1 Executive Summary

  • The aim of this report is to get a glimpse on the overview of residential property prices of Hong Kong based on our sample dataset.
  • The main discoveries is that while the average HongKong property prices are high,purchasers are still able to find a suitable property that is best of value within a particular budget.

With reference to our aim, we looked into a research paper that goes in depth and analyzes the property prices has increased drastically while average income has remained unchanged. The report states “The market is chronically undersupplied. So over the last decade its affordability has fallen the most among the cities considered in this study.”with reference to https://www.globalpropertyguide.com/Asia/Hong-Kong/Price-History

2 Full Report

2.1 Initial Data Analysis (IDA)

library(readxl)
HKproperty <- read_excel("~/Desktop/DATA1001/HKproperty.xlsx")
View(HKproperty)

-Our dataset is secondary data, as it was collected from reputable real estate websites.

  • The limitations were the biases in the selection of our data, and the variability of our sources.

  • We predetermined locations that we would collect data on for both cheap and expensive housing. We were more prone to choosing properties with prices that suited our assumed knowledge of that area.

  • For some districts all 10 property information were chosen from one agency, while for other districts, they were collected from a different agency/multiple agencies.

  • This complicates our data because different agencies target customers with different budgets. For example, the property information shown on the website of an agency known for selling expensive properties will mostly likely show expensive housing instead of the general type of property within the area.

-Our dataset is reliable for our aim, we chose to focus on 6 districts(3 affluent, 3 non affluent districts)in order to provide a balanced and unbiased view on the property prices. After wrangling our data we collated total of 10 properties from each of the 6 districts we wanted to investigate. Our observations are the properties and key features of each property we established 9 variables.

# Top 5 rows of data
HKproperty=read.csv("~/Desktop/DATA1001/DATA1001_GROUP3.csv",header=T)
HKproperty <- HKproperty[1:60,]

head(HKproperty)
##           Agent       Type
## 1      Midland  Apartment 
## 2    Squarefoot Apartment 
## 3 HongKongHomes Apartment 
## 4 HongKongHomes Apartment 
## 5 HongKongHomes Apartment 
## 6 HongKongHomes  Apartment
##                                                                                                                       Property
## 1 https://en.midland.com.hk/find-property-detail/Flat-2-Middle-Floor-Block-1-May-Tower-Central-Mid-Levels-Admiralty-M100001917
## 2                                                                                                                Estoril Court
## 3                      https://www.hongkonghomes.com/en/hong-kong-property/for-sale/mid-levels-central/tregunter-tower-3/94415
## 4                              https://www.hongkonghomes.com/en/hong-kong-property/for-sale/mid-levels-central/the-albany/5819
## 5                        https://www.hongkonghomes.com/en/hong-kong-property/for-sale/mid-levels-central/century-tower-ii/4898
## 6                                                                                                        Century-tower-i/12000
##   Bedrooms Bathrooms Carspots Building.age   Location  Price Size  X
## 1        3       3.5        0         1975 Mid-Levels  93.00 2850 NA
## 2        5       3.0        1         1983 Mid-Levels  79.88 3347 NA
## 3        4       3.0        1         1993 Mid-Levels 120.00 3639 NA
## 4        2       2.0        1         1989 Mid-Levels  82.00 1755 NA
## 5        4       3.5        1         1992 Mid-Levels 135.00 3663 NA
## 6        4       5.0        2          n/a Mid-Levels 268.00 4172 NA
## Size of data
dim(HKproperty)
## [1] 60 11
## R's classification of data
class(HKproperty)
## [1] "data.frame"
## R's classification of variables
str(HKproperty)
## 'data.frame':    60 obs. of  11 variables:
##  $ Agent       : Factor w/ 9 levels "","28Hse","HongKongHomes",..: 5 9 3 3 3 3 3 3 3 3 ...
##  $ Type        : Factor w/ 4 levels "","Apartment",..: 3 3 3 3 3 2 2 2 2 2 ...
##  $ Property    : Factor w/ 57 levels "","140 Waterloo Road",..: 24 6 32 30 29 4 10 7 55 31 ...
##  $ Bedrooms    : int  3 5 4 2 4 4 4 3 3 3 ...
##  $ Bathrooms   : num  3.5 3 3 2 3.5 5 4 4 2 3 ...
##  $ Carspots    : int  0 1 1 1 1 2 2 1 1 1 ...
##  $ Building.age: Factor w/ 27 levels "","1961","1963",..: 7 12 19 16 18 25 25 25 25 20 ...
##  $ Location    : Factor w/ 7 levels "","Kowloon Tong",..: 5 5 5 5 5 5 5 5 5 5 ...
##  $ Price       : num  93 79.9 120 82 135 ...
##  $ Size        : int  2850 3347 3639 1755 3663 4172 2828 2888 1282 2918 ...
##  $ X           : logi  NA NA NA NA NA NA ...
sapply(HKproperty, class)
##        Agent         Type     Property     Bedrooms    Bathrooms 
##     "factor"     "factor"     "factor"    "integer"    "numeric" 
##     Carspots Building.age     Location        Price         Size 
##    "integer"     "factor"     "factor"    "numeric"    "integer" 
##            X 
##    "logical"

Summary:

mean(HKproperty$Price)
## [1] 36.499
36.499
## [1] 36.499
median(HKproperty$Price)
## [1] 12.8
12.8
## [1] 12.8
mean(HKproperty$Size)
## [1] 1298.85
1298.86 
## [1] 1298.86
median(HKproperty$Size)
## [1] 834
834 
## [1] 834

-Each row represents a particular property and each column comprise of the apartment features.


