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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.

By considering the rapid and continuous increase of housing prices in Turkey recently, this study aims to examine the determinants of the residential property price index (RPPI). In this context, a total of 12 explanatory (3 macroeconomic, 8 markets and 1 pandemic) variables are included in the analysis. Moreover, the residential property price index for new dwellings (NRPPI) and the residential property price index for old dwellings (ORPPI) are considered for robustness checks.

A quantile regression (QR) model is used to examine the main determinants of RPPI in Turkey. A monthly time series data set for the period between January 2010 and October 2020 is included. Moreover, NRPPI and ORPPI are examined for robustness.

Predictions for RPPI, NRPPI and ORPPI are carried out separately at the country (Turkey) level. The results show that market variables are more important than macroeconomic variables; the pandemic and rent have the highest effect on the indices; The effects of the explanatory variables on housing prices do not change much from low to high levels, the COVID-19 pandemic and weighted average cost of funding have a decreasing effect on indices while other variables have an increasing effect in low quantiles; the pandemic and monetary policy indicators have a negative and significant effect in low quantiles whereas they are not effective in high quantiles; the results for RPPI, NRPPI and ORPPI are consistent and robust.

The results of the study emphasize the importance of the pandemic, rent, monetary policy indicators and interest rates on the indices, respectively. On the other hand, focusing solely on Turkey and excluding global variables is the main limitation of this study. Therefore, the authors encourage researchers to work on other emerging countries by considering global variables. Hence, future studies may extend this study.

The COVID-19 pandemic and market variables are determined as influential variables on housing prices in Turkey whereas macroeconomic variables are not effective, which does not mean that macroeconomic variables can be fully ignored. Hence, the main priority should be on focusing on market variables by also considering the development in macroeconomic variables.

Emerging countries can make housing prices stable and affordable, which will increase homeownership. Hence, they can benefit from stability in housing markets.

The QR method is performed for the first time to examine housing prices in Turkey at the country level according to the existing literature. The results obtained from the QR analysis and policy implications can also be used by other emerging countries that would like to increase homeownership to provide better living conditions to citizens by making housing prices stable and keeping them under control. Hence, countries can control housing prices and stimulate housing affordability for citizens.

This study aims to analyze the impact of technology-based corporation relocation on housing price indices during COVID-19 within the metropolitan areas of Austin, Texas and Seattle/Bellevue, Washington.The corporations under observation were Tesla and Amazon, respectively. The analysis intends to understand economic drivers behind the housing market and the radius of its effect while including fixed and random effects.

This study used a difference-in-difference (DID) method to evaluate changes in housing price index near and further away from Tesla’s and Amazon’s new corporate locations. The DID method allows for the capture of unique regional characteristics, as it requires a treatment and control group: housing price index and 5-mile and 10-mile search radii centered from the new corporate location.

The results indicated that corporate relocation announcements had a positive effect on housing price index post-pandemic. Specifically, the effect of Tesla’s relocation in Austin on the housing price index was not concentrated near the relocation site, but beyond the 5- and 10-mile radii. For Seattle/Bellevue, the effect of Amazon’s relocation announcement on housing price index was concentrated near the relocation site as well as beyond a 10-mile radius. Interestingly, these findings suggest housing markets incorporate speculation of prospective economic expansion linked with a corporate relocation.

Previous literature assessed COVID-19 housing market conditions and the economic effects of corporate relocation separately, whereas this study analyzed the housing price effects of corporate relocation during COVID-19. The DID method includes spatial and temporal analyses that allow for the impact of housing price to be observed across specified radii rather than a city-wide impact analysis.

This paper studies the dynamic effects of selected macroeconomic factors on the performance of the housing market in Kenya using Autoregressive Distributed Lag (ARDL) Models. This study aims to explain the dynamic effects of the macroeconomic factors on the three indicators of the housing market performance: housing prices growth, sales index and rent index.

This study used ARDL Models on time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited.

The results indicate that household income, gross domestic product (GDP), inflation rates and exchange rates have both short-run and long-run effects on housing prices while interest rates, diaspora remittance, construction output and urban population have no significant effects on housing prices both in the short and long run. However, only household income, interest rates, private capital inflows and exchange rates have a significant effect on housing sales both in the short and long run. Furthermore, household income, GDP, interest rates and exchange rates significantly affect housing rental growth in the short and long run. The findings are key for policymaking, especially at the appraisal stages of real estate investments by the developers.

The authors recommend the use of both the traditional hedonic models in conjunction with the dynamic models during real estate project appraisals as this would ensure that developers only invest in the right projects in the right economic situations.

The imbalance between housing demand and supply has prompted an investigation into the role of macroeconomic variables on the housing market in Kenya. Although the effects of the variables have been documented, there is a need to document the short-run and long-term effects of the factors to precisely understand the behavior of the housing market as a way of shielding developers from economic losses.

This study aims to establish the relationship between house acquisitions by foreigners (HAF) and house price index (HPI) in Turkey.

Due to the nature of this study, the data spans from January 2020 to March 2022. The house price index and the number of foreign house acquisitions across three provinces: Ankara, Izmir and Bursa, and national-level data were obtained from the TurkStat database. Consumer price index (CPI) and Turkish interest rates are control variables. In addition, monthly Turkish interest rates and CPI were obtained from the investing.com and TurkStat database, respectively. Furthermore, this study used autoregressive-distributed lag and Toda Yamamoto Granger causality models to avoid analysis bias. HPI and HAF are the variables used to accomplish the objectives of this study.

