
    colour="black", 
    alpha=0.9, 
    size=1.5) +
  geom_point(aes
           (x=town, 
            y=mean), 
           stat="identity", 
           color="red",
           size = 1.5,
           alpha=1) +
  theme_classic() +
  theme(plot.title = element_text(size=16, hjust=0.5),
        axis.title.x = element_text(size=12),
        axis.text.x = element_text(angle=60, hjust=1),
        axis.title.y = element_text(size=12)) + 
  ggtitle("Standard error of 4 Room prices_mean_by_town")

Show the code 
ggarrange(prices_by_town, prices_mean_by_town,ncol = 1, nrow =2 )

The above charts are showing CENTRAL AREA, QUEENSTOWN and KALANG are the top 3 areas which have the higest mean price with 90% of confidence interval.

Show the code 
type <- '5 ROOM'
prices_by_town <- resale_prices_2022 %>% filter(flat_type==type) %>% group_by(town)  
ggplot(prices_by_town, aes(x=reorder(town, price_psm), y=price_psm, color = flat_type)) +
  geom_boxplot() +
  labs(title="Flat Resale Price (psm) by Town for 5 Room ", 
       x="Town", 
       y="Resale Price ($ psm)") +
  theme_classic() +
  theme(plot.title = element_text(size=16, hjust=0.5),
        axis.title.x = element_text(size=15),
        axis.text.x = element_text(angle=45, hjust=1),
        axis.title.y = element_text(size=15))

Show the code 
prices_mean_by_town%>% 
  filter(flat_type==type) 
# A tibble: 6,716 × 6
# Groups:   town [26]
   town       flat_type     n  mean    sd    se
   <chr>      <chr>     <int> <dbl> <dbl> <dbl>
 1 ANG MO KIO 5 ROOM      987  5940 1511.  48.1
 2 ANG MO KIO 5 ROOM      987  5940 1511.  48.1
 3 ANG MO KIO 5 ROOM      987  5940 1511.  48.1
 4 ANG MO KIO 5 ROOM      987  5940 1511.  48.1
 5 ANG MO KIO 5 ROOM      987  5940 1511.  48.1
 6 ANG MO KIO 5 ROOM      987  5940 1511.  48.1
 7 ANG MO KIO 5 ROOM      987  5940 1511.  48.1
 8 ANG MO KIO 5 ROOM      987  5940 1511.  48.1
 9 ANG MO KIO 5 ROOM      987  5940 1511.  48.1
10 ANG MO KIO 5 ROOM      987  5940 1511.  48.1
# … with 6,706 more rows
Show the code 
ggplot(prices_mean_by_town) +
  geom_errorbar(
    aes(x=reorder(town,mean), 
        ymin=mean-se, 
        ymax=mean+se), 
    width=0.2, 
    colour="black", 
    alpha=0.9, 
    size=1.5) +
  geom_point(aes
           (x=town, 
            y=mean), 
           stat="identity", 
           color="red",
           size = 1.5,
           alpha=1) +

  theme_classic() +
  theme(plot.title = element_text(size=16, hjust=0.5),
        axis.title.x = element_text(size=12),
        axis.text.x = element_text(angle=60, hjust=1),
        axis.title.y = element_text(size=12)) + 
  ggtitle("Standard error of 5 Room prices_mean_by_town")

Show the code 
ggarrange(prices_by_town, prices_mean_by_town,ncol = 1, nrow =2 )

The above charts are showing CENTRAL AREA, QUEENSTOWN and KALANG are the top 3 areas which have the higest mean price with 90% of confidence interval.

5.5 Resale price by remaining_lease_year

Show the code 
type <- "3 ROOM"
ggscatterstats(
 data = resale_prices_2022 %>% filter(flat_type==type),
x = remaining_lease_years,
y = price_thousand,
marginal = FALSE)+
  theme_minimal() +
  labs(title=paste("Correlation of Resale Price and Remaining_lease_years", lapply(type, tolower), "flats"), x="Remaining_lease_years", y="Resale Price (in thousand)", fill="Resale Price (in thousand)")+
theme(
        plot.title = element_text(hjust = 0.2, size = 15, face = 'bold'),
        plot.margin = margin(20, 20, 20, 20),
        legend.position = "bottom",
        
        axis.text = element_text(size = 10, face = "bold"),
        axis.title = element_text(size = 12, face = "bold"))

The above chart show the higher remaining lease years, the higher unit price. The coefficient is 0.61 which suggests there is a slight correlation.

