Windfall Gains: The Impact of the Gautrain on Property Prices

Transcription

1 Windfall Gains: The Impact of the Gautrain on Property Prices A Research Report Copyright Presented to UCT The Graduate School of Business University of Cape Town In partial fulfilment of the requirements for the Masters of Business Administration Degree by Craig Ridgard 6 December 2010 Supervisor: Barry Standish

2 Acknowledgements I would like to express my gratitude and thanks to all the people involved in this study. I would like to thank Barry Standish for his insights and guidance as supervisor on the study. Special thanks to Kutlwano Ramaboa from the University of Cape Town s Statistical Department for her assistance with the statistical modelling, data analysis and interpretation. I would also like to thank my partner Monique for her presence and support during the MBA. 2

3 Plagiarism Statement University of Cape Town Graduate School of Business MBA Fulltime 2010 Declaration 1. I know that plagiarism is wrong. Plagiarism is to use another s work and pretend that it is one s own. 2. I have used a recognised convention for citation and referencing. Each significant contribution and quotation from the works of other people has been attributed, cited and referenced. 3. I certify that this submission is all my own work. 4. I have not allowed and will not allow anyone to copy this essay with the intention of passing it off as his or her own work. Student Number: RDGCRA001 Craig Ridgard MBA F/T

4 TABLE OF CONTENTS List of Figures... 6 List of Tables... 7 Abstract Area of Study The Gautrain Rapid Rail Link Property values in South Africa Purpose and objective of the study Background of the study Gautrain station selection Background to Rosebank area Background to Centurion and Lyttelton area Research Hypothesis Rationale for hypothesis Primary hypothesis Literature Review A brief history of estimating land values Positive effects on property values near transport nodes Negative effects on property values near transport nodes Distance from the central business district High income/low income areas Planning, construction and operational phases of railways Subway systems Developed economies Developing economies Conclusion to literature review

5 4. Methodology: Literature Review Hedonic pricing regression Repeat sales model Research Methodology Data collection Data preparation Overview of variables Hedonic pricing model Limitations and assumptions Data Modelling Rosebank variables and correlation analysis Rosebank outlier analysis Rosebank distribution Forward stepwise regression of Rosebank model Final Rosebank model Centurion variables and correlation analysis Centurion outlier analysis Centurion distribution Forward stepwise regression of Centurion model Final Centurion model Empirical Results Primary Rosebank results Secondary Rosebank results Primary Centurion results Secondary Centurion results Discussion and Conclusion Areas for Further Study Bibliography

6 11. Appendices Appendix 1 Sample of data Appendix 2 Aerial view of Rosebank and Centurion Appendix 3 Artist impression of completed stations LIST OF FIGURES Figure 1 Gautrain railway transit stations Figure 2 Average house price inflation Figure 3 House price inflation by area price category Figure 4 Rosebank 1 km impact area Figure 5 Aerial view of Rosebank subway station under construction Figure 6 Centurion l km impact area Figure 7 Aerial view of Centurion above ground station under construction Figure 8 Histogram of residuals for Rosebank showing a normal distribution Figure 9 Histogram of residuals for Centurion showing a normal distribution...47 Figure 10 Aerial view of Rosebank Figure 11 Aerial view of Centurion Figure 12 Artist impression of Rosebank station..63 Figure 13 Artist impression of Centurion station..63 6

7 LIST OF TABLES Table 1 Transit station and neighbourhood attributes Table 2 List of variables Table 3 Summary of correlation analysis and significance level (P-Value)-Rosebank...39 Table 4 Sample of Cook statistic for Rosebank data set Table 5 Rosebank stepwise regression results.. 42 Table 6 Variables deleted from the Rosebank data set.43 Table 7 Variables in the final Rosebank model Table 8 Final statistical model used for Rosebank...44 Table 9 Summary of correlation analysis and significance level (P-Value)-Centurion...45 Table 10 Sample of Cook statistic for Centurion Table 11 Centurion stepwise regression results Table 12 Variables deleted from the Centurion data set...49 Table 13 Variables in the final Centurion model..49 Table 14 Final statistical model used for Centurion.50 Table 15 Sample of data 61 7

8 ABSTRACT This study analyses the effects of the Gautrain Rapid Rail Link on residential sectional title property prices within close proximity to the railway transit stations. Two railway stations Rosebank and Centurion are examined. The study focuses on the planning and construction phase of the railway line and examines sectional title property transactions from 2000 to A hedonic pricing model is estimated accounting for the individual property characteristics, neighbourhood demographics and accessibility features of each property. The hedonic methodology aims to highlight and evaluate each individual characteristic or explanatory variable of a property s value. The study employs a cross-sectional forward stepwise regression analysis to discard insignificant or highly correlated variables and thereby strengthen the robustness of the model. A model was generated for both the Rosebank and Centurion station for comparative purposes. The study concluded that residential sectional title property values within a 1km distance of the Gautrain transit stations have been positively affected during the planning and construction phase of the Gautrain project. The estimated coefficient of the distance and Copyright transaction variable were found to be significant and correlated, UCT thereby confirming the study s hypothesis. 8

9 1. AREA OF STUDY 1.1 THE GAUTRAIN RAPID RAIL LINK The Gautrain Rapid Rail Link project was announced in February 2000 and was the largest transport project in Africa at the time of announcement. Johannesburg is recognised as the financial and economic capital of Africa and has experienced substantial economic growth over the last decade and a half. This economic growth has increased traffic congestion in and around the Gauteng region. These worsening traffic problems, together with the growing need for access to reliable transport, may shape land usage, land values and densities around the new transport nodes provided by the Gautrain Rapid Rail Link. On 25 th April 2004 the final environmental authorisation was given and the final route was confirmed for the Gautrain Rapid Rail Link. On 28 th September 2006, official construction began on the Gautrain and on 8 th June 2010 Phase 1 of the Gautrain was opened to the public. This first phase included the link between Sandton station, Marlboro station, Rhodesfield station and OR Tambo airport. The second phase will be completed during 2011 and will include all the remaining stations. When complete, the Gautrain will consist of an 80km ten- station commuter railway line linking Johannesburg and Pretoria with OR Tambo airport 1. Figure 1: Gautrain railway transit stations Source 1 Information retrieved from 9

10 1.2 PROPERTY VALUES IN SOUTH AFRICA South Africa experienced a boom in the residential property market beginning in late 2002 and rising to its peak in 2005 (Figure 1 below). Real house price inflation has been declining since early 2005 from a level of 30% year-on-year. The period 2005 through to 2007 still saw exceptional growth in real property prices, although at a declining year-on-year rate. Real property price growth has been negative for a two-year period between 2008 and This was largely due to the global economic crisis in A pick up in real property price inflation once again occurred in 2010 (First National Bank, 2010). Housing markets are intrinsically local in character, with effects differing in each geography, price band and neighbourhood (Tsatsaronis & Zhu, 2004). However, the growth in the South African property market has been experienced across all price bands (Figure 2 below). The influences on property values over the long term include growth in household disposable income, gradual shifts in demographics, level of interest rates and permanent features of the tax system that might encourage home ownership. The determinants of house prices can also include the availability and cost of land and the cost of construction and investments to improve the quality of the existing housing stock (Tsatsaronis & Zhu, 2004). Access to amenities within the environment such as commercial facilities and reliable transport can also have a positive impact on property values (Bowes & Ihlanfeldt, 2001). Importantly, little research is available on the topic in developing economies (Cervero & Susantono, 1999: Li, 2006). Influences such as access to transport have not been well documented in South Africa and this study aims to explore some of these effects. Figure 2 Average house price inflation Source First National Bank 10

11 Figure 3 House price inflation by area price category Source First National Bank 1.3 PURPOSE AND OBJECTIVE OF THE STUDY The purpose of this study is to ascertain empirically whether the Gautrain Rapid Rail Link, during the planning and construction phase of the railway line, has had an effect on residential sectional title property prices within close proximity of the railway transit stations. Two transit stations Rosebank Station and Centurion Station were chosen for examining these effects. The stations are situated in neighbourhoods that have different suburb and station characteristics. The study will seek to explore the local conditions at the two different stations, to see if any differing localised effects and influences are attributable to each station and how these effects may vary. The objective is to conduct a quantitative empirical study, using a hedonic methodology, to ascertain the effects that each station has had on surrounding residential property values. While extensive literature exists on the impact of rail transit systems on residential property values in developed economies, little research is available on the same topic in developing economies. This may be because more of these rail systems are prevalent in developed economies. Additionally, developed economies may also provide better records of data in terms of property transactions than developing economies (Cervero & Susantono, 1999: Li, 2006). The empirical results derived from this study will therefore contribute to the literature currently available in this field of research. The need for public transport infrastructure in South Africa is also apparent. Land values and the patterns of land use in a city can be linked to urban problems such as neighbourhood development and transportation (Brodsky, 1970). This study will contribute to the literature and particularly, future South African research on this subject. 11

