Search results

The purpose of this paper is to extract the location attributes, which are the most important for market value of real estate in countries with well‐developed markets.

In this paper meta‐analysis is applied for extraction of location attributes and the weights of their importance. The outcomes of existing regression models created in different countries mainly with a developed real estate market are used. A total of 81 models described in 39 sources are analysed.

The paper finds that the lists of statistically significant location attributes, which influence market value, are obtained for different real estate types. The weights of attributes' relative influence are compared, where possible.

In the paper meta‐analysis is also applied for a limited number of empirical studies. However, for land and residential real estate the number of sources is sufficient to make a substantiated conclusion. The application of the outlined location attributes is a subject for future research.

The paper shows that the lists of important location attributes can be used for practical specification of the valuation models for urban land and other real estate in countries where the market is underdeveloped, to increase the degree of objectivity and market orientation.

The paper is one of the few studies which synthesize the findings of existing regression models with respect to location attributes generally. The method of weights' estimation is original. The result of the paper has practical value for real estate valuation in countries with an underdeveloped market.

The purpose of this paper is to better understand the spatial structure of the Lyon urban area focusing on real estate. For this, two aims are formulated. The first aim is to identify and geographically analyse latent structure underlying apartment variables and location. The second aim is to decrease a number of explanatory variables in a hedonic model of real estate prices applying latent constructs.

For the first aim of a parsimonious representation among measured variables, exploratory factor analysis is applied. For the second aim of data reduction, principal component analysis (PCA) is used. The exploited regression methodologies are global and geographically weighted ordinary least squares.

Four factors are extracted, of which two represent apartment attributes and other two – location attributes. Principal components provide better insight into location attributes dividing the service employment centres into two geographical groups. The inclusion of principal components in hedonic price equation instead of initial location variables decreases goodness of fit, but does not gradually change non‐location estimates and other parameters.

Differently from previous applications of factor analysis and PCA in the real estate domain, oblique rotation is applied, which allows the extracted factors or components to be correlated. The scores of factors and components are interpolated from points to raster maps creating a continuous geographical distribution. Hedonic models with and without principal components are compared in detail.

The purpose of the paper is to verify whether the version of neighbourhoods created from the lowest geographical level improve a predictive accuracy of hedonic model in comparison with those based on upper geographical levels.

The paper proposes a method for defining neighbourhoods using Thiessen polygons. The clustering technique is based on fuzzy equality. Clustering is started at different geographical levels: municipalities, traffic analysis zones, and apartment blocks' Thiessen polygons. Delineated neighbourhoods are incorporated into hedonic model of apartment prices, the applied methodologies are ordinary least squares and spatial error.

With ordinary least squares regression, the slight superiority of Thiessen polygons is found in both in‐sample analysis and ex‐sample prediction. With spatial error technique, the clusters of Thiessen polygons do not always provide the best outcome, and their superiority is contested by the highest geographical level of municipalities.

This paper is the first attempt to apply the proposed method, which not always demonstrates clear superiority. In future study, the method of neighbourhood delineation could be used in combination with market segmentation.

The proposal to use Thiessen polygons as a transition from points to continuous space can outline a base for the use of different clustering techniques, which are applicable to delineate neighbourhoods in housing market studies, in particular for the assessment purpose. The fuzzy equality clustering algorithm itself can be applied to polygonal data.

The originality of the proposed method is that it defines neighbourhoods starting from individual observations applying fuzzy equality. Its advantages are an increased independence from existing boundaries, self‐determination of a number of clusters, and total coverage of an area.

This paper is devoted to urban land zoning for taxation purposes in advance of the establishment of a land market. It typifies most urban areas in Ukraine, a country in transition to a market economy. The assessed values in Ukraine do not reflect market values, as the land market is immature. The disadvantages of the current assessment system are described. The reasons why the usual Western valuation methods cannot be applied are also discussed. A proposal is presented here for a new approach to the analysis of land values and real estate markets. For this purpose, three groups of methods are described and evaluated. In the proposed structure of assessment zoning, several possible approaches to zones formation are foreseen. While the paper proposes methods that could be used, these are not backed up with empirical data. The implementation of the proposed approach is the subject for future research.