Real House Prices Research Articles

Financial technologies and the effectiveness of monetary policy transmission

This study investigates whether and how financial technologies (FinTech) influence the effectiveness of monetary policy transmission. We use an interacted panel vector autoregression model to explore how the effects of monetary policy shocks change with regional-level FinTech adoption. Results indicate that FinTech adoption generally mitigates the transmission of monetary policy to real GDP, consumer prices, bank loans, and housing prices, with the most significant impact observed in the weakened transmission to bank loan growth. The relaxed financial constraints, regulatory arbitrage, and intensified competition are the possible mechanisms underlying the mitigated transmission.

The dynamic effect of macroeconomic factors on housing prices: Evidence from South Africa.

This study examines the dynamic short- and long-run causal relationship between South African real house prices and key macroeconomic fundamentals (gross domestic product(GDP), mortgage rate, exchange rate-USDZAR, affordability, household debt to disposable income, unemployment rate, share prices (JSE ALL share index), foreign direct investment, and producer price index) over the period 2000Q1-2019Q4. The study uses a vector error correction model (VECM) to estimate the relationships while accounting for endogeneity and reverse causality. Although, there seems to be a significant association(both short and long-run) between house prices and all macroeconomic fundamental variables, GDP and producer price index appear to have the greatest impact. Further, our results suggest that any short-term disequilibrium in house prices always self-corrects in the long-run.

Don’t Look Up: House Prices in Emerging Europe

Abstract This paper investigates how housing prices respond to economic, financial and demographic conditions in emerging markets in Europe. We use quarterly data covering 10 countries over the period 1998–2022 and implement a panel quantile regression approach to obtain a granular analysis of real estate markets. Overall, economic, financial and demographic developments explain real house price growth in emerging Europe, with income growth having the most significant impact. Quantile regression estimations show that income growth matters more for higher quantiles of the property market. We also find that an increase in short-term or long-term interest rates has a price-dampening impact, indicating that a higher cost of borrowing is associated with lower real house price growth. These results indicate that the downturn in house prices could deepen with the looming economic recession and soaring interest rates.

Bubble detective: City‐level analysis of house price cycles

AbstractThis paper investigates house price dynamics at high frequency using city‐level observations during the period 1994–2022 in Lithuania. We employ multiple time series‐based econometric procedures to examine whether real house prices and house price‐to‐rent ratios exhibit explosive behaviour. According to these recursive right‐tailed test results, we reject the null hypothesis of no‐bubble and find evidence for long and multiple periods of explosive behaviour in the housing market in all major cities during the sample period. While the size of bubbles varies across cities, especially when we use the house price‐to‐rent ratio, there is clearly a similar boom‐bust pattern in Lithuania. Large house price corrections can in turn have adverse effects on economic performance and financial stability, as experienced during the global financial crisis and other episodes in history.

Drivers of Nigeria’s real house prices: long-run analysis and short-run dynamics

Purpose The purpose of this paper is to assess the long-run and short-run drivers of real house prices in Nigeria from 1991Q1 to 2020Q4. Design/methodology/approach Vector autoregression and cointegration tests were used to assess the key drivers of Nigeria’s real house prices in the long run and short run. Findings The empirical findings revealed that household disposable income is the most important determinant of house prices in Nigeria. House prices increased by 1.6% and 60.8% in response to a 1% increase in disposable income in the long run and short run, respectively, while real mortgage credits pushed up house prices by 5% and have no long-run effects, suggesting that most Nigerians depend on their money income rather than credits in securing a home. In addition, prices of oil sector products and real interest rates had negative and significant relationship with house prices, while positive correlations were found for real effective exchange rate and real housing investments regardless of the time horizon. The impact of construction costs and cement prices was also documented. Originality/value This is likely a pioneering study of its kind to focus on the determinants of real house prices in Nigeria. It is probably the first study, the best of the author’s knowledge, to empirically examine the impact of the oil sector on house prices in the country.

Has the Fed Responded to House and Stock Prices? A Time-Varying Analysis

Abstract We investigate whether the Federal Reserve has responded systematically to house and stock prices and whether this response has changed over time using a Bayesian structural VAR model with time-varying parameters and stochastic volatility. To recover the systematic component of monetary policy, we interpret the interest rate equation in the VAR as an extended monetary policy rule responding to inflation, the output gap, house prices, and stock prices. Our results indicate that the systematic component of monetary policy in the United States responded to real stock price growth significantly but episodically, mainly around recessions and periods of financial instability, and took real house price growth into account only in the years preceding the Great Recession. Around half of the estimated response captures the predictor role of asset prices for future inflation and real economic activity, while the remaining component reflects a direct response to stock prices and house prices.

