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This paper aims to bring together research on housing market area, submarket and household migration into a systems approach that helps us gain a better understanding of the structure and dynamics of a housing market and identify housing problems for a large metropolitan area.

The paper uses a geographic information system (GIS)-based method with simple quantitative techniques, including spatial analysis, location analysis, house price clustering and cross-tabulation. The analysis is based on migration data from the 2011 Census, house price data from the Land Registry in 2011 for Greater Manchester at the ward level and the output areas level.

The results show that different submarkets and housing market areas had different patterns of spatial migration and connections with other areas. Through a systematic analysis of migration and house price in combination, it also found a close connection between destination submarkets and the ages of migrants and identified specific problematic patterns for a large metropolitan area.

The interactions between the owner-occupied sector and the social and private rented sectors are arguably an important omission from the analysis. Also, it is acknowledged that clustering ward units based on price differentials is subject to distortions in terms of specification, size and shape. Moreover, the use of the large samples may result in very small p-values, leading to the problem of the rejection of the predefined hypothesis.

A systematic analysis of migration and house price in combination may be used to gain a better understanding of the housing market dynamics and identify housing problems systematically for a large metropolitan. It may help to identify low-demand areas, high-demand areas and assist planners with decisions in allocating suitable land for new housing constructions.

The GIS-based method introduced in the paper could be considered as an effective approach to provide a better basis for determining policy interventions and public investment designed to allocate land resources effectively and improve transport systems to change existing problematic migration patterns.

This paper fills a gap in the international literature in relation to adopting a systems approach that analyses migration and house price data sets in combination to systematically explore migration patterns and linkages and identify housing problems for a large metropolitan area. This systems approach can be applied in any metropolitan area where migration and house price data are available.

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 author invokes the concept of strategic adaptation to first specify the evolutionary as well as the strategic character of the causal mechanism (“intra-industry exit”), and second to explain its effect on the evolution of firms' within-industry geographic scope. The author reconciles the two competing logics for firm behavior – strategic choice and environmental selection – that underpin alternate explanations for the relationship between intra-industry exit and the evolution of geographic scope. This paper contributes to both theory and empirics concerning the dynamics of firms' competitive scope, in general, and within-industry geographic scope, in particular.

The US long-distance telecom services industry during the period 1984–1996, which satisfies the empirical requirements of a geographically fragmented industry characterized by demand-side heterogeneity across the submarkets, provides the research setting and panel data to test the empirical hypotheses.

The author finds that while the firms' overall performance influences their intra-industry exit decisions, it is the firm-in-market performance that influences their decision to exit a specific submarket. The author also finds that intra-industry exit decision, when influenced by firm performance, does lead to reduction in geographic scope.

This context-specific theory, which conceptualizes the dynamics of firms' geographic scope as an evolutionary process, explains the temporal change in the geographic scope of firms during the latter part of the demand growth stage of a geographically fragmented industry.

This analysis of the demand-side dynamics of firms' within-industry geographic scope focuses on the hypothetical causal effect of intra-industry exit, a pervasive business phenomenon. First, the demand-side analysis of the evolution of geographic scope is grounded in a theoretical framework that melds firm dynamics with submarket dynamics and industry dynamics. Second, this analysis explicates the demand-side underpinnings of the strategic adaptive mechanism.

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.

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.

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.

The 21st century is a new century of environmental protection. Environmental protection is one of the most important subject matters yet to come. Moreover, as the public pays more attention to environmental problems, enterprises should increase their investment in environmental management. Therefore, determining the investment level for environmental management and allocating the investment to associated environmental management activities has become a major task. The principal and agent theory and sales response functions are used for analysis in this research. The allocation of capital investment in environmental management is found to have significant impact on the aggregate sales response, aggregate profit and investment level. Therefore, in preparing the budget for environmental management, enterprises should focus on investment allocation decisions, determine the investment level and allocation method using integrated means, and apply submarket data in the allocation decision‐making process. In other words, in setting the investment level, executive management should take managers’ willingness into consideration. In allocating capital investment, managers should identify the optimal allocation method based on submarket characteristics.

The purpose of this paper is to comment upon the use of hedonic pricing models for the valuation of property. This model can be particularly useful for some housing markets.

This Education Briefing is an explanation of the how hedonic pricing can be useful in looking at the effect of “location” on the house prices within different submarkets using the Italian real estate market as an example.

Although, this case study is relatively straightforward, it shows how the application of the market approach can provide insights in cases where the comparable properties belong to different submarkets with relatively few transactions.

In cases of mass appraisals, hedonic pricing models can provide a broad indication of value across submarkets.

This paper develops a general framework that connects multiple regression analysis, direct comparison model and submarket binary variables.

With access to the large-scale search data from Rightmove plc, the paper firstly indicated the possibility of using user-generated data from online property portals to predict housing market activities and secondly embraced a GIS approach to explore what people search for housing and what they chose and investigated the issue of mismatch between search patterns and revealed patterns. Based on the analysis, the paper contributes a visual GIS-based approach which may help planners and designers to make more informed decisions related to new housing supply, particularly where to build, what to build and how many to build.

The paper used the 2013 housing search data from Rightmove and the 2013 price data from Land Registry with transactions made after the search period and embraced a GIS approach to explore the potential housing demand patterns and the mismatch between searches and sales. In the analysis, the paper employed the K-means approach to group prices into five levels and used GIS software to draw maps based on these price levels. The paper also employed a simple analysis of linear regression based on the coefficient of determination to investigate the relationship between online property views and values of house sales.

The result indicated the strong relationship between online property views and the values of house sales, implying the possibility of using search data from online property portals to predict housing market activities. It then explore the spatial housing demand patterns based on searches and showed a mismatch between the spatial patterns of housing search and actual moves across submarkets. The findings may not be very surprising but the main objective of the paper is to open up a potentially useful methodological approach which could be extended in future research.

It is important to identify search patterns from people who search with the intention to buy houses and from people who search with no intention to purchase properties. Rightmove data do not adequately represent housing search activity, and therefore more attention should be paid to this issue. The analysis of housing search helps us have a better understanding of households' preferences to better estimate housing demand and develop search-based prediction models. It also helps us identify spatial and structural submarkets and examine the mismatches between current housing stock and housing demand in submarkets.

The GIS approach in this paper may help planners and designers better allocate land resources for new housing supply based on households' spatial and structural preferences by identifying high and low demand areas with high searches relative to low housing stocks. Furthermore, the analysis of housing search patterns helps identify areas with latent demand, and when combined with the analysis of transaction patterns, it is possible to realise the areas with a lack of housing supply relative to excess demand or a lack of latent demand relative to the housing stock.

The paper proves the usefulness of a GIS approach to investigate households' preferences and aspirations through search data from online property portals. The contribution of the paper is the visual GIS-based approach, and based on this approach the paper fills the international knowledge gap in exploring effective approaches to analysing user-generated search data and market outcome data in combination.

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.