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This study aims to address the factors or attributes that would influence the price of residential products in Jakarta Metropolitan Region.

In total, 202 respondents from all across Jakarta Metropolitan Region participated in the questionnaire for this study. Demographic questions are categorized into age, gender and preferences for real estate locations. The questionnaire was made based on the author’s previous studies. Of the total respondents, 127 were males and 75 were females with age ranging from 18 to 56 years old. For data analysis, the authors utilized factor analysis, Cronbach’ α test and analysis of correlation to reach the conclusion of this study.

The findings suggested that from the initial three factors groups, there are five new groups that emerge as influencing factors for housing prices. Cronbach’ α score were verified (α = 0.906). Correlation study result suggested that the initial three factors groups produce a significant correlation between each of them, except for the factor of “overall location” and “located near family.” After factor analysis, the research results show that there are two new additional groups of factors that emerge as influences to housing prices. There are significant scores of differences between gender and real estate location preference toward the groups of factors.

This study shows how physical qualities, concept and location factors influence the housing price perception of their consumers. The result shows to be relatively reliable and valid.

The study is the first to analyze the relationship between the factors for preferences on residential products and housing price in Indonesia. This paper is also intended to be the first to pioneer the study on factors of preferences on residential products in Indonesia. The findings will be useful to develop pricing models for housing product in Indonesia.

This paper aims to reveal major factors affecting housing prices (flats and houses) in Baku, the capital of Azerbaijan Republic.

Based on cross-sectional data set of 497 flats and 443 houses, polynomial regression models are estimated for flats and houses separately. Regression models are estimated by using ordinary least squares.

Location, largeness, repair level and existence of bill of sale are major price determinants for flats. For houses, number of rooms also matters. Findings reveals that houses are land intensive (more floors, less land area) toward city center, and vice versa. Price difference due to existence of bill of sale diminishes significantly toward the surrounding areas.

The data set represents view of sellers and does not take into consideration price bargaining in time of sale; probability of information asymmetries exists which not could accounted for, and urgency of sale is not considered.

Estimation results can be used for housing valuation by real estate market participants and investors.

Research findings reveal importance of bill of sale as a major price determinant and expected to attract policymakers’ attention to solve such a big social problem. Additionally, models can be based for price estimations in Baku housing market.

The study contributes to the literature by empirically analyzing housing market in Baku, Azerbaijan. Research produces new practically valuable findings.

The purpose of the paper is to investigate a cross section of Hungarian settlement-level unit housing prices with a special emphasis on measuring the effect of population and its growth, along with accessibility to the centre of an aggregated spatial unit such as a micro-region, county or region, for the period of 2001-2011.

The analysis uses cross-sectional ordinary least squares techniques with Moulton-corrected standard errors. The estimation is guided by the implications of a simplified monocentric urbanized area framework following the model of DiPasquale and Wheaton (1996), and the econometric model is augmented with population growth rate at the settlement level to bridge the theory explaining rents and data base containing prices instead.

The location is a key factor in determining housing prices: living 10 min further from the centre results in 11 per cent cheaper housing. When estimating bid-rent curves, results show that it is crucial to control for city size and the income effect. The elasticity of housing price with respect to city size is 0.09 according to our preferred model. Population growth has an asymmetric impact on housing prices: municipalities with positive expected population growth have higher prices today.

Estimating the quantitative relationship between commuting time and housing price is crucial for a cautious infrastructure development. The benefits of improved roads and faster access could be capitalized in appreciating the housing stock. Estimating the slope of the bid-rent curve is one possible ex ante quantification of the benefits of a public development.

One contribution of this research is providing empirical evidence to surprisingly limited applied work in the field of (monocentric) urban models using data from the CEE region. Second, to the best of the authors’ knowledge, this is the first study to investigate Hungarian settlement-level unit prices from an urban economic point of view.

