Welcome to the third issue in the nineteenth volume of the International Journal of Housing Markets and Analysis. This issue publishes 12 research papers from a diverse range of countries which conduct up-to-date research into current challenges in housing markets. The findings each provide a significant contribution and expand our knowledge about global housing markets. The authors have each identified a unique facet of the vast housing research landscape and focused on an outcome which will assist to address the increasing pressure to solve many of the current housing issues including affordability and sustainability. Every paper has successfully passed through a rigorous double blind refereed process and substantial credit is given to the academics and industry experts who contributed to this process.
The first paper from Ghana examines the social, economic, physical and environmental factors which influence the residential neighbourhood choices of renters. It is argued an understanding of these factors would assist policymakers and town planners in urban planning decision-making. The methodology employed a cross-sectional survey approach to ascertain the critical factors that influence tenants’ choice of residential neighbourhood and the relative importance in that decision. The findings showed that out of the 28 indicators, only seven were critical to determining tenants’ choice of neighbourhood. The indicators that most influence renters’ choice of neighbourhood were noted as the availability of water, availability and reliability of electricity, quiet and peaceful neighbourhood, aesthetic impression of housing, access to schools, commuting costs to work and also the terrain of the neighbourhood.
The second paper from Europe examines the influence of economic policy uncertainty (EPU) on residential property prices across 10 distinct nations in Europe, specifically in the northern and southern regions. The sample period of nearly 40 years includes several business cycles, as well as the global financial crisis, Brexit process and COVID-19 pandemic. The methodology used the autoregressive distributed lag (ARDL) bounded cointegration test to conduct an empirical analysis. The findings confirmed there is a stable long-term cointegration between house prices and their determining factors, including EPU. The outcomes also indicated that EPU has a negative long and short-term effect on house prices, apart from Spain where uncertainty had a positive effect.
The third paper from Spain evaluates the effect of Catalonia’s 20231 “Housing Law”, aimed at controlling rental prices in high-demand areas like Barcelona. The study investigates if the policy effectively reduced rental prices by comparing trends both before and after the law’s implementation to other Spanish regions. A differences-in-differences (DiD) methodology was applied with Catalonia as the treatment group and other Spanish regions as the control. Also the study controls for macroeconomic factors such as GDP and inflation, using administrative data on actual rental prices for accuracy. The findings show that while rental prices in Catalonia decreased slightly after the policy, the reduction was not statistically significant compared to other regions. Macroeconomic factors, especially GDP growth and housing supply, had a stronger influence on rental price trends than the rent control measure.
The fourth paper from South Korea analysed the impact of reverse jeonse on the risk of non-return of security deposits in the country’s housing market. In this context reverse jeonse refers to a situation where the market price of a jeonse contract drops below the agreed price, then increasing the likelihood of landlords being unable to return deposits. The analysis uses data from 17 South Korean regions, covering apartments and multi-family dwellings from 2017 to 2022. Key indicators of non-return risk include leasehold registration orders, court auctions and jeonse deposit guarantee incidents. A panel regression model is applied to quantify the correlation between reverse jeonse incidents and the increased risk of non-return. The findings show that an increase in reverse jeonse incidents correlates with a higher frequency of leasehold registration orders, court auctions and deposit guarantee cases, particularly after 2021. Economic disruptions and policy changes exacerbated landlords’ financial burdens, highlighting the need for interventions to stabilise the jeonse market.
The fifth paper from Chile builds a set of long-term, geographically controlled, land value indices for Santiago de Chile to test land rent theory predictions regarding macroeconomic impacts. The methodology uses a geographic cluster approach to the Laspeyres estimator, weighted by the stock of available land plots and their market offers per zone, to create two quarterly land value indices. This is followed by implementing a dynamic time series methods (VEC) as a baseline to determine the effect of economic performance and interest rate on urban land values. The findings confirmed the two land value indices were correctly predicted by economic and interest rate shocks. In addition it was found that land values grew faster than predicted during the period of the so-called “Chilean Miracle” (1992–1998), a situation associated with worsened housing affordability and socio-spatial inequality.
The sixth paper from India investigates a household’s residential search process by examining various decision dimensions including search tenure, housing typology, search criteria, information sources, search extent and search duration. It also identifies factors influencing each dimension. The methodology developed separate logit models to analyse the decision dimensions. The findings identified the influence of household typology, size, income, car ownership, origin, education, travel-attitudes, relocation reasons and also urgency on decision dimensions. It was shown that households use five dominant search criteria to orient themselves spatially, also affecting the search extent. Also it was noted that some households enter the housing market without housing typology preferences, exploring all options.
