Real estate markets and bank distress

We investigate the relationship between real estate markets and bank distress among German universal and specialized mortgage banks between 1995 and 2004. Higher house prices increase the value of collateral, which reduces the probability of bank distress (PDs). But higher prices at given rents may also indicate excessive expectations regarding the present value of real estate assets, which can increase PDs. Increasing price-to-rent ratios are positively related to PDs and larger real estate exposures amplify this effect. Rising real estate price levels alone reduce bank PDs, but only for banks with large real estate market exposure. This suggests a positive, but relatively small 'collateral' effect for banks with more expertise in specialized mortgage lending. Likewise, lower price-to-rent ratios are estimated to reduce the riskiness of banks. The multilevel logit model used here further shows that real estate markets are regionally segmented and location-specific effects contribute significantly to predicted bank PDs. --Real estate,distress,universal vs. specialized banks

The Spatial Dimension of House Prices

The economic reform in China, launched in the late 1970s, gradually promotes the free mobility of capital and labour between rural and urban areas, and between cities. The following housing market reform in the late 1990s thoroughly terminates the socialist allocation of housing and introduces market forces into the housing sector. Such institutional shifts have profound effects on the evolution of the Chinese interurban housing market. Yet, little is known about the spatial behaviour of house prices across cities in the post-reform era. How do the housing markets of different cities organise across space? What is the relationship between the house price dynamics of different cities? To answer these questions, this research performs economic and econometric analysis of the spatial dimension of the Chinese interurban housing market. In addition, this research also concerns the construction of a reliable house price index in the presence of spatial heterogeneity and dependence in the urban housing market of China. A reliable house price index is essential to the analysis of house price dynamic behaviour. However, owing to the data problem, this part is conducted based on the housing market of a Dutch city. This research discovers the spatial regularities of house prices across Chinese prefecture cities in an economic common area and investigates the underlying formation process. It reveals an uneven distribution of house prices across cities, with those large and/or higher-tier cities and their neighbours having significantly higher house prices. Such an uneven pattern of house prices demonstrates the agglomeration spillovers in the interurban housing market. Two forms of spillovers are empirically examined. The first is the urban hierarchy distance effect, which is related to the position of a city in a hierarchical urban system. In general, the distance penalty of higher-tier urban centres is confirmed, that is, all else being equal, the further away a city is from the higher-tier city, the lower the house price. The second form of spillovers relates to a city’s position in a city network system, in which no hierarchical structure is imposed. In such a situation, the spillovers arise from the interaction with neighbouring cities and it is found that a city that has larger neighbours tends to have higher house prices. These two forms of spillovers are somewhat correlated with each other because a higher-tier city is always associated with a larger urban size. It is argued that the spillovers in the interurban housing market work through two channels: the productivity and amenity channel. First, because of the economies of agglomeration, a location that has good access to large urban concentrations is likely to enjoy some productivity advantages and thus can bear higher house prices. Second, a location that is surrounded by large urban concentrations can easily get access to some unique amenities that need a large market potential to survive; households value such access and thus bid up the house price there. However, it seems that the role that the productivity channel plays is much more important than the role of the amenity channel. In addition to the static distribution of house prices across space, this research also concerns the time series behaviour of house price dynamics across Chinese cities. Geography plays an important role in explaining the cross-city differences of house price dynamics. For the housing markets of major cities across the whole of China, the cluster analysis generally uncovers two relatively homogeneous groups, within which the house price growth series share a similar dynamic pattern. One cluster contains mainly the cities in the undeveloped central, western and northeast China, whereas the other is composed of the most important economic centres in eastern China. However, the spatial segregation of housing markets is more likely to occur in the most recent period. In the early period before 2010, the house price dynamics of cities are much more homogeneous. The similarities and/or dissimilarities among house price dynamics of different cities indicate the complicated interrelationships between each of the markets. This research further examines various spatial interrelationships between the housing markets of an economic common area in south China. The spatial causal relationships between housing markets are first tested by the Granger causality test. The results reveal a complicated pattern, but it can be tentatively confirmed that house price changes in the developed eastern-central markets ‘cause’ the house price dynamics in the less-developed western markets. Then a spatial-temporal model is built to depict the diffusion pattern of house prices between markets. In general, a shock given to the house price of a certain market gradually spreads to its neighbouring cities. However, the interurban housing market can hardly remain an equilibrium relationship in the long-run, that is, it tends to be divergent. The last part of this research concerns the treatment of spatial effects in the hedonic house price model as well as its influence on the construction of hedonic imputation indexes, which measure the pure house price changes over time. It is argued that the value of a dwelling can be split into the value of the land and the value of the structure, and that the value of the location characteristics of a dwelling is capitalised into the price of the land. Thus, land prices can be expected to vary significantly across space. Indeed, the mixed geographically weighted regression framework adopted in this research, which allows the shadow price of structure to be constant across space and the implicit price of land to be property-based, is found to be superior to, in terms of model prediction, those models that restrict the spatial variation of land prices. Nevertheless, the Fisher imputation house price index based on the most sophisticated model is almost identical to those based on the simple specifications. The land and structure price indexes, on the other hand, are sensitive to the treatment of location in land prices. This research underlines market forces in the operation of Chinese interurban housing markets in the post-reform era, and contributes to the understanding of spatial dimension of house prices, not only in China, but also in other market-oriented economies