Show the code 
type <- '4 ROOM'
ggscatterstats(
 data = resale_prices_2022 %>% filter(flat_type==type),
x = remaining_lease_years,
y = price_thousand,
marginal = FALSE) +
  theme_minimal() +
  labs(title=paste("Correlation of Resale Price and Remaining_lease_years", lapply(type, tolower), "flats"), x="Remaining_lease_years", y="Resale Price (in thousand)", fill="Resale Price (in thousand)")+
theme(
        plot.title = element_text(hjust = 0.2, size = 15, face = 'bold'),
        plot.margin = margin(20, 20, 20, 20),
        legend.position = "bottom",
        
        axis.text = element_text(size = 10, face = "bold"),
        axis.title = element_text(size = 12, face = "bold"))

The above chart show the higher remaining lease years, the higher unit price. The coefficient is 0.41 which suggests correlation is not strong.

Show the code 
type <- '5 ROOM'
ggscatterstats(
 data = resale_prices_2022 %>% filter(flat_type==type),
x = remaining_lease_years,
y = price_thousand,
marginal = FALSE) +
  theme_minimal() +
  labs(title=paste("Correlation of Resale Price and Remaining_lease_years", lapply(type, tolower), "flats"), x="Remaining_lease_years", y="Resale Price (in thousand)", fill="Resale Price (in thousand)")+
theme(
        plot.title = element_text(hjust = 0.2, size = 15, face = 'bold'),
        plot.margin = margin(20, 20, 20, 20),
        legend.position = "bottom",
        
        axis.text = element_text(size = 10, face = "bold"),
        axis.title = element_text(size = 12, face = "bold"))

The above chart show the higher remaining lease years, the higher unit price. The coefficient is 0.18 which suggests correlation is not strong.

5.6 Resale prices by floor_area_sqm

Show the code 
type <- '3 ROOM'
ggscatterstats(
 data = resale_prices_2022 %>% filter(flat_type==type),
x = floor_area_sqm,
y = price_thousand,
marginal = FALSE) +
  theme_minimal() +
  labs(title=paste("Correlation of Resale Price and floor_area_sqm", lapply(type, tolower), "flats"), x="floor_area_sqm", y="Resale Price (in thousand)", fill="Resale Price (psm)")+
theme(
        plot.title = element_text(hjust = 0.2, size = 15, face = 'bold'),
        plot.margin = margin(20, 20, 20, 20),
        legend.position = "bottom",
        
        axis.text = element_text(size = 10, face = "bold"),
        axis.title = element_text(size = 12, face = "bold"))

The above chart show the higher remaining lease years, the higher unit price. The coefficient is 0.25 which suggests correlation is not strong.

Show the code 
type <- '4 ROOM'
ggscatterstats(
 data = resale_prices_2022 %>% filter(flat_type==type),
x = floor_area_sqm,
y = price_thousand,
marginal = FALSE) +
  theme_minimal() +
  labs(title=paste("Correlation of Resale Price (psm) and floor_area_sqm", lapply(type, tolower), "flats"), x="floor_area_sqm", y="Resale Price (in thousand)", fill="Resale Price (in thousand)")+
theme(
        plot.title = element_text(hjust = 0.2, size = 15, face = 'bold'),
        plot.margin = margin(20, 20, 20, 20),
        legend.position = "bottom",
        
        axis.text = element_text(size = 10, face = "bold"),
        axis.title = element_text(size = 12, face = "bold"))

The above chart show the higher remaining lease years, the higher unit price. The coefficient is -0.05 which suggests correlation is not strong.

Show the code 
type <- '5 ROOM'
ggscatterstats(
 data = resale_prices_2022 %>% filter(flat_type==type),
x = floor_area_sqm,
y = price_thousand,
marginal = FALSE) +
  
  theme_minimal() +
  
  labs(title=paste("Correlation of Resale Price (psm) and floor_area_sqm", lapply(type, tolower), "flats"), x="floor_area_sqm", y="Resale Price ($ psm)", fill="Resale Price (psm)")+
theme(
        plot.title = element_text(hjust = 0.2, size = 15, face = 'bold'),
        plot.margin = margin(20, 20, 20, 20),
        legend.position = "bottom",
        
        axis.text = element_text(size = 10, face = "bold"),
        axis.title = element_text(size = 12, face = "bold"))

The above chart show the higher remaining lease years, the higher unit price. The coefficient is 0.02 which suggests correlation is not strong.

5.7 resalse price by time(sale months)

Show the code 
type <- '3 ROOM'
na.omit(resale_prices_2022) %>% 
  filter(flat_type == type) %>% 
  ggplot(aes(x = flat_type, y = price_thousand)) +
  
  geom_boxplot(aes(fill = as.factor(Month)), color = "grey") +
  
  stat_summary(fun = "mean", geom = "point", color = "black") +
  
  theme_minimal() +
  
  scale_fill_brewer(palette = "Paired") +

 labs( title = "Monthly Resale prices by Flat Types",
       y = "Resale price (in thousand)",
       x = "Flat Type",
       fill = "Month") +theme_classic()+

theme(
        plot.title = element_text(hjust = 0.5, size = 12, face = 'bold'),
        plot.margin = margin(20, 20, 20, 20),
        legend.position = "bottom",
        
        axis.text = element_text(size = 10, face = "bold"),
        axis.title.x = element_text(hjust = 0.5, size = 12, face = "bold"),
        axis.title.y = element_text(hjust = 0.5, size = 12, face = "bold"))

The above chart shows there is no significant increasing or decreasing trend of resale price over months.

