Transportation infrastructure impacts on house prices and firms location: the effect of a new metro line in suburbs of Madrid

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(1)E.T.S.I. CAMINOS, CANALES Y PUERTOS DEPARTAMENTO DE INGENIERÍA CIVIL: TRANSPORTES. The effect of a new metro line in the suburbs of Madrid. Lucía Mejía‐Dorantes Civil Engineer Supervisor: José‐Manuel Vassallo Co‐Supervisor: Antonio Páez. Madrid, 2011.

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(3) D. 15 Tribunal nombrado por el Mgfco. y Excmo. Sr. Rector de la Universidad Politécnica de Madrid, el día ………………………………….. Presidente D.. …………………………………………………………... Vocal. D.. …………………………………………………………... Vocal. D.. …………………………………………………………... Vocal. D.. …………………………………………………………... Secretario. D.. …………………………………………………………... Suplente. D.. …………………………………………………………... Suplente. D.. …………………………………………………………... Realizado el acto de defensa y lectura de la Tesis el día…... de .......................... de 2011 en la E.T.S. de Ingenieros de Caminos, Canales y Puertos de la U.P.M. Calificación: ............................................................................. EL PRESIDENTE. LOS VOCALES. EL SECRETARIO.

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(5) Domitrix omnium patientia.

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(7) AGRADECIMIENTOS A José Manuel Vassallo, director de esta tesis, por su enorme y constante apoyo a lo largo de todos estos años. A Antonio Páez, co-director y valioso sostén de esta memoria. A todos los miembros de TRANSyT, quienes me han enseñado muchísimo y me han hecho más llevadero este trabajo. A los Centros en donde he realizado las diferentes estancias.. Finalmente, quiero agradecer a mis padres, a mi hermana, al resto de mi familia y a mis amigos su INFINITA paciencia.. Y a los que no he nombrado pero que han sido importantes: GRACIAS..

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(9) ABSTRACT Transportation infrastructure, is known to impact urban areas in a number of significant ways. Those effects may be classified as: transportation-related effects, land-use effects and effects on jobs and business activities. Commuter rail and metro stations can have both positive and negative effects. On the positive side metro stations increase the accessibility to public transportation for the people living nearby and reduce their travel time and costs to other destinations in urban areas. Similarly, business activities near the stations also enjoy some advantages. On the one hand, jobs and shops are now more accessible for those coming from any destination. On the other hand, business activities close to the stations—especially shops—can also benefit from the increase in the number of people who pass by the shops in their way to or from the stations. Many negative effects associated with metro stations are spatial externalities, including noise and changes to the urban landscape. The effects described above depend on the type of transport infrastructure, its location, and its specific characteristics. A problem of interest whenever a new transportation infrastructure and/or services are introduced is to assess the net impact of the positive and negative effects listed above. However, many times it is complicated due to the lack of availability of adequate datasets, therefore, new approaches must be used. The aim of this thesis is to assess the impact of a new public transport infrastructure on house prices and on the location of economic activities using two different approaches. The empirical case study presented herein is the Madrid Metro Line 12 (known as Metrosur) in the southwest of the Madrid Region. In both cases different spatial models and geo-statistical techniques are used along with micro-level data bases. The results indicate that first, a better accessibility to Metrosur stations has a positive impact on land values, and that the effect is particularly marked when selling a house. Second, the location of economic activities in many cases is related to the proximity to transport stations; however, it also depends on other factors, such as agglomeration economies. Third, the results differ among municipalities. The results presented in this thesis provide important evidence, useful to inform efficient transportation, urban and regional economic planning. Moreover, the proposed methodology could be useful to assess cities with similar databases..

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(11) RESUMEN Las infraestructuras de transporte impactan de forma significativa, en las áreas urbanas en diferentes formas. Estos efectos se pueden clasificar como: efectos relacionados con el transporte, efectos en el uso de suelo y efectos en las actividades comerciales y en el empleo. Las estaciones de metro y tren urbano pueden producir tanto externalidades positivas como externalidades negativas. Por una parte, una estación de metro incrementa la accesibilidad del transporte para las personas que viven alrededor, reduciendo su tiempo de viaje y el coste. De forma similar, las actividades comerciales se ven beneficiadas por el incremento de personas que accede o sale de la estación. Del mismo modo existen efectos negativos producto de externalidades espaciales, como son el ruido y los cambios en el paisaje. Los efectos descritos anteriormente dependen del tipo de infraestructura de transporte, su localización y sus características específicas. Un problema frecuente es cómo calcular el impacto neto que una nueva infraestructura de transporte produce. La mayoría de las veces evaluar lo anterior resulta complicado debido a la poca disponibilidad de bases de datos detalladas. Por lo tanto, es necesario utilizar nuevos métodos para la correcta evaluación. El objetivo de esta tesis es evaluar el impacto de una nueva infraestructura de transporte en el precio de la vivienda y en la localización de las actividades comerciales utilizando dos métodos diferentes. El caso de estudio utilizado es el de la línea 12 de Madrid, Metrosur, localizada al suroeste de la Comunidad de Madrid. En ambos casos se utilizan modelos espaciales y técnicas geoestadísticas junto con una base de datos muy detallada. Los resultados indican que, primero, una mejor accesibilidad a las estaciones de Metrosur tiene un impacto positivo en el precio de la vivienda y éste se nota cuando se vende una casa. Segundo, la localización de las diferentes actividades económicas en muchos casos está relacionada con la cercanía a diversas infraestructuras de transporte, sin embargo también depende de otros factores, como la economía de aglomeración. Tercero, los resultados varían dependiendo del municipio. Los resultados que se presentan en esta tesis proporcionan conclusiones muy importantes y útiles para llevar a cabo una planificación eficiente, tanto del transporte como a nivel urbano y regional. Asimismo, la metodología propuesta podría ser aplicable a ciudades con bases de datos similares a las utilizadas en este análisis..

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(13) INDEX Index of Figures .................................................................................................................................. 15 Index of Tables ................................................................................................................................... 17. 1. INTRODUCTION .............................................................................................................. 19 2. OBJECTIVES...................................................................................................................... 27 3. STATE OF THE ART ON THE ASSESSMENT OF TRANSPORTATION INFRASTRUCTURE IMPACTS .......................................................................................... 31 3.1 An Overview on the Effects of Transport Investments. ................................................................. 33 3.2 Theoretical Background on Hedonic Models and Spatial Analysis ................................................. 38 3.2.1 Spatial Analysis ............................................................................................................................. 41 3.2.2 Housing Submarkets .................................................................................................................... 45 3.2.3 Marginal Benefits ......................................................................................................................... 46 3.3 Theoretical Background on Firms’ Location Patterns..................................................................... 47 3.3.1 Methods and Models ................................................................................................................... 49. 4. MADRID AND METROSUR STUDY AREA ................................................................. 57 4.1 Study Area .................................................................................................................................... 59 4.2 Metrosur ...................................................................................................................................... 69. 5. VALUE CAPTURE STRATEGIES IN SPAIN ................................................................ 87 5.1 International Experiences ............................................................................................................. 89 5.2 The Case of Spain ......................................................................................................................... 90 5.2.1 Property Tax (IBI) ......................................................................................................................... 90 5.2.2 Urban Land Value Increase Tax .................................................................................................... 91 5.2.3 Special Contributions ................................................................................................................... 91 5.2.4 Developers’ Fee............................................................................................................................ 91. 6. METHODOLOGY .............................................................................................................. 93 6.1 Methodological Approach on House prices ................................................................................... 95 6.2 Methodological Approach on the Location of Economic Activities ................................................ 98. 7. DATA SOURCES .............................................................................................................105 7.1 Data Sources for House Prices .................................................................................................... 107 7.2 Data Sources for Firms Location Choices ..................................................................................... 109. 13.