This study established a short-run equilibrium between foreign house acquisitions at the provincial and national levels. The short-run deviations were adjusted faster, ranging from 57.53% to 89.24% for some provinces, while Izmir is struggling to adjust at 6.48%. Both unidirectional and bidirectional Granger causality evidence suggests that the Turkish house price index increases at the national and provincial levels. This finding suggests the need for continuous policy intervention in the Turkish housing market because house prices play a pivotal role in Turkish economic development and daily lives.

This study’s scope and single-country study are its limitations. However, those limitations make the findings appropriate for the country of the study rather than generalising the results.

The study provides empirical evidence that foreign housing acquisition contributes negatively to housing affordability in Turkey and calls for authority intervention. This is because housing is considered shelter, a fundamental need to which citizens are expected to be entitled. Most citizens are low- and medium-income earners who may be unable to afford a house out of their income if it becomes costly. Once the expenditure to secure housing exceeds 30% of their income, it is considered unaffordable.

To the authors' best knowledge, this is the first empirical study that established the influence of foreign house acquisitions on Turkish house price increases and adversely reduced house affordability by Turkish citizens. The study is the first on foreign Turkish housing acquisition that used both theory of ownership and justice motivation theory to explain HAF.

The purpose of this study is to make an analysis of the short- and long-term effects of inflation, exchange rate, housing interest rate, industrial production index, total housing loans and housing volume on housing inflation in Turkey, taking into account the multiple structural breaks.

Multiple structural break Lee–Strazicich unit root test, autoregressive distributed lag bound test and Granger causality test based on error correction model were used.

There is both a short- and long-term relationship between housing prices and macrovariables. Housing prices are mostly affected by housing interest rates, housing volume, real exchange rate and total housing loans in the short run. In the long run, it is mostly affected by total housing loans, housing volume and housing interest rates.

The variables used in the analysis are: housing price index, consumer price index, dollar rate, housing interest rate, industrial production index, total housing loan amount and domestic loan volume. Because the data that variables started common is 2010:M01, the period starting from this date until 2021:M12 is considered. The research covers only Turkey as a country. Determining the micro- and macroeffects of housing prices can always offer solutions for the problems experienced in housing supply and housing demand.

While investigating housing prices, there are no studies in which total housing loans and housing volume are included in the model together. However, it is important to analyze the effect of the current conjuncture, in which there has been constant increases in foreign exchange rates and high inflation in recent years, on housing prices in Turkey. In this study, investigating these effects by using econometric methods that include structural breaks also increases the original value of this study.

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.

The purpose of this study is designed to achieve the learning process in producing studies involving economic issues and scenarios in business management in Malaysia. In addition, this study will provide exposure to the integration of managerial skills by using both microeconomics and macroeconomics concepts and theories to aid decision-making in a business environment.

The research method comprised qualitative methodology of literature review, case study and quantitative methodology of multiple linear regression (MLR). In this case, seven microeconomics and macroeconomics factors which are believed to significantly affect house price index (HPI) are taken into consideration which includes gross domestic product, consumer price index (CPI), government tax and subsidy on housing, overnight policy rate, unemployment rate (UNEMP), the median income (INC) and cost of production index.

This research has resulted in three significant factors affecting HPI from MLR, which include CPI, UNEMP and INC where the increase of these factors will cause a high increment of HPI. The other four factors are not significant.

Malaysia has been facing the stagnancy in house market these recent years due to issues such as massive oversupply, impacting Malaysia’s economy specifically focusing on domestic direct investment. To avoid oversupply issues, the vitality of future house demand and pricing forecast should be comprehended by involved bodies for more effective planning for the house development industry. To make a better and bigger impact, this research is intended to analyse the microeconomic and macroeconomic factors affecting the HPI to better understand the significance of each of these factors to the changes of HPI to resolve these economic issues.

This study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and housing sales on the real housing prices.

This study uses a nonlinear autoregressive distributed lag (NARDL) model in the monthly period of 2010:1–2021:10.

The real effective exchange rate has a positive and symmetric effect. The decreasing effect of negative changes in real money supply on real housing prices is higher than the increasing effect of positive changes. Only positive changes in the real construction cost index have an increasing and statistically significant effect on real house prices, while only negative changes in housing sales have a small negative sign and a small increasing effect on housing prices. The fact that the positive and negative changes in real mortgage rates are negative and positive, respectively, indicates that both have a reducing effect on real housing prices.

This study suggests the first NARDL model that investigates the asymmetric effects on real housing prices instead of nominal housing prices for Turkey. In addition, the study is the first, to the best of the authors’ knowledge, to examine the effects of the five real variables on real housing prices.

Increasing overhang of serviced apartments poses a serious concern to the national property market. This study aims to examine the impacts of macroeconomic determinants, namely, gross domestic product (GDP), consumer confidence index (CF), existing stocks (ES), incoming supply (IS) and completed project (CP) on serviced apartment price changes.

To achieve more accurate, quality price changes, a serviced apartment price index (SAPI) was constructed through a self-developed hedonic price index model. This study has collected 1,567 transaction data in Kuala Lumpur, covering 2009Q1–2018Q4 for price index construction and data were analysed using the vector autoregressive model, the vector error correction model and the fully modified ordinary least squares (OLS) (FMOLS).

Results of the regression model show that only GDP, ES and IS were significantly associated with SAPI, with an R² of 0.7, where both ES and IS have inverse relationships with SAPI. More precisely, it is predicted that the price of serviced apartments will be reduced by 0.56% and 0.21% for every 1% increase in ES and IS, respectively.

Therefore, government monitoring of serviced apartments’ future supply is crucial by enforcing land use-planning regulations via stricter development approval of serviced apartments to safeguard and achieve more stable property prices.

By adopting an innovative approach to estimating the response of price change to supply and demand in a situation where there is no price indicator for serviced apartments, the study addresses the knowledge gap, especially in terms of understanding what are the key determinants of, and to what extent they influence, the SAPI.