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L. Miguel Martínez and José Manuel Viegas Effects of Transportation

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L. Miguel Martínez and José Manuel Viegas Effects of Transportation L. Miguel Martínez and José Manuel Viegas Effects of Transportation Accessibility on Residential Property Values: A Hedonic Price Model in the Lisbon Metropolitan Area L. Miguel Martínez PhD Candidate CESUR, Department of Civil Engineering Instituto Superior Técnico Lisbon Technical University Av. Rovisco Pais 1049 - 001 Lisboa, Portugal. Phone: +351-21-8418425 Fax: +351-21-840 9884 Email: [email protected] José Manuel Viegas Professor of Civil Engineering CESUR, Department of Civil Engineering Instituto Superior Técnico Lisbon Technical University Av. Rovisco Pais 1049 - 001 Lisboa, Portugal. Phone: +351-21-8418413 Fax: +351-21-840 9884 Email: [email protected] The total number of words is 8,389 (5,889 words + 4tables + 6figures) Submitted to the 88th Annual Meeting of the Transportation Research Board 15 th of November, 2008 L. Miguel Martínez and José Manuel Viegas 1 ABSTRACT The aim of this paper is to examine the relationship between the availability of transportation infrastructure and services and the pattern of house prices in an urban area, and to assess whether public investment in transportation can really modify residential property values. This study was developed for the Lisbon Metropolitan Area (LMA) as part of a broader study that intends to develop new value capture financing schemes for public transportation in the LMA. The paper focuses in three central municipalities (Amadora, Lisbon, Odivelas) where these effects could be more easily measured due to the existence of a significant variability of public transportation services. The paper tries to determine, using different spatial hedonic pricing models, the extent to which access to transportation infrastructure currently is capitalized into house prices, isolating the influence of three different transportation infrastructures: metro, rail and road. The results suggest that the proximity to one or two metro lines leads to significant property value changes and that the classic hedonic price model (ordinary least squares estimation) leads to similar coefficient values of the local accessibility dummy variables compared to the spatial lag model, thus providing a steady basis to forecast the property values changes derived from transportation investment for the study area in the absence of a significant property values database. L. Miguel Martínez and José Manuel Viegas 2 INTRODUCTION For decades, there has been considerable discussion about the effects of transportation accessibility on the housing prices. It is well known that a good public transport system provides a high level of access to work and other activities for households, and to customers and employees for businesses. The monetary value of this accessibility will be reflected in the value of a home or a business, in addition to the value of other features such as the specific physical attributes of the building and neighbourhood characteristics. The impact of public transport on property values has been studied from many perspectives, including analyses of different types of systems (e.g., rapid, commuter, light rail), of residential versus commercial impacts, and studies that have attempted to isolate both positive and negative effects. The varied approaches make it difficult to compare the results of one study to another. Further, some of the contradictory results over the years have often been due to differing methods of analysis, data quality, and regional differences. This paper examines the relationship between the availability of transportation infrastructure and services and the house prices in an urban area, trying to assess the impact of public investment in transportation on residential property values. This study was developed for the Lisbon Metropolitan Area (LMA) as part of a broader study that intends to develop new value capture financing scheme for public transportation in the LMA. The available data focuses in three central municipalities (Amadora, Lisbon, Odivelas) where these effects could be more easily measured due to the existence of a significant variability of public transportation services. This study presents several hedonic pricing models to assess the relationship between transportation accessibility and house values, ranging from the classic model to spatial hedonic price models (spatial lag) and including local and systemwide accessibility indicators. The results of the different models are assessed and compared having in mind the need to forecast house prices in subsequent phases of the research project. LITERATURE REVIEW In the 1960s, economists like Alonso and Muth developed the theory for determining residential location in the urban land market (1, 2). The theory illustrates a model where a household chooses to locate at a point where its bid-rent curve intersects with the actual one, in which the bid rent curves have a declining gradient with the distance from the residential location to the central business district (CBD). However it might be necessary to consider the effect of other variables such as neighborhood characteristics. The introduction of the hedonic pricing methodology by Rosen (3) led to an easier way of attributing value to different properties’ features. A number of studies have observed the integration of physical, neighborhood and accessibility characteristics of the property in models trying to explain the differences in property values or house prices (4-35). The hedonic price model is a multivariate regression model for housing values, as well as a common robust indirect approach to valuation in that its estimates represent the implied prices that people place on obtaining desirable features of a property and avoiding undesirable ones (20, 36). Most commonly, hedonic price models have used ordinary least squares (OLS) estimation (22, 33, 37-39), but more recently these models have been extended to incorporate L. Miguel Martínez and José Manuel Viegas 3 spatial effects in multiple ways: feasible generalized least square estimation (34) and spatial econometric models (spatial lag and spatial error models) (20, 40). There are several empirical evidences relating the changes in commercial and residential property market values and transport investment. Table 1 presents the information from the Europe, whilst Table 2 does the same for North America. As can be seen from the tables, the evidence is broadly positive with the widest difference being found between the residential and commercial markets. Parsons Brinkerhoff (41) concludes that proximity to rail systems is valued by property owners and there is little support that this proximity can decrease property values. Much of the European research (Table 1) has focused mainly on the residential market, but in the US research (Table 2) where the commercial market has been the main target. Almost uniformly, the impacts are seen as positive, with some very large percentage increases particularly in commercial property values. The enormous variability in (positive) impact points towards either the importance of other factors, or the specificity of results, or the limitations of the methods used – or a combination of all these factors. TABLE 1 Property value impacts of public transport proximity in European cities. Case/Location Impact on Impact Source Bremen Office rents +50% in most cases (42) Some localized positive Croydon Tramlink Residential property (43) impacts Freiburg Office rent +15-20% (42) Freiburg Residential rent +3% (42) Greater Manchester Not stated +10% (42) Hannover Residential rent +5% (42) Helsinki Metro Property values +7.5-11% (44) Residential and commercial London Crossrail Positive (45) property Residential and commercial London Docklands LRT Positive (44) property Residential and commercial London JLE Positive (46, 47) property Manchester Metrolink House Prices Unable to identify (48) Montpellier Property values Positive (42) Nantes LRT Commercial property Higher values (42) Nantes LRT Not stated Small increase (42) Nantes LRT Number of commercial premises +13% (44) Nantes LRT Number of offices +25% (44) Nantes LRT Number of residential dwellings +25% (44) Newcastle upon Tyne House prices +20% (42) None-initially negative due Orléans Apartment rents (42) to noise Rouen Rent and houses +10% most cases (42) Saarbr űcken Not stated None (42) Sheffield Supertram Property values Unable to identify (16, 48) Strasbourg Office rent +10-15% (42) Strasbourg Residential rent +7% (42) Tyne and Wear Metro Property values +2% (49) Vienna S-Bahn Housing units +18.7% (44) L. Miguel Martínez and José Manuel Viegas 4 TABLE 2 Property value impacts of public transport proximity in North American cities. Case/Location Impact on Impact Source Atlanta Office rents Positive (8, 50) Baltimore LRT Not stated Unable to identify (44) Boston Residential property +6.7% (50, 51) Buffalo, New York House prices +4-11% (23) Chicago MTA House prices +20% (52) Dallas DART Commercial rents +64.8% (53) Dallas DART Property values +25% (53, 54) Linden, New Jersey Residential property Positive (55) Los Angeles Property values Higher values (56) Miami House prices +5% (7) New Jersey SEPTA rail House Prices +7.5-8% (57) New Jersey PACTO rail House Prices +10% (57) New York Not stated Positive (58) Pennsylvania SEPTA rail House Prices +3.8% (57) Portland House prices +10% (42) Portland Gresham Residential rent >5% (42) Portland Metropolitan House prices +10.5% (17, 19) Express San Diego Trolley Not stated Positive (44) San Francisco Bay Area Property values Positive (59) BART San Francisco Bay
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