How Do House Prices Affect Consumption? Evidence From Micro Data

Housing is a major component of wealth. Since house prices fluctuate considerably over time, it is important to understand how these fluctuations affect households' consumption decisions. Rising house prices may stimulate consumption by increasing households' perceived wealth, or by relaxing borrowing constraints. This paper investigates the response of household consumption to house prices using UK micro data. We estimate the largest effect of house prices on consumption for older homeowners, and the smallest effect, insignificantly different from zero, for younger renters. This finding is consistent with heterogeneity in the wealth effect across these groups. It suggests that as the population ages and becomes more concentrated in the old homeowners group, aggregate consumption may become more responsive to house prices. In addition, we find that regional house prices affect regional consumption growth. Predictable changes in house prices are correlated with predictable changes in consumption, particularly for households that are more likely to be borrowing constrained, but this effect is driven by national rather than regional house prices and is important for renters as well as homeowners, suggesting that UK house prices are correlated with aggregate financial market conditions.

Housing is a major component of wealth. Since house prices fluctuate considerably over time, it is important to understand how these fluctuations affect households’ consumption decisions. Rising house prices may stimulate consumption by increasing households’ perceived wealth, or by relaxing borrowing constraints. This paper investigates the response of household consumption to house prices using UK micro data. We estimate the largest effect of house prices on consumption for older homeowners, and the smallest effect, insignificantly different from zero, for younger renters. This finding is consistent with heterogeneity in the wealth effect across these groups. In addition, we find that regional house prices affect regional consumption growth. Predictable changes in house prices are correlated with predictable changes in consumption, particularly for households that are more likely to be borrowing constrained, but this effect is driven by national rather than regional house prices and is important for renters as well as homeowners, suggesting that UK house prices are correlated with aggregate financial market conditions.

The impact of housing price on non-housing consumption of the Chinese households: A general equilibrium analysis

The commercial residential industry has high added value and comprehensive economic benefit so the commercial residential industry is naturally a hot-spot issue. The core issue of the commercial housing is the price. This thesis conducts the descriptive statistic analysis of residential real estate prices, urban resident income and other relevant data in China’s 30 provinces (excluding Tibet) from 1998 to 2006. The change trend and the difference feature of both the residential real estate price and the urban resident income in those regions are revealed, which is expected to make a contribution to the macro-control in China’s real estate. In recent years, the real estate market in China is growing rapidly. On the one hand, it plays a vital role in both promoting the national economic growth and improving the living standards of urban residents. On the other hand, some problems in the development of China’s current real estate market have been fully exposed, such as the overheated investment, the unbalance in supply and demand, insufficient financing channels, soaring property prices and so on. In particular, the rapid growth in the housing price has brought challenges to the sound development in both China’s real estate market and the whole national economy and it has also become a hot-spot and difficult issue in the current academia. Such relevant research as whether the rapid growth of China’s housing prices has become disjointed with resident income seriously or not and what the rules of the changes in income and housing prices in China’s different regions are is realistically significant for guiding the micro-control in China’s real estate. I. Index Selection and Disposal of Comparability The samples selected in this thesis are composed of the fluctuating residential real estate prices in China’s 30 provinces (excluding Tibet) from 1998 to 2006, urban resident income and other relevant panel data that are from various years of China Statistical Yearbook. The data of the real estate prices adopts the real estate prices in urban areas. The income indexes adopt the annual per capita disposable income of urban residents. In order to remove the impacts of the price and make indexes of various types had comparability in time series, the disposal of comparability has been conducted in indexes of different types in the thesis and their present value has been turned into the value of the constant price, namely, on the basis of the constant price in 1998, the concrete calculation method is that the housing price is deflated by the housing sales price index and the disposable income is deflated by the consumer price index of urban residents. II. Analysis of Commercial Housing Price Variance among Provinces According to the average housing prices and their growth rates in provinces from 1998 to 2006, thirty provinces, cities and autonomous regions across the country can be divided into three types in accordance with the mean and the growth rate of their average housing prices. It’s found that the provinces, cities and autonomous regions of the three types also have common in geographic areas so they can be divided into such three regions as the eastern region, the central region and the western region on the basis of their geographic areas. For the regional division of the average housing price in China, Figure 1 compares the changes in the average housing prices in the central, western and eastern regions from 1998 to 2006. It’s found that the average housing price in the east is prominently higher than those in the western and International Conference on Education Technology, Management and Humanities Science (ETMHS 2015) © 2015. The authors Published by Atlantis Press 1100 central regions and the trend of its average housing prices is on the rise. In particular, the rising trend of the average housing prices is obvious after 2004. The changes in the average housing prices in eastern and western regions are comparatively similar. However, the average prices in the central region are rising slowly while for the western region, a small decline also appears in its slightly rising process. Besides, the average housing prices in the eastern region surpass those in the western region after 2004. III. Analysis of the Differences in the Income Change among Regions Figure 1: Changes in the housing prices in the central, eastern and western regions Figure 2: Changes in the income in the central, eastern and western regions For the regional division of the average housing price in China, Figure 2 compares the changes in the income in the central, western and eastern regions from 1998 to 2006. It’s found that the per capita income in the east is prominently higher than those in the western and central regions and its trend is uniformly on the rise.