12 1.4 BACKGROUND OF THE STUDY The Gautrain Rapid Rail Link represents a groundbreaking infrastructural development that has far-reaching economical, social and demographic implications for the areas in close proximity. The very nature of the accessibility allowed by the Gautrain stations may in fact have a bearing on land use patterns that may emerge in the future for areas in close proximity to the stations. The evidence suggests that land use patterns are already changing, as substantial and significant urban renewal projects are already progressing around the Gautrain s ten stations. The Zone Mall is part of one of the growing commercial precincts in Johannesburg, with direct pedestrian access to the Gautrain Rosebank station. A R600 million mixed-use development, which aims to upgrade and integrate the Zone Mall, the Firs Shopping Centre and the Galleria Mall, is currently under development and in progress. In Hatfield, Pretoria, the construction of 4000sqm of retail space and 693 residential units is underway with an initial investment of R280 million. Properties close to the Rhodesfield station have been redeveloped and rezoned, with units being sold to developers for retail, mixed use and highdensity residential purposes. Sandton and Midrand have been experiencing high growth and rezoning and the advent of the Gautrain has bolstered some pre-existing projects, such as the multi-billion rand Zonki lzizwe development in Midrand 2. The question then arises as to how the Gautrain project has affected existing residential property values in close proximity to the railway transit stations. The following additional anecdotal evidence about the effects of the Gautrain on residential property values was collected: - Gisella Seidel from Pam Golding Properties, a residential estate agent in the Rosebank area, was contacted telephonically. Ms. Seidel reported that the market reacted negatively upon the announcement of the Gautrain project but that residential property around the Rosebank station is now more popular. She did, however, report that property prices have not been affected to date but that she anticipates property prices in proximity to the Rosebank station to command a premium in future. Lydia Johnston from Lew Geffen Sotheby s, a residential estate agent in the Rosebank area, was contacted telephonically. Ms. Johnston reported that the initial anticipatory effects of the Gautrain project were negative in the Rosebank area. She reported that she initially experienced difficulties selling residential property in close proximity to the 2 Information retrieved from Lightstone Risk Management, 12

13 Gautrain route. She felt that that the current effects are neutral, while the projected effects are positive concurring with Ms. Seidel s opinion above. Mariandel Pelser from Seeff Properties, a residential estate agent in the Centurion area, was contacted telephonically. Ms. Pelser reported a negative anticipatory reaction to the Gautrain project, with no effect currently being experienced for properties in proximity to the Centurion station. She did, however, anticipate a positive effect going forward again concurring with Ms. Seidel and Ms. Johnston s opinions. By contrast, a 2008 press release by Lightstone 3 indicated that the Gautrain had already had a positive effect on property values within close proximity to stations. Lightstone analysed repeat sales within 2km and 3km of each station and found an increase in residential property activity and value in these areas. The anecdotal qualitative evidence collected regarding the effects of the Gautrain on residential property values was contrasting. It was the opinion of the estate agents interviewed that the Gautrain has not had an effect to date on residential property values for properties situated within close proximity of the railway transit stations. This study aims to empirically prove the effects of the Gautrain on residential property values. 1.5 GAUTRAIN STATION SELECTION The purpose of this section is to explain the station selection process that was followed in identifying the two stations to examine for this study. The objective of the selection process was to select two stations with differing neighbourhood and station characteristics but similar stages in the neighbourhood development lifecycle. The Gautrain s ten stations are situated in unique neighbourhoods with varying local characteristics and demographics. The station neighbourhoods all have differing residential mosaics, reflecting the three dimensions of social space (Prinsloo & Prinsloo, 2004): 1. Income levels and residential property values; 2. Ethnicity; and 3. Stage in the neighbourhood lifecycle. The Gautrain railway transit stations are situated in neighbourhoods that are experiencing different stages in the neighbourhood lifecycle and with differing residential property values. 3 Lightstone provides a live interface from the South African Deeds registry and provides up-to-date residential sales information for various suburbs of South Africa 13

14 The station characteristics, as well as the sectional title property values in each suburb, are shown in table 1. Table 1 Transit station and neighbourhood attributes STATION CHARACTERISTIC PHASE NEIGHBOURHOOD LIFECYCLE 4 SECTIONAL TITLE PROPERTY HIGH MEDIAN VALUES IN 2009/ SECTIONAL TITLE PROPERTY LOW MEDIAN VALUES IN 2009/2010 RESIDENTIAL PROPERTY VALUES PARK CENTRAL UNDERGROUND CONSTRUCTION DOWNGRADING R R LOW ROSEBANK UNDERGROUND CONSTRUCTION FULL OCCUPANCY R R HIGH SANDTON UNDERGROUND OPERATIONAL FULL OCCUPANCY R R HIGH MARLBORO ABOVE GROUND OPERATIONAL RENEWAL N/A N/A MEDIUM RHODESFIELD ABOVE GROUND OPERATIONAL DOWNGRADING R R LOW OR TAMBO ABOVE GROUND OPERATIONAL DEVELOPMENT R R MEDIUM MIDRAND ABOVE GROUND CONSTRUCTION DEVELOPMENT R R MEDIUM CENTURION ABOVE GROUND CONSTRUCTION DEVELOPMENT R R MEDIUM PRETORIA CENTRAL ABOVE GROUND CONSTRUCTION DOWNGRADING R R LOW HATFIELD ABOVE GROUND CONSTRUCTION RENEWAL R R MEDIUM The first step in the station selection process was to group the stations according to their differing neighbourhood and station characteristics. These groupings were done according to subway stations and above ground stations and the value of sectional title residential property within each station neighbourhood. The purpose of this exercise was to select two stations with dissimilar characteristics - one station with high neighbourhood residential property values and one station with medium neighbourhood residential property values. The next step was to select two stations that are located in neighbourhoods undergoing similar phases of the neighbourhood lifecycle. For the purposes of this study it was decided to select two stations that are situated in neighbourhoods experiencing development, as opposed to decline. It was also decided to select two stations in areas that have not been experiencing large scale commercial development over the last couple of decades. Furthermore, as the study will 4 Stages of residential development (Prinsloo & Prinsloo, 2004) 5 Sectional title property median values: High is the median for the top 50% of properties sold and Low is the median for the bottom 50% of properties sold during the 2009/2010 period. Source: SAPTG 14

15 focus on the effects of the Gautrain stations on residential property values, stations such as OR Tambo station and Marlboro station could not be selected due to the lack of residential property within close proximity to these stations. High residential property values/subway - Sandton Station Rosebank Station Medium residential property values/above ground line - Centurion Station Midrand Station The Sandton and Midrand stations are situated in proximity to decentralised commercial nodes that have been experiencing substantial growth over the two decades. The Rosebank and Centurion stations are situated in two areas that have not experienced substantial commercial development over the last two decades. The Rosebank and Centurion stations were therefore selected for comparative purposes for this study. 1.6 BACKGROUND TO ROSEBANK AREA Rosebank is situated in one of the oldest areas in Johannesburg and was part of the original farm Rosemill Orchards owned by a Mr. Lorenco. Auctioneer Richard Currie sold the first plots in the area in The city council renamed all the streets in 1919 in honour of British admirals of World War One. From 1950 onwards Rosebank attracted many commercial developers and the area became one of the prime shopping areas in Johannesburg. In the late 1990s the area experienced some decline but this reversed in the early 2000s, with new developments taking place and the nightlife trade being monitored and controlled. Very little undeveloped land exists in Rosebank and most new redevelopments require demolition of older existing properties. Rosebank was also one of the first suburbs in Johannesburg to have the trolley-bus transport that ran along Oxford Road (Joburg Happenings, 2010). Today, Rosebank remains one of Johannesburg s up-market multi cultural suburbs that is home to one of Johannesburg s largest business districts. This business district is home to some of Johannesburg s better-known shopping malls, including The Mall of Rosebank, The Firs, The Zone Shopping Centre and The Mews. The Gautrain station is located within the same precinct as these shopping centres. Some of the best schools in Johannesburg are also situated in Rosebank and the surrounding suburbs. These include Rosebank Convent, Kingsmead, St Johns and Rodean. 15

16 Figure 4 - Rosebank 1 km impact area Source Google Maps Crime levels in Rosebank and the surrounding areas are varied. The areas with high crime near the Rosebank station are: Melrose, Melrose Estate, Dunkeld West and Parkwood. These areas are one step above the areas with medium crime: Rosebank, Birdhaven, Parktown North and Melrose North (South African Property Transfer Guide, 2010). The Rosebank precinct consists of approximately: 251 Full title properties consisting of plots with a median land size of square metres. 463 Sectional title properties with a median floor area of 68 square metres. The predominant price band for full title properties is between R and R The predominant price band for sectional title properties is between R and R The median sales price for full title properties was R and the average sales price was R This was for a total of 5 transactions during the first half of The median sales price for sectional title properties was R and the average sales price was R This was for a total of 12 transactions during the first half of 2010 (South African Property Transfer Guide, 2010). 16

17 Approximately 65% of the land in Rosebank is full title, while 35% of the land is sectional title. Figure 5 Aerial view of Rosebank subway station under construction Source Copyright 1.7 BACKGROUND TO CENTURION AND LYTTELTON AREA UCT Centurion originated from the Lyttelton Township that was formed from the Droogegrond farm in Lyttelton grew from a number of townships and farming areas to cover an area of twenty thousand hectares in Lyttelton was renamed as the City of Verwoerdburg in 1967 after Prime Minister Hendrik Verwoerd. After South Africa s first democratic election in 1994 the Verwoerdburg City Council was disbanded with new local authorities coming into existence. The name Centurion was accepted in 1995 by the new city council (CenturionNet, 2010). The main business district of Centurion and Lyttelton is Centurion Central. This area is a mix of shopping malls, offices and light industrial space, including car dealerships. The central business district was in decline but has been experiencing some rejuvenation over the last decade. An example of this rejuvenation is Centurion Mall, the regional shopping centre, which underwent a major renovation in

18 Figure 6 - Centurion l km impact area Source Google Maps Over the last two decades Centurion and the greater Lyttelton area have seen rapid development. New estates and townhouse complexes have been erected on once open rural land. These extensions contain many new complexes that are contained within gated security communities. These gated communities are perceived to have less crime and the higher property prices reflect the premiums that are paid for extra security measures. The Lyttelton precinct consists of approximately: 402 Full title properties with a median land size of 322 square metres Sectional title properties with a median floor area of 97 square metres. The predominant price band for full title properties is between R and R The predominant price band for sectional title properties is between R and R The median sales price for full title properties was R and the average sales price was R This was for a total of 3 transactions during the first half of The median sales price for sectional title properties was R and the average sales price was R This was for a total of 180 transactions during the first half of 2010 (South African Property Transfer Guide, 2010). Approximately 7% of the land in Lyttelton is full title, while 93% of the land is sectional title. Figure 7 - Aerial view of Centurion above ground station under construction 18