2.2 Research Question 1 What is the current state of Hong Kong’s property market?

hist(HKproperty$Price)
abline(v = mean(HKproperty$Price), col = "pink")
abline(v = median(HKproperty$Price), col = "blue")

#Analysis of Graph 1 The mean is being pulled up by the expensive villa properties(outliers).

  • From the perspective of a purchaser, it is more useful to use the median as an indication of what sort of budget is needed to be in the market and is robust.This is because our histogram is right skewed, hence the middle data point (median line) is lower than our balancing point (mean line).

-Our median indicates that 50% of the properties for sale are above and below 12.8 million HKD.A purchaser should be looking at properties below or around 12.8millionHKD. This is a high average considering how small the apartments are. Size(square metre) and the number of bedrooms are important features to also consider with purchasing a house.

median(HKproperty$Size)
## [1] 834
library(plotly) 
p2=ggplot(HKproperty,aes(x=Size, y=Price))
p2+geom_point(aes(col=Location))

p1 = plot_ly(HKproperty, x = ~Size, y = ~Price, main = "scatter")

Graph showing Property Price(Millions HKD) compared to Size(square feet) according to Location. #Analysis of Graph 2
-The median property size of HK apartments are 834 square metres. The purchaser would be looking at spending less/around 12.8 million HKD for a 834 square metre apartment.

-Clusters of properties show to buy an HK apartment between 250 - 1000 square feet you would need a budget of at least 8-10million HKD. In HK there is a large variety of apartments prices, you can find a apartment costing 3 million HKD(KwaiChung)up to 268million HKD(Midlevels), and it all depends on the district/location. From a purchaser’s perspective, it is best to pick a district with house prices within a budget and then from there to consider the prices, and size.

-While this reflects the current state of property market in HKthat the properties of Mid-Levels (affluent district) have overall very expensive properties, Kowloon Tong also has a few properties with very high prices (almost comparable to the ones in Midlevels) and generally a purchaser with a sensible budget would not be interested in a Midlevels. Most purchasers would prefer a more cost-effective apartment within the average property price of 12million and roughly less than 1000 square feet.

median(HKproperty$Bedrooms) 
## [1] 2
2 
## [1] 2

-The median number of Bedrooms is 2,hence 50% of the apartments have less than 2 bedrooms , which corresponds to the results from our scattergraph where the majority of properties are smaller than 1000 square feet.

median(HKproperty$Price[HKproperty$Type == "Apartment" & HKproperty$Bedrooms == "2"])
## [1] 7.8
7.8
## [1] 7.8

  • A first time purchaser who is looking for a 2 bedroom apartment would need a budget of at least 7.8million HKD ,as indicated by our median price of HK apartments with 2 bedrooms.

  • There’s a property in LammaIsland district that has 3 bedrooms,5.80million HKD, and 1400 square feet(twice the median average size of 834 square feet.) This property is of excellent value and cost efficient!

2.3 Research Question 2 Of the 6 districts we have analysed, which of them would be suitable for a purchaser with a budget of approximately $10 million HKD?

We had set the budget to 10million from answering the previous research question.

boxplot(HKproperty$Price~HKproperty$Location, main="Boxplot on property prices by location", ylab = "Price of property in millions (HKD)", xlab = "", cex.axis=0.8,las=2)

title(xlab="Locations in Hong Kong", line=5)

-By looking at the comparative boxplots above, we can see that all of the districts except Mid-Levels have a similar condensed spread meaning that property prices will approximately be similar to the median. When compared to the IQR where 50% of properties in HK costs 18.6million HKD, the cost effectiveness of Kwai Chung is emphasised.

-Kwai Chung is the ideal district as it has the smallest spread and no outliers, with the purchaser expecting to pay around 6million HKD.

-Midlevels has the most spread out property prices, with a median price of 130millionHKD. It’s not a cost effective district to be looking at unless you have a large budget.

-Comparatively, Mid-Levels has the largest spread with a 130million median price, its minimum value exceeding the average property price in HK thus making it the least cost-effective district to buy property in. But in Kowloon it is not cost effective district, as you would expect to pay a bit more around 20million HKD for most apartments, and a few outliers (very expensive property).

Thus a buyer with a 10million budget would find it most suitable to buy a property within either Kwai Chung, Lamma Island of Sham Shui Po.

quantile(HKproperty$Price)
##      0%     25%     50%     75%    100% 
##   3.000   6.385  12.800  25.000 268.000
quantile(HKproperty$Price)[4] - quantile(HKproperty$Price)[2]
##    75% 
## 18.615

Interquartile range reveals that 50% of HK property costs 18.6million HKD.

We conclude that a purchaser with a budget of $10,000,000 HKD to purchase property from the following districts: Sham Shui Po, Kwai Chung, and Lamma Island.With a budget of 10million HKD, it will be the most cost effective by getting a property with 2 bedrooms, in Kwai Chung,Lamma Island and ShamShui Po.

Summary: - While the affordability on HK apartments may have fallen, purchasers with specific budget of 10,000,000 HKD are still able to find a suitable property within their budget that has 2 bedrooms depending on the district. -Ultimately, it all depends on the purchaser’s budget, their intententions, and what they are looking for.