National House Price Research Articles

How supply and demand affect national house prices: The case of Ireland

While many western economies experienced substantial fluctuations in house prices since the turning of the century, the Irish residential market stands out as a particular case. Irish house prices experienced profound increases in the period leading up to the global financial crisis (GFC); thereafter the concomitant downturn in both the real Irish economy and financial sector precipitated a dramatic decline in prices between 2007 and 2012. However, since 2012 prices have increased in a sustained and persistent manner. A number of possible reasons are commonly cited for the recovery. In this paper we avail of a new housing and financial sector model, which are part of a broader macro-econometric model, COSMO, of the Irish economy to characterise the determinants of Irish house prices over the period 1995–2019 and in particular to examine the reasons for the post 2012 recovery. The suite of models are used to examine the contribution of developments in both monetary policy and financial stability as well as the performance of the real economy. The role played by the sluggish response of the supply-side of the Irish residential sector is also assessed. The supply-side of the Irish market was especially impacted by the GFC and has struggled to respond to the surge in housing demand which has accompanied the general economic recovery since 2012. Our results have interesting implications for the growing literature examining the intersection of monetary policy and financial stability on house prices.

Contradictory Results: Reassessing the Impact of Loan‐to‐Value Restrictions on House Price Inflation

AbstractUsing Bayesian Structural Time Series analysis, this study examines the causal impact of loan‐to‐value (LTV) restrictions imposed by the Reserve Bank of New Zealand in October 2013. By incorporating state‐space components, such as local linear trend, seasonality and regression, counterfactual values of house price indices are predicted. Surprisingly, the study reveals that the implementation of LTV restrictions had no significant effect on national house price indices, contradicting prior Central Bank studies that reported a nearly 3 percentage‐point decrease in housing cost inflation. This contradictory evidence challenges existing perceptions of the effectiveness of LTV restrictions in curbing house price inflation.

Housing Price Cycle Interdependencies and Comovement: A Markov-Switching Approach

This paper uses a Markov-switching approach to examine why there is house price cycle comovement across some U.S. metropolitan areas (MSAs) but not others, and which MSAs cluster together for each of these reasons. Past studies have attributed common housing downturns in different regions as possible explanations for comovement. We explore other channels, and find some clusters based on common industry concentration (such as information technology), house price elasticity, as well as a cluster of MSAs that are desirable for retirees (in the sun belt). We find seven clusters of MSAs, where each cluster experiences idiosyncratic house price downturns, plus one distinct national house price cycle. Notably, only the housing downturn associated with the Great Recession spread across all the MSAs in our sample; all other house price downturns remained contained to a single cluster. We also identify MSA economic and geographic characteristics that correlate with housing price cluster membership, which implies comovement due to mobility of residents. In addition, while prior research has found housing and business cycles to be related closely at the national level, we find very different house price comovement and employment comovement across clusters and across MSAs.

Nature of comovements in US state and MSA housing prices

AbstractThis study analyzes the time‐varying nature and determinants of comovements in US housing prices using state and metropolitan statistical area (MSA) data. We employ dynamic factor models with time‐varying loadings and stochastic volatility (DFM‐TV‐SV) to estimate the national, regional, and state factors. The time‐varying factor loadings and stochastic volatility features enrich the dynamic factor model structures and are an effective tool to examine the comovements in housing prices. We find that the national factor is the dominant factor in explaining the movement of housing prices. The national factor accounts for 79% of the variation in state‐level housing prices on average, with the greatest magnitude occurring during the housing boom and bust periods in many regions and states. We also find that the factors and synchronization effects are time‐varying and heterogeneous across regions. The state‐level housing prices contain higher national housing factor components in states with more diverse economies, higher wages and house prices, and lower unemployment rates. These findings shed light on the effectiveness of residential real estate diversification across the United States and the potential for elevated national housing risk amid economic downturns due to increased national housing price integration.

Regional divergence and house prices

We document a new fact: regional divergence, the rate at which rich states grow faster than poor states, explains most U.S. house price movements since 1939, including the post-2000 boom-bust-boom cycle. An industry-share instrument provides evidence the relationship is causal, implying the location of economic growth affects national house prices. We propose a model to learn why regional divergence and house prices are related. In the model, high interstate inequality raises rents on average because relative demand for living in a high-income state increases and housing supply in low-income states is elastic. Regional divergence leads to higher expected future interstate inequality, which implies higher expected future rents, and therefore, higher current house prices. The model accurately predicts rents since 1929, which are quite different than prices, as well as cross-sectional moments of prices, rents, construction, and migration.