19 Source 2. RESEARCH HYPOTHESIS 2.1 RATIONALE FOR HYPOTHESIS The literature review suggests that there is a strong correlation between residential property values and access to railway transit stations. However, these effects may vary and in some instances a negative correlation is experienced. Most of the research has taken place in developed economies with little research available on the effects in developing economies. This study seeks to explore the effects of the Gautrain or residential property values during the planning and construction phase of the railway line. 2.2 PRIMARY HYPOTHESIS This study aims to test the following primary hypothesis: Primary hypothesis Residential property values within a 1km distance of the Gautrain transit stations have been positively affected during the planning and construction phase of the Gautrain project. 19

20 3. LITERATURE REVIEW The purpose of this section is to review the effects that commuter railway transit stations can have on the value of residential property values. It does this by reviewing each salient contributing factor and the effects, both positive and negative, which have had a bearing on the results of studies conducted internationally. 3.1 A BRIEF HISTORY OF ESTIMATING LAND VALUES The estimation of land values can be traced back to von Thünen (1830), who attempted to explain variations in the pricing and value of farmland. Von Thünen (1830) postulated, by comparing farmland with similar fertility attributes, that distance to the market place where produce was sold had an impact on the value of the land. Robert Murray Haig (1926) was one of the first economists to build on the work of von Thünen and to explicitly investigate the relationship between transport and land values. He postulated that site rents and transport costs are vitally connected through their relationship to the distance travelled and the costs of travel. Higher site rentals can therefore be made for sites with accessibility to low transport costs (Alcaly, 1976). The bid-rent model, introduced by Alonso (1964), is one in which an agent will be prepared to pay a certain amount for land, depending on the location of the land. This leads to a rent yield gradient that declines, for sites with similar utilities, as the distance increases from the central business district. In Alonso s (1964) studies, the dominant factor explaining the difference in land values was the distance from the central business district and the associated transport costs. These early investigations were not without their shortcomings and the body of literature investigating the relationship between land values and access to transportation has since grown into a complex field of study. Brodsky (1970) associated land values and the patterns of land use in a city and linked land values to urban problems such as neighbourhood development and transportation. Brodsky (1970) put forward the theory that an increase in the value of land and an increase in residential density occur as one approaches the centre of a large city. This is attributable to the competitive bidding process of potential residents seeking land where transport costs to their places of employment are the lowest. Furthermore, Brodsky (1970) investigated how potential residents are willing to substitute higher residential densities for lower transport costs. He found that lower transport costs increase residential densities and that transport costs and residential densities act together to create an increase in land values as the city centre is approached (Brodsky, 1970). 20

21 Brodsky s (1976) theory has been accepted by other scholars in the field and may be summarised as follows: as one approaches the centre of a city and the attractiveness of property increases, the property prices are pushed up by higher demand and by the bidding process. While the attractiveness of living close to the city centre increases property prices, investments in transport infrastructure reduce this demand by some degree by allowing residents to settle around railway stations (Fejarang, 1994). It follows that properties close to these new transport investment areas will enjoy the benefits of decreased transport costs and decreased travelling time. It is then to be expected that property values close to these railway stations will increase, in relation to their proximity to these railway transit stations (Fejarang, 1994). Recent studies have shown that proximity to transport nodes and specific commuter railway stations impacts upon property value because of four main factors two having a positive effect, two a negative effect (Bowes & Ihlanfeldt, 2001). 3.2 POSITIVE EFFECTS ON PROPERTY VALUES NEAR TRANSPORT NODES The first positive factor is the access advantage that living close to a station provides in terms of reduced travelling time. This translates into increased residential property values, as many commuters are willing to pay for the convenience. Reliable transport and a reduced commuting time can also come with the added but less tangible benefit of reduced stress for the commuters (Bowes & Ihlanfeldt, 2001). The second positive factor is the development of neighbourhood commercial services. The development of retail and commercial space often occurs in close proximity to the railway stations, due to the advantages of increased rail accessibility. Access to these commercial services is an added benefit for the households close by, irrespective of whether they use the train service or not. This access to additional commercial facilities can have a positive impact on property values (Bowes & Ihlanfeldt, 2001). Examples of this kind of urban renewal and development can be seen around the stations of the Gautrain. An example in Rosebank is the R600 million rand mixed use development that is currently in progress. This development includes the upgrading of the commercial precinct comprising The Zone Mall, together with the Firs Mall and the Galleria Shopping Centre. A further example is the urban renewal project that includes an investment of R280 million for retail and residential space in Hatfield. 21

22 These improvements to local neighbourhood commercial services will positively influence residential property values in much the same way as additions to individual properties amenities such as the addition of an extra room or a swimming pool - would (Carruthers, Kilpatrick, Krause, & Throupe, 2007). 3.3 NEGATIVE EFFECTS ON PROPERTY VALUES NEAR TRANSPORT NODES The first negative factor affecting property value in proximity to stations is the externalities that come with the development of the railway stations. These externalities can include noise pollution and unsightliness, especially if the station includes an open parking lot (Bowes & Ihlanfeldt, 2001: Brons, 2003). The second negative factor is crime. Crime may arise as a result of the improved accessibility to the surrounding areas that the station provides (Bowes & Ihlanfeldt, 2001). In the study conducted by Bowes & Ihlanfeldt (2001), the negative factors listed above were found to have had an impact on property values in high-income areas. However, the areas studied were near downtown Atlanta, Georgia and the stations had adjacent open parking lots. The study showed a reduction in the value of properties within 0.4 km of one station but an increase in the value of properties between 0.4km and 0.8km from the station. Debrezion, Pels, & Rietveld, (2006) found that the value of properties situated alongside railway lines is negatively influenced by their proximity to the lines, postulating that this is due to the noise levels and unsightly views. However, they found a positive effect on residential property values for properties that were located near railway stations. Almeida & Hess (2007) conjectured that residential property immediately adjacent to a railway station might be expected to decrease in value due to traffic congestion, while properties radiating out and within walking distance from the station should increase in value. 3.4 DISTANCE FROM THE CENTRAL BUSINESS DISTRICT Bowes & Ihlanfeldt (2001) found that people are prepared to pay a premium for accessibility to transport options and these premiums increase as the distance from the central business district increases. They found the largest positive effect occurs at stations that are located the furthest away from the central business district and within a radius of 0.4kms to 0.8kms of the station. This corroborates the findings of Fejarang (1994) that residential property values increase more markedly with proximity to the stations, the further one gets from the central business district. It was postulated that this was due to the added accessibility and ease of transport that these stations offer. Fejarang s (1994) findings were substantiated by a study by 22

23 Lin (2010) on the effects of the Shanghai Rapid Transit Line 8 on residential property values. The study revealed that the transit line had significant incremental effects on the value of residential property located within 1.5km of each station. More importantly, the study found that each station had a varying degree of impact on residential property values. The stations closer to the central business district had a weaker incremental effect on residential housing prices than stations further from the central business district. 3.5 HIGH INCOME/LOW INCOME AREAS Gatzlaff and Smith (1993) found that the Miami Metrorail had a weak but positive effect on property values in high-priced neighbourhoods experiencing growth. However, they found no impact on property values in low-income neighbourhoods experiencing decline. This stands in contrast to the study conducted by Bowes and Ihlanfeldt (2001), who found that some highincome neighbourhoods were negatively affected. This was explained by the fact that these high-income neighbourhoods were in close proximity to downtown and the station studied had an adjacent open parking lot. Bowes and Ihlanfeldt (2001) deduced that the parking lot had a resultant effect on crime levels, which in turn affected residential property values in close proximity to the station (Bowes & Ihlanfeldt, 2001). 3.6 PLANNING, CONSTRUCTION AND OPERATIONAL PHASES OF RAILWAYS The impact on residential property values within close proximity to railway transit stations may differ, depending on which stage the railway line is at within its lifecycle. The different life cycle stages are the planning, construction and operational phases of the railway line. The results may differ depending on when the research was conducted and which stage of the railway lifecycle the research focused on (Nelson, 1999). A study done by Gatlaff & Smith (1993) on the impacts of the new development of the Miami Metrorail found some evidence of an increase in property values upon the announcement of the development of the Miami Metrorail. Corroborating these findings, a study by Chau & Ng (1998) found that the expectation effect of the Hong Kong Rapid Transit System was positive, as residential property values increased before the railway line become operational. A study conducted by McMillen & McDonald (2004) on the Midway Rapid Transit Line in Chicago showed a significant increase in property values in anticipation of the line being built. They found an overestimation of home values occurred in anticipation of the railway transit line opening but that a substantial increase in home values was maintained in the subsequent operational years of the transit line. The overall result showed that the railway 23

24 transit line created additional value for residential property values located near the stations during all the lifecycle phases of the railway transit line (McMillen & McDonald, 2004). A study by Lin (2010) on the Shanghai Rapid Transit Line revealed an increase in residential property values in proximity to the stations during the planning, construction and operating phases of the railway transit line. The research also showed that the incremental effects on residential property value were strongest in the early building and operating phases and that a fallback of prices was experienced during the midterm building period of the line (Lin, 2010). Lee (1997) chose to study a specific individual subway station, Kundae-Ipku in Korea, in which he found land values rising directly before and after the opening of the subway station. 3.7 SUBWAY SYSTEMS Various studies investigating the effects on residential property values in proximity to subway stations have been conducted. Most of these studies are consistent in their findings that subway stations exert a positive influence on residential property values for properties located close to these stations. Studies conducted in Toronto (Bajic, 1983) and Atlanta (Nelson, 1992) showed a positive influence on residential property values in relation to proximity of subway stations. However, the impact is different within each subway station submarket, with influencing factors being property location in relation to central business district, property position in relation to subway stations, land use zoning and building type (Bajic, 1983: Nelson, 1992). A study done by Dewees (1976) after the construction of the Toronto Subway also revealed positive effects on residential property values. The Toronto Subway was built to replace an existing tramway and the study found a major impact on the value of residential properties situated within 0.5kms from the subway stations. A Study by Chi-Hau & Jen-Jia (2004) on the newly constructed Taipei Subway, the first subway infrastructure project in Taiwan, supports these findings. The study found that property values increased significantly after the opening of the Taipei Subway. Additionally, the subway system was found to have a positive impact by the proliferation of commercial facilities in the area following its construction (Chi- Hau & Jen-Jia, 2004). Interestingly, a study conducted by Chang-Hee, Myung-Jin, & Hyeon, (2003) on the impact of the Seoul Subway Line 5 on residential property values revealed that the line only had an impact in anticipation of the line opening, with very modest gains experienced thereafter. It was argued that Seoul s accessible and expansive transportation network meant that proximity 24

