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Automated valuation models have been in use at least for the last 50 years in both academia and practice, while automated valuation recently re-emerged as very important with the rise of digital infrastructure. The current state of the art, therefore, justifies the dual contributions of this paper: organising existing knowledge and providing a new framework.

This paper provides much-needed analysis and synthesis of the accumulated body of knowledge by proposing an updated classification of automated valuation approaches based on two criteria, and a taxonomy adapted to new trends. The latter requires a paradigm shift from models to automated valuation systems. Both classification and taxonomy arose after literature review.

This paper provides a framework for an explicit context under which automated valuation is carried out. To do so, authors propose a definition of automation valuation systems; contextualise the differences among theories, approaches, methods, models and systems present in automated valuation and introduce a classification of automated valuation approaches and a non-hierarchical taxonomy of automated valuation systems.

Perhaps, a systematic literature review process instead of a selective list of 100 references could additionally validate the proposed classification and taxonomy.

The new framework, underlying various dimensions of the automated valuation process, can help practitioners surpass judging models based purely on their predictive accuracy. Also, the automated valuation system is a more generic term that can better accommodate future research coming from a multitude of disciplines, more diverse business areas and enlarged variety of practical users.

This is the first paper that develops a taxonomy of automated valuation systems.

The paper's purpose is to review the growth of computer supported valuation models and the increased access via information technology to property data in the world of property taxation. The paper aims to stimulate debate on what the short/medium term future may hold. Is there room for both traditional valuation surveying skills and computer mass appraisal models in the enlightened property taxation world, where transparency and access to property data is expected?

The paper compares and contrasts developments and trends in the use of automated valuation models (AVMs) across the world to assess property for local taxation purposes. It focuses in detail on three automated property taxation valuation systems of which the author has working knowledge and experience: Valuation Office Agency – Council Tax (Dwellings) and Non Domestic Rating (Commercial); Northern Ireland Valuation and Lands Agency – Domestic (Dwellings); Hong Kong Rating and Valuation Department (Dwellings and Commercial) property. The paper also considers the progress made in access to property data and data storage/retrieval.

Automated valuation programmes assist in the production of a valuation but its quality and accuracy are data and valuer led. One size does not fit all and there is no automated replacement for the subjective professional judgement of the valuer.

This paper considers the challenges, opportunities and possible problems when using computer driven valuation models for property taxation purposes.

The current state-of-the-art recognises three traditional valuation approaches. The current division is not sufficient to explain systematically all features that drive the development and usage of automated valuation models.

This practice briefing reviews existing valuation approaches, their pros and cons and more critical other automated valuation aspects or features; both based on a literature review.

This paper discusses and lists the six critical aspects or features, besides the valuation approaches.

This paper reveals the list of aspects or features that are important to consider when designing an automated valuation model.

This practice briefing discusses the inclusion of a multitude of aspects when considering an automated valuation model design.

This paper aims to construct an appropriate automated valuation model to value Housing and Development Board resale flats in Singapore. The paper also aims to test the accuracy of the model by comparing the values generated with actual valuations performed by a property firm in Singapore. In addition, it seeks to examine whether models for the sub‐markets of Housing and Development Board resale flats based on location or type of flat are more “sufficiently accurate” than the general model.

Using transacted data of 1,483 HDB resale flats, a hedonic price model is used to estimate housing price. The variables adopted include floor area of the housing unit, floor level of the housing unit, age, distance from central business district and distance from the mass rapid transit station.

The study found that the general model provides sufficient accuracy when producing valuations. The models based on sub‐markets, namely, “location” and “type of flats” produced reasonable levels of accuracy, although more variables could be added to the “type of flats” model to improve its reliability.

The research is limited to a few locations in Singapore. Future studies can include data from all over the island to provide better coverage.

The automated valuation model could bring time and cost savings, which could result in higher profit margin for property firms. Thus, valuers could spend more time on complex valuations and issues. The model can also be modified to fit other property markets with appropriate characteristics (for example, high volume transactions).