Land valuation using public records and kriging: Implications for land versus property taxation in cities

We construct land values for each parcel in Maricopa County (Phoenix), Arizona, from 2000 through 2018 using a novel public dataset containing the universe of land sales and parcel records in the county. We then compare residential land values constructed using two classes of source data, vacant land sales and land under existing structures. Between 2012 and 2018, estimated land values for developed parcels are, on average, 14% higher when estimated using vacant land due to plattage effects and other unobserved factors. Growth rates are similar, facilitating the use of vacant land price indices to trace valuations over time from an accurate base year valuation. Dynamics between prices of Maricopa County land and housing suggest hypothetical land value tax revenues are more pro-cyclical than property tax revenues, with elasticities of 3.3 and 2.3 with respect to national house prices, respectively. By 2018, houses had recovered 96% of pre-crisis (2007) values, but land had only recovered 66%. These findings demonstrate a source of risk of dependence on public revenues from land value taxes versus a base-period revenue-neutral property tax.

Dynamic Characteristics of Excess Returns in Seoul Housing Market

In Korea, the increase in individuals incomes appears to lead to the bigger accumulation of housing assets than in other developed countries. And the accumulation is more concentrated in Seoul, especially in South Seoul. This study analyzes the development process of excess returns of apartment prices in Seoul and South Seoul compared to the national housing price based on the threshold regression model and the ESTAR model. The analysis result shows that excess returns in Seoul and South Seoul increase non-linearly as national income increases. This implies that apartment growth rates in Seoul and South Seoul continue to rise more than in other regions as the economy develops. In this case, the economic wealth will be concentrated in the Seoul area. In addition, if national income enters a high-growth regime with the appreciation of the won, housing investment will be strengthened in the Seoul area. This result implies that the dynamic accumulation of excess returns with a spillover effect from Seoul is the cause of housing instability of non-homeowners and imbalanced regional development.

Monetary policy, hot money and housing price growth across Chinese cities

ABSTRACT We use a dynamic hierarchical factor model to identify the national, regional and local factors of the city-level housing price growth in China. During the zero-lower-bound (ZLB) episode in the U.S., local factors account for 78% of variations in the month-on-month city-level housing price growth. However, as the time horizon extends, the national factor gets a larger variance share, reaching 51% in a half-year horizon. This indicates that the city-level housing price growth in China is more of a national phenomenon in the long run. We then use a VAR model to investigate the driving forces of the national factor and find that monetary policy and hot money shocks affect the national housing price growth significantly. A tightening monetary policy shock has a significant negative impact on the national factor, which lasts for more than 2 years. An increase in hot money inflows causes a significant but transitory rise in the national factor. Moreover, we find that the quantitative easing measure adopted by the U.S. Fed is behind the surge of capital inflows into China.

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.

Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty

We analyze the role of macroeconomic uncertainty in predicting synchronization in housing price movements across all the United States (US) states plus District of Columbia (DC). We first use a Bayesian dynamic factor model to decompose the house price movements into a national, four regional (Northeast, South, Midwest, and West), and state-specific factors. We then study the ability of macroeconomic uncertainty in forecasting the comovements in housing prices, by controlling for a wide-array of predictors, such as factors derived from a large macroeconomic dataset, oil shocks, and financial market-related uncertainties. To accommodate for multiple predictors and nonlinearities, we take a machine learning approach of random forests. Our results provide strong evidence of forecastability of the national house price factor based on the information content of macroeconomic uncertainties over and above the other predictors. This result also carries over, albeit by a varying degree, to the factors associated with the four census regions, and the overall house price growth of the US economy. Moreover, macroeconomic uncertainty is found to have predictive content for (stochastic) volatility of the national factor and aggregate US house price. Our results have important implications for policymakers and investors.

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.

