House Price Cycles Research Articles

Regional composition of national house price cycles in the US

House price cycles may have considerable macroeconomic effects even if they evolve heterogeneously across local markets. In this paper, we use a panel Markov-switching model allowing for time-varying volatility to jointly analyze national- and state-level house price regimes for the US over the period 1976 to 2017. Our approach identifies three house price regimes endogenously, namely, a nationwide boom regime, a spatially limited bust regime and a nationwide bust regime. The spatially limited bust regime occurs in the coastal states where, compared to other states, the population density is high and the unemployment rate, the housing density as well as the land supply elasticity are low. This spatially limited bust regime usually follows a nationwide house price boom. Hence, house price movements in the coastal states usually determine the nationwide cycle in the US. Moreover, boom and bust cycles are accompanied by an exaggeration of house price increases during the boom in this group of states. In contrast, a bubble in the housing market occurred in almost all states prior to the Great Recession. This is one explanation for the severity of the Great Recession.

Do Credit Conditions Move House Prices?

To what extent did an expansion and contraction of credit drive the 2000s housing boom and bust? The existing literature lacks consensus, with findings ranging from credit having no effect to credit driving most of the house price cycle. We show that the key difference behind these disparate results is the extent to which credit insensitive agents such as landlords and unconstrained savers absorb credit-driven demand, which depends on the degree of segmentation in housing markets. We develop a model with frictional rental markets that allows us to consider cases in between the extremes of no segmentation and perfect segmentation typically assumed in the literature. We argue that the relative elasticities of the price-rent ratio and homeownership with respect to an identified credit shock is a sufficient statistic to measure the degree of segmentation. We estimate this moment using three different credit supply instruments and use it to calibrate our model. Our results reveal that rental markets are highly frictional and closer to fully segmented, which implies large effects of credit on house prices. In particular, changes to credit standards can explain between 34% and 55% of the rise in price-rent ratios over the boom.

(A)Synchronous Housing Markets of Global Cities

In this paper we examine house price synchronization in 15 global cities using real house price data from 1995:Q1-2020:Q2. We find that although there is evidence for bilateral positive phase synchronization, there is no evidence for an integrated global housing market for our sample of cities. Using a hierarchical clustering approach, we identify three clusters of cities with similar housing price cycles that are not solely determined by geographic proximity. We interpret this finding as suggestive of a rather segmented housing market for the global cities in our sample. Using a dynamic factor model with time-varying stochastic volatility we decompose a city's real housing price growth into a global component, a cluster-based component, and an idiosyncratic component. For most cities in our sample, the global component plays a minor role, whereas the cluster-based factor explains a large fraction of the observed variation in real house price growth with its contribution peaking during the Great Recession of 2007-09.

우리나라 주택가격의 국면별 시계열 특성 비교

This study analyzes how the time series characteristics of two phases (upwards and downwards) in Korea’s housing price cycle are different. Two phases are identified by the algorithm suggested by Bry and Boschan. The minimum period of each phase is set to 6 months. The target data are the Korea National House Price Index and the house price index of seven metropolitan cities. Data was collected from November 2003 to June 2020. The findings are as follows; First, the whole country and the seven metropolitan cities have their own distinctive cyclical features. Second, statistical properties, such as high volatility, skewness, kurtosis, and no n-normality, are getting prominent in the upwards phase. Third, the estimated AR coefficients indicates that dynamic effects of market shocks on the housing price changes are stronger and longer in the upward phase. Fourth, the volatility clustering mainly emerges in the period of the upward phase.

A study of financial cycles and the macroeconomy in Taiwan

This paper studies the characteristics of financial cycles (credit and house prices) and their interactions with business cycles in Taiwan. We employ multivariate structural time series model to estimate trend and cyclical components in real bank credit, real house prices, and real GDP. We find that financial cycles are roughly twice the length of the business cycles, and house price cycles lead both credit and business cycles. Nevertheless, the estimated length of business and financial cycles in Taiwan is much shorter than those in industrialized economies. We then use machine learning to evaluate the importance of a macroeconomic variable that predicts downturns of financial cycles, by conducting both in-sample fitting and out-of-sample forecasting. Those macrovariables selected by machine learning reflects Taiwan’s close linkage in trades and financial interdependence with other countries such as China and spillover effects from the Fed’s monetary policy.

Macroprudential Policy, Mortgage Cycles and Distributional Effects: Evidence from the UK

Macroprudential regulators worldwide have introduced regulations to limit household leverage in light of existing evidence which suggests that high leverage is associated with household distress during crisis. We analyse the distributional effects of such a macroprudential policy on mortgage and house price cycles. For identification, we exploit the universe of UK mortgages and a 15%-limit imposed in 2014 on lenders — not households — for high loan-to-income ratio (LTI) mortgages. Despite some regulatory arbitrage (eg increases in LTV and average loan size), more-constrained lenders issue fewer high-LTI mortgages. Partial substitution by less-constrained lenders leads to overall credit contraction to low-income borrowers in local-areas more exposed to constrained-lenders, lowering house price growth. Following the Brexit referendum (which led to house-price correction), the 2014-policy strongly implies — via lower pre-correction debt — better house prices and mortgage defaults during an episode of house price correction.

