Contents

1. Problem/Task Specification 4

1.1 Introductions 4

1.2 Objective 5

1.3 Significance of research 5

2. Literature Review 6

2.1 Urban Economic Stimuli 6

2.1.1 Residential property value 6

2.1.2 The role of Shopping Centres 7

2.1.3 Externalities 8

Air Pollution 9

Noise 9

Accessibility 9

2.2 Review of past literature 10

2.2.1 Asia 11

2.2.2 United States 12

2.2.3 Canada 13

2.2.4 Africa 14

2.3 The Hedonic Model 14

3. Methodology 16

3.1 Selection of proposed shopping centres 16

3.1.1 East Gardens 16

3.1.2 Baulkham Hills 17

3.1.3 Carnes Hill 18

3.2 Criteria for residential property 18

3.3 Application of the Hedonic Model 19

3.4 Price 20

3.5 NSW Globe 21

3.6 Structural Details 24

3.7 Distance 24

3.8 Expected Outcomes 25

• Identify the ways in which shopping centres affects residential property value 25

3.9 Progress in the first 10 Weeks 25

3.10 Proposed Time Schedule 27

Winter, Ian and Wendy Stone, 1998. Social Polarisation and Housing Careers: Exploring the Interrelationship of Labour and Housing Markets in Australia. Australian Institute of Family Studies. March.              28

Badcock, Blair and Andrew Beer. Home Truths: Property Ownership and Housing Wealth in Australia. Melbourne: Melbourne University Press, 2000, p2-100.              28

1. Problem/Task Specification

1.1 Introductions

The Australian property market sparked itself arguably from the origins of the traditional culture known as the “Australian Dream” (Winter, Ian and Stone, 1998). Typically, this comprises of owning a fenced off single storey house on a quarter acre block of land. Since its beginnings, Australians have seen a steady increase in the demand for residential housing. A survey in 2010 on the topic of annual income and residential property showed that 33% of Australians outright owned their own house, and 36% were currently on a mortgage (Badcock, Blair and Beer, 2000). From the period of 1996 to 2010, the number of mortgages rose of 28% to 36%, indicating that there has been a growth overtime (Winter, Ian and Wendy Stone, 1998).  Figure 1.1 also highlights the general trend of property prices over time.

The areas or Eastgardens, Baulkham Hills and Carnes Hill in NSW have experienced strong rates of growth over the past decade (ABS, 2017). It is advocated that property values are appropriated from the complex mix of service accessibility and location and ease of transport between (Berry and Bednarz, 1979).  As such, it is proposed that houses are the effect of positive and negative externalities. Some elements are more prominent than others, such as shopping centres.

With Australia being within the top 20 nations of the highest GDP per capita (World Economic Outlook Database, 2017), infrastructure developments and works are always prominent. This can be seen from the multitude of shopping centres being constructed in varying suburbs. Since 1957, there has been an average of around 24 shopping centres being opened each year; resulting in Australia containing the worlds 3rd largest amount of of market size (22.2 million sq.m) leading to annual retail sales of up to $120 billion ((Shopping Centre Council of Australia, 2017).

Thus, with the large consumer market in retail, this produces the notion that houses being within proximity of these shopping centres would have an influence in its value as compared to other real estates which lack these facilities. Kahn & Case (1977) labelled residential properties which are within the vicinity of a shopping centre, as ‘enhanced’.  Further, Tse and Love (2000) believe that housing prices are affected not by the dwellings themselves, but by the location of the estate.

1.2 Objective

The main aim of this study is to investigate the effects of proximity to shopping centres on the price of  residential properties. Additionally, the study aim to:

• Find the qualitative relationship between the prices of residential properties and the proximity to shopping centers.

• To allow comparisons between the effect of proximity to shopping centers on the price of adjacent residential properties in the Australian and international property markets.

