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The paper aims to describe, construct, and compare alternative price indices for real estate in Malta over the period 1980‐2010.

The paper utilises the technique of hedonic regression analysis to construct four hedonic price indices. One of the constructed indices is based the unconstrained hedonic methodology. Two other indices are variants of the constrained hedonic technique, while the fourth consists of an imputed hedonic index. The hedonic indices are then compared to other 12 conventional indices, namely the Laspeyres, Paasche and Fisher indices (constant weight and chain linked) that are constructed by utilizing the mean and median house prices pertaining to 14 different types of houses.

All indices are found to move closely together, growing between six and seven times between 1980 and 2010. The average annual compound growth rate of the 16 indices was found to be 6.5126 percent. The paper also shows how the estimated hedonic coefficients can be used to construct regional price indices for different combinations of housing characteristics.

The paper builds on previous work related to house prices in Malta. Its main contribution is the construction of hedonic indices that are based on advertised prices that span over a relatively long period of 31 years, together with the construction of constant weight and chain linked Laspeyres, Paasche and Fisher indices.

The purpose of this study is to examine the relationship and pricing effects of physical infrastructure on house rents using the hedonic technique.

Primary data are derived through a questionnaire survey and secondary data from existing literature. Sampling data on 211 detached residential buildings with a range of physical infrastructure attributes within Lekki Phase 1 area of Lagos are analysed with the hedonic regression technique.

Results reveal significant impacts and a range of price premium estimates of physical infrastructure on house rents in the study area.

The study suggests a nouvelle and contextualized approach for sustainable infrastructure delivery, improvement and maintenance. Appropriate pricing will help to guide and support physical infrastructure development and sustainability. When tailored in line with market support, achievable pricing can be attained in setting land-based user charges and tariffs for cost recovery on projected developments and reform. Results from empirical market evidence also provide demand and viability indicators that offer invaluable blueprints, by which governments, policy/decision makers, investors, town-planning authorities and other stakeholders can take sustainable decisions based on priority, in the face of budgetary constraints – a significant characteristic of the Nigerian economy.

The primary objective of this study is to apply hedonic regression techniques to an office market to identify and quantify the significant contribution of the different attributes to office rents. This technique is widely used in the analysis of housing markets but an extensive literature review reveals little application in commercial property markets. The study analyses a sample of 477 asking rents, together with a series of locational and physical attributes, for the City of Glasgow. The results explain approximately 60 per cent of variation in rents across the city, emphasizing the importance of age and location as principal determinants of rents.

The purpose of this paper is to synthesise the plethora of research that has been conducted into the relationship between sustainability and market value in real estate, by critically analysing the research and the applicability of sustainability and value research in valuation practice.

The research on the relationship between sustainability and market value in real estate is examined from the perspective of its usefulness to the valuation profession in providing guidance, information and evidence to be used in valuation practice.

Existing research conducted into the relationship between sustainability and market value has not provided the valuation profession with evidence which would allow the incorporation of normative theories on the value of sustainability in valuation practice. This review highlights the lack of evidence, and the applicability of current research into sustainability and value to the valuation profession in providing guidance and information in valuing real estate incorporating sustainability.

This paper highlights the limited applicability of research to date in regard to the relationship between sustainability and market value for the valuation profession. The lack of historical evidence, data or information on the quantifiable effects on market value of this new trend (sustainability), leaves the valuation profession uncertain as to the relationship between sustainability and market value. There is a probable risk of valuers interpreting strategic research incorrectly, and making inappropriate adjustments or comparisons because of their lack of knowledge and limited sustainability assessment skills. Although there is an evolving body of knowledge, there is a need for extensive analysis of unbiased, evidence‐based research in individual and broader markets to provide guidance, evidence and knowledge of the implications of sustainability in the valuation of real estate.

The examination of research investigating the relationship between sustainability and value from a valuation perspective provides an alternative insight into the applicability of current research in valuation practice. The increasing profile and role of sustainability in the real estate sector needs to be addressed in valuation practice; however, the variety of research to date needs to be interpreted by valuers in the correct context. This paper brings to light the applicability of sustainability and value research for the broader valuation profession, and the potential implications of misuse or misunderstanding of that research.

