Search results

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.

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.

A panel error correction model has been developed to investigate the spatial correlation patterns among house prices. This paper aims to identify a dominant housing market in the ripple down process.

Seemingly unrelated regression estimators are adapted to deal with the contemporary correlations and heterogeneity across cities. Impulse response functions are subsequently implemented to simulate the spatial correlation patterns. The newly developed approach is then applied to the Australian capital city house price indices.

The results suggest that Melbourne should be recognised as the dominant housing market. Four levels were classified within the Australian house price interconnections, namely: Melbourne; Adelaide, Canberra, Perth and Sydney; Brisbane and Hobart; and Darwin.

This research develops a panel regression framework in addressing the spatial correlation patterns of house prices across cities. The ripple-down process of house price dynamics across cities was explored by capturing both the contemporary correlations and heterogeneity, and by identifying the dominant housing market.

The paper sets out a conceptualisation of the housing cycle centring on households' desire to upgrade their housing consumption.

The paper begins by studying house price trends and cycles in OECD countries since 2000 to identify housing cycle patterns. It then assesses existing theories partly in relation to these patterns. It then proposes a new conceptualisation of the housing cycle.

The paper finds the central role of supply lags in housing cycles is not warranted. Instead, a demand cycle generated by upgrading desires better explains an initial boom followed by a slow recovery.

The paper challenges existing orthodoxy on housing cycle dynamics and proposes an alternative perspective.

Many people living in and around Mumbai face space scarcity within homes, an issue exacerbated for families with members having differing and conflicting spatial requirements. By investigating how families live in existing residential environments, planners and designers can enable families to cope better with space scarcity.

A case study approach is used to examine four small home settings in and around Mumbai, followed by a thematic analysis of the data collected.

This study contributes a framework for studying small homes in and around Mumbai wherein they are characterized by five categories of information: internal zoning pattern, expansion pattern, spatial specialization and stratification pattern, outdoor space appropriation pattern and household adjustment pattern. Analysis through this framework gives insight into how small home settings are used by residents.

To the best of the authors’ knowledge, this is the first study offering in-depth comparative analysis of small home typologies in India.

This paper focusses on analysing the impact of crime on the housing market in Los Angeles (LA) County. By looking at different types of crime instead of general crime measures and controlling by spatial dimension of prices and crime as well as endogeneity, a model is developed that allows for the understanding of how a specific crime impacts the housing market transaction price. To perform the analysis, the paper merges different data sets (crime, housing transaction and census data) and then computes the distances to crucial transport modes to control the accessibility features affecting housing prices. The latter allows estimating the association of housing prices and crime in the distance and estimating the impact on housing depending on it.

This paper focusses on the following crimes: aggravated assault, burglary (property crime), narcotics, non-aggravated assault and vandalism. The paper shows firstly how incidents of reported crime are distributed across space and how they are related to each other – thus highlighting crime models with spatial influences. Secondly, the research utilises instrumental variables within the methodology to estimate house prices using spatial analysis techniques while controlling for endogeneity. Thirdly, it estimates the direct impact of crime on house prices and explores the impact of housing and neighbourhood features.

Results suggest that house transaction prices and crime are closely correlated in two senses. Housing prices are endogenously negatively associated with the levels of narcotics and aggravated assaults. For narcotics, the impact of distance is shorter (1,000 m). However, for burglary, vandalism and non-aggravated assaults, the price reaction suggests a positive association: the further away the crime occurs, the higher the prices. The paper also shows the large spatial association of different crimes suggesting that they occur together and that their accumulation would make negative externalities appear affecting the whole neighbourhood.

The use of a huge database allows interesting findings, but one limitation can be to not have longer time observations to identify the crime evolution and its impact on housing prices.

Large implications as the relationship identified in this paper allow defining precise policies to avoid crime in different areas in LA. In addition, crime has significant but quantitative small effects on LA housing transaction prices suggesting that the effect depends on the spatial scale as well as lack on information about where the crimes are committed. Lack on information suggests low transparency in the market, affecting the transaction decision-taken process, affecting the risk perception and with relevant implications over household welfare.

This paper relates the spatial association among crimes defining the hotspots and their impacts on housing transaction prices.

