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On 1 April 1997 the rating revaluation of non‐domestic property in Northern Ireland came into force, representing a period of 19 years since property values were last reviewed. During that period commercial property values have changed significantly across property types and locations. The primary purpose of the revaluation is not, as one might expect, to increase the total amount of revenue to be raised, but rather to ensure that value‐based relativities between properties are fairly reflected. Results from the analysis of changes in Net Annual Values indicate substantial changes in retail and office property values and in addition significant increases in rate liability. Measures the shifts in assessed values and rates liability impact across District Council areas and examines the implications of introducing a transitional relief scheme to cushion the impact of the revaluation. Concludes by recommending that as an ad valorem tax, rates should be based on the regular revaluation of the tax base.

Buildings contribute significantly to CO₂ production. They are also subject to considerable taxation based on value. Analysis shows that while similar attributes contribute to both value and CO₂ production, there is only a loose relationship between the two. If we wish to use taxation to affect policy change (drive energy efficiency behaviour), we are unlikely to achieve this using only the current tax base (value), or by increasing the tax take off this current tax base (unlike extra taxation of cigarettes to discourage smoking, for example). Taxation of buildings on the basis of energy efficiency is hampered by the lack of current evidence of performance. This paper aims to model the now-obligatory (at sale or letting) energy performance certificate (EPC) data to derive an acceptable appraisal model (marked to market, being the EPC scores) and deploys this to the entire population of properties. This provides an alternative tax base with which to model the effects of a tax base switch to energy efficiency and to understand the tax incidence effects of such a policy.

The research uses a multiplicative hedonic approach to model energy efficiency utilising EPC holding properties in a UK jurisdiction [Northern Ireland (NI)] as the sample. This model is then used to estimate discrete energy assessments for each property in the wider population, using attributes held in the domestic rating (property tax) database for NI (700,000+ properties). This produces a robust estimate of the EPC for every property in its current condition and its cost-effective improved condition. This energy assessment based tax base is further used to estimate a new millage rate and property tax bill (green property tax) which is compared against the existing property tax based on value to allow tax incidence changes to be analysed.

The findings show that such a policy would significantly redistribute the tax burden and would have a variety of expected and some unexpected effects. The results indicate that while assessing the energy performance of houses can be a complex process involving many parameters, much of the explanatory power can be achieved via a relatively small number of input variables, often already held by property tax jurisdictions. This offers the opportunity for useful housing stock modelling – such as the savings possible from power switching. The research also identifies that whilst urban areas display the expected “heat island” effect in terms of energy consumption, urban properties are on average more efficient than suburban/rural properties. This facilitates spatial targeting of policy messages and initiatives.

Analogous with other studies, data deficiencies introduce the risk of omitted variable bias. Modelling of the energy efficiency in the sample is limited to property attributes that are available for the wider population of properties. While this limits the modelling exercise, it is a perennial issue facing mass appraisal worldwide (where knowledge of the transacted sample attributes generally exceeds knowledge of the unsold properties). That said, the research demonstrates the benefits of sharing data and improving knowledge of the housing stock, as taxation databases would be stronger, augmented with EPC-derived property attributes for example.

The EPC lead in time for wide residential coverage is likely to be considerable. The paper contributes to emerging literature and policy debate surrounding the effect, performance measurement and implementation of energy efficiency certification, through a greater understanding of the sectorial and geographical dispersion of energy efficiency. It provides high level research to help guide policy and decision-making, identifying key locales where there is more of a physical problem and locations where there is more to gain in terms of targeting energy improvement and/or encouraging behavioural change. The paper also allows a glimpse of the implications of a change towards a taxation regime based on energy efficiency, which contributes to the debate surrounding the “greening” of property based taxes.