The impact of house prices on consumption is an important concern for academics and policy makers. Several studies have documented that house price changes have an impact on consumption; however, there is far less consensus on how house price changes affect consumption in China. The purpose of this paper is to examine the impact of house prices on household consumption in urban China and to identify the mechanisms behind the impact. This study measures the impact of housing price changes on consumption at the household level, using research data from 71,548 home-owning households in the 2011–2019 China Household Finance Survey database. Our analysis shows that housing prices have a significant negative effect on consumption, with a 1% increase in the value of a household’s property causing a 0.0034% decrease in household consumption, an effect that is significant for households that own a home and for those in the eastern and central regions. We find that rising house prices cause an increase in households’ precautionary savings, which is the main mechanism through which house prices affect consumption in China. Furthermore, the impact of house prices on consumption is asymmetric, with consumption moving in the opposite direction when house prices rise but not when house prices fall. This study provides meaningful insights for policy makers on the usefulness of building a healthy and stable housing market to expand consumption and revitalize the economy.

Purpose– The purpose of this paper is to address a key issue fundamental to the operation of land and housing markets, that is, the relationship between land and house prices. The study identifies possible causation between established house and vacant allotment prices using the metropolitan area of Adelaide, Australia as a case study.Design/methodology/approach– A key outcome of the study is the construction of a Site Adjusted Land Price Index against which a Quality Adjusted House Price Index is compared.Findings– The results show that there is a lagged effect of land prices on house prices and that this is significant at an interval of eight lag periods. The results also imply that the lead lag relationship between established house and vacant allotment prices is not unidirectional. This suggests that, while a change in house prices leads to a change in land prices in the short-run, the long-run position is for increasing land prices to lead to a delayed increase in house prices.Research limitations/implications– Rising house prices do not simply and solely reflect a shortage of land. There are suggested effects both immediate from house to land and delayed from land to house, particularly in a rising market.Originality/value– The lead lag relationships of both indexes are tested using Granger causality estimates to assess whether theoretical Ricardian concepts still hold in a modern urban land market.

Purpose – This paper aims to use Markov switching vector auto regression (MSVAR) methods to examine UK house price cycles in UK regions at NUTS1 level. There is extensive literature on UK regional house price dynamics, yet empirical work focusing on the duration and magnitude of regional housing cycles has received little attention. The research findings indicate that the regional structure of UK exhibits that UK house price changes are best described as two large groups of regions with marked differences in the amplitude and duration of the cyclical regimes between the two groups. Design/methodology/approach – MSVAR principal component analysis NUTS1 data are used. Findings – The housing cycles can be divided into two super regions based on magnitude, duration and the way they behave during recession, boom and sluggish periods. A north-south divide, a uniform housing policy and a monetary policy increase the diversion among the regions. Research limitations/implications – Markov switching needs high-frequency data and long time spans. Practical implications – Questions a uniform housing policy in a heterogeneous housing market. Questions the impact of monetary policy on a heterogeneous housing market. The way the recovery of the housing market varies among regions depends on regional economic performance, housing market structure and the labour market. House price convergence, beta-convergence. Originality/value – No such work has been done looking at duration and magnitude of regional housing cycles. A new econometric method was used.

PurposeThe purpose of this paper is to examine the house market in Malaysia from 2002 to 2015. Specifically, the macroeconomic determinants on the house price and house demand are investigated.Design/methodology/approachStructural Vector Autoregressive Regression was adopted to estimate the unexpected changes in both house demand (residential transaction volume) and prices based on economic theoretical reasoning that consider shock from macroeconomic determinants.FindingsThe transaction volume and real house prices respond to most of the macroeconomic shocks. While the impact of real gross domestic product (GDP) on house prices appears to be stronger and longer in comparison to other macroeconomic shocks, a 60 per cent change in house prices can be explained by real GDP regardless of whether it is in the short run or the long run. The studies also reveal that a positive effective exchange rate plays an important role when demonstrating the transaction volume. Moreover, monetary liquidity plays a major role in justifying the transaction volume. This implies that mortgage lending may have an impact on housing demand. Meanwhile, movements of house prices cannot be explained by the demand in quantity. This signifies that supply has a strong influence in determining the price.Research limitations/implicationsThis study has implications on policymakers of which the interest rate as a cooling measure might not be effective in the short run. The interest rate has very little impact on housing prices. Furthermore, policymakers should address the concerns on speculations, as the results reveal that monetary liquidity and the exchange rate have a strong impact on the housing demand.Originality/valueThis study seeks to provide answers regarding the recent upsurge of Malaysian housing prices. Besides focusing on the house price changes, this study addresses the role of transaction volume while evaluating the house market, as housing prices are usually downwards rigid. Since the price and transaction volume are both related to the transaction activity, this study is significant and could be a good reflection on the actual demand behaviour in the residential market.