(14) 8. RESULTS ......................................................................................................................... 111 8.1 Hedonic Models for House Prices ............................................................................................... 113 8.2 Analyses for Firm’s Location Choice............................................................................................ 121 8.2.1 Alcorcon ..................................................................................................................................... 122 8.2.2 Mostoles ..................................................................................................................................... 127 8.2.3 Leganes ....................................................................................................................................... 130 8.2.4 Getafe ......................................................................................................................................... 132 8.2.5 Fuenlabrada................................................................................................................................ 134 8.2.6 Overall analysis of municipalities ............................................................................................... 137 8.2.7 Summary .................................................................................................................................... 140. 9. CONCLUSIONS AND DISCUSSION ............................................................................ 143 9.1 Conclusions on House prices ...................................................................................................... 145 9.2 Conclusions on Firms’ Location Choice ....................................................................................... 147 9.3 Discussion .................................................................................................................................. 149. 10. FURTHER RESEARCH............................................................................................... 157 11. REFERENCES .............................................................................................................. 161 12. APPENDIX ................................................................................................................... 173. 14.

(15) INDEX OF FIGURES Figure 3.01 – Kernel estimation of point pattern ................................................................................ 50 Figure 4.01 – Madrid Map and inhabitants of the Municipalities connected by Metrosur ................... 59 Figure 4.02 – Population per year and per Municipality ...................................................................... 60 Figure 4.03 – Population per year and per Municipality per age. ........................................................ 61 Figure 4.04 – Gross Domestic Product per year and per Municipality.................................................. 62 Figure 4.05 – Modal split: urban trips, 2004 ........................................................................................ 66 Figure 4.06 – Modal split: metropolitan trips, 2004 ............................................................................ 66 Figure 4.07 – Madrid Metro System ................................................................................................... 70 Figure 4.08 – Metrosur Map ............................................................................................................... 71 Figure 4.09 – Metrosur map per Municipality: Alcorcon ..................................................................... 71 Figure 4.10 – Metrosur map per Municipality: Fuenlabrada................................................................ 72 Figure 4.11 – Metrosur map per Municipality: Leganes ...................................................................... 72 Figure 4.12 – Metrosur map per Municipality:Getafe ......................................................................... 73 Figure 4.13 – Metrosur map per Municipality: Mostoles..................................................................... 74 Figure 4.14 – Pictures of Metrosur stations ........................................................................................ 75 Figure 4.15 – Pictures of Metrosur stations ........................................................................................ 76 Figure 4.16 – Madrid zonal fares ........................................................................................................ 77 Figure 4.17 – Trips with origin and destination at the Metrosur transfer stations ............................... 79 Figure 4.18 – Trips with origin at the Metrosur transfer stations ........................................................ 81 Figure 5.01 – Project cycle and value‐capture opportunities ............................................................... 92 Figure 6.01 – Scheme of the analysis in the case of house prices ........................................................ 95 Figure 6.02 – Euclidean distance and street network distance. ........................................................... 96 Figure 6.03 – Scheme of the analysis in the case of location of economic activities........................... 102 Figure 8.01 – Moran’s I analysis for dependent variable. .................................................................. 114 Figure 8.02 – LISA significant maps. .................................................................................................. 114 Figure 8.03 – LISA cluster maps. ....................................................................................................... 115 Figure 8.04 – Moran’s I analysis for SEM and SLM residuals .............................................................. 116. 15.

(16) Figure 8.05 – Map of OLS residuals within the five municipalities analyzed ...................................... 118 Figure 8.06 – Impact in house value related to its distance to the closest Metrosur station.............. 120 Figure 8.07 – Impact in house value related to its distance to the closest commuter rail station (Cercanías) ....................................................................................................................................... 120 Figure 9.01 – Ranking of Municipalities per type of analysis. Number 1 means the Municipality most benefited by Metrosur ..................................................................................................................... 150 Figure 9.02 – Summary of results per type of analysis ...................................................................... 150 Figure 9.03 – Neighbourhood around Manuela Malasaña Metro station .......................................... 152. 16.

(17) INDEX OF TABLES Table 4.01 – Income Per Capita (IPC) per year and per Municipality (Euros)........................................ 62 Table 4.02 – Socio‐economic and other characteristics of Metrosur municipalities ............................. 63 Table 4.03 – Employment and polulation in the different areas of Madrid ......................................... 65 Table 4.04 – Vehicles in the Madrid region in 2004 ............................................................................. 67 Table 4.05 – Time average of the most important combinations using different transport modes with origin/destination within the Madrid region in 2004 .......................................................................... 68 Table 4.06 – Ranking of Metrosur stations in 2007 per number of passengers using each station and its ratio of passengers compared to the total metro system.................................................................... 78 Table 4.07 – Passengers with origin at any Metrosur station in 2003, 2004 and 2007. ........................ 80 Table 4.08 – How people access Metrosur stations in different years. ................................................ 82 Table 4.09– Origin / Destination share of trips per Metrosur station and time of the day ................... 83 Table 4.10 – Share of origin trips per Metrosur station and time of the day........................................ 84 Table 4.11 – Share of destination trips per Metrosur station and time of the day ............................... 85 Table 6.01 – Variables used for housing analysis ................................................................................ 97 Table 6.02 – Example of Model without occupancy ratios (Alcorcon) ................................................. 99 Table 6.03 – Economic activity sectors.............................................................................................. 101 Table 6.04 – Variables used in the specification of the multinomial logit models .............................. 104 Table 7.01 – Descriptive statistics of variables for housing analysis .................................................. 108 Table 8.01 – Results hedonic models ................................................................................................ 119 Table 8.02 – Alcorcon multinomial logit model summary: parameter estimates by activity sector with information about economic activities ............................................................................................. 126 Table 8.03 – Mostoles multinomial logit model summary: parameter estimates by activity sector with information about economic activities in year 1998 ......................................................................... 129 Table 8.04 – Leganes multinomial logit model summary: parameter estimates by activity sector with information about economic activities in year 1998 ......................................................................... 131 Table 8.05 – Getafe multinomial logit model summary: parameter estimates by activity sector with information about economic activities ............................................................................................. 133 Table 8.06 – Fuenlabrada multinomial logit model summary: parameter estimates by activity sector with information about economic activities in year 1998 ................................................................. 136. 17.

