Long-run drivers and integration in interprovincial Canadian housing price relations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vijay Kumar Vishwakarma
Keyword(s):  
Housing Market ◽  
Granger Causality ◽  
Global Financial Crisis ◽  
Housing Prices ◽  
Housing Price ◽  
Housing Markets ◽  
Content Type ◽  
Stochastic Trend ◽  
Long Run ◽  
Long Run Equilibrium

Purpose This paper aims to examine the integration of housing markets in Canada by examining housing price data (1999–2016) of six metropolitan areas in different provinces, namely, Calgary, Vancouver, Winnipeg, Toronto, Montreal and Halifax. The authors test for cointegration, driver cities of long-run relationships, long-run Granger causality and instantaneous causality in light of the global financial crisis (GFC) (2007–2008). Design/methodology/approach The authors use Johansen’s system cointegration approach with structural breaks. Moving average representation is used for common stochastic trend(s) analysis. Finally, the authors apply vector error correction model-based Granger causality and instantaneous causality. Findings Cities’ housing prices are in long-run equilibrium. Post-crisis Canadian housing markets became more integrated. The Calgary, Vancouver, Toronto and Montreal markets drive the Canadian housing market, leading all cities toward long-run equilibrium. Strong long-run Granger causality exists, but the authors observe no instantaneous causality. Price information takes time to disseminate, and long-run price adjustments play a significant role in causation. Practical implications The findings of cointegration increasing after the GFC and strong lead–lag can be used by investors to arbitrage and optimize portfolios. This can also help national and local policymakers in mitigating risk. Incorporating these findings can lead to better price forecasting. Originality/value This study presents many novelties for the Canadian housing market: it is the first to use repeat-sales regional pricing indices to test long-run behaviors, conduct common stochastic trend analyzes and present causality relations.

Housing market networks in China's major cities: a conditional causality approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lu Yang ◽  
Nannan Yuan ◽  
Shichao Hu
Keyword(s):  
Housing Market ◽  
Granger Causality ◽  
House Price ◽  
Housing Markets ◽  
Content Type ◽  
Scale Free ◽  
Second Tier ◽  
The Difference ◽  
Secondary Housing Market ◽  
Market Networks

PurposeTo explore the state of this conditional Granger causality when other cities are not factors, we investigate housing market networks in China's major cities by using a combination of conditional Granger causality and network analysis.Design/methodology/approachAlthough housing market networks have been well discussed for different countries, the question of housing market networks in China's major cities based on the conditional causality perspective has yet to be answered.FindingsWe discover that second-tier cities are more influential than first-tier cities. Although the connectivity of the primary housing market is more complex than the diversified connectivity observed in the secondary housing market, both markets are scale-free networks that exhibit high stability. Moreover, we reveal that geographic conditions and economic development jointly determine the housing market's modular hierarchical structure. Our results provide meaningful information for both Chinese policymakers and investors.Originality/valueBy excluding the influence of other cities, our conditional Granger causality identifies the true casual relation between cities' housing markets. Moreover, it is the first paper to consider the primary housing market and secondary housing market separately. Specifically, Chinese prefer new house rather than second-hand house from both speculative and self-housing. Generally speaking, the new house price is lower than the second-hand house price since the new house is off-plan property. Therefore, understanding the difference between primary and secondary housing markets will provide useful information for both policymakers and speculators.

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.

Testing for bubbles in housing markets: some evidence for Brazil

2018 ◽  
Vol 11 (5) ◽  
pp. 754-770 ◽  
Author(s):  
Cássio da Nóbrega Besarria ◽  
Nelson Leitão Paes ◽  
Marcelo Eduardo Alves Silva
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Housing Prices ◽  
Market Price ◽  
Housing Markets ◽  
Cointegration Analysis ◽  
Small Samples ◽  
Content Type ◽  
Long Run ◽  
Cointegration Tests

Purpose Housing prices in Brazil have displayed an impressive growth in recent years, raising some concerns about the existence of a bubble in housing markets. In this paper, the authors implement an empirical methodology to identify whether or not there is a bubble in housing markets in Brazil. Design/methodology/approach Based on a theoretical model that establish that, in the absence of a bubble, a long-run equilibrium relationship should be observed between the market price of an asset and its dividends. The authors implement two methodologies. First, the authors assess whether there is a cointegration relationship between housing prices and housing rental prices. Second, the authors test whether the price-to-rent ratio is stationary. Findings The authors’ results show that there is evidence of a bubble in housing prices in Brazil. However, given the short span of the data, the authors perform a Monte Carlo simulation and show that the cointegration tests may be biased in small samples. Therefore, the authors should be caution when assessing the results. Research limitations/implications The results obtained from the cointegration analysis can be biased for small samples. Practical implications The information on the excessive increase of the prices of the properties in relation to their fundamental value can help in the decision-making on investment of the economic agents. Social implications These results corroborate the hypothesis that Brazil has an excessive appreciation in housing prices, and, as Silva and Besarria (2018) have suggested, this behavior explains, in part, the fact that the central bank has taken this issue into account when deciding about the stance of monetary policy of Brazil. Originality/value The originality is linked to the use of the Gregory-Hansen method of cointegration in the identification of bubbles and discussion of the limitations of the research through Monte Carlo simulation.

