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Article
Publication date: 10 June 2021

Abhijat Arun Abhyankar and Harish Kumar Singla

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…

Abstract

Purpose

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.”

Design/methodology/approach

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).

Findings

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%).

Research limitations/implications

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.

Practical implications

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.

Originality/value

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.

Details

International Journal of Housing Markets and Analysis, vol. 15 no. 2
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 5 June 2017

Genanew Bekele Worku

This paper aims to examine house price drivers in Dubai, addressing nonlinearity and heterogeneity.

Abstract

Purpose

This paper aims to examine house price drivers in Dubai, addressing nonlinearity and heterogeneity.

Design/methodology/approach

The study applies a combination of linear and nonlinear, as well as quantile regression, specifications to address these concerns and better explain the real-world phenomenon.

Findings

The study shows the double-log quantile regression approach is an overarching description of house price drivers, confirming that not only the price of housing and its determinants are non-linearly related but also that their relationship is heterogeneous across house price quantiles. The findings reveal the prevalence of sub-market differentials in house price sensitivity to house attributes such as size (in square meters), location and type of house, as well as government laws. The study also identifies the peaks and deflation, as well as the rebounding nature of the house price bubble in Dubai.

Research limitations/implications

The data used are limited, in that information on only a few house attributes was available. Future research should include data on other house attributes such as house quality, zip codes and composition.

Practical implications

The findings of this study are expected to suggest results with significant ramifications for researchers, practitioners and policy makers. From a policy perspective, there is an obvious interest in understanding whether the price of housing is affected by different attributes differently along its distribution.

Social implications

This study allows policy makers, developers and buyers of higher-priced houses to behave differently from buyers of lower-priced or medium-priced houses.

Originality/value

Methodologically, it demonstrates alternative linear and nonlinear, as well as quantile regression, specifications to address two increasing concerns in the house price literature: nonlinearity and heterogeneity. Unlike most other studies, this study used a rich data (140,039 day-to-day transactions of 10 years’ pooled data). The Dubai housing market presents an interesting case. UAE (Dubai, in particular) is named as the second-hottest marketplace for global residential property investors, ahead of Singapore, the UK and Hong Kong (Savills plc, 2015).

Details

International Journal of Housing Markets and Analysis, vol. 10 no. 3
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 23 January 2024

Zoltán Pápai, Péter Nagy and Aliz McLean

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Digital Policy, Regulation and Governance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2398-5038

Keywords

Article
Publication date: 9 May 2018

Debarpita Roy

This paper aims to understand housing demand of urban Indian households in terms of housing and household-level characteristics. Because a house is a bundle of certain…

Abstract

Purpose

This paper aims to understand housing demand of urban Indian households in terms of housing and household-level characteristics. Because a house is a bundle of certain characteristics which vary across houses, each characteristic has an implicit price. Finding this implicit price for certain important characteristics is the first objective of this study. The second objective of the paper is to compute the income elasticity and price elasticity of housing demand for these cities.

Design/methodology/approach

To achieve comparable estimates, household-level data from India’s National Sample Survey Organisation housing surveys for the years 2002 and 2008-2009 have been used. A hedonic price function is estimated using ordinary least squares (OLS) and Box-Cox functional forms to estimate the implicit prices of housing characteristics. This exercise is attempted for owned and rented houses separately. Demand function required for computing the elasticities, uses the hedonic price index derived from the implicit prices and household characteristics.

Findings

The study finds housing demand to be income elastic and price inelastic for the six cities across both the time periods.

Originality/value

Firstly, this study includes housing characteristics such as individual access to drinking water, modern sanitation facility, separate kitchen, condition of the structure, existence of a road with street light and whether the house is in a slum or non-slum area in the hedonic price function. These variables were not used in any of the earlier studies pertaining to India. Secondly, it uses the Box-Cox non-linear form to derive the hedonic price function, a specification not used earlier. Thirdly, this is the first study analysing housing demand across the six largest Indian cities.

Details

International Journal of Housing Markets and Analysis, vol. 13 no. 1
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 17 April 2007

David P. Lorenz, Stefan Trück and Thomas Lützkendorf

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…

4785

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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

Details

Property Management, vol. 25 no. 2
Type: Research Article
ISSN: 0263-7472

Keywords

Article
Publication date: 29 December 2023

Prabhat Kumar Rao and Arindam Biswas

This study aims to assess housing affordability and estimate demand using a hedonic regression model in the context of Lucknow city, India. This study assesses housing…

Abstract

Purpose

This study aims to assess housing affordability and estimate demand using a hedonic regression model in the context of Lucknow city, India. This study assesses housing affordability by considering various housing and household-related variables. This study focuses on the impoverished urban population, as they experience the most severe housing scarcity. This study’s primary objective is to understand the demand dynamics within the market comprehensively. An understanding of housing demand can be achieved through an examination of its characteristics and components. Individuals consider the implicit values associated with various components when deciding to purchase or rent a home. The components and characteristics have been obtained from variables relating to housing and households.

Design/methodology/approach

A socioeconomic survey was conducted for 450 households from slums in Lucknow city. Two-stage regression models were developed for this research paper. A hedonic price index was prepared for the first model to understand the relationship between housing expenditure and various housing characteristics. The housing characteristics considered for the hedonic model are dwelling unit size, typology, condition, amenities and infrastructure. In the second stage, a regression model is created between household characteristics. The household characteristics considered for the demand estimation model are household size, age, education, social category, income, nonhousing expenditure, migration and overcrowding.

