A Study on Automated Valuation Model in Mass Appraisal System for Real Property Tax

Zhang, Rui

doi:10.1109/ecbi.2009.89

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Mostly, the urge of mass appraisal process is purpose-driven and obligatory in nature because it is done for taxation and other land-related transactions of the government (Demetriou et al , 2012; Omari, 2008). Hence, the process must be dealt with high level of consistency and transparency so that policy implementation and tax enforcement would be successful (Bidanset and Lombard, 2014; Zhang and Chen, 2009). In addition, it is essential to consult with various experts during the drafting of policies for land administration (e.g.…”

Section: Introductionmentioning

confidence: 99%

Factors affecting decision-making in land valuation process using AHP: a case in the Philippines

Bencure¹,

Tripathi²,

Miyazaki³

et al. 2021

IJHMA

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Purpose The research aims to establish importance scheme of geospatial factors for land valuation activities that may serve as an eye-opener and aid the concerned government agencies in drafting land valuation policies and guidelines to achieve a sound land governance and administration. It specifically identifies and weighs geospatial valuation factors to establish their importance. Design/methodology/approach The research involves discussions and survey questionnaires given to land experts (i.e. appraisers, environmental planners, land economist, geodetic engineers and assessors) who indicated their opinions on influence of geospatial factors on land value. The analytic hierarchy process (AHP) is then used to weigh the factors in terms of its importance. Findings The result was then compared with the multiple regression analysis (MRA) taking into consideration the standardized regression coefficient of the 15 factors. The AHP method found out the major road accessibility and slope direction as the most and least influential factors, respectively, while surprisingly MRA found major road accessibility not significant at p < 0.05 level of significance. Research limitations/implications The research generally reflects the sub-urban type of study area; hence, inclusion of other road types such as express ways and subways and performing sensitivity analysis of AHP are suggested in future studies. Practical implications The findings of the study will provide information of concerned government agencies in improving valuation activities, as well as to update values regularly based on the geospatial factors. Originality/value To the best of the authors’ knowledge, this study is the first effort to rank geospatial factors with analytic hierarchy analytic process that further considered both their negative and positive influences on land value. The approach surmounts the flaw and shortcomings of empirical methods of identifying importance of factors.

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Section: Introductionmentioning

confidence: 99%

Factors affecting decision-making in land valuation process using AHP: a case in the Philippines

Bencure¹,

Tripathi²,

Miyazaki³

et al. 2021

IJHMA

View full text Add to dashboard Cite

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Comparison of Mass Appraisal Models for Effective Prediction of Property Values

Yacim¹,

Bosho²

2016

Sustainable Multi-Sectoral Real Estate Development in Emerging Economies - The 16th African Real Estate Society Conference

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There are a number of models that are used for mass appraisal of properties. However, the choice of a model is predicated on a number of criteria. Notable among these is to compare models predictive accuracies relative to the property market context where appraisal is undertaken or being contemplated. This study focuses on comparing predictive accuracies of mass appraisal models with a dataset of 3494 single-family property transactions in the city of Cape Town, South Africa, from 2012-2014. Five mass appraisal models including back propagation (BP) trained artificial neural networks, multiple regression model, M5P trees, support vector machine optimise with sequential minimal optimisation and additive nonparametric regression were used for the simulations. Waikato Environment for Knowledge Analysis (WEKA) explorer; an open source data mining software was used to pre-processed property data to normalised values and model property prices. The analysis shows that BP trained artificial neural networks (BP-ANNs) and M5P trees utilised in this study predicted better results with root mean squared error and mean absolute error within the acceptable threshold of 5%. But M5P trees demonstrate distinctiveness in predicted results between normalised and absolute values which require further examination. The other three mass appraisal models including multiple regression model, additive nonparametric regression and support vector machines with sequential minimal optimisation predicted results with RMSE that are higher than 5% acceptable threshold. Furthermore, contextual application of results with other studies reveal that BP-ANNs and M5P trees do not have power of universal acceptability because of varied results in other context. Therefore these models are particularly relevant to mortgage lenders, valuation offices, etc. in South Africa, but should the scope be extended to other context, application should be based on the property market features.

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A Study on Automated Valuation Model in Mass Appraisal System for Real Property Tax

Cited by 2 publications

References 3 publications

Factors affecting decision-making in land valuation process using AHP: a case in the Philippines

Factors affecting decision-making in land valuation process using AHP: a case in the Philippines

Comparison of Mass Appraisal Models for Effective Prediction of Property Values

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