Housing prices in emerging countries during COVID-19: evidence from Turkey

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mustafa Tevfik Kartal ◽  
Serpil Kılıç Depren ◽  
Özer Depren
Keyword(s):  
Monetary Policy ◽  
Price Index ◽  
Housing Prices ◽  
Emerging Countries ◽  
Residential Property ◽  
Content Type ◽  
Macroeconomic Variables ◽  
Global Variables ◽  
Property Price ◽  
Market Variables

Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Social implications Emerging countries can make housing prices stable and affordable, which will increase homeownership. Hence, they can benefit from stability in housing markets. Originality/value 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.

Responsiveness of residential construction-production progress to house price dynamics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Le Ma ◽  
Henry Liu ◽  
Michael Sing
Keyword(s):  
Research Output ◽  
Housing Prices ◽  
Activity Level ◽  
Residential Construction ◽  
Residential Property ◽  
Price Dynamics ◽  
Content Type ◽  
Property Price ◽  
Under Construction ◽  
Practical Implications

Purpose This study aims to address the gap by empirically exploring how residential construction-production progress, which includes project commencement, under-construction and project completion, responds dynamically to fluctuations in house prices. Design/methodology/approach A vector autoregressive model and an impulse response function are applied to simulate and analyse the circle of the stage-responsiveness of residential construction to residential property price dynamics in the state of Victoria, Australia. The quarterly numbers of dwelling units commenced, under-construction and completed are used as the proxy for the residential construction activities at three stages over the construction progress. Findings The analysis indicates that the dynamics are essentially transmitted throughout the construction process and can substantially impact the pace of production progress. The findings from this study provide an empirical base that should be useful in developing price-elasticity and production theories applicable to the context of residential property construction. Research limitations/implications The findings described above have been generated basically by examining the case of Victoria, Australia at a macro level. The generalisation of the research output needs to be verified further by future researchers using data collected from other regions/countries. Nevertheless, the reliability of the conclusions with particular practical implications can be substantially improved by future researchers by analysing more markets and production proxies at the activity level. Practical implications Based on new empirical findings, this research argues that building activity (i.e. under construction) played as a gateway between the construction and housing sectors, via which the inter-responsiveness of the housing supply in terms of construction activities and housing prices are transmitted. Originality/value This research firstly attempts to explore the inter-responsiveness between the real estate and construction sectors. A simulated circle of the stage-responsiveness of residential construction to residential property price dynamics is proposed, which can serve as a significant foundation for developing the theory of construction production.

House price index (HPI) and Covid-19 pandemic shocks: evidence from Turkey and Kazakhstan

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yeşim Aliefendioğlu ◽  
Harun Tanrivermis ◽  
Monsurat Ayojimi Salami
Keyword(s):  
Real Estate ◽  
Price Index ◽  
Housing Prices ◽  
House Price ◽  
Monthly Basis ◽  
Data Set ◽  
House Price Index ◽  
Content Type ◽  
Asymmetric Pricing ◽  
Long Run

Purpose This paper aims to investigate asymmetric pricing behaviour and impact of coronavirus (Covid-19) pandemic shocks on house price index (HPI) of Turkey and Kazakhstan. Design/methodology/approach Monthly HPIs and consumer price index (CPI) data ranges from 2010M1 to 2020M5 are used. This study uses a nonlinear autoregressive distributed lag model for empirical analysis. Findings The findings of this study reveal that the Covid-19 pandemic exerted both long-run and short-run asymmetric relationship on HPI of Turkey while in Kazakhstan, the long-run impact of Covid-19 pandemic shock is symmetrical long-run positive effect is similar in both HPI markets. Research limitations/implications The main limitations of this study are the study scope and data set due to data constraint. Several other macroeconomic variables may affect housing prices; however, variables used in this study satisfy the focus of this study in the presence of data constraint. HPI and CPI variables were made available on monthly basis for a considerably longer period which guaranteed the ranges of data set used in this study. Practical implications Despite the limitation, this study provides necessary information for authorities and prospective investors in HPI to make a sound investment decision. Originality/value This is the first study that rigorously and simultaneously examines the pricing behaviour of Turkey and Kazakhstan HPIs in relation to the Covid-19 pandemic shocks at the regional level. HPI of Kazakhstan is recognized in the global real estate transparency index but the study is rare. The study contributes to regional studies on housing price by bridging this gap in the real estate literature.

