Urban land value map: a case study in Eldorado do Sul - BrazilGeisa BugsErasmus Mundus Master Program in Geospatial TechnologyUniversidade Nova de LisboaInstituto Superior de Estatística e Gestão da Informaçãom2007153@isegi.unl.ptAbstractGeographic Information System is a valorous toll in local administration to provide decisionrelevant information for urban planning and management. Land valuation is an importantdynamic in a city addressing many urban studies. The geographical location is commonunderstood as the main factor influencing the potential value of a property. In order tounderstand the land value activity in a city, this paper describes a GIS application projectstudying land valuation factors in the urban area known as “Sede” in Eldorado do Sul –Brazil, an attractive case study once it is suffering a tremendous demographic growing. Theproject rises from the questions: What are the spatial patterns of land value in a city? Whatbetter reflects the reality of land market? The aim is to analyze the land value patterns;visualize GIS tools application for urban management; and test if useful results can be donewith a restricted amount of initial data and with few procedures or not high technologydemanding in an objective way. The project of the urban land value map for Eldorado do Sulused weighted overlay of thematic maps of selected land valuation factors and the value ofmarket comparison approaches. This comparison between the market price and the predictedland value map has proved that the model is reasonably appropriate in Eldorado do Sul’s“Sede” urban area. As final result, a single map represents all factor estimated and can beeasily understood.Keywords: GIS, urban management, urban planning, land value, weighted overlay.1 IntroductionThe process of assessing the characteristics of a given piece of land may describe carefullyestimates of the worth landed property based on experience and judgment. It’s known that thevalue and potential of propriety are fundamentally determined by location (Ping, 2005). Theseemphasize the significance of spatial factors in decision making for urban management of landvaluation and the use of Geographic Information System tools.An adjusted estimation can be done by analyzing a certain amount of land characteristics orfactor influencing the value (Ping, 2005). It is important to highlight that the process is reallycomplex and would be almost impossible to estimate an exact land value. But a sufficientestimation and useful results can be done with a restricted amount of initial data and fewprocedures in an objective way. To archive this goal is significant have a good previousknowing of the study area, and may be interesting analyze first the attributes behaviour whichcould be affecting the land value, in order to select the optimal factors to be considered in themodel and avoid data redundancy.
2 Problem definition and objectiveLand value map is used by local governments to collect land taxes, so a properly valuated landvalue map may not diverge from the actual market value as well as not distort marketmechanism whereas discourage speculation. The property valorisation is created mainly bychanges that are not the result of the landowners own effort. According to these arguments,citizens should not pay an unfairly high or low amount of tax. On the other hand the taxcollection would work as a tool for benefits redistribution mechanism on a city. For thesereasons and many other that do not fit here, a land value map is a critical instrument for urbanmanagement in a city.GIS plays a critical whole in urban management mainly due to its capability to deal with largeamounts of data and spatial related events. It is clear that most of the urban issues are spatiallydependent, and that urban decisions must take into consideration many factors. The landvaluation issue was choose for be considered a dynamic process dealing with almost all thefactors acting in a city like transportation, environment, location, neighbourhood, buildingquality, public equipments and so on. Those are directly consequences of urban planning andmanagement, whereas the value of a property is largely used in all urban studies for planning.According to the above mentioned the valuation process is similar to retro alimented systemwhere a deep understand of the influencing factors and constant monitoring is vital.Through the exposition of GIS application resources to urban planning and management, thegoal is to understand deeply the phenomenon of valorisation of urban land. Furthermore,evaluate which spatial factors influence the land value in a town by comparing with the salesprice on the free market.3 Project frameworkThe steps followed in this project are summarized in the figure below and each one isdescribed in the sequence. Study area definition Literature review Data collection Model attributes definition Data pre processing Market value analysis Analysis phase Input influence factors Thematic maps Weighted overlay Figure 1: project framework.
