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A trend-cycle analysis of the evolutionary trajectory of Australian housing prices
Abstract
Time-frequency domain analysis of housing prices can provide insights into significant periodic patterns in the pricing dynamics for modelling and forecasting purposes. This study applied wavelet and information entropy analyses to examine the periodic patterns and evolution of housing prices in Australia’s eight capital cities from 1980 to 2023, using quarterly median house pricing data. Our findings revealed consistent patterns of higher variability in housing prices at high frequencies across all cities, with Melbourne exhibiting the highest variability. This indicates that short-term price fluctuations were higher than long-term changes. Perth And Brisbane demonstrated notable cyclical patterns with recurring periods of growth and decline. Coherence analyses revealed dynamic lead–lag relationships, both positive and negative,between housing prices of various cities, suggesting interconnectedness but not always synchronisation. Some cities’ housing prices dominated the information sharing, indicating varying degrees of influence within the national market. These findings highlight the complex, dynamic interdependencies among Australia’s major city housing markets, providing valuable insights for policymakers,investors, and stakeholders in economic forecasting, policy development and strategic planning within these markets.
Supplementary resource (1)
Data
January 2025
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Objective: The article aims to investigate the synchronisation of metropolitan house prices from 2000 to 2020 based on novel empirical data. We applied several econometric procedures to address the cyclicity of housing markets.
Research Design & Methods: Using a unique dataset of over four million offers, we investigated the synchronisation of 18 regional housing markets in Poland from 2000 to 2020. The study used spectral analysis.
Findings: The study proved the strong synchronisation of housing cycles in Polish regional housing markets. Moreover, when the cycle amplitude was considered, the econometric analysis showed regional disparities in the cyclical behaviour of housing prices. According to the research, Lublin and Opole had the lowest housing cycle magnitude, and Gdańsk, Katowice, and Łódź had the largest magnitude.
Implications & Recommendations: The information about housing cycles has important policy implications. Housing wealth effects generated by cyclical house prices may be more persistent than financial wealth effects. Consequently, they may substantially affect consumption dynamics. Therefore, precisely identifying market cycles and differences in cycle synchronisation is vital when constructing an effective, selective, and well-timed monetary policy.
Contribution & Value Added: The research narrows the knowledge gap on the synchronisation of housing markets in an emerging economic environment. With their distinct institutional structures, Eastern European countries have little knowledge of this issue.
Despite the extensive theoretical connections between defense budget growth and inflation, empirical findings based on traditional time-domain methods have been inconclusive. This study reexamines the issue from a time-frequency perspective. Applying continuous wavelet analysis to the U.S. and Britain, it shows empirical evidence in support of positive bilateral effects in both cases. In the bivariate context, U.S. defense budget growth promoted inflation at 2-to 4-year cycles in the 1840s and at 8-to 24-year cycles between 1825 and 1940. Conversely, inflation accelerated defense spending growth at 5-to 7-year cycles in the 1830s and at 25-to 64-year cycles between 1825 and 1940. Similarly, British defense budget growth spurred inflation at 8-to 48-year cycles between 1890 and 1940 and at 50-to 65-year cycles between 1790 and 1860. Inflation fueled the growth of defense spending at 7-to 20-year cycles between 1840 and 1870, in the 1940s, and in the 1980s. Preliminary results from multivariate analyses are also supportive, though there is a need for further research that is contingent on advancements in the wavelet method in the direction of simulation-based significance tests.
Study region
Upper Blue Nile River Basin, Ethiopia
Study focus
This study aims to determine possible trends in monthly, seasonal and annual precipitation and streamflow series. The Discrete Wavelet Transform (DWT) was used to obtain detailed information about the time-frequency conditions of six meteorological and five streamflow stations during 1984–2014. The Mann-Kendall test was also applied to the original and the DWT components to determine which periodicities are dominant in reproducing the observed trends. The combined DWT and Mann-Kendall test has not been explored in the UBNRB context. We used three criteria to determine a suitable wavelet, decomposition level, and signal extension mode. From these criteria, the bias ratio between the wavelet approximation series and the original time series was proposed and explored in this study.
New hydrological insights for the region
Results show that almost all the precipitation stations experience an insignificant but increasing trend, whereas the streamflow stations exhibit an increasing trend at a 5% significance level. The combined use of DWT and the Mann-Kendall test show that periodic events up to 4 years are more influential in affecting the observed trends.
The purpose of this paper is to provide a dating system for the Italian residential real estate market from 1927 to 2019 and investigate its interaction with credit and business cycles.
To detect the local turning point of the Italian residential real estate market, we apply the honeycomb cycle developed by Janssen et al. (1994) based on the joint analysis of house prices and the number of transactions. To this end, we use a unique historical reconstruction of house price levels by Baffigi and Piselli (2019) in addition to data on transactions.
Our study confirms the validity of the honeycomb model for the last four decades of the Italian housing market. In addition, our results show that the severe downsizing of the housing market is largely associated with business and credit contraction, certainly contributing to exacerbating the severity of the recession. Finally, preliminary evidence suggests that whenever a price bubble occurs, it is coincident with the start of phase 2 of the honeycomb cycle.