Housing Wealth Effects: The Long View

AbstractWe provide new time-varying estimates of the housing wealth effect back to the 1980s. We use three identification strategies: ordinary least squares with a rich set of controls, the Saiz housing supply elasticity instrument, and a new instrument that exploits systematic differences in city-level exposure to regional house price cycles. All three identification strategies indicate that housing wealth elasticities were if anything slightly smaller in the 2000s than in earlier time periods. This implies that the important role housing played in the boom and bust of the 2000s was due to larger price movements rather than an increase in the sensitivity of consumption to house prices. Full-sample estimates based on our new instrument are smaller than recent estimates, though they remain economically important. We find no significant evidence of a boom–bust asymmetry in the housing wealth elasticity. We show that these empirical results are consistent with the behaviour of the housing wealth elasticity in a standard life-cycle model with borrowing constraints, uninsurable income risk, illiquid housing, and long-term mortgages. In our model, the housing wealth elasticity is relatively insensitive to changes in the distribution of loan-to-value (LTV) for two reasons: first, low-leverage homeowners account for a substantial and stable part of the aggregate housing wealth elasticity; second, a rightward shift in the LTV distribution increases not only the number of highly sensitive constrained agents but also the number of underwater agents whose consumption is insensitive to house prices.

House prices and credit cycles: the case of Cyprus

PurposeThe purpose of this paper is to assess whether the rapid accumulation of bank deposits before the global financial crisis and their subsequent drastic reduction was the main driving force of the Cyprus house price cycle over the period 2006–2015.Design/methodology/approachTo this aim we estimate a three-equation model in which house prices are determined by housing loans, among other factors, and housing loans are determined by bank deposits. All equations are estimated using partial adjustment model specifications.FindingsOur findings indicate that housing loans, which capture the effect of credit availability on housing demand, had the smallest effect on house prices, thus providing little support to our proposition of a deposits-driven cycle in house prices.Research limitations/implicationsThe main limitation of the study is the use of the housing loan stock instead of the actual volume of housing loans in each period due to lack of such data. As a result our econometric estimates may not accurately capture the magnitude of the effect of housing loans on house prices.Practical implicationsThe study has important practical implications for policy makers as it highlights the importance of availability of credit in supporting effective demand for housing during periods of economic growth. Furthermore, it highlights the key role of house price increases in combination with the collateral effect in driving the house price cycle.Originality/valueThis is among the few studies internationally and the first study in Cyprus that attempts to link econometrically the credit and house price cycles that were caused by the global financial crisis.

Making sense of increased synchronization in global house prices

Purpose The purpose of this paper is to provide perspective on whether and why global metro house prices have become more synchronized, and perspective on the limited implications of this for investing in international real estate. Design/methodology/approach This paper reviews main findings from the literature on house price determination, reviews the emerging literature on global synchronization, and provides graphs to illustrate main points and trends. Findings House prices have become somewhat more synchronized likely reflecting greater correlation in long-term interest rates and macroeconomic cycles related to trends in globalization and international portfolio diversification. Nevertheless, this trend has not been continuous, reflecting that house prices depend on other fundamentals, which are not uniform across areas. Theory and evidence indicate that the more common are fundamentals, the more synchronized are house price cycles and the more substitution effects may matter. Also, real estate markets that are open to immigration and foreign investment have become more sensitive to shifts in the international demand for property by migrants or investors. Research limitations/implications Changes in international house price synchronization stem from variation in two categories of key drivers of house prices. The first are traditional supply and demand fundamentals. The second include international capital flows and immigration. Both sets of factors are sensitive to the economic environment and public policy. Increased synchronization of business cycles, the Euro currency union, and more common monetary policy strategies and tactics have fostered greater correlation of real interest rates across countries, which tend to increase house price synchronization. These effects can be amplified by the tendency for property owners to use extrapolative expectations of future house prices. Practical implications Shifts in prospective returns and the synchronization of international property returns not only on arbitrage of general property price differentials but also on underlying factors driving those differentials. Investors need to be mindful of the risks that metro prices sometimes reflect bubble-builder dynamics that can give rise to over-shooting of house prices. Observing simple correlations and changes in those correlations does not do away with the need for careful analysis of property investment, and if anything, warrant analysis of both how and why one may observe changes in the extent to which international house prices is synchronized. Social implications Despite the rise of globalization and of new technologies, the author has seen substantial divergences in house prices emerge across gateway cities and metros in less vibrant areas within countries. These reflect not only the impact of stronger income and population in more tech, educated and global oriented cities but also changes in the demand for amenities toward more culturally appealing cities, often – but not exclusively in – warmer or coastal areas where the supply elasticity of housing is often limited. Further complicating investment decisions are potential shifts in housing or immigration policy that can notably affect the demand for housing. Originality/value The paper provides practical perspective on why different groups of international cities have seen their house prices become more sychronized. Nevertheless, increased synchronization has occurred within an elite set of major cities, but in an environment house prices have diverged across gateway cities and metros in less vibrant areas within countries. The paper helps investors make sense of some recent patterns and recent prospects for investing in international real estate.