1.3 Significance of research

Through the analysis of the effects which shopping centres impose on the neighbouring residential property value, this may lead to a more sound understanding through the scientific domain. The findings of the study may assist professionals in urban planning, economics, government representatives and to the general population as a means of informing and identifying the key concepts which affect property prices (Rosiers et al., 1996). Additionally, this information also serves the Australian market and would not require assumptions to be made as may be the case for global studies.

2. Literature Review

This literature review shall be separated into 3 sections. The first section will explore the basics of urban economic stimuli as well as discuss the influencing factors which arise from shopping centres in order to understand the motivation for property pricing. The second section will be a thorough examination on previously conducted research literature which relays on the influences of shopping centres on the nearby residential housing prices at a global scale and finally, a brief outlook on the hedonic price modelling in literature.

2.1 Urban Economic Stimuli

This section of the literature review explores basic economic principles and aims to provide a foundation on the concepts which are known to influence real estate appraisal.

2.1.1 Residential property value

At its core, residential property prices are influenced by the laws of supply and demand. This crucial economic principle defines the ‘rate of consumers wanting to purchase a product or asset’ as the demand whereas the supply is the capability ‘to supply goods to consumers’ (Whelan, 1996).

This is further reinforced by Kimmons on the topic of real estate trends, such that the increase in demand of residential property in an area leads to a total increase in the prices for that given area. Conversely, a lack of demand in a residential suburb results in house prices decreasing due to the competition and excess supply of houses available (Kimmons, 2016).

The factors which influence the demand for residential property, (discussed below), directly influence the market value (Colwell et. Al., 1985). As of current in Australia, formal property valuations are conducted by third party companies and not by the government. However, they are required to conform to the International Valuation Standards (IVSC, 2013). Care should be taken to not misinterpret the definition of market value to price. The Australian Government does not have a formal definition of ‘market value’, but was first evoked by Griffith CJ in the court case of Spencer v. Commonwealth from the question: “What would a man desiring to buy…have had to pay for it on that day to a vendor willing to sell…but not desirous to sell?” (Griffith, 1907) (Australian Government, 2017).  Thus, the discrepancy in residential prices arises from the dissimilitude in the amount a vendor (or owner) is willing to accept and what a buyer is willing to pay (Elli et. al, 2003). This generates the topic of market value vs the price.

Residential property price differences stem from the agreed contract price to the actual worth of the property (market value). The price which a buyer pays might not reflect the actual market value of the residential property. In reality, one common case of the general populace which alters prices is being misinformed (Schram, 2006). This situation arises when a contractual agreement is reached between the buyer and the vendor, however either or both may be misinformed of the property’s true market value (Maliene et. al, 2010). Conversely, another case involves the buyer valuing the property at a higher rate compared to the general market value. This premium arises from the demand as well as being subjected to investment value (BIS Shrapnel, 2016). Havard advocates that the worth of assets such as property is based on its capacity in producing equity and thus, revenue (Havard, 2014).  Therefore, the factors which cause variations in residential prices are reflective of the externalities which exist.

2.1.2 The role of Shopping Centres

The International Council of Shopping Centre puts the formal definition of a shopping centre as “a group of retail and other commercial establishments that is planned, developed and managed as a single property” (ICSC, 2017). Shopping Centres became very popular since the beginnings in May 1957. They provide the local community with convenience of access, low cost parking and combines both retail, recreational and entertainment facilities for consumers (Shopping Centre Council of Australia, 2017). Apart from serving as an outlet for necessities, shopping centres have become a common place for both events and as a means of socialising in the Australian domain.

This characteristic conforms with the global platform. A UK research by Thomas et al. (2008) highlighted that shopping centres served the needs of the local community in the social, psychological and spatial needs.  Similarly, shopping centres are also used for a variety of passive activities such as wandering and relaxing (Martin et al., 2007). A research in Nigeria further establishes that shopping centres experienced social gatherings, a place served for recreation and window-shopping as much as active buying and selling (Allen, 2006). Finally, Aliyu et al. (2011) states that shopping centres should serve as a centralised location for shopping, leisure, mental and physical repose and serve as a social location.  This evidence above showcases the importance of shopping centres for the local community. Additionally, it also demonstrates the attractiveness of being within the close proximity of one.