Compares the predictive performance of artificial neural networks to hedonic pricing models, a more traditional valuation tool. The results document similar predictive performance evidenced from both techniques, which contradicts some of the earlier studies which support a position of artificial neural network superiority. Demonstrates that at least 18 per cent of the “normal” property predictions and over 70 per cent of the “outlier” property predictions contained valuation errors greater than 15 per cent of the actual sales price. The combination of these substantial errors and the model‐optimization costs incurred motivate a message of caution before artificial neural networks are adopted by the real estate valuation and/or lending industries.

The purpose of this paper is to demonstrate the application of robust techniques to the estimation of hedonic house price indexes.

The authors use simulation analysis to compare an index estimated using ordinary least squares (OLS) with several indexes estimated using robust techniques. The analysis uses sales transactions data from a US city. The authors then explore how robust methods can correct for omitted variables under some circumstances and how they affect the revision problem that occurs when longitudinal hedonic indexes are updated.

Robust methods can resolve missing variable problems in some circumstances and also can substantially reduce the revision problem in longitudinal hedonic indexes.

Robust techniques may be preferable to OLS when constructing longitudinal hedonic indexes.

This is the first paper to undertake a systematic analysis of the applicability of robust techniques in constructing hedonic house price indexes.

Focusing on managerial problems related to the measurement of intangibles, this paper develops and validates a hedonic-pricing methodology for the evaluation of the intangible resources of companies obtaining their shadow prices.

The paper adapts a hedonic-pricing methodology developed primarily for markets in real estate and secondhand cars to define how much intangibles may contribute to companies' market value. A certain calibration of the original tool has been developed to make this methodology appropriate for interpretation and practical use. The main advantage of this approach is that it allows for an evaluation of the shadow prices of intangible resources. These prices can be interpreted as the market value of the intangible resources which are not reflected on the balance sheet.

The results of this study demonstrate that hedonic pricing with a self-selection correction generates robust estimates. As one can see, the positive contribution of a high endowment of intangibles for all shadow prices is confirmed through estimations using two different techniques. Meanwhile, the negative effect of a low endowment is even more evident for the baseline model. This model shows consistent negative shadow prices for the majority of underinvested intangibles. Brands have the highest shadow prices in the introduced models; human capital, as measured by the qualification of top management and investments in employees, has likewise demonstrated high prices. However, most structural resources seem to be not reflected to a large degree in companies' market value.

This paper brings new opportunities to obtain the monetary value of intangible resources based on estimated market prices of a corporation's resource portfolio. These prices may be used for several purposes – for example, benchmarking for performance management, capital budgeting or knowledge-management practices. Moreover, by having methodological value, this study opens ways to evaluate any other intangibles which are not explicitly discussed in the empirical test of this particular study.

This study primarily contributes to the methodological advancement of evaluation of corporate intangible resources. It departs from the conventional hedonic-pricing mechanism to identify cogent estimates to intangibles in monetary terms. Importantly, this mechanism implies individual shadow prices for specific intangible resources which makes the contribution of this study unique for the existing literature, both within resource-based and value-based views.

The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.”

Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016).

While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%).

The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices.

The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence.

To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

The basic purpose of this paper is to explore the relationship between the sustainability of construction on the one hand and market value, worth and property investment performance on the other hand. This paper aims to analyse price movements and price differences caused by different property characteristics.

Based on the estimated log‐linear hedonic regression model, a hedonic price index is calculated. Price movements subject to different property characteristics are examined by constructing various conditional hedonic price indexes.

The results reveal that, high‐quality flats or flats within preferred locations clearly outperform their competitors in terms of price stability during an overall market downturn. However, it is also shown that contemporary building descriptions or specifications of transactions within property databases are not yet sufficient and need to be widened to meet forthcoming challenges. Therefore, an “integrated building performance approach” is introduced and a proposal for the step‐wise improvement of building descriptions is made.

The paper shows that efforts need to be undertaken by the property profession in combining and transferring financial performance data along with information that is indicative of a building's contribution to sustainable development.

The paper offers insights into the relationship between the sustainability of construction and market value.

This paper examines commonly used property price indices in several Commonwealth countries. It finds that many of the measures may be flawed owing to two issues relating to the index construction methodology: the quality change problem and the choice of an index number algorithm. Using data that comprises the universe of transactions for the Singapore residential market, alternative indices based on more rigourous estimation models are constructed that aim to mitigate these problems. When compared to the official benchmark indices, deviations in time series price behaviour are evident particularly for short‐run dynamics. A key implication of the results is the importance of explicitly recognizing the biases that can arise from using extant indices. Otherwise, a reliance on flawed index signals for decision‐making may result in distorted allocations.