Geography HL's Sample Extended Essays

Geography HL's Sample Extended Essays

What are the most significant geographical factors affecting the price of public housing in Singapore?

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Table of content

Introduction

This paper aims to determine the most significant geographical factors affecting the price of public housing in Singapore. Understanding the importance of location as a determination of value lends a greater comprehension of consumer decision-making and property pricing in urban areas. This paper explores the significance of geographical factors at different scales through a combination of primary and secondary research.

Geographical context

Singapore, a city-state in Southeast Asia, is well-known for its high standard of public housing. In 2020, 80% of the Singaporean population lived in housing provided by the Housing and Development Board (HDB), a government-owned corporation (Statista, 2020). HDB flats are allocated to individuals based on requirements including age and marital status. Flats are heavily subsidised by the Singaporean government, encouraging the purchasing of flats over renting.

 

Singapore is divided into 26 HDB towns, totalling 1.09 million housing units (Statista, 2021). The towns have a mean size of 7.54 km2 and mean population of 125,000 (data.gov 2020).

Figure 1 - A Map Of Singapore, Showing The 26 HDB Towns (Google Earth).

Method

In order to answer the research question, a combination of primary and secondary data was collected at two scales: HDB towns and individual HDB estates. Data from the larger HDB towns/areas indicate large-scale trends in housing prices e.g. distance to the CBD, whereas the smaller-scale HDB estates indicate more local factors e.g. distance to a mall.

 

To determine the HDB estates that were investigated, a combination of stratified and random sampling was used. 26 HDB estates were chosen, one in each HDB town. This was to ensure a geographical spread of data was used. South-East Asia’s leading real-estate platform, PropertyGuru, was used to select a 3-bedroom, for-sale, HDB flat in each town. PropertyGuru provided recent transaction prices allowing the mean to be taken. Figure 2.1 shows the distribution of the 26 estates used in the study.

Figure 2 - A Map Of Singapore, Showing The 26 Estates Surveyed In This Investigation (Google Earth).

A 2018 study determined the following as factors affecting housing prices in Dalian, China (Yang et al, 2018): Distance to the airport, distance to the railway station, distance to the nearest park, the number of medical facilities within 1000 metres, transport accessibility, age, distance to education, distance to the coastline and a series of building attributes. These factors were adjusted to Singapore’s context and assigned to either the HDB Towns data set or the HDB estate data set based on the nature of the factor. If the factor referred to the distance to a less common place, like the airport, it was analysed under the HDB Town data set. However, frequently occurring variables, like malls, were analysed under the HDB Estate data set as there may be more than one mall in each HDB Town.

 

The distance to the closest hospital was measured using Google Earth in reference to the 14 major hospitals in Singapore (MOH). These hospitals are shown in Figure 3.1.

Figure 3 - A Map Of Singapore Showing The Locations Of The 14 Main Hospitals in Singapore (Google Earth)

The distance to the closest MRT station and the MRT station density were found using the 130 MRT stations in Singapore (LTA).

Figure 4 - A Sample Of How The Distance To An MRT Station Was Measured (Google Earth)

The density of bus stops was calculated using information from the map in Figure 3.3.

Figure 5 - A Map Showing All The Bus Stops In Singapore (Busrouter.sg)

In an informal interview with two HDB owners, the following was determined as qualitative factors affecting the value of an HDB: building condition, general housekeeping, greenery, facilities, aesthetic appeal, air quality, and safety. These factors were cumulated into an Environmental Quality (EQ) survey conducted at all 26 HDB estates. Each factor was given a score out of 10 based on the criteria below. A pilot study was conducted to determine the standards for each category.

 

The pilot study was conducted on June 24, 2022, at 13:50. The chosen site was an HDB estate at Redhill Close in the South of Singapore. The estate scored extremely low on building condition (3), general housekeeping (2), facilities (3), and safety (3). This was due to it being a relatively old estate (built in 1955) and a lack of maintenance (PropertyGuru). The estate scored highly in greenery (7), accessibility (7), and air quality (9). Older HDB estates tend to be less dense than recently built estates. This is due to rising concerns surrounding Singapore’s ability to house its population, leading to more dense HDB estates being built. These tend to have less communal and green spaces. Since the estate at Redhill is older, there are much larger open areas, leading to better air quality and more greenery. Furthermore, a survey among HDB residents found that 86% have a ‘grow in place’ mindset, this means older HDBs will tend to have older residents (Channel News Asia). As the residents of an HDB estate age, its town council adapts to provide for the needs of the elderly, making it more accessible.

Figure 7 - An Infographic Describing The Pilot Site Surveyed For This Study (Made By Author)

Qualitative factors were cumulated into an Environmental Quality (EQ) survey conducted at all 26 HDB estates. Each factor was given a score out of 10 based on the criteria in Table 1 (Appendix).

Data presentation and analysis

Multiple Spearman’s Rank tests were done to mathematically compare the significance of factors affecting the price of HDBs. This eliminates the issue of normal distribution in data sets. The Spearman’s Rank test was conducted for all factors against price. The outcome (-1 < r < 1) must then be assessed for statistical significance. The Spearman’s Rank test is conducted using the formula below.

 

\(p=1\frac{6∑d^2}{n(n^2-1)}\)

 

Where ⍴ is the Spearman’s Rank Correlation Coefficient,

 

d is the difference between the ranks of a pair of data,

 

and n is the number of pairs of data.

Figure 8 -

Figure 9 - A Map Showing The Average Price Of HDS 's In Each Town (Googel Earth)

Figure 10 - A Map Showing The Distance To CBD Frome Each Town (Google Earth)

Figure 11 - A Map Showing The Bus Stop Density In Each Town (Google Earth)

Figure 12 - A Map Showing The MRT Station Density In Each Town (Google Earth)

Figure 13 - A Map Showing The Distance To The Airport Frome Each Town (Google Earth)

A series of figures demonstrating the distribution of the different factors across Singapore.

Figure 14 - A Scatter Graph Of The Distance To The CBD Versus Price (Made By Author)

The most statistically significant factor, determined by the Spearman’s Rank test, is VCBD. There is a strong negative correlation between price and VCBD. Housing in this area is of much higher value for a number of reasons. 12.9% of Singapore’s resident workforce worked in the CBD in 2021, decreasing the land available for real estate and increasing its demand, leading to higher prices (The Straits Times). The CBD is relatively new with major developments occurring in the 1980s, thus HDB blocks tend to be relatively new, averaging 35 years old (PropertyGuru). Newer HDBs tend to have better value as they use more advanced technology, have better layouts, and are more aesthetically pleasing. In addition, since the CBD tends to be a very publicised area, it is in the best interest of the government to display the highest quality and most modern HDBs nearby, driving up construction prices.

 

This trend is explained in bid-rent theory, which states that land prices are higher closer to a city’s centre, see Figure 4.7 (Trussell, 2010). The trend in Singapore follows the bid-rent theory extremely well, with the initial steep negative gradient becoming a gentle negative gradient. According to figure 4.6, the initial steep gradient begins to become less steep after 2500 metres from the CBD, demonstrating a large difference in value between living in the CBD versus living 2500 metres away from it. This is likely due to the ability to walk to the city centre compared to taking a form of transportation as 2500 metres is a walkable distance. From about 7500 metres and above, the difference between commuting 7500 metres and 10,000 metres becomes negligible and thus prices of land decrease at a lesser rate.