Understanding the key locational and neighbourhood determinants and their accessibility is a topic of great interest to policymakers, planners and property valuers. In Northern Ireland, the high level of market segregation means that it is problematic to understand the nature of the relationship between house prices and the accessibility to services and prominent neighbourhood landmarks and amenities. Therefore, this paper aims to quantify and measure the (dis)amenity effects on house pricing levels within particular geographic housing sub-markets.

Most hedonic models are estimated using regression techniques which produce one coefficient for the entirety of the pricing distribution, culminating in a single marginal implicit price. This paper uses a quantile regression (QR) approach that provides a “more complete” depiction of the marginal impacts for different quantiles of the price distribution using sales data obtained from 3,780 house sales transactions within the Belfast Housing market over 2014.

The findings emerging from this research demonstrate that housing and market characteristics are valued differently across the quantile values and that conditional quantiles are asymmetrical. Pertinently, the findings demonstrate that ordinary least squares (OLS) coefficient estimates have a tendency to over or under specify the marginal mean conditional pricing effects because of their inability to adequately capture and comprehend the complex spatial relationships which exist across the pricing distribution.

Numerous studies have used OLS regression to measure the impact of key housing market externalities on house prices, providing a single estimate. This paper uses a QR approach to examine the impact of local amenities on house prices across the house price distribution.

Provides an outline of research seeking to apply to computer assisted mass appraisal (CAMA) model capable of use within a geographic information system (GIS). The end product will be a working GIS/valuation integrated model. The model, in an operational context, can be utilized for property taxation purposes, to facilitate the rating and revaluation of residential properties in Northern Ireland. As the value of land and property is a function of economic, legal, physical and locational factors, consequently access to comprehensive, reliable and up‐to‐date transaction evidence is a prerequisite to property valuation. Valuation techniques depend on the collection and analysis of relevant data. Historically, the application of these techniques took place within a non‐spatial environment. Ultimately, market data support any estimate of value. Data searches and collection can prove both time consuming and expensive in relation to the fee earning potential of a valuation report. GIS can facilitate, in a spatial and aspatial context, the storage, manipulation and analysis of data, in a fraction of the time previously required. Current techniques for the mass appraisal of property, and for the prediction of residential property values, can be enhanced by utilizing the data handling capacity of GIS. Integration of a mass appraisal model within a GIS will add value to the valuation process.

This paper seeks to examine the potential for simplified market value and non market value based banded approaches to be utilised for residential property tax purposes. The broad aim is to ascertain whether relatively low complexity approaches to establishing a property tax base can perform adequately in comparison to established best practice – in essence whether there is evidence of equifinality (equivalent performance from approaches of substantially different complexity) between simpler and more complex approaches.

The research comprises empirical analysis of a database of property sales and property attribute data drawn from a UK District Council area. Several simplified methods are used to create different tax base scenarios and the outflowing tax incidence is compared with that of using a complex, industry standard market value approach. The methods of comparison are regression and spline regression based models testing for tax inequity, drawn from the literature. The approach here differs from previous work in that it occurs at the actual tax bill level allowing the comparison of value, non‐value and banded approaches.

The findings of the research indicate that simplified approaches to establishing a property tax base can perform in a broadly similar fashion to more complex systems currently practiced in developed economies and therefore evidence of equifinality exists.

The research provides useful tools to property tax policy makers and practitioners in developing and transitional economies in furthering their aspirations of embedding robust property taxes for the furtherance of socio‐economic and political development and the general wellbeing of society and they are of value to property tax policy makers and to academics in the field.

The paper provides evidence of the efficacy of simplified and banded approaches as an option for jurisdictions in developing and transitional economic circumstances or elsewhere in circumstances which mitigate against full scale appraisal of the property tax base to discrete market values. The approaches and techniques pioneered open up opportunities to carry out a range of new comparative analysis of tax base options.

The purpose of this paper is to apply boosted regression trees (BRT) to a heterogeneous data set of residential property drawn from a jurisdiction in Malaysia, with the objective to evaluate its application within the mass appraisal environment in Malaysia. Machine learning (ML) techniques have been applied to real estate mass appraisal with varying degrees of success.