scholarly journals Untangling the nonlinear “knots” of UK’s housing prices

2019 ◽  
Vol 46 (5) ◽  
pp. 1083-1103
Author(s):  
Constantinos Alexiou ◽  
Sofoklis Vogiazas
Keyword(s):  
Housing Market ◽  
House Prices ◽  
Housing Prices ◽  
Positive Association ◽  
Research Area ◽  
Content Type ◽  
Nonlinear Ardl ◽  
Long Run ◽  
Short Run ◽  
The Uk

Purpose Housing prices in the UK offer an inspiring, yet a complex and under-explored research area. The purpose of this paper is to investigate the critical factors that affect UK’s housing prices. Design/methodology/approach The authors utilize the recently developed nonlinear ARDL approach of Shin et al. (2014) over the period 1969–2016. Findings The authors find that both the long-run and short-run impact of the price-to-rent (PTR) ratio and credit-to-GDP ratio on house prices (HP) is asymmetric whilst ambiguous results are established for mortgage rates, industrial production and equities. Apart from the novel framework of analysis, this study also establishes a positive association between HP and the PTR ratio which suggests a speculative behaviour and could imply the formation of a housing bubble. Originality/value It is the first study for the UK housing market that explores the underlying fundamental relationships by looking at nonlinearities hence, allowing HP to be tied by asymmetric relationships in the long as well as in the short run. Modelling the inherent nonlinearities enhances significantly the understanding of UK housing market which can prove useful for policymaking and forecasting purposes.

Modelling housing prices and market fundamentals: evidence from the Sydney housing market

2019 ◽  
Vol 12 (4) ◽  
pp. 746-762 ◽  
Author(s):  
Md Abdullah Al-Masum ◽  
Chyi Lin Lee
Keyword(s):  
Investment Decision ◽  
Housing Prices ◽  
National Level ◽  
Housing Price ◽  
Capital City ◽  
Content Type ◽  
Price Series ◽  
Long Run ◽  
Investment Decision Making ◽  
Market Fundamentals

PurposeHousing prices in Sydney have increased rapidly in the past three decades. This leads to a debate of whether Sydney housing prices have departed from macroeconomic fundamentals. However, little research has been devoted to this area. Therefore, this study aims to fill this gap by examining the long-run association between housing prices and market fundamentals. Further, it also examines the long-run determinants of housing prices in Greater Sydney.Design/methodology/approachThe analysis of this study involves two stages. The first stage is to estimate the presence of long-run relationship between housing prices and market fundamentals with the Johansen and Juselius Cointegration test. Thereafter, the determinants of housing prices in Greater Sydney is assessed by using a vector error correction model.FindingsThe empirical results show that Sydney housing prices are cointegrated with market fundamentals in the long run. In addition, there is evidence to suggest that market fundamentals such as gross disposable income, housing supply, unemployment rate and gross domestic product are the key long-run determinants of Sydney housing prices, reflecting that Sydney housing prices, in general, can be explained by market fundamentals in the long run.Research limitations/implicationsThe findings enable more informed and practical policy and investment decision-making regarding the relation between housing prices and market fundamentals.Originality/valueThis paper is the first study to offer empirical evidence of the degree to which the behaviour of housing prices can be explained by market fundamentals, from a capital city instead of at a national level, using a relatively disaggregated dataset of housing price series for Greater Sydney.

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.

scholarly journals International Real Estate Review

2017 ◽  
Vol 20 (3) ◽  
pp. 375-396
Author(s):  
Gary Wai Chung Wong ◽  
◽  
Lok Sang Ho ◽  
Keyword(s):  
Interest Rate ◽  
Housing Market ◽  
Supply And Demand ◽  
Housing Prices ◽  
Housing Price ◽  
Price Expectations ◽  
Price Expectation ◽  
Long Run ◽  
Property Price ◽  
Explicit Consideration

This paper builds on the literature that shows policy often plays a key role in housing cycles. Using the cointegration approach which focuses on the supply and demand dynamics of the housing market, and with explicit consideration of housing price expectations proxied by the price-earning ratio in financial markets, this paper identifies two cointegrating relations: a long run demand-side relation that involves housing property price, interest rate, price expectation and income; and a supply-side relation that involves private housing completion, property price, interest rate, and building and land costs. Based on Hong Kong data from 1990 a£á¡§ 2012, which covers big cycles in the housing market, this paper suggests that policies to augment or restrain housing supply in the attempt to stabilize housing prices have been counterproductive.

