Spatial multicriteria analysis for home buyers

Transcription

1 Spatial multicriteria analysis for home buyers Xavier Albacete University of Eastern Finland, Department of Environmental Science, Research Group of Environmental Informatics, Yliopistoranta 1 E, P.O. Box 1627, Kuopio, Finland xavier.albacete@uef.fi Kari Pasanen University of Eastern Finland, Department of Environmental Science, Research Group of Environmental Informatics, Yliopistoranta 1 E, P.O. Box 1627, Kuopio, Finland kari.pasanen@uef.fi Mikko Kolehmainen University of Eastern Finland, Department of Environmental Science, Research Group of Environmental Informatics, Yliopistoranta 1 E, P.O. Box 1627, Kuopio, Finland mikko.kolehmainen@uef.fi Abstract Home buyers are often interested in both accessibility of services and the quality of the local environment, while real estate agents frequently offer some web-based systems for home searches. However, there is hardly any information about the quality of local living environments in those web-based systems. In our study, multicriteria spatial analysis method was applied to home buyers selection process using data from the city of Kuopio, Finland. Several spatial variables were applied, including environmental and service factors in the home searching process. A geographical information system (GIS) was used for creating maps for decision variables and mapping suitable areas. A method for ranking alternative dwellings based on the criteria given by the user was developed and demonstrated. This kind of decision analysis tool will be useful for both customers and real estate agents, and can also be used for city planning. The availability of spatial data on the living environment in the web-based services for home buyers is likely to have effects on customers requirements and house markets, and in the long run also promote better spatial city organization. Keywords: spatial multicriteria analyses, web-based systems, house market, social services, environmental variables.

2 WS-03: SPATIAL MULTI-CRITERIA ANALYSIS FOR HOME BUYERS Introduction According to the Population Reference Bureau, in the 2009 World Population Data Sheet, by 2025 the world population will be over 8,000 million, more than a half of whom will be living in urban areas. In several studies, there have been proposals for quantifying the level of happiness in urban areas depending on different factors (Ferrer-i-Carbonell 2004, Liao 2009, MacKerron 2009). Although these studies represent an approach for the identification of the variables that promote happiness in the population, they do not have direct effects on the city and housing development. On the other hand, since the development of GIS (Geographical Information Systems), there have been many attempts to evaluate the existing urban and rural spaces and the quality of life of their citizens (Apparicio 2008, Villa 1996, Rinner 2006, Vreeker 2006). The main focus in the scientific discussion has been based on the methodology used to quantify and integrate the different variables involved in the urban spatial analyses (Banai 2005, Saaty 2005, Strager 2006, Rosenberger 2006, Karnatak 2007). These studies have been useful for the small-scale spatial decision process, and most of them have been used as a support system by city planners and designers rather than by ordinary people. The aim of our paper is to propose a spatial analysis method for the home buyer s selection process. Several spatial variables including environmental and service factors (e.g. noise level, children s playgrounds, sports centers, public health, connectivity, nature contact) were applied in the home selection. ArcGIS 9.3 software was used for creating the decision variables, and multivariate method for ranking the alternative dwellings was proposed. Material and Methods Data collection Original spatial data were obtained from the National Land Survey of Finland, the Finnish Transport Agency, the Finnish Forest Research Institute, the Finnish Meteorological Institute and municipal officials of the city of Kuopio. The spatial data applied on a basic map of the study area are shown in Table 1: Table 1. Original data characteristics. Data Type Related Attributes 1. Dwellings Point Square meters, price, number of rooms, number of bathrooms, and garage. 2. Forest Raster 3. Water Feature 4. Noise level Feature Noise level in 1 dba resolution for day and night hours, originating from main roads and the railway. 5. PM 10 concentration Feature Annual average concentration in µg/m 3 6. Children s playgrounds Point

