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The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

Sustainability represents innovative elements in determining the profitability of real estate investments, among other factors, including the green component in real estate. Evidence from the literature has pointed out that incorporating green features into residential buildings can reduce operational costs and increase the building’s value. Although green real estate is considered the future trend of choice, it is still being determined whether prospective buyers are willing to accept the extra cost of green residential investment. Therefore, this study aims to investigate the effect of housing attributes and green certification on residential real estate prices.

The impact of the housing attribute and green certification in the residential sectors was assessed using a transaction data set comprising approximately 861 residential units sold in Selangor, Malaysia, between 2014 and 2022. Linear and quantile regression were used in this study by using SPSS software for a robust result.

The findings indicate that the market price of residential properties in Malaysia is influenced by housing attributes, transaction types and Green Building Index certification. The empirical evidence from this study suggests that green certification significantly affects the sales price of residential properties in Malaysia. The findings of this research will help investors identify measurable factors that affect the transaction prices of green-certified residential real estate. These identifications will facilitate the development of strategic plans aimed at achieving sustainable rates of return in the sustainable residential real estate market.

Specifically, this research will contribute to achieving area 4 of the 11th Malaysia Plan, which pertains to pursuing green growth for sustainability and resilience. This will be achieved by enhancing awareness among investors and homebuyers regarding the importance of green residential buildings in contributing to the environment, the economy and society.

The regression model for housing attributes and green certification on house price developed in this study could offer valuable benefits to support and advance Malaysia in realising its medium and long-term goals for green technology.

The purpose of this study is to analyse numerous aspects affecting residential property price in Malaysia against macroeconomics issues such as gross domestic product (GDP), exchange rate, unemployment and wage.

The hedonic pricing model has been adopted as econometric model for this research to investigate the relationship between residential property price against macroeconomics indicator. The data for residential property price and macroeconomic variables were collected from 1991 to 2019. Multiple linear regression had been adopted to find the relationship between the dependent and independent variables.

The result shows that the GDP has a significant positive impact on residential property price, while exchange rate has no significant impact although it was positive. In addition, the unemployment rate has a significant impact on the residential property price and has a negative relationship. Similar to the wage that shows the negative relationship with residential property prices. Moreover, during the pandemic COVID-19 in Malaysia, this research shows a more transparent view of the relationship between residential property price and the macroeconomic issues of GDP, exchange rate, unemployment and wage.

The findings of this research found that macroeconomics issue cannot be eliminated due to Malaysia is a developing country, and there will always be an issue that will happen, but the issues can be reduced to maximise the advantages, e.g. during COVID-19, the solution to fight against COVID-19 were crucial and weaken the macroeconomics issues.

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.

The purpose of this study is to identify the situation of spatial inequality in the residential system of Tehran city in terms of housing prices in the year 2021 and to examine its changes over time (1991–2021).

In terms of purpose, this study is applied research and has used a descriptive-analytical method. The statistical population of this research is the residential units in Tehran city 2021. The average per square meter of a residential unit in the level of city neighborhoods was entered in the geographical information system (GIS) in 2021. Moran’s spatial autocorrelation method, map cluster analysis (hot and cold spots) and Kriging interpolation have been used for spatial analysis of points. Then, the change in spatial inequality in the residential system of Tehran city has been studied and measured based on the price per square meter of a residential unit for 30 years in the 22 districts of Tehran by using statistical clustering based on distance with standard deviation.

The result of spatial autocorrelation analysis with a score of 0.873872 and a p-value equal to 0.000000 indicates a cluster distribution of housing prices throughout the city. The results of hot spots show that the highest concentration of hot spots (the highest price) is in the northern part of the city, and the highest concentration of cold spots (the lowest price) is in the southern part of Tehran city. Calculating the area and estimating the quantitative values of data-free points by the use of the Kriging interpolation method indicates that 9.95% of Tehran’s area has a price of less than US$800, 17.68% of it has a price of US$800 to US$1,200, 25.40% has the price of US$1,200 to US$1,600, 17.61% has the price of US$1,600 to US$2,000, 9.54% has the price of US$2,000 to US$2,200, 6.69% has the price of US$2,200 to US$2,600, 5.38% has the price of US$2,600 to US$2,800, 4.59% has the price of US$2,800 to US$3,200 and finally, the 3.16% has a price more than US$3,200. The highest price concentration (above US$3,200) is in five neighborhoods (Zafaranieh, Mahmoudieh, Tajrish, Bagh-Ferdows and Hesar Bou-Ali). The findings from the study of changes in housing prices in the period (1991–2021) indicate that the southern part of Tehran has grown slightly compared to the average range, and the western part of Tehran, which includes the 21st and 22nd regions with much more growth than the average price.

There is massive inequality in housing prices in different areas and neighborhoods of Tehran city in 2021. In the period under study, spatial inequality in the residential system of Tehran intensified. The considerable increase in housing prices in the housing market of Tehran has made this sector a commodity, intensifying the inequality between owners and non-owners. This increase in housing price inequality has caused an increase in the informal living for the population of the southern part. This population is experiencing a living situation that contrasts with the urban plans and policies.

This study aims to investigate dynamic relationships among residential housing price indices of ten major Chinese cities for the years 2005–2021.

Using monthly data, this study uses vector error correction modeling and the directed acyclic graph for characterization of contemporaneous causality among the ten indices.

The PC algorithm identifies the causal pattern and the Linear Non-Gaussian Acyclic Model algorithm further determines the causal path, from which this study conducts innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tiers of cities.

This study suggests that policies on residential housing prices in the long run might need to be planned with particular attention paid to these top tiers of cities.

