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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.

This paper aims to formulate a hedonic pricing model for Japanese rice wine, sake, via hierarchical Bayesian modeling estimated using an efficient Markov chain Monte Carlo (MCMC) method. Using the estimated model, the authors examine how producing regions, rice breeds and taste characteristics affect sake prices.

The datasets in the estimation consist of cross-sectional observations of 403 sake brands, which include sake prices, taste indicators, premium categories, rice breeds and regional dummy variables. Data were retrieved from Rakuten, Japan’s largest online shopping site. The authors used the Bayesian estimation of the hedonic pricing model and used an ancillarity–sufficiency interweaving strategy to improve the sampling efficiency of MCMC.

The estimation results indicate that Japanese consumers value sweeter sake more, and the price of sake reflects the cost of rice preprocessing only for the most-expensive category of sake. No distinctive differences were identified among rice breeds or producing regions in the hedonic pricing model.

To the best of the authors’ knowledge, this study is the first to estimate a hedonic pricing model of sake, despite the rich literature on alcoholic beverages. The findings may contribute new insights into consumer preference and proper pricing for sake breweries and distributors venturing into the e-commerce market.

Real estate is one of the fundamental growth engines for developing economies as it contributes urbanization and infrastructure development. In recent years, Somalia has witnessed massive real estate development in both housing and commercial buildings. The purpose of this study is twofold. First, the study examines the determinants of residential property rents. Second, it investigates whether residential property rents are fairly valued.

This study uses two-stage modeling. A hedonic regression model is used in the first stage, while an artificial neural network is applied in the second stage.

After analysis, this study established that size, location and security of a residential property have a significant influence on its monthly rents. Alternatively, the study identified that residential property rents are not fairly valued in Mogadishu and overvaluation is more frequent than undervaluation.

This implies that Somalia’s real estate industry is more speculative-driven than real demand-driven. Though Somali real estate is an infant industry with huge potentials in the long run, it may end up disastrously following the well-known bubble-then-burst behavior. To avoid such crisis, this study recommends formulating government policies that regulates, supervises and protects the infant real estate industry without undermining the needs of the poor and low-income citizens.

The purpose of this paper is to test the asymptotic properties and prediction accuracy of two innovative methods proposed along the hedonic debate: the geographically weighted regression (GWR) and the generalized additive model (GAM).

The authors assess the asymptotic properties of linear, spatial and non-linear hedonic models based on a very large data set in Germany. The employed functional form is based on the OLS, GWR and the GAM, while the estimation methodology was chosen to be iterative in forecasting, the fitted rents for each quarter based on their 1-quarter-prior functional form. The performance accuracy is measured by traditional indicators such as the error variance and the mean squared (percentage) error.

The results provide evidence for a clear disadvantage of the GWR model in out-of-sample forecasts. There exists a strong out-of-sample discrepancy between the GWR and the GAM models, whereas the simplicity of the OLS approach is not substantially outperformed by the GAM approach.

For policymakers, a more accurate knowledge on market dynamics via hedonic models leads to a more precise market control and to a better understanding of the local factors affecting current and future rents. For institutional researchers, instead, the findings are essential and might be used as a guide when valuing residential portfolios and forecasting cashflows. Even though this study analyses residential real estate, the results should be of interest to all forms of real estate investments.

Sample size is essential when deriving the asymptotic properties of hedonic models. Whit this study covering more than 570,000 observations, this study constitutes – to the authors’ knowledge – one of the largest data sets used for spatial real estate analysis.

The housing sector is one of the main sources of economic growth in both developing and developed countries. Although many methods for modeling house prices have been proposed, each has its own limitations. The present paper aims to propose gene expression programming (GEP) as a new approach for prediction of housing price.

This study introduces gene expression programming (GEP) as a new approach for predicting housing price. This is the first time that this metaheuristic method is used in the housing literature.

The housing price model based on the gene expression programming is compared with a least square regression model that is derived from a stepwise process. The results indicate that the GEP‐based model provides superior performance to the traditional regression.

Data used in this study is derived from the Household Income and Expenditure Survey (HIES) in Iran that is conducted by the Statistical Center of Iran (SCI). Housing price model is estimated by administering the questionnaires of this survey in Hamedan Province. To show the applicability of the derived model by GEP technique, it is verified applying parts of the data, namely test data sets that were not included in the modeling process.

This study aims to show how hedonic methods can be used to compare the performance of the public and private sector housing markets in Costa Rica.

Hedonic price indexes are computed using the adjacent-period method. Average housing quality is measured by comparing hedonic and median price indexes. The relative performance of the public and private sector residential construction is compared by estimating separate hedonic models for each sector. A private sector price is then imputed for each house built in the public sector, and a public sector price is imputed for each house built in the private sector.

The real quality-adjusted price of private housing rose by 12 per cent between 2000 and 2013, whereas the price of private housing rose by 9 per cent. The average quality of private housing rose by 45 per cent, whereas that of public housing fell by 18 per cent. Nevertheless, the hedonic imputation analysis reveals that public housing could not be produced more cheaply in the private sector.

