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The purpose of this paper is to test the accuracy of the explicit time variable (ETV) and the strictly cross-sectional (SCS) hedonic models when constructing house price indices in developing markets using Ghana as a case study.

The quantitative research methodology is adopted where the accuracy of the two hedonic models used in the construction of house price indices is examined using the mean squared error (MSE) and out-of-sample technique. Yearly indices are constructed for each of the models using 60 per cent of the sample data and 40 per cent is used to forecast house prices for each observations based on which the MSEs are calculated.

The two models produce similar house price trend but the SCS model is more volatile. The ETV model produces the lower MSE, suggesting that it is better to pool data together and includes time dummies (ETV) to estimate indices rather than running separate regressions (SCS) to estimate the index. Using the Morgan–Granger–Newbold test, it is found that indeed the difference between the forecast errors of the two models are statistically significant on a 1 per cent level confirming the accuracy of the ETV model over the SCS model.

This paper has produced convincing results recommending the use of the ETV hedonic model to construct house price indices which is of use to practitioners and academics.

The introduction of financial products like the property derivatives and home equity insurances to the financial market calls for accurate and robust property price indices and the hedonic method is mostly used to construct these indices. While there have been a lot of test conducted as to which variant of the hedonic method to use in developed markets, little is known about the developing markets. This paper contributes to fill these gaps.

The aim of this paper is to present estimation results of hedonic price models as well as housing price indices for the Warsaw secondary market.

Three direct methods of constructing a hedonic price index and four indices that allow for quality adjustment are presented. The paper also discusses theoretical issues related to the estimation and interpretation of hedonic models.

It is shown that the imputation and the time dummy variable indices are subject to less variation than the characteristic price index. It is also shown that in comparison to the mean and the median, hedonic indices are less variable, which can be interpreted as partial control for quality changes in dwellings sold.

As this research project represents one of the first attempts of hedonic modelling applied to the Polish housing market, its results may be employed by appraisers to gain insight into behaviour of the Warsaw housing market. Practical implications focus on reliable measurement of house price dynamics in Poland. This paper supplies an appropriate methodology for addressing this question and offers empirical solutions.

Employment of hedonic models for construction of quality‐adjusted housing price indices has not yet been explored in Poland. The theoretical and practical aspects of hedonic indices presented in the paper open promising directions for the development of Polish statistics of real estate prices.

This study aims to estimate the quality-adjusted changes in residential mobile consumer prices by controlling for the changes in the relevant service characteristics and quality, in a case study on Hungary between 2015 and 2021; compare the results with changes measured by the traditionally calculated official telecommunications price index of the Statistical Office; and discuss separating the hedonic price changes from the effect of a specific government intervention that occurred in Hungary, namely, the significant reduction in the value added tax rate (VAT) levied on internet services.

Since the price of commercial mobile offers does not directly reflect the continuous improvements in service characteristics and functionalities over time, the price changes need to be adjusted for changes in quality. The authors use hedonic regression analysis to address this issue.

The results show significant hedonic price changes over the observed seven-year period of over 30%, which turns out to be primarily driven by the significant developments in the comprising service characteristics and not the VAT policy change.

This paper contributes to the literature on hedonic price analyses on complex telecommunications service plans and enhances this methodology by using weights and analysing the content-related features of the mobile packages.

The purpose of this paper is to provide an integrated approach that combines the two methods usually used in the real estate appraisals, namely, the income capitalisation method and the hedonic model.

In order to pull out the link between the income capitalisation approach and the hedonic model, the standard hedonic price function is introduced into the basic model of income capitalisation instead of the house market value. It follows that, from the partial derivative, a direct relation between hedonic prices and discount rate can be obtained. Finally, by using the close relationship between income capitalisation and direct capitalisation, a mathematical relation between hedonic prices and capitalisation rate is also obtained.

The developed method allows to estimate the capitalisation rate using only hedonic prices. Indeed, selling and hedonic prices incorporate all of the information required to correctly estimate the capitalisation rate. Furthermore, given the close relation among going-in and going-out capitalisation rates and discount rate, the proposed method could also be useful for determining both the going-out capitalisation rate and the discount rate.

Obviously, it is always preferable to estimate the capitalisation rate by just using comparable transactional data. Nevertheless, the method developed in this paper is especially useful when: the rental income data are missing and/or not entirely reliable; the data on rental income and house price are related to different homes; the capitalisation rate, in fact, should compare the rent and value of identical homes. In these cases, therefore, the method can be a valuable alternative to direct estimation.

The large and important literature on real estate economics and real estate appraisal neglects the relationship between hedonic prices and capitalisation rate, thus considering the hedonic model and the income capitalisation approach as two separate and alternative methods. This paper, instead, shows that integration is possible and relatively simple.

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 aim of this education briefing is to comment upon how basic hedonic pricing models for the valuation of property can be expanded and developed. In this case, the briefing illustrates the use of the new economic approach to the analysis of housing markets, namely the search-and-matching models.

This education briefing discusses the connection of two important economic theories: the hedonic price theory and the search-and-matching theory.

This education briefing gives an example of a (non-linear) form of the hedonic price function.