To the best of our knowledge, this is the first time that the honeycomb approach has been tested over such a long historical period and compared to the cyclic features of financial and real aggregates. In addition, even if the honeycomb cycle is not a model for detecting booms and busts in the housing market, our preliminary evidence might suggest a role for volume/transactions in detecting housing market bubbles.
We study the cross‐country dimension of financial cycles for six euro area countries using wavelet analysis. Estimated wavelet cohesions show that cycles in equity prices and interest rates display stronger synchronization across countries than real output cycles, whereas credit variables and house prices show lower cross‐country synchronization. We propose a wavelet‐based extension to the spectral envelope that is similar to a frequency‐based time‐varying principal component analysis. The country loadings show that, contrary to all other variables, cycles in loans to households and house prices in Germany and the Netherlands are negatively or less strongly correlated with the common cycles.
Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. The assumption of fixed time delay also exists in Transfer Entropy, which is considered to be a non-linear version of Granger causality. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop Variable-lag Granger causality and Variable-lag Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the fixed time delay and allow causes to influence effects with arbitrary time delays. In addition, we propose methods for inferring both Variable-lag Granger causality and Transfer Entropy relations. In our approaches, we utilize an optimal warping path of Dynamic Time Warping to infer variable-lag causal relations. We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets. Our approaches can be applied in any domain of time series analysis. The software of this work is available in the R-CRAN package: VLTimeCausality.
This article argues that the explosion of mortgage finance has not led to a proportional expansion of housing supply across 17 countries in a historical perspective (1913-2016). Based on a collection of housing construction data, it shows that the co-cyclical behavior of construction, prices and mortgage credit has been followed by a decoupling of house-price mortgage spirals from the underlying stagnating or declining construction activity since the 1980s. Mortgage debt is nonlinearly associated with new construction: positive up to a threshold, negative thereafter. The article argues that the increasing use of housing as an asset, or housing financialization, can explain why mortgages grow without construction, i.e. through privatization of state housing and supply restrictions as a result of rentier strategies of housing-market insiders and private developers. Private mortgage markets have thus been a less reliable policy alternative to traditional state-led housing construction policies. The article confirms for housing what has previously been found for growth or capital formation: beyond a certain threshold, there is a curse of too much finance.
Recognising the rapid increase in housing prices and the presence of socio-economic and demographic disparities that often characterise a metropolitan city, we adopted a sub-city approach and deployed an array of methods to detect bubbles in the different regions of Greater Sydney – western, inner-west, southern, eastern and northern – over 1991 to 2016, using Westerlund error correction-based panel cointegration, backward supremum augmented Dickey–Fuller (BSADF) procedure, and dynamic ordinary least square (DOLS) tests. Our cointegration results show no evidence of cointegration between real house price and rent in the western region. However, there is strong evidence of cointegration in the eastern and northern regions. This confirms the existence of housing submarkets in Greater Sydney, and an indication of housing price bubbles in Western Sydney. Further, the formal BSADF bubble tests reveal strong evidence of explosive price bubbles in Western Sydney, while there is no comparable evidence for the other regions of Sydney, which further highlights the importance of submarket analysis. Importantly, the DOLS results suggest that housing investment plays a major role in the build-up of housing bubbles in Western Sydney, supporting Shiller's Psychological Theory of bubbles which posits that bubbles occur via the speculative behaviour of investors. The implications of the findings are also discussed.
Noninvasive brain stimulation (NIBS) techniques, such as transcranial magnetic stimulation or transcranial direct and alternating current stimulation, are advocated as measures to enable causal inference in cognitive neuroscience experiments. Transcending the limitations of purely correlative neuroimaging measures and experimental sensory stimulation, they allow to experimentally manipulate brain activity and study its consequences for perception, cognition, and eventually, behavior. Although this is true in principle, particular caution is advised when interpreting brain stimulation experiments in a causal manner. Research hypotheses are often oversimplified, disregarding the underlying (implicitly assumed) complex chain of causation, namely, that the stimulation technique has to generate an electric field in the brain tissue, which then evokes or modulates neuronal activity both locally in the target region and in connected remote sites of the network, which in consequence affects the cognitive function of interest and eventually results in a change of the behavioral measure. Importantly, every link in this causal chain of effects can be confounded by several factors that have to be experimentally eliminated or controlled to attribute the observed results to their assumed cause. This is complicated by the fact that many of the mediating and confounding variables are not directly observable and dose–response relationships are often nonlinear. We will walk the reader through the chain of causation for a generic cognitive neuroscience NIBS study, discuss possible confounds, and advise appropriate control conditions. If crucial assumptions are explicitly tested (where possible) and confounds are experimentally well controlled, NIBS can indeed reveal cause–effect relationships in cognitive neuroscience studies.