2.1.3 Externalities

In order to understand the reason for these variations in property values, it is important to consider the concept of externalities.  Externalities as defined by Miller as the outcome from an economic activity that has a direct affect onto an external party (Miller, 1999). Similarly, Do et al. (1994) advocates that such externalities cause a change in the values of the neighbouring residential housing indefinitely. Shopping Centres can simultaneously employ both positive and negative externalities.

Positive externalities promote the appeal of living within a close proximity to the shopping centres. Shopping centres promote the reduction of time required for shopping by converging multiple outlets within a single complex which allows consumers to save expenditures on fuel and provide convenience. Accessibility is also increased as most shopping centres provide consumers with low cost parking options, with some potentially for free (Addae-Dapaah et al., 2010). This may increase the value of nearby property as there is a reduced need to compete for car parking spaces. Finally, it also serves as a place for entertainment and relaxation, as discussed previously (Sirpal, 1992).

Conversely, negative externalities also arise for matters such as an increase in traffic, resulting in congested roads for the surrounding area. This leads to more time and cost required for travel. The surge in traffic would also result in an increase in noise and negative effects to air quality. Furthermore, residential houses may be exposed to unwanted lighting during the night.

Colwell et al. (1985) and Sirpal (1994) propose that the positive externalities which arise from shopping centres outweigh the negatives, effectively making the shopping centre a positive externality itself. Further, Reilly’s law of retail gravitation (1931) deduces that nearby residential property can only be positively influenced in proportion to the size of a shopping centre.  However, this contradicts with the notion whereby there would be an overall increase in negative externalities due to the magnification of the effects with the larger sized shopping centres (Addae-Dapaah et al., 2010). Addae-Depaah et al. further supports that shopping centres are a ‘blessing or a curse’ to the neighbouring residential property.

The extent of impact the discussed externalities have may vary from different shopping centres and residential suburbs. This section attempts to supplement the effects of the proximity of shopping centres on residential prices by examining the underlying externalities which results from shopping centres and its impact on the residential proximity.

International studies have been conducted on externalities attributed to shopping centres with conclusions on both positive and negative.

Air Pollution

In Australia, the general consensus for air quality is positive for the average residential suburb (Environmental Justice Australia, 2014).  However, air pollution is well viewed as a negative characteristic on the evaluation of property. Literature reviews have reinforced the impact which air pollution may have on the price of residential property. A study in America by Ridker and Henning (1967) deduced that the several areas containing high levels of air borne particulates had a negative impact on the value of residential property. Similarly, a study by Murdoch and Thayer (1988) confirmed the positive relationship of house prices in California’s South Coast and clean air (by using visibility as a medium). Furthermore, Chau states that homes which are “located in places with better air quality are expected to have higher value” (Chau et. al., 2006. pg.1)

Conversely, Smith and Deyak (1974 attempted to challenge these results by investigating in 85 central cities and concluded that there was an insignificant effect of air pollution to the house prices. Likewise, Li and Brown (1980) examined 15 suburban towns in the Boston metropolitan area and reinforced the insignificance of air pollution on property value. However, they formally announced that there may have been an issue with their regression analysis.

Therefore, the above studies advocate that air pollution have a negative effect on property values.

Noise

Previous studies have demonstrated the correlations of noise on property values. Analogous to the effects of air quality, noise is often viewed in a negative way due to the adverse consequences on human wellbeing and productivity (Ng, 2009). A study in the Boston metropolitan area directly measured the ambient noise level in 1971 (Li and Brown, 1980). Another study illustrated the effects of road noise by using the hedonic model incorporated with a Geographic Information System (GIS) and correlated the noise effects on the house values in Glasgow (Lake et. al, 2000).  The above studies provide strong indications of the adverse effect on residential value.

