Journal of Property Tax Assessment & Administration

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Volume 23, Issue 1 (2026)Read More

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      • Articles21 August 2026

        A comparative study on artificial intelligence based automated valuation models

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        This paper presents a comparative study of traditional linear regression approaches, and a range of Artificial Intelligence (AI) based automated valuation models (AVMs) in the context of mass appraisal valuation. Using datasets from Adams County (USA), Belfast (UK), and Eindhoven (Netherlands), the research evaluates model performance across a diverse range of market structures and data characteristics. A suite of models including Decision Tree, Random Forest, Gradient Boosting, Light Gradient Boosting, Generalized Regression Neural Network, Multi-Layer Perceptron, and Dense Neural Network were developed and tested against standardised ratio study metrics from the International Association of Assessing Officers (IAAO), along with other statistical measures for evaluating model performance. The findings reveal that ensemble machine learning models consistently outperform traditional regression methods across all the jurisdictions examined, exhibiting superior accuracy, precision, and equity even with limited predictor datasets. Furthermore, the application of hyperparameter tuning was observed to enhance model performance, particularly for boosting based algorithms. The study highlights the adaptability of AI-based AVMs to heterogeneous and non-linear property markets, while emphasizing the continued need for transparency in variable selection and communication around the variables used within automated valuation models. The findings underscore the transformative potential of AI in advancing equitable and accurate property assessment practices, and for the development of industry guidance on the ethical and transparent integration of AI and generative AI technologies within valuation frameworks and assessment practices.
      • Articles21 August 2026

        Adaption of property assessment and taxation policies in response to extreme weather events in Canada, and the United States

        Laura Maclean, Stephanie Vieille
        The increased intensity, and frequency of extreme weather events pose a different problem to property assessment practices, and insurance, and tax policies, which deserves to be examined separately. This research relies on qualitative methods, including a review of existing literature, and interviews with field experts, to gain insights into current practices, and emerging strategies. A case study format is proposed to investigate the Canadian, and U.S. assessment, tax, and insurance sectors’ responses to extreme weather events in Alberta, Nova Scotia, and Maui, Hawaii. It is expected that findings will be transferable, and lessons learned will help guide action in other jurisdictions. This proposed study seeks answers to the following question: How can the Canadian, and American communities affected by extreme weather events integrate a climate change lens into their property assessment practices, and taxation policies?
      • Articles21 August 2026

        A Gaussian Markov Random Field model for the valuation of domestic properties in Wales, UK, using INLA

        Elizabeth Osuno
        Location is a key factor in the valuation of residential properties, but it can be a challenge to capture its impact well. This paper considers a Gaussian Markov Random Field (GMRF) approach to modelling property values that enables the location element of the model to be considered as a smooth process and allows a more flexible correlation structure than alternatives. The model was built to value approximately 1.5 million properties in Wales, UK, for a revaluation planned by the Welsh Government. Due to the large scale of the model a Bayesian technique, known as the Integrated Nested Laplace Approximation (INLA), was employed to make the model computationally feasible. This method allowed spatial dependence to be accounted for and improved model performance over a mixed model with nested geographical variables.

    Most Popular Articles

    • Articles
      1 June 2019

      State and Provincial property tax policies and administrative practices (PTAPP): 2017 findings and report

      Alan Dornfest, Jennifer Rearich, T. Brydon, Richard Almy
      In response to a need for current information about property tax systems, since 1990 IAAO has conducted surveys of the features of property tax systems in Canada and the United States and published the results. Although the main audience comprises property tax administrators and policy makers in the two countries, readers from other countries may find the results helpful as well. The compilations of survey results published in 1990, 1991, 1992, 2000, 2009, and 2012 were based on a questionnaire sent to each Canadian province and territory and to each U.S. state and the District of Columbia. Similar to the 2012 survey the 2017 survey was limited in scope, representing an update of information and additional exploration of emerging topics. The most important focus of the 2017 survey was state appeals processes and reassessment practices.
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    • Articles
      1 June 2018

      Commercial big-box retail: a guide to market-based valuation

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      This IAAO position paper provides guidance for the valuation of big-box retail properties. Over the last several years, issues involving these properties and theories about how to value them, such as the dark store theory, have resulted in great debate within both the appraisal and legal communities. Even though this paper concentrates on arriving at the market value of the fee simple interest of these properties, it provides guidance regardless of the specific law of a jurisdiction.
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    • Articles
      1 June 2017

      Understanding intangible assets and real estate: a guide for real property valuation professionals

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      This guide is intended to assist assessors in understanding and addressing intangible assets in property tax valuation and does not represent a policy position of IAAO. Laws can vary from state to state, but for the majority of jurisdictions, intangible assets are not taxable, at least not as part of the real estate assessment. As a result, assessors must ensure their real estate assessments are free of any intangible value. To help determine whether something is an intangible asset, a four-part test can be applied. This guide highlights many property types that potentially include intangible assets, such as hotels, senior care facilities, and properties with valuable trade names and franchises.
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    • Articles
      1 December 2021

      The potential of artificial intelligence in property assessment

      Robert Gloudemans, Paul Sanderson
      What is artificial intelligence? Although many definitions are available, in the context of property valuation, IPTI defines artificial intelligence (AI) as machine learning designed to predict an outcome or provide an estimate, e.g., most probable sale price. AI, at least in our use of the term, is based on pattern and image recognition. It has the ability to process large volumes of data and requires intensive computer power of the type available in today’s higher-end PCs and cloud services. For purposes of this white paper, AI does not include standard statistical algorithms — most prominently multiple regression analysis (MRA) — in which the user specifies and calibrates a prediction model. Although users specify the dependent and independent variables, AI models produce no tangible equation. This white paper is therefore distinct from the International Association of Assessing Officers (IAAO) standards on Mass Appraisal of Real Property and Automated Valuation Models, which focus on equation-based applications of the three approaches to value in mass appraisal. The vision of this white paper is to provide a framework or first step toward the production of a standard on the use of AI in property assessment administration. While existing mass appraisal tools can be highly effective and produce excellent performance results, AI offers another viable tool that, if used properly, can efficiently produce equally or, arguably, more accurate valuations for many jurisdictions. Therefore, the authors believe the time has come for serious consideration of AI by the assessment community, and we hope to see the guidance offered in this paper considered and debated as part of a process to have AI adopted on a more formal basis, either as a stand-alone standard or as an addition to an existing standard.
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    • Articles
      31 December 2020

      A Gini measure for vertical equity in property assessments

      Carmela Quintos
      This paper aims to show how the Gini-based measures for inequality, commonly used in the socioeconomic literature, can be applied to property assessments. The index for vertical equity is the ratio of the Gini-based coefficient of assessment to the Gini coefficient of price. It is interpreted as the elasticity of shares of assessments to shares of prices when prices are ordered from the lowest to highest price levels. A second index is based on the difference, rather than the ratio, of the Gini-based coefficients. An important distinction between both indexes and the price-related differential (PRD) and currently used measures is that Gini-based analyses do not use sales ratios (assessment/price ratios), which are basically the behavior of the appraisal errors. Instead, they are based on measures that capture the cumulative distributional behavior of assessments relative to the cumulative distributional behavior of prices across ordered price levels. Both indexes are summary measures that are simple to calculate without regression, although there are regression-equivalent formulations that are used to statistically test for vertical equity. Because Gini-based measurements of inequality have a long history in economics, their introduction to property assessment aligns the measurement and interpretation of vertical equity with its application in other fields.
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