1# RECORD 5cf7ffdc50779ba179e5d6c37bf66eba2f4f81092# TITLE: The value capture potential of the Lisbon Subway3# YEAR: 2012.0 VENUE: nan DOI: 10.5198/jtlu.v5i1.2514# AUTHORS: L. Martinez; J. Viegas5# PAGES: 18 CANDIDATE TABLE PAGES (1-based): [7]6# ABSTRACT: This paper tries to build on traditional value capture measures, to estimate the potential of application of some of these mechanisms to the Lisbon subway, examining their ability to contribute to cover the financial costs of the system operation and development. The study will just focus on the municipality of Lisbon where this system mainly operates. This research uses spatial hedonic pricing models of the real estate of the region, calibrated on previous stages of the study, to asses, to which extent, transportation infrastructure is currently capitalised into the real estate market. The paper uses a Monte Carlo simulation procedure to estimate a synthetic population of residential and non residential properties that matches the census blocks statistics, allowing, measuring the subway valuation for each synthetic property and aggregate the results for the whole municipality. This potential value capture estimate is then used to estimate an annual tax that could be charged under different value capture measures configurations (i.e. land value tax, special assessment). The results suggest that there is a significant potential of the use of this instrument to finance the subway infrastructure.789===== PAGE 1 (score 11, numeric-paren 0, table-words 0, var-words 11) =====101112http://jtlu.org13. 5 . 1 [Spring 2012] pp. 65–8214doi: 10.5198/jtlu.v5i1.25115The value capture potential of the Lisbon subway16Luis Miguel Garrido Martínez17Instituto Superior Técnico, Lisbon a18José Manuel Viegas19Instituto Superior Técnico, Lisbon b20Abstract:21is paper tries to build on traditional value capture measures, to estimate the potential of some of these mechanisms for the Lisbon22subway by examining their ability to mitigate the system’s operation and development costs. e study focus is on the municipality of Lisbon23where this system mainly operates. is research uses spatial hedonic pricing models of the real estate of the region, calibrated on previous24stages of the study, to assess the extent to which transportation infrastructure is currently capitalized into the real estate market. e paper uses25a Monte Carlo simulation procedure to estimate a synthetic population of residential and non-residential properties that matches the census26blocks statistics, measuring the subway valuation for each synthetic property and aggregating the results for the whole municipality. is po-27tential value capture estimate is then used to estimate an annual tax that could be charged under different value capture measure configurations28(i.e., land value tax, special assessment). e results suggest that there is significant potential for the use of this instrument to finance the subway29infrastructure.30Keywords:31Value capture; Transport financing; Fiscal simulation; Real estate hedonic price models; Subways; Lisbon32133Introduction34Most of the cities across the world are currently facing chal-35lenges in financing public transport. For several decades now,36Urban Public Transport (UPT) has been unable to collect37enough revenues from clients to cover its production costs.38e fact that public transit is seen as an indispensable service39in all medium and large cities, accompanied by the operating40constraints and the multiple (and sometimes conflicting) ob-41jectives under which it performs, have largely prevented radi-42cal political moves to force its cost coverage to be more in line43with the normal market economy (Viegas 2005).44Parallelwiththesefinancialdifficulties,theserviceprovided45by UPT, even with younger fleets and more comfortable vehi-46cles, has not been attractive enough to resist the higher avail-47ability and comfort provided by the private car. us the48long term market share of UPT has been declining in all cities49around the world, with only a few recent short term excep-50tions. e financing problem of UPT, partly explained by51this loss of competitiveness and patronage, is accompanied by52the problem of generally low quality and sustainability (Vie-53gas 2005).54Civil Engineering Department. martinez@civil.ist.utl.pt55Civil Engineering Department. viegas@civil.ist.utl.pt56Furthermore, accessibility to desired destinations tends to57play a major role in location decisions of activities and resi-58dential location, which raises land value in highly accessible59locations. e monetary value of this accessibility is reflected60in the value of a home or a business, in addition to the value61of other features such as the specific physical attributes of the62building and neighborhood