(18) Table 8.07 – Metrosur multinomial logit model summary: parameter estimates by activity sector with information about economic activities in year 1998 ......................................................................... 138 Table 8.08 – Changing patterns by economic activity using Kernel density maps between 1998 and 2007 ................................................................................................................................................ 140 Table 8.09 – Multinomial logit models summary (per municipality) for public transportation values 141. 18.

(19) 1. INTRODUCTION.

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(21) Chapter 1: Introduction. Public transportation infrastructure in urban areas has significant impacts. Banister and Berechman (2000) classify those varied effects, among other things on: transportation-related effects, land-use effects, and effects on jobs and business activities. It can also be a powerful driver of urban form.. Those effects can be classified as short-term and long-term (Boarnet, 2006). Short-term effects are those related to reductions in travel time for the population living or working around the infrastructure. Long-term effects are mostly those triggered by the existence of positive externalities that improve the efficiency of firms due to economies of scale, and by the advantages that these areas provide to people to live around them (Fujita, 1989; Graham, 2005). As a result, the effects on jobs and businesses due to a new transport infrastructure are usually noticed over the long term. Moreover, researchers such as Mas and Maudos (2004) have shown that transportation infrastructure does not only have significant effects on the area directly influenced by it, but also on areas close or connected by it.. Commuter rail and metro stations often have positive rather than negative effects. A greater number of metro stations increase the accessibility to public transportation for the people living nearby and reduce their travel time to other destinations in urban areas. In multiple cases it has been noted that a new transport infrastructure may boost land values in the area due to a better accessibility. Similarly, business activities (both offices and shops) near the stations also enjoy some advantages: jobs and shops are now more accessible for those coming from any destination; business activities close to the stations enjoy either the increase in the number of people who pass by near the shops in their way to or from the stations, or the increase in the qualified labor force they can draw upon.. Many negative effects associated with metro stations are spatial externalities, including noise and changes to the urban landscape. The effects described above depend on the type of transport infrastructure (Cambridge Systematics, 1998; Mas and Maudos, 2004), its location, and its specific characteristics.. Authors, such as Maoh et al. (2010), affirm that there is a spatial relationship among residential and commercial land uses, where different land uses tend to colocate depending on their interrelationships, taking advantage on its proximity.. 21.

(22) Their findings suggest that there is interdependence between the locational patterns of residential and commercial land development.. On the other hand, literature, both theoretical and empirical, on industrial location shows that the location of firms is not a random process, but rather the result of an analysis aimed at maximizing location benefits for individual firms, where the location decision is based on the future profits that a firm expects to earn in that location (Holl, 2004). In this respect, authors such as Mori and Nishikimi (2002) point out that there is a process of reciprocal reinforcement between industrial agglomeration and transport. In spite of that, the location of businesses does not depend only on a reliable transportation infrastructure by itself, but also on a combination of factors which include, among others: firms’ agglomeration, labor market characteristics, land market, and enhancement of environmental quality (Banister and Berechman, 2001). Some researchers point out that due to agglomeration economies and the advantages of easy access, most business activities are concentrated very close to transportation stations (Cambridge Systematics, 1998). Authors suggest that agglomeration is caused by clustering of firms which eventually become employment nodes. Over the time, those nodes define urban form (Maoh and Kanaroglou, 2007).. In effect, economic development and economic growth are the result of the longterm increase in economic activities which can be attributed in part to the direct impact of improvements in the transportation infrastructure, such as travel time reductions that promote industrial agglomeration. However, economic development requires a social and political framework that prompts such economic growth. Banister and Berechman (2001) state that in order to maximize benefits some requirements must be achieved: -. Political factors related to policy actions and institutional support.. -. Availability of funds for investment and its efficient implementation.. -. Economic conditions such as labor force and other positive externalities.. However, the study of public transportation impacts is very limited, and most importantly, they are in general only focused on one aspect of the multiple impacts brought about by the new infrastructure. There are only relatively few cases where an in depth research has been carried out, for example, the Jubilee line in London or the BART in San Francisco.. 22.

(23) Chapter 1: Introduction. Many of the studies fail to record long term effects, for different reasons, such as, the availability of large temporal and detailed datasets, because of the short length of the study or even because the investment is still too recent. Therefore, in many cases the studies are only limited to one aspect of the multiple possible effects.. For example, compared to housing land values, scarce research has been carried out to evaluate in detail the benefits due to a transport infrastructure on the economic activities, taking into account the type of businesses affected, demographic and economic characteristics (Vuchic, 2005) due to the fact that datasets are not always available at a very detailed local or regional level (Boarnet, 2006). Nevertheless, there are some studies that have attempted to quantify those effects (Banister and Goodwin, 2010; Cuthbert and Anderson, 2002; Manzato et al., 2011; Melo and Graham, 2009). As it is discussed in the following chapters, in Spain the availability of this kind of analyses is even more pronounced.. The goal of this research is to assess the long term effect that an increase in accessibility caused by the construction of a new metro line using two different approaches, analyzing both the - changes in housing land values and the impact that metro stations have had on the location of business activities. The empirical analysis is focused on Metro line 12 (known as Metrosur) located in the Madrid Region.. In this research, two main hypotheses are tested: First, that people living within the municipalities covered by Metrosur may be benefited by the proximity to Metrosur stations, and that this benefit is noticed in land markets. Second, that the impact wrought by this new infrastructure, affects the locational patterns of businesses and generates a preference to locate near transit facilities. However, it is also hypothesized that the location of firms is affected by the surrounding opportunity landscape, and the presence or absence of other firms that may generate economies of agglomeration and/or competition.. According to different studies such as the TRCP Report 35 (Cambridge Systematics, 1998), a sufficient period of time has now elapsed in order to evaluate the economic impact of the public transport infrastructure. Therefore, reliable estimates may be obtained of the capitalized impact of Metrosur within the five municipalities. The methodology used herein is based on different approaches,. 23.