3.1 Study areaThe case study proposed has as spatial base limit the urban nucleus called "Sede" in Eldoradodo Sul/Brazil. Eldorado do Sul is located in the Metropolitan Region of Porto Alegre in thesouthern more state of Brazil called Rio Grande do Sul. With only 18 years, it posses alreadyapproximately 35.000 inhabitants, and presents a dizzy demographic growth of 5% per year.The accelerated growth configured a perturbed urban structure. The territory occupation isscarce and disconnected. The urban fabric, consequence of the ‘sewing’ of successiveoccupations, reflects the problem of irregularity, with big empty urban areas and roaddiscontinuity in some points. The city was born as powerful and consolidated industrialplacement, located at the margins of a main road. But in general, the residential use prevailsconsiderably.The city has five spread urban areas along its territory. The Sede urban nucleus was choosebecause usually local people prefer to live in this area, it is the origin of city settlement, themain services and facilities are located there, in addition to better availability of valuable data.The study area consists of five neighborhoods with inherent unique characteristics. The parcelsize and shape plus a substantial occurrence of shanty within some neighbourhoods are themain difference in between them.The case study is interesting because the urbanization is sprawl and disconnected asking formuch more infrastructure and consequently high costs for the municipality; and even being ayoung city lacking many facilities the construction market is growing as well as takingadvantages of the low terrain value comparing to the rapid valorization of a built. On the otherhand the local administration is not. The municipality of Eldorado do Sul is using the sameland value map for land value taxation since the city creation (1988).Figure 2 and 3: Eldorado do Sul location in Brazil (left) and in Rio Grande do Sul (right).
Figure 4: Sede urban area location in Eldorado do Sul territory. Figure 5: Study area.3.2 Data collectionThe data used on the project was collected during the urban studies for Eldorado do Sul masterplan elaboration in 2006 and is summirazed in the table 1. Table 1.Data description and input format. Data description Format Spatial a. Roads CAD b. Blocks and land Shape file c. Equipments CAD d. Risk areas CAD f. City boundary CAD g. Census data Shape file and excel Non-spatial h. Market value samples Internet advertisements
3.3 Data pre processingDuring this step the CAD data was converted into Shape files and assigned to a geodatabase onArcCatalog. Some edition was required using topology editing tool in ArcView mainly on theroads intersections to create the network dataset. At the same time the data was projected onSouth American Datum (SAD) 1969, Universal Transversal Mercator (UTM), zone 22S.3.4 Literature reviewThe spatial pattern of land value has been studied by various scholars and researches. Thepurpose here is to give an overview of recent works on urban land value which inspirate thepresent assignment.Ping, 2005, analyzed the spatial land value patterns of the residential land value in Hankowtown in China, and developed a land valuation model in order to update regularly thebenchmark price. This master thesis has been constructed using the sale comparison approachand multiple regression analysis. In this method to determine the value of a land, some landvaluation criteria were selected and formulated so that property values were assigned bynumerical parameters. These parameters were derived from a combination of selected factorswhich could be spatially analyzed using GIS.Lake at all, 1998, did a research whose aim was to assign money values to the negativeimpacts associated with road development. These impacts do not have observable prices and sohad to be calculated indirectly by examining their effect upon house prices. The valuationssuch a method produces can then be included alongside other costs and benefits in theappraisal of a road development. The project used GIS and large-scale digital data to derive allthe required variables. The paper describes how such a dataset was modeled and priceestimates for road noise and the visual intrusion extracted by a method to extract individualvariable coefficients.Girelli and Gomes, 2003, intended to build a new value mapping for urban land in Guaporé –Brazil. They used a methodoly which maximizes the centrality of the original town nucleousand the main distances of the avenues, even the negative distance related to the risk areas, tothe detriment of infrastructure features. The aim of the project was to show in the case studythat centrality and distance to urban equipments explain in a deeper way and with moreproperty the phenomenon of valorization of urban areas in comparison to infrastructure data.3.5 Model attribute definitionIn applying the Hedonic Pricing Method (HP) to the property market, the determinants ofhouse prices can be divided into four groups (Lake at al, 1998): 1. Structural variables (e.g.: the number of rooms in each house, land area and floor area ratio, public illumination): traditionally the land value map used in municipalities maximizes these variables. 2. Accessibility and location variables (e.g.: proximity of schools and facilities like hospital and markets): the relationship between prices and locational factors is the result of unobservable variation in the location across properties coupled with the heterogeneity of the market. 3. Neighborhood variables (e.g.: local unemployment rates, neighborhood quality, and presence of amenities such as views, parks, and community services): the land value is strongly related to the social and economic characteristics of the neighborhood.