Show the code 
type <- '4 ROOM'
na.omit(resale_prices_2022) %>% 
  filter(flat_type == type) %>% 
  ggplot(aes(x = flat_type, y = price_thousand)) +
  
  geom_boxplot(aes(fill = as.factor(Month)), color = "grey") +
  
  stat_summary(fun = "mean", geom = "point", color = "black") +
  
  theme_minimal() +
  
  scale_fill_brewer(palette = "Paired") +

 labs( title = "Monthly Resale prices by Flat Types",
       y = "Resale price (in thousand)",
       x = "Flat Type",
       fill = "Month") +theme_classic()+

theme(
        plot.title = element_text(hjust = 0.5, size = 12, face = 'bold'),
        plot.margin = margin(20, 20, 20, 20),
        legend.position = "bottom",
        
        axis.text = element_text(size = 10, face = "bold"),
        axis.title.x = element_text(hjust = 0.5, size = 12, face = "bold"),
        axis.title.y = element_text(hjust = 0.5, size = 12, face = "bold"))

The above chart shows there is no significant increasing or decreasing trend of resale price over months.

Show the code 
type <- '5 ROOM'
na.omit(resale_prices_2022) %>% 
  filter(flat_type == type) %>% 
  ggplot(aes(x = flat_type, y = price_thousand)) +
  
  geom_boxplot(aes(fill = as.factor(Month)), color = "grey") +
  
  stat_summary(fun = "mean", geom = "point", color = "black") +
  
  theme_minimal() +
  
  scale_fill_brewer(palette = "Paired") +

 labs( title = "Monthly Resale prices by Flat Types",
       y = "Resale price (in thousand)",
       x = "Flat Type",
       fill = "Month") +theme_classic()+

theme(
        plot.title = element_text(hjust = 0.5, size = 12, face = 'bold'),
        plot.margin = margin(20, 20, 20, 20),
        legend.position = "bottom",
        
        axis.text = element_text(size = 10, face = "bold"),
        axis.title.x = element_text(hjust = 0.5, size = 12, face = "bold"),
        axis.title.y = element_text(hjust = 0.5, size = 12, face = "bold"))

The above chart shows there is no significant increasing or decreasing trend of resale price over months.

5.7 Resale price by storey_range

Treemap

Show the code 
#treemap  

treemap_storey <- treemap (resale_prices_2022,
        index= c("flat_type", "town"),
        vSize= "price_thousand",
        vColor = "storey_range",
        type="categorical",
        palette = "Blues",
        title="Resale price by storey_range",
        title.legend = "Price_thousand"
        )

The above chart shows: For 3 Room flat type, the unit prices are higher at YISHUN, ANG MIAO KIAO, BEDOK, storey ranging from 04-06 to 07-09. For 4 Room flat type, the unit prices are higher at SENGKANG, PUNGGOL, YISHUN, storey ranging from 07-09, 10-12 to 13-15. For 5 Room flat type, the unit prices are higher at SENGKANG, PUNGGOL, JURONG WEST, storey ranging from 07-09, 10-12 to 13-15.

6.0 Summary of insights

Finding 1 - Geography Overall, the top 1 proportion flat type is 4 Room, followed by 3 Room and 5 Room in Singapore in 2022. When looking at the numbers of flats, we can see that SENGKANG, PUNGGOL and YISHUN have the most of the flats of all flat types.This could be seen in section 5.3 Proportion and absolute numbers of flat types in Singapore.

Location had a great effect on flat resale prices. Generally, the CENTRAL AREA had the most expensive flats by mean price per square meter followed by QUEENSTOWN and KALLANG. This could be seen from the box plots and uncertainty of point estimates in section 5.4.2 Average resale prices by room type by planning areas.

Finding 2 - Resale price and remaining lease year, floor area sqm, Time(months) Generally, there is no strong correlation between the unit price and remaining lease, unit price and floor area sqm. This could be seen in section 5.5 Resale price by remaining_lease_year and 5.6 Resale prices by floor_area_sqm.

There is no significant increasing or decreasing trend of resale price over months.This could be seen in section 5.7 resalse price by time(sale months).

Finding 3 - Resale price by storey_range For 3 Room flat type, the unit prices are higher at YISHUN, ANG MIAO KIAO, BEDOK, storey ranging from 04-06 to 07-09. For 4 Room flat type, the unit prices are higher at SENGKANG, PUNGGOL, YISHUN, storey ranging from 07-09, 10-12 to 13-15. For 5 Room flat type, the unit prices are higher at SENGKANG, PUNGGOL, JURONG WEST, storey ranging from 07-09, 10-12 to 13-15. This could be seen in section 5.7 Resale price by storey_range.