(24) mainly due to the availability of micro-level data bases. The analyses are focused on two sectors: residential land values and location of economic activities.. In the case of residential land values, land prices were analyzed through a spatial econometric approach in order to answer whether or not land markets within the municipalities connected by Metrosur have been benefited by its proximity to metro stations. Meanwhile, for the analysis of firm location decisions, multiple objectives were considered, such as labor force availability, market opportunities, and transportation costs. Many of these factors are influenced by changes in accessibility wrought by new transportation infrastructure. In this case spatial statistical techniques and discrete choice models are used to evaluate the effects of Madrid’s Metrosur expansion in business location patterns. Specifically, the location patterns are explored by different industry sectors, to evaluate if the new metro line has encouraged the emergence of a “Metrosur spatial economy”.. The methodology to carry out this research is based on the first law of geography: “Everything is related to everything else but near things are more related than distant things” (Tobler, 1970), which has been the core of spatial analysis and modeling since 1970. Herein, data processing was conducted using a Geographic Information System (GIS). GIS integrates the information to be managed under one system. It provides important tools for transportation research and planning, from pre-processing and processing of data, to fundamental spatial analysis operations such as the calculation of distances, areas, frequencies, and spatial relationships accessibility (Hsiao et al., 1997; Miller and Shaw, 2001; Miller, 2004). For example, this research takes into account the distances from each location to the different facilities evaluated, such as the closest metro station through the street network, which increases the confidence in the analysis through the use of real distances between points (Gutiérrez and García-Palomares, 2008; MejiaDorantes et al., 2010).. Herein, different findings are stated. First, in the case of housing, the results indicate that better accessibility to Metrosur stations has a positive impact on house values, and that the effect is particularly marked when selling a house. Moreover, they also show the presence of submarkets which are well defined by geographic boundaries as well as by transport fares, which imply that the economic benefits differ among municipalities. Second, in the case of firm location decisions, results indicate that the pattern of economic activity location is related to. 24.

(25) Chapter 1: Introduction. urban accessibility and that agglomeration, through economies of scale, also plays an important role. As in the case of housing, results differ among municipalities. Finally, it is discussed the reasons that have yielded some municipalities being more benefited than the others, such as Alcorcon, and some recommendations are drawn to improve positive land use effects and to take into account when planning a new transport infrastructure.. The peculiarities of Metrosur make this research a very singular one. Herein, some remarkable conclusions provided by the results are drawn. As far as it is known, this is the first time that such a detailed assessment has been carried out in Spain. For all the reasons explained above, it is presented a novel research in terms of its methodology and case study. This thesis contributes to the literature because it combines geostadistical techniques along with spatial econometric and discrete choice models to assess the economic impact on different types of land uses wrought by a new transport infrastructure. It is worth noting that the lack of availability of micro-level data bases has yielded that methodologies not commonly used for the assessment of land uses became an alternative in this research. The methodology proposed herein shows not only the potential of the different techniques and models used herein when using a micro level data base but it proposes a different approach which could be applied to other cities with similar micro-data bases, such as the ones in the EU. I am sure that the results presented in this thesis provide important evidence to inform efficient transportation, urban and regional economic planning.. This thesis is organized in the following way. After introduction, the second section presents the objectives, which are followed by state of the art. Afterwards, the characteristics of the study area are presented. Chapter six presents the methodology used to carry out the analysis and Chapter seven contains information about the data sources. In the eighth section, results are discussed. Finally, the last two sections offer the conclusions, a final discussion about the most relevant findings along with the outlines for future research.. 25.

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(27) 2. OBJECTIVES.

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(29) Chapter 2: Objectives. This thesis focuses on long term impacts due to a new public infrastructure, specifically, the effects on house prices and on business activities. For that sake, it proposes a double methodology, one based on housing and the other one based on the location of firms, which is a novel one. The empirical analysis focuses on Metro line 12 (known as Metrosur), which is a circular line that connects five municipalities at the south of the Madrid Region. This case study was chosen because this metro line started operations in 2003, thus and according to different studies, such as the TRCP Report 35 by Cambridge Systematics (1998), land market adjustments were likely to occur even before 2003, due to the anticipation effect (Tsutsumi, 2008). Hence, from 2003 to the datasets of the analyses, the lag is sufficient enough in order to obtain reliable estimates of the capitalized impact of Metrosur in land uses. Moreover, this metro line was chosen because it is a huge metro line (it corresponded to the 20% of the total metro infrastructure at the time it was built) and at the same time it is a peculiar one because it intends to unify five municipalities with huge expectations (Authorities expected to create the third most important “city” in Spain). Finally, the population covered by Metrosur is of about 1 million inhabitants while there are 3 million people living in Madrid City.. Due to the lack of availability of data, the approach used in this research is different. In the case of residential land values, it was possible to assess the implicit impact that a new metro line may have had on land values using hedonic models, because a cross sectional dataset was obtained through a real estate web page at the beginning of 2009. Meanwhile, in the case of the location of business activities, the real estate market is very limited; therefore, observations were not sufficient to carry out a hedonic approach and another approach was needed. In this case, it was possible to use a detailed firmographic dataset obtained through the Bureau of Statistics of the Madrid Region (Instituto de Estadística de la Comunidad de Madrid), where it was possible to locate any type of firm activity at each municipality in 1998 and in 2007.. The goal of this research is threefold. First, to evaluate the influence of Metrosur stations for house values. Second, to evaluate how economic activities are benefited by its proximity to Metrosur stations by analyzing how they are located over space and over time. Third, to analyze both impacts together in order to discuss the reasons for those impacts to come up, and to draw some policy recommendations for efficient urban planning. More specifically the objectives are:. 29.

(30) In the case of housing: -. To evaluate how house prices are influenced by their real distance to Metro stations.. -. To asses which structural, location and neighbourhood variables play an important role in the implicit house prices.. -. To evaluate whether the impacts are brought about by the infrastructure or by other factors.. -. To conduct a spatial econometric approach to obtain accurate estimates and to compare the differences among them.. -. To analyze if there are evident submarkets and to show how are those submarkets defined.. In the case of firm’s location choice: -. To evaluate which characteristics are significant to firm’s location choice, mainly those related to labor force availability, market opportunities and transportation costs.. -. To explore location patterns by different industry sectors.. -. To assess if changes in accessibility wrought by new transportation infrastructure have impacted its location patterns over time.. -. To propose a methodology that combines spatial statistical techniques along with a micro-level data base to evaluate the effects of Madrid’s Metrosur expansion in business location patterns.. -. To evaluate whether the impacts are brought about by the infrastructure or by other factors.. -. To evaluate if a “Metrosur spatial economy” has emerged.. Finally, taking into account the above findings, the objective is to discuss the different effects triggered by the new transport infrastructure, to assess if the benefits are similar among municipalities or the reasons that have yielded some municipalities. being. more. benefited. than. others.. Finally,. to. draw. some. recommendations in order to improve positive land use effects and to efficiently manage public resources.. The approach used herein is based on a Spatial Analysis perspective, where “near” is the key concept. Therefore, different specialized software for spatial data analyses were used.. 30.

(31) 3. STATE OF THE ART ON THE ASSESSMENT OF TRANSPORTATION INFRASTRUCTURE IMPACTS.