The Spatial Dimension of House Prices

The economic reform in China, launched in the late 1970s, gradually promotes the free mobility of capital and labour between rural and urban areas, and between cities. The following housing market reform in the late 1990s thoroughly terminates the socialist allocation of housing and introduces market forces into the housing sector. Such institutional shifts have profound effects on the evolution of the Chinese interurban housing market. Yet, little is known about the spatial behaviour of house prices across cities in the post-reform era. How do the housing markets of different cities organise across space? What is the relationship between the house price dynamics of different cities? To answer these questions, this research performs economic and econometric analysis of the spatial dimension of the Chinese interurban housing market. In addition, this research also concerns the construction of a reliable house price index in the presence of spatial heterogeneity and dependence in the urban housing market of China. A reliable house price index is essential to the analysis of house price dynamic behaviour. However, owing to the data problem, this part is conducted based on the housing market of a Dutch city.This research discovers the spatial regularities of house prices across Chinese prefecture cities in an economic common area and investigates the underlying formation process. It reveals an uneven distribution of house prices across cities, with those large and/or higher-tier cities and their neighbours having significantly higher house prices. Such an uneven pattern of house prices demonstrates the agglomeration spillovers in the interurban housing market. Two forms of spillovers are empirically examined. The first is the urban hierarchy distance effect, which is related to the position of a city in a hierarchical urban system. In general, the distance penalty of higher-tier urban centres is confirmed, that is, all else being equal, the further away a city is from the higher-tier city, the lower the house price. The second form of spillovers relates to a city’s position in a city network system, in which no hierarchical structure is imposed. In such a situation, the spillovers arise from the interaction with neighbouring cities and it is found that a city that has larger neighbours tends to have higher house prices. These two forms of spillovers are somewhat correlated with each other because a higher-tier city is always associated with a larger urban size.It is argued that the spillovers in the interurban housing market work through two channels: the productivity and amenity channel. First, because of the economies of agglomeration, a location that has good access to large urban concentrations is likely to enjoy some productivity advantages and thus can bear higher house prices. Second, a location that is surrounded by large urban concentrations can easily get access to some unique amenities that need a large market potential to survive; households value such access and thus bid up the house price there. However, it seems that the role that the productivity channel plays is much more important than the role of the amenity channel.In addition to the static distribution of house prices across space, this research also concerns the time series behaviour of house price dynamics across Chinese cities. Geography plays an important role in explaining the cross-city differences of house price dynamics. For the housing markets of major cities across the whole of China, the cluster analysis generally uncovers two relatively homogeneous groups, within which the house price growth series share a similar dynamic pattern. One cluster contains mainly the cities in the undeveloped central, western and northeast China, whereas the other is composed of the most important economic centres in eastern China. However, the spatial segregation of housing markets is more likely to occur in the most recent period. In the early period before 2010, the house price dynamics of cities are much more homogeneous.The similarities and/or dissimilarities among house price dynamics of different cities indicate the complicated interrelationships between each of the markets. This research further examines various spatial interrelationships between the housing markets of an economic common area in south China. The spatial causal relationships between housing markets are first tested by the Granger causality test. The results reveal a complicated pattern, but it can be tentatively confirmed that house price changes in the developed eastern-central markets ‘cause’ the house price dynamics in theless-developed western markets. Then a spatial-temporal model is built to depict the diffusion pattern of house prices between markets. In general, a shock given to the house price of a certain market gradually spreads to its neighbouring cities. However, the interurban housing market can hardly remain an equilibrium relationship in the long-run, that is, it tends to be divergent.The last part of this research concerns the treatment of spatial effects in the hedonic house price model as well as its influence on the construction of hedonic imputation indexes, which measure the pure house price changes over time. It is argued that the value of a dwelling can be split into the value of the land and the value of the structure, and that the value of the location characteristics of a dwelling is capitalised into the price of the land. Thus, land prices can be expected to vary significantly across space. Indeed, the mixed geographically weighted regression framework adopted in this research, which allows the shadow price of structure to be constant across space and the implicit price of land to be property-based, is found to be superior to, in terms of model prediction, those models that restrict the spatial variation of land prices. Nevertheless, the Fisher imputation house price index based on the most sophisticated model is almost identical to those based on the simple specifications. The land and structure price indexes, on the other hand, are sensitive to the treatment of location in land prices.This research underlines market forces in the operation of Chinese interurban housing markets in the post-reform era, and contributes to the understanding of spatial dimension of house prices, not only in China, but also in other market-oriented economies