In cases of mass appraisals, hedonic pricing models can provide a broad indication of value across submarkets and this education briefing demonstrates a theoretical model that can be used to provide a theoretical groundwork for the use of a concave hedonic price function in empirical estimates.

This education briefing shows how basic hedonic pricing models can be enhanced by a search-and-matching approach to determine property values.

An economic indicator faces two requirements. It should be reported in a timely manner and should not be significantly altered afterward to avoid erroneous messages. At the same time, it should reflect changing market conditions constantly and appropriately. These requirements are particularly challenging for housing price indices, since housing markets are subject to large temporal/seasonal changes and occasional structural changes. The purpose of this paper is to estimate a hedonic price index of condominiums of Tokyo, taking account of seasonal sample selection biases and structural changes in a way it enables us to report the index in a manner which is timely and not subject to change after reporting.

The paper proposes an overlapping‐period hedonic model (OPHM), in which a hedonic price index is calculated every month based on data in the “window” of a year ending this month (this month and previous 11 months). It also estimates standard hedonic housing price indexes under alternative assumptions: no structural change (“structurally restricted”: restricted hedonic model) and different structure for every month (“structurally unrestricted”: unrestricted hedonic model).

Results suggest that the structure of the housing market, including seasonality, changes over time, and these changes occur continuously over time. It is also demonstrated that structurally restricted indices that do not account for structural changes involve a large time lag compared with indices that do account for structural changes during periods with significant price fluctuations.

Following the financial crisis triggered by the US housing market, housing price index guidelines are currently being developed, with the United Nations, International Monetary Fund, and Organization for Economic Co‐operation and Development leading the way. These guidelines recommend that indices be estimated based on the hedonic method. We believe that the hedonic method proposed here will serve as a reference for countries that develop hedonic method‐based housing price indices in future.

In the many studies involving conventional housing price indices, whether those using the repeat‐sales method or hedonic method, there are few that have analyzed the problem of market structural changes. This paper is the first to construct a large database and systematically estimate the effect that changes in market structure have on housing price indices.

The purpose of this article, starting from linear regression, was to estimate a switching regression model, nonparametric model and generalized additive model as a semi-parametric model, perform function estimation with multiple nonlinear estimation methods and conduct comparative analysis of their predictive accuracy. The theoretical importance of estimating hedonic functions using a nonlinear function form has been pointed out in ample previous research (e.g. Heckman et al. (2010).

The distinctive features of this study include not only our estimation of multiple nonlinear model function forms but also the method of verifying predictive accuracy. Using out-of-sample testing, we predicted and verified predictive accuracy by performing random sampling 500 times without replacement for 9,682 data items (the same number used in model estimation), based on data for the years before and after the year used for model estimation.

As a result of estimating multiple models, we believe that when it comes to hedonic function estimation, nonlinear models are superior based on the strength of predictive accuracy viewed in statistical terms and on graphic comparisons. However, when we examined predictive accuracy using out-of-sample testing, we found that the predictive accuracy was inferior to linear models for all nonlinear models.

In terms of the reason why the predictive accuracy was inferior, it is possible that there was an overfitting in the function estimation. Because this research was conducted for a specific period of time, it needs to be developed by expanding it to multiple periods over which the market fluctuates dynamically and conducting further analysis.

Many studies compare predictive accuracy by separating the estimation model and verification model using data at the same point in time. However, when attempting practical application for auto-appraisal systems and the like, it is necessary to estimate a model using past data and make predictions with respect to current transactions. It is possible to apply this study to auto-appraisal systems.

It is recognized that housing price fluctuations caused by the subprime crisis had a massive impact on the financial system. The findings of this study are expected to serve as a tool for measuring housing price fluctuation risks in the financial system.

While the importance of nonlinear estimation when estimating hedonic functions has been pointed out in theoretical terms, there is a noticeable lag when it comes to testing based on actual data. Given this, we believe that our verification of nonlinear estimation’s validity using multiple nonlinear models is significant not just from an academic perspective – it may also have practical applications.

This study aims to examine the impact of relative importance of local characteristics, distance from the city centre and unobservable spatial relation in explaining values of constant‐quality apartment units in Vienna.

Drawing on recent developments in spatial econometrics and spatial hedonic house price modelling, the rent gradient hypothesis is examined by means of hedonic regression and spatial hedonic regression. Spatial autocorrelation tests are applied in order to assess possible presence of spatial dependence. The authors borrow Florax et al.'s specification search strategy in order to choose the most appropriate spatial model specification.

This research shows that local characteristics – or particularities – proxied by district and distance from the city centre are important location variables with regard to the Viennese apartment market. The spatial analysis suggests that the apartment prices are spatially autocorrelated and the Viennese apartment market has a distance‐based neighbourhood structure. The main finding is, however, that residents are willing to bid more for constant‐quality apartment units that are close to the centre of the city.

Rent gradient hypothesis is usually tested within non‐spatial hedonic frameworks: this study estimates a spatial hedonic model additionally in order to allow for comparison of results. This is also the first article to apply recent developments in spatial econometrics to examine explicitly rent gradient theory in the context of the Viennese apartment market.

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.