This paper presents a fresh understanding of the vigorous connection between inward FDI, Renewable Energy Consumption, Economic Growth and Carbon Emission in the Chinese economy employing novel Morlet’ wavelet analysis. Wavelet correlation, continuous wavelet transform and partial and the multiple wavelet coherence analyses are applied on variables under study for data acquired during the period 1979 to 2017. The outcome of these analyses reveals that the connections among the variables progress over frequency and time. From the frequency domain point of view, the current study discovers noteworthy wavelet coherence and robust lead and lag linkages, although, time-domain reveals in-consistent associations among the considered variables. The wavelet analysis, according to the economic point of view supports that inward foreign direct investment (FDI) and renewable energy consumption helps to enhance economic condition in the Chinese economy. The results also suggested that inward FDI enhances environmental degradation in the medium and long run in China. The results emphasize the significance of having organized strategies by the policymakers to cope with huge environmental degradation occurred for a couple of decades in China.
Researchers increasingly wish to estimate time-varying parameter (TVP) regressions which involve a large number of explanatory variables. Including prior information to mitigate over-parameterization concerns has led to many using Bayesian methods. However, Bayesian Markov Chain Monte Carlo (MCMC) methods can be very computationally demanding. In this paper, we develop computationally efficient Bayesian methods for estimating TVP models using an integrated rotated Gaussian approximation (IRGA). This exploits the fact that whereas constant coefficients on regressors are often important, most of the TVPs are often unimportant. Since Gaussian distributions are invariant to rotations we can split the the posterior into two parts: one involving the constant coefficients, the other involving the TVPs. Approximate methods are used on the latter and, conditional on these, the former are estimated with precision using MCMC methods. In empirical exercises involving artificial data and a large macroeconomic data set, we show the accuracy and computational benefits of IRGA methods.
Despite numerous studies investigating house price diffusion between regional cities, few have considered a spillover effect among housing submarkets within a metropolitan city. This study expands upon the limited literature to examine house price diffusion of housing submarkets (namely, low-priced and high-priced submarkets) in Greater Sydney, one of the most diverse housing markets in Australia, using convergence tests, cointegration techniques, Granger causality and dynamic ordinary least square cointegration tests. The results show that a long-run relationship in house prices exists between these two submarkets in Greater Sydney. Importantly, the empirical results show that a large degree of diffusion takes place from the less prosperous submarket to the high-end submarket. This supports the equity transfer hypothesis via a filtering process in which house prices in the low-priced submarket will be transmitted into the high-priced submarket. The study also finds that the low-priced submarket is the primary reactor to changes in economic fundamentals. These findings have some profound implications for policy-makers and housing investors.
Non-stationary time series (TS) analysis has gained an explosive interest over the recent decades in different applied sciences. In fact, several decomposition methods were developed in order to extract various components (e.g., seasonal, trend and abrupt components) from the non-stationary TS, which allows for an improved interpretation of the temporal variability. The wavelet transform (WT) has been successfully applied over an extraordinary range of fields in order to decompose the non-stationary TS into time-frequency domain. For this reason, the WT method is briefly introduced and reviewed in this paper. In addition, this latter includes different research and applications of the WT to non-stationary TS in seven different applied sciences fields, namely the geo-sciences and geophysics, remote sensing in vegetation analysis, engineering, hydrology, finance, medicine, and other fields, such as ecology, renewable energy, chemistry and history. Finally, five challenges and future works, such as the selection of the type of wavelet, selection of the adequate mother wavelet, selection of the scale, the combination between wavelet transform and machine learning algorithm and the interpretation of the obtained components, are also discussed.
This paper proposes a hedonic regression model to estimate housing prices and the spatial variability of prices over multiple years. Using the model, maps are obtained that represent areas of the city where there have been positive or negative changes in housing prices. The regression-cokriging (RCK) method is used to predict housing prices. The results are compared to the cokriging with external drift (CKED) model, also known as universal cokriging (UCK). To apply the model, heterotopic data of homes for sale at different moments in time are used. The procedure is applied to predict the spatial variability of housing prices in multi-years and to obtain isovalue maps of these variations for the city of Granada, Spain. The research is useful for the fields of urban studies, economics, real estate, real estate valuations, urban planning, and for scholars.
This paper examines the asymmetric behaviour of house prices in large metropolitan areas. Using a sample of large cities, in several countries, it is shown that real estate prices cycles are largely nonlinear. It is found that dynamic asymmetries in the housing market cycle can well be modelled using a logistic smooth transition model (LSTAR). Further, it is shown that the LSTAR model has better forecasting properties with respect to a linear autoregressive model
This paper analyses the low‐frequency temporal variation of unit land prices in two single localities, the municipalities of Espoo and Nurmijärvi, of the Finnish real estate markets by the use of the wavelet transforms. These transforms are nonparametric orthogonal series estimators, which are capable of providing the necessary time and frequency information of land prices simultaneously in a highly flexible fashion. In the empirical section of this paper both the raw and the quality‐adjusted unit price series are analysed. The estimated cycles and trends are all nonlinear and, in particular, the behavior of cyclical component is highly curvelinear and transient in time. The findings strongly suggest that the sub‐markets in question are not in a steady state, but are continuously evolving in time. It seems that much of the temporal variation present in the untransformed series is, in fact, explained by the quality differences in the attribute variables. The use of quality corrections produced significant improvements in the internal reliability of results.