US House Price Projections from the Economic Impact of the Coronavirus

Could the coronavirus-induced economic shocks hurt US house prices as much as the global financial crisis (GFC) did? This article identifies four drivers that could produce much milder house price depreciation (HPD) this time: early and broad mortgage- and housing-relief policy responses, the apparent lack of a preceding house price bubble, historically moderate mortgage- and consumer-debt levels, and lower mortgage rates. The authors apply a previously published time series–based house price model, combined with these exogenous drivers. Fitted to historical data between 1987 and 2020, the model estimates a 1.66 transmission rate from GDP underperformance to HPA underperformance, a ratio of 0.3 between HPA and a mortgage rate relative percentage rally. If mortgage rates rally from 3.7% in 2019 to 2.7% and the GDP takes a hit of 11% below-trend growth, then the model estimates roughly 10% below-trend growth for national HPA, which is much milder than the experience during the GFC. <b>TOPICS:</b>Financial crises and financial market history, MBS and residential mortgage loans <b>Key Findings</b> • National house price depreciation due to the coronavirus may be much milder than the previous crisis, due to effective mortgage and housing relief policy, lack of a preceding house price bubble and mortgage debt bubble, and lower mortgage rates. • Our house price model combines a time-series property with these exogenous drivers and estimates the sensitivity to each driver. • Our model projects 10% below-trend growth for national house price appreciation (HPA) if the US GDP takes a hit of 11% below-trend growth, supported with a 1% mortgage rate rally.

Modeling House Price Synchronization across the U.S. States and their Time-Varying Macroeconomic Linkages

Abstract This paper analyzes the time-varying impact of macroeconomic forces on the synchronization in housing movements across all the U.S. states. Using a Bayesian modeling approach, the house price movements are decomposed into national, regional and state-specific factors. We then analyze the time-varying impact of macroeconomic forces on these national and regional factors. Evidence suggests that in several Western and Eastern states the house price variations are dominated by the national factor, whereas the regional factor dominates the Southern and Midwestern markets. These factors are found to have a time-varying relationship with most macroeconomic indicators with particularly pronounced time-variation caused by national house prices, inflation rate and consumer sentiments.

Las Cruces housing price fluctuations

Purpose This paper aims to analyze the median price for existing single-family housing units in Las Cruces, New Mexico. The proposed theoretical model accounts for the interplay between supply and demand sides of a metropolitan housing market. Design/methodology/approach This study analyzes the median price for existing single-family housing units in Las Cruces, New Mexico. The proposed theoretical model accounts for the interplay between supply and demand sides of a metropolitan housing market. Explanatory variables used in the analysis are real per capita income, the housing stock, real mortgage rates, real apartment rents and the median real price of single-family units in the USA. Annual frequency data are collected for a 1971–2017 sample period. Parameter estimation is completed using two-stage generalized least squares. Empirical results confirm several, but not all, of the hypotheses associated with the underlying analytical model. In particular, Las Cruces housing prices are found to be reliably correlated with local income and national housing prices. Findings Empirical results confirm several of the hypotheses associated with the underlying analytical model. In particular, Las Cruces housing prices are found to be reliably correlated with local income and national housing prices. Research limitations/implications Results obtained support only a subset of the hypothetical relationships associated with the theoretical model. Additional testing for other small and/or medium sized is required to clarify whether these outcomes are unique to Las Cruces. Practical implications Local income fluctuations and national housing price fluctuations appear to be reliably related to housing price fluctuations for this metropolitan economy. Originality/value Comparatively little housing market research has been conducted for small and medium size urban economies. There is no guarantee that results obtained for large metropolitan housing markets are representative of smaller regional housing markets. The model developed has fairly moderate data requirements and may be applicable to other small and medium size economies such as Las Cruces.

FA Cup, UK GDP, FTSE Growth and National House Prices

The current paper examines national sporting events related market anomaly. It assesses an interrelationship between the Football Association Challenge Cup, known as FA Cup, winning team and annual returns for UK GDP, FTSE All Share index over the 133-year competition period as well as house price inflation starting from 1931. To test the hypothesis, the study employs a combination of non-parametric and regression techniques. The former is Kruskal-Wallis (KW) test. The latter is dummy variable regression. As the results of the study suggest, there is no statistical relationship between FA Cup winners and the annual growth in macro-series. All in all, analysts should simply enjoy ‘o jogo bonito’ (the beautiful game) rather than use it’s outcomes to gauge annual market trends.