characteristics (Lari et al. 2009).63Value capture then arises as the process whereby a funding64agency (i.e., the city, the region, or the state) attempts to re-65cover a share of the value added to property resulting from any66infrastructure development (Hass-Klau 2006).67is paper examines the value capture potential of Lisbon’s68subway by testing several different mechanisms, and looks at69its integration with the current fiscal system in order to de-70velop a new financing framework for the system operation and71infrastructure development within the municipality of Lis-72bon.73is study uses a Monte Carlo simulation procedure in or-74der to estimate a synthetic market composition of the residen-75tial, retail, and office sectors from the aggregated statistical76data available at city block level, leading to an estimate of the77value capture potential of the subway in the Lisbon munici-78pality.79e potential value capture estimate is then analyzed in or-80der to extract an annual tax for dwellings and commercial ar-81Copyright 2012 Luis Miguel Garrido Martínez and José Manuel Viegas.82Licensed under the Creative Commons Attribution – NonCommercial License 3.0.838485===== PAGE 7 (score 54, numeric-paren 36, table-words 4, var-words 10) =====8687e value capture potential of the Lisbon subway8889Table 3: Summary of the hedonic price models used for property value estimation.90Residential model (Martínez and Viegas 2009)91Retail & office model (Martínez 2010)92Variables93Coef.94Std. Error95Variables96Coef.97Std. Error98SP_LAG_LOGPRICE990.3561***1000.0085101SP_LAG_LOGPRICE1020.0394***1030.0088104Constant1056.9089***1060.0999107Constant10810.264 ***1090.1143110Structural attributes111Bedrooms1120.0427***1130.003114Store1150.4147***1160.0446117House1180.1685***1190.0154120Office1210.3892***1220.053123Floor1240.0155***1250.0009126Floor1270.0227**1280.0108129Area1300.0064***1310.0001132Area11330.0079***1340.0002135Area21360.0018***1370.0001138Area31390.0005***1400.0001141Age2142−0.1034***1430.0063144Age2145−0.1775***1460.0281147Age3148−0.0729***1490.0068150Age3151−0.156 ***1520.0263153Garage1540.1126***1550.0059156Garage1570.1316***1580.0344159Neighborhood attributes160Educational Index1610.416 ***1620.0225163Educational Index1640.9892***1650.0939166Entropy Index1670.2312***1680.0234169Shopping Center1700.23081710.1518172Accessibility Attributes1732MAccess1740.0916***1750.01331762MAccess1770.2163***1780.04661791Maccess1800.0652***1810.00841821Maccess1830.0918**1840.0357185Network1186−0.0732***1870.0069188Network1189−0.127 ***1900.036191Network21920.0458***1930.0064194Network21950.1029***1960.0302197Network3198−0.038 ***1990.006200Sintra201−0.0614***2020.0134203Cascais2040.1517***2050.0259206Pseudo R22070.795208Pseudo R22090.76210LM statistic2111154.496 ***212LM statistic21319.940 ***214Log likelihood215236.608216Log likelihood217-693.486218Note: ***, **, and * denote coefficient significantly different from zero at the 1%, 5%, and 10% level of signifi-219cance, respectively.2204.2221Model description222In order to estimate the market value of the real estate stock223and the value capture potential of the Lisbon subway, two224simulation procedures were developed: one for the residen-225tial market and another for the non-residential market (retail,226office, and warehouse). Figure 2 presents the initial data pro-227cessing and estimate of the attributes of each property simu-228lated in the model for the residential market, while Figure 3229illustrates the structure of the simulation model with all the230cycles and processes invoked by the simulation model. A sim-231ilar structure was adopted for the non-residential market sim-232ulation model.233For each iteration, the model generates a synthetic popula-234tion of residential properties for Lisbon based on the statisti-235cal distributions available for the census block using a Monte236Carlo simulation procedure. All the structural attributes gen-237erated from the statistical census data are considered as inde-238pendent variables with the exception of area, which depends239on the number of bedrooms generated. is simplification as-240sumptionsignificantlyreduces thecomplexityof theinterrela-241tion between the different attributes; however it might lead to242some unreliable configurations in some low probability cases.2434.3244Results discussion245For the current study, 100 iterations for the residential and the246non-residentialsimulationmodelswereused. esummaryof247the average values obtained from the simulation are presented248in Table 4. e resulting values show that approximately 2.5249250251===== PAGE 8 (score 0, numeric-paren 0, table-words 0, var-words 0) =====252253254.255Figure 2: Property attributes generation of the synthetic properties model for market value estimation.256