However identifying all relevant neighborhood characteristics within urban areas is difficult. 4. Environmental variables (e.g.: noise, visibility, pollution, water access): the price paid for a property directly reflects the benefits of the environmental characteristics.Due to time limitation and cost on data acquisition only a selected combination of landvaluation factors were used. Location and accessibility are the main features interesting theproject scope. Since the town suffers from successive floods due to it slope and proximity tothe Jacuí River and Guaíba Lake, also this environmental feature was taken into consideration.Besides, in other to improve the results one neighborhood variable was added during theanalysis phase. The outcomes revealed that the land values had been affected also byneighborhood quality or more explicitly differentiations in social classes. In this manner for thepresent propose the factors taken into account were regrouped as follow: 1. Accessibility features: distance from principal and semi principal avenues. 2. Location features: distance from schools, health center and city center. 3. Environmental features: risk flood area contours 4 and 6. 4. Neighborhood features: salary wage per census track.Once more, the goal of the project is to understand which spatial patterns better reflect thereality of the market. For this reason structural variables were not analyzed since the map usedin municipalities does maximize this feature and is assumed as not manifesting well themarket’s values.3.6 Market value analysisSamplings of land price were collected by research on sales advertisments on internet. Sincethe city is quite small only 10 samples were found. It is not an enough number but fortunatelycovering the whole study area and well distributed in each neighborhood. The samples will beused to validate the model by comparing if the estimated land value is close or not to themarket price.Large variations were observed in built prices among the samples. The general variation rangesfrom R$ 50.000,00 to R$ 205.000,00. Even inside one neighborhood such as the center there isa large variation from R$ 70.000,00 to R$ 205.000,00. A previous conclusion based on thisinformation is that not only the spatial location is influencing the market value. Whenexamining the relation between the built area and the market value, as exposed in figure 6, it isclear that the price is not totally correlated to the area. Therefore also only the built area is notsufficient to explain the reality of the sale’s prices. correlation of market value and built area 250.000,00 market value (R$) 200.000,00 150.000,00 100.000,00 50.000,00 0,00 0,00 50,00 100,00 150,00 200,00 250,00 300,00 350,00 built area (m2) Figure 6: Correlation of market values and built area.
4 Analysis phaseThis section will review all the processes executed during the called analysis phase: inputfeatures, clip and union to study area, convert to raster and reclassify, to end with weightedoverlay. The present stage was totally carried out in ArcView software. Figure 7 condense theoperations realized throughout the procedures till the final single map representing allestimated factors. Buffer + Network Analyst (input features) Clip and union to study area Convert to raster and reclassify (thematic maps) Weight overlay (single map) Figure 7: Analysis steps.4.1 Thematic mapsDuring this step the thematic maps were created for each land valuation variable to further beweighted overlaid. In order to get maps with areas under influence within certain establisheddistances from the selected features, equally buffer and service area tolls were used. The buffertool was used to get the distance areas from principal and semi principal avenues, as similar forthe negative distance from risk flood areas contours 4 and 6. The distance from both types ofavenues utilized was 200m due to the limited extend of the area, so a larger distance wouldcover the whole city. The streets classification employed the master plan determinations.While the risk flood area maps basically contain or not the risk of flood for the two limitingcontours.Instead, to obtain the distance areas from schools, health centers and city center, the servicearea operation from network analyst was applied. The schools and health center distances have4 levels ranging from 0m to over 1000m; where 0m to 250m is considered as high influence,251m to 500m as neighborhood influence, 501m to 1000m as the maximum walking distance,and greater then 1000m as no influenced areas. Accordingly the 4 levels of city centerdistances range from 0m to more than 1500m; where 0m to 500m is related with the centerneighborhood itself, 501m to 1000m with the maximum walking distance, 1001m to 1500mwith the maximum distance area under influence, and superior to 1500m with no influencedareas.Afterwards all the results were clipped to the study area and ultimately union. This lastfunction was required because of the necessity to have a zero value for the no affected areas.Till this point the vector format had been used. However it is important to remember that theweighted overlay works with raster format. For this reason each vector thematic map resultingfrom the buffer or network analyst were converted from vector to raster format and reclassifiedwith spatial analyst tools. The figures 8 to 14 show the resulting thematic maps. Likewise, asmentioned before in order to improve the results a neighbohood feature were included later on.This variable contain minimum wage census data per track in 3 levels: till 2 minimum wages,
2 to 10 minumum wages, and 10 to 20 minumum wages. This thematic map is illutrated infigure 15. Figure 8: Distance from semi principal avenues. Figure 9: Distance from principal avenues.