WaveletComp is an R package for continuous wavelet-based analysis of univariate and bivariate time series. Wavelet functions are implemented in WaveletComp such that a wide range of intermediate and final results are easily accessible. The null hypothesis that there is no (joint) periodicity in the series is tested via p-values obtained from simulation, where the model to be simulated can be chosen from a wide variety of options. The reconstruction, and thus filtering, of a given series from its wavelet decomposition, subject to a range of possible constraints, is also possible. WaveletComp provides extended plotting functionality — which objects should be added to a plot (for example, the ridge of wavelet power, contour lines indicating significant periodicity, arrows indicating the leading/lagging series), which kind and degree of smoothing is desired in wavelet coherence plots, which color palette to use, how to define the layout of the time axis (using POSIXct conventions), and others. Technically, we have developed vector- and matrix-based implementations of algorithms to reduce computation time. Easy and intuitive handling was given high priority.
Even though we provide some details concerning the mathematical foundation of the methodology implemented in WaveletComp, the present guide is not intended to give an introduction to wavelet analysis. The goal here is to give a series of constructed as well as real-world examples to illustrate the use and functionality of WaveletComp, with statistical arguments in mind.
This study explores multi-scale causality and extreme tail dependence structures among housing prices in four cities: Seoul, Hong Kong, Tokyo, and New York. We apply two different and unique approaches in our analysis of monthly housing price data: (i) the frequency domain Granger casualty test and (ii) the non-parametric copula test. Employing the frequency domain casualty test, we find both bi-directional and uni-directional causalities at different frequency bands. Additionally, the nonlinear copula estimates indicate asymmetric tail dependence for housing price pairs in all four cities. Finally, the Hong Kong housing market has a greater effect on the Seoul and Tokyo housing markets than does the New York housing market.
Housing markets as well as the overall economy develop unevenly. Business cycles are the result of the diverse dynamics of their development. The housing market, as one of the components of economic systems, is influenced by business cycles, at the same time affecting them as well. It should be noted, however, that the special nature of this market may determine a different course of housing market cycles in comparison with changes in business cycles. The aim of the paper is to identify similarities and differences in the shaping of the housing market cycle and the business cycle. The analysis will be conducted on the basis of experience from the Polish market and selected Western markets.
In the present paper, shrinkage for time-varying parameter (TVP) models is investigated within a Bayesian framework, with the aim to automatically reduce time-varying parameters to static ones, if the model is overfitting. This goal is achieved by formulating appropriate shrinkage priors, in particular, for the process variances, based on the normal-gamma prior (Griffin and Brown, 2010). In this way, we extend previous work based on spike-and-slab priors (Fr\"uhwirth-Schnatter and Wagner, 2010) and Bayesian Lasso type priors (Belmonte et al., 2014). We develop an efficient MCMC estimation scheme, exploiting boosting ideas such as ancillarity-sufficiency interweaving (Yu and Meng, 2011). Furthermore, we investigate different priors, including the common inverted gamma prior for the process variances, using a predictive analysis and highlight the advantages of using a Kalman mixture approximation to evaluate one-step ahead predictive densities. Our method is applicable both to TVP models for univariate as well as to multivariate time series. This is exemplified through EU area inflation modelling based on the generalized Phillips curve as well as estimating a time-varying covariance matrix based on a TVP Cholesky stochastic volatility model for a multivariate time series of returns derived from the DAX-30 index. Overall, our findings suggest that the family of shrinkage priors introduced in this paper for TVP models is successful in avoiding overfitting, if potentially time-varying parameters are, indeed, static or even insignificant.
House prices in some Australian capital cities have recently been on the rise to the extent that some describe this as an emerging bubble, but this claim remains formally untested to date. We apply a recently developed time series procedure to detect, and time stamp, bubbles in house price to rent ratios in Australian capital cities. The results show a sustained, yet varying, degree of speculative behaviour in all capital cities in the early 2000s. The onset of these bubbles were soon after the federal government introduced a major change to the capital gains tax law, and all of these bubbles collapsed with or before the global financial crisis. Recently, only Sydney and, with some delay, Melbourne have exhibited significant evidence of an exuberant rise in house prices compared to rents. We believe that the method that we apply has the potential to be a general early warning indicator to detect housing bubbles in other countries/markets. We provide the programming files to enable researchers to implement this test in other countries. The link for our program is:
https://sites.google.com/site/shupingshi/PrgGSADF.zip?attredirects=0&d=1
The paper deals with the applications and comparison of various filtering techniques, which is an integral part of the business cycles identification and their further analysis. In particular, the paper examines four high-pass and band-pass filters and compares them to an ideal filter. The gain function is estimated and shown by the periodogram to assess the filters' efficiency in the extraction of undesired periods in the time series. The monthly data of industrial production indices of chosen EU countries are used to analyse the filtering techniques. The results show the Butterworth filter as a high pass and the Christiano-Fitzgerald filter as a band-pass to be closer to an ideal filter than the Hodrick- Prescott and Baxter-King filters.