Accessibility

The accessibility of shopping centres plays an important role in the demand for neighbouring residential properties (Addae-Dapaah et al., 2010). This appeal of accessibility stems from the convenience which shopping centres provide for the local community, in particular to those who are closer to the centres. The close proximity results in lower times of travel, which can be spent elsewhere. Additionally, this would also result in a reducing in costs associated with transportation requirements. Moreover, accessibility to shopping centres boasts as a vital impact on the surrounding property (Basu et al., 1998).

Although the proximity to shopping centres may potentially arouse negative externalities, Mills (1979) argues that such negative diseconomies are compensated by the benefits of having the convenience of accessibility. Mills continues on that residential values will fall with distance away from the shopping centre. Further, Tse et al. (2000) states that there is a strong relationship between residential property prices and accessibility to amenities,

A predominant aspect of accessibility created by shopping centres on residential property is the resulting increase in walkability for the households. Walkability is defined as the ease of access via walking from a neighbourhood to the desired amenity (in which the shopping centre will be the focus amenity for this report (Weissbourd, 2009).  A study by Pivo et al. (2011) suggest the positive impact which walkability to amenities have upon the neighbouring residential prices. Research on walkability conducted by Rauterkus et. al (2011) proposed that neighbourhoods with high walkability to amenities reduces the need for travel associated costs and thus, would better allow residents to financially support the property. Rauterkus further argues that this in turn would drive up the demand for the property due to the willingness to pay.  Further, properties that experience the effects of inconvenience generally would result in a reduction in value (Galster, 1986).

A study on the effects of walkability on residential property pricing was conducted in Alabama, USA by Miller and Rauterkus, (2011).  5,603 property sales between the years of 2004 and 2008 were analysed and placed under OLS regression models. The method used was via Walk Score, which scores out of 100 the ease of living without a vehicle for transportation purposes. The sizing of the residential land as well as the growth of population was controlled. The investigation concluded that property value increases according to Walk Score. Furthermore, the study advocates that walkability increases property value from a financial perspective (Miller et al., 2011).

Therefore, the following literature strongly supports the positive effect which accessibility has upon property value.

2.2 Review of past literature

This section of the literature review shall focus on the thorough analysis of past academic literature from a selection of accredited sources. The studies being examined shall be relate to the effects of shopping centres on the neighbouring residential property prices. As there is a lack of academic research being conducted in Australia, the majority of sources are of international origins. The aim of this review will serve as a validation tool on the findings conducted in NSW, Australia.

2.2.1 Asia

A Hong Kong study on the effects of valued attributes on residential property prices was conducted by Tse et al. (2000). A hedonic regression model was employed in the analysis of floor area, age, views and the availability and ease of access to amenities and recreational facilities. The study focused on small to medium sized dwellings in four different housing estates, with an average age of 12.4 years. It was predicted that the accessibility to shopping centres is favourable and produce a positive influence on the housing prices.  It is also noteworthy that the authors regarded shopping centres are accessible only if it is within 10 minutes of walking distance. However, this may pose as an issue in the data as walking duration is subjective to the individual (Fujiyama, 2004). Interestingly, the study concludes that ease of accessibility to a shopping centre is not a favourable attribute in the influence of residential house prices due to it being insignificant. Tse et al. (2000) suggests this is due to the dynamic nature of the Hong Kong housing market, the demand for alternative motivators outweigh the effects which the proximity of shopping centres have on houses. Although reflective of reality, the multiple variables imposed by differing motivators and interests of individuals pose an issue in accurately estimating the true price influence on the houses (Evans, 1973).