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(33) Chapter 3: State of the art on the assessment of transportation infrastructure impacts. 3.1 AN OVERVIEW ON THE EFFECTS OF TRANSPORT INVESTMENTS During the last decades, there has been an increasing interest on examining the effects that a new infrastructure produces to the area where it is located, not only related to transport use, but also on the economies of local areas served by new stations and its spillover effects. However, there are only few examples of thorough analysis of the long term effects of a major public transport infrastructure. In every case the analyses make use of different methodologies, basically due to the lack of data availability . Moreover, many of these studies fail to record those impacts due to different reasons, such as: that the length of the study is too short, that there has not been a sufficient period of time to allow for these effects to come up or that there is no detailed data available. Many other studies only use one specific approach to assess long term effects.. As pointed out by Lucas and Jones (Lucas and Jones, 1998) even in Britain only very few opportunities have come up to assess the effects of major investments: the Victoria Line (1963-1965), the Glasgow Rail Improvements (1979-1983), the Tyne and Wear Metro (1979-1986), the Manchester Metrolink (1990-1996), the South Yorkshire Supertram (1992-1996). More recently, a good example of appraisal was the one of the Jubilee Line Extension (JLE) Impact Study, which is without doubt one of the most complete studies that exist on the topic. It was carried out by the University of Westminster which coordinated the programme that aimed to analyze before and after the infrastructure was built. The specific goals of this study were (Wofinden, 1998a): •. To understand how the extension has affected London.. •. To improve the appraisal and forecasting techniques.. This study included the analysis of four broad categories: •. Transport impacts and accessibility changes. •. Residential and commercial development, including impacts on land values. •. Employment and impacts on the economy. •. Impacts on residents and their travel patterns. It was a thorough study which used a complete set of different techniques for the analyses (Arup Economics and Planning, 1999), which included among others,. 33.

(34) household and employees’ surveys (Wofinden, 1998a; Wofinden, 1998b), and provided very interesting results.. The Jubilee line extension was the first new underground line built in London for over twenty years. It is of about sixteen kilometres long and added eleven new stations to the line, six of which will be in locations served by the underground network for the first time.. The primary objective of the JLE was to assist the regeneration of Docklands (Jones et al., 2004). It started operations in autumn 1999. The expectation was that this line would produce substantial benefits from the regeneration of the South Bank and the creation of new jobs in Canary Wharf.. As reckoned by Jones (2004) the availability of datasets was a major issue which at the end determines the extent of the analysis. Their principal problems were related to the availability of datasets in the property market values, related to a long time lag to evaluate long time effects or related to detailed datasets such as census, among other problems. Du and Mulley (2006) also comment that this study failed to identify any significant effect in phase 1 by using a hedonic price models, because this analysis was substituted by different surveys in phase 2.. Another good example of this type of assessment is the one carried out for the Bay Area Rapid Transit (BART) which was planned as a means of guiding future population and employment growth in the San Francisco Bay Area.. The original BART Impact Study was carried out in the mid-1970s, only a few years after the 1972 opening of the 140-mile BART system. This study is known as the most extensive study carried out to date on the development impacts of a US transit system (Arup Economics and Planning, 1999; Cervero and Landis, 1995). This study found that BART did not induce significant development impacts, especially outside of downtown San Francisco. The reasons for this finding might be related to the short period of time when it was evaluated (Cervero and Landis, 1995).. Afterwards, the study was headed by Cervero and Landis (1997) aimed at examining land use changes that occurred twenty years after the original evaluation.. The. analysis. concentrated 34. on. residential. and. non-residential.

(35) Chapter 3: State of the art on the assessment of transportation infrastructure impacts. development of land around a collection of new stations. Data was collected and examined at the macro level for the area around the line and at micro level for the individual station catchment areas.. Cervero and Landis (1995) concluded that BART’s influence on office development in the east Bay was spotty, because its major influence was found in downtown San Francisco while in the East Bay was weak. They also state that employment growth occurred in non-BART-served corridors. In general terms, the study concludes that BART played a fairly modest, though not inconsequential, role in shaping metropolitan growth in the San Francisco Bay Area. The authors also conclude that proximity is capitalized into land prices, however; the most important factor is the quality and scope of the service. Therefore, not all the systems are equally appreciated.. Finally, the study states that benefits are most evident in highly accessible, nonresidential areas where a variety of other influences are also present, which is related to a strong regional vision about the desired urban form along with adequate political conditions to support and foster public transport policies.. As it was said before, there are several studies that account for transport effects in land value using only a certain approach (Duncan, 2008; Yoo and Kyriakidis, 2009; Perk et al., 2010; Andersson et al., 2008; Habib and Miller, 2008). However; most of them only focus on a specific area, or do not use really disaggregated datasets or fail to include the whole extent of the infrastructure for different types of analysis. Above all, most of the analyses do not assess the impacts of an infrastructure using different perspectives; they only centre their attention in one type of impact, such as housing. Moreover, many times, the infrastructure is only analyzed once the infrastructure started operations.. On the other hand, in practice, empirical analysis of spatial and temporal patterns of firm location is even more complicated due to lack of detailed firmographic data. Very few studies are available that examine these issues (Baumont et al., 2004; Manzato et al., 2011; Maoh and Kanaroglou, 2007; Montero-Lorenzo et al., 2009). The studies of Maoh and colleagues (Maoh and Kanaroglou, 2007; Maoh, 2007; Maoh and Kanaroglou, 2009; Maoh et al., 2010; Ryan et al., 2009) for example, were facilitated by access to firm micro-data through a special program with. 35.

(36) Statistics Canada that allowed the researchers to work at a secure data facility site in Ottawa. Other databases are relatively inaccessible or simply do not exist. In the case of Spain, no one has carry out this research before.. Moreover, as noted as well by different meta-analyses, such as the ones by Martinez and Viegas (2009) and Du and Mulley (2006), it was found that there is a lack of studies related to the impact of transport infrastructure improvements in Spain. This may be due to the lack of micro data sets available. Only very few detailed Spanish case studies related to spatial hedonic models are available, however they are not focus on the relation of transportation and housing. See for example the research carried out by Bengochea-Morancho (2003), Militino et al. (2004) and Montero-Lorenzo et al. (2009). In the case of firms, in general, the empirical analysis of spatial and temporal patterns of firm location is even more complicated due to lack of detailed firmographic data. Only one study related to the impact on retail activity using a survey approach for a middle size city (Seville) was found in Spain (Castillo-Manzano and López-Valpuesta, 2009). Similar studies are related to average data.. In conclusion, there is a number of issues that should be taken into account before analyzing the long term effects of an infrastructure: •. Confusion might arise due to the impacts that are caused by the infrastructure or by other factors.. •. Time-scale is necessary to obtain reliable estimates: While there may be effects caused by anticipation effects, it is also possible to be unable to appreciate effects in less than a certain period of time.. •. Effective evaluation of either positive or negative externalities.The use of different techniques to have a wider approach.. •. Correlation of results along with the geographic, economic, planning and policy context that may boost or diminish the expected impacts.. Regarding the last statement, different studies such as the one carried out by Cervero (1998), mention that Transport Oriented Development (TOD) initiatives are important urban policy actions to increase the opportunities driven by metro stations. These initiatives include among others: high density developments near transit stations, pedestrian-friendly neighborhood design especially through dense street patterns, and mixed land uses. Different studies note as well, that the characteristics of the street network design determine the attractiveness of transit 36.