Work from Home and Urban Structure

The sustained increase in working from home in the wake of Covid has the potential to reshape the US urban landscape. This article describes the big picture of pre2020 remote work in the US and summarizes how that picture changed during the subsequent three years. It then introduces a mathematical model designed to calculate the possible long-run impacts of increased remote work on where and how Americans work and live. This model predicts that the increased prevalence of remote and hybrid work arrangements will induce workers with remote-capable jobs to find housing farther away from their job locations, increasing the length of the average commute while cutting the time actually spent commuting. Jobs that produce goods and services which must be consumed locally will follow the bulk of the population to suburbs and smaller cities, while jobs producing tradable output will increase both in low-cost and high-productivity locations, at the expense of the middle. In the long run, the reallocation of demand to lower density locations with fewer legal restrictions on housing development should reduce the real price of housing by at least 1 per cent, but these changes depend on adjustments to the housing stock, both through new construction and through re-purposing commercial real estate in city centres. The model predicts a partial reversal of the decades-long concentration of talent and income in the centres of the biggest cities. Data on changes 2019–2022 suggest that some of this reversal is already happening.

Impacts of monetary policy on housing prices in five emerging economies during the Covid-19 pandemic

Purpose This paper aims to examine the dynamic impacts of the COVID-19 pandemic and government policy on real house price indices in five emerging economies, namely, Brazil, China, Thailand, Turkey and South Africa. Design/methodology/approach The authors use the local projection method with a panel data set of these countries spanning from January 2020 to July 2021. Findings The number of COVID-19 confirmed positive cases raised housing prices, whereas government containment measures reduced them. Both conventional and unconventional monetary policy implemented by central banks to cope with the COVID-19 helped increase housing prices. These effects were strengthened by the US monetary policy via globalized financial markets. Originality/value First, while previous researches typically concentrated on developed countries, the authors investigate emerging economies where proportionally more people were badly affected by the pandemic. Second, a panel data set of five emerging economies enabled the authors to examine the dynamic effects of the COVID-19 crisis on housing prices. Third, to the best of the authors’ knowledge, this is the first study evaluating the influences of easing monetary policy on housing prices in emerging economies during the pandemic.

The long-run impact of remittances on house prices in Kenya

PurposeGiven the coincidental and sustained rise in house prices and foreign capital flows in Kenya, this study aims to understand whether a long-run relationship exists between real diaspora remittances and real house prices.Design/methodology/approachThis study uses data from 2004-Q1 to 2020-Q4 and applies an autoregressive distributed lag model for estimation.FindingsThe results indicate that a positive and significant relationship exists between real remittances and real house prices in Kenya in the long run.Originality/valueTo the best of the authors’ knowledge, there is no study exploring the relationship between real remittance inflows and house prices in Kenya, after controlling for other key macroeconomic determinants of house prices. This study addresses this research gap.

Applying the AD-AS Model to the Housing Market of Post-Socialist Economies

We propose applying the standard aggregate demand and aggregate supply model (AD-AS) to the housing market. It is a very simple and intuitive tool that can help shed light on the major forces at play in the market and that can supplement the use of the general equilibrium and dynamic stochastic equilibrium models (DSGE). The latter models are very sophisticated and aim to cover many aspects of the economy, but they require a significant number of long time series to estimate the model parameters. However, in many countries, such as post-socialist countries, the time series are short. Those models moreover cover only real house prices, but in certain situations we should consider real and nominal prices at the same time.

Comparison of Real Estate Price Prediction Based on LSTM and LGBM

Housing price prediction is one of the widely discussed topics, and the application of machine learning and deep learning models to housing price prediction is a research hotspot. Exploring which models are suitable for predicting real housing price datasets has significant implications for guiding government and homebuyers' decision-making. In this study, the LSTM and LightGBM models were selected as research objects, and the suitability of the models was explored and compared using the second-hand housing price dataset in Beijing. Based on the analysis, in the task of pure time series prediction based on historical housing prices, the LSTM model had a better fit (R2=0.91), while in the task of housing price prediction based on multiple influencing factors, the LightGBM model had better comprehensive evaluation index results (R2=0.53). Both models can be used to predict pure time series, but the LSTM model is not suitable for predicting multi-factor input models that are not time series.