Real-Time Warning Signs of Emerging and Collapsing Chinese House Price Bubbles

The recent increase in Chinese house prices has led to concerns that China is vulnerable to asset price shocks. In this paper, we apply recently developed recursive unit root tests to spot the beginning and the end of potential speculative bubbles in Chinese house price cycles. Overall, we find that except for 2009–2010 actual house prices are not significantly disconnected from fundamentals. Thus, the evidence for speculative house price bubbles in China is in general weak.

Do Credit Conditions Move House Prices?

To what extent did an expansion and contraction of credit drive the 2000s housing boom and bust? The existing literature lacks consensus, with findings ranging from credit having no effect to credit driving most of the house price cycle. We show that the key difference behind these disparate results is the extent to which credit insensitive agents such as landlords and unconstrained savers absorb credit-driven demand, which depends on the degree of segmentation in housing markets. We develop a model with frictional rental markets that allows us to consider cases in between the extremes of no segmentation and perfect segmentation typically assumed in the literature. We argue that the relative elasticities of the price-rent ratio and homeownership with respect to an identified credit shock is a sufficient statistic to measure the degree of segmentation. We estimate this moment using three different credit supply instruments and use it to calibrate our model. Our results reveal that rental markets are highly frictional and closer to fully segmented, which implies large effects of credit on house prices. In particular, changes to credit standards can explain between 34% and 55% of the rise in price-rent ratios over the boom. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

New synchronicity indices between real and financial cycles: Is there any link to structural characteristics and recessions in European Union countries?

AbstractIn this paper, we examine the comovements and relationships between real, credit, house, and equity price cycles in European Union countries looking at their link to structural characteristics and their predictive power for future recessions. We make use of new within country synchronicity indices, finding that among the different credit cycles and between the house price cycle and the real cycle, there is a relatively high level of synchronicity. Credit and gross domestic product (GDP) fluctuations seem to be less synchronised, mostly because credit volumes tend to lag the real cycle by several quarters. The high rates of private homeownership tend to be associated with larger cycles in GDP, credit, and house prices. Higher loan‐to‐value ratios, seen as a proxy of borrowing constraints, and a higher percentage of flexible‐rate mortgages, could also indicate that a country is more sensitive to shocks and possibly increase procyclicality and increase cycle volatility. Finally, the procyclicality of the credit and housing market to the GDP cycle can be linked to the fluctuation in current accounts and their misalignments with respect to the theoretical equilibrium value. Some synchronicity measures and, above all, the credit cycles may also be considered for signalling future recessions.

Online Appendix for: Is the Behavior of Sellers with Expected Gains and Losses Relevant to Cycles in House Prices?

This is the Online Appendix for: Is the Behavior of Sellers with Expected Gains and Losses Relevant to Cycles in House Prices? Appendix 1 provides support for using assessed value to mitigate unobserved quality. Appendix 2 describes the calculation of normalized assessed value (NAV) and summarizes results of leave-one-out (LOO) cross-validation. Appendix 3 summarizes the analytical framework. Appendix 4 displays our sample construction process. Appendix 5 reports the first-stage hedonic regression results. Appendix 6 shows the comparison between repeat sales index (based on repeat sales pairs), loss index and gain index. Appendix 7 provides additional summary statistics that supplement Table 2. Appendix 8 summarizes double results using asking price. Appendix 9 summarizes results using an alternative house price cycle. Appendix 10 reports additional robustness tests. • A10.1 report robustness tests with various combinations of spatial and temporal fixed effects. • A10.2 report robustness tests by restricting to the repeat sale sample only. • A10.3 report results using asking price as the dependent variable. • A10.4 reports robustness tests by adding the loan-to-value ratio. Appendix 11 summarizes Clapp and Zhou (2019)’s method for correcting unobserved quality and report results using their method. Appendix 12 presents and discusses contrast-relative analyses holding loss/gain coefficients constant.

Global Cities and Local Housing Market Cycles

In this paper, we consider the dynamic features of house price in metropolises that are characterised by a high degree of internationalisation. Using a generalised smooth transition (GSTAR) model we show that the dynamic symmetry in house price cycles is strongly rejected for the housing markets considered in this paper. Further, we conduct an out-of-sample forecast comparison of the GSTAR with a linear AR model for the metropolises under consideration. We find that the use of nonlinear models to forecast house prices, in most cases, generate improvements in forecast performance.