Ankara has experienced a recent rapid increase of residential communities that promise residents a comfortable lifestyle with strictly controlled entrances and several amenities. These luxury residential developments come in different forms, and are distributed unevenly across Ankara’s neighborhoods. Some neighborhoods become the center of gravity, and act like a magnet for these high-cost residential developments; however, most developers avoid making such costly investments in some other neighborhoods, which leads to an unfair distribution of profit across different locations in Ankara. The purpose of this paper is to examine the different factors affecting the site selection of different types of these real estate developments.

The aim of this study has been to examine the factors affecting the site selection of different types of access-restricted housing developments through a multiple discriminant analysis (DA).

The results of the analysis revealed that distance from the city center and the unit price of the current housing stock are associated with high scores in the discrimination of neighborhoods of different types of residential communities.

This paper implied a DA to create a better understanding of the site selection criteria behind access-restricted housing developments for policy makers to make better investment and management decisions.

The paper discusses the factors affecting the site selection of access-restricted residential communities in Ankara in order to show uneven distribution of profit across different locations caused by these developments.

This paper aims to scrutinize the value of houses explained by their location in Central Asia’s largest city – Tashkent, the capital of Uzbekistan. The city was completely rebuilt after a devastating earthquake of 1961 and since the country was under the centrally planned regime, houses followed a common standard and style. This allows minimizing the error from omitting possible intrinsic characteristics in a hedonic model, while measuring the impact of location on housing values.

This study uses unique data on housing values and characteristics harvested from classified adverts. This study deliberately focused on data between 2013 and 2016, when Uzbekistan was characterized as hesitant in economic reforms. Therefore, residential buildings constructed during the Former Soviet Union dominated all parts of the city in the analysis period. The results are based on the hedonic pricing model.

The results suggest that locational characteristics are relatively more important than intrinsic characteristics. This study finds that proximity to the city center and access to subway stations significantly increase housing values. The opposite is true for the case of proximity to a wastewater treatment plant. Surprisingly, on the contrary to the authors’ expectations access to a park has no economically significant impact on housing values. The results, therefore, call for an integrated approach for city planning.

The first and most important contribution of this study to the field of housing economics is that it is the first study done for Uzbekistan, the most populated Central Asian country, and it is a case study for other Central Asian countries. The second contribution of the study is that, after a long and meticulous study, the determinants of housing prices were analyzed by taking into account all possible intrinsic and extrinsic properties of houses with the similar characteristics, over a unique data set created from approximately 18,000 observations from classified pages of a major newspaper published every Thursday in Tashkent City. The final contribution of the study is that it offers new perspectives and policy recommendations to the housing market actors in Uzbekistan.

The study of house prices has become more relevant in recent times after the global financial crisis. Using a housing data set from three regions of Ghana (collated from real estate agents), the purpose of this paper is to estimate the relative importance of housing attributes to house prices.

The hedonic regression analysis conducted indicates that location is the most powerful determinant of house prices. Other relevant factors are the number of bedrooms, the number of floors, the total floor area, land size, age of the house and luxury finishing.

The implications of these results are many. Policy wise, the study provides an evidence-based empirical study that supports the need for better urban planning to improve communities, which in turn is associated with house price appreciations. Homeowners, investors and creditors, particularly mortgage lenders could be the immediate beneficiaries. Drawing on this, improved urban planning could mitigate strategic defaults that results from house prices falling below mortgage loan balances. This is important for financial market stability.

The paper provides a comprehensive and unique understanding of the hedonic determinants of house prices in Ghana. Future studies could examine the effect of location upon mortgage lending in Ghana.

Tobler's law of geography states that things that are close to one another tend to be more alike than things that are far apart. In this regard, the spatial pattern of price distribution is defined by the arrangement of individual entities in space and the geographic relationships among them. The purpose of this paper is to provide emerging findings of research analysing the salient factors which impact on the sale price of residential properties using a spatial regression approach.