3 7. Kindergartens Point 8. Primary and high Point schools 9. Libraries Point 10. Sports centers Point Gyms, swimming pools and sports halls. 11. Food shops Point 12. Public health centers Point Public hospitals and clinics. 13. Streets Feature 14. Pedestrian and bike lanes Feature Mapping the decision variables Raster maps were created for each decision variable using ArcGIS 9.3 software (Table 2). For the distance-based variables (e.g. distance to the closest shop), cost-distance analysis via road network was applied. Two variables deserve special mention: centers of neighborhoods and special interest points. The purpose of the first variable was to express proximity to social and commercial centers located in every neighborhood of the city. These centers (Tori or Keskus in Finnish) are potential spaces for developing social networks in Finnish cities. The proximity to special interest point was conceived as an option where the customer could define some important locations that they want to reach easily. Table 2. Raster maps for decision variables calculated using GIS. Variables Data used Purpose of the variable 1. Nature Pedestrian and bike lanes, streets, forest, water. Measure the proximity to a natural environment. 2. Water Water. Measure the proximity to water areas. 3. Noise level from road traffic: day and night 4. Noise level from rail traffic: day and night Noise level. Noise level. Measure levels of road traffic noise in the city distinguishing night hours from day hours. Measure levels of rail noise in the city distinguishing night hours from day hours. 5. PM 10 concentration Annual average concentration in µg/m 3. Measure the polution level. 6. Children s playgrounds. Pedestrian and bike lanes, streets, children s Measure the proximity to public children s playgrounds.

4 WS-03: SPATIAL MULTI-CRITERIA ANALYSIS FOR HOME BUYERS playgrounds. 7. Children s daycare centers. Pedestrian and bike lanes, streets, kindergarten. Measure the proximity to children s daycare centers. 8. Schools Pedestrian and bike lanes, streets, primary and high schools. 9. Libraries Pedestrian and bike lanes, streets, libraries. 10. Sports centers Pedestrian and bike lanes, streets, sports centers. 11. Markets Pedestrian and bike lanes, streets, food markets. 12. Public health centers Pedestrian and bike lanes, streets, public health centers. Measure the proximity to public educational centers for children and young people. Measure the distance to libraries. Measure the distance to the main sports centers. Nearby sports spaces are especially important in Finnish societies during winter time. Measure the distance to food markets. Measure the distance to the public health centers. 13. Centers of neighborhoods 14. Proximity to special interest points Pedestrian and bike lanes, streets. Pedestrian and bike lanes, streets. Measure the distance to the social center of neighborhoods. Measure the distance to the points of interest chosen by the customer and not included in other variables. Furthermore, for the noise variable, the noise curve data was transformed into a raster format layer. The nature variable was constructed as a combination of dense forest areas and natural water areas. In order to make the decision variables easy to understand and apply, we classified the variables into five levels, where 5 is the best and 1 is the worst. Moreover, a uniform objective classification method was selected because for most of the variables no previous studies were available. Classification of the distance-based variables was based on the percentage of the maximum distance between two input locations of the variable (e.g. distance between two shops). This presented some advantages and disadvantages further discussed in the conclusions section. The percentages and real values for each levels are shown in Table 3:

5 Table 3. Classification of the decision criteria maps with three examples. Level Percentage of the maximum distance Example 1: Distance to Schools (m) Example 2: Noise Level Traffic Day (dba) Example 3: PM 10 Concentration (µg/m 3 ) 5 5% % % % % In the case of noise levels, the classification of the variables was done according to the Caltrans Transportation Laboratory Noise Manual (1982) (Jones & Stokes 2004) and the modification by the Environmental Science Associates adapted to more demanding levels for the night cases. Maximum noise levels were fixed at 65 dba for night and 75 dba for day time, following the advice of local experts. The minimum noise level was 35 dba, which is considered to be the natural background noise level. Finally, GIS tools were applied to link the levels of decision variable maps to the locations of alternative dwellings. Ranking of suitable dwellings There were three main steps in the decision analysis. First, the user chose the relevant variables for the dwelling selection, and for each of them a target level (between 1 and 5) was given. Second, the raster maps of areas that were desirable according to the given criteria were calculated and visualised. Finally, the best homes were found using the following multivariate ranking method based on the variables and target levels selected by the user. For every dwelling, target levels and real values were compared using two index variables (Difference1 and Difference2) as follows: 1 = ) ) (1) 2 = ( ) (2) where V i is the real level of variable i for the dwelling, P i is the target level of variable i given by the user and n is the number of considered decision variables. The variables that the user did not mark as relevant were excluded from the ranking procedure. The equation 1 is formed by two sums. The first is sum of the absolute differences between real and target levels of each variable. The second (Difference 2) is sum of the real differences between real and target levels. The minimun value of Difference1 is zero and will be reached for dwellings that fullfill all targets given by the user. Difference2 is needed to rank dwellings having the same value for Difference1.