25 to Seoul Subway Line 5 had less impact on nearby residents and, therefore, on property prices (Chang-Hee et al., 2003). In contrast, a study conducted by So, Tse, & Ganesan (1997) on the effect of subway stations in Hong Kong on residential property prices revealed significant increases in property values for properties located near the subway stations. 3.8 DEVELOPED ECONOMIES There have been numerous studies in developed economies that have estimated the effects of transport on property prices, with the majority of these studies being fairly consistent in their findings. The predominant norm is that proximity to railway transit stations exerts a positive influence on residential property values (Cervero & Susantono, 1999). However, as to be expected, there are some contrary findings in the literature with regard to the effects of commuter rail investments on residential property value. The explanation for these contrary findings may be the result of local factors in each city, combined with the long-term effects of land use value and the economic periods during which the studies focused (Gatzlaff & Smith, 1993). Research is often done in thriving cities where population and economic growth is being experienced, while contradictory research is often conducted in cities that are experiencing population and economic decline (Almeida & Hess, 2007). Commuter railway stations have had some of the most significant documented impacts on property values in Toronto, Portland, Oregon, Washington D.C and Philadelphia. The Toronto case is interesting in that the first line was opened in 1954 and the last completed in 1974, resulting in a total of sixty stations spread over 57.6km. One study showed that property values were higher near the stations (Bajic 1983). Another study highlighted the fact that Toronto has strong development controls, which has resulted in a lack of highways being built. This may have influenced the stations strong impact on residential property prices (Gatzlaff & Smith, 1993). A study carried out by Al-Mosaind, Dueker & Strathman (1993) examined the value of single-family homes within five hundred meters of Portland Oregon s Eastside Light Rail Transit Line. The study found that these homes netted a 10% sales premium compared with those further from the line. The Metro Rail in Washington comprises an extensive 161km network, in which the first station was opened in In the areas around some of the suburban stations the impacts on land value were marked, with large-scale commercial developments happening in close proximity to the station (Rice Center, 1987). At the Pentagon City Station, townhouses within 1km of the station sold for an average price of $ more than comparable units farther away from the Metro service (Rice Center, 1987). 25

26 Studies that have been done on the Lindenwold Line, a 183km thirteen station line that runs through the New Jersey suburbs of Philadelphia, have shown modest but significant increases in residential property values in proximity to the stations (Rice Center, 1987). The study completed by the Rice Center showed an increase in value of approximately 7% per house for properties in proximity to the stations. A more recent study conducted by Voith (1991) found that properties with easy access to the Philadelphia Rail Network commanded an average value of 6,4% more than other similar properties in the area. Similarly, a study by Brod and Lewis-Workman (1997) found a significant impact on residential property values within 0.76km and 1.6km of the Portland Light Rapid Transit System. This positive effect on residential property values of close proximity to transit stations was further supported by the two studies conducted by Cervero and Duncan (2002). They found that proximity to two transit railway stations in Los Angeles had a positive effect on the value of residential properties situated within a distance of 0.4km to 0.8km of the stations. A study done by Debrezion, Pels, & Rietveld (2006) on the impact of commuter railway networks on house prices in the Netherlands revealed a positive correlation between house value and proximity to railway stations. The study, which was conducted using house sales over a seventeen year period from 1985 to 2001, revealed that houses within close proximity to the stations earned a premium of 25% compared to houses that were situated 15km or more from the stations. Interestingly, they also found that doubling the frequency of the rail services to some stations resulted in an additional premium of 2,5% to 3,5% for houses situated closer to the railway stations with these increased services. 3.9 DEVELOPING ECONOMIES While extensive literature exists on the impact of rail transit systems on residential property values in developed economies, little research is available on the same topic in developing economies. This may be because more of these rail systems are prevalent in developed economies. Additionally, developed economies may also provide better records of data in terms of property transactions than developing economies (Cervero & Susantono, 1999: Li, 2006). It is widely accepted that improvements in transport infrastructure are critical in building and achieving sustained economic growth and development in developing economies (Button, 1993: Leinbach, 1995). It can be conjectured that any access to transport infrastructure in developing economies should increase land value. This idea is supported by research as far back as the theory first proposed by von Thünen (1830) and is borne out by the studies that 26

27 have been done. For example in Bogota, Columbia, access to the Bus Rapid Transport System has resulted in an increase in residential property values in close proximity to the Bus Rapid Transport stations (Rodríguez & Targa, 2004). A study by Lin (2010) of the Shanghai Rapid Railway Transit Line 8 revealed that the transit line had significant incremental effects on the value of residential property values located within 1.5km of a station. The study found that each station had a varying degree of impact on property values, with stations closer to the central business district having a weaker incremental effect on residential housing prices. The study showed an increase in property values of 7.2% for properties within 0.5km of the stations and 3.2% for properties between 0.5km and 1km of the stations. It also found the radius of influence to be 1km for central city stations and 1.5km for suburban stations. Another study by Li (2006) on the recently constructed Guangzhou Metro Line 2 in China revealed that the line had a positive impact on the valuations of residential properties near the stations. It was found that even in a dense city where public transport has been dominant, the construction of a metro line rail could still have a significant effect on residential real estate values within walking distance of the metro stations. Corroborating these findings, a study done by Celik & Yankaya (2006) revealed that investments in commuter rail lines in the Izmir region of Turkey have increased residential property values for properties in the vicinity of the railway stations CONCLUSION TO LITERATURE REVIEW Most of the studies described in the literature concur that there exists a positive correlation between residential property values and proximity to commuter railway stations. The extent of the influence may vary depending on unique local factors, with other considerations, such as distance from the central business district, distance from the railway transit station or access to a commercial core also having a bearing (Armstrong, 1994: Cervero, 1984: Cervero & Duncan, 2002a, 2002b). In some cases railway transit stations have been shown to have a negative influence due to crime, pollution or aesthetics (Bowes & Ihlanfeldt, 2001: Brons, 2003). The results may also differ depending on when the research was conducted and which stage of the railway lifecycle the research focused on (Nelson, 1999). The literature points to the fact that the effects of railway transit stations are the greatest as distance increases from the central business district (Brodsky, 1970). There may be variations 27

28 in this study due to Johannesburg and Pretoria not having one central business district but a number of decentralised commercial nodes that exist in proximity to the Gautrain stations. The literature is fairly extensive with regards the effects of commuter railway transit stations on residential property prices in developed economies; however, there is not a great deal of depth in the literature with regards to the effects in developing economies. This study will aim to explore these effects and the gaps that may exist in the literature around the effects of railway transit stations on residential property prices in developing economies. 4. METHODOLOGY: LITERATURE REVIEW The purpose of this section is to review the literature as to the types of methodologies that are used for studies of this nature. Two predominant methodologies have been identified within the literature. 4.1 HEDONIC PRICING REGRESSION Hedonic Pricing Regression (Andersson & Shyr, 2010: Bowes & Ihlanfeldt, 2001: Debrezion, Pels & Rietveld, 2006: Dewees, 1976: Gatzlaff & Smith, 1993: Lin, 2010: Li, 2006) The Hedonic methodology has emerged as the accepted methodology for studies of this nature and can be traced back to Rosen (1974). This methodology aims to highlight and evaluate each individual characteristic or explanatory variable of a property. It is based on the theory that the value of a house is representative of the amenities and services that are available to it. The theory is to reproduce these individual characteristics in different combinations, using regression analysis, in order to estimate each characteristic independently (Rosen, 1974). The sum of the different characteristics will theoretically add up to the total value of the property. For instance, ease of access to transportation may have a direct bearing on the price of the property and can be awarded a material value. The types of amenities and services that are captured in this type of study are usually twofold. Firstly, there are the unique characteristics of each property such as building age, size and the amenities that are available within each property. Then there are the characteristics of the environment in which the property is found, such as distance from amenities such as schools and shopping malls. Neighbourhood demographics such as income levels, age and crime are also captured as part of the external environmental characteristic. 28

29 The key with this type of methodology is defining and choosing the explanatory variables that will be used in the regression analysis. In a study done by Dewees (1976), the explanatory variables were grouped under four headings: Structure quality Structure quality refers to the age, overall condition and floor space or lot size of the property. Structure quantity Structure quantity refers to property size, number of rooms and square meterage of each residence. Neighbourhood characteristics Neighbourhood characteristics include zoning restrictions, distance to environmental amenities, neighbourhood demographics, crime levels and neighbourhood income levels. Transportation access The linear distance from the commuter railway station to each individual property. Bowes & Ihlanfeldt (2001) used the physical characteristics of the property as well as the neighbourhood characteristics, including access to transport, as the explanatory variables. The explanatory variables used in Gatzlaff & Smith s (1993) study included total living area, lot size, age of property and distance of the property from the station. Transaction time to identify property transactions before and after the announcement of the Metrorail project was used as a dummy variable. This was to compare the effects of the stations before it was announced and after it was constructed. In the Li (2006) study the explanatory variables included the building characteristics, the surrounding facilities and accessibility features of each property. In the Hedonic model used in the study conducted by Bowes & Ihlanfeldt (2001) the sale price was deflated by the consumer price index to gain an accurate picture of property value as the dependent variable A hedonic study conducted by Chi-Hau & Jen-Jia (2004) highlighted some of the problems that have been encountered in previous hedonic studies. Firstly, the explanatory variables, if not chosen correctly, may bias the results. Secondly, too many independent variables will negatively influence the statistical freedom and theoretical assumptions of the model. Thirdly, the assumptions of the models are generally incompletely examined. For the Chi-Hau & Jen- Jia study, price was used as the dependent variable. Floor space, building age, distance from public facilities, distance from the CBD, economic growth rate and consumer price index were used as the explanatory variables. Prices of property before and after the announcement 29

