Geisa Bugs. Trabalho final da disciplina GIS Applications, UNL, 2007

1. Urban land value map: a case study in Eldorado do Sul - Brazil
Geisa Bugs
Erasmus Mundus Master Program in Geospatial Technology
Universidade Nova de Lisboa
Instituto Superior de Estatística e Gestão da Informação
m2007153@isegi.unl.pt
Abstract
Geographic Information System is a valorous toll in local administration to provide decision
relevant information for urban planning and management. Land valuation is an important
dynamic in a city addressing many urban studies. The geographical location is common
understood as the main factor influencing the potential value of a property. In order to
understand the land value activity in a city, this paper describes a GIS application project
studying 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. The
project rises from the questions: What are the spatial patterns of land value in a city? What
better 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 done
with a restricted amount of initial data and with few procedures or not high technology
demanding in an objective way. The project of the urban land value map for Eldorado do Sul
used weighted overlay of thematic maps of selected land valuation factors and the value of
market comparison approaches. This comparison between the market price and the predicted
land 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 be
easily understood.
Keywords: GIS, urban management, urban planning, land value, weighted overlay.
1 Introduction
The process of assessing the characteristics of a given piece of land may describe carefully
estimates of the worth landed property based on experience and judgment. It’s known that the
value and potential of propriety are fundamentally determined by location (Ping, 2005). These
emphasize the significance of spatial factors in decision making for urban management of land
valuation and the use of Geographic Information System tools.
An adjusted estimation can be done by analyzing a certain amount of land characteristics or
factor influencing the value (Ping, 2005). It is important to highlight that the process is really
complex and would be almost impossible to estimate an exact land value. But a sufficient
estimation and useful results can be done with a restricted amount of initial data and few
procedures in an objective way. To archive this goal is significant have a good previous
knowing of the study area, and may be interesting analyze first the attributes behaviour which
could be affecting the land value, in order to select the optimal factors to be considered in the
model and avoid data redundancy.

2. 2 Problem definition and objective
Land value map is used by local governments to collect land taxes, so a properly valuated land
value map may not diverge from the actual market value as well as not distort market
mechanism whereas discourage speculation. The property valorisation is created mainly by
changes that are not the result of the landowner's own effort. According to these arguments,
citizens should not pay an unfairly high or low amount of tax. On the other hand the tax
collection would work as a tool for benefits redistribution mechanism on a city. For these
reasons and many other that do not fit here, a land value map is a critical instrument for urban
management in a city.
GIS plays a critical whole in urban management mainly due to its capability to deal with large
amounts of data and spatial related events. It is clear that most of the urban issues are spatially
dependent, and that urban decisions must take into consideration many factors. The land
valuation issue was choose for be considered a dynamic process dealing with almost all the
factors acting in a city like transportation, environment, location, neighbourhood, building
quality, public equipments and so on. Those are directly consequences of urban planning and
management, 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 system
where a deep understand of the influencing factors and constant monitoring is vital.
Through the exposition of GIS application resources to urban planning and management, the
goal 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 sales
price on the free market.
3 Project framework
The steps followed in this project are summarized in the figure below and each one is
described 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. 3.1 Study area
The case study proposed has as spatial base limit the urban nucleus called "Sede" in Eldorado
do Sul/Brazil. Eldorado do Sul is located in the Metropolitan Region of Porto Alegre in the
southern more state of Brazil called Rio Grande do Sul. With only 18 years, it posses already
approximately 35.000 inhabitants, and presents a dizzy demographic growth of 5% per year.
The accelerated growth configured a perturbed urban structure. The territory occupation is
scarce and disconnected. The urban fabric, consequence of the ‘sewing’ of successive
occupations, reflects the problem of irregularity, with big empty urban areas and road
discontinuity in some points. The city was born as powerful and consolidated industrial
placement, located at the margins of a main road. But in general, the residential use prevails
considerably.
The city has five spread urban areas along its territory. The Sede urban nucleus was choose
because usually local people prefer to live in this area, it is the origin of city settlement, the
main 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 parcel
size and shape plus a substantial occurrence of shanty within some neighbourhoods are the
main difference in between them.
The case study is interesting because the urbanization is sprawl and disconnected asking for
much more infrastructure and consequently high costs for the municipality; and even being a
young city lacking many facilities the construction market is growing as well as taking
advantages of the low terrain value comparing to the rapid valorization of a built. On the other
hand the local administration is not. The municipality of Eldorado do Sul is using the same
land 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).