The purpose of this paper is to investigate the impact of social and economic factors on home ownership as an investment in American urban areas.

The authors run a spatial analysis using home ownership data on 817 American counties from US Census Bureau’s 2013 American Community Survey.

While the amenity value of home ownership is found to be important to overall housing tenure decisions, it is found to be less so for the ownership cohort without mortgages. Economic factors are found to impact the spatial pattern of owner-occupied housing without mortgage differently than that of all owner-occupied housing. The implications of these differences for investors are explored.

The results may lack generalizability outside of the American urban areas.

As a result of the findings of this study, a shift in investor focus from minimizing initial housing costs to sustainable housing costs is recommended.

This study provides insights into the social and economic dimensions of owner-occupied housing in order to create a more profitable investing policy for promoting home ownership.

Several post-disaster housing extension and modification studies have indicated that owner-driven modification behavior relates to socio-economic and livelihood factors. This study aims to clarify housing extension patterns and examine the relationships among spatial characteristics, sociocultural factors, livelihood factors and housing extensions. This research also highlights the implications of post-disaster housing design for indigenous communities.

An indigenous community case study was conducted using a literature review. Moreover, interview surveys and housing measurements were implemented based on purposive sampling to diversify interviewees’ backgrounds and the extent of housing extensions.

This study confirms that housing extensions are closely related to the number of household members and their associated functions and cultural and livelihood factors that were ignored during the design stage. Furthermore, the housing extension process was confirmed to match households’ economic recovery. A post-disaster housing implementation framework for the indigenous population is proposed.

This research only targeted one indigenous community with a limited number of interviewees and samples because of the connection with households.

The study’s proposed resilience post-disaster housing framework can be used to develop post-disaster housing design guidelines, which can benefit policymaking. The proposed participatory concept can be further adopted in future disaster risk-reduction programs.

This study uniquely focuses on the pre- and post-disaster housing layout and the livelihood of an indigenous community. It offers valuable insights for post-disaster reconstruction planners and practitioners.

The purpose of this paper is to compare rural houses reconstructed by core housing method after the Manjil earthquake in 1990 and traditional housing. Also, other factors such as residential space needs of residents, the role of the used structure in building the core units and the role of climatic conditions in the expansion of residential units have been investigated.

Some rural houses which were reconstructed by using core housing method – 86 rural houses – in 20 different villages were investigated using quantity and quality method. Also, the rate of satisfaction of inhabitants with their reconstructed houses was evaluated.

Although rural houses were reconstructed with inhabitants' participation and with use of local technology and materials, those houses had some characteristics contrary to traditional houses. Another problem was in the gradual expansion of spaces, which led to disharmonious and unbalanced growth and converted the houses into a complex and closed entity. It caused trouble in privacy and connections of family members and in the use of spaces.

The paper reports research generally done on rural houses which were reconstructed after the Manjil earthquake in 1990 and shows that this reconstruction experience was done by using core housing method. Also, how the core units were expanded was analyzed and advantages and disadvantages of this reconstruction experience were extracted. These results can be useful in planning and designing shelters for survivors of future disasters.

Over the past three decades, a small community of eighty-four Chilean low-income families has built and improved their home incrementally, without any technical assistance, showing an impressive performance. A six square meters bathroom on a serviced plot of land with individual connection to potable water, sewerage, electricity and access roads, worked as a starting point back in 1974. However particular their rationale may seem, the individual history of their housing process reveals some general regularities in occurrence and duration of self-build activities, as well as size and allocation of the domestic spaces. A small random sample of fifteen households was selected to tell the story and explain the whys, hows, and whens of an ever-evolving housing process. Semi-structured interviews and building surveys were both combined to reconstruct the sequence of states of each housing process, with the awareness of the characteristic imprecision of oral information transfer. Alternative states were explored by constraint programming methods and spatial qualitative reasoning. Considering the hard constraints over the site morphology and services allocation, the results of the exploration stress how extraordinary lucid and intuitive the surveyed families are when making their design decisions. The article exposes a reconstructive case study on spontaneous growth patterns underlying an unassisted, incremental self-build housing dynamics.