30 of the railway project were used as a dummy variable, to compare the effects of the stations before and after it was constructed. 4.2 REPEAT SALES MODEL Repeat Sales Model (Gatzlaff & Smith, 1993: Li 2006: McMillen & McDonald, 2004) Along with the hedonic methodology, the repeat sales methodology is also used for studies of this nature. The repeat sales methodology uses transactional data over time for the same property. A property price index can be constructed for properties whose value is affected by the close proximity and access to railway transit stations. This can then be compared with an overall property price index, to observe the difference. This technique has come under scrutiny and been criticised because of the small sample sizes that are obtained after discarding the properties that have only been sold once from the overall data sample (Bover & Velilla, 2002). The assumption implicit in the model is that the individual property attributes or characteristics have not changed as time has elapsed this not always being the case. In addition, certain property and neighbourhood characteristics will not be captured using this model. 5. RESEARCH METHODOLOGY The purpose of this section is to explain how the most popular methodology was adapted for this study. This study adopts a deductive approach using quantitative data only. First the data collection and preparation is discussed; this is followed by an overview of the variables selected for the study. The statistical hedonic pricing model is then discussed and finally the limitation and assumptions of the study are covered. 5.1 DATA COLLECTION All primary data for the study was sourced through South African Property Guide (SAPTG) and the Knowledge Factory. SAPTG provides property related reports sourced from the South African Deeds office. The source is one of the leading service providers in South Africa, providing general statistical information to estate agents, property professionals and the general public. The Knowledge Factory is a marketing insights company that provides demographic information, census statistics and property related information. The study made no use of qualitative data such as interviews or questionnaires. In order to select all sectional title properties within the 1km impact distance of the two stations, the following steps were followed. Individual property deed reports were drawn for 30

31 all sectional title schemes in the neighbourhoods surrounding the stations. These individual reports included all sale transactions for the period 2000 through to These reports included the individual property s X and Y Latitude and Longitude co-ordinates. The X and Y Latitude and Longitude co-ordinates for the Rosebank and Centurion stations were obtained from the Gautrain website. The shortest straight-line distance, in kilometres, was calculated between the two sets of co-ordinates. For comparative purposes all sectional title schemes within a 2km radius of each station were kept in the dataset. Any sectional title scheme that was further than a 2km distance from the station was excluded from the dataset. A total number of 1278 property transactions for the Rosebank station and 4929 property transactions for the Centurion station were used in the analysis. The sectional title schemes contained within the impact area for the Rosebank station fell within the following suburbs - Rosebank, Melrose, Melrose North, Melrose Estate, Dunkeld, Dunkeld West, Birdhaven, Parkwood, Parktown North and Riviera. The sectional title schemes contained within the impact area for the Centurion station fell with the following suburbs Centurion, Lyttelton and Die Hoewes. Copyright The data obtained for each sectional title scheme included; UCT Sectional title scheme name; Suburb; Registered owner; Sales date; Unit floor area in square metres; Distance to closest amenities (shopping centre, medical, education); Average income levels; Neighbourhood crime levels; The individual property X and Y co-ordinates; and All sales for the specific sectional title scheme for the period 2000 to The data was compiled from individual property reports and aggregated into one database for analysis. 31

32 5.2 DATA PREPARATION The following steps were taken to clean and prepare the data for analysis:- The individual sectional title scheme data was sourced and aggregated into one database Data that had blank or incomplete information was excluded from the analysis for example a transaction could include a garage that was registered under a separate title deed to the main property but transferred to a buyer. The selling price for this transaction would be nil as the garage has been included in the main property transaction s selling price. Properties that were further than 2km from the respective stations were excluded. Transactions that were not at arm s length were excluded from the analysis this was determined by removing any property transaction that had an unrealistic nominal value as the sale price. Industry experts were consulted and this was determined to be a sales price that was less than fifty percent of comparable units sold in the sectional title scheme within the same time frame. Categorical data such as crime levels and the stations impact areas were put into numerical format, as described in the following section OVERVIEW OF VARIABLES The value of a property is made up of many different factors, both tangible and intangible. It would be impossible to include all variables that influence a buyer in the regression analysis. Therefore variable selection must be based on assumptions and the availability of data. In this study all the data that was available was considered in the analysis. The explanatory variables used in this study consisted of: the individual property characteristics (Prop), the neighbourhood characteristics (Nei) and the property location and accessibility characteristics (Loc). The sale transaction time variable (Trn) was also introduced into the model to compare residential property values before and during the planning and construction phase of the railway line. The explanatory variables consisted of: Property Characteristics (Prop) Floor size for sectional title properties; and Building age for sectional title properties. 32

33 Neighbourhood demographics (Nei) Suburb demographics: Suburb population, crime levels and average annual household income range. Property location (Loc) Distance from the railway transit stations; and Distance from external amenities: The closest shopping centre, school and medical facility from each property. Time variable (Trn) Sectional title property transactions from 2000 to 2010 within a 2km radius of each station. This will enable comparison of property values both before and after 25 th April 2004 The date that the final environmental impact assessment was approved and the final Gautrain Route confirmed. The explanatory variables used in the study were similar to the variables used in the study done by Li (2006). As described by Hardy (1993) and as used in the Li (2006) study, dummy variables were used to identify the following: Transaction time (TRN) The dummy variable has a value of one if the sales date of the property falls between 25 th April 2004 and 8 th June 2010 notwithstanding the fact that at the time of the study the Centurion and Rosebank stations were still under construction. The dummy variable has a value of zero if the sales date for the property was before 25 th April 2004 the date of the final environmental authorisation and construction period of the Railway transit line. Distance (DIST) The dummy variable has a value of one if the sectional title scheme falls within a 1km radius of the respective station. If the sectional title scheme falls within a 1-2km radius of the respective station the dummy variable has a value of zero. Crime (CRI) The dummy variable has a value of one if the sectional title scheme falls within a suburb that has medium crime levels. If the sectional title scheme falls within a suburb that has high crime levels, the dummy variable has a value of zero 33

34 The dependent variable used in this study is REALPRICE (P) - the sales price of each property deflated by the consumer price index. This was done to gain an accurate picture of the property REALPRICE (P) over time, as the dependent variable (Bowes & Ihlanfeldt, 2001). The table below shows all the specific variables that were used in the regression analysis: Table 2 List of variables VARIABLE DEPENDENT VARIABLE REALPRICE DESCRIPTION The property sale price as recorded at the deeds office and adjusted for inflation using the Consumer price index (CPI) index as provided by Statistics South Africa (Statssa) being the base year with an index of 100. INDEPENDENT VARIABLES Physical Attributes of property M² Unit floor size in M² Amenities in the Environment STATION SCHOOL MEDICAL SHOP Demographics POP CRI Distance DIST Transaction time TRN Distance in km's to Gautrain station Distance in km's to closest school Distance in km's to closest medical facility Distance in km's to closest shopping mall Suburb Population Dummy variable 1 if medium crime, otherwise 0 for high crime Dummy variable 1 if the property is located within 1km of the station, otherwise 0 between 1km and 2km of the station Dummy variable 1 if property sale date is after 25th April 2004, otherwise 0 for transaction between 1st January 2000 and 25th April

35 5.4 HEDONIC PRICING MODEL The hedonic methodology has become the accepted methodology for studies of this nature (Andersson & Shyr, 2010: Bowes & Ihlanfeldt, 2001: Debrezion, Pels & Rietveld, 2006: Dewees, 1976: Gatzlaff & Smith, 1993: Lin, 2010: Li, 2006). The study adopted a similar hedonic regression model as used by Li (2006), with reference made to Lin (2010). The MBA master thesis conducted by Roodt & Standish (2006) provided an additional guide for this study. The following hedonic equation was applied to this study: With: P = factor (Prop, Nei, Loc, Trn) (P) Being the real sales price of the property; (Prop) Being the individual property characteristics; (Nei) Being neighbourhood demographics; Copyright (Loc) Being the property location in relation to amenities - such UCT as the Gautrain station; and (Trn) Being the property sales transaction time variable. The sum of the different characteristics will theoretically add up to the total value of the property. For instance, ease of access to transportation (Loc) or in the case of this study access to the Gauteng transit station may have a direct bearing on the price of the property and can be awarded a material value. The regression equation used in the study can thus be identified as follows:- Y ˆ ( price ) = α + ˆ β 1 Pr op + ˆ β 2 Nei + ˆ β 3 Loc + ˆ β 4 Trn In the equation ˆ Υ is the real price of the property, while β represents the estimated coefficients of the Prop, Nei, Loc and Trn variable. Each significant variable will have a direct bearing on the price of the property and can be awarded a material value. From this process the empirical proven extent and influence of the property location (Loc) variable will be highlighted and observed. To strengthen the model a Cook statistic method was used in order to check for the presence of outliers in the data set. These outliers may have an influence the statistical impact of the 35