Property investor decisions using income and rental ratio signals

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Billie Ann Brotman
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Discount Rate ◽  
Price Index ◽  
Housing Prices ◽  
Housing Price ◽  
Consumer Sentiment ◽  
Content Type ◽  
Income Ratio ◽  
Real Estate Prices

PurposeThis paper, a case study, aims to consider whether the income ratio and rental ratio tracks the formation of residential housing price spikes and their collapse. The ratios are measuring the risk associated with house price stability. They may signal whether a real estate investor should consider purchasing real property, continue holding it or consider selling it. The Federal Reserve Bank of Dallas (Dallas Fed) calculates and publishes income ratios for Organization for Economic Cooperation and Development countries to measure “irrational exuberance,” which is a measure of housing price risk for a given country's housing market. The USA is a member of the organization. The income ratio idea is being repurposed to act as a buy/sell signal for real estate investors.Design/methodology/approachThe income ratio calculated by the Dallas Fed and this case study's ratio were date-stamped and graphed to determine whether the 2006–2008 housing “bubble and burst” could be visually detected. An ordinary least squares regression with the data transformed into logs and a regression with structural data breaks for the years 1990 through 2019 were modeled using the independent variables income ratio, rent ratio and the University of Michigan Consumer Sentiment Index. The descriptive statistics show a gradual increase in the ratios prior to exposure to an unexpected, exogenous financial shock, which took several months to grow and collapse. The regression analysis with breaks indicates that the income ratio can predict changes in housing prices using a lead of 2 months.FindingsThe gradual increases in the ratios with predetermine limits set by the real estate investor may trigger a sell decision when a specified rate is reached for the ratios even when housing prices are still rising. The independent variables were significant, but the rent ratio had the correct sign only with the regression with time breaks model was used. The housing spike using the Dallas Fed's income ratio and this study's income ratio indicated that the housing boom and collapse occurred rapidly. The boom does not appear to be a continuous housing price increase followed by a sudden price drop when ratio analysis is used. The income ratio is significant through time, but the rental ratio and Consumer Sentiment Index are insignificant for multiple-time breaks.Research limitations/implicationsInvestors should consider the relative prices of residential housing in a neighborhood when purchasing a property coupled with income and rental ratio trends that are taking place in the local market. High relative income ratios may signal that when an unexpected adverse event occurs the housing market may enter a state of crisis. The relative housing prices to income ratio indicates there is rising housing price stability risk. Aggregate data for the country are used, whereas real estate prices are also significantly impacted by local conditions.Practical implicationsRatio trends might enable real estate investors and homeowners to determine when to sell real estate investments prior to a price collapse and preserve wealth, which would otherwise result in the loss of equity. Higher exuberance ratios should result in an increase in the discount rate, which results in lower valuations as measured by the formula net operating income dividend by the discount rate. It can also signal when to start reinvesting in real estate, because real estate prices are rising, and the ratios are relative low compared to income.Social implicationsThe graphical descriptive depictions seem to suggest that government intervention into the housing market while a spike is forming may not be possible due to the speed with which a spike forms and collapses. Expected income declines would cause the income ratios to change and signal that housing prices will start declining. Both the income and rental ratios in the US housing market have continued to increase since 2008.Originality/valueA consumer sentiment variable was added to the analysis. Prior researchers have suggested adding a consumer sentiment explanatory variable to the model. The results generated for this variable were counterintuitive. The Federal Housing Finance Agency (FHFA) price index results signaled a change during a different year than when the S&P/Case–Shiller Home Price Index is used. Many prior studies used the FHFA price index. They emphasized regulatory issues associated with changing exuberance ratio levels. This case study applies these ideas to measure relative increases in risk, which should impact the discount rate used to estimate the intrinsic value of a residential property.

scholarly journals Are House Prices Driven by Capital Flows? Evidence from Singapore

2016 ◽  
Vol 07 (01) ◽  
pp. 1650006 ◽  
Author(s):  
Hwee Kwan Chow ◽  
Taojun Xie
Keyword(s):  
Capital Flows ◽  
Price Index ◽  
House Prices ◽  
House Price ◽  
Capital Inflows ◽  
Residential Property ◽  
Foreign Direct Investments ◽  
Short Term ◽  
Property Price ◽  
The Impact

This paper investigates whether real house price appreciations can be attributed to the surge in real capital inflows into Singapore. We proxy capital flows by using the amount of Foreign Direct Investments (FDI) to real estate capturing the foreign purchases of property in Singapore which we deflate by the private residential property price index. Notwithstanding the absence of a cointegrating relationship, our results support the hypothesis that lagged short term fluctuations in capital inflows are positively associated with the growth rates of house prices over the last decade. We also provide evidence that macroprudential measures implemented by Singapore reduced the impact of capital inflows on house price appreciation by more than half, suggesting the effectiveness of such market cooling measures in weakening the credit growth channel.