This paper represents an initial attempt to apply the automated valuation model in the valuation of Housing and Development Board resale flats.

The continuous redesign of processes is crucial for companies in times of tough competition and fast‐changing surrounding conditions. Since the manual redesign of processes is a time‐ and resource‐consuming task, automated redesign will increasingly become a useful alternative. Hence, future redesign projects need to be valuated based on both a manual and an automated redesign approach. The purpose of this paper is to compare the manual and automated process redesign on the basis of the Business Process Management (BPM) lifecycle.

In this paper, the authors compare the manual and automated process redesign on the basis of the Business Process Management (BPM) lifecycle. The results form the basis for a mathematical model that outlines the general economic characteristics of process redesign as well as for the manual and automated approaches. Subsequently, the authors exemplarily apply their model to a set of empirical data with respective assumptions on particular aspects of the automated approach.

In the problem setting described in the paper, the valuation model shows that automated process redesign induces an equal or higher number of optimized processes in a company. Therefore, the authors present a decision support that outlines how much to invest in automated process redesign.

The model considers the cost side of automated process redesign; therefore, further research should be conducted to analyze the possibility of higher returns induced by automated redesign (e.g., through a quicker adaption to real‐world changes). Moreover, for automated redesign, there is no requirement for broad empirical data that should be collected and analyzed as soon as this approach leaves the basic research and prototyping stages.

This paper presents an approach that can be used by companies to estimate the upper limit for investments in manual and automated process redesign. Working under certain general assumptions and independently from actual cost and return values, the paper demonstrates that automated process redesign induces an equal or higher ratio of optimized processes. Thus, companies introducing automated redesign cannot only apply the model to evaluate their investments but can also expect a higher ratio of optimized processes for this approach.

As existing literature primarily focuses on the technical aspects of automated process redesign, these findings contribute to the current body of literature. This paper discusses a first decision‐support for the economic aspects of automated process redesign, particularly with regard to the investments that are required for it. This information is relevant as soon as the approach leaves the stage of a prototype.

Automated valuation models (AVMs) are statistical asset pricing models omnipresent in residential real estate markets, where they inform property tax assessment, mortgage underwriting and marketing. Use of these asset pricing models outside of residential real estate is rare. The purpose of the paper is to explore key characteristics of commercial office lease contracts and test an application in estimating office market rental prices using an AVM.

The authors apply a semi-log ordinary least squares hedonic regression approach to estimate either contract rent or the total costs of occupancy (TOC) (“grossed up” rent). Furthermore, the authors adopt a training/test split in the observed leasing data to evaluate the accuracy of using these pricing models for prediction. In the study, 80% of the samples are randomly selected to train the AVM and 20% was held back to test accuracy out of sample. A naive prediction model is used to establish accuracy prediction benchmarks for the AVM using the out-of-sample test data. To evaluate the performance of the AVM, the authors use a Monte Carlo simulation to run the selection process 100 times and calculate the test dataset's mean error (ME), mean absolute error (MAE), mean absolute percentage error (MAPE), median absolute percentage error (MdAPE), coefficient of dispersion (COD) and the training model's r-squared statistic (R²) for each run.

Using a sample of office lease transactions in Sydney CBD (Central Business District), Australia, the authors demonstrate accuracy statistics that are comparable to those used in residential valuation and outperform a naive model.

AVMs in an office leasing context have significant implications for practice. First, an AVM can act as an impartial arbiter in market rent review disputes. Second, the technology may enable frequent market rent reviews as a lease negotiation strategy that allows tenants and property owners to share market risk by limiting concerns over high costs and adversarial litigation that can emerge in a market rent review dispute.

The intent of this paper is to identify uncertainty surrounding automated valuation models (AVMs) valuations and the criteria by which a valuer could judge the accuracy of an AVM estimate of value when being assisted by such AVM as a valuation tool.