Housing prices during the COVID-19 pandemic: insights from Sweden

The COVID-19 pandemic triggered a major global economic recession, to which policymakers around the world responded with massive fiscal and monetary support. While housing prices generally fall during economic downturns, they have risen during the pandemic in all OECD countries. A number of factors may have contributed, including expansionary monetary policy, the lifting of some macro-prudential constraints and a shift in housing preferences. This paper uses monthly data to examine the behaviour of real house and flat prices during the pandemic in Sweden, at the national level and in the three biggest cities. While a model containing usual determinants of housing prices tracked price developments well before the pandemic, it underestimates house prices and generally overestimates flat prices in the pandemic period. This suggests a preference shift from flats towards houses, which is consistent with findings from the recent literature on other countries. The introduction of mortgage amortisation requirements in 2016 and 2018 is estimated to have lowered housing prices. However, their lifting during the pandemic seems to have had a relatively minor effect on housing prices.

Identifying Common and Idiosyncratic Explosive Behaviors in the Large Dimensional Factor Model with an Application to U.S. State-Level House Prices

Abstract This study applies the date-stamping methodologies for explosive behaviors proposed in the seminal work of Phillips, P. C. B., and J. Yu. (2011. “Dating the Timeline of Financial Bubbles during the Subprime Crisis.” Quantitative Economics 2 (3): 455–91), Phillips, P. C. B., S. Shi, and J. Yu. (2015a. “Testing for Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500.” International Economic Review 56 (4): 1043–78), and Phillips, P. C. B., S. Shi, and J. Yu. (2015b. “Testing for Multiple Bubbles: Limit Theory of Real Time Detectors.” International Economic Review 56 (4): 1079–134) to a large dimensional factor model. To this end, we compare two methods of identifying common and idiosyncratic components: the Panel Analysis of Nonstationarity in Idiosyncratic and Common Components (PANIC) method by Bai, J., and S. Ng. (2004. “A Panic Attack on Unit Roots and Cointegration.” Econometrica 72 (4): 1127–77) and the Cross-Sectional regression (CS) method by Yamamoto, Y., and T. Horie. (2022. “A Cross-Sectional Method for Right-Tailed PANIC Tests under a Moderately Local to Unity Framework.” Econometric Theory (forthcoming)). We show that, when the explosive behavior lies only in the common component, the origination and termination dates are precisely estimated by either method. However, when the explosive behaviors exist in idiosyncratic components, only the CS method can detect them. We apply our method to the U.S. state-level real house price indices. We find that the 2000s boom was driven by not only the national bubble factors but also local components, while the 2010s onward expansion is dominated by the effect of national components.

Analysis of Factors Affecting the Dynamics of Residential Real Estate Prices in Russia

In this paper, we have constructed a VAR model to identify and assess the impact of real interest rate shocks, real estate demand, oil prices, uncertainty, and aggregate business activity on residential real estate prices in Russia. The relevance of the research is due to the following: the dynamics of real estate prices determines the consumer and investment behavior of households, and serious fluctuations in real estate prices lead to adverse consequences in many areas of life, so more and more researchers are asking questions about the presence of bubbles in the real estate market, which can be dangerous to the stability of the economy. In addition, a sharp increase in the cost of housing in Russia in 2020 is an open question for researchers. Our goal is to determine what factors caused the rise in real estate prices in Russia in the time interval from the Q1 of 2000 to the Q2 of 2022. A VAR model with a Cholesky decomposition was used for the evaluation. Several specifications were considered with the inclusion of the real oil price as an exogenous variable and a set of endogenous variables: real GDP, real interest rate, uncertainty index and housing price index. The main conclusion of the paper is that the housing market is sensitive to identified macroeconomic shocks, and a decrease in the interest rate leads to an increase in demand and real estate prices. The estimate of the long-term elasticity of housing prices for oil prices was 0.35, the dynamics of oil prices explained a significant proportion of the variation in real estate prices, but the predominant role in housing price fluctuations is given to housing demand shocks. The housing demand shocks in Russia itself had a negligible impact on GDP.

Two-Way Fixed Effects Estimation on the Impact of rising house prices on the capital structure of real enterprises in China

Since the implementation of China's reform and opening-up policy, rapid economic growth has led to the rapid development of the real estate market and housing prices have entered the fast lane of growth. As housing prices rise, the allocation of credit funds in China will continue to be tilted towards the real estate sector. This has a "crowding out" effect on the real economy and is not conducive to the positive optimisation of corporate capital structure. Little research exists on the lagged nature of house price inflation and its impact on the capital structure of real enterprises. To fill this gap, this paper investigates the lagged effect of house price changes on the adjustment of capital structure of real enterprises and the heterogeneity between regions based on a two-way fixed effects model with a sample of 30 Chinese provinces over the period 2000-2020. The study finds that in the short run, rising house prices do not have a significant impact on the capital structure of real enterprises. In the long run, enterprises' debt levels rise and their capital structure adjusts negatively. There is a lag in the central and western regions and no lag in the eastern regions. After a series of robustness tests, the results remain significant and robust. This study provides a reference for the government to establish a long-term mechanism for the stable development of the real estate market.