Figure 10: Risk flood areas contour 4.Figure 11: Risk flood areas contour 6.
Figure 12: Distance from health centers.Figure 13: Distance from health centers.
Figure 14: Distance from city center.Figure 15: Minimum wage per census track.
4.2 Weighted overlayThroughout this stage all the attributes were added to model builder, once the model whitinfluencing percentages defined for each variable was run for 4 different hypotheses. Threehypotheses were tested before including the neighbourhood feature: maximizing accessibilityand location attributes with 6 degrees, and maximizing location with 4 degrees. Theclassification rank with 6 levels has very low, low, medium low, medium, high and very highrates; whereas the rank with 4 levels has low, medium, high and very high rates. For thehypotheses containing the minimum wage feature, the 4 levels with location maximizedapproach was adopted hence the results revealed to be better by means of this parameters.In the sequence a correlation function in Excel was executed with the intention of evaluate theoutcomes. The class on each market sample fell was compared with it ‘real’ value. Thesupposition that the accessibility features could be affecting the sale price proved to becomplete wrong as can be visualized in the correlation table 4. In contrast the resulting fromthe maps maximizing the location features reached more satisfactory products. The on with 6levels reached 70% of correlation while the one with 4 levels achieved 72% as can be observedin table 4 as well. Although, the results still were not considered suitable. As a consequencewas observed that differentiations between neighbourhood social classes were underestimatedsince some samples whit high vales had been classified in lower parts of the grade. That is whythe last hypothesis was tested incorporating the minimum wage per census track; closing with78% of correlation.Figure 16 shows the first hypothesis and figure 17 the second with the respective appliedpowers on tables 2 and 3 and correlation assessment on table 4. The third proposition can beobserved in figure 18 and tables 4 and 5. Lastly the final map corresponds to figure 19 andtables 7 and 8. Figure 16: weighted overlay with acessibility maximized.
