Regional House Prices Research Articles

Inter-regional rail travel and housing markets connectedness between London and other regions

Taking London as a location with which to measure the ripple effect in the UK housing market, this study aims to explain and verify the high degree of correlation between inter-regional transportation and the regional correlation of the housing market. Based on the literature on the relationship between short-term mobility and long-term migration, this paper illustrates that the extent to which people use trains for travel across regions will be related to the ripple effect in the regional housing markets. Frequent railway transport behavior, whether for commuting or traveling, might increase people's desire to relocate between regions, and thus leading to information transmission effects across regional housing prices and transaction volume. First, we estimate a dynamic indicator for the ripple effect. Then, the empirical tests use panel data, including the ripple indicator and passenger number data across time (1996–2022) and regions (nine regions). It is found that if London house prices drive other regional house prices to rise, inter-regional transportation demand will increase, and in turn, the increase of house prices in other housing markets will again drive up London house prices. The number of passengers will affect the information transmitted by the housing market transaction volume in other regions to the London housing market. This implies that higher inter-regional transport needs may lead to migration between London and other property markets, causing their transaction volumes to change in the same direction. The results of this paper verify that travel behavior between regions is a crucial factor in the leading/lagging behavior of regional housing market performance, implying a relationship between short-term travel and long-term migration. The results also indicate that incorporating variables of housing market correlations may help in the prediction of passenger numbers or transportation demand.

Aviation Discontinuity and Regional House Prices: A Quasi-Experiment in New Zealand

Although there is evidence that more regional airport activities can increase regional house prices, no study has examined the relevant impacts of aviation discontinuity. My unique quasi-experiment data comes from New Zealand where Jetstar withdrew its domestic services from four airports/regions in December 2019. The synthetic control method (SCM)'s results reveal that the withdrawal created different impacts on the house prices of the regions involved, in which the overall effect was negative in the short run but the housing market in New Zealand has recovered in the long run.

Monetary policy and house price heterogeneity: Evidence from the U.K

This paper investigates the effects of monetary policy shocks on regional house prices in the U.K. The empirical results indicate significant heterogeneity in the impact of monetary policy on regional house prices. Focusing on the source of heterogeneity, the paper suggests a demand-side factor to be a major driver. The regions with relatively affordable housing exhibit higher sensitivity to monetary policy than those with less affordable housing. The prevailing view that region-specific housing supply constraints and regional housing supply elasticity are responsible for the heterogeneous response of regional house prices to monetary policy shocks is not confirmed by the empirical findings.

Analysis of Factors Affecting Regional Housing Prices : Using Markov Switching Approach

Analysis of Factors Affecting Regional Housing Prices : Using Markov Switching Approach

Spatial heterogeneity analysis of biased land resource supply policies on housing prices and innovation efficiency

To rationally allocate land resources, the government has formulated biased land resource supply policies. However, this policy will promote the fluctuation of regional housing prices, thereby affecting regional innovation efficiency. To elucidate the intrinsic logical relationship among these factors, this study investigates the influence of biased land resource supply policies on housing prices and innovation across 31 Chinese provinces over a 16-year period. It places particular emphasis on probing into the spatial disparities in this impact. The innovation of this paper lies in the development of a theoretical analysis model, which creatively utilizes the regional land supply status as an instrumental variable to address the endogeneity problem. At the same time, a double robustness test was conducted by substituting the explanatory variables and employing the fixed effects model. The results of the TSLS regression indicate: (1) The land resource supply policy, which prioritizes the central and western regions, has resulted in an increase in housing prices across the majority of regions in China, with the eastern region experiencing a more rapid rise compared to the central and western regions. (2) The accelerated growth of housing prices in the eastern region is likely to exert a more pronounced inhibitory effect on innovation compared to the central and western regions. In summary, China’s land resource supply policy, which favors the central and western regions, has exacerbated the rise in housing prices in the eastern region, and rising housing prices will inhibit the innovation efficiency of the region. Hence, it is imperative for the government to continuously optimize and adjust land resource supply policies, ensuring their alignment with population migration patterns, stabilizing the real estate market, while also considering the innovation efficiency of regional development.