Contrastingly, a study on the shopping centres effects on the proximate residential property prices in Singapore argues against the results above. Addae-Dapaah et al. (2010) hypothesised three main concepts: (1) That units near shopping centres command a premium in pricing, (2) Value of residential property will decrease as the distance increases between the property and shopping centre and (3) A residential house near a shopping centre would be valued greater than the same identical property with the same distance to a conventional town centre of stores.  Using the hedonic pricing model, a total of 8627 sales were analysed and reveals that the prices of houses within close proximity of the shopping centre were on average 4.7% higher, with the highest premium being units within 100m of the centre. Addae-Dapaah states that the results are supportive of a positive price-proximate distance relationship for residential houses and may stretch beyond 500m. Additionally, units near a shopping centre command a premium of 6.1% compared to units near a town centre, which reinforces the appeal of shopping centres (Addae-Dapaah et al., 2010).

2.2.2 United States

Colwell, Gujral and Coley (1985) deduced that shopping centres can result in both positive and negative effects on housing value in their study of comparing the prices of property before and after the announcement of the Southgate Shopping Centre in Illinois. They found that after the development was publicised, properties within a 1500 feet distance suffered a decline in value and beyond this point, there was a net increase. However, Colwel et al. did not comment on the nature of the upward slope of value once past this 1500 feet distance.

Similarly, Li and Brown (1980) investigated the combination of positive and negative externalities which the development of shopping centres provide for the neighbouring households. Specifically, the study explored the offset of accessibility to the shopping centre to the undesirable effects of noise and aesthetics due to the close proximity. The findings were essentially homogeneous to those found by Colwell et al. (1985) whereby homes within 1760 feet experienced a loss of value. Likewise, residential prices augmented beyond this point. Li and Brown concluded that it is at the critical distance where the desirable effects of accessibility offset the reduced effects of the negative externalities.

Nevertheless, the outcomes of both studies above reinforces the hypothesis that such effects of externalities are highly localised within a small section (Grether and Mieszkowski, 1980)

Kholdy, Muhtaseb and Yu (2014) further reaffirms the above results in the study of an open-air, mixed use shopping centre on the surrounding properties in Victoria Gardens, California. Using the hedonic pricing model, the study examined 4168 single storey properties sold over the period of 2001-2010 within a 3-mile radius of Victoria Gardens. The study concludes that within a 1.3-mile radius, residential housing is impacted in a negative way, implying that the effects of noise and traffic dominate over the desired accessibility. Beyond this critical radius, prices experience an increase in value. However, this study only involves an isolated location and would require more locations to ensure consistency. Furthermore, such results are reflective on the previous above deductions [Colwell, Gujral and Coley (1985), Li and Brown (1980)] in that the impact of the shopping centre persists overtime from the developmental stages.

Sirpal (1992), examined the effects of shopping centres on the values of adjacent residential homes in Gainesville, Florida. Three different shopping centres were chosen in the NW quadrant of Florida and the house transactions within a circular area for a given radius of the shopping centre were used in the study. Sirpal assigned the circular area as the critical distance and would serve to be the boundaries for the study. Distances taken are 9000 feet between shopping centres and 6000 feet within the 2 individual shopping centre complex’s  and 3000 feet for the smaller shopping centre.

Sirpal concluded that the price-distance relationship between residential houses is in a concaved shape. House prices near the complex are lower but increase with distance away from the shopping centre to a peak, then prices fall beyond this distance. This can be explained by the prominence of undesired externalities associated with the shopping centre such as noise and traffic (Ridker and Henning, 1967). The results also confirm to the findings of Tideman (1970) in that beyond the critical point, residents do not view a slight reduction of accessibility as being a negative influence on their property’s value. Additionally, this undesired region will extend further out with larger shopping centres. Although Sirpal defined that the results would be based on the individual shopping centres at 6000 feet apart, the residential property within the two boarders would have external factors due to the availability of two shopping centres within the nearby proximity, which would impact the overall values of the houses.