This paper aims to analyze the factors that affect housing prices and predict the trend of housing prices through a linear regression model. This article will use a linear regression model to analyze data on UK house prices from 1952 to 2022. And selected 12 different factors that affect housing prices, using a linear regression model to predict the fluctuations of housing prices. At the same time, a multiple prior regression model was used to explore the importance of each factor in affecting house prices. And the significance of these variables was also compared. The results show that the location of the house, per capita income, housing environment, immigration rate, actual deposit rate and actual loan rate have a significant linear relationship with house price changes, while per capita productivity and house type after the epidemic do not have a significant linear relationship with house price changes. Therefore, UK house prices can be predicted based on the above factors.

PurposeVarious empirical studies have demonstrated that house prices in different geographic regions have a tendency to co‐move. But these studies have focused on developed economies in the west. The purpose of this paper is to test this hypothesis in the case of three major urban areas in the rapidly developing economy of Malaysia, namely Klang Valley, Penang and Johor.Design/methodology/approachUsing Pesaran et al.'s bounds testing approach to cointegration and Granger non‐causality analysis, the short‐run and long‐run dynamics of regional house prices are analysed.FindingsFirst, house prices in all three regions appear to be cointegrated. Second, there is evidence of short‐run bi‐directional causality between house prices in all regions. Third, long‐run house price movements in Johor are not Granger‐caused by house prices in Klang Valley and Penang. This observation could be rationalised in the light of the argument that Johor house values may be more closely aligned with activities in the Singaporean economy, given the region's geographic proximity to Singapore.Practical implicationsThe findings have several practical implications for housing investors who intend to optimise investment decisions on housing purchases. The understanding of the nature of regional house dynamics could also enrich the government's knowledge of how the local housing markets work and enable the design and implementation of relevant housing policies.Originality/valueThis is the first known study that establishes stylised facts of lead‐lag relationships for regional house prices, while also providing short‐run and long‐run estimates of regional house price interactions for Malaysia.

Purpose: This paper examines the existence of the ripple effect from Amsterdam to the housing markets of other regions in the Netherlands. It identifies which regional housing markets are influenced by house price movements in Amsterdam. Design/methodology/approach: The paper considers the ripple effect as a lead-lag effect and a long-run convergence between the Amsterdam and regional house prices. Using the real house prices for second-hand owner-occupied dwellings from 1995q1 to 2016q2, the paper adopts the Toda-Yamamoto Granger Causality approach to study the lead-lag effects. It uses the ARDL-Bounds cointegration techniques to examine the long-run convergence between the regional and the Amsterdam house prices. The paper controls for house price fundamentals to eliminate possible confounding effects of common shocks. Findings: The cumulative evidence suggests that Amsterdam house prices have influence on (or ripple to) all the Dutch regions, except one. In particular, the Granger Causality test concludes that a lead-lag effect of house prices exists from Amsterdam to all the regions, apart from Zeeland. The cointegration test shows evidence of a long-convergence between Amsterdam house prices and six regions: Friesland, Groningen, Limburg, Overijssel, Utrecht and Zuid-Holland. Research limitations/implications: The paper adopts an econometric approach to examine the Amsterdam ripple effect. More sophisticated economic models that consider the asymmetric properties of house prices and the patterns of interregional socio-economic activities into the modelling approach are recommended for further investigation. Originality/value: This paper focuses on the Netherlands for which the ripple effect has not yet been researched to our knowledge. Given the substantial wealth effects associated with house price changes that may shape economic activity through consumption, evidence for ripples may be helpful to policy makers for uncovering trends that have implications for the entire economy. Moreover, our analysis controls for common house price fundamentals which most previous papers ignored.

