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The purpose of this paper is to empirically test for submarket existence based on an understanding of the residential rental housing market in Ghana.

Based on extant literature and market observations, the authors provide key concepts and an overview of the residential rental market dynamics in Ghana. Reseachers appreciate that submarkets may exist in the Ghanaian rental market but have ignored the empirical testing for submarket existence due to data asymmetries. Based on real estate experts and stakeholder consultations, a priori delineation of submarkets are constructed based on spatial, structural and a nested approach. Submarket existence is tested using the Kruskal–Wallis H test and Hedonic modelling techniques.

By using fieldwork data from Accra rental market, the analysis provides credence to the conceptualisation of submarkets and how to empirically test for same. It is argued that researchers should use alternative methods to compare results to make far-reaching conclusions.

Examining the hypothesis that differential rental values exists for submarkets has implications for policy decisions to target submarket constructs differently to improve market maturity.

The research provides stakeholder investors in the rental space an understanding of market dynamics for profit maximisation, and end-users to maximise utility in deciding where to live – and as such households could benefit from making informed investment decisions on housing.

This research is one of the first attempts to empirically identify and test for submarkets existence in Ghana’s residential rental housing market.

There is a general consensus that residential submarkets exist, but the basis upon which these are specified remains the subject of debate. The purpose of this paper is to model data on different residential locations in Ghana to show how the submarkets have performed over the past 16 years.

The paper employs hedonic modelling based on 3,250 sale transactions and 1,130 rental transactions from 1992 to 2007.

The results demonstrate that five residential real estate characteristics – location, detached, landscaping quality, gross internal areas and plot size – predominate in the explanation of both rental and transactions prices across all submarkets. They also highlight points of variation between the submarkets. An understanding of the impact of these features on residential price and rent is important for capital and rental valuation.

This paper analyses historic performance of the residential market, both at the aggregate and disaggregate level to place the housing market in an investment context.

The purpose of this paper is to describe a segmentation technique based on geostatistical modeling methods utilizing geographically weighted regression (GWR) to identify submarkets which could be applied within the mass appraisal environment.

Given the spatial dimension within which neighbourhoods/submarkets exist, this paper has sought to utilize the geostatistical technique of GWR to identify them.

The efficacy of the procedure is established by demonstrating improvements in predictive accuracy of the resultant segmented market models as compared to a baseline global unsegmented model for each of the study areas. Optimal number of segments is obtained by measures of predictive accuracy, spatial autocorrelation in the residual errors and the Akaike information criterion.

The three datasets used allowed for an evaluation of the robustness of the method. Nonetheless it would be beneficial to test it on other datasets, particularly from different regions of the world.

Many researchers and mass appraisal practitioners have established the benefit of segmenting a study area into two or more submarkets as a means of incorporating the effects of location within mass valuation models. This approach develops the existing knowledge.

The research ultimately is developing more accurate valuation models upon which the property tax is based. This should create an environment of fair and acceptable assessed values by the tax paying community.

The contribution of this work lies in the methodological approach adopted which incorporates a market basket approach developed through a process of GWR. The importance of the research findings illustrate that submarket segmentation need no longer be an arbitrary process.

The purpose of this paper is to provide technically robust indicators of housing market performance from the records held by the Ghana Land Valuation Board, through the construction of the first ever residential price and rent indices for the aggregate and disaggregate markets.

The approach involved time series produced from hedonic models using 3,250 transaction‐based data, running from 1992 to 2007, and documents on movements in capital and rental values in Accra and Tema, the dominant commercial conurbations in the country.

The paper makes a major contribution to knowledge and understanding of housing market dynamics in Ghana. The results suggest that the derived price and rent indices look, at first sight, reasonably plausible with cyclical trends showing weak and strong patches.

The paper focuses on the development of formal housing markets through a detailed case study of Ghana, and provides findings and models of a wider application in other emerging economies.

In this paper, it is argued that neo‐classical location theory is of limited value in conceptualising the structure of urban office markets. Rather there are sound theoretical and technical arguments for segmenting office markets into distinct submarkets. It is further argued that submarkets, rather than being based on prior knowledge of agents or researchers, should be derived empirically. As an illustration the authors use principal components analysis and cluster analysis to construct office submarkets. The results reported are based on the analysis of a unique dataset of asking rents, physical and locational characteristics of properties on the market in the cities of Glasgow and Edinburgh in the 1990s. From the empirical evidence, it is clear that different factors are important in influencing the structure of the office market in Scotland’s major urban centres.

This paper presents a mixture of linear models (or hedonic regressions) for defining housing submarkets. Two different mixture models are considered: the first model allows all the regression coefficients to vary among the clusters (random coefficients); and the second model allows only the intercept term to change (random intercept). The model with a random intercept can be seen as a linear mixed model where the random effects distribution is estimated via non-parametric maximum likelihood (NPML). The models are illustrated using a real data set of 293 properties in Pamplona, Spain. These mixture models provide a classification of the dwellings into homogeneous groups that determine the structure of the submarkets.