36 regression analysis. These observations were excluded from the analysis to further strengthen the model. Multi-collinearity between the variables was addressed by using a forward stepwise regression model. The forward stepwise cross-sectional regression analysis excluded any insignificant variables from the statistical model and also strengthened the robustness of the final model. In many similar studies in the literature a logarithmic or semi-logarithmic form is used as opposed to a linear functional form. The theory offers no guide as to selecting the best model and one should therefore select a model based on best fit (Gatzlaff & Smith, 1993). As the independent variables are identical, the best fit was determined by the model with the highest R 2 as done in the study by Gatlaff & Smith (1993). The final model was run in linear form and logarithmic form for comparative purposes. Both the Rosebank and Centurion models showed a greater R 2 for the linear model compared to the logarithmic model. The Rosebank linear model had an R 2 of 0.74, while the logarithmic model had a R 2 of The Centurion linear model had an R 2 of 0.744, while the logarithmic model had a R 2 of Importantly, the distribution of the data set a requirement for regression analysis was normal for the linear model. It was therefore not necessary to make any transformations to the data. This followed the study done by Li (2006) in which a linear model was also used and no data transformations were made. 5.6 LIMITATIONS AND ASSUMPTIONS The following limitations have been identified: The potential shortcomings of hedonic studies are firstly in choosing the correct explanatory variables (Chi-Hau & Jen-Jia, 2004). Many studies are limited by the data that are available, while even the most comprehensive data will not be able to fully capture all the unique attributes of a property. This study used all the available data and could not include additional explanatory variables that might have an effect on the property value. These variables could include amenities within a property, the position of the property, the build quality and finishes in each property, the property location within the neighbourhood and street, the views the property may have, the zoning or potential sub-division rights of the property, the gardens and street appeal of the property, security features and the suburb demographics etc. 36

37 This study has focused on sectional title properties (flats and townhouses). The exact square meterage of each individual sectional title unit are available through the South African deeds office. The exact square meterage of full title properties (houses) is not available. Information gathered from property experts highlights the fact that a large portion of the value of sectional title property is reflected in the size of each sectional title unit and such information was available for this study. It is common practice amongst property experts to use a price per square metre for sectional title units. Furthermore, 93% of the Lyttelton area is covered by sectional title properties, with more than 5000 sectional title transactions recorded in 2010 compared to just over 400 for full title properties. For this reason the study has focused on sectional title units only. This, however, may not be representative of all residential property within close proximity to the railway transit stations. Studies of this nature are usually varied in that they can concentrate on three different phases of commuter rail transport systems, namely: - o The planning stage and the anticipatory affects on residential property values; o The construction phase and the effects on residential property values; and o The operational phase and the effects on residential property values. As Phase 1 of the Gautrain only became operational on 8 th June 2010, the study focuses solely on the planning and construction phase of the Gautrain project. The planning stage is defined in this study as the date that the final environmental impact assessment was approved and the Gautrain Route confirmed 25 th April Prior to this date the Gautrain route was not confirmed and therefore one can assume minimal impact would have been experienced on residential property values. The final route confirmed was amongst a number of different proposed routes. The planning stage is therefore defined in this study as the period from 25 th April 2004 through to the 27 th September The construction phase is defined as the period from 28 th September 2006 through to 8 th June 2010; notwithstanding the fact that at the time of writing the Rosebank and Centurion stations were still under construction. The effects, if any, which the Gautrain railway transit stations have on residential property values, may vary in the different neighbourhoods in which the stations are located. Due to the constraints on the study, only two railway transit stations were examined. Due to the varying local neighbourhood characteristics and demographics, it would be impossible to 37

38 select two railway transit stations that are representative or a proxy for all the stations along the Gautrain route. The study only examined sectional title property between a 0-1km radius and a 1-2km radius of each station. It is assumed that the effects of the Gautrain stations on property values will weaken the further a property is located from the station. However, this study did not examine these incremental effects or property sale transactions further than 2km from each station. The statistical hedonic model is dependent on accurate data. It is assumed that the data obtained from SAPTG and the Knowledge Factory is accurate and complete. 6. DATA MODELLING The purpose of this section is to go through the steps that were taken to develop a final statistical model for analysis and interpretation. A correlation analysis was conducted to identify highly correlated variables that could affect the statistical freedom of the model. A Cook statistic diagnostic was used to identify any outliers in the datasets and these outliers were then removed to strengthen the output of the model. The distribution of the data set was checked to ensure a normal distribution was present a requirement for regression analysis. A forward stepwise regression was then employed to eliminate highly correlated and insignificant variables. The objective was to find a model with a relatively small number of variables but with a high coefficient of multiple determination R 2 the model with the highest percent of variation in the dependent variable explained or accounted for by the explanatory variables. The following sections highlight the steps taken at each stage of the analysis for both datasets ROSEBANK VARIABLES AND CORRELATION ANALYSIS The ten Rosebank variables represent six continuous and four categorical variables (Dummy variables) as follows:- Continuous variables REAL PRICE, STATION, SCHOOL, MEDICAL, SHOP, POP and M2 Categorical variables DIST, CRI, TRN 38

39 A correlation analysis was conducted with these variables in order to determine the variable correlations and their significance levels (P-Values). If a strong collinearity exists between any of the variables, then these variables may have to be excluded from the regression analysis (Chi-Hau & Jen-Jia, 2004). The results of the correlation analysis indicated that the DIST and STATION variable were strongly correlated. This was to be expected as the DIST dummy variable was derived from the STATION variable. Because of multi-collinearity between the variables, a forward stepwise regression analysis was utilised. Importantly, it was observed that the DIST and STATION variable has a significance level (P-Value) of less than 0.01 or 1%. The SHOP variable was the only variable not to have a significance level (P- Value) of less than 0.05 or 5%. Table 3 Summary of correlation analysis and significance level (P-Value) - Rosebank Correlations (Rosebank) N=1296 REAL PRICE DIST STATION SCHOOL MEDICAL SHOP CRI POP M² TRN REAL PRICE 1 DIST *** 1 STATION.034 *** *** 1 SCHOOL.177 *** ***.7141 *** 1 MEDICAL *** ***.4790 ***.3225 *** 1 SHOP ***.6222 ***.4616 ***.7475 *** 1 CRI **.3350 *** *** *** *** POP.088 ***.1043 ***.0838 *** **.2325 ***.2508 ***.3133 *** 1 M².702 *** *** *** *** *** *** 1 TRN.492 *** *** ** p-value < 0.05; *** p-value < ROSEBANK OUTLIER ANALYSIS As the presence of outliers in the data set may influence the statistical impact of the regression analysis, the data set was checked for any outliers using the Cook statistic method. The results confirmed that there were indeed outliers present in the data set. By using a cut-off point, the outliers in the dataset were highlighted. Each of the outliers in the dataset was examined individually to confirm that these were in fact outliers. In order to strengthen the results of the final forward stepwise regression, a total of eighteen outliers were removed from the Rosebank dataset. 39

40 Table 4 Sample of Cook statistic for Rosebank data set Cook's Distances: REAL PRICE (Rosebank) Sorted Data Number Cook's - Distance * * * * * * * * * * * * * * * * * * * * * * * * ROSEBANK DISTRIBUTION A blind regression analysis was conducted to establish the distribution in the data set. From this a histogram of residuals was generated in order to observe the distribution of the data set. The REALPRICE histogram showed a normal distribution of the data set a requirement for regression analysis and as such REALPRICE was used in the analysis. As discussed in section 5.3, it was not necessary to make any transformations to the dependent variable REALPRICE such as using the natural logarithm of REALPRICE. This was due to REALPRICE providing the best fit model (Gatzlaff & Smith, 1993). To maintain integrity of the analysis, additional data transformations to both the dependant and explanatory variables were deemed not necessary for the study. 40

41 Figure 8 Histogram of residuals for Rosebank showing a normal distribution 500 Distribution of Raw residuals (Rosebank) Expected Normal No of obs FORWARD STEPWISE REGRESSION OF ROSEBANK MODEL To establish the most appropriate model to use for this study, a forward stepwise regression was used. The forward stepwise regression analysis would address any multi-collinearity that may exist between the variables. The various steps undertaken in the stepwise regression are highlighted in table 5. 41

42 Table 5 Rosebank stepwise regression results Model Step b* b Std.Err. - of b t(1274) p-value 1 1 (Constant) M_ (Constant) M_ TRN (Constant) M_ TRN SCHOOL (Constant) M_ TRN SCHOOL DIST (Constant) M_ TRN SCHOOL DIST STATION (Constant) M_ TRN SCHOOL DIST STATION CRI (Constant) M_ TRN SCHOOL DIST STATION CRI POP (Constant) M_ TRN SCHOOL DIST STATION CRI POP MEDICAL (Constant) M_ TRN SCHOOL DIST CRI POP MEDICAL

43 For the purposes of this study a significance level or P-Value, of 0.05 or 5% was used to select the data to include in the final model. This level of significance or P-Value, was chosen as it is the most widely used in statistical significance testing (Lind, Marchal, & Wathen, 2008). Any variable that had a P-Value of less than 0.05 was kept and any variable that had a P-Value of more than 0.05 was discarded. Furthermore, any variables with strong collinearity were not included in the final model. The variable SHOP was excluded from the final model as it had a P-Value greater than 0.05 or 5%. The variable STATION was excluded from the final model due to its high collinearity with the variable DIST. Table 6 Variables deleted from the Rosebank data set VARIABLE REASON FOR DISCARDING SHOP P-Value greater than 0.05 or 5% STATION High collinearity with variable DIST The seven remaining explanatory variables were then introduced into the primary statistical model. These variables were made up of four continuous variables and three categorical (dummy) variables. The statistical summation of all these variables is shown in the table 7. Table 7 Variables in the final Rosebank model Model Step b* b Std.Err. - of b t(1274) p-value 45 9 (Constant) M² TRN SCHOOL DIST CRI POP MEDICAL The results listed in table 7 show that most of the coefficients had P-Values less than 0.01 or 1% and were significant and therefore considered in the final statistical model FINAL ROSEBANK MODEL After all the steps were conducted, a final statistical model was available to be used in the analysis and interpretation. The initial regression analysis, before removing any variables, had a R 2 of After discarding any insignificant or highly correlated variables, the final model had a R 2 of 0.74 indicating that 74% of the variation in the dependent variable 43