2.2.3 Canada

Rosiers. F.D, Lagana. A, Thériault. M, Beaudoin. M (1996) investigated the influences of the relative proximity and size of a shopping centre on house prices; linked by the combination of positive and negative externalities in the Canadian market. From the territory of Quebec, a total of 4000 detached single storey houses and 87 shopping centres with differing sizes are examined under hedonic price modelling. The centres are separated into 3 categories of sizing: neighbourhood, (up to 42 shops), community (from 44 -90 shops) and regional (101 to 476 shops) in order to maximise the applicability of this study. Interestingly, the findings indicate that going away from shopping centres, housing prices rise then peak at 215m, 310m and 532m for the neighbourhood, community and regional shopping centres respectively, but then decline constantly thereafter. This evidence suggests that due to the increasing shopping centre related negative externalities from size, it essentially shifts the ‘optimal distance, defined as a balance between positive and negative externalities, further out away from the shopping centre. This optimal distance results in the highest residential prices and hence, demand.  The results reassert the findings of Sirpal (1994) and suggests that the relationship between distance to shopping centres and house prices is not monotonic and decreases with distance away from the shopping centre.

Thus, the modelling of house locations using the gamma function was executed where it proved to be successful in predicting household’s locational preferences influenced by shopping centre externalities.

2.2.4 Africa

Aliyu, Kasim and Martin (2011) investigated the impact of a neighbourhood shopping on the surrounding residential property in Bauchi, Nigeria through the analysis of the prices of 43 single family homes within ¾ miles from the shopping centre and were sold between the years of 2003 and 2009. The results indicate that residential houses within a proximity of 1500 feet there is a dominance of premium pricing whereas beyond 1500 feet, there is a general reduced pricing, which confirms with the results by Addae-Dapaah et al. (2010). Further, Aliyu, Kasim and Martin suggest that there would be an “optimal spatial frequency” (Aliyu et al., 2011. pg. 1) whereby the most suitable location would incorporate a trade-off of reduced negative externalities whilst still being served by the shopping centre and is accessible with a reduced residential value [Kholdy, Muhtaseb and Yu (2014), Colwell, Gujral and Coley (1985), Li and Brown (1980)].

However, this study fails to comment on the availability of trends in residential housing price reduction beyond the 1500 feet. Additionally, this study only isolates a single location out and thus is limited in its applicability.

Sale (2015) investigates the influence of the Walmer Park Shopping Centre on the surrounding properties in Nelson Mandela Bay under the application of the hedonic rice model. From the sample pool of 170 transactions between the years of 1995 and 2009, it was discovered that there is a strong relationship between the proximity to the shopping centre and local property prices. The study also reinforces the concept that the benefits of convenience to the shopping centre offsets the negative externalities. Similar to Aliyu, Kasim and Martin (2011), this study only focused on a single neighbourhood and the results are not indicative of the general case.

2.3 The Hedonic Model

The hedonic model is well regarded as one of the most often used method in the evaluation of property prices with multiple factors [Humavindu and Stage (2003), Walsh, Milon and Scrogin (2011)]. The model is believed to have originated in a conceptual form from the study by Waugh (1929) in the ambition to achieve methods which adjusts the value of vegetables in relation to quality. The powerful ability which this model has on adjusting prices whilst factoring in multiple variables because widely appreciated from the 1960s (Palmquist et al., 2001).

However, the first regarded application in literature of the hedonic pricing model was documented in Lancaster’s (1966) paper: “A new approach to consumer theory”. It applies the concept that it is the various services which consumables offer that entices individuals towards the good, however, the good itself is traded in transactions, not the actual services themselves. The hedonic pricing model was used to account for the implicit costs during the transactions. This was followed by Rosen’s (1974) study on the effects of Hedonic prices in implicit markets, to which afterwards, lead to a multitude of uses on pricing studies.

The hedonic model has since earned a reputation as an effective tool in the exploration of locational influences to the likes of hospitals (Huh and Kwak, 1997), schools (Haurin and Brasington, 1996), churches (Carroll, Clauretie and Jensen, 1996) and cemeteries (Tse and Love, 2000).  Care must be taken in order for researchers to properly specify the variables in forming the hedonic relationship (Linneman, 1980).