This paper aims to evaluate the impact of submarkets on water view premiums of residential properties and investigate the correlation between submarket view premium and socio‐economic status.

Over 53,000 residential sales transactions from 2004 to 2006 are analysed using the hedonic method. The Auckland region is divided into 17 submarkets with similar water view scarcities. The region is analysed along with each individual submarket in order to determine if significant differences in view premiums exist.

The empirical results indicate that the regionwide model chronically over‐ or under‐estimates view premiums, for example, the regionwide model estimates that a wide water view adds 18 per cent to a home's value while the same view amenity adds only 5 per cent in modest West Harbour but 54 per cent in posh Mission Bay.

The study's findings can be directly applied to residential valuation practice and in particular mass appraisal systems.

This research fills a gap in the body of knowledge relating to water view externalities by investigating the differing price impacts across submarkets.

Prior research indicates a discount for foreclosures sold through the multiple listing service (MLS). The purpose of this paper is to examine whether the effect of foreclosure on house value is consistent over submarkets based on property size in the US single family home market. The paper also tests whether the spillover effect of a nearby foreclosure on the specific property value varies across submarkets.

The full sample is split into four quartiles based on the square feet of all observations. The hedonic pricing models are estimated across full sample and three subsamples, in order to examine the effect of foreclosure on selling price. The number of neighborhood foreclosures within each combination of radii and timing intervals is used to investigate the spillover effect of a nearby foreclosure on the specific property value.

It is found that the quartile with smaller houses have the largest discount associated with a foreclosure of approximately 24 percent, while the medium and larger houses have a discount of approximately 19 percent. The results are robust after including a proxy for property quality. Second, the spillover effects of nearby foreclosures are lowest for small properties and highest for large properties. Adding additional controls for housing quality reduces the observed spillover effect.

The findings on foreclosure discount are consistent with Pennington‐Cross's argument, that the foreclosures in the smaller properties have lower appreciation than the larger ones. The paper's results about spillover effects also support the previous research, implying a greater stigma for foreclosed houses in neighborhoods with larger, more expensive houses.

The paper provides potential explanation for foreclosure discount and spillover effects of nearby foreclosure in the US single family residential markets.

Since the 1980s UK academics have promoted the use of multiple regression analysis in property valuation. Recently, however, there has been growing recognition that regression models will be subject to aggregation bias if they fail to accommodate the existence of housing market segmentation (submarkets). In this study, we compare the empirical performance of a standard hedonic house price regression model for the city of Glasgow with a segmented model which recognises the importance of understanding the underlying market structure and, in particular, the existence of submarkets for different dwelling types. The results show that the (weighted) standard error of the segmented model is significantly lower than that of the market wide model. Consequently, we propose a two‐stage approach to the application of MRA techniques to residential valuation. First, following traditional institutional analysis of housing markets, the market should be subdivided into distinct structurally differentiated market segments. These segments can usefully be identified by principal components factor analysis which allows the identification of the most important common components in the housing bundle. Second, separate house price equations should be estimated for each market segment. Although the best‐fit equation may vary from sector to sector this is likely to reflect the behavioural realities of the property market, and will provide the basis for more accurate valuations.

This paper examines the price setting behaviour over time and space in the Athens residential market. In periods of house price inflation asking prices are often based upon the last observed highest selling price achieved for a similar property in the same micro-location. However, in a falling market, prices may be rigid downwards and less sensitive to the most recent transaction prices, weakening spatial effects. Furthermore, the paper considers whether future price expectations affect price setting behaviour.

The paper employs a dataset of approximately 24,500 property values from 2007 until 2014 in Athens incorporating characteristics and locational variables. The authors begin by estimating a baseline hedonic price model using property characteristics, neighbourhood amenities and location effects. Following this, a spatio-temporal autoregressive (STAR) model is estimated. Running separate models, the authors account for spatial dependence from historic valuations, contemporaneous peer effects and expectations effects.

The initial STAR model shows significant spatial and temporal effects, the former remaining important in a falling market contrasting with previous literature findings. In the second STAR model, whilst past sales effects remain significant although smaller, contemporaneous and price expectations effects are also found to be significant, the latter capturing anchoring and slow adjustment heuristics in price setting behaviour.

As valuations used in the database are based upon comparable sales, then in the recessionary periods covered in the dataset, finding comparables may have become more difficult, and hence this, in turn, may have impacted on valuation accuracy.

In addition to past effects, contemporaneous transactions and expected future values need to be taken in consideration in analysing spatial interactions in housing markets. These factors will influence housing markets in different cities and countries.

The information content of property valuations should more carefully consider the relative importance of different components of asking prices.

This is the first paper to use transactions data over a period of falling house prices in Athens and to consider current and future values in addition to past values in a spatio-temporal context.