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This paper aims to deal with the sale of the bare ownership subject to the lifetime usufruct to the seller as an alternative channel for housing investments in Italian cities. The first aim of the study is to understand in greater detail the bare ownership market while also stimulating the interest of researchers in this segment of the housing market that up until now has been marginally analyzed so far. The second aim is to make the estimation of the bare ownership easier and more reliable for market investors and institutions. The third aim is to quantify on the markets investigated in this paper, the “actual” appraisal coefficients for the usufructuary presence, as well as compare these coefficients with the ones set for tax purposes to highlight either the concordance or discordance.

A hedonic price model is developed, with which it is possible to evaluate the market price of the bare ownership as a function of the determinants of value in the market segments in question, in particular the life expectancy of the usufructuary.

Through the hedonic models defined in this work, it is possible to appraise the market value of the bare ownership of residential flats in a building located in the same districts overcoming the limitations of the procedures currently used. Comparing the appraisal coefficients for the usufructuary presence estimated through the hedonic models defined in this work, with the legally defined coefficients applied countrywide for tax purpose, it is possible to conclude that the latter markedly underestimate the market value of bare ownership, thereby leading to fiscal iniquities and substantial social costs.

It has not been possible to know the gender of the usufructuary. Rather limited size of the apartment sales samples.

The bare ownership market is currently an important sector that deserves greater attention from the institutions, market investors and real estate research. The model developed allows to estimate the “actual” coefficients for the determination of the market value of the bare ownership of residential units.

There are currently no studies nor descriptive investigations relating to either the bare ownership market segments or recent cases of estimates. The present study is the first in current literature that both systematically deals with the quantification of the “actual” appraisal coefficients for the usufructuary presence, as well as compares these coefficients with the ones set for tax purposes.

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 study is to enhance the estimation of vertical and horizontal inequity within property valuation. Property taxation is a crucial source of finance for local government around the world – based on a presumptive tax base underpinned by estimates of property value, inaccurate real estate valuations used for such ad valorem or value-based property tax calculations potentially lead to a variety of costs, both financial and other, for tax payers and governments alike. More common are increased costs in time, staff and, in some cases, legal fees. Some governments are even bound by acceptability thresholds to promote fairness, equitability and overall government accountability with respect to valuation.

There exist a number of vertical inequity measurements that have undergone academic testing and scrutiny within the property tax industry since the 1970s. While these approaches have proved successful in detecting horizontal and vertical inequity, one recurring disadvantage pertains to measurement error/omitted variable bias, stemming largely from a failure to accurately account for location. A natural progression within property tax research is the application of a more spatially local weighted modelling approach to examine vertical and horizontal inequity. This research, therefore, specifies a geographically weighted regression (GWR) methodology to detect and measure vertical inequity in property valuations.

The findings show the efficacy of using more applied spatial approaches for vertical tax estimation and indeed the limitations of employing conditional mean estimates coupled with delineated boundaries for assessing property tax inequity. The GWR model findings highlight the more fluctuating nature of vertical inequity across the Belfast market for the apartment sector both in a progressive and regressive sense and at different magnitudes. Moreover, the results reveal spatial clustering in the effects and are indicative of systematic inequities related to location inferring that spatial (horizontal) tax inequities are not random. The findings further show increased GWR model predictability overall.

This research adds to the existing literature base for evaluating both vertical and horizontal inequity in value-based property taxation at the intra-neighbourhood level. This is accomplished by modifying the Birch–Sunderman approach by transforming the traditional OLS model architecture to a GWR model, thereby allowing coefficient estimates of inequity to vary not only across a jurisdiction, but also at a more local level, while incorporating property characteristic variables. This arguably allows assessors to identify specific geographical areas of concern, saving them money, time and resources on identifying, addressing and correcting for inequity.

An appraisal by an accredited appraiser is often necessary before a proposed development can receive a commitment for financing from a financial institution. Such pre‐construction appraisals are often more difficult to complete than those where the subject property is already developed and accessible for inspection. In this article we are performing a valuation of a proposed townhouse complex prior to its construction. Hedonic estimation is used to estimate the market value of the proposed development. The estimated value of the complex as a whole, and of the individual units, is compared to the prices obtained as the townhouses were built and sold. Insights with respect to the feasibility of the approach for the estimation of the gross retail value of proposed developments are discussed.

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 purpose of this study is to investigate whether increases in homeowner green amenities occurred because of income tax credits to the degree that changes in housing prices are measurable. Are higher incomes, lower mortgage rates and green income-tax credits impacting housing price changes?

The paper uses the least-squares regression model with natural log specifications. The log of income and a dummy variable, which was assigned to the Energy Policy Act (2005) and the American Recovery and Reinvestment Act (2009) coverage dates are used as independent variables. Two regression models were examined using monthly housing price data from January 1990 through the year 2018. The first regression model used a single dummy variable for credits available under the Policy Act of 2005 and the Recovery Act of 2009. The second regression model considered the credits granted under these two laws separately. Disposable income per capita impacts demands for housing while green upgrade expenditures affect the cost of housing.

