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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 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.

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.

A common approach to predicting the price of residential properties uses the hedonic price model and its spatial extensions. Within the hedonic approach, real estate prices are decomposed into internal characteristics of an apartment, apartment characteristics and external characteristics. To account for the unobserved quality of the surrounding environment, price models include spatial price correlation factors, where the distance is usually measured as the distance in geographic space. In determining the price, a seller focuses not only on the observed and unobserved factors of the apartment and its environment but also on the prices of similar marketed objects that can be selected both by geographic proximity and by characteristics similarity. The purpose of this study is to show the latter point empirically.

This study uses an ensemble clustering approach to measure objects' proximity and test whether the proximity of objects in the property characteristics space along with spatial correlation explain the significant variation in prices.

In this paper, the pricing behaviour of sellers in a reselling market in Perm, Russia is studied. This study shows that the price transmission mechanism includes both geographic and characteristics spaces.

After testing on market data, the proposed framework for the distance construct could be used to obtain higher predictive power for price predictive models and construction of automated valuation services.

This study tests the higher explanatory power of the model that includes both the distance measured in geographic and property characteristics spaces.

In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.

All tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.

Machine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.

AVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.

According to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.

Explores the use which is made of strategic management accounting in the hotel sector through case studies at six major UK hotel groups. Uses the definition of strategic management accounting – “the provision and analysis of management accounting data relating to business strategy: particularly the relative levels and trends in real costs and prices, volumes, market share, cash flow and the demands on a firm′s total resources”. The results demonstrate that the accounting function in hotel groups is becoming increasingly involved in strategic management accounting, both in planning and in ad hoc exercises on the market conditions and competitor analysis. The widespread adoption of strategic management accounting is consistent with the open and relatively homogeneous nature of the industry and the high degree of competitiveness among the hotel groups in the market.

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.

Examines some of the improvements in personal computer software in recent years and the uptake of this technology in the surveying industry. Discusses the extent to which bespoke software systems have found acceptance in the industry and points to the greatly increased professional familiarity with generic software systems and its effect on valuation activities. Stresses the importance of user involvement in the system development process. Suggests that many problems with computing stem from an unwillingness to be involved in the developmental process.

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.