44 (REALPRICE) is explained or accounted for, by the explanatory variables. The final model used for all the analysis is shown below in table 8. Table 8 Final statistical model used for Rosebank Regression Summary for Dependent Variable: REAL PRICE (Rosebank) R=.860 R²=.740 Adjusted R²=.738 F(7,1274)= p<0.000 Std.Error of estimate: 2498E2 b* Std.Err. - of b* b Std.Err. - of b t(1274) p-value Intercept M² TRN SCHOOL DIST CRI POP MEDICAL The final Rosebank statistical model is represented by the following equation: ˆ Y (REALPRICE ) = (M2) (TRN ) (SCHOOL ) (DIST ) 92380(CRI) + 55(POP ) 63410(MEDICAL ) CENTURION VARIABLES AND CORRELATION ANALYSIS The ten Centurion variables represent six continuous and four categorical variables (Dummy variables) as follows: - Continuous variables REAL PRICE, STATION, SCHOOL, MEDICAL, SHOP, POP and M2 Categorical variables DIST, CRI, TRN A correlation analysis was conducted with these variables in order to get a feel for each of the variable correlations and their significance levels (P-Values). If a strong collinearity exists between any of the variables, then these variables may have to be excluded from the regression analysis (Chi-Hau & Jen-Jia, 2004). The results of the correlation analysis indicated the DIST and STATION variable were strongly correlated. This was to be expected, as the DIST dummy variable was derived from the station variable. As in the Rosebank model, the multi-collinearity between the variables would be addressed by using a forward stepwise regression analysis. Importantly, it was observed that the DIST and STATION variable has a significance level (P-Value) of less than 0.01 or 1%. 44

45 Table 9 Summary of correlation analysis and significance level (P-Value) - Centurion Correlations (centurion) Marked correlations are significant at p < N=4948 (Casewise deletion of missing data) REAL PRICE DIST STATION SCHOOL MEDICAL SHOP CRI POP M² TRN REAL PRICE 1 DIST.1019 ** 1 STATION ** *** 1 SCHOOL ***.3573 *** *** 1 MEDICAL ***.4236 ***.2342 *** 1 SHOP *** ***.4694 ***.1989 ***.6462 *** 1 CRI.0238 ***.0952 *** ***.1592 *** POP.0613 *** ***.2960 *** *** *** 1 M².5911 *** *** *** *** *** 1 TRN.5967 ***.0889 *** ** *** *** ** 1 ** p-value < 0.05; *** p-value < CENTURION OUTLIER ANALYSIS As the presence of outliers in the data set may influence the statistical impact of the regression analysis, the data set was checked for any outliers using the Cook statistic method. The results confirmed that there were indeed outliers present in the data set. By using a cut-off point, the outliers in the dataset were highlighted. Each of the outliers in the dataset was examined individually to confirm that these were in fact outliers. In order to strengthen the results of the final forward stepwise regression, a total of nineteen outliers were removed from the Centurion dataset. 45

46 Table 10 Sample of Cook statistic for Centurion Cook's Distances: REAL PRICE (Centurion) Sorted Cook's - Distance * * * * * * * * * * * * * * * * * * * * * * * * CENTURION DISTRIBUTION To establish the distribution in the data set, a blind regression analysis was conducted. From this a histogram of residuals was generated in order to observe the distribution of the data set. The REALPRICE histogram showed a normal distribution of the data set a requirement for regression analysis and as such REALPRICE was used in the analysis. As discussed in section 5.3, it was not necessary to make any transformations to the dependent variable REALPRICE, such as using the natural logarithm of REALPRICE. This was due to REALPRICE providing the best fit model (Gatzlaff & Smith, 1993). To maintain integrity of the analysis, additional data transformations to both the dependant and explanatory variables were deemed not necessary for the study. 46

47 Figure 9 Histogram of residuals for Centurion showing a normal distribution 2400 Distribution of Raw residuals (Centurion) Expected Normal No of obs FORWARD STEPWISE REGRESSION OF CENTURION MODEL To establish the most appropriate model to use for this study, a forward stepwise regression was used. The forward stepwise regression analysis would address any multi-collinearity that may exist between the variables. The various steps undertaken in the stepwise regression are highlighted in table

48 Table 11 Centurion stepwise regression results Model Step b* b Std.Err. - of b t(1274) p-value 1 1 (Constant) TRN (Constant) TRN M² (Constant) TRN M² SHOP (Constant) TRN M² SHOP DIST (Constant) TRN M² SHOP DIST SCHOOL (Constant) TRN M² SHOP DIST SCHOOL POP (Constant) TRN M² SHOP DIST SCHOOL POP CRI As in the Rosebank model, a significance level or P-Value, of 0.05 or 5% was used to select the data to include in the final model. Any variable that had a P-Value of less than 0.05 was kept and any variable that had a P-Value of more than 0.05 was discarded. Furthermore, any variables with strong collinearity were not included in the final model. The variable MEDICAL was excluded from the final model as it had a P-Value greater than 0.05 or 5%. The variable STATION was excluded from the final model due to its high collinearity with the variable DIST. 48

49 Table 12 Variables deleted from the Centurion data set VARIABLE REASON FOR DISCARDING MEDICAL P-Value greater than 0.05 or 5% STATION High collinearity with variable DIST The seven remaining explanatory variables were then introduced into the primary statistical model. These variables were made up of four continuous variables and three categorical (dummy) variables. The statistical summation of all these variables is shown in table 13. Table 13 Variables in the final Centurion model Model Step b* B Std.Err. - of b t(1274) p-value 28 7 (Constant) TRN M² SHOP DIST SCHOOL POP CRI The results listed in table 13 show that most of the coefficients had P-Values less than 0.01 or 1% and were significant and therefore considered in the final statistical model FINAL CENTURION MODEL After all the steps were conducted, a final statistical model was available to be used in the analysis and interpretation. The initial regression analysis, before removing any variables, had an R 2 of After discarding any insignificant or highly correlated variables, the final model had an R 2 of indicating that 74.4% of the variation in the dependent variable (REALPRICE) is explained or accounted for, by the explanatory variables. The final model used for all the analysis is shown below in table

50 Table 14 Final statistical model used for Centurion Regression Summary for Dependent Variable: REAL PRICE (Centurion) R=.862 R²=.744 Adjusted R²=.743 F(7,4922)= p<0.000 Std.Error of estimate: 955 b* Std.Err. - of b* b Std.Err. - of b t(4922) p-value Intercept TRN M² SHOP DIST SCHOOL POP CRI The final statistical model for Centurion is represented by the following equation: Y ˆ( REALPRICE) = ( TRN) ( M 2) ( SHOP) ( DIST) ( SCHOOL) + 2.6( POP) ( CRI) 7. EMPIRICAL RESULTS The purpose of this section is to interpret the results of the final statistical model for each station. Both models discarded the STATION variable due to the high collinearity with the DIST variable. The SHOP variable was discarded from the Rosebank model and the MEDICAL variable was discarded from the Centurion Model. These variables were discarded as they had P-Values greater than 0.05 or 5%. The estimated coefficient for CRI for Rosebank and Centurion was contrasting and had a different sign. The estimated coefficient for SCHOOL for Rosebank and Centurion was contrasting and had a different sign. These contrasting coefficients between the two station models are discussed in detail in section PRIMARY ROSEBANK RESULTS The final statistical model used for the Rosebank data showed the following primary results:- Distance from station (DST) = This dummy variable captured the properties distance from each station between 0-1km and 1-2km of each station. This indicated that the REALPRICE of the average property would be R higher within a 0-1km radius than a 1-2km radius of each station. This assumes that all other variables are held constant and represents the average for the sample. This variable confirmed the primary hypothesis of the study residential property values within a 1km distance of the Gautrain 50

51 transit stations have been positively affected during the planning and construction phase of the Gautrain project. Transaction period (TRN) = This dummy variable captured transactions that took place either before or during the planning and construction phase of the Gautrain (25 th April 2004 to 8 th June 2010). The estimated coefficient indicated that the REALPRICE would be on average R rand more for properties during the planning and construction phase than before. This assumes that all other variables are held constant and represents the average for the sample. These primary results confirmed the primary hypothesis of the study residential property values within a 1km distance of the Gautrain transit stations have been positively affected during the planning and construction phase of the Gautrain project. 7.2 SECONDARY ROSEBANK RESULTS The results of the estimated coefficients of the remaining variables in the model were as follows:- Copyright Unit floor size (M²) = Indicating that, holding all other UCT variables constant, the REALPRICE will increase on average by R6 685 for every 1 square metre increase in the size of an apartment. Distance from closest school (SCHOOL) = Indicating that, holding all other variables constant, the REALPRICE will increase on average by R for every 100 meters closer the property is to a school. Distance from closest medical (MEDICAL) = Indicating that holding all the other variables constant, the REALPRICE will decrease on average by R6 341 for every 100 meters closer the property is to medical facilities. Crime levels in the suburb (CRI) = Indicating that, holding all the other variables constant, the REALPRICE will decrease on average by R rand for areas with medium crime compared to areas with high crime. Suburb population (POP) = 55. Indicating that, holding all the other variables constant, the REALPRICE will increase on average by R55 for each additional person in the suburb. 51

52 7.3 PRIMARY CENTURION RESULTS The final statistical model used for the Centurion data showed the following primary results. Distance from station (DST) = This dummy variable captured the property s distance from each station between 0-1km and 1-2km of each station. This indicated that the REALPRICE of the average property would be R higher within a 0-1km radius than a 1-2km radius of each station. This assumes that all other variables are held constant and represents the average for the sample. This variable confirmed the primary hypothesis of the study residential property values within a 1km distance of the Gautrain transit stations have been positively affected during the planning and construction phase of the Gautrain project. Transaction period (TRN) = This dummy variable captured transactions that took place either before or during the planning and construction phase of the Gautrain (25 th April 2004 to 8 th June 2010). This variable confirmed the primary hypothesis of the study that property prices were indeed influenced during the planning and construction phase of the Gautrain. The estimated coefficient indicated that the REALPRICE would be on average R more for properties during the planning and construction phase of the railway line than before. This assumes that all other variables are held constant and represents the average for the sample. These primary results confirmed the primary hypothesis of the study residential property values within a 1km distance of the Gautrain transit stations have been positively affected during the planning and construction phase of the Gautrain project. 7.4 SECONDARY CENTURION RESULTS The results of the estimated coefficients of the remaining variables in the model were as follows:- Unit floor size (M²) = Indicating that, holding all other variables constant, the REALPRICE will increase on average by R3 569 units for every 1 square metre increase in the size of an apartment. Distance from closest school (SCHOOL) = Indicating that, holding all other variables constant, the REALPRICE will decrease on average by R1 752 for every 100 meters closer the property is to a school. 52