Purpose: This paper examines the existence of the ripple effect from Amsterdam to the housing markets of other regions in the Netherlands. It identifies which regional housing markets are influenced by house price movements in Amsterdam. Design/methodology/approach: The paper considers the ripple effect as a lead-lag effect and a long-run convergence between the Amsterdam and regional house prices. Using the real house prices for second-hand owner-occupied dwellings from 1995q1 to 2016q2, the paper adopts the Toda-Yamamoto Granger Causality approach to study the lead-lag effects. It uses the ARDL-Bounds cointegration techniques to examine the long-run convergence between the regional and the Amsterdam house prices. The paper controls for house price fundamentals to eliminate possible confounding effects of common shocks. Findings: The cumulative evidence suggests that Amsterdam house prices have influence on (or ripple to) all the Dutch regions, except one. In particular, the Granger Causality test concludes that a lead-lag effect of house prices exists from Amsterdam to all the regions, apart from Zeeland. The cointegration test shows evidence of a long-convergence between Amsterdam house prices and six regions: Friesland, Groningen, Limburg, Overijssel, Utrecht and Zuid-Holland. Research limitations/implications: The paper adopts an econometric approach to examine the Amsterdam ripple effect. More sophisticated economic models that consider the asymmetric properties of house prices and the patterns of interregional socio-economic activities into the modelling approach are recommended for further investigation. Originality/value: This paper focuses on the Netherlands for which the ripple effect has not yet been researched to our knowledge. Given the substantial wealth effects associated with house price changes that may shape economic activity through consumption, evidence for ripples may be helpful to policy makers for uncovering trends that have implications for the entire economy. Moreover, our analysis controls for common house price fundamentals which most previous papers ignored.

PurposeThis paper aims to examine the existence of the ripple effect from Amsterdam to the housing markets of other regions in The Netherlands. It identifies which regional housing markets are influenced by house price movements in Amsterdam.Design/methodology/approachThe paper considers the ripple effect as a lead-lag effect and a long-run convergence between the Amsterdam and regional house prices. Using the real house prices for second-hand owner-occupied dwellings from 1995q1 to 2016q2, the paper adopts the Toda–Yamamoto Granger Causality approach to study the lead-lag effects. It uses the autoregressive distributed lags (ARDL)-Bounds cointegration techniques to examine the long-run convergence between the regional and the Amsterdam house prices. The paper controls for house price fundamentals to eliminate possible confounding effects of common shocks.FindingsThe cumulative evidence suggests that Amsterdam house prices have influence on (or ripple to) all the Dutch regions, except one. In particular, the Granger Causality test concludes that a lead-lag effect of house prices exists from Amsterdam to all the regions, apart from Zeeland. The cointegration test shows evidence of a long-convergence between Amsterdam house prices and six regions: Friesland, Groningen, Limburg, Overijssel, Utrecht and Zuid-Holland.Research limitations/implicationsThe paper adopts an econometric approach to examine the Amsterdam ripple effect. More sophisticated economic models that consider the asymmetric properties of house prices and the patterns of interregional socio-economic activities into the modelling approach are recommended for further investigation.Originality/valueThis paper focuses on The Netherlands for which the ripple effect has not yet been researched to the authors’ knowledge. Given the substantial wealth effects associated with house price changes that may shape economic activity through consumption, evidence for ripples may be helpful to policy makers for uncovering trends that have implications for the entire economy. Moreover, the analysis controls for common house price fundamentals which most previous papers ignored.

House prices, homeownership, and household consumption: Evidence from household panel data in Korea

Housing price fluctuation has an important effect on employment, which has not been paid enough attention to. Since the global financial crisis in 2007, declining housing price, decreasing consumption and increasing unemployment have coincided, which promotes academia to think more deeply about the impact of housing price fluctuation on the real economy. The research on the impact of housing price fluctuation on the real economy gradually expands to the impact on employment. In the recent twenty years, the housing price has been in a rising cycle in China. This paper studies the impact of the rising housing price on the employment in China. It is mainly devoted to solving the following two problems: one is how the rising housing price in China affects the employment in industrial enterprises; the other is how the impact is transmitted. The impact mechanism of housing price changes on the employment in industrial enterprises mainly includes the following three types: crowding-out effect, demand effect and collateral effect. Subsequently, this paper empirically examines the impact of the rising housing price on the employment in industrial enterprises by using the data from the database of all state-owned industrial enterprises and non-state-owned industrial enterprises above the designated size from the National Bureau of Statistics. After controlling a series of individual and regional characteristic variables, the results of ordinary least square regression based on mixed cross-sectional data and fixed effect regression based on panel data both show that the rising housing price increases the number of employees. Further, all enterprises are divided into two categories: those producing tradable goods and those producing non-tradable goods. Since the demand for non-tradable goods mainly depends on the local market and the demand for tradable goods depends on the national or even global market, the demand effect expects that the rising housing price will have a greater impact on the employment in enterprises producing non-tradable goods than on that in enterprises producing tradable goods. But the collateral effect has no such difference. These results show that the rising housing price increases the employment both in enterprises producing tradable goods and in enterprises producing non-tradable goods. And the increase of the employment in enterprises producing non-tradable goods is greater than that in enterprises producing tradable goods. Therefore, both the demand effect and the collateral effect exist. This paper will help to deepen the understanding of how the housing price affects the real economy from the perspective of employment, and provide empirical support for the discussion of the housing price regulation and employment-related policies in China.