4. Figure 4: Sede urban area location in Eldorado do Sul territory.
Figure 5: Study area.
3.2 Data collection
The data used on the project was collected during the urban studies for Eldorado do Sul master
plan 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

5. 3.3 Data pre processing
During this step the CAD data was converted into Shape files and assigned to a geodatabase on
ArcCatalog. Some edition was required using topology editing tool in ArcView mainly on the
roads intersections to create the network dataset. At the same time the data was projected on
South American Datum (SAD) 1969, Universal Transversal Mercator (UTM), zone 22S.
3.4 Literature review
The spatial pattern of land value has been studied by various scholars and researches. The
purpose here is to give an overview of recent works on urban land value which inspirate the
present assignment.
Ping, 2005, analyzed the spatial land value patterns of the residential land value in Hankow
town in China, and developed a land valuation model in order to update regularly the
benchmark price. This master thesis has been constructed using the sale comparison approach
and multiple regression analysis. In this method to determine the value of a land, some land
valuation criteria were selected and formulated so that property values were assigned by
numerical parameters. These parameters were derived from a combination of selected factors
which could be spatially analyzed using GIS.
Lake at all, 1998, did a research whose aim was to assign money values to the negative
impacts associated with road development. These impacts do not have observable prices and so
had to be calculated indirectly by examining their effect upon house prices. The valuations
such a method produces can then be included alongside other costs and benefits in the
appraisal of a road development. The project used GIS and large-scale digital data to derive all
the required variables. The paper describes how such a dataset was modeled and price
estimates for road noise and the visual intrusion extracted by a method to extract individual
variable 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 nucleous
and the main distances of the avenues, even the negative distance related to the risk areas, to
the detriment of infrastructure features. The aim of the project was to show in the case study
that centrality and distance to urban equipments explain in a deeper way and with more
property the phenomenon of valorization of urban areas in comparison to infrastructure data.
3.5 Model attribute definition
In applying the Hedonic Pricing Method (HP) to the property market, the determinants of
house 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.

6. 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 land
valuation factors were used. Location and accessibility are the main features interesting the
project scope. Since the town suffers from successive floods due to it slope and proximity to
the 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 the
analysis phase. The outcomes revealed that the land values had been affected also by
neighborhood quality or more explicitly differentiations in social classes. In this manner for the
present 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 the
reality of the market. For this reason structural variables were not analyzed since the map used
in municipalities does maximize this feature and is assumed as not manifesting well the
market’s values.
3.6 Market value analysis
Samplings of land price were collected by research on sales advertisments on internet. Since
the city is quite small only 10 samples were found. It is not an enough number but fortunately
covering the whole study area and well distributed in each neighborhood. The samples will be
used to validate the model by comparing if the estimated land value is close or not to the
market price.
Large variations were observed in built prices among the samples. The general variation ranges
from R$ 50.000,00 to R$ 205.000,00. Even inside one neighborhood such as the center there is
a large variation from R$ 70.000,00 to R$ 205.000,00. A previous conclusion based on this
information is that not only the spatial location is influencing the market value. When
examining the relation between the built area and the market value, as exposed in figure 6, it is
clear that the price is not totally correlated to the area. Therefore also only the built area is not
sufficient 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.

7. 4 Analysis phase
This section will review all the processes executed during the called analysis phase: input
features, clip and union to study area, convert to raster and reclassify, to end with weighted
overlay. The present stage was totally carried out in ArcView software. Figure 7 condense the
operations realized throughout the procedures till the final single map representing all
estimated 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 maps
During this step the thematic maps were created for each land valuation variable to further be
weighted overlaid. In order to get maps with areas under influence within certain established
distances from the selected features, equally buffer and service area tolls were used. The buffer
tool was used to get the distance areas from principal and semi principal avenues, as similar for
the negative distance from risk flood areas contours 4 and 6. The distance from both types of
avenues utilized was 200m due to the limited extend of the area, so a larger distance would
cover 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 limiting
contours.
Instead, to obtain the distance areas from schools, health centers and city center, the service
area operation from network analyst was applied. The schools and health center distances have
4 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 center
distances range from 0m to more than 1500m; where 0m to 500m is related with the center
neighborhood itself, 501m to 1000m with the maximum walking distance, 1001m to 1500m
with the maximum distance area under influence, and superior to 1500m with no influenced
areas.
Afterwards all the results were clipped to the study area and ultimately union. This last
function 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 the
weighted overlay works with raster format. For this reason each vector thematic map resulting
from the buffer or network analyst were converted from vector to raster format and reclassified
with spatial analyst tools. The figures 8 to 14 show the resulting thematic maps. Likewise, as
mentioned 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,

8. 2 to 10 minumum wages, and 10 to 20 minumum wages. This thematic map is illutrated in
figure 15.
Figure 8: Distance from semi principal avenues.
Figure 9: Distance from principal avenues.

