Prediction accuracy in mass appraisal: a comparison of modern approaches

McCluskey, William; McCord, Michael; Davis, Peadar; Haran, Martin; McIlhatton, David

doi:10.1080/09599916.2013.781204

Cited by 140 publications

(158 citation statements)

References 57 publications

Supporting

Mentioning

140

Contrasting

Unclassified

Order By: Relevance

“…In 1991, Skinner asked “If Agricultural Land Taxation Is So Efficient, Why Is It So Rarely Used?” His conclusion was the administrative cost of land taxation (Skinner, ). Since then, mass valuation (McCluskey et al ., ; Almy, ) and comprehensive international support for land administration (Enemark et al ., ) have been developed, so that it may be time to reconsider using land rent taxation.…”

Section: Resultsmentioning

confidence: 99%

Taxation of Economic Rents

Schwerhoff

Edenhofer

Fleurbaey

2019

Journal of Economic Surveys

View full text Add to dashboard Cite

Economic rents have long been identified as an efficient tax base. In addition, the recent literature documents that rent income is highly concentrated and that rents are quickly increasing. Rent taxation thus seems attractive for reasons of both efficiency and equity. Nevertheless, rent taxation remains a marginal topic in research and policy making. In a systematic review of the neoclassical literature on different rent types, we find that some types of rents reflect inefficiencies and should thus be minimized, while others reward investments and should be supported in line with social welfare. What remains for taxation are land rents, one of the few true scarcity rents. Land rents have significant potential to improve the efficiency of the tax system. We then begin to develop a comprehensive theory of land rent taxation by identifying relevant efficiency and equity effects. The interaction of many of these effects remains unexplored, which might explain policymakers' hesitation in using land taxes to date.

show abstract

Section: Resultsmentioning

confidence: 99%

Taxation of Economic Rents

Schwerhoff

Edenhofer

Fleurbaey

2019

Journal of Economic Surveys

View full text Add to dashboard Cite

show abstract

“…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 of real estate with similar attributes will be estimated by using the known mathematical relationship. Multiple regression analysis (MRA) is a statistical method to predict the real estate value (dependent variable) based on two or more other relevant attributes (independent variables) [8].…”

Section: Multiple Regression Analysismentioning

confidence: 99%

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

Wang

2019

Sustainability

View full text Add to dashboard Cite

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.

show abstract

“…These modelling techniques are becoming more and more popular, including for house price modelling. Applications in this field include: Worzala et al (1995), Din et al (2001), Peterson and Flanagan (2009), Zurada et al (2011), McCluskey et al (2013, and Chiarazzo et al (2014) for neural networks; Yoo et al (2012) and Antipov and Pokryshevskaya (2012) for random forests; Kagie and Van Wezel (2007), Lu et al (2017), Gu andXu (2017), andSangani et al (2017) (Zurada et al, 2011;Antipov and Pokryshevskaya, 2012). In most but not all cases, researchers have concluded that machine learning techniques yield more accurate predictions than standard linear models.…”

mentioning

confidence: 99%

Estimation and Updating Methods for Hedonic Valuation

et al. 2018

View full text Add to dashboard Cite

Purpose -We use a large and rich data set consisting of over 123,000 single-family houses sold in Switzerland between 2005 and 2017 to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models.Design/methodology/approach -We 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).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.

show abstract

Prediction accuracy in mass appraisal: a comparison of modern approaches

Cited by 140 publications

References 57 publications

Taxation of Economic Rents

Taxation of Economic Rents

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

Estimation and Updating Methods for Hedonic Valuation

Contact Info

Product

Resources

About