European law and European Valuation Standards allow valuers to use AVMs as one tool among others in reaching an estimation of Market Value, but only insofar as the valuer is able to satisfy him/herself and the client of the relevance of the AVM report, its inputs and outputs. To enable this, it thus becomes essential that AVMs be more transparent and their accuracy verified.

This paper recommends minimum reporting requirements thereby enabling an assessment of AVM valuations. At the outset, a distinction needs to be made between two groups of AVM users: banks and valuers. Banks will require considerably more information, including details of the types of models employed and “Bulk” accuracy test results.

This paper addresses the minimum information needed by valuers in order to gauge the usefulness and accuracy of the AVMs they propose to use as one of their valuation tools.

This paper provides guidance on minimum information requirements for AVMs. Indeed, it may be that the AVM vendors' industry will recognise that providing more transparency in their reports along the lines suggested would facilitate a wider and more supportive acceptance of AVMs.

As regards the assessment of the market values of properties that compose real estate portfolios, the purpose of this paper is to propose and test an automated valuation model. In particular, the method defined allows for providing for objective, reliable and “quick” valuations of the assets in the phases of periodic reviews of the property values.

Aiming at both predictive and interpretative purposes, the method, based on multi-objective genetic algorithms to search those model expressions that simultaneously maximize the accuracy of the data and the parsimony of the mathematical functions, is applied to a sample data of office properties characterized by medium and large size, located in the city of Milan (Italy) and sold in the period between 2004 and 2015.

The model obtained could be an integration of the canonical methodologies (market approach, income approach, cost approach) implemented in the assessment of the market values of properties, so as to provide an additional tool to verify the results. In particular, the inclusion of economic variables in the model is consistent with the need to reiterate the valuations, contextualizing them to the locational characteristics and to the current property cycle phase in the specific area.

The model can be applied by all the operators involved in the periodic reviews of the values of property portfolios: from real estate funds’ insiders, in order to monitor the values obtained through the canonical approaches, to the public institutions, such as the revenue agencies, in order to ensure the fair payment of the taxes through the updating values of the properties according to the actual and current market trends.

The method proposed can be a valid support for all public and private entities that hold significant property assets and that, for various reasons (periodic reviews of the balance sheets, sales, enhancement, investment, etc.), require cyclical updated values of the properties. The automated valuation model developed can be used for the assessment of “comparison” values with the estimates values obtained by other assessment techniques, in order to ensure a further monitoring tool of the results from the subjects involved.

In this study the authors aim to outline new ways of information extraction for automated valuation models, which in turn would help to increase transparency in valuation procedures and thus contribute to more reliable statements about the value of real estate.

The authors hypothesize that empirical error in the interpretation and qualitative assessment of visual content can be minimized by collating the assessments of multiple individuals and through use of repeated trials. Motivated by this problem, the authors developed an experimental approach for semi-automatic extraction of qualitative real estate metadata based on Comparative Judgments and Deep Learning. The authors evaluate the feasibility of our approach with the help of Hedonic Models.

The results show that the collated assessments of qualitative features of interior images show a notable effect on the price models and thus over potential for further research within this paradigm.

To the best of the authors’ knowledge, this is the first approach that combines and collates the subjective ratings of visual features and deep learning for real estate use cases.

The purpose of this paper is to review the issues involved in the implementation of mass valuation systems and the conditions needed for doing so.

The method makes use of case studies of and fieldwork in countries that have either recently introduced mass valuations, brought about major changes in their systems or have been working towards introducing mass valuations.

Mass valuation depends upon a degree of development and transparency in property markets and an institutional structure capable of collecting and maintaining up-to-date price data and attributes of properties. Countries introducing mass valuation may need to undertake work on improving the institutional basis for this as a pre-condition for successful implementation of mass valuation.

Although much of the literature is concerned with how to improve the statistical modelling of market prices, there are significant issues concerned with the type and quality of the data used in mass valuation models and the requirements for successful use of mass valuations.

Much of the literature on mass valuation takes the form of the development of statistical models of value. There has been much less attention given to the issues involved in the implementation of mass valuation.