Findings

Based on the findings of regression model results, it is evident that the hedonic model is an effective tool for the estimation of housing affordability and housing demand for urban poor. Various housing and household-related variables affect housing expenditure positively or negatively. The two-stage hedonic regression model can define willingness to pay for a particular set of housing with various attributes of a particular household. The results show the significance of dwelling unit size, quality and amenities (R2 > 0.9, p < 0.05) for rent/imputed rent. The demand function shows that income has a direct effect, whereas other variables have mixed effects.

Research limitations/implications

This study is case-specific and uses a data set generated from a primary survey. Although household surveys for a large sample size are resource-intensive exercises, they provide an opportunity to exploit microdata for a better understanding of the complex housing situation in slums.

Practical implications

All the stakeholders can use the findings to create an effective housing policy. The variables that are statistically significant and have a positive relationship with housing costs should be deliberated upon to provide the basic standard of living for the urban poor. The formulation of policies should duly include the housing preferences of the economically disadvantaged population residing in slum areas.

Originality/value

This paper uses primary survey data (collected by the authors) to assess housing affordability for the urban poor of Lucknow city. It makes the results of the study credible and useful for further applications.

Details

International Journal of Housing Markets and Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 1 July 2014

Funlola Famuyiwa and Gabriel Kayode Babawale

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

Abstract

Purpose

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

Design/methodology/approach

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.

Findings

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

Originality/value

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.

Details

Journal of Facilities Management, vol. 12 no. 3
Type: Research Article
ISSN: 1472-5967

Keywords

Article
Publication date: 18 March 2021

Onur Özsoy and Hasan Şahin

The purpose of this paper is to investigate empirically the main factors that affect the house prices in Izmir, Turkey using the quantile regression and ordinary least square…

743

Abstract

Purpose

The purpose of this paper is to investigate empirically the main factors that affect the house prices in Izmir, Turkey using the quantile regression and ordinary least square approaches.

Design/methodology/approach

Sample data about the housing market for Izmir collected from the web pages of various real estate agencies during June 2018. Following this, the quantile regression method is used to estimate all possible effects of variables on each interested quantile to determine the factors that affect house prices to guide the potential consumers, house developers, city planners and the policymakers in Izmir, Turkey.

Findings

Results show that the age of the house, central heating and parking have no significant effect on prices. The size of the house, the existence of an elevator, fire and security have a positive and significant effect on prices. The number of rooms has lower values for high-priced houses, while the floor, the number of balconies, air conditioning, proximity to schools have a higher value for high-priced houses. The number of toilets, the number of bathrooms and the distance to the hospital have a lower value on the high-priced housing. The value of the distance from the city center and the shopping center is almost uniform in all quantiles and lowers the value of the higher-priced houses. With the exception of the value of the houses in the 10th percentile in Balcova district, the value of the houses in Konak, Balcova and Narlidere is lower prices in Karsiyaka.

Originality/value

This is the first comprehensive research to determine the major factors that affect house prices in Izmir. The second contribution of this paper is that it includes all possible variables and accordingly derives adequate policy implications, which could be used both by the public housing authority and private housing constructing companies in designing and implementing effective housing policies.

Details

International Journal of Housing Markets and Analysis, vol. 15 no. 1
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 18 June 2020

Devindi Geekiyanage and Thanuja Ramachandra

Traditionally, early-stage investment decisions on buildings purely based initial capital costs and simply ignored running costs and total lifecycle cost. This was basically due…

Abstract

Purpose

Traditionally, early-stage investment decisions on buildings purely based initial capital costs and simply ignored running costs and total lifecycle cost. This was basically due to the absence of estimating models that yield running costs at the early design stage. Often, when the design of a building, which is responsible for 10–15% of its total cost, is completed, 80% of the total cost is committed. This study aims to develop a building characteristic-based model, which is an early-stage determinant of running costs of buildings, to predict the running costs of commercial buildings.

Design/methodology/approach

A desk study was carried out to collect running costs data and building characteristics of 35 commercial buildings in Sri Lanka. A Pareto analysis, bivariate correlation analysis and hedonic regression modelling were performed on collected data.

Findings

According to Pareto analysis, utilities, services, admin work and cleaning are four main cost constituents, responsible for 80% of running costs, which can be represented by highly correlated building characteristics of building height, number of floors and size. Approximately 94% of the variance in annual running costs/sq. m is expressed by variables of number of floors, net floor area and working hours/day together with a mean prediction accuracy of 2.89%.

Research limitations/implications

The study has utilised a sample of 35 commercial buildings due to non-availability and difficulty in accessing running cost data.

Originality/value

Early-stage supportive running costs estimation model proposed by the study would enable construction professionals to benchmark the running costs and thereby optimise the building design. The developed hedonic model illustrated the variance of running costs concerning the changes in characteristics of a building.

Details

Built Environment Project and Asset Management, vol. 10 no. 3
Type: Research Article
ISSN: 2044-124X

Keywords

Article
Publication date: 2 August 2013

Shanaka Herath and Gunther Maier

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Journal of European Real Estate Research, vol. 6 no. 2
Type: Research Article
ISSN: 1753-9269

Keywords

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