Mauritius’s residential real estate sector has undergone an increase in foreign investment over the past decades. This study aims to establish if the increasing level of foreign real estate investments (FREI) has increased land demand and land prices. The study also aims to depict whether the relation between FREI and land prices prevails at an aggregate and/ or a regional level.

Data from 26 regions, classified as urban, rural and coastal is collected on an annual basis over the period 2000 to 2019, and a dynamic panel regression framework, namely, an autoregressive distributed lag model, is used to take into account the dynamic nature of land price modeling.

The findings show that, at the aggregate level, in the long-term, FREI does not have a significant influence on land prices, while in the short term, a positive significant relationship is noted between the two variables. A regional breakdown of the data into urban, rural and coastal was done. In the long term, only in coastal regions, a positive significant link was observed, whereas in urban and rural regions FREI did not influence land prices. In the short term, the positive link subsists in the coastal regions, and in rural regions also land prices are positively affected by FREI.

Unlike other studies which have used quite general measures of FREI, the present research has focused on FREI mainly undertaken in the residential real estate market and how these have affected residential land prices. This study also contributes to research on the determinants of land prices which is relatively scarce compared to research on housing prices.

While the declining rate of urban security and its potential effects have been globally acknowledged, the ways urban neighborhood security shapes real estate markets in African cities remain largely unexplained. The purpose of this paper therefore is to present the findings from a study of the nexus between urban neighborhood security and home rental prices in Lagos, Nigeria.

This paper is based on the hedonic price theory, an objectively derived urban neighborhood security index (UNSI) and property rental price data in Ojo, Lagos, Nigeria. This is a quantitative cross-sectional study that employs multistage sampling survey procedure. Data are analyzed using descriptive statistics, nonparametric correlation and hedonic price function with ordinary least squares (OLS).

Results show that nearly 50% of the study area is prone to insecurity and average rental values in Ojo, Lagos range from N151329.41 ($302.66) to N167333.33 ($334.67) per annum. Correlation analysis shows that home rental prices have high, positive and significant correlations (rs = 0.725 and p < 0) with UNSI. After controlling for neighborhood and structural factors, it is found that urban neighborhood security positively influences home rental values as a unit improvement in security leads to N81000.00 ($162.00) increase in rental value per annum.

Urban neighborhood security risk threatens residential property values, creates unintended residential mobility and destabilizes families. Findings from this study point to the facts that security is a key component of urban housing values and developers, and real estate investors must ensure that this component is well factored into property design, construction and valuation.

This is perhaps the first study that uses an objectively derived UNSI to study home rental price dynamics in Nigeria. The study extends knowledge on urban housing price determinants and contributes to literature on the crucial place of security in property management.

This study aims to examine the impact of lending liquidity on house prices especially during black swan events such as the Global Financial Crisis of 2007–08 and COVID-19. Homeownership is an important goal for many, and house prices are a significant driver of household wealth and the wider economy. This study argues that excessive liquidity from central banks may be driving house price increases, despite negative changes to fundamental drivers. This study contributes to the literature by examining lending liquidity as a driver of house prices and evaluating the efficacy of fiscal policies aimed at boosting liquidity during black swan events.

This study aims to examine the impact of quantitative easing on Australian house prices during back swan events using data from 2004 to 2021. All macroeconomic and financial data are freely available from official sources such as the Australian Bureau of Statistics and the nation's Central Bank. Methodology wise, given the problematic nature of the data such as a mixed order of integration and the possibility of cointegration among some of the I(1) variables, the auto-regressive distributed lag model was selected given its flexibility and relative lack of assumptions.

The Australian housing market continued to perform well during the COVID-19 pandemic, with the house price index reaching an unprecedented high towards the end of 2021. Research using data from 2004 to 2021 found a consistent positive relationship between house prices and housing finance, as well as population growth and the value of work commenced on residential properties. Other traditional drivers such as the unemployment rate, economic activity, stock prices and income levels were found to be less significant. This study suggests that quantitative easing implemented during the pandemic played a significant role in the housing market's performance.

Given the severity of COVID-19, policymakers have responded with fiscal and monetary measures that are unprecedented in scale and scope. The full implications of these responses are yet to be completely understood. In Australia, the policy interest rate was reduced to a historic low of 0.1%. In the following periods house prices appreciated by over 20%. The efficacy of quantitative easing and associated fiscal policies aimed at boosting liquidity to mitigate the impact of black swan events such as the pandemic has yet to be tested empirically. This study aims to address that paucity in literature by providing such evidence.

This paper aims to examine the impact of coastline on the rental value of residential property in proximity to the coastline, using the hedonic pricing model from two perspectives. First, Model 1A–C accounted for estimating the influence of coastal amenities while controlling for other housing attributes influencing rent. Second, Model 2A–C accounted for the interaction between coastal amenities/disamenities and other housing attributes influencing rent.

A survey approach was adopted for the data collection process. For both models, property values were measured in proximity to coastline using 0–250 m, 251–500 m and 0–500 m.

Findings revealed that property rental value increases as we move away from the coastline when disamenities are not controlled. The results suggested that for a mean-priced home (N2,941,029 or $8,170) at the mean distance from the coastline (301.83 m), a 1% increase in distance from the coastline would result in a 0.001% or N9.77 ($0.03) increase in rental value.

The implication to real estate valuers is that varying premiums should be considered when valuing a property depending on the distance to the coastline while considering other housing attributes.

This research introduces a novel approach to the hedonic model for determining property values in proximity to coastal environment by estimating the influence of coastal amenities while controlling for other housing attributes influencing rent, on the one hand, and accounting for the interaction between coastal amenities/disamenities and other housing attributes influencing rent, on the other.