The Multilevel Model in the Computer-Generated Appraisal: A Case in Palermo

Ciuna, Marina; Salvo, Francesca; Simonotti, Marco

doi:10.1007/978-3-319-49746-4_14

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…To better account for spatial information in the data, the classical linear model can be extended to a hierarchical or multilevel (mixed effects) model by adding the municipality and possibly other higher-level administrative units as random intercepts in the model equation. Such models are, for example, applied by Brown and Uyar (2004), Ciuna et al (2017) and Keskin et al (2017) in the framework of hedonic price modeling. Numerous publications, such as Orford (2002), Goodman and Thibodeau (2003), Bourassa et al (2003), Case et al (2004) and Bourassa et al (2007, 2010), use related approaches in the context of market segmentation.…”

Section: Introductionmentioning

confidence: 99%

Estimation and updating methods for hedonic valuation

Mayer¹,

Bourassa

Hoesli

et al. 2019

JERER

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Purpose The purpose of this paper is to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models. Design/methodology/approach The authors apply six estimation methods (linear least squares, robust regression, mixed-effects regression, random forests, gradient boosting and neural networks) and two updating methods (moving and extending windows). They use a large and rich data set consisting of over 123,000 single-family houses sold in Switzerland between 2005 and 2017. Findings The gradient boosting method yields the greatest accuracy, while the robust method provides the least volatile predictions. There is a clear trade-off across methods depending on whether the goal is to improve accuracy or avoid volatility. The choice between moving and extending windows has only a modest effect on the results. Originality/value This paper compares a range of linear and machine learning techniques in the context of moving or extending window scenarios that are used in practice but which have not been considered in prior research. The techniques include robust regression, which has not previously been used in this context. The data updating allows for analysis of the volatility in addition to the accuracy of predictions. The results should prove useful in improving hedonic models used by property tax assessors, mortgage underwriters, valuation firms and regulatory authorities.

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Section: Introductionmentioning

confidence: 99%

Estimation and updating methods for hedonic valuation

Mayer¹,

Bourassa

Hoesli

et al. 2019

JERER

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“…Generally, researchers apply the ideas, models and methods of other fields, such as statistics, computer science or geographic science, to the field of real estate mass appraisal. Some scholars have made beneficial attempts to summarize the models from different perspectives, such as academic discipline [7], prediction accuracy [8], computational intelligence [9][10][11], taxation purpose [12][13][14], mortgage purpose [15] and automated valuation application in relevant countries and regions [6,[16][17][18][19][20]. Through the classification of various models, a 3I-trend is summarized, namely AI-Based Model, GIS-Based model and MIX-Based Model.…”

Section: Overview Of the Selected Papersmentioning

confidence: 99%

Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review

Wang

2019

Sustainability

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With the increasing volume and active transaction of real estate properties, mass appraisal has been widely adopted in many countries for different purposes, including assessment of property tax. In this paper, 104 papers are selected for the systematic literature review of mass appraisal models and methods from 2000 to 2018. The review focuses on the application trend and classification of mass appraisal and highlights a 3I-trend, namely AI-Based model, GIS-Based model and MIX-Based model. The characteristics of different mass appraisal models are analyzed and compared. Finally, the future trend of mass appraisal based on model perspective is defined as “mass appraisal 2.0”: mass appraisal is the appraisal procedure of model establishment, analysis and test of group of properties as of a given date, combined with artificial intelligence, geo-information systems, and mixed methods, to better model the real estate value of non-spatial and spatial data.

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“…Following the non-agency mortgage crisis, numerous contributions have been offered in order to improve the efficiency and quality of an automated valuation methodology (AVM) dealing with emerging problems and different contexts. Spatial issues [29,30], evolution of AVM standards [31][32][33], multilevel models [34], fuzzy and rough set applications [35] and quantitative methods to define comparables are just some of the topics discussed.…”

Section: Literature Reviewmentioning

confidence: 99%

A Mass Appraisal Model Based on Market Segment Parameters

2017

Self Cite

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Abstract:The proposed evaluation scheme is a uni-equation model to evaluate properties of Mass Appraisal (MA) in terms of widespread availability of sample data. It all allows the use of statistical models and in the opposite conditions of the absence of data of comparable properties, the functions of similar market areas are known as well as the ones near to those for which you want to estimate the function. Of course, the accuracy of the evaluation increases with the amount of available data, with other equal conditions and evaluations carried out without data (but in the presence of other market information). It requires extra-statistical appraisal procedures involving a complete knowledge of the real estate market. However, such knowledge is also required in the MA performed by quantitative models with regard to the data sampling and performance monitoring process. The model considers micro-level characteristics of the properties and macro-level parameters of the real estate market segments. The appraisal model defines the prediction function with both the statistical models and estimation procedures. For this purpose, the model considers four specific situations: the construction of a statistical model operating with a sufficiently large sample of market prices; the construction of a prediction function operating with a very few number of market prices samples; in this situation, the appraisal function of market value is defined by using a sample of market prices referred to comparable properties, and these are few for statistical use but perfectly suitable to the appraisal process; the construction of a prediction function operating with only one market price; the construction of a prediction function operating in the absence of real estate data but with similar functions of market areas with other estimated proprieties. The presented model provides a uniform method of estimating the market value of properties (and fees), through the modular functions. The model studied is able to operate also with reduced information, considering the practical circumstances, the boundary conditions, the application precautions and the significance of the results.

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The Multilevel Model in the Computer-Generated Appraisal: A Case in Palermo

Cited by 6 publications

References 21 publications

Estimation and updating methods for hedonic valuation

Estimation and updating methods for hedonic valuation

Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review

A Mass Appraisal Model Based on Market Segment Parameters

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