Making Sense of Increased Syncrhonization in Global Housing Prices

Evidence indicates that house prices have become somewhat more synchronized during this century, likely reflecting more correlated movements in long-term interest rates and macroeconomic cycles that are related to trends in globalization and international portfolio diversification. Nevertheless, the trend toward increased synchronization has not been continuous, reflecting that house prices depend on other fundamentals, which are not uniform across countries or cities. Theory and limited econometric evidence indicate that the more common are fundamentals, the more in-synch house price cycles will become and the more substitution effects may matter. In addition, real estate markets that are open to immigration and foreign investment have become more sensitive to shifts in the international demand for property by migrants or investors.

Real and Financial Cycles in Euro Area Economies: Results from Wavelet Analysis

AbstractWe study the within-country dimension of financial cycles in the four largest euro area economies using tools from wavelet analysis. We focus on credit and house price cycles which are most commonly used to represent the financial cycle. With the exception of Germany, the variables contain important common cycles within each country close to the upper bound of business cycle length and beyond which can be interpreted as financial cycles. These cycles are closely linked to domestic cycles in real activity showing financial and real economic cycles as interconnected phenomena. For these common cycles, credit and house prices lag real GDP.

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

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.

How do housing cycles influence listed firms’ R&D investment: evidence from the collateral channel

ABSTRACTFirm innovation is essential to long-run economic growth. Financially constrained R&D firms may use firm-owned properties as collateral to finance their R&D projects. Therefore, the housing price cycle can affect firms’ R&D investment through influencing their real estate value. By examining listed R&D firms during the housing boom period 2002–2006 in the U.S., we find that a $1 increase in real estate value leads a firm to increase its R&D investment by $0.38. We also find that this collateral effect is more pronounced among financially constrained R&D firms than that among unconstrained ones. Additionally, we examine the housing bust period 2008–2012, and find that real estate depreciation retarded R&D investment, especially among constrained R&D firms.

An international housing market in the British Isles: Evidence from business and medium-term cycles using a Friedman test

It has been averred that there is an international market in housing (inter alia, Adams Z and Füss R (2010) Macroeconomic determinants of international housing markets. Journal of Housing Economics 19(1): 38–50; Helbling T and Terrones M (2003) When Bubbles Burst – Chapter II, World Economic Outlook. April. Washington, DC: IMF; Pomogajko K and Voigtländer M (2012) Co-movement of house price cycles – A factor analysis. International Journal of Housing Markets & Analysis 5(4): 414–427). Although some authors examine the business cycle range, the mechanism by which this comes about is more likely to reflect financial market integration and evidence for this should be found in the medium-term cycle range. As a case study, there is an analysis of three associated ‘regional’ housing markets and their price movements. Northern Ireland’s economy is distinct but should be linked to the rest of the UK by common policy shocks, and to Eire through a mutual border. Using a Friedman test, it is found that even this small cluster is not integrated. It is asserted that a housing cycle has both a financial and a business component. As Drehmann et al. (Drehmann M, Borio C and Tstasaronis K (2012) Characterising the financial cycle: Don’t lose sight of the medium term! BIS Working Papers, No. 380, Bank for International Settlements, June) argue, cycles in the medium-term range are significant in housing. A dual component approach highlights the relevance of cyclical interaction for both revealing integration and the need to intervene to moderate cyclical reinforcement for crisis-avoidance. After 1995, Dublin and London are found to be more integrated than before, which is consistent with the international city integration thesis of Holly et al. (Holly S, Pesaran H and Yamagata T (2011) The spatio-temporal diffusion of house prices in the UK. Journal of Urban Economics 69(1): 2–23). Lastly, a simple test for cyclical asymmetry indicates that there is highness (the obverse of deepness, where the troughs are deeper than peaks) in the UK financial cycle.

Financial Cycles Across G7 Economies: A View from Wavelet Analysis

We analyse the cross-country dimension of financial cycles by studying cyclical co-movements in credit, house prices, equity prices and interest rates across the G7 economies. We use wavelet-based statistics to assess at which frequencies cyclical fluctuations and their crosscountry co-movements are important and how these change over time. We show cycles in interest rates and equity prices to be at least as synchronised as cycles in real GDP while cycles in credit and house prices are less synchronised. As a result, cross-country common cycles in equity prices and long-term interest rates account for a larger share of the volatility of these variables at the country level than common cycles in credit aggregates and house prices. A cluster analysis shows a high degree of similarity in the spectral characteristics of cycles in interest rates and equity prices across all countries but less similarities for cycles in credit and house price. For credit and house price cycles country-specific developments turn out to be more important than the common cross-country cycles.