The laws set low credit limits of $500 followed by $1,500 but because of the multiplier effect, the spending appears to have magnified and been much higher. The credit availability variables have positive coefficients and were significant at 1 per cent. This implies that single-family housing prices were sensitive to the existence of residential energy property income-tax credits. The R² results were 0.93 or above for both models.

The data used was aggregated and publicly available online. Many studies use aggregated macroeconomic data when modeling housing prices using the exogenous variable of disposable income but there is no substitute for examining individual homes by location and their sales price to see under what conditions green income-tax credits have the most impact. There could be demographic issues that are missed when using aggregated information.

Spending on heating/cooling systems, dual pane windows and other green amenities keeps the housing stock modernized and housing prices steady or rising. An additional benefit is that spending motivated by self-interest can simulate household consumption spending. Houses deteriorate due to wear and tear. Physical-repairable depreciation represents a situation where maintenance funds are continuously needing to be spent. Repairs and upgrades to the structure of the property keep its price stable by stopping the physical depreciation that would otherwise occur with the passage of time.

The paper provides support for the idea that residential green amenity upgrades positively impact the value of a house. These green-amenity upgrades, which other research studies have suggested should be included explicitly in the appraisal process, are a major characteristic of a property when a price estimate is being done. Housing being sold should have a section on the information sheet noting the property green upgrades that exist and an energy efficiency score should be assigned to each house listed for sale.

There are few (if any) academic research papers studying the impact of green tax credits available under the Energy Policy Act (2005) and under the American Recovery and Reinvestment Act (2009). The degree to which green income-tax credits stimulate spending on housing has not been addressed by researchers. This paper is an initial research attempt to quantify whether these legislative efforts measurably encouraged homeowners to adopt newer, greener technologies.

The purpose of this paper is to describe a segmentation technique based on geostatistical modeling methods utilizing geographically weighted regression (GWR) to identify submarkets which could be applied within the mass appraisal environment.

Given the spatial dimension within which neighbourhoods/submarkets exist, this paper has sought to utilize the geostatistical technique of GWR to identify them.

The efficacy of the procedure is established by demonstrating improvements in predictive accuracy of the resultant segmented market models as compared to a baseline global unsegmented model for each of the study areas. Optimal number of segments is obtained by measures of predictive accuracy, spatial autocorrelation in the residual errors and the Akaike information criterion.

The three datasets used allowed for an evaluation of the robustness of the method. Nonetheless it would be beneficial to test it on other datasets, particularly from different regions of the world.

Many researchers and mass appraisal practitioners have established the benefit of segmenting a study area into two or more submarkets as a means of incorporating the effects of location within mass valuation models. This approach develops the existing knowledge.

The research ultimately is developing more accurate valuation models upon which the property tax is based. This should create an environment of fair and acceptable assessed values by the tax paying community.

The contribution of this work lies in the methodological approach adopted which incorporates a market basket approach developed through a process of GWR. The importance of the research findings illustrate that submarket segmentation need no longer be an arbitrary process.

Hybrid systems as the next generation of intelligent applications within the field of mass appraisal and valuation are investigated. Motivated by the obvious limitations of paradigms that are being used in isolation or as stand‐alone techniques such as multiple regression analysis, artificial neural networks and expert systems. Clearly, there are distinct advantages in integrating two or more information processing systems that would address some of the discrete problems of individual techniques. Examines first, the strategic development of mass appraisal approaches which have traditionally been based on “stand‐alone” techniques; second, the potential application of an intelligent hybrid system. Highlights possible solutions by investigating various hybrid systems that may be developed incorporating a nearest neighbour algorithm (k‐NN). The enhancements are aimed at two major deficiencies in traditional distance metrics; user dependence for attribute weights and biases in the distance metric towards matching categorical variables in the retrieval of neighbours. Solutions include statistical techniques: mean, coefficient of variation and significant mean. Data mining paradigms based on a loosely coupled neural network or alternatively a tight coupling with genetic algorithms are used to discover attribute weights. The hybrid architectures developed are applied to a property data set and their performance measured based on their predictive value as well as perspicuity. Concludes by considering the application and the relevance of these techniques within the field of computer assisted mass appraisal.

Sees the objective of teaching financial management to be to help managers and potential managers to make sensible investment and financing decisions. Acknowledges that financial theory teaches that investment and financing decisions should be based on cash flow and risk. Provides information on payback period; return on capital employed, earnings per share effect, working capital, profit planning, standard costing, financial statement planning and ratio analysis. Seeks to combine the practical rules of thumb of the traditionalists with the ideas of the financial theorists to form a balanced approach to practical financial management for MBA students, financial managers and undergraduates.

The purpose of this paper is to examine the usefulness of a heuristic expert system, to show its applicability to real‐world valuation problems, and to suggest several avenues for statistical testing.

The expert systems follow a traditional sales adjustment grid format, with sufficient data for non‐parametric testing.

The paper finds that, while non‐parametric statistics provide weaker results than traditional (e.g. hedonic regression) modeling, the technique provides a statistically testable model useful in situations with limited data and/or poorly characterized probability functions.

This paper addresses the conundrum faced by real estate valuers on the lack of statistical underpinnings of traditional heuristic models.

This is one of the first empirical studies in the valuation literature exploring statistical characterization of heuristic valuation methods.