The quality of public housing has declined over time. The hedonic analysis shows that the decline is not because of a lack of competition between construction firms in the public sector. An alternative demand side explanation is provided.

This study applies hedonic methods in novel ways to compare the relative performance of the public and private housing sectors in Costa Rica. The results shed new light on the effectiveness of public sector housing programs.

Hedonic models are commonly used in housing markets studies to obtain quantitative measures of various implicit prices. The use of panel data in other fields of research has proved to be valuable when accounting for unobserved heterogeneity. Given that houses are extremely heterogeneous, and given that it is impossible to include all relevant attributes in hedonic models, removing unobserved heterogeneity by basic panel data models sounds appealing. This paper seeks to compare results between models that use pooled cross section data and panel data. The main research question is whether the pooled model gives unbiased estimates on some basic implicit prices.

The paper applies the hedonic methodology. It uses regression analysis and estimate basic and parsimonious models that use either pooled time series and cross section data or panel data. The empirical results when using the two different approaches are compared.

The paper illustrates that the results from the pooled timeseries and cross section model could be biased for some basic implicit prices. With some nuances, it is illustrated that in specific situations the use of a basic panel data estimator could be a simple solution to the problem of misspecification due to omitted, time‐invariant explanatory variables.

Most of the included variables do not change over time, however. In these cases potential bias using a basic fixed effects approach could not be checked for. It is also problematic that the variation in some of the time‐varying variables is not reliable and small. Finally, there could be a problem with sample selection bias. This may limit the usefulness of using panel data in disaggregated hedonic house price studies.

Hedonic house price models are frequently used in housing market research. It is therefore important to study in various ways whether the traditional approaches provide unbiased results. In this paper models that use panel data are compared to models that use more traditional time series and cross section data. To the author's knowledge, this approach has not been followed before.

Hedonic price modeling (HPM) plays an important role in identifying factors affecting the price of goods or services. Many researchers have extensively used this modeling approach in their research work. However, there has been no bibliometric review that analyses the existing research, which makes use of HPM modeling. This study aims to provide a comprehensive visualized bibliometric and scientometric view of HPM usage in real estate research.

The Scopus database was used to collect the bibliographic data of 269 publications from 1970 to 2019. CiteSpace software was then used to analyze and visualize the data.

The results revealed a significant increase in the annual number of HPM study publications. Core authors, participating countries and representative references have been identified. Research hotspots for the HPM studies were identified.

While the study provides a methodological bibliometric review of HPM literature, the findings provide a very good platform for understanding the important elements of HPM usage in real estate research.

This paper aims to assess the marginal value of several types of open space in a single family residential market. It also aims to test the hypothesis that locating closer to open spaces might be a substitute for the size of a homeowner's own yard.

Using data from Wake County, North Carolina in the US hedonic modeling is used to estimate the house price as a function of the quantities of a property's characteristics, including the property's access to different types of open spaces, property structural features, public services, disamenity features, neighborhood socio‐economic characteristics, and accessibility measures.

The findings were that housing prices increase with a property's proximity to certain types of open land uses, and that the size of those nearby open spaces also impacts home price. More importantly, the findings concluded that the value of being adjacent to public open spaces, having more public open spaces within walking distance of the property, and being closer to the nearest open space is greater for properties with smaller private yards.

This paper explicitly tests the relationship between yard size and the proximity value of various types of open spaces. The paper also discusses the implications of the research findings for land use planning and smart growth development.

Urbanisation, environmental sustainability and property markets are intertwined. Consequently, studies on any of these three topics need to take the other two topics into consideration. By critically reviewing 33 hedonic pricing studies in 16 key journals in the urban studies and environmental policies areas, we summarise quantitative evidence on the price of environmental externalities resulting from China's urbanisation process. We find that Chinese residents are willing to pay more for the access to green space and waterbody as well as the treatment of urban pollution. The cost and benefit of these amenities and disamenities have already been capitalised in house prices. The central and local government in China can leverage market force to encourage, support and facilitate sustainable urban development and environmental protection, instead of directly intervening in the property market by using public resources. Meanwhile, the estimated hedonic price of Urban Green, Urban Blue and Urban Grey helps policymakers to understand the cost and benefit of their urban development decisions. Our review of the papers on Urban Green, Urban Blue and Urban Grey suggests that there have been promising and encouraging development in studies on all three topics in the last decade. The quality and quantity of hedonic price research has been improving notably. However, it is also clear that there is virtually no empirical evidence from the second- or third-tier cities, particularly, regarding Urban Green and Urban Blue investigations. The small number of existing hedonic studies is far from sufficient to draw reliable conclusions about the costs of environmental externality for cities that have not been studied. What works in first-tier cities may not hold elsewhere in China due to the large geographical variation in natural endowment, economic development status and local customs. There are many pieces that are missing from this big picture. More hedonic price studies are needed.