6 WS-03: SPATIAL MULTI-CRITERIA ANALYSIS FOR HOME BUYERS Thus, for ranking dwellings that fullfils all target levels (Difference 1= 0), ranking is based on the value of Difference2, i.e. the best solution is the one that has the highest value for Difference 2. If there are still dwellings that have the same value for difference 2 they are ranked equal. If there are no dwellings that fullfil all targets levels (lowest value for Difference1 >0), the ranking procedure is as follows. Firstly, the lower the value of Difference1 is the better ranking is given. The alternatives that have same value for Difference 1 are ranked is based on the value of Difference 2 and dwellings that have the same value for Difference 2 they are ranked equal. Case Study We performed a demonstration using two examples. From a real estate company s web site (Etuovi.com), 50 dwellings in Kuopio with an area bigger than 70 square meters and more than 3 rooms were selected. This was done using the exisiting searching tools offered on the web site. In our simulated cases, the proximity to special interest point was focused on natural spaces and the city center. In a second step, two cases were simulated, each with different target levels for some variables (Table 4). Table 4. Target levels for Case 1 and Case 2, where level 0 means not chosen. Variables Level Case 1 Level Case 2 Nature 1 0 Water 0 3 Road traffic noise (day) 0 2 Road traffic noise (night) 4 3 Rail traffic noise (day) 0 2 Rail traffic noise (night) 3 4 PM 10 concentration 3 2 Children s playgrounds 2 0 Children s daycare centers 2 0 Schools 3 0 Libraries 3 0 Sports centers 2 2 Markets 4 4

7 Public health centers 0 0 Centers of neighborhoods 0 3 Proximity to special interest points 0 2 Based on the target levels, suitable areas were calculated using raster analysis tools of ArcGIS Spatial Analyst software (Figure 1). In the second step, the ranking procedure was applied in order to find the best homes according to the users criteria. All the dwellings which had a result value of 0 in Equation 1 were found to be located inside the suitable area (Figure 1). Figure 1. Suitable areas and ranked suitable dwellings for Case 1 and Case 2. Source: Pohjakartta (C) Maanmittauslaitos lupanro 51/MML/10, Copyright Liikennevirasto /Digiroad 2010

8 WS-03: SPATIAL MULTI-CRITERIA ANALYSIS FOR HOME BUYERS Conclusions The method gave useful results for analyzing a home buyer s spatial decision. It allows the addition, suppression and modification of variables so that it adapts to the social and cultural needs of every area. Moreover, users can identify interest points of their own, resulting in an individual final result for every user. In addition, it is possible in further developments to collect prospective buyers input data and indicate general trends to the city planner. The raster-based spatial analysis allowed us to create result maps showing suitable areas in addition to suitable dwellings. This opens the possibility to city planners and real estate companies to check which are the unbuilt areas suitable for further development. Moreover, the decision maps were useful for the classification of both suitable areas and dwellings. The case study showed a direct relation between the desirable areas and suitable dwellings location. Although the equations are based on simple mathematical operations, they were able to show the real proximity between the dwellings and the selected criteria. However, there are some limitations in the procedure as a consequence of the original data and the data processing. When measuring the distances, all the roads and streets were unified in a single network layer. As a consequence, we could not discriminate the accessibility to services between the different transport possibilities. In addition, there were some paths through natural areas that could not be included in the general network layer. Percentages of the distance between the two furthest points were used for setting the classes for every variable. This classification method seemed to work in the case studies in the Kuopio area, but it will be modified for further applications to be suitable for any selected area. Acknowledgements I want to especially thank the support of CIMO fellowship programme, as well as the National Land Survey of Finland, the Finnish Transport Agency, the Finnish Forest Research Institute, the Finnish Meteorological Institute and municipal officials of the city of Kuopio for granting me access to the data. References Apparicio, P. 2008, "The quality of the urban environment around public housing buildings in Montreal: An objective approach based on GIS and multivariate statistical analysis", Social Indicators Research, vol. 86, no. 3, pp Banai, R. 2005, "Land Resource Sustainability for Urban Development: Spatial Decision Support System Prototype", Environmental Management [Environ.Manage.].Vol.36, vol. 36, no. 2, pp Ferrer-i-Carbonell, A. 2004, "How Important is Methodology for the estimates of the determinants of Happiness?", Economic Journal, vol. 114, no. 497, pp Jones & Stokes Transportation- and construction-induced vibration guidance manual.

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