Does urbanization drive up housing prices? Novel evidence from remote sensing and dynamic panel quantile regression

Purpose This study aims to quantify the influence of urbanization on housing prices at the district-based level, while also investigating the heterogeneous impacts across different quantiles of housing prices. Design/methodology/approach The study uses remote-sensed spectral images from the Landsat 7 ETM+ satellite to measure urbanization, replacing prior reliance solely on urban population metrics. Subsequently, the two-step system generalized method of moments is used to evaluate how urbanization influences district-based housing prices through three spectrometries: Urban Index (UI), Normalized Difference Built-up Index (NDBI) and Built-Up Index (BUI). Finally, this study examines the heterogeneous impacts across various housing price quantiles through Dynamic Panel Quantile Regression with non-additive fixed effects under Markov Chain Monte Carlo simulation. Findings The study demonstrates that urbanization leads to an increase in regional housing prices. However, these impact magnitudes vary across housing price quantiles. Specifically, the impact exhibits an inverse V-shaped curve, with urbanization exerting a more pronounced influence on the 60th percentile of housing prices, while its effect on the 10th and 90th percentiles is comparatively weaker. Originality/value This study uses a novel method of remote sensing to measure urbanization and investigates its effects on housing prices. Furthermore, it provides an empirical application of non-additive fixed effect quantile regression for analyzing heterogeneity.

The nonlinear impacts of macroeconomic shocks on the US regional housing markets

ABSTRACT This paper examines the asymmetrical impacts of aggregate macroeconomic shocks on the US regional housing markets by estimating nonlinear Vector Autoregressive models. We show how aggregate output, monetary, inflation, and credit shocks result in heterogenous impulse responses of the housing market volatilities across growing and declining housing market regimes in nine census divisions and 19 metropolitan cities. We find that output and inflation shocks cause qualitatively asymmetrical responses across regimes in majority of regions. A positive output shock and a negative inflation shock reduce (increase) housing price uncertainties when housing markets are in a declining (booming) regime in most regions. The responses of regional housing price volatilities to monetary and credit shocks are quantitatively asymmetrical across regimes in most regions. We also find that aggregate shocks have larger contributions on changing the probabilities of regime switching when a regional housing market is initially expanding than declining. Among aggregate shocks, inflation shock has the most prominent contributions across most regions. It also presents the strongest asymmetrical effects across positive and negative shocks. Our results can help regional decision makers utilize fiscal policies, regulatory measures, and regional planning to mitigate the destabilizing effects of macroeconomic shocks on regional housing markets.

The Impact of Urban Planning on House Prices in China

Urban planning is a major part of urban development and leads the future direction of the city. In different times and social situations, urban planning can change, and this change can have an impact on house prices. This paper analyzes the changes in house prices due to urban planning, with China as the main subject. China's real estate market has changed significantly. Housing prices have changed more dramatically and are more susceptible to external factors. Through the collection of data on house prices in Beijing, Jiangsu, and other regions and the development of urban planning, it summarizes the important factors of urban planning affecting house prices and provides an in-depth analysis of different factors. Through the conclusions obtained from different analyses, this essay summarizes the relationship between urban planning and house prices and provides certain suggestions to the government on plans and improvements to address the issues involved.

House prices and fertility: Can the Dutch housing crisis explain the post‐2010 fertility decline?

AbstractFertility has declined in many rich societies after 2010. However, the factors that explain this fertility decline remain poorly understood. In particular, little is known about how changes on the housing market contributed to the fertility decline. This study examines the links between house prices and fertility in the Netherlands, a country where house prices have risen dramatically in the past decade. Regional (NUTS 3) data for the period 1995–2022 are analysed using within‐between models. The results for the within‐effect of house prices show that after 2010, fertility decreased (more) in regions where house prices increased (more), suggesting that the housing crisis in the Netherlands can explain a substantial part of the post‐2010 fertility decline. Before 2010, however, increases in regional house prices were associated with small increases in fertility, showing that the direction of the within‐effect of house prices depends on the time period that is studied. Finally, differences in house prices between regions are not consistently associated with fertility.

