Welcome to the fourth issue in the sixteenth volume of the International Journal of Housing Markets and Analysis. The following 10 research papers ensure this issue of the journal retains a high level of housing market research as well as being passed through a rigorous blind review process. The diversity of research topics and their geographical regions, including a paper investigating G7 countries, ensures there is a strong contribution to knowledge as they individually and collectively solve current housing market challenges and problems. This journal undertakes a unique approach to housing market research and analysis, being centred on publishing research which is timely and relevant to global researchers rather than being restricted to a limited geographical base. This remains a core strength for the authors and their outstanding research publications.
The first paper from Iran examines the interaction between housing inflation and stock market price with the emphasis placed on the role of key time series variables including the exchange rate. To improve the level of accuracy, there were three control and determinate variables analysed in the models including consumer price index as the proxy for total inflation, real interest rate and real GDP. Therefore, the methodology investigates the symmetric and asymmetric relationship between housing prices, exchange rates and stock market prices using linear autoregressive distributed lag bound test (ARDL) and non-linear ARDL models. The findings confirmed there was no co-integration between the variables in the three models. However, in the non-linear ARDL model, there is a significant correlation between exchange rate, shock and housing price, although the positive and negative shocks of stock price changes had a negative effect on the housing price.
The second paper from the USA investigates the immediate effects of hurricanes upon the time on the market, share of houses sold and also the percentage of houses with price decreases identified in the housing market using MSA level data in Florida. Previous research concluded the intensity and the number of hurricanes is likely to increase in the future. The methodology uses a difference-in-difference method where the impact that a hurricane has upon the housing market is estimated. The findings showed a hurricane has a positive and significant effect on the time on the market. Furthermore, the occurrence of a hurricane leads to a delay in the sale of a typical house in Florida by five days. The analysis tests for within-year seasonality and confirmed that these effects change with seasonality of the housing market, where markets with seasonal housing prices tend to be affected more by hurricanes in contrast to those where housing prices are not seasonal. Also, it was demonstrated that the effects of a hurricane are transient and dissipate within a few months. Note the results remain significant as the hurricane intensity changes.
The third paper from Canada models temporal variations in housing prices to predict price trends within the context of land use-transportation interactions using machine learning methods based on longitudinal observations of housing transaction prices. The methodology examines three machine learning algorithms, namely linear regression machine learning, random forest and decision trees, being applied to housing price trends from 2001 to 2016 in the Great Toronto and Hamilton Area. The emphasis was placed on the role of accessibility in modelling house prices. Furthermore, it compares the performance of the ML algorithms with traditional temporal lagged regression models. The results confirmed both the ML algorithms and also the temporal regression achieved a relatively high level of accuracy. Temporal lag effects were found to play a key role in housing price modelling, along with physical conditions and socio-economic factors. Variations in accessibility effects on housing prices were observed by mode and activity type.
The fourth paper from Hong Kong examined how the rental housing market responds to urban renewal strategy via urban renewal projects. A broad range of urban renewal projects were incorporated including urban redevelopment projects, urban rehabilitation projects and urban revitalisation projects are within the scope of this study. The methodology was based on a two-stage linear Time on Market model. The findings supported the existing market search theory that the marketing time period for rental property is positively associated with the listed rental price. It was shown that the urban renewal projects in the city of Hong Kong were associated with a shorter marketing time for surrounding rental properties.
The objective of the fifth paper from Japan is based on the underlying premise that houses are durable, where an imbalance between demand and supply occurs over time since initial construction. Therefore, the aim is to quantify the extent of this imbalance for existing houses while focusing on the heterogeneity across property segments. The data set was compiled based on the “inquiry volume” that each property received from an online real estate portal to measure the volume of demand in relation to supply. The methodology used regression models to analyse the resale condominium market across the Tokyo metropolitan area. In the findings, the inquiry volume successfully tracked a recent expected trend in which demand relative to supply is stronger for:
condominiums in reasonably priced areas;
condominiums located in convenient, accessible locations;
condominiums built within the last 20 years; and
compact and spacious units.
It was argued these trends cannot be captured through heterogeneity in price levels as has been widely used in previous studies to measure housing preferences.
The sixth paper from India investigated the status of private off-campus student accommodation. The study examined the geographical variability of student’s private rental housing in Prayagraj city via semi-structured questionnaires and also interviews. The methodology used Moran’s I and LISA to determine the spatial clustering of rental levels paid by both male and female students. The findings confirm the prevalence of unregulated and poor-quality of off-campus housing in general. Furthermore, there were comparatively higher rental levels paid by females and also more expensive rents observed in the proximity of University of Allahabad. It was argued there was a relatively low level of coordination between central government policies, educational administrators and municipal officials.
The seventh paper investigates house prices volatility and economic policy uncertainty nexus within G7 countries. During recent years there have been numerous economic uncertainty challenges that have been observed globally. The methodology used a relatively newly introduced econometric technique being the GARCH MIDAS model to the data set covering the period between January 1998 and May 2021. The results highlighted a significant relationship between house prices volatility and economic policy uncertainty. Furthermore, economic policy uncertainty acts as a significant determinant of house price volatility. The out-of-sample also showed that the economic policy uncertainty is an effective predictor, whereas the GARCH-MIDAS has a superior predictive ability.
The eighth paper from China investigates dynamic relationships in residential housing price indices for ten major cities between 2005 and 2021. The data set was based on monthly data where the methodology used monthly data to use vector error correction modelling with the directed acyclic graph examining contemporaneous causality among the 10 indices. The PC algorithm identified the causal pattern with the LiNGAM algorithm, further determining the causal path followed by innovation accounting analysis. Sophisticated price dynamics were found in price adjustment processes following price shocks which were generally dominated by the top-tier cities.
The ninth paper from Turkey is based on an analysis of the short-term and long-term effects of inflation, the exchange rate, the housing interest rate, the industrial production index, total housing loans and housing volume on housing inflation whilst taking into account the multiple structural breaks. The methodology uses the multiple structural break Lee Strazicich unit root test, ARDL test and Granger causality test based on an error correction model. The findings confirmed there was both a short and long-term relationship between housing prices and macro variables. Housing prices were predominantly affected by housing interest rates, housing volume, real exchange rate and total housing loans in the short-run. In the long-run, it was predominantly affected by total housing loans, housing volume and housing interest rates.
The tenth paper from Saudi Arabia examines the effect of Saudi Vision 2030 and associated government initiatives upon macroeconomic variables related to housing. The methodology used an empirical-analytical approach with a set of hypotheses examined to verify whether there has been any change in the trends of macroeconomic variables related to housing after the Saudi Vision 2030 commenced. The results confirmed the trend in the proportion of housing ownership reversed from a continuous downturn to accelerated growth since the Saudi Vision 2030 was implemented. However, the effect of these advances is not observed in non-oil GDP or in the economic activities of the construction, real estate and financial services sectors. Therefore, government entities should review the levels to which other economic activities contribute to non-oil GDP.
In this sixteenth year as editor of this journal, I continue to observe increasing submission rates every year as demand to be published in this journal increases. As always, I encourage authors with prospective papers to consider engaging with myself, the editor prior to submission to ensure their paper is in an acceptable format for publication including the scope of their research. This includes ensuring the submitted paper conforms to the journal’s stated author guidelines for the journal as listed on the website. Most importantly, this will reduce the time the paper spends in the review process. If you are interested in submitting a research paper or reviewing potential publications, then please contact the editor direct at ijhma@ijhma.com
