Health Effects of Short-Term Fluctuations in Macroeconomic Conditions: The Case of Hypertension for Older Americans.

This project investigates how changes in Metropolitan Statistical Area (MSA)-level housing prices affect household fertility decisions. Recognizing that housing is a major cost associated with child rearing, and assuming that children are normal goods, we hypothesize that an increase in house prices will have a negative price effect on current period fertility. This applies to both potential first-time homeowners and current homeowners who might upgrade to a bigger house with the addition of a child. On the other hand, for current homeowners, an increase in MSAlevel house prices will increase home equity, leading to a positive effect on birth rates. Our results suggest that indeed, short-term increases in house prices lead to a decline in births among non-owners and a net increase among owners. The estimates imply that a $10,000 increase leads to a 5 percent increase in fertility rates among owners and a 2.4 percent decrease among nonowners. At the mean U.S. home ownership rate, these estimates imply that the net effect of a $10,000 increase in house prices is a 0.8 percent increase in current period fertility rates. Given underlying differences in home ownership rates, the predicted net effect of house price changes varies across demographic groups. In addition, we find that changes in house prices exert a larger effect on current period birth rates than do changes in unemployment rates. This project investigates how changes in Metropolitan Statistical Area (MSA)-level housing prices affect household fertility decisions. Recognizing that housing is a major cost associated with child rearing, and assuming that children are normal goods, we hypothesize that an increase in house prices will have a negative price effect on current period fertility. This applies to both potential first-time homeowners and current homeowners who might upgrade to a bigger house with the addition of a child. On the other hand, for current homeowners, an increase in MSA-level house prices will increase home equity, leading to a positive effect on birth rates. Our results suggest that indeed, short-term increases in house prices lead to a decline in births among non-owners and a net increase among owners. The estimates imply that a $10,000 increase leads to a 5 percent increase in fertility rates among owners and a 2.4 percent decrease among non-owners. At the mean U.S. home ownership rate, these estimates imply that the net effect of a $10,000 increase in house prices is a 0.8 percent increase in current period fertility rates. Given underlying differences in home ownership rates, the predicted net effect of house price changes varies across demographic groups. In addition, we find that changes in house prices exert a larger effect on current period birth rates than do changes in unemployment rates.

House prices and birth rates: The impact of the real estate market on the decision to have a baby

Subprime mortgage default rates have led to a crisis in the residential mortgage markets unprecedented since the Great Depression. Information reported on these defaults has demonstrated that they vary by location and region. To properly understand the causes of such high rates of mortgage defaults, we undertook this study. It reports on a cross-section analysis of default rates of residential, subprime and Alt-A mortgages aggregated to the metropolitan areas (MSA) level. The hypotheses tested here represent the effects of loan and borrower level characteristics and MSA economic factors, such as MSA employment growth, unemployment rate, household income and housing price changes and their volatility on the level of default rates on subprime mortgages. We test these by origination vintage for 2005 and 2006 and project default rates by MSA into 2007 using 2006 vintage estimated parameters. We find that loan and borrower level characteristics such as loan-to-value ratio weighted by original loan balances, the weighted proportion of loans that have no documentation or the borrowers’ weighted FICO score and MSA economic factors of housing price changes and employment growth are highly statistically significant and economically important in explaining MSA subpirme residential mortgage default rates over the 359 MSAs for 2005 and 2006 vintages. The data used are from Loan Performance data base for 6 million subprime loans separated into 2005 and 2006 vintages, the OFHEO (now FHFA) Housing Price Index and household employment Dept. of Labor. Projections of housing price changes and employment growth for each MSA were used based on a linear extrapolation of the past 9 months of 2006 and the first 3 months of 2007. The main finding is that the projected default rates for all of 2007 are larger than those of 2005 and 2006 on average.

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.

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.

Being able to quantify the probability of large price changes in stock markets is of crucial importance in understanding financial crises that affect the lives of people worldwide. Large changes in stock market prices can arise abruptly, within a matter of minutes, or develop across much longer time scales. Here, we analyze a dataset comprising the stocks forming the Dow Jones Industrial Average at a second by second resolution in the period from January 2008 to July 2010 in order to quantify the distribution of changes in market prices at a range of time scales. We find that the tails of the distributions of logarithmic price changes, or returns, exhibit power law decays for time scales ranging from 300 seconds to 3600 seconds. For larger time scales, we find that the distributions tails exhibit exponential decay. Our findings may inform the development of models of market behavior across varying time scales.