Influential Factors on California Regional Housing Price Analysed by Multiple Linear Regression

Influential Factors on California Regional Housing Price Analysed by Multiple Linear Regression

The non-linear dynamics of South Australian regional housing markets: A machine learning approach

Traditional linear models often struggle to capture regional housing markets' complex, non-linear dynamics. This study addresses this gap by developing and applying advanced machine learning algorithms to unlock unique insights into South Australian housing price behavior. Leveraging a comprehensive dataset of over 10,000 regional house sales from 2010 to 2021, we explore the non-linear relationships between housing prices and microeconomic factors (e.g., house size, land area, building quality) and socioeconomic characteristics (e.g., proximity to amenities and income levels). Our analysis employs a multi-step approach, including feature engineering, spatial data integration, correlation tests, multilevel modeling, and non-linear machine learning algorithms including Decision Tree, Random Forest, Gradient-Boosted Tree, and Artificial Neural Network. The key finding is that machine learning models outperform traditional econometric models in predicting regional housing prices, with higher accuracy and greater goodness of fit. Furthermore, we identify specific non-linear relationships, such as the increasing marginal impact of proximity to the sea on house prices as distance decreases. These findings offer valuable insights for policymakers, real estate professionals, and stakeholders, informing regional planning, infrastructure provision, and economic development strategies. This study sheds light on the complex dynamics of South Australian housing markets and lays the foundation for further research.

Adjust for non-ignorable panel attrition in the analysis of leaving the parental home

Leaving the parental home is an important life event that has received significant attention in the literature. Research on this topic relies heavily on panel data; however, panel data faces the issue of serious non-ignorable panel attrition associated with leaving the parental home. This paper addresses this issue using the UK Household Longitudinal Study (UKHLS) as a case study. It introduces an adjustment procedure that combines panel gap imputation via the next observation carried backward and inverse probability weighting based on the retrieved information about leaving the parental home. The results show that this adjustment method yields more precise model estimates for leaving the parental home, and after the adjustment, the positive marginal effects of age and living with non-biological parents, as well as the negative marginal effects of Asian ethnicity and regional house prices, become more pronounced. This adjustment method has the potential to be applied to address non-ignorable panel attrition associated with other events in different panel data.

Estimating regional house price levels

While quality‐adjusted indices tracing the evolutions of regional house prices are increasingly available, this is less the case for data on regional house price levels. Yet, such information is fundamental to assess housing affordability and barriers to labour mobility. This article puts forward a method to compile regional house price levels that are consistent with the evolutions given by house price indices, representative of the underlying stock of dwellings, and based on the information on house price levels that is available at all dates rather than in a single reference period. Results for Spain show that the decline in house prices following the 2008–2009 crisis initially reduced the dispersion in house prices across Spanish regions, but this dispersion has increased again afterwards. Moreover, the relative housing affordability of Madrid deteriorated compared to all other Spanish regions in the last decade. Monitoring whether COVID‐19 will reverse this trend will be key.

Regional House Price Cohesion in the UK: Recent Trends and Covid

Regional House Price Cohesion in the UK: Recent Trends and Covid

Regional house price convergence: implications of monetary policy

ABSTRACT Motivated by the structural change since the adoption of inflation targeting in October 1992, this research investigates regional house price convergence in the UK over the period 1973:Q4–2020:Q4. By using subsample comparisons, results show that all regions in the inflation-targeting regime converges to one club until the Global Financial Crisis. The finding suggests that the successful inflation-targeting policy can promote integration among regions.

House price connectedness and consumer sentiment in an era of destabilizing macroeconomic conditions: Empirical evidence from Türkiye

This study analyzes changes in the connectedness of 26 regional house prices in Türkiye and determines how consumer sentiments affect connectedness in the era of destabilizing macroeconomic conditions over the period from January 2010 to April 2022. To this end, we estimate network connectedness using a Lasso VAR model and time-varying analyses using a rolling linear VAR model for the pre-crisis and post-crisis periods. Our empirical results provide evidence of a high connectedness of house prices, with a greater degree of connectedness witnessed during the post-crisis period across regions in Türkiye. The results further divulge that Ankara (region TR51) is a major transmitter of connectedness during the pre-crisis period, while Istanbul (region TR10) is a major transmitter of connectedness during the post-crisis period. By and large, our results show that the degree of connectedness is associated with the period of financial and economic destabilization, characterized by a significant depreciation of the local currency, rising inflation rates, and an increasing cost of borrowing accompanied by rising loan restructurings and defaults. Furthermore, the empirical results indicate substantial evidence of a positive impact of consumer sentiment on the degree of connectedness during the post-crisis period. These empirical results are validated by the results of the time-varying Granger causality tests.

