The mass appraisal of the real estate by computational intelligence

Kontrimas, Vilius; Verikas, Antanas

doi:10.1016/j.asoc.2009.12.003

Cited by 149 publications

(90 citation statements)

References 18 publications

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“…This large database contrasts with those used in most of the studies in this field, regardless of the valuation method applied, e.g. Brown et al (2004) considered data for 725 dwellings and only one explanatory variable; d'Amato (2007) worked with 390 observations; García et al (2008) used 591 sample cases; in Kontrimas et al (2011) the sample size was 100; and Narula et al (2012) considered 54 observations.…”

Section: Databasementioning

confidence: 99%

“…The former includes regression models and their multiple variants. The second group is well represented in the literature and includes approaches like decision trees (Fan et al 2006), rough set theory (D'Amato 2007), artificial neural networks (Tay and Ho 1991;García et al 2008;Selim 2009), support vector machines (Kontrimas and Verikas 2011) and random forest (Antipov and Pokryshevskaya 2012).…”

Section: Introductionmentioning

confidence: 99%

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Mass Appraisal of Residential Real Estate Using Multilevel Modelling

Arribas

García

Guijarro

et al. 2016

International Journal of Strategic Property Management

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ABSTRACT. Mass appraisal, or the automatic valuation of a large number of real estate assets, has attracted the attention of many researchers, who have mainly approached this issue employing traditional econometric models such as Ordinary Least Squares (OLS). However, this method does not consider the hierarchical structure of the data and therefore assumes the unrealistic hypothesis of the independence of the individuals in the sample. This paper proposes the use of the Hierarchical Linear Model (HLM) to overcome this limitation. The HLM also gives valuable information on the percentage of the variance error caused by each level in the hierarchical model. In this study HLM was applied to a large dataset of 2,149 apartments, which included 17 variables belonging to two hierarchical levels: apartment and neighbourhood. The model obtained high goodness of fit and all the estimated variances of the parameters in HLM were lower than those calculated by OLS. It can be concluded as well that no further neighbourhood variables need be added to the model to improve the goodness of fit, since almost all the residual variance can be attributed to the first hierarchical level of the model, the apartment level.

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Section: Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mass Appraisal of Residential Real Estate Using Multilevel Modelling

Arribas

García

Guijarro

et al. 2016

International Journal of Strategic Property Management

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show abstract

“…Hedonic models are widespread in urban studies, but for them statistical techniques as genetic algorithms, linear programming, semi-parametric or non-parametric regressions, artificial neural networks are certainly less common [3][4][5][6][7][8][9][10][11][12][13]28,29,[53][54][55][56][57][58][59][60][61][62][63][64][65].…”

Section: Discussionmentioning

confidence: 99%

Using Genetic Algorithms for Real Estate Appraisals

2017

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Abstract:The main aim of this paper is the interpretation of the existing relationship between real estate rental prices and geographical location of housing units in a central urban area of Naples (Santa Lucia and Riviera of Chiaia neighborhoods). Genetic algorithms (GA) are used for this purpose. Also, to verify the reliability of genetic algorithms for real estate appraisals and, at the same time, to show the forecasting potentialities of these techniques in the analysis of housing markets, a multiple regression analysis (MRA) was applied comparing results of GA and MRA.

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“…mass valuation of real property is a systematic valuation of real property items performed on a specific date using standard procedures and statistical analysis, while individual valuation determines the value of an individual property item. mass valuation models are commonly based on the sales comparison method and the ordinary least squares (ols) linear regression is the traditional method used to build models in this approach (Kontrimas and verikas, 2006(Kontrimas and verikas, , 2007(Kontrimas and verikas, , 2009.…”

Section: Introductionmentioning

confidence: 99%

Land Value Tax in the Context of Sustainable Urban Development and Assessment. Part Ii - Analysis of Land Valuation Techniques: The Case of Vilnius

Raslanas

Zavadskas

Kaklauskas

et al. 2010

International Journal of Strategic Property Management

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abstraCt. part two of this article deals with problems related to land valuation. for that purpose, a site of 6.89 ares located in Švitrigailos Street XX, Vilnius, was selected as the research object and appraised using three approaches: the sales comparison approach, the mamva method and the mass valuation approach. peculiarities of these methods in land valuation were reviewed and the recommendations were provided for land valuation for taxation purposes applicable in a new model for lithuanian real property taxation system.

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The mass appraisal of the real estate by computational intelligence

Cited by 149 publications

References 18 publications

Mass Appraisal of Residential Real Estate Using Multilevel Modelling

Mass Appraisal of Residential Real Estate Using Multilevel Modelling

Using Genetic Algorithms for Real Estate Appraisals

Land Value Tax in the Context of Sustainable Urban Development and Assessment. Part Ii - Analysis of Land Valuation Techniques: The Case of Vilnius

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