53 Distance from closest shopping centre (SHOP) = Indicating that, holding all the other variables constant, the REALPRICE will decrease on average by R1 899 for every 100 meters the closer the property is to a shopping centre. Crime levels in the suburb (CRI) = Indicating that, holding all the other variables constant, the REALPRICE will increase on average by R for areas with medium crime compared to areas with high crime. Suburb population (POP) = 2.6 Indicating that, holding all the other variables constant, the REALPRICE will increase on average by R2.6 for each additional person in the suburb. 8. DISCUSSION AND CONCLUSION This study analyses the effects of the Gautrain on property prices within close proximity to the railway transit stations. The study focuses on the planning and construction phase of the railway line and examines sectional title property transactions from 2000 to Two railway stations Rosebank and Centurion were examined. A hedonic pricing model is estimated accounting for the individual property characteristics, neighbourhood demographics and accessibility features of each property. However, the main focus of the study is to examine the effects that proximity to the Gautrain railway transit stations may have on residential property values. The primary results for the Rosebank and Centurion stations support the study s primary hypothesis. The study has found that average property values are higher within a 1km radius of each station compared with a 1-2km radius of each station. The sign and significance of each of the estimated coefficients distance (DST) and transaction time (TRN) test the hypothesis. The positive estimated coefficient for distance from station (DST) for both stations Rosebank (124304) and Centurion (19957) suggests that residential sectional title property values are greater within a 1km radius of each station compared to a 1-2km radius of each station. The significance level for DST was statistically significant, below 1%, for both models. The estimated coefficient for Rosebank is greater than the coefficient for Centurion. This makes intuitive sense, as the average price for sectional title property is higher in Rosebank than in Centurion (see Section 1). Additionally, Rosebank and the surrounding suburbs have been historically more developed than the Centurion area. Over the last decade far more undeveloped land has been available in Centurion, while very little undeveloped land 53

54 existed in Rosebank. It could therefore be argued that a greater impact is felt on property values in Rosebank when going from a 1-2km radius to within a 1km radius of the station. The positive estimated coefficients for transaction time (TRN) for both stations Rosebank (448996) and Centurion (240268) should be interpreted cautiously. The positive coefficient suggests that residential sectional title property values have been positively affected during the planning and construction phase of the Gautrain project. The data analysed included property transactions (TRN) between 2000 and 2010, with the planning and construction phase defined as the period 25 th April 2004 through to 8 th June The study made use of real property prices adjusted for inflation. However, average real property prices have increased year on year between 2004 and 2008 and again in 2010 (see figure 2). Over the last decade the average real price of property in South Africa, in all price bands, has only decreased for a two-year period between 2000 and 2010 (see figure 3). Therefore the magnitude of the TRN coefficients in both models could largely be the result of the increase in real property prices over this period. The significance level for TRN was statistically significant, below 1%. The difference between the coefficients could reflect the difference in property values between the two areas Rosebank being a high value area and Centurion being a medium value area. The Rosebank model (R 2 = 74%) and the Centurion model (R 2 = 74,4%) had almost identical coefficients of multiple determination the model with the highest percent of variation in the dependent variable explained or accounted for, by the independent variables. However, some of the secondary variables and the signs of the secondary variables were contrasting. This may be explained by unique local factors present and attributable to each neighbourhood. The estimated coefficient for crime (CRI) for Rosebank (-92380) and Centurion (57921) were contrasting and had a different sign. The Rosebank coefficient suggests that the average real price will decrease for areas with medium crime compared to high crime. The Centurion coefficient suggests that the average price will increase for areas with medium crime compared to high crime. Arguably, this may be down to criminals targeting high property value areas that have been in existence for many decades close to the Rosebank station. The areas with high crime near the Rosebank station are Melrose, Melrose Estate, Dunkeld West and Parkwood. These areas are one step above the areas with medium crime Rosebank, Birdhaven, Parktown North and Melrose North. In contrast, many of the areas and complexes close to the Centurion station have only been developed in the last decade. The Lyttelton area has been expanded over the last ten years and these new extensions contain many gated 54

55 security communities. These gated communities are perceived to have less crime and the property prices reflect the higher prices that are paid for extra security measures. The estimated coefficient for shop (SHOP) for Centurion (-18991), which was removed in the Rosebank model, suggests that the average real price will decrease the closer the property is to a shopping centre. Arguably, this may be due to older shopping centres and the CBD of Centurion, which are not desirable to live close to. The estimated coefficient for medical (MEDICAL) for Rosebank (-63410), which was removed in the Centurion model, suggests that the average real price will decrease the closer the property is to medical facilities. Arguably, this may be due to the medical facilities in Rosebank being located in Sturdee Avenue close to an area that historically had nightlife trade and attracted an undesirable element. The estimated coefficient for school (SCHOOL) for Rosebank (209590) and Centurion (-17522) were contrasting and had a different sign. The Rosebank coefficient suggests that the average real price will increase the closer one gets to a school, while the Centurion coefficient suggests the opposite. It could be argued that Rosebank and the surrounding suburbs contain some of the best schools in Johannesburg, which are desirable to live close to. The same cannot be said for Centurion and surrounding suburbs. The key with using a hedonic methodology is defining and choosing the explanatory variables used in the regression analysis (Dewees, 1976). Many cases are limited by the data that is available, while even the most comprehensive data will not be able to fully capture all the unique attributes of a property s value. These attributes can include a property s position, the build quality and finishes in each property, the property s location within the neighbourhood and street, the views the property may have, the zoning or potential sub-division rights of the property, the garden and street appeal of the property and the security features of the property. The influences on property values over the long term include growth in household disposable income, gradual shifts in demographics, level of interest rates and permanent features of the tax system that might encourage home ownership. The determinants of house prices can also include the availability and cost of land, the cost of construction and investments to improve the quality of the existing housing stock (Tsatsaronis & Zhu, 2004). Importantly, access to amenities within the environment such as commercial facilities and reliable transport can also have a positive impact on property values (Bowes & Ihlanfeldt, 2001). The hypothesis of this study states that Residential property values within a 1km distance of the Gautrain transit stations have been positively affected during the planning and 55

56 construction phase of the Gautrain project. This study confirms that prices are indeed greater, for sectional title properties, within a 1km radius of each station compared with a 1-2km radius of each station. The hypothesis of the study is therefore empirically proven. 9. AREAS FOR FURTHER STUDY Very little research exists on the impact of rail transit systems on residential property in developing economies. This may be because more of these rail systems are prevalent in developed economies. Additionally, developed economies may also provide better records of data in terms of property transactions than developing economies (Cervero & Susantono, 1999: Li, 2006). The access to property related data is widely available in South Africa. The Gautrain and the South African Metro line service can provide an opportunity to study the effects of rail transit systems on property values in greater detail. Due to the limitations and assumptions of this study, there are areas that are open to further research. The following studies may provide greater understanding on the topic of railway transit lines and property values: - A study to establish the impact of all of the Gautrain s stations on residential property values. A study to investigate the effects of the Gautrain on residential property in the operational phase of the railway line. A study to investigate the impact of the Gautrain on freehold and on commercial property. A study to include qualitative aspects of the impact of the Gautrain on property values. This could be conducted to include the opinions of industry insiders and experts. A study to establish the impact of the South African Metro railway lines on residential property values. 56

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61 11. APPENDICES APPENDIX 1 SAMPLE OF DATA Table 15 Sample of data Suburb ROSEBANK ROSEBANK ROSEBANK ROSEBANK ROSEBANK DUNKELD WEST DUNKELD WEST DUNKELD WEST DUNKELD WEST DUNKELD WEST DUNKELD WEST Scheme Name Sale Date Title Deed Sale price CPI index REAL PRICE DIST STATION SCHOOL MEDICAL SHOP CRI POP M² TRN 2 HOOD AVENUE 07/31/2008 ST46492/ ,85 0,40 0,53 0, HOOD AVENUE 07/04/2008 ST46162/ ,85 0,40 0,53 0, HOOD AVENUE 06/05/2008 ST36773/ ,85 0,40 0,53 0, HOOD AVENUE 06/05/2007 ST62492/ , ,85 0,40 0,53 0, HOOD AVENUE 10/30/2006 ST13499/ , ,85 0,40 0,53 0, NORTH HYDE 10/04/2009 ST3701/ , ,7083 1,24 0,23 0, NORTH HYDE 07/07/2009 ST74741/ , ,7083 1,24 0,23 0, NORTH HYDE 07/08/2008 ST94053/ ,7083 1,24 0,23 0, NORTH HYDE 11/10/2007 ST18424/ , ,7083 1,24 0,23 0, NORTH HYDE 08/21/2007 ST1696/ , ,7083 1,24 0,23 0, NORTH HYDE 05/22/2007 ST86097/ , ,7083 1,24 0,23 0,

62 APPENDIX 2 AERIAL VIEW OF ROSEBANK AND CENTURION Figure 10 Aerial view of Rosebank Figure 11 Aerial view of Centurion Source Google Maps 62

63 APPENDIX 3 ARTIST IMPRESSION OF COMPLETED STATIONS Figure 12 Artist impression of Rosebank station Figure 13 Artist impression of Centurion station Source 63