On employing fuzzy modeling algorithms for the valuation of residential premises

Lughofer, Edwin; Trawiński, Bogdan; Trawiński, Krzysztof; Kempa, Olgierd; Lasota, Tadeusz

doi:10.1016/j.ins.2011.07.012

Cited by 52 publications

(10 citation statements)

References 37 publications

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“…In our investigations we have employed nonparametric Friedman and Wilcoxon paired comparison tests Smętek and Trawiński, 2011) as well as post-hoc procedures devoted to multiple comparisons Lughofer et al, 2011). The Wilcoxon sign rank test was applied also by Zaman and Hirose (2011) to pairwise comparisons in their research into ensemble methods with small training sets.…”

Section: B Trawiński Et Almentioning

confidence: 99%

Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms

Trawiński¹,

Smźtek²,

Telec³

et al. 2012

International Journal of Applied Mathematics and Computer Science

Self Cite

137

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In the paper we present some guidelines for the application of nonparametric statistical tests and post-hoc procedures devised to perform multiple comparisons of machine learning algorithms. We emphasize that it is necessary to distinguish between pairwise and multiple comparison tests. We show that the pairwise Wilcoxon test, when employed to multiple comparisons, will lead to overoptimistic conclusions. We carry out intensive normality examination employing ten different tests showing that the output of machine learning algorithms for regression problems does not satisfy normality requirements. We conduct experiments on nonparametric statistical tests and post-hoc procedures designed for multiple 1 × N and N × N comparisons with six different neural regression algorithms over 29 benchmark regression data sets. Our investigation proves the usefulness and strength of multiple comparison statistical procedures to analyse and select machine learning algorithms.

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Section: B Trawiński Et Almentioning

confidence: 99%

Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms

Trawiński¹,

Smźtek²,

Telec³

et al. 2012

International Journal of Applied Mathematics and Computer Science

Self Cite

137

View full text Add to dashboard Cite

show abstract

“…In its initial formulation, the hedonic approach is based on multiple regression models where the value of a property is given in a predefined form as a weighted sum of the various characteristics of the said property (Curry, 2002;Rosen, 1974). Notwithstanding neural networks, other approaches have come up recently to extend or replace hedonic models, such as the fuzzy logic approach (Lughofer, Trawinski, Trawinski, Kempa, & Lasota, 2011), the support vector machine technique (Lam, Yu, & Lam, 2009), the rulebased expert systems (Kauko, 2003), the Geographically Weighted Regression model (Huang, Wu, & Barry, 2010;Osland, 2010) and from the geostatistics field, cokriging (Chica-Olmo, 2007) which was used to estimate housing prices. Further spatial econometric techniques have extended the hedonic models in order to consider the spatial autocorrelation and the spatial heterogeneity of housing prices (Bourassa, Cantoni, & Hoesli, 2010;Osland, 2010;Zietz, Zietz, & Sirmans, 2008).…”

Section: Introductionmentioning

confidence: 99%

Property valuation with artificial neural network: the case of Athens

Mimis

Rovolis

Stamou

2013

Journal of Property Research

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The purpose of this article is to examine the application of an artificial neural network (ANN) approach in property valuation. The approach has been enhanced by the use of a geographic information system (GIS) to enrich the explanatory variables and model the spatial dimension of the problem. The sample data used contain information of 3150 properties in the broader area of Athens. Various internal physical (structure quality and quantity) and external environmental characteristics (neighbourhood characteristics and transportation access) of the properties are available. In order to incorporate these environmental variables, the GIS was used to employ location-based characteristics. In our approach, the multilayer perception network has been employed and the results have been compared with the traditional approach of the spatial lag model. The comparison demonstrates that ANN gives more consistent predictions in the area of Athens. Our results reveal the non-linear relationships of the value of a property with respect to floor space and age. Finally, spatial variation of the values of the properties in broader area of Athens is illustrated.

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“…( 9) were used in the performance analyses to compare the model values and the market values obtained from the criteria. The performance of the model is investigated with these error rates especially in the studies in which the method is developed for real estate valuation (Fernandez-Martinez, Fernandez-Ceniceros, Sanz-Garcia, Lostado-Lorza, & Martinez-De-Pison- Ascacibar, 2011;Kavas, 2014;Kuşan, Aytekin, & Özdemir, 2010;Lin, 2010;Lughofer, Trawinski, Trawinski, Kempa, & Lasota, 2011;Saraç, 2012).…”

Section: Performance Analysismentioning

confidence: 99%

Reduction of mass appraisal criteria with principal component analysis and integration to GIS

Ünel

Yalpır

2019

International Journal of Engineering and Geosciences

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In real estate, mass appraisal is a very important subject in the valuation of two and more properties. It can be of benefit in a number of fields including taxation, banking transactions, expropriation, etc. The base problem is which criteria to use for mass appraisal. Because the number of criteria and the criteria themselves vary according to people, regions and countries, they are uncertain. They should be optimum in order to save on time, labour and cost. The aim of this study is to reduce the criteria by determining which ones affect the plot value. A survey which was answered by a total of 2,531 participants was conducted in Turkey. Principal Component Analysis (PCA), one of the criteria analysis methods, was applied to the survey data. The number of criteria was reduced to 14 with separation and to 30 according to the results of PCA. But they decreased in the model verification when criteria data for 558 samples were collected in the Konya study area. An index of the neighbourhood and locational features of these criteria was created by using GIS. Three models were established using Multiple Regression Analysis (MRA) and the performance of the models was examined. The prediction values and the market value were integrated into the GIS to compare the spatial distributions of plot values.

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On employing fuzzy modeling algorithms for the valuation of residential premises

Cited by 52 publications

References 37 publications

Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms

Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms

Property valuation with artificial neural network: the case of Athens

Reduction of mass appraisal criteria with principal component analysis and integration to GIS

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