Furthermore, Sales (2015) affirms that all factors in regards to housing should be accounted for when evaluating the property using the hedonic technique. However, this poses as an issue as there are cases where the focus is on collinearity with specified variables and so, multiples factors are dismissed in the hedonic model (Constantine, 1994). Consumer Reports (1996)

argues that this omission of certain inputs in the model will create errors and results in a biased outcome. However, Mok, Chan and Cho (1995) argue that a small number of independent variables in the hedonic model would bring about small variations. Similarly, Butler (1982) concluded that there is limited bias in a highly restricted configurations input in the model.

Thus, users of the hedonic method should account that using highly correlated variables would reduce the effect of the varying independent elements and enhances collinearity while the dismissal of variables may lead to a bias in the outcome (Sale, 2015).

3. Methodology

3.1 Selection of proposed shopping centres

For this study of the Effects of Proximity to Shopping Centres on the price of Residential Property, three shopping centres at East Gardens, Baulkham Hills and Carnes Hill shall be investigated. Below seeks to analyse the different locations and to provide brief information for each site.

3.1.1 East Gardens

Eastgardens is a Sydney suburb as part of the Bayside Council. Eastgardens is relatively small, with a total land area of 0.2m2 with a population of just 911 (Census-EG, 2011). The suburb is also served as a bus deport for the Australian Government.

Eastgarden attributes:

• Westfields Shopping Centre (source: Scentregroup.com, 2017)

o 303 total stores inside

o Total floor space of 84, 627 m2

o Total Car spaces of 3,263

o Bus Service in adjacent roads

• Zoned R2: low density detached dwelling houses dominating the south sector of the suburb (see Figure 3.1).

The Eastgardens local area is being served by the major Westfields Shopping Centre and provides a good location for analysis due to insignificant number of variables that may influence the model, such as train stations.

Figure 3.1 illustrates the Eastgardens map, with the location of the Westfields as per the red highlight.

3.1.2 Baulkham Hills

Baulkham Hills is located within ‘The Hills’ and with a small part in the ‘City of Parramatta’ LGA. In contrast to Eastgardens, Baulkham Hills covers an area of 121, 000 m2 with a population of 33, 945 as of 2011 (Census-BH, 2011). Baulkham Hills also resides in close proximity to the M2 Motorway and also incorporates services such as schools, a hospital, various parks, a railway station in the works and a bus service by the Hillsbus bus company (The Hills Shire Council Forward Planning Team, 2017).

In the interest of this study, the focus shall be on:

• Baulkham Hills Shopping Centre (Stockland.com.au, 2017)

o 80 stores

o 850 car park spaces

o Bus service to surrounding suburb

• Zoned R2: majority of suburb is zoned for low density residential housing with small sections for RE1 (public recreation) and minor instances of R3 (as indicated in Figure 3.2 below; red circle).

The local area around the Stocklands shopping centre was deemed more suitable in the examination as the shopping centre is surrounded by residential housing with limited number of variables needing to be considered.

3.1.3 Carnes Hill

Carnes Hill serves over 4,200 residents with over 1,200 dwellings in the nearby proximity (Liverpool.nsw.gov.au, 2017). Carnes hill was rezoned in the middle of the 1990s and is still classified as a developing suburb by the Liverpool Local Council (LCCA, 2015). A bus transit system also runs along the road along the shopping centre and around the surrounding local neighbourhood. Although only established in 2010, it is predicted that the suburb will exceed a population of 100,000 (LCCA, 2015).

The focus of study is on the Carnes Hill suburb is:

• Carnes Hill Marketplace Shopping Centre

o 53 stores

o 926 car park spaces

o Bus service outside centre

• Zoned R2: Most the surrounding neighbourhood is zoned R2, with a small section around the centre zoned at R3 (as per the figure below, with the centre in blue).