scholarly journals Determinant of Residential Property Price Index in Five Asian Emerging Market Countries: A Demand and Supply Approach

2021 ◽  
Vol 7 (08) ◽  
Author(s):  
Silma Fikria Balqis ◽  
Rudi Purwono
Keyword(s):  
Economic Growth ◽  
Interest Rate ◽  
Real Interest Rate ◽  
Price Index ◽  
Emerging Market ◽  
Residential Property ◽  
Demand And Supply ◽  
Independent Variables ◽  
Emerging Market Countries ◽  
Property Price

This study aims to analyze the factors influencing the Residential Property Price Index (RPPI) from the demand and supply sides in five Asian emerging market countries. The data used are semi-annual data from the first semester of 2009 until the second semester of 2019 because this study aims to denote the impact of RPPI toward the demand and supply indicators after the global crisis in 2008. The dependent variable of this study is the RPPI, while the independent variables include the number of workers, real interest rate, economic growth, and the Real Effective Exchange Rate (REER). The Fixed Effects Model (FEM) is thus the applied method to process the data. In the end, the results indicate that all independent variables are significant toward the RPPI. The number of workers, real interest rate, and REER negatively affect the RPPI, while economic growth positively affects the RPPI.

Impact of macroeconomic indicators on housing prices

2019 ◽  
Vol 12 (6) ◽  
pp. 1055-1071 ◽  
Author(s):  
Satish Mohan ◽  
Alan Hutson ◽  
Ian MacDonald ◽  
Chung Chun Lin
Keyword(s):  
Price Index ◽  
Time Series Data ◽  
Statistical Significance ◽  
Housing Prices ◽  
Consumer Price Index ◽  
New York State ◽  
Housing Price ◽  
Series Data ◽  
Content Type ◽  
Macroeconomic Indicators

Purpose This paper uses statistical analyses to quantify the effects of five major macroeconomic indicators, namely crude oil price, 30-year mortgage interest rate (IR), Consumer Price Index (CPI), Dow Jones Industrial Average (DJIA), and unemployment rate (UR), on housing prices over time. Design/methodology/approach Housing price is measured as housing price index (HPI) and is treated as a variable affecting itself. Actual housing sale prices in the Town of Amherst, New York State, USA, 1999-2008, and time-series data of the macroeconomic indicators, 2000-2017, were used in a vector autoregression statistical model to examine the data that show the greatest statistical significance and exert maximum quantitative effects of macroeconomic indicators on housing prices. Findings The analyses concluded that the 30-year IR and HPI have statistically significant effects on housing prices. IR has the highest effect, contributing 5.0 per cent of variance in the first month to 8.5 per cent in the twelfth. The UR has the next greatest influence followed by DJIA and CPI. The disturbance from HPI itself causes the greatest variability in future prices: up to 92.7 per cent in variance 1 month ahead and approximately 74.5 per cent 12 months ahead. This result indicates that current changes in house prices heavily influence people’s expectation of future prices. The total effect of the error variance of the macroeconomic indicators ranged from 7.3 per cent in the first month to 25.5 per cent in the twelfth. Originality/value The conclusions in this paper, along with related tables and figures, will be useful to the housing and real estate communities in planning their business for the next years.

Asymmetric sensitivities of house prices to housing fundamentals: evidence from UK regions

2019 ◽  
Vol 12 (3) ◽  
pp. 442-455 ◽  
Author(s):  
Huthaifa Alqaralleh
Keyword(s):  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Housing Affordability ◽  
Star Model ◽  
Content Type ◽  
Macroeconomic Variables ◽  
North West ◽  
Country Level ◽  
The North

Purpose This paper aims to examine asymmetries in the house price cycle and to understand the dynamic of housing prices, incorporating macroeconomic variables at regional and country level, namely, housing affordability, the unemployment rate, mortgage rate and inflation rate. Design/methodology/approach To highlight significant differences in the asymmetric patterns of house prices between regions, the STAR model is adopted. Findings The authors highlight significant differences in the asymmetric patterns of house prices between regions, in which some areas showed asymmetric response over the housing cycle; here the LSTAR model outperforms other models. In contrast, some regions (the South West and the North West) showed symmetric properties in the tails of the cycle; therefore, the ESTAR model was adopted in their case. Practical implications Being limited to a few fundamentals, this study opens an avenue for further research to investigate this dynamic using in addition such demand-supply factors as land supply, construction cost and loans made for housing. These findings can also be used to examine whether other models such as ARIMA, exponential smoothing or artificial neural networks can more accurately forecast housing prices. Originality/value The present paper aims to highlight housing affordability as a cause of asymmetric behaviour in house prices. Put differently, the authors seek to understand the dynamics of housing prices with other fundamentals incorporating macroeconomic variables in regions and country level data as a means of achieving a more concise result.