금리가 주택가격에 미치는 영향과 요인

This study examined the effect of CD rates on the national(regional) housing price index with the two and four variable TVC-VAR (4). The four variable model is constructed by adding domestic industrial production index and consumer price index. According to the empirical results, the effect of the CD rate on the housing prices is negative value before the Asian foreign exchange crisis, and after positive value until 2012. The relationship between CD rate and housing prices is weakened sharply with the reinforce of the low level of global interest rate and quantitative easing policies. However, the relationship between CD rate and housing prices is rebounded, after the US has finished the quantitative easing and has increased interest rate. The change between CD rate and housing prices by LTV, DTI, and real estate policies have declined slightly after the global financial crisis, while the influence on expected inflation, long-term and short-term interest rates, and domestic and overseas policy rates have become stronger clearly. Also, it was confirmed that the relationship between interest rates and house prices could be changed according to the expectation of interest rate rise. Global interest rate hikes have raised expectations for a increase in domestic interest rates. Accordingly, it is necessary to carefully consider the timing of the domestic benchmark rate hike considering the domestic economic situation and the real estate market.

Modeling House Price Synchronization Across the U.S. States and Their Time-Varying Macroeconomic Linkages

This paper analyzes the time-varying impact of macroeconomic forces on the synchronization in housing movements across all the U.S. states. Using a Bayesian modeling approach, the house price movements are decomposed into national, regional, and state-specific factors. We then analyze the time-varying impact of macroeconomic forces on these national and regional factors. Evidence suggests that in several Western and Eastern states the house price variations are dominated by the national factor, whereas the regional factor dominates the Southern and Midwestern markets. These factors are found to have a time-varying relationship with most macroeconomic indicators with particularly pronounced time-variation caused by national house prices, inflation rate, and consumer sentiments.

A stochastic approach to model housing markets: The US housing market case

This study aims to estimate the price changes in housing markets using a stochastic process, which is defined in the form of stochastic differential equations (SDEs). It proposes a general SDEs system on the price structure in terms of house price index and mortgage rate to establish an effective process. As an empirical analysis, it applies a calibration procedure to an SDE on monthly S&P/Case-Shiller US National Home Price Index (HPI) and 30-year fixed mortgage rate to estimate parameters of differentiable functions defined in SDEs. The prediction power of the proposed stochastic model is justified through a Monte Carlo algorithm for one-year ahead monthly forecasts of the HPI returns. The results of the study show that the stochastic processes are flexible in terms of the choice of structure, compact with respect to the number of exogenous variables involved, and it is a literal method. Furthermore, this approach has a relatively high estimation power in forecasting the national house prices.

Household Debt and Housing Price: An Empirical Study across 36 Countries

This paper investigates the effect of the change of household debt ratio (household debt to GDP) on national housing price by using unbalanced panel data in 36 countries during 1981-2015. We employ Two Stage Least Square and GMM method to analyze the fixed effect model, after controlling the demand, supply, other assets prices and endogeneity. Our findings are that household debt ratio and housing price are positively significantly related. Household debt ratio promotes the growth rate of housing prices. The findings remain robust by separating countries into two groups, European countries and non-European countries, and using nominal housing price as explanatory variable.

RIPPLE EFFECT IN REGIONAL HOUSING AND LAND MARKETS IN IRAN: IMPLICATIONS FOR PORTFOLIO DIVERSIFICATION

In recent years, real estate has become a very popular investment choice for Iranian investors due to several interrelated economic and political reasons. The purpose of this study is to find out how real estate investors can gain diversification benefits from investing within the real estate sector across provinces of Iran. We use semi-annual data from selected provinces of Iran over the period of 1993–2014 and apply univariate Lagrange multiplier unit root tests with one and two structural breaks to the ratio of the provincial to national house and residential land prices respectively. We find diversification benefit can be gained by investing in housing markets across provinces because house prices in half of the sample provinces tend to drift away from house prices in the rest of the country. In addition, our results show that it is difficult to create an adequately diversified portfolio in a residential land market because shocks to the residential land prices of provinces ripple out across the nation. These findings should be valuable to domestic and foreign investors who are interested in the Iranian real estate sector, especially after the lifting of several international economic sanctions.