(37) Chapter 3: State of the art on the assessment of transportation infrastructure impacts. stations because most of the riders get to the stations by walking. Therefore, traditional street patterns (narrow roads with frequent crossings) enhance pedestrian access; while, newly developed areas designed for private transport (longer and wider roads, irregular patterns) limit pedestrian access to the stations (Gutiérrez and García-Palomares, 2008; Mejia and Vassallo, 2010).. The Netherlands is a very interesting case of spatial planning where important urban policies have been implemented over the years. Government has always been really concerned in the development of urban forms. Many policies have come up in order to foster compact urban growth, to limit sprawl and to encourage the use of public transport by people living and working nearby (Dijst, 1997). The Randstad is the densely populated western part of the Netherlands, where all the large cities of the country are located (Amsterdam, Rotterdam, The Hague and Utrecht along with smaller cities). The A-B-C firms’ location policy was formulated to discourage the use of private car and to promote the use of public transport together with cycling and walking in those areas where it was implemented. “A” locations are centrally located sites close to rail or metro stations, which imply that they are very accessible by public transport. In this zone, there may be no more than ten spaces for every 100 employees or twenty if it is outside the Randstad. “B” locations are generally located in developments outside the CBD areas, reasonably well connected by public transport and very accessible by car. In this case, parking spaces may be up to 20 for every 100 employees within the Randstad and forty outside it. “C” locations typically have very good motorway access and no parking limitations (Bertolini and Dijst, 2003; Dijst, 1997; Schwanen et al., 2004). It imposes location guidelines to companies and organizations in order to drive employment and public services towards A and B locations, avoiding C zones. It is also interesting to note that housing policy has been used to support spatial planning objectives. The government has a very strong regulation of the residential development processes.. Next sections deal with different methodologies that may be used to analyze the changes brought about by a new transport infrastructure.. 37.

(38) 3.2 THEORETICAL BACKGROUND ON HEDONIC MODELS AND SPATIAL ANALYSIS In literature, there are basically two different methods to assess the real estate markets: Repeated sales models and hedonic models. Hybrid models, which are the combination of those methodologies, have also been used. Repeated sales deals with a regression of the difference in sale prices for the same set of homes and a set of time dummies. It is known also as BMN-model, first presented by Bailey, Muth and Nourse (1963). Its main problem is caused by the small sample data available and the difficulties to collect the data due to the required time frame. Compared to hedonic models, only few studies have been carried out using this method (Sommervoll, 2006).. Hedonic models are based on a different utility theory point of view. They consider a product Z which is composed by different n attributes or characteristics, which are objectively measured by consumers’ perceptions. Z = (z1 , z 2 ,..., z n ). (3.01). Of course, consumers’ appreciation about each characteristic might differ among observations, but in general it is assumed a unique value. For that sake, it has to be a sufficiently large number of available products, so that choice among products with different combinations of z is possible. Moreover, it is assumed perfect information, so that consumers are aware of all the relevant characteristics that compose the good.. At the beginning, hedonic models’ use was focused on consumer models until Rosen (1974) generalized it for market equilibrium, where the individual’s utility is a function of the utility attributes that compose a product whereas producer costs depend on the characteristics of that good. Equilibrium prices are determined so that buyers and sellers are perfectly matched.. In the case of land market, the decision problem involves a maximizing utility subject to an income constraint, therefore, for a house i:. Max. U ( xi , Si , Di ,.Li ). 38. (3.02).

(39) Chapter 3: State of the art on the assessment of transportation infrastructure impacts. Subjected to. I = Pi + x. (3.03). Where the utility of a household is a function of a composite good xi, a vector of structural characteristics Si, a vector of social and neighbourhood characteristics Di and a vector of locational characteristics Li. Subjected to I which is the household income; a function of the house price Pi and the composite good x (Freeman, 2003).. Hedonic regression analysis is very popular for land market studies. Literature using this technique is extremely extensive. Hedonic models were popularized by Zvi Griliches in the early 1960s. Its history dates back to 1939 when it is said that Andrew Court proposed the first hedonic analysis (Goodman, 1998), although according to Colwell and Dilmore (1999), the origins of this methodology date back to the 1920s. At the beginning, hedonic models’ use was focused on consumer models until Rosen (1974) generalized it for market equilibrium, where the individual’s utility is a function of the utility of the attributes that compose a product whereas producer costs depend on the characteristics of that good. Equilibrium prices are determined so that buyers and sellers are perfectly matched.. In order to obtain reliable results, the correct specification of the hedonic model is essential. There is no consensus in the literature regarding the variables that should be included in the model, basically because models depend on the information available. However, there are three basic categories that are generally accepted:. structural. characteristics,. location. attributes. according. to. the. neighbourhood, and accessibility characteristics such as transport and other services. Therefore, hedonic house price functions are typically expressed as:. Pi = α i + ∑ η ji S ji + ∑ λ li D l i + ∑ ϕ mi Ami + ε i j. l. (3.04). m. As in any regression model, i is equal to the observations available in the dataset. P is a vector of selling prices. S is a vector of structural characteristics, D is the vector of neighbourhood attributes and A is a vector representing the accessibility attributes. εi is the random error term vector. Its matrix notation is:. 39.

(40) P = Xβ + ε. (3.05). Where P is an (n x 1) vector of selling prices, X is an (n x k) matrix with observations on structural or neighborhood or accessibility characteristics, β is the (k x 1) vector of unknown regression coefficients and, ε is assumed to be a vector of independent and identically distributed (i.i.d.) error terms.. Traditional hedonic functions are basically linear econometric regression models. The classical linear regression models consist of five basic assumptions: linearity, the expected value of disturbance term should be zero, disturbance terms should have uniform and uncorrelated variance, it should have a correct specification and, no exact linear relationships should exist within the model (Kennedy, 2003). Its unknown parameter is generally estimated by ordinary least squares (OLS), such that:. βˆ = ( X ' X )−1 X 'Y. (3.06). The OLS method generates the set of values of the parameters that minimizes the sum of squared residuals. Estimators should be consistent, unbiased and efficient. Finally, it is important to remark that regression coefficient estimates provide a constant value for the entire sample.. In the case of real estate market, different studies show that there is not a unique global market but rather market segments, where locational and adjacent factors affect house prices (Can, 1992). Thanks to technologies like geographic information systems and the progress in fields such as spatial econometrics and statistics, it has been possible to analyze datasets taking into account the spatial nature of real estate data. If no spatial considerations are taken into account when modelling spatial data, errors such as spatial dependence (or spatial correlation) and spatial heterogeneity may be present in the analysis (Can and Megbolugbe, 1997; Dubin, 1988; Dubin, 1998; Paez et al., 2001; Won Kim et al., 2003). The consequences are inefficient estimates, the standard errors may be biased and finally, predicted values may be inaccurate (Dubin, 1988).. 40.