Purpose
– This paper aims to use Markov switching vector auto regression (MSVAR) methods to examine UK house price cycles in UK regions at NUTS1 level. There is extensive literature on UK regional house price dynamics, yet empirical work focusing on the duration and magnitude of regional housing cycles has received little attention. The research findings indicate that the regional structure of UK exhibits that UK house price changes are best described as two large groups of regions with marked differences in the amplitude and duration of the cyclical regimes between the two groups.
Design/methodology/approach
– MSVAR principal component analysis NUTS1 data are used.
Findings
– The housing cycles can be divided into two super regions based on magnitude, duration and the way they behave during recession, boom and sluggish periods. A north-south divide, a uniform housing policy and a monetary policy increase the diversion among the regions.
Research limitations/implications
– Markov switching needs high-frequency data and long time spans.
Practical implications
– Questions a uniform housing policy in a heterogeneous housing market. Questions the impact of monetary policy on a heterogeneous housing market. The way the recovery of the housing market varies among regions depends on regional economic performance, housing market structure and the labour market. House price convergence, beta-convergence.
Originality/value
– No such work has been done looking at duration and magnitude of regional housing cycles. A new econometric method was used.
Purpose:
Between 2000 and 2010, air pollutant levels in counties throughout the United States changed significantly, with fine particulate matter (PM2.5) declining over 30% in some counties and ozone (O3) exhibiting large variations from year to year. This history provides an opportunity to compare county-level changes in average annual ambient pollutant levels to corresponding changes in all-cause (AC) and cardiovascular disease (CVD) mortality rates over the course of a decade. Past studies have demonstrated associations and subsequently either interpreted associations causally or relied on subjective judgments to infer causation. This article applies more quantitative methods to assess causality.
Methods:
This article examines data from these "natural experiments" of changing pollutant levels for 483 counties in the 15 most populated US states using quantitative methods for causal hypothesis testing, such as conditional independence and Granger causality tests. We assessed whether changes in historical pollution levels helped to predict and explain changes in CVD and AC mortality rates.
Results:
A causal relation between pollutant concentrations and AC or CVD mortality rates cannot be inferred from these historical data, although a statistical association between them is well supported. There were no significant positive associations between changes in PM2.5 or O3 levels and corresponding changes in disease mortality rates between 2000 and 2010, nor for shorter time intervals of 1 to 3 years.
Conclusions:
These findings suggest that predicted substantial human longevity benefits resulting from reducing PM2.5 and O3 may not occur or may be smaller than previously estimated. Our results highlight the potential for heterogeneity in air pollution health effects across regions, and the high potential value of accountability research comparing model-based predictions of health benefits from reducing air pollutants to historical records of what actually occurred.
The dynamics that have led to the dramatic increase in the Istanbul housing market in recent years have revealed housing affordability concerns. Although the dynamics affecting housing market disruptions are generally attributed to demand and supply side factors, it is also necessary to take into account the macroeconomic factors causing this. We concordantly demonstrate the strong connections between the increase in housing prices and the dynamics affecting this increase by observing the recent developments in the Istanbul housing market. We then utilize a time-varying approach by transforming a linear model into a nonlinear model to investigate the mechanisms underlying this relationship. Afterward, we observe the impulse-response graphs. On the supply side, increases in construction costs and builders’ sales price expectations augment real housing prices. However, excessive borrowing by construction companies has a decreasing impact on prices. On the demand side, housing prices in Istanbul grow because mortgage interest rates decrease and housing sales to foreigners and refugee migration increase. In the macroeconomic sight, real exchange rate, inflation, growth rate, and money supply boosts enhance prices. At the same time, housing prices rise as long as the unemployment rate and policy interest decrease. While housing prices respond most to construction costs among supply and demand variables, they react to money supply among macroeconomic variables. Eventually, after all our findings, we recommend socioeconomic policies for housing affordability problems.
Monetary policy and commodity prices are key drivers of Australian house prices, but endogeneity between these variables complicates empirical research. To address this, we use local projection methods and instrumental variables for estimation. We find that one standard deviation expansionary monetary and commodity shocks increase house prices at peak by 1.5 per cent and 1.4 per cent, respectively. Using geographically disaggregated panel data, we exploit the heterogeneity in house price responses to study the different channels through which these shocks affect housing markets. We find that both monetary policy and commodity price shocks have significant effects on housing markets through income channels.
Economists have traditionally been interested in the problem of separating trend, cyclical and seasonal components in observed data series. In recent years, this interest has been reflected in an increasing use of band-pass filtering to define and extract business cycles in macroeconomic time series. However, there is an extensive class of filters in the field of DSP (digital signal processing), which has virtually escaped the attention of economists. The aim of this paper is two-fold firstly, to discuss the properties of some of the more promising filters in this class and secondly to evaluate the relative performance of the DSP band-pass filters vis-à-vis two band-pass filters particularly popular in the economics literature viz. the Baxter-King and Christiano-Fitzgerald filters. The comparative evaluation is attempted via Monte Carlo simulation. The paper concludes with a macroeconomic illustration for Indian data.