The seventh paper from Ethiopia identifies the determinants of single-family residential property values by analysing respondents’ willingness-to-pay/accept data alongside real transaction data. The methodology employed ordinal logistic regression to analyse willingness-to-pay/accept data, accommodating the ordered nature of property value responses while incorporating multiple influencing factors. Ordinal least square regression quantified the impact of continuous and categorical predictors on real transaction data. The findings highlighted strong associations between property values and several variables where the analysis of willingness-to-pay/accept data showed significant impacts of factors such as the number of rooms, site area, construction material, property orientation, property age and proximity to bus stations and the central business district. The ordinal least square regression analysis of transaction data confirmed the significance of most factors, except for property orientation.
The eighth paper from Ghana is based on the premise that creating green design capability readiness has become an emerging necessity towards increasing sustainable performance. Since a comprehensive understanding of the green design readiness markers for housing delivery is needed, this study develops a green design capability readiness model for affordable housing delivery. The methodology adopted the use of the self-determination theory and the TOE, Where a comprehensive review revealed 23 indicators on motivational, technological, organisational and environmental markers for green design practices capability readiness for affordable housing delivery. In addition means score analysis and fuzzy synthetic evaluation (FSE) were used to develop the capability readiness model. The study confirmed the top indicators in each of the markers which accounted for green design capability readiness for affordable housing delivery. Technological and motivational markers had the greatest contributions to green design readiness for affordable housing followed by an environmental marker. This model will assist in benchmarking the readiness potential of future regulations, policies and motivations towards green design practices, concepts and technologies for housing delivery.
The ninth paper from Malaysia states that the Housing Price Index (HPI) is a crucial economic indicator which reflects trends in residential property prices, influencing decisions by policymakers, investors and homeowners. Accurate forecasting of the HPI is particularly important since Malaysia is noted for significant regional diversity and seasonal variations. The methodology used to predict the HPI is the ARIMA approach, being recognised for its capacity to model both seasonality and trends. The findings demonstrated the ARIMA model improves accuracy in capturing seasonal and trend components in the housing market, offering actionable insights for strategic decision-making. The findings also presents point and annual changes across different housing types, offering a detailed view of price movements. By providing a reliable five-year forecast of the Housing Price Index HPI, this study enables more informed decision-making in housing policy, investment strategy and financial planning.
The tenth paper from Jordan develops a novel two-stage machine learning framework for real estate price prediction, integrating advanced preprocessing and evaluation techniques. It bridges the gap between traditional appraisal methods and modern predictive analytics, offering practical and academic value. The underlying aim is to replace traditional, subjective appraisal methods with an objective, data-driven framework, enhancing decision-making in the dynamic real estate market. This methodology presents a two-stage prediction framework using support vector regression (SVR) and gradient boosting machine (GBM) to enhance real estate price prediction. Model performance was evaluated using RMSE, MAE and MAPE metrics to compare SVR and GBM, highlighting their complementary strengths. The findings showed that the SVR and GBM models demonstrated high accuracy, with SVR outperforming GBM in RMSE and MAPE. The two-stage framework effectively handled data heterogeneity, providing consistent and reliable price predictions. The findings validated the robustness of machine learning for real estate appraisals.
The eleventh paper from India examines the housing market dynamics in Doha Metropolitan in the context of projected demographic changes by 2035, which are expected to significantly influence the housing sector. Focusing on downtown, waterfront and suburban areas within a rapidly developing urban environment, the study analyses four major factors: (i) residential land use policy and capacity, (ii) government housing policies, (iii) housing supply and demand and (iv) housing preferences. The methodology used a multi-methodological approach, including an assessment of housing policies, content analysis of supply-demand dynamics and a survey recording population housing preferences. The findings identified key insights for policymakers, urban planners and developers, highlighting the need for strategic housing distribution to address misalignments between supply and demand.
The twelfth paper from India investigates the impact of financial stress on house price expectations. It used household-level data from the Inflation Expectation Survey of Households (IESH) of the Reserve Bank of India (RBI) and the Financial Stress Index (FSI) released by Tracking Asian Integration (AIRC) of the Asian Development Bank. The methodology analysed unit-level data on household expectations of house prices comes for 19 major Indian cities involving approximately 5,500 participants. The approach examined the aggregate effect of the overall index (FSI) and augmented the analysis to explore the impact of individual components of FSI on a household’s economic sentiments. The findings showed that increased financial stress results in lower house price expectations among households and has a stronger impact for the near term compared to a year ahead. Heterogeneity analysis reveals that a rise in FSI leads to an increase in expectations of house prices among women, Expectations are lower for the older population and people with less income stability. Asymmetric analysis reveals that house price expectations are more sensitive to high financial stress offering new insights into the cyclical nature of housing sentiment. The equity market and the foreign exchange market have the highest negative impact on house price expectations during financial stress.
All prospective authors are welcome to contact the editor prior to submission to ensure their paper is in an acceptable format for publication. This includes ensuring the submitted paper conforms to the published author guidelines for the journal which can reduce the time the paper spends in the double blind review process. Please contact the editor directly if I can be of assistance prior to submission and/or discuss the procedure for admission into the review process. If you are interested in submitting a research paper or reviewing potential publications, please contact the editor direct at ijhma@ijhma.com.