(41) Chapter 3: State of the art on the assessment of transportation infrastructure impacts. 3.2.1 Spatial Analysis. Tests for spatial dependence are generally based on Exploratory Spatial Data Analysis (ESDA), which are a set of techniques designed to describe and visualize spatial distributions, to identify atypical points or spatial outliers and to detect spatial pattern associations, such as clusters, which therefore suggest spatial autocorrelation or spatial heterogeneity (Anselin, 1998).. Moran’s I is a very well known local statistic for measuring spatial dependency. Formally, it gives an indication on the degree of linear association between the vector zt of observed values and the vector Wijzi of spatially weighted averages of neighboring values, called the spatially lagged vector. Values of I larger than the expected value E(I)=-1/(n-1) indicate positive spatial autocorrelation, while values smaller than the expected indicate negative spatial autocorrelation. It is based on a permutation approach, where it is assumed that, under the null hypothesis, each observed value could have occurred at all locations with equal likelihood (Anselin, 1995). A high degree of spatial autocorrelation implies small differences at close distances and increasing differences at higher distances (Anselin, 1998; Cressie, 1991). It is a manner to express the coincidence of similar data in a certain place, or clustering.. Further information about this statistic can be found at (Anselin,. 1988; Anselin, 1995; Anselin, 1998). It is defined by:. I=. (3.07). n ∑ i ∑ j W ij Z i Z j S0. ∑i Z i. 2. Where : •. Zi is the deviation of the variable of interest with respect to the mean: (x-μ). •. Wij is the matrix of weights, where i is a neighbour of observation j, and equal to zero otherwise. •. n is the number of observations. However, the above statistic is a global statistic which yields only one statistic to provide overall information about the entire dataset. It does not provide information about clusters at local level, which would mean local spatial autocorrelation. To account for that fact, it is necessary to use the Local Moran’s I, known also as the. 41.

(42) Local Indicator of Spatial Association (LISA) (Anselin, 1995), which evaluates the statistical significance for each Ii . It is defined as a local indicator of spatial association which satisfies two criteria: the LISA for each observation gives an indication of significant spatial clustering of similar values around that observation and, the sum of the LISA for all observations is proportional to a global indicator of spatial association. It is calculated as:. Ii =. (3.08). Zi ∑ Wij Z j m0 j. Where:. m0 =. ∑i Z i. (3.09). 2. n. And:. I =∑ i. (3.10). Ii n. In practice Moran’s I test is implemented on the basis of an asymptotically normal standardized z-value, obtained by subtracting the expected value and dividing by the standard deviation. Further information about this statistic can be found at (Anselin, 1995). There are different software programs that compute for this statistics. Programs such as ArcGis or Geoda model these spatial relationships. Each one uses different approaches such as randomization or normalization equations or Monte Carlo simulation, which is many times preferred when analyzing socioeconomic processes.. In the presence of spatial dependence, there are two different hedonic methods to analyze geographically distributed data: spatial econometrics and spatial statistics. Both are increasingly used in the analysis of property prices (Páez, 2009). Spatial econometrics may only be used if the dataset analyzed is a disaggregated one (Tsutsumi and Seya, 2009b). In this case, the most commonly known techniques are: Spatial-Lag Models (SLM) and Spatial Error Models (SEM) (Anselin and Gallo, 2006). Both models are based on the principle that there are either similarities or a strong relationship among properties located nearby.. 42.

(43) Chapter 3: State of the art on the assessment of transportation infrastructure impacts. 3.2.1.1 The Spatial-Lag Model (SLM) It is appropriate if the process is endogenous, or in other words, if property prices are affected by the price of other properties in the neighborhood. This insight is widely used in assessment, for example by considering comparative sales. In order to capture the endogenous component of the process, the model considers the spatially weighted average of house prices at each location of interest. Since the characteristics of the neighborhood influence the price of each house this in effect captures a spatial multiplier. Technically, this effect is characterized by means of a new variable on the right hand side of the equation that represents the interaction effect as a weighted average of neighboring observations (Anselin, 1988). This model is suitable when the modeler wants to know the strength of this relationship and the true effect of the explanatory variables (Cho et al., 2010; Won Kim et al., 2003). It is usually written in the matrix form as follows:. P = ρWP + X i β i + ε. (3.11). Where P is an (n x 1) vector of selling prices, X is an (n x k) matrix with observations on structural or neighborhood characteristics, β is the (k x 1) vector of unknown regression coefficients and, ε is assumed to be a vector of independent and identically distributed (i.i.d.) error terms. ρ is a spatial autocorrelation parameter (scalar). The spatial lag, WP, introduces endogeneity to the model (since price becomes an exogenous and endogenous variable); therefore OLS estimators are inconsistent and biased. Maximum-likelihood estimation or other instrumental variables are necessary to obtain consistent estimators (Anselin, 1988; Ord, 1975; Won Kim et al., 2003).. 3.2.1.2 Spatial Error Models (SEM) They are based on the assumption that there are omitted variables within the model that follow a spatial pattern, which leads to spatial autocorrelation within the error term. Therefore the lack of an adequate analysis produces inefficient but unbiased and consistent estimators. Again, estimation must be based on the method of Maximum Likelihood or the generalized moments approach (Ord, 1975). It is generally written as:. 43.

(44) P = Xi β i + ε. ε = λWε + u. (3.12). Where λ is the spatial autoregressive coefficient and u is assumed to be the vector of i.i.d. errors.. In both spatial analysis, W is a matrix that accounts for interactions between locations, size n (number of observations). It defines the relation among observations that are assumed to interact. It is a matrix that has positive wij elements, its diagonal elements are set to zero, where i and j are neighbors and equal to zero otherwise. Usually its row elements are standardized such that the sum is equal to one. There are different ways to define the spatial weight matrices, for example, contiguity and distance based or k-nearest neighbors (Anselin, 1988; Dubin, 1998). There is no formal guidance in order to choose the “correct” specification for spatial weight, therefore choosing the correct specification is a difficult work, which implies that many trials are needed.. 3.2.1.3 Other Models There are other spatial modelling techniques to account for spatial dependency and non-stationarity: Kriging which is able to incorporate systematic residual information (i.e. error autocorrelation) to obtain an improved predictive model. On the other hand, Moving Windows Regression (MWR) and its enhanced counterpart, Geographically Weighted Regression (GWR) are methods designed to model spatially heterogeneous processes. Finally, Moving Windows Kriging (MWK) is able to incorporate locational effects and spatial heterogeneity at the same time (Paez et al., 2008).. Geographically. weighted. regression. (GWR). aims. at. identifying. spatial. heterogeneities in regression models of geo-referenced data. The spatial variability of the estimated local regression coefficients is usually examined to determine whether the underlying data generating process exhibits spatial heterogeneities or local deviations from a global regression model.. Although GWR is drawing attention as a statistical method to estimate regression models with spatially varying relationships between explanatory variables and a 44.