Dynamic implications of GDP, interest rates, taxes, income, foreign direct investments, housing prices on property NPLs

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kuen-Wei Tham ◽  
Rosli Said ◽  
Yasmin Mohd Adnan
Keyword(s):  
Interest Rates ◽  
Global Financial Crisis ◽  
Housing Prices ◽  
Economic Environment ◽  
Macroeconomic Factors ◽  
Content Type ◽  
Macroeconomic Variables ◽  
Long Run ◽  
First Time

Purpose The study on how macroeconomic factors affect non-performing loans (NPLs) have not been focussed on property loans, which had been amongst the largest contributor of NPLs in many countries. At the same time, whilst there are many studies that focusses on NPLs during the recession and financial crises, not many studies focus on how macroeconomic factors affect property NPLs in a recovering economic environment. The purpose of this study seeks to fill the gap by analysing the relationships between gross domestic product (GDP), interest rates, income, foreign direct investments (FDI), housing prices and taxes on property NPLs with Malaysia as a case study in which NPLs rose for the first time after declining for almost a decade since the 2008–2009 global financial crisis. This study aims to understand the dynamics and direction of causation in relationships. Design/methodology/approach The author uses the auto regressive distribution lag analysis between the independent variables of GDP, interest rates, housing prices, service taxes, percapita income and FDI affecting the dependent variable of property NPLs from 2009 to 2017, during a unique period of recovering economic environment where NPLs rose for the first time in almost a decade of decline. Findings This study found that interest rates, housing prices, income, GDP and service taxes were found to possess long cause effects and long run elasticity with NPLs. At the same time, interest rates were found to implicate property NPLs significantly in longer periods, followed by GDP, housing prices, service taxes and income. FDIs were found to be insignificantly negative in implicating property NPLs in the long run. Research limitations/implications This paper allows policymakers to understand the dynamic implications of crucial macroeconomic factors in affecting NPLs so that appropriate strategic monetary policies could be formulated towards addressing them. More focus shall be given to addressing the long term implications of these factors on NPLs. Practical implications Appropriate strategic monetary policy making can be channelled towards addressing these factors via understanding the short and long term implications of macroeconomic variables on property NPLs. Policymakers can take note of the long cause effects and long run elasticity of average interest rates, housing prices, income levels, GDP and service taxes with property NPLs so that appropriate long term policies can be addressed to control the rise of property NPLs in the country. At the same time, priority should be given towards strengthening of the GDP of the country due to its strongest impact in long term effects with reduction of NPLs in the country. Social implications The insights from the present study suggest policymakers interested in bringing stability in the real estate finance system need to account for the various macroeconomic variables found in this study. Originality/value The paper is novel on at least two dimensions. First, this study involves focussing on a unique period of recovering economic environment where NPLs rose for the first time after a decade of decline since recovering from the 2008–2009 global financial crisis. At the same time, this study focusses on property NPLs, which is unique in nature compared to general NPLs. This study had enabled policymakers to better understand the dynamic implications of several macroeconomic variables affecting property NPLs and assist them in strategic monetary policy making.

The role of house price indices in managing the integration of finance and housing markets in the European Union

2014 ◽  
Vol 7 (3) ◽  
pp. 270-294 ◽  
Author(s):  
Richard Grover ◽  
Christine Grover
Keyword(s):  
European Union ◽  
Financial Integration ◽  
House Price ◽  
Quality Data ◽  
Residential Property ◽  
The European Union ◽  
Price Indices ◽  
Content Type ◽  
Price Bubbles ◽  
Property Price

Purpose – The article aims to examine why residential property price indices (RPPI) are important, particularly in the European Union (EU) with its highly integrated financial system and examines the problems in developing a pan-European price index that aggregates the indices of different countries. Design/methodology/approach – The reasons why RPPI are important is explored through a review of the literature on residential price bubbles and the issues with the indices through studies of individual examples. Findings – Financial integration in the EU has taken place without adequate consideration having been given to diversity in residential property markets. The development of means of monitoring them has lagged behind integration with the national price indices using a variety of methods and approaches to data that limit the extent to which they can be aggregated. Originality/value – The article shows the need for better quality data about house price trends in Europe if the consequences of future bubbles are to be avoided. Current initiatives are unlikely to satisfy this, as they leave too many choices about methodology and data in the hands of individual countries.

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