Behavioral finance theories explain "why" individuals exhibit behaviors that do not maximize expected utility. This study explores how projection bias, as explained by regret theory, may shape financial risk tolerance attitudes. The results suggest that gender, income, and stock market price changes, as measured by the NASDAQ, the Dow Jones Industrial Average, and the Standard & Poor's 500 indexes, help explain risk attitudes. Risk tolerance appears to be an elastic and changeable attitude. This research expands on the work of Shefrin [2000], who reported that recent stock market price changes exert a strong influence on risk tolerance attitudes and behaviors.

Sequential monitoring of stock market price changes

This paper investigates the relationship between changes in interest rate and house prices from the period 1975 to 2023 in five countries: United States, United Kingdom, China, Germany and Indonesia. Results show that contrary to the paradigm of real estate pricing, housing prices increase with interest rates. Furthermore we find that changes in house prices are independent of monetary policies and interest rate changes set by the central bank. Economically developed nations exhibit a stronger correlation between the two variables. We demonstrate that this is due to the 2008 financial crisis, where houses fell by nearly 30%, creating a fear of recession and the resulting policy of low interest rates to encourage investment.

The financial crisis of 2007-2009 had divesting effects around the globe. Many financial institutions and government officials failed to see the build up of problems predicting the crisis and hence failed to take actions to keep the crisis from breaking out. Thus, it is important to see if the emerging problems could have been identified in advance in order to develop types of analysis that could help us avoid future crises. A full investigation of such possibilities will require many different studies taking different approaches. This dissertation contributes to that collective effort by investigating the extent to which balance sheet information could have been used to identify the emerging problems. We implement our research strategy by analyzing what types of balance sheet information did the best job of explaining how hard different major financial institutions were hit during the crisis. We constructed a large data set of financial variables from the financial reports of financial institutions over the years 2002 to 2011. We used this data to developed models to predict the damage to an individual firm when a systemic crisis occurred based on its financial position and performance over varying time periods and relative to other institutions’ characteristics. We used changes in stock market prices as our measure of performance. We found that the financial leverage ratio and the mismatch between current assets and current liabilities are the most significant ratios to predict the degree of stock market declines each institution would face if a systemic crisis occurred. We quantified the degree of the financial leverage and current ratios in two different ways, an average level and accumulated time-weighted rate of change over different lags of periods using two different estimation techniques. We found that the financial leverage and current ratios can be used as early warning signals based on both the multivariable fractional polynomials estimation technique and structural equation modeling. However, the out-of-sample tests showed that the imbalance between current assets and current liability would be the only significant predictor of the changes in stock market prices. The test confirmed that the changes in pre-crisis stock prices are less sensitive to the leverage ratio but more sensitive during crisis.

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

PurposeThe purpose of this paper is to investigate the effects of macroeconomic factors on secured and unsecured household loans from UK banks.Design/methodology/approachThe approach uses Vector auto‐regression models to test the relationship between macroeconomic factors such as interest rates, house prices, unemployment rates, disposable income and bank write‐offs to discern the main factors which could impact on banks' losses.FindingsThis paper identifies several macroeconomic factors that influence loan losses. The influence however depends on the type of arrears. Changes in house prices, interest rates and unemployment rates have a significant impact on secured loans. There is however, minimal impact on unsecured loans. Unemployment stands out as the major factor that influences both mortgage and credit card arrears. The estimated results show that the main factors impacting on credit cards are disposable income and unemployment rates, while changes in interest rates have no impact on credit card write‐offs.Originality/valueThis paper's value lies in providing methods by which commercial banks could manage household loans better by reducing the effects of macroeconomic factors.

Individual Borrower and Regional Factors Contributing to Subprime and Prime Mortgage Delinquency and Default Rates: An Analysis by Origination Vintages and Projections for 2009

We take a closer look at changes in county unemployment rates in Indiana during the Great Recession and evaluate how local population and the mix of sectoral employment influence these patterns. Using a quantile regression approach, we specifically observe the impacts on counties on both tails of the changes in unemployment distribution. We find the impact of sectoral composition of a county’s workforce depends on its geographical classification. Overall, greater reliance on pro-cyclical industries, most notably manufacturing, magnifies the increases in unemployment during the recession. This effect is further amplified for MSA counties. In contrast, counter-cyclical industries, education in particular, insulates the counties in the top 10th percentile of the distribution of changes in unemployment rates, and a stronger insulation effect is observed for MSA counties. At the bottom 10th percentile, education marginally amplifies changes in unemployment rates for MSA counties, whereas it insulates non-MSA counties from the same distribution.

The labor force projections