Introduction

China has been undergoing significant social and economic structural changes since launching its policy of economic reform and opening up in 1978. This has involved a transformation from a centrally planned economy, where there is no role for the market, to a market-oriented economy in which market principles play a major role. During the last four decades, great achievements have been made in terms of economic growth and social well-being. To name a few indicators: the Gross Domestic Product (GDP) of the country increased from USD 189.65 billion in 1980 to USD 10.866 trillion in 2015, positioning China as the second largest economy in the world, with an average annual growth rate over 10%. Meanwhile, poverty levels have greatly improved. The poverty headcount ratio at USD 1.90 a day (2011 PPP) has decreased dramatically, from 42.15% in 1981 to 10.68% in 2013. The rapid economic growth, combined with the reform of the Hukou registration system, has also accelerated the migration flow from rural areas to urban areas. The population living in urban China in 2015 reached 763 million, making the urbanisation level of 55.61%, almost three times that in 1980. With the rapid growth of the urban population, the welfare-based public housing provision system founded in the central planning era could no longer meet the increasing housing demand of urban residents. Thus, in 1994, comprehensive housing reforms were implemented, aiming to privatize the public housing sector and promote a housing allocation system based on market principles. The milestone of housing reform occurred in 1998, when the government completely suspended the traditional housing allocation system, making the housing market the only way to access housing services (Wang et al. 2012). The emergence of the private urban housing market spurred both housing transactions and prices. In 1998, the housing area traded on the market was approximately 108 million square metres on an average transaction price of 1854 yuan/m2. These two figures were nearly ten and three times higher in 2014, soaring to 1.05 billion square metres and 5933 yuan/m2, respectively. At the regional level, rapid economic development has been accompanied by increasing inequality. Soon after the launch of the economic reforms, some coastal regions, Guangdong and Zhejiang in Eastern China, for example, grew quickly, due to the influx of foreign direct investment (FDI), advanced technologies and equipment, and favourable policies of the central government. The ‘core’ position of these regions in the national economy was further enhanced through a self-reinforcing process (Anderson 2012, p.127), shaping a core-periphery economic structure in China. In 1980, the regional gross product of Eastern China accounted for 43.69% of total GDP in China, while in 2014 this ratio increased to 51.16%, reflecting the polarization of economic activities. Reflecting the distribution of economic activities, the inequality in the cost of housing between regions is also striking. In 2014, the average sale price in 35 main cities in mainland China was approximately 8599 yuan/m2, with the standard error also high, at 4651 yuan/m2, making the coefficient of variance 0.54, thus indicating a high degree of heterogeneity across this city-level housing market. The left panel of Figure 1.1 shows the spatial distribution of average house prices. It is apparent that the prices in the coastal cities of Eastern China are generally greater than the prices of inland cities. However, the picture of house price dynamics is a little different. From 2002 to 2014, the rapid growth in house prices, on average 11.38% per year, seems to be anational phenomenon and there is very little variance between the annual growth rates in different cities; the coefficient of variance is only 0.18, much lower than that of the house price level. Perhaps the most prominent spatial pattern of house price growth rate is that the northeastern cities experienced the lowest price appreciation during the period 2002-2014. This dissertation is fundamentally concerned with the spatial patterns of house prices and their dynamics across cities in China. Although literature on the Chinese housing market has been emerging in recent years, little is known about the spatial interaction of regional housing markets. The following four chapters will be dedicated to responding to questions concerning the emerging market: Why is there a core-periphery structure in the distribution of interurban house prices? To what extent are the house price developments across cities similar? How do house price dynamics in one city affect the house price changes in other cities? The investigation of the spatial dimension of the Chinese housing market has been always hampered by the quality of the data, especially when analysing house price dynamics. This situation has inspired the pursuit of research to construct house price indexes that reflect the house price changes as accurately as possible. In line with a key theme of this study, particular