Long-run dynamics between CFP and CSP in the GCC banking sector: estimation of non-stationary heterogeneous panels allowing for cross-sectional dependence

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ismail Ben Douissa ◽  
Tawfik Azrak
Keyword(s):  
Financial Performance ◽  
Granger Causality ◽  
Banking Sector ◽  
Social Performance ◽  
Content Type ◽  
Long Run ◽  
Equilibrium Relationship ◽  
Corporate Social ◽  
The Relationship ◽  
Long Run Equilibrium

Purpose Causality between corporate financial performance (CFP) and corporate social performance (CSP) has been extensively debated in previous research works; however, little research has been done to investigate the long-run dynamics between these two constructs. The purpose of this paper is to enrich the CFP–CSP literature by estimating the long-run equilibrium relationship between financial performance and social performance in the banking sector in the Gulf Cooperation Council countries over the period 2009–2019. Design/methodology/approach The paper adopts an approach that is primarily used in financial economics: first, the authors perform panel long-run Granger causality following Canning and Pedroni’s procedure to indicate the direction of the causal relationship. Second, the authors estimate an error correction model using Chudik and Pesaran’s (2015) dynamic common correlated effects mean group estimator to determine the sign of the relationship. Findings The present research findings prove the existence of a long-run equilibrium relationship between CFP and CSP, while indicating at the same time that panel Granger causality runs positively from CSP to CFP, which means that changes in CSP produce lasting changes in CFP. Practical implications The findings of the paper would guide strategists to build fit for purpose corporate social responsibility (CSR) strategies in their firms and establish a continuous investment in CSR activities in the long run rather than harshly investing in CSR activities in the short run. Originality/value To the best of the authors’ knowledge, this paper is the first one to address heterogeneity in long-run Granger causality tests to estimate the relationship between CSP and CFP.

scholarly journals Residential property market in Malaysia: an analysis of price drivers and co-movements

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hassanudin Mohd Thas Thaker ◽  
Mohamed Ariff ◽  
Niviethan Rao Subramaniam
Keyword(s):  
Housing Prices ◽  
Housing Price ◽  
Government Support ◽  
Policy Implications ◽  
Kuala Lumpur ◽  
Content Type ◽  
Time Frequency ◽  
Long Run ◽  
Residential Properties ◽  
Practical Implications

PurposeThe purpose of this paper is to identify the drivers of residential price as well as the degree co-movement of housing among different states in Malaysia.Design/methodology/approachThis study adopted an advanced econometrics technique: the dynamic autoregressive-distributed lag (DARDL) and – the time-frequency domain approach known as the wavelet coherence test. The DARDL model was applied to identify the cointegrating relationships and the CWT was used to analyze the co-movement and lead–lag relationships among four states’ regional housing prices. The extracted data were mainly on annual basis and comprised macroeconomics and financial factors. Information with regard to residential prices and other variables was extracted from the National Property Information Centre (NAPIC) website, the Central Bank of Malaysia Statistics Report, the Department of Statistics, Malaysia, I-Property.com and the World Bank (WB). The data covered in this study were the pool data from four main states in Malaysia and different categories of residential properties.FindingsThe empirical results indicate that there were long-run cointegration relationships between the housing price and capital gain and loss, rental per square feet, disposable income, inflation, number of marriages, deposit rate, risk premium and loan-to-value (LTV) ratio. While the wavelet analysis shows that (1) in the long run, Kuala Lumpur housing price having strong co-movement with Selangor, Penang and Melaka housing prices except for Johor and (2) the lead–lag relationship also postulates Kuala Lumpur housing price having in-phase category with Selangor, Penang and Melaka housing prices except for Johor.Practical implicationsThis study offers relevant practical implications. First, the study proposes an active collaboration between the private sector and government support which may help to smooth the pricing issue of residential properties. More low-cost residential projects are needed for focus groups including middle- and low-income earners. Furthermore, the results are expected to provide real estate investor in Malaysia, an improved understanding of the regional housing market price dynamics.Originality/valueThe findings of this study were obtained from various reliable sources; therefore, the results reflected the analysis of price drivers and co-movements. Furthermore, findings from this study lend some support to the argument on the rise of residential prices and offer several policy implications from a practical point of view with regard to the residential market.

scholarly journals Housing Price, Mortgage Interest Rate and Immigration

2020 ◽  
Vol 28 (3) ◽  
pp. 36-44
Author(s):  
Fennee Chong
Keyword(s):  
Interest Rate ◽  
Global Financial Crisis ◽  
Housing Prices ◽  
Housing Price ◽  
Mortgage Interest ◽  
Long Run ◽  
Mortgage Interest Rate ◽  
Short Run ◽  
The Global Financial Crisis ◽  
The Impact

AbstractHousing price in New Zealand has appreciated substantially after the Global Financial Crisis, resulting in an affordability problem for first home buyers. This paper studies whether changes in immigration activity and mortgage interest rate influence housing price. Empirical findings derived using VECM confirm the impact of immigration and mortgage interest rate on housing property price. Both variables explain 11.4 percent of the variation of Housing Index. An increase of 1 percent in mortgage interest rate would reduce the housing index movement by 1.44 percent whilst a 1 percent increase in immigrants would increase the housing index by 0.30 percent. In addition, about 2 percent of the short-run deviations of housing prices are adjusted towards the long-run equilibrium each month.

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