(45) Chapter 3: State of the art on the assessment of transportation infrastructure impacts. response variable, it is presented as a standard too in exploratory spatial data analysis. However, different analyses show that local regression coefficients are potentially collinear and across space, even if the underlying exogenous variables in the data generating process are uncorrelated (Wheeler and Tiefelsdorf, 2005; Wheeler, 2007). The former leads to GWR coefficient estimates have inflated variances, and are at times counterintuitive and contradictory in sign to the global regression estimates. The presence of local collinearity in the absence of global collinearity necessitates the use of diagnostic tools in the local regression model building process to highlight areas in which the results are not reliable for statistical inference (Wheeler, 2007).. Paez (2005) compared GWR and the expansion method in a simulation study; he found that both approaches are able to provide a reasonable representation of the spatial patterns inherent in the simulated data.. 3.2.2 Housing Submarkets Spatial dependence and housing submarkets are related. The presence of submarkets implies that several housing units share similarities in prices with its neighbors. Controlling submarkets may reduce estimation errors (Redfearn, 2009). Geographical areas are natural submarkets, but they can also be specified by dummy variables, neighbors, postcodes, by estimating a separate equation for each submarket, by adjusting predicted values using the errors within each submarket or by more sophisticated statistical techniques (Dubin, 1988; Bourassa et al., 2007). However, as note by Bourassa et al., geographical subdivisions are easier to implement and perform better than spatial statistical methods (Bourassa et al., 2003; Bourassa et al., 2007). Their findings show that even an OLS model, when correctly specified with submarket dummy variables may obtain better results than geostatistical methods or lattice models and that these submarkets can be defined as geographical areas. In the same line with Bourassa, Paez et al. (2008) state. that. market. segmentations. may. dependencies.. 45. be. more. important. than. spatial.

(46) 3.2.3 Marginal Benefits As stated before, hedonic price function is an equilibrium price equation where the price of house i is defined as a function of the house characteristics. Therefore, for each characteristic of interest, theory says that the first order condition defines the marginal willingness to pay for any characteristic that enters the utility function. Once the estimates for the coefficients are obtained, it is possible to estimate the person’s marginal willingness to pay for any characteristic that enters the utility function.. The above implies that for OLS and SEM the marginal benefits for the ith variable are given by the following expression:. ∂y = βi y ∂x'i. (3.13). The above expression is a differentiating equation with respect to the characteristic of interest. It can be interpreted as the price that it is accepted to pay for that characteristic.. In the case of SLM, the marginal benefit is given by:. ∂y −1 = β i [I − ρW] y ∂x'i. (3.14). Where I is an identity matrix. Small and Steimetz (2006) state that if property values are affected by pecuniary externalities, equation (3.13) may be used for SLM while equation (3.14) should be used if there are technological externalities. Further information on marginal benefits can be found at (Anselin and Gallo, 2006; Small and Steimetz, 2006; Tsutsumi, 2008; Won Kim et al., 2003) Moreover, Tsusumi and Seya (2009) state that the use of the spatial econometric approach for assessing marginal benefits requires spatial tessellation data because it uses a spatial weight matrix.. 46.

(47) Chapter 3: State of the art on the assessment of transportation infrastructure impacts. 3.3 THEORETICAL BACKGROUND ON FIRMS’ LOCATION PATTERNS Firm location patterns are a consequence of many single and unique decisions of business activities. Firms select a site for a business based on numerous factors, including whether the firm is market- or resource-oriented, access to markets, labour and resources, and the availability of appropriate real estate. Once a firm is established and settled down in a specific location, it also may generate interactions with other firms. It may attract complementary activities, developing local markets, or it may repel other business activities that try to avoid competition. Firms can find proximity to other business advantageous or disadvantageous depending on the character of interactions. Most importantly, there are reasons to believe that firms are not indifferent to the presence or absence of other firms in their neighbourhood.. The theoretical foundation for the location patterns of firms is given by the concept of market areas. A market area is the geographical extent of a firm’s consumer base –where firm’s products are being sought–, and is determined by the firm’s spatial pricing. In order to maximize revenue and profit, firms consider how locating at a specific location would affect their market potential based on their ability to set a price consistent with their marginal revenue and marginal costs. Consumers, it is assumed, will consider the delivered price of competing firms and will try to minimize their cost. Other things being equal, the cost of transportation generates a tendency to prefer geographically proximate firms. Location, pricing policies, and consumer’s willingness to pay (based on access) all combine to generate market areas, and as a consequence firm locational patterns (Hoover and Giarratani, 1971).. Several location patterns may occur when a population of firms is examined. The case of firm repulsion may take place when enterprises are market-oriented and the market is dispersed, or alternatively when firms are resource-oriented and the sources of input are dispersed. Hence, firms may be competing for the same market or raw materials. Firm clusters, on the other hand, may result from demand or production characteristics of the activity in question. Foremost, agglomerative forces arise from the external economies of a cluster which is linked to input suppliers (Cohen and Paul, 2005). Sometimes spatial competition may lead to. 47.

(48) mutual attraction of sellers as well. Moreover, as urban areas grow, they become increasingly capable of supporting activities and services that are external to any cluster but which generate economies for a number of clusters. Finally, repulsion and attraction may operate jointly when sellers have market areas and buyers at the same time have established supply areas. Therefore the firm benefits from demand of the product at a well-known specific location (Hoover and Giarratani, 1971). The net effect of this joint process is an empirical question that must be examined in the context of the various factors that influence location.. During the past few decades, urban areas have experienced important changes due to decentralization of activities. Urban form has in many places evolved, and is increasingly less defined by a unique pole of economic activity, i.e. a traditional Central Business District (CBD), and more by multiple suburbanized economic poles. Employment, housing and population are therefore, reorganized in new areas (Cuthbert and Anderson, 2002). Transportation, it is commonly agreed, has played a key role in facilitating and even encouraging this type of development.. In general, enterprises judge that certain factors are important when considering a location: available labour force and its cost, market opportunities, taxes and subsidies (if available), infrastructure, transportation accessibility, space, location amenities and even personal decisions (Banister and Berechman, 2001; Beckmann, 1999; Small, 1982). Competition among business firms is also an important factor (Hoover and Giarratani, 1971). Furthermore, the location of firms and industries is influenced by geographic factors and agglomeration (Yrigoyen and Garcıa, 2009). The latter is a consequence of economies of scale, and means that the profit of some firms is improved when they operate in the context of a larger local economy, taking advantage of being closer to related firms (Cohen and Paul, 2005; Johansson and Quigley, 2003). When interdependent industries are attracted due to their economic linkages, it is even possible that these linkages attract them to other locations, such as out of urban centers. Whenever different types of firms locate closer, they eventually create clusters, which impact the way commercial and industrial land uses are defined over space. Eventually, such a process of co-location can have broader implications for urban form (Maoh and Kanaroglou, 2007). If different industrial sectors take advantage of physical proximity to related firms, these agglomerations become economic poles. In order to create agglomeration economies, a powerful labour force should be present (Feser and Sweeney, 2000; Maoh, 2005). 48.