An improved spatial error model for the mass appraisal of commercial real estate based on spatial analysis: Shenzhen as a case study

Zhang, Ran; Du, Qingyun; Geng, Jijin; Liu, Biao; Huang, Yikun

doi:10.1016/j.habitatint.2014.12.001

Cited by 48 publications

(32 citation statements)

References 27 publications

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“…Regression models with geographically specific dummy variables and distance coefficients have been considered by several authors [14][15][16]. To improve the valuation accuracy other authors, consider spatial information in pricing models using the direct spatial modelling with Cartesian coordinates [17,18], geostatistical models [19], or response surfaces [20][21][22]. Other research [23][24][25][26] has focused on submarkets in which the marginal price contributions of independent variables are more likely to be similar.…”

Section: Literature Reviewmentioning

confidence: 99%

A Mass Appraisal Model Based on Market Segment Parameters

2017

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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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Section: Literature Reviewmentioning

confidence: 99%

A Mass Appraisal Model Based on Market Segment Parameters

2017

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“…Zhang and colleagues extended the construction of the spatial error model (SEM) from a single variable to multiple variables using fuzzy mathematics; this extension increases the applicability of our technique and provides technical support for the ongoing property tax reforms in the Chinese real estate market [33]. To assist in decision-making regarding a reference sample, the proposed model uses fuzzy mathematics to establish fuzzy sets of samples and automatically pushes the most similar samples based on neartude, which is an important index introduced by Wang that describes the degree of similarity between two fuzzy sets [34].…”

Section: Neartude-based Transaction Sample Push Modelmentioning

confidence: 99%

A GIS- and Fuzzy Set-Based Online Land Price Evaluation Approach Supported by Intelligence-Aided Decision-Making

Qiao

2016

IJGI

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Abstract:In recent years, with the reforms to the land use system and the development of urbanization in China, land price evaluation has tended towards marketization. Prices are determined by the government, the land transaction market and the public. It is necessary to propose higher standards to be used in the evaluation process. This paper presents an online land price evaluation approach for convenience in evaluation. In a network environment, taking advantage of the data services provided by various departments, we propose two models to assist in decision-making: (1) a geographic information system (GIS)-and fuzzy set-based location factor quantification model, which adopts dynamic data, rules and quantification measures (based on the road network) to dynamically quantify location factors, thus transforming fuzzy sets into appropriate values; and (2) a neartude-based transaction sample push model, which quantifies the similarity between a given land and other samples, thus providing a basis for decision-making by an appraiser. This approach is applied in Shenzhen to evaluate its ability to simplify the work of appraisers and make their decisions more intuitive and objective in a real case.

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“…Сформулированные в статье тенденции характерны для городов-миллионников. Одним из ключевых факторов, влияющих на деятельность торговых организаций, осуществляющих торговую деятельность, является состояние рынка торговой недвижимости (Акулова, 2013;Жарков, 2012;Кузьмина, 2008;Мотылев, 2014;Сидячев, 2009;Слизяк, 2012;Смирнова, Щесняк, 2011;Чкалова, 2014;Cozmei, Onofrei, 2012;Deng, McMillen, Sing, 2014;Nguyen, Krabben, Samsura, 2014;Stanescu, Tunaru, Candradewi, 2014;Toivonen, Viitanen, 2015;Zhang, Du, Geng, Liu, Huang, 2015).…”

Section: королевский институт дипломированных оценщиков (The Royal Inunclassified

Оценка Тенденций Развития Рынка Торговой Недвижимости В 2015 Году

Кочетова¹

2015

Russian Journal of Retail Management

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В статье представлены результаты исследования развития сегмента торговой недвижимости в 2014–2015 г. Автором систематизированы факторы, повлиявшие на динамику развития рынка недвижимости, представлены данные характеризующие объем предложения и обеспеченность торговыми площадями по округам Москвы, систематизированы данные об условиях аренды площадей в торговых центрах, сформулированы причины изменения условий сотрудничества девелоперов и арендаторов. В статье отмечена значимая роль внешних факторов, влияющих на изменение ситуации на рынке торговой недвижимости. Целью статьи является исследование состояния и тенденций развития спроса и предложения на рынке недвижимости и определение перспективных направлений и рисков, влияющих на формирование инфраструктуры розничной торговли. Сформулированные в статье тенденции характерны для городов–миллионников.

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An improved spatial error model for the mass appraisal of commercial real estate based on spatial analysis: Shenzhen as a case study

Cited by 48 publications

References 27 publications

A Mass Appraisal Model Based on Market Segment Parameters

A Mass Appraisal Model Based on Market Segment Parameters

A GIS- and Fuzzy Set-Based Online Land Price Evaluation Approach Supported by Intelligence-Aided Decision-Making

Оценка Тенденций Развития Рынка Торговой Недвижимости В 2015 Году

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