A Two-Stage Approach to Spatio-Temporal Analysis with Strong and Weak Cross-Sectional Dependence

© 2016 John Wiley & Sons, Ltd. An understanding of the spatial dimension of economic and social activity requires methods that can separate out the relationship between spatial units that is due to the effect of common factors from that which is purely spatial even in an abstract sense. The same applies to the empirical analysis of networks in general. We use cross-unit averages to extract common factors (viewed as a source of strong cross-sectional dependence) and compare the results with the principal components approach widely used in the literature. We then apply multiple testing procedures to the de-factored observations in order to determine significant bilateral correlations (signifying connections) between spatial units and compare this to an approach that just uses distance to determine units that are neighbours. We apply these methods to real house price changes at the level of Metropolitan Statistical Areas in the USA, and estimate a heterogeneous spatio-temporal model for the de-factored real house price changes and obtain significant evidence of spatial connections, both positive and negative.ESRC Grant. Grant Number: ES/I031626/

FLOODING RISK AND HOUSING VALUES: AN ECONOMIC ASSESSMENT OF ENVIRONMENTAL HAZARD

Climate change, the ‘boom and bust’ cycles of rivers, and altered water resource management practice have caused significant changes in the spatial distribution of the risk of flooding. Hedonic pricing studies, predominantly for the US, have assessed the spatial incidence of risk and the associated implicit price of flooding risk. Using these implicit price estimates and their associated standard errors, we perform a meta-analysis and find that houses located in the 100-year floodplain have a –0.3 to –0.8% lower price. The actual occurrence of a flooding event or increased stringency in disclosure rules causes ex ante prices to differ from ex post prices, but these effects are small. The marginal willingness to pay for reduced risk exposure has increased over time, and it is slightly lower for areas with a higher per capita income. We show that obfuscating amenity effects and risk exposure associated with proximity to water causes systematic bias in the implicit price of flooding risk.Manufactured Housing; valuation, environmental risk, meta-analysis, hedonic pricing

Neighborhood Loyalty or Neighborhood Entrapment? Explaining Unmeasured Sources of Reduced Geographic Mobility

This paper develops a simple self-selection utility model for leaving a neighborhood. This opens the door for a simple reduced form approach that leverages a hierarchical Bayesian model to obtain an annualized latent push and pull factor for each neighborhood. Posterior analysis indicates that common predictors of neighborhood quality and inputs to classical utility functions systematically under-predict the number of people who stay in a neighborhood. Such underprediction of out-migration can either be viewed as an unexplained variation due to neighborhood \loyalty or as financial barriers to mobility. I isolate this residual, referred to as an \inertia, isolated using a quasi-experimental matching method that uses variation in push factor to isolate eects on pull factors. I show the inertia measure can be explained by measures of financial access including distance to a bank branch, local rates of second mortgage, and redevelopment certifications. The residual from these financial measures is shown to correlate with existence of anchor institutions like charter schools. This methodology creates a robust measure of not only the local push- and pull-factors by neighborhood, but also is suggestive of an economic approach to appraising local community strength