The research develops and formulates a geographically weighted regression (GWR) model to incorporate residential sales transactions within the Belfast Metropolitan Area over the course of 2010. Transaction data were sourced from the University of Ulster House Price Index survey (2010, Q1‐Q4). The GWR approach was then evaluated relative to a standard hedonic model to determine the spatial heterogeneity of residential property price within the Belfast Metropolitan Area.

This investigation finds that the GWR technique provides increased accuracy in predicting marginal price estimates, in comparison with traditional hedonic modelling, within the Belfast housing market.

This study is one of only a few investigations of spatial house price variation applying the GWR methodology within the confines of a UK housing market. In this respect it enhances applied based knowledge and understanding of geographically weighted regression.

This paper presents a procedure for considering interactions of neighbourhood quality and property specifics within hedonic models of housing price. It handles interactions between geographical factors and the marginal contribution of each property attribute for enhancing values assessment. Making use of simulation procedures, it is combining GIS technology and spatial statistics to define principal components of accessibility and socio‐economic census related to transaction prices of single‐family homes. An application to the housing market of the Quebec Urban Community (more than 3,600 bungalows transacted in 1990 and 1991) illustrates its usefulness for building spatial hedonic models, while controlling for multicollinearity, spatial autocorrelation and heteroskedasticity. Distance‐weighted averages of each property attribute in the neighbourhood and interactions of property attributes with each principal component are used to detect any spatial effect on sale price variations. This first‐stage spatial hedonic model approximates market prices, which are then used in order to compare “expected” and actual property tax amounts, which are added to obtain a second‐stage model incorporating fiscal effects on house values. Interactions between geographical factors and property specifics are computed using formulae avoiding multicollinearity problems, while considering several processes responsible for spatial variability. For each property attribute, they define sub‐models which can be used to map variations, across the city, of its marginal value, assessing the cross‐effect of geographical location (in terms of neighbourhood profiles and accessibility to services) and its own valuation parameters. Moreover, this procedure distinguishes property attributes, exerting a stable contribution to value (constant over the entire region) from those whose implicit price significantly varies over space.

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

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.

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.

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.

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.

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

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).

The purpose of the study is to understand the relative impacts of structural characteristics of house, its locational attributes and neighbourhood characteristics on the choice of house and the rent paid by the individual tenants. The micro level study helps in understanding the issues of urban housing and help in policy formulation.

This paper tried to identify the socio-economic, locational and neighbourhood factors that influence tenant households in determining their residential choices in an urban area of North-east India. Also, the extent of impacts of those characteristics on the monthly rent for housing is analysed. The analysis is based on the primary data collected from the sample residents of Dimapur Town in Nagaland. The sample units are selected by cluster sampling technique from all the wards. Regression technique is used under hedonic pricing technique to examine the impacts of various potential factors on the rent.

The analysis reveals that family size, income, education of the head of family, water availability, security, convenience to access workplace, road conditions, etc. have significant positive impacts on the monthly rent. However, the impacts of some locational and neighbourhood variables vary across social and economic groups.

Time series data on the growth of house price are not available, so that the authors could examine the escalation of house price and rising scarcity of houses in the selected town.

Rental housing accounts for a significant percentage of housing in many urban centres. It is particularly important for the migrants and urban poor for whom it is the only source of accommodation. Location choice depending on capability, availability, requirement and neighbourhood conditions are integral parts of selection of accommodation in a city. Hence the present study has its relevant practical implications.

Housing choice in a hilly urban area like Dimapur assumes a special significance for the presence of heterogeneous ethnic, social and cultural groups with majority of Tribal inhabitants with varied requirements and lifestyle.

No study on housing choice or pricing of residential accommodation has been done in North-East India. Here along with the socio-economic characteristics of the individuals, structural characteristics of house, neighbourhood and locational characteristics are used simultaneously to find out the impact of various factors on the total prices through hedonic pricing method.