Towards Better Banking Crisis Prediction: Could an Automatic Variable Selection Process Improve the Performance?*

This study proposes using the Least Absolute Shrinkage and Selection Operator (LASSO) method with cross‐validation to automate the variable selection process of the conventional multivariate logit early warning system (EWS), the purpose being to improve the prediction of systemic banking crises. Using a dataset covering 23 OECD countries with quarterly data from 1970Q1 to 2018Q3, model performance is evaluated in a recursive out‐of‐sample forecasting exercise, taking policy‐makers' preference of missed crises and false alarms into account. The results suggest that the automatic variable selection process can enhance the predictive performance of the EWS. It also highlights the importance of extracting information from variable interactions and lags that may not be easily identified and accessed by typical subjective variable pre‐selection. This simple approach is easy to interpret and is transparent, which are important aspects for effective policy communication. Five variables, namely credit growth, domestic and global credit gaps, real house price growth and the real effective exchange rate, are identified as the most important key indicators of systemic banking crises.

The tremendous household debt in Asia Pacific countries: is it determined by financial development

Since the early twentieth century, the tremendous increase in household debt has motivated researchers to find the determinant factors of this phenomenon. The paper uses data from Asia Pacific countries and dynamic heterogeneous panel method analysis to determine factors of household debt. The results find that financial development and financial institution access have a long-run positive relationship, stable and convergent with household debt. This implies that as the financial system developed, as evidenced by easy access to financial institutions, it provided some attractive financial instruments and financial markets became liquid. As a result, household debt increased due to easier access to financing and credit options that matched people's needs. However, in the short run, financial development and financial institution access do not strongly affect household debt of countries overall. Also, the real interest rate and housing price have a positive impact, and stock capital accumulation and household consumption harm household debt in the long run. Therefore, supervision of the volatility of household debt and financial development are important for sustainable economic growth.

Examining the Presence of Balloons in Turkey's House Prices

Financial bubbles have a significant effect on economic indicators. Bubbles in the housing market may herald the existence of financial crises, especially those arising from the housing market. In this study, it was aimed to test the existence of bubbles in housing price formations by considering the housing price index of Turkey in general and the housing price index of 26 sub- provinces/regions, including Turkey's three big cities. For this purpose, the existence of bubbles in housing prices were examined using the monthly frequency data of 27 housing price indexes for the 2010M1 – 2022M3 periods, with the methods of the Sup-Augmented Dickey Fuller and Generalized Sup-Augmented Dickey Fuller methods and then the periods of the bubbles determined form each province/region were expressed. In the findings obtained, the presence of undeflated balloons was determined in Turkey in general and in all 26 sub-provinces/regions. Considering the economic risks that balloons indicate, it is clear that necesserity of constantly controlled with regulations both of the housing market and the financial markets associated with the housing market. It is thought that the study will contribute to the literature in terms of handling the period including the COVID-19 pandemic process, using real housing prices, and comprehensively examining the existence of bubbles for both Turkey in general and all sub-regions of Turkey.

What causes house prices to fluctuate? Evidence from South Korea

AbstractIn this study, we build a structural vector autoregressive model of the housing market with supply and demand shocks to determine the main causes of house price movements in South Korea. We include housing permits, basic housing demand, and the growth rates of real housing prices in the model. Changes in house prices are decomposed into three structural components: housing supply shocks, shocks to basic housing demand, and shocks to housing market‐specific demand. We find that the main driver of the movements in house prices is housing market‐specific demand shocks, and these are associated with beliefs regarding future house prices and the real estate market, as measured by the Consumer Sentiment Indices for the housing market, rather than credit conditions or financial variables such as interest rates or stock returns.

The asymmetric effects of real variables on real housing prices: a nonlinear ARDL analysis for Turkey

PurposeThis study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and housing sales on the real housing prices.Design/methodology/approachThis study uses a nonlinear autoregressive distributed lag (NARDL) model in the monthly period of 2010:1–2021:10.FindingsThe real effective exchange rate has a positive and symmetric effect. The decreasing effect of negative changes in real money supply on real housing prices is higher than the increasing effect of positive changes. Only positive changes in the real construction cost index have an increasing and statistically significant effect on real house prices, while only negative changes in housing sales have a small negative sign and a small increasing effect on housing prices. The fact that the positive and negative changes in real mortgage rates are negative and positive, respectively, indicates that both have a reducing effect on real housing prices.Originality/valueThis study suggests the first NARDL model that investigates the asymmetric effects on real housing prices instead of nominal housing prices for Turkey. In addition, the study is the first, to the best of the authors’ knowledge, to examine the effects of the five real variables on real housing prices.