Gali and Gambetti (2015) found protracted episodes in which stock prices rise in response to monetary policy tightening. This counter-intuitive result suggests that raising the policy rate in response to a perceived asset price deviation from fundamentals may fail to contain an emerging bubble. Since housing is often at the epicenter of deep and protracted recessions, it is essential (from a monetary policy perspective) to assess whether the result from Gali and Gambetti (2015) also holds when one considers housing instead of stock prices. Thus, we estimated a Bayesian VAR model based on an asset-pricing framework allowing for rational bubbles in the United States, the United Kingdom, and Canada. In addition, this estimation framework separates the fundamental component of housing prices from its bubble component, derived as the deviation of observed prices from the fundamental values. This allowed us to examine the responses of both components to a monetary policy shock and assess how bubbles may affect the responses of housing prices to monetary policy tightening. According to the results, we found that housing prices respond negatively to an interest rate hike, as common intuition would imply. This indicates that monetary policy may play a role in fighting housing price bubbles.
The role of real estate during the global financial and economic crisis has prompted efforts to better incorporate housing and financial channels into macro models, improve housing models, develop macroprudential tools, and reform the financial system. This article provides an overview of major, recent contributions to the literature in relation to earlier research on what drives housing prices and how they affect economic activity. Particularly emphasized are studies, both theoretical and more strongly evidence-based, that connect housing markets with credit markets, house price expectations, financial stability, and the wider economy. The literature reveals much diversity in the international and regional behavior of house prices and the need to improve data tracking key housing supply and demand influences. Also reviewed are studies examining how monetary, macroprudential, and other policies affect house prices and access to housing. This survey is designed to help readers navigate the plethora of recent studies and understand the unsettled issues and avenues for further research. The findings should be of interest to policy makers concerned with financial stability as well as those dealing with the role of housing in the wider economy (JEL E32, E44, E63, G01, G21, R31).
In this paper, I assess the evidence for a structural break in labor productivity growth in the years before the Great Recession with the use of out-of-sample forecasting exercises for the years 2010 to 2019 and the recently developed Beveridge–Nelson filter. Models based on a Beveridge–Nelson filter with no structural breaks outperform those allowing for a structural break, and there is statistically significant evidence that they outperform the random walk, though all models were too optimistic about labor productivity growth. Recently developed statistical tests do point to the presence of a structural break before the Great Recession, but uncertainty about the data-generating process for labor productivity growth or the timing and magnitude of the break may be too great to be helpful in forecast preparation.
House prices have risen substantially faster than the prices of consumer goods in most G7 countries over the past few decades. This raises major policy issues: such rises affect the distribution of wealth within and between generations, the mobility of labour and financial stability. This paper explores why such rises have happened, and what the policy implications are. The price rises have been greatest in the UK, where real house prices have risen more than three and a half times since the 1970s, substantially outpacing real income growth. Meanwhile, rental yields have been trending downwards—particularly since the mid-90s. This paper reconciles these observations by analysing the contribution of a number of drivers of house prices. It shows that the rise in house prices relative to incomes between 1985 and 2018 in the UK can be more than accounted for by the substantial decline in real risk-free interest rates over the period. This is slightly offset by net increases in home-ownership costs from higher rates of tax. Changes in the risk-free real rate are likely to have been a major driver of changes in house prices. We analyse why they have driven house prices up faster in the UK than in other advanced economies. Our model predicts that a 1% sustained increase in index-linked gilt yields from current rates could ultimately result in a fall in real house prices of around 20%.
The paper proposes a novel hybrid method that extends previous work incorporating the fractionally cointegrated vector autoregressive and the permanent-transitory decomposition model. Using the hybrid method, we investigate whether the rapid rise in China’s housing prices is a trend or cyclical fluctuation as well as whether there exists a housing price bubble. The findings indicate there exists a fractional cointegration relationship between housing prices and macroeconomic fundamentals in China, and that the fundamental value of housing prices is determined by macroeconomic factors. In particular, the upward trend in China’s housing prices has shifted and decelerated since 2012, in line with GDP and money supply. Moreover, we provide evidence of the existence of a housing price bubble in China since 2012, particularly during the 2015–2018 period. We demonstrate that the hybrid model has a superior forecasting performance and decomposes the trend and cycle components more accurately than the conventional methods when estimating a system of possibly fractional cointegration relationship.
We study the relationship between government imposed land price discrimination and housing price fluctuation in China. Providing the first DSGE model considering such discrimination, we show that land finance, monetary easing policy, labor preference and housing demand shocks accounts for 39.01%, 24.89%, 14.45% and 9.26% of housing price variation from 2008Q1 to 2017Q4, respectively. Counterfactual simulation proves that the current land price policy in China stimulates the growth of manufacturing sector at the expense of inflated housing cost and reduced multiplier of government spending. We also find that easing monetary policy with land price discrimination contributes to elevated housing price inflation and lowered consumption good price inflation.
Trend elimination and business cycle estimation are analyzed by finite sample and asymptotic methods. An overview history is provided, operator theory is developed, limit theory as the sample size n → ∞ is derived, and filtered series properties are studied relative to smoothing parameter (λ) behavior. Simulations reveal that limit theory with λ =O(n⁴) delivers excellent approximations to the HP filter for common sample sizes but fails to remove stochastic trends, contrary to standard thinking in macroeconomics and thereby explaining ‘spurious cycle’ effects of the HP filter. The findings are related to the long run effects of the GFC.
