Valuating residential real estate using parametric programming

Narula, Subhash C.; Wellington, John F.; Lewis, S. A.

doi:10.1016/j.ejor.2011.08.014

Cited by 28 publications

(15 citation statements)

References 33 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%

“…A large number of academic studies have employed regression models for real estate valuation from the decade of the 80s to the present. These studies apply different econometric models with different complexity levels, like the traditional hedonic regression models (Palmquist 1984;Isakson 2001;Downes and Zabel 2002), ridge regression (Ferreira and Sirmans 1988) or quantile regression (Farmer and Lipscomb 2010;Narula et al 2012), just to mention some examples.…”

Section: The Use Of Econometric Models For Mass Appraisal Of Residentmentioning

confidence: 99%

“…So, for example, habitual and well-established methodologies like DEA (Titko et al 2014), AHP (Ecer 2014) or econometric models (Mihi-Ramirez et al 2013;Rudzkis and Valkaviciene 2014) have been introduced into the appraisers' practice as well (D'Amato 2010; Aznar et al 2011;Narula et al 2012;Cervelló et al 2013).…”

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: The Use Of Econometric Models For Mass Appraisal Of Residentmentioning

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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“…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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“…Furthermore, the analysis is usually based on small samples (less than 500 properties) at regional level, (Landajo et al, 2012;Kilpatrick, 2011;Kusan et al, 2010;Selim, 2009;Kontrimas and Verikas, 2011;Narula et al, 2012;Brasington and Hite, 2008). In this study the proposed AVMs are tested in a new market still at its infancy with lots of unique characteristics.…”

Section: Introductionmentioning

confidence: 99%

Real Estate valuation and forecasting in non-homogeneous markets: A case study in Greece during the financial crisis

Alexandridis

Karlis

Papastamos³

et al. 2018

Journal of the Operational Research Society

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In this paper we develop an automatic valuation model for property valuation using a large database of historical prices from Greece. The Greek property market is an inefficient, non-homogeneous market, still at its infancy and governed by lack of information. As a result modelling the Greek real estate market is a very interesting and challenging problem. The available data covers a big range of properties across time and includes the Greek financial crisis period which led to tremendous changes in the dynamics of the real estate market. We formulate and compare linear and nonlinear models based on regression, hedonic equations, spatial analysis and artificial neural networks. The forecasting ability of each method is evaluated out-of-sample. Special care is given on measuring the success of the forecasts but also to identify the property characteristics that lead to large forecasting errors. Finally, by examining the strengths and the performance of each method we apply a combined forecasting rule to improve performance. Our results indicate that the proposed methodology constitutes an accurate tool for property valuation in non-homogeneous, newly developed markets.

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Valuating residential real estate using parametric programming

Cited by 28 publications

References 33 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

Real Estate valuation and forecasting in non-homogeneous markets: A case study in Greece during the financial crisis

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