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.

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.

This study aims to explore and analyze the disparities in the distribution of housing types and characteristics among households in Saudi Arabia, taking into consideration the regional perspective.

This study uses quantitative data obtained from the General Authority for Statistics, specifically from the Saudi 2022 Statistical Census. The data were analyzed using descriptive statistics (percentages) as well as inferential statistics, including correlation analysis (Pearson correlation) and t-tests.

The study found a distinct preference among Saudis for villas, with 85.3% choosing this housing type, while only 14.7% of non-Saudis opted for villas. The statistical analysis confirmed the significance of housing type for Saudi citizens (t = 2.561, p = 0.037), while non-Saudis did not show a statistically significant preference (t = 1.703, p = 0.132). The Pearson correlation results revealed a moderate positive correlation (r = 0.641, p = 0.009) between regional landmass and the number of houses, and a very strong positive relationship (r = 0.984) between population and the number of houses across the 13 regions. As expected, with increasing population, there was a significant increase in the number of houses (p = 0.001).

This study fills a research gap by investigating regional disparities in housing characteristics in Saudi Arabia. The findings are valuable for policymakers, housing developers and the housing market in understanding these disparities. The insights from this research can inform decision-making to promote equitable access to housing types and foster social inclusivity in the housing sector.

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.

Several studies have examined the impact of market fundamentals on house prices. However, the effect of economic sectors on housing prices is limited despite the existence of two-speed economies in some countries, such as South Africa. Therefore, this study aims to examine the impact of mining activities on house prices. This intends to understand the direction of house price spreads and their duration so policymakers can provide remediation to the housing market disturbance swiftly.

This study investigated the effect of mining activities on house prices in South Africa, using quarterly data from 2000Q1 to 2019Q1 and deploying an auto-regressive distributed lag model.

In the short run, we found that changes in mining activities, as measured by the contribution of this sector to gross domestic product, impact the housing price of mining towns directly after the first quarter and after the second quarter in the non-mining cities. Second, we found that inflationary pressure is instantaneous and impacts house prices in mining towns only in the short run but not in the long run, while increasing housing supply will help cushion house prices in both submarkets. This study extended the analysis by examining a possible spillover in house prices between mining and non-mining towns. This study found evidence of spillover in housing prices from mining towns to non-mining towns without any reciprocity. In the long run, a mortgage lending rate and housing supply are significant, while all the explanatory variables in the non-mining towns are insignificant.

These results reveal that enhanced mining activities will increase housing prices in mining towns after the first quarter, which is expected to spill over to non-mining towns in the next quarter. These findings will inform housing policymakers about stabilising the housing market in mining and non-mining towns. To the best of the authors’ knowledge, this study is the first to measure the contribution of mining to house price spillover.

Understandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations.

Analysis here is facilitated through network analysis together with topological and hierarchical characterizations of price comovements.

This study determines eight sectoral groups of cities whose house price indices are directly connected and the price synchronization within each group is higher than that at the national level, although each shows rather idiosyncratic patterns. Degrees of house price comovements are generally lower starting from 2018 at the national level and for the eight sectoral groups. Similarly, this study finds that the synchronization intensity associated with the house price index of each city generally switches to a lower level starting from early 2019.

Results here should be of use to policy design and analysis aiming at housing market evaluations and monitoring.

This paper aims to examine the convergence pattern of residential house prices in a panel of 55 major cities in Turkey over the period between 2010 and 2018 and to investigate the determinants of convergence club formations.

The authors applied the log t-test to identify the convergence clubs and estimated ordered logit model to determine the key drivers.

The results suggest that there are five convergence clubs and confirm the heterogeneity of the Turkish housing market. Istanbul, the commercial capital, and Mugla, an attractive tourist destination, are at the top of the housing market and followed by the cities located in the western part, particularly along the Aegean and Mediterranean coasts of Turkey. Moreover, the ordered logit model results point out that the differences in employment rate, climate, population density and having a metropolitan municipality play a significant role in determining convergence club membership.

Large-scale policy measures aiming to increase employment opportunities in rural cities of central and eastern provinces and providing lower land prices and property taxes in the metropolitan cities of Turkey can help mitigate some of the divergence in the house prices across cities.

The novelty of this study lies in employing a new data set at the city level containing 55 cities in Turkey, which is by far the largest in terms of city coverage among emerging market economies to implement the log t-test. It also contributes to the literature on city-specific determinants of convergence club formation in the case of an emerging economy.

From the perspective of housing affordability, Portugal is an interesting case study, considering that Portugal ranks 5th in terms of price-to-income ratio and has experienced, since 2015, a significant increase in real estate prices.

The provision of housing is a critical social development factor. With the growing worldwide urbanization and the demand pressure over real estate in many cities, the problem of affordability has gained increase attention by policy makers. Housing affordability is hardly a new topic from a literature perspective, but the recent post-pandemic worldwide inflation growth has re-centered affordability as key topic in the housing agenda. This paper provides a comprehensive overview on past literature and a detailed analysis on the Portuguese market at the municipal level, by analyzing the changes in housing affordability in recent years.

Despite this growth, overall, affordability has improved. The study also shows the importance of municipal-level analysis, given the significant geographical differences. The authors' study confirms that many municipalities, outside metropolitan areas, exhibit low levels of affordability. Nevertheless, markets with higher average real estate values tend to exhibit even lower affordability, outpacing the higher levels of income.

Previous studies have focused on affordability issues on a national or highly aggregated level or focusing only on the two largest metropolitan areas in the country. This paper provides a deeper understanding on the inequalities of housing affordability between Portuguese municipalities.

Housing market research involves observing the relationships between housing value and its indicators. However, recent literature indicates that the disruption of the COVID-19 pandemic could have an impact on the forecasting properties of some of the housing indicators. This paper aims to observe the relationships between the home value index and three potential indicators to verify their forecasting properties pre- and post-COVID-19 and provide general recommendations for time series research post-pandemic.

This study features three vector autoregression (VAR) models constructed using the home value index of the USA, together with three indicators that are of interest according to recent literature: the national unemployment rate, private residential construction spending (PRCS) and the housing consumer price index (HCPI).

Unemployment, one of the prevalent indicators for housing values, was compromised as a result of the COVID-19 pandemic, and a new indicator for housing value in the USA, PRCS, whose relationship with housing value is robust even during the COVID-19 pandemic and HCPI is a more significant indicator for housing value than the prevalently cited All-Item consumer price index (CPI).

The study adds residential construction spending into the pool of housing indicators, proves that the finding of region-specific study indicating the unbounding of housing prices from unemployment is applicable to the aggregate housing market in the USA, and improves upon such widely accepted belief that overall inflation is a key indicator for housing prices and proves that the CPI for housing is a vastly more significant indicator.

The present study, based on a comparative analysis of several historic houses in Shiraz, presents a new perspective on uncovering the role of the social lives of homeowners in the formation of the spatial configurations of their houses.

Twelve plans were selected and analyzed using space syntax techniques. The spatial characteristics were compared mathematically using integration, intelligibility and axial synergy.

The results of the research showed that spatial configurations reflected the social identity of their homeowners as well as their cultural and private beliefs. The results further highlighted the syntactical differences between different types of one courtyard houses, two courtyard houses and three courtyard houses. While privacy was maintained across all housing layouts, centralized one-courtyard typography had the highest synergy while the three-courtyard typology had the highest intelligibility.

This research advances the use of space syntax approach in the design of contemporary housing by recognizing the socio-cultural values of its occupants.