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Bayesian data analysis (BDA) is a powerful tool for making inference from ecological data, but its full potential has yet to be realized. Despite a generally positive trajectory in research surrounding model development and assessment, far too little attention has been given to prior specification.
Default priors, a sub‐class of non‐informative prior distributions that are often chosen without critical thought or evaluation, are commonly used in practice. We believe the fear of being too ‘subjective’ has prevented many researchers from using any prior information in their analyses despite the fact that defending prior choice (informative or not) promotes good statistical practice.
In this commentary, we provide an overview of how BDA is currently being used in a random sample of articles, discuss implications for inference if current bad practices continue, and highlight sub‐fields where knowledge about the system has improved inference and promoted good statistical practices through the careful and justified use of informative priors.
We hope to inspire a renewed discussion about the use of Bayesian priors in Ecology with particular attention paid to specification and justification. We also emphasize that all priors are the result of a subjective choice, and should be discussed in that way.
The present paper endeavors to analyze and provide fresh insights from the dynamic association between tourist arrivals, transportation services, growth and carbon dioxide emanation in the United States. The analysis employs a unique Morlet’s Wavelet method. Precisely, this paper implements Partial and Multiple Wavelet Coherence techniques to the monthly dataset spanning from 2001-2017. From the frequency perspective, this research finds remarkable wavelet coherence and vigorous lead and lag associations. The analysis discovers significant progress in variables over frequency and time. The variables display strong but inconsistent associations between them. There exist a strong co-movement among the variables considered, which is not equal across the time scales. The study may help the policymakers and regulars to devise strategies and formulate policies pertaining to tourism development, which can contribute towards environmentally sustainable economic growth.
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.
The recent U.S. subprime crisis provides us with a perfect framework to study cross-asset contagion mechanisms in the U.S. financial markets. Specifically, we look at how and to what extent a negative shock that initially occurred in the asset-backed security (ABS) low-quality market propagated to ABS higher grade, Treasury repos, Treasury note, corporate bond, and stock markets. We rely on dynamic time series models estimated with Bayesian methods to capture the (potentially) time-varying relation among the different financial markets. We provide evidence of structural changes in the cross-asset relationships and therefore of contagion. Moreover, by observing the impulse response functions of the models, we conclude that contagion mainly occurred through the flight-to-liquidity, risk premium, and the correlated information channels.
A large number of recent proofs indicate the irrationality of optimal resource allocation assumption in traditional economic growth theory, wherein resource misallocation (misallocation of capital and labor among heterogeneous enterprises) is regarded as the important reason for the difference in total factor productivity (TFP). In this article, we study resource allocation from the perspective of housing price, and measure the resource allocation efficiency of industrial enterprises in China based on industrial enterprises database covering the period of 1999–2007. We find that to some extent rising housing price has ameliorated resource allocation efficiency in China recently. We further find that rising housing price ameliorates resource allocation mainly in Central and Western areas and in labor-intensive industries.
Purpose
This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city centre, sub-centres and outskirts or suburbs. It estimates the Italian long-term house price index, city based in real terms, and shows a combination of methods to deal with large time-series data.
Design/methodology/approach
This paper builds long-term cycles based on the city (real) data by estimating the common components of cointegrated time series and extracting the unobservable signals to build real house price index for sub-regions in Italy. Three different econometric methodologies are used: Johansen cointegration test and VAR models to identify the long-term pattern of prices at the estimated aggregate level; principal components to obtain the common (permanent and transitory) components; and signal extraction in ARIMA time series–model-based approach method to extract the unobserved time signals.
Findings
Results show three long-term cycle-trends during the period and identify several one-direction causal non-permanent relationships among house prices from different Italian areas. There is no evidence of convergence among regional’s house prices suggesting that the Italian housing prices converge inside the local market with only short diffusion effects at larger regional level.
Research limitations/implications
Data are measured as the average price in squared meters, and the resulting index is not quality controlled.
Practical implications
The long-term trends on housing prices serve to implement further research and know deeply the evolution of Italian housing prices.
Originality/value
This paper contains new and unknown information about the evolution of housing prices in Italian regions and cities.
Here's why. (a) The Hodrick-Prescott (HP) filter introduces spurious dynamic relations that have no basis in the underlying data-generating process. (b) Filtered values at the end of the sample are very different from those in the middle and are also characterized by spurious dynamics. (c) A statistical formalization of the problem typically produces values for the smoothing parameter vastly at odds with common practice. (d) There is a better alternative. A regression of the variable at date t on the four most recent values as of date t - h achieves all the objectives sought by users of the HP filter with none of its drawbacks. © 2018 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.