Table 2: weights for accessibility maximized. Maximizing accessibility featuresFeature Attribute Influence WeightLocation School 15% 5–3–1–035% Health center 15% 5–3–1–0(4 levels) Center 20% 5–3–1–0Accessibility Principal avenue 10% 4–245% Semi-principal avenue 10% 5–3Environment Cote 6 10% 4–120% Cote 4 20% 5–0 Figure 17: weighted overlay with location maximized. Table 3: weights for location maximized. Maximizing location featuresFeature Attribute Influence WeightLocation School 15% 5–3–1–050% Health center 15% 5–3–1–0(4 levels) Center 20% 5–3–1–0Accessibility Principal avenue 10% 4–220% Semi-principal avenue 10% 5–3Environment Cote 6 10% 4–130% Cote 4 20% 5–0
Table 4: correlation for acessibility and location maximization.sale value (R$) location value loc. 6 levels value access. value loc. 4 levels 80.000,00 centro 4 4 3 205.000,00 centro 5 4 4 95.000,00 centro 3 3 3 140.000,00 centro 4 3 3 70.000,00 centro 2 2 2 120.000,00 chácara 2 2 2 50.000,00 c. verde 2 2 2 115.000,00 itaí 3 1 3 80.000,00 residencial 3 1 3 135.000,00 residencial 3 1 3 correlation with sale value 0,7042 (70%) 0,3181 (32%) 0,7210 (72%) Figure 18: weighted overlay for location maximized with 4 levels. Table 5: weights for location maximized with 4 levels. Feature Attribute Influence Location School 15% 50% Health center 15% (4 levels) Center 20% Accessibility Principal avenue 10% 20% Semi-principal avenue 15% Environment Cote 6 10% 30% Cote 4 20%
Figure 18: weighted overlay with census data. Table 6: weights including census data. Feature Attribute Influence Location School 15% 50% Health center 15% (4 levels) Center 20% Accessibility Principal avenue 10% 25% Semi-principal avenue 15% Environment Cote 6 10% 15% Cote 4 5% Neighborhood 10% Minimum wage 10% Table 7: correlation results with census data.sale value (R$) location value location with census data 80.000,00 centro 3 205.000,00 centro 4 95.000,00 centro 3 140.000,00 centro 3 70.000,00 centro 2 120.000,00 chácara 2 50.000,00 c. verde 2 115.000,00 itaí 3 80.000,00 residencial 2 135.000,00 residencial 3 correlation with sale value 0,7827 (78%)
5 ConclusionsThe comparison between the market price and the predicted land value map has proved that themodel is reasonably appropriate in Eldorado do Sul’s Sede urban area. Given that the veryfinal map has a 78% of relation with the market reality it can be assumed that despite thelimited features analyzed the results are pretty close to the truth. Consequently an adjustedestimation can be done by analyzing a certain amount of land characteristics or factorsinfluencing the value.Nevertheless the 10 market value samples are not enough to evaluate the model with efficacybecause some distinct regions like industrial for example were not included on. In this sensewould be extremely useful take into account more samples to enhance the accuracyassessment. Furthermore with the purpose of improve the results other attributes related withneighborhood and environmental factors could be included in the analysis.The project demonstrates that even though the land valuation is a complex process addressingmany dynamics in a city, a sufficient estimation and useful results can be archived using GIStools. Other important point it the fact that the project did not use high technology ormethodology making explicit that local governments should do the effort to include this kindof analysis for planning and management.As suggestion would be really interesting to compare the outcomes of this assignment and theland value map used by the Eldorado do Sul municipality; what was the very initial intentionbut unfortunately impossible to make use of the data. Finally, further research could focus ongeographically weighted regression approaches.ReferencesBatty, M. and Densham, P. J., 1996, Decision support, GIS, and urban planning. London, UCL, Centre forAdvanced Spatial Analysis. Available at http://www.geog.ucl.ac.uk/~pdensham/sdss/s_t_paper.html (lastaccessed 17 December 2007).Câmara, G., Sistemas de informação geográfica para aplicações ambientais e cadastrais: uma visão geral.São José dos Campos/Brasil, Divisão de Processamento de Imagens – DPI, Instituto Nacional de PesquisasEspaciais – INPE. Available at www.dpi.inpe.br/geopro/trabalhos/analise.pdf (last accessed 17 December2007).Gilfoyle, I., 2004, Geographic information management in local government. Florida, CRC press.Girelli, G. and Gomes, F. S. G., 2004, Geoprocessamento aplicado ao mapa de valores de Guaporé/ RS.Porto Alegre, PROPUR, UFRGS, Disciplina de Geoprocessamento Aplicado ao Planejamento Urbano eAmbiental. Available at http://www.ufrgs.br/propur/frame_entrada.html (last accessed 17 December2007).Lake, I.R., Lovett, A. A., Baterman, I. J. and Langford, I. H., 1998, Modelling environmental influenceson property prices in an urban environment. Computers, Environment and Urban Systems, 22 (2), pp. 121-136.Malczewski, J., 1999, GIS and multicriteria decision analysis. New York: Willey.Ping, A., 2005, Residential land value modeling: case study of Hankou, China. Enschede, ITC, 73 pp.