This location provides an ideal setup in terms of orientation around the shopping centre and provides services similar to those of Eastgardens and Baulkham Hills.

3.2 Criteria for residential property

This study will adopt only the residential property which conforms to the following criteria:

• Sold since the beginning of 2015

• Constructed before 1985

• Residing in a R2 zone

• 3 bedroom, detached single storey house

Within the proximity of the above three shopping centres, the houses must have been sold since the beginning of 2015. This ensures that there is consistency and that the data obtained is reliable and up to date. A timeframe since the beginning of 2015 also ensures that there are enough elements for the study to utilise. Additionally, by having a smaller time period, this eliminates the variations which may occur in property value due to inflation. Although this study will attempt to account for such variations, it is best to ensure that influences on the prices are minimised to the study.

Additionally, houses being examined must also have been constructed before the year of 1985. It is important to note that with newer properties comes the additional of newer technologies and modern innovations being incorporated as part of the household. This will inevitably affect the price of the property as a whole. Thus, in order to ensure that the prices are solely based on its location and for it to be only seen as a form of shelter, the property must have been constructed at a date before 1985. This factor shall be determined electronically or by best means of visual inspection. However, no control could be placed for internal renovations and so is dismissed within this study due to the difficulty in accountability of the variable.

Furthermore, houses must also be zoned as ‘R2’. R2 zones are classified as low density residential environments where there are certain restrictions of land usage. This restriction further enhances the reliability in the data and ensures that the zoned area contains residential houses are designed and built in accordance to a specific outline (The Hills Shire Council Forward Planning Team, 2017).

Finally, due to the great variation in prices for houses of varying structural elements, this study will only focus on 3 bedrooms, detached single storey houses. The most common number of bedrooms in the Australian property market is 3 per household (ABS, 2017). Additionally, in restricting the sample pool to only a specific number of bedrooms and storeys, it increases the reliability of the data obtained. As well, this ensures that price variations are only based on location by replicating homogeneity throughout the houses.

3.3 Application of the Hedonic Model

The hedonic pricing model will be employed for this study due to its solid reputation and reliability in past cases and use. It incorporates the different factors which relate to the external and internal attributes of residential prices. The following equation below is the hedonic model equation at its simplest form:

Y= (Sx,Lx,Nx) +εx

• Sx = structural element factors such as the number of bedrooms, bathrooms, car spaces, and lot sizing.

• Lx = incorporates location factors by considering the proximity to local points of interest.

• Nx = neighbourhood accessibility influencing factors including but not limited to transport lines, hospitals etc.

This study has defined restrictive controls which attempts to isolate only the location factors in order to maximise the accuracy of the effects associated in relation to the real estate. Therefore, this eliminates the requirements for structural and accessibility factors due to varying accessibility by suburb through the use of a governing selection criteria and specification of suburb locations. This results in the one variable being location or proximity and the hedonic pricing model equation becomes:

Y= (Lx)+εx

Thus, the required independent variables are identified below. The independent coefficients will determine the dependent variable, which is the expected property price.

Y(price)=β_1 (dist.shop.)+β_2 (lot.area)+β_3 (no.bath.)+β_5 (garage)+ε

3.4 Price

All prices obtained in the study shall be adjusted using the Residential Property Price Index for Sydney. This will assist in rectifying the differing sale years and the variation in the currency at that time. This data is provided by the Australian Bureau of Statistics the values are calculated according to every quarter.  The following table below (Table 3.1) illustrates the differences in changes as a percentage to the last known change according to each quarter.

By utilizing these values, multipliers for the property can be calculated using compound theory in economics and is reliant on which quarter the house was sold in.

Table 3.1 documents the changes in percentage over the last quarters from 2015

Quarter measured Percentage change since last quarter (%)

Mar-2015 3.1

Jun-2015 8.9

Sep-2015 3.1

Dec-2015 -1.6

Mar-2016 -0.7

Jun-2016 2.8

Sep-2016 2.6

Dec-2016 5.2