The aims of this paper are twofold. First, we propose a threshold cube equation, augmented with the optimal number of autoregressive terms, to model the likely future path of house prices in Australia’s four major capital cities. The proposed framework can be adopted to simulate house prices in other cities experiencing similar price cycles (spirals) such as London and Vancouver. Second, using a proprietary dataset not publically available (1995m12-2015m10), we use the proposed model to simulate house prices in Brisbane, Melbourne, Perth and Sydney, which have been on a steep upward trajectory since 2013. To check the sensitivity and robustness of our results, we evaluate the model in terms of the out-of-sample accuracy for two separate 12-month periods. We find that the forecasting performance of the model appears to be reasonable. Then we use the model to provide the ex ante future path of house prices during the next twelve months (2015m11-2016m10). Although we do not seek to forecast all see-saw changes in property prices, based on the historical length of boom and bust cycles in the past, some interesting overall price paths are detected. It is observed that during the next year house prices in all capital cities will start plateauing with some likely moderate falls. This downward price adjustment is particularly more noticeable in Perth.
Time-series studies that have tried to establish the long-run relationship between house prices and economic fundamentals have been criticized due to low power of their cointegration tests. On the other hand, those who have used panel data and panel tests to increase the power have found mixed results. Both groups have assumed that changes in the fundamentals have symmetric effects on house prices. In this article, we use nonlinear ARDL approach to cointegration and error-correction modelling and quarterly data from each of the states in the US to show that changes in the fundamentals have asymmetric effects on house prices, in the short run as well as in the long run. Cointegration between house prices and fundamentals is established in 30 states and in District of Columbia.
Many of the ideas behind wavelet transforms have been around for a long time. Despite efforts, wavelet analysis initially remained confined to a small, mainly mathematical, community with only a handful of scientific papers being published each year. By the start of the 1990s, the stage was set for the practical application of wavelet analysis in science and engineering. As more and more researchers spotted the potential of the technique, a flurry of papers began to appear. That flurry has since turned into a blizzard, with over 1000 peer-reviewed papers now appearing each year.
We investigate whether expectations that are not fully rational have the potential to explain the evolution of house prices and the price-to-rent ratio in the United States. First, a stylized asset-pricing model solved under rational expectations is used to derive a fundamental value for house prices and the price-rent ratio. Although the model can explain the sample average of the price-rent ratio, it does not generate the large and persistent fluctuations observed in the data. Then, we consider a rational bubble solution, an extrapolative expectations solution and a near rational bubble solution. In this last solution agents extrapolate the future from the latest realizations and the degree of extrapolation is stronger in good times than in bad times, generating waves of over-optimism. We show that under this solution the model not only is able to match key moments of the data but can also replicate the run up in the U.S. house prices observed over the 2000-2006 period and the subsequent sharp downturn.
We employ a wavelet approach and conduct a time-frequency analysis of dynamic correlations between pairs of key traded assets (gold, oil, and stocks) covering the period from 1987 to 2012. The analysis is performed on both intra-day and daily data. We show that heterogeneity in correlations across a number of investment horizons between pairs of assets is a dominant feature during times of economic downturn and financial turbulence for all three pairs of the assets under research. Heterogeneity prevails in correlations between gold and stocks. After the 2008 crisis, correlations among all three assets increase and become homogenous: the timing differs for the three pairs but coincides with the structural breaks that are identified in specific correlation dynamics. A strong implication emerges: during the period under research, and from a different-investment-horizons perspective, all three assets could be used in a well-diversified portfolio only during relatively short periods.
Monetary policy and the private sector behaviour of the U.S. economy are modelled as a time varying structural vector autoregression, where the sources of time variation are both the coefficients and the variance covariance matrix of the innovations. The paper develops a new, simple modelling strategy for the law of motion of the variance covariance matrix and proposes an efficient Markov chain Monte Carlo algorithm for the model likelihood/posterior numerical evaluation. The main empirical conclusions are: (1) both systematic and non-systematic monetary policy have changed during the last 40 years - in particular, systematic responses of the interest rate to inflation and unemployment exhibit a trend toward a more aggressive behaviour, despite remarkable oscillations; (2) this has had a negligible effect on the rest of the economy. The role played by exogenous non-policy shocks seems more important than interest rate policy in explaining the high inflation and unemployment episodes in recent U.S. economic history.
This paper studies the demand for and supply of residential housing in urban China since the late 1980s when the urban housing market became commercialized. Using aggregated annual data from 1987 to 2012 in a simultaneous equations framework we show that the rapid increase in the urban residential housing price can be well explained by the forces of demand and supply, with income determining demand and cost of construction affecting supply. We find the income elasticity of demand for urban housing to be approximately 1, the price elasticity of demand to be approximately −1.1 and the price elasticity of supply of the total housing stock to be approximately 0.5. The resulting long-run effect of income on urban housing prices in elasticity terms is approximately 0.7, because the increase in income has shifted the demand curve outward more rapidly than the supply curve.
Radial basis function methods are modern ways to approximate multivariate functions, especially in the absence of grid data. They have been known, tested and analysed for several years now and many positive properties have been identified. This paper gives a selective but up-to-date survey of several recent developments that explains their usefulness from the theoretical point of view and contributes useful new classes of radial basis function. We consider particularly the new results on convergence rates of interpolation with radial basis functions, as well as some of the various achievements on approximation on spheres, and the efficient numerical computation of interpolants for very large sets of data. Several examples of useful applications are stated at the end of the paper.