To evaluate the performance of the BRT model two multiple regression analysis (MRA) models have been specified (linear and non-linear). One of the weaknesses of traditional regression is the need to a priori specify the functional form of the model and to ensure that all non-linearities have been accounted for. For a BRT model the algorithm does not require any predetermined model or variable transformations, making the process much simpler.

The results show that the BRT model outperformed the MRA-specified models in terms of the coefficient of dispersion and mean absolute percentage error. While the results are encouraging, BRT models still lack transparency and suffer from the inability to translate variable importance into quantifiable variable effects.

This paper presents a useful alternative modelling technique, BRT, for use within the mass appraisal environment in Malaysia. Its advantages include less intensive data cleansing, no requirement to specify the predictive underlying model, ability to utilise categorical variables without the need to transform them and not as data hungry, as for example, MRA.

This paper adds to the knowledge in this area by applying a relatively new ML model, BRT to residential property data from a jurisdiction in Malaysia. BRT has shown promise as a strong predictive model when applied in other disciplines; therefore this research empirically tests this finding within real estate valuation.

The aim of this paper is to attempt to measure the effect of location on residential house prices and to endeavour to integrate spatial and aspatial data in terms of developing a hybrid predictive model. The research methodology investigates the traditional hedonic approach to modelling location using multiple regression techniques. Alternative approaches are considered which specifically model the spatial distribution of house prices with the objective of developing location adjustment factors. These approaches are based on the development of surface response techniques such as inverse distance weighting and universal kriging. The results generated from the surfaces created are then calibrated within MRA.

The aim of this paper is to investigate the comparative performance of an artificial neural network (ANN) and several multiple regression techniques in terms of their predictive accuracy and capability of being used within the mass appraisal industry.

The methodology first tested that the data set had neglected non‐linearity which suggested that a non‐linear modelling technique should be applied. Given the capability of ANNs to model non‐linear data, this technique was used along with an OLS regression model (baseline model) and two non‐linear multiple regression techniques. In addition, the models were evaluated in terms of predictive accuracy and their capability of use within the mass appraisal environment.

Previous studies which have compared the predictive performance of an ANN model against multiple regression techniques are inconclusive. Having superior predictive capability is important but equally important is whether the technique can be successfully employed for the mass appraisal of residential property. This research found that a non‐linear regression model had higher predictive accuracy than the ANN. Also the output of the ANN was not sufficiently transparent to provide an unambiguous appraisal model upon which predicted values could be defended against objections.

The research provides an informative view as to the efficacy of ANN methodology within the real estate field. A number of issues have been raised on the applicability of ANN models within the mass appraisal environment.

This work demonstrates that ANNs whilst useful as a predictive tool have a limited practical role for the assessment of residential property values for property tax purposes.

The work has taken forward the debate on the usefulness of ANN techniques within the mass appraisal environment.

Since 1993 the UK has used a “banded” property tax as opposed to discrete values for the assessment of residential property. Explains both the advantages and disadvantages of the system. In addition, summarises the main results of empirical research into the use of banded property values which have been unaltered for ten years. In summary, aims to present findings on the continued operation of this unique system, highlighting strengths and weaknesses and its viability/applicability in other countries and jurisdictions in the light of empirical evidence based on the analysis of open market transactions. Discusses both the assessment and administration process and, with the analysis of sales data, demonstrates the importance of regular and frequent revaluations of the tax base in order to ensure a reasonable level of both vertical and horizontal equity. Speculates on the potential application of a banded system of property values in other countries, in the light of the advantages of the banded system which could lend themselves to jurisdictions where an ad valorem system of land taxation is inappropriate; where resources are limited in terms of experienced valuers, or where the availability of technology to undertake mass appraisal would provide added advantages. Concludes by drawing together recommendations in relation to how the system in the UK can be improved and makes recommendations for policy‐makers in other jurisdictions.