Losing a job and (dis)incentives to move: Interregional migration in Finland

Traditional theories of migration frequently fail to fully explain the real-world patterns of interregional mobility. Empirical studies indicate that individuals may not always migrate for economic reasons such as poor employment and income prospects. Which characteristics drive people to stay or move after job displacement? Using information on establishment closures, we analyse the economic and social determinants of interregional mobility following job loss. We base our empirical analysis on nationwide individual-level register data from Finland for 1997–2015. We find that receiving earnings-based unemployment benefits substantially weakens the economic incentives for interregional mobility. This negative association is particularly strong for the lower educated and those living in more rural areas. Moreover, our results show that the migration decisions of displaced workers are strongly affected by homeownership, differences in regional housing prices, and social connections, as measured by childhood family relations.

THE EFFECTS OF COVID-19 ON RESIDENTIAL PROPERTY PRICES IN MALAYSIA

The physical distancing, lockdown, and other measures due to the COVID-19 pandemic have caused massive job loss and economic hardship, which would result in people not being able to pay their mortgages or rent. Thus, the main objective of this study is to examine the effects of the Gross Domestic Product (GDP), inflation, investment, and interest rate on residential property prices in Malaysia before and during COVID-19. This research utilises the annual time series data from 1990 to 2020, and the empirical analysis is conducted using the multiple linear regression analysis modelling approach. The finding shows that GDP and interest rates are positive and statistically significant with residential property prices. Practitioners can use these findings to understand the pandemic's effect on house prices. This will provide some guidelines for a policy formulation to moderate the increase in residential property prices to maintain the country's economic stability and indirectly help the government realise its vision of becoming a high-income country between 2024 and 2028. This is the first study that examines the pricing behaviour of Malaysian house prices concerning the COVID-19 shocks. In addition, the study contributes to regional housing price studies by bridging the real estate literature gap.

Do Community Banks Stabilize Housing Prices?

This paper examines the influence that community bank residential mortgage lending has on regional housing prices and ultimately the housing price bubble that culminated with the Great Recession of 2007–09. Previous research suggests that community bank lending is influenced by financial intermediation frictions, such as difficulty attracting uninsured deposits and limited access to the bond market. Such frictions cause bank liquidity to play an important role in the ability of community banks to navigate swings in the local economy as well as real estate prices. We take these frictions as the starting point in our analysis and contribute to literature by focusing on the impact that community banks exert on housing prices. Specifically, we find that while community bank residential mortgage holdings are marginally related to housing price cycles, their mortgage lending does not contribute in an economically meaningful way to housing price bubbles. Even though bank liquidity, defined as the ratio of securities-to-assets, plays a role in the degree of community bank residential mortgage lending, it is not sufficiently strong enough to influence housing prices.

Housing networks in urban China: A panel VAR model with Bayesian stochastic search

This paper analyzes housing networks in China established through the inter-city information spillover. Unlike conventional methods that either fail to identify the city-level linkage or impose arbitrary restrictions, we investigate both the direction and magnitude of cross-city housing spillover in a data-driven manner by employing a panel VAR framework with Bayesian stochastic search. Through this, significance of the pair-linkages is examined from both perspectives of return and shock with insignificant ones being detected. Using a monthly dataset of housing prices covering 70 major China's cities over 2005–2021, our results confirm the presence of positive housing spillover that 11.68 % and 12.92 % of the total city-paired linkages are found to exist in terms of return and shock, respectively. With a particular focus on China's key urban agglomerations, housing spillovers exist not only across city-tiers in forms of ‘top-down’ and ‘bottom-up’ but also within the same tier in a ‘peer learning’ pattern, and the spillover magnitude of the latter is shown to be greater than that of the former. Despite the above common pattern, housing networks also demonstrate heterogeneous features across city clusters. Additional analyses reassure robustness of our findings that possess implications for clear comprehension of regional housing price dynamics.