9. Figure 10: Risk flood areas contour 4.
Figure 11: Risk flood areas contour 6.

10. Figure 12: Distance from health centers.
Figure 13: Distance from health centers.

11. Figure 14: Distance from city center.
Figure 15: Minimum wage per census track.

12. 4.2 Weighted overlay
Throughout this stage all the attributes were added to model builder, once the model whit
influencing percentages defined for each variable was run for 4 different hypotheses. Three
hypotheses were tested before including the neighbourhood feature: maximizing accessibility
and location attributes with 6 degrees, and maximizing location with 4 degrees. The
classification rank with 6 levels has very low, low, medium low, medium, high and very high
rates; whereas the rank with 4 levels has low, medium, high and very high rates. For the
hypotheses containing the minimum wage feature, the 4 levels with location maximized
approach 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 the
outcomes. The class on each market sample fell was compared with it ‘real’ value. The
supposition that the accessibility features could be affecting the sale price proved to be
complete wrong as can be visualized in the correlation table 4. In contrast the resulting from
the maps maximizing the location features reached more satisfactory products. The on with 6
levels reached 70% of correlation while the one with 4 levels achieved 72% as can be observed
in table 4 as well. Although, the results still were not considered suitable. As a consequence
was observed that differentiations between neighbourhood social classes were underestimated
since some samples whit high vales had been classified in lower parts of the grade. That is why
the last hypothesis was tested incorporating the minimum wage per census track; closing with
78% of correlation.
Figure 16 shows the first hypothesis and figure 17 the second with the respective applied
powers on tables 2 and 3 and correlation assessment on table 4. The third proposition can be
observed in figure 18 and tables 4 and 5. Lastly the final map corresponds to figure 19 and
tables 7 and 8.
Figure 16: weighted overlay with acessibility maximized.

13. Table 2: weights for accessibility maximized.
Maximizing accessibility features
Feature Attribute Influence Weight
Location School 15% 5–3–1–0
35% Health center 15% 5–3–1–0
(4 levels) Center 20% 5–3–1–0
Accessibility Principal avenue 10% 4–2
45% Semi-principal avenue 10% 5–3
Environment Cote 6 10% 4–1
20% Cote 4 20% 5–0
Figure 17: weighted overlay with location maximized.
Table 3: weights for location maximized.
Maximizing location features
Feature Attribute Influence Weight
Location School 15% 5–3–1–0
50% Health center 15% 5–3–1–0
(4 levels) Center 20% 5–3–1–0
Accessibility Principal avenue 10% 4–2
20% Semi-principal avenue 10% 5–3
Environment Cote 6 10% 4–1
30% Cote 4 20% 5–0

14. 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%

15. 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%)

16. 5 Conclusions
The comparison between the market price and the predicted land value map has proved that the
model is reasonably appropriate in Eldorado do Sul’s Sede urban area. Given that the very
final map has a 78% of relation with the market reality it can be assumed that despite the
limited features analyzed the results are pretty close to the truth. Consequently an adjusted
estimation can be done by analyzing a certain amount of land characteristics or factors
influencing the value.
Nevertheless the 10 market value samples are not enough to evaluate the model with efficacy
because some distinct regions like industrial for example were not included on. In this sense
would be extremely useful take into account more samples to enhance the accuracy
assessment. Furthermore with the purpose of improve the results other attributes related with
neighborhood and environmental factors could be included in the analysis.
The project demonstrates that even though the land valuation is a complex process addressing
many dynamics in a city, a sufficient estimation and useful results can be archived using GIS
tools. Other important point it the fact that the project did not use high technology or
methodology making explicit that local governments should do the effort to include this kind
of analysis for planning and management.
As suggestion would be really interesting to compare the outcomes of this assignment and the
land value map used by the Eldorado do Sul municipality; what was the very initial intention
but unfortunately impossible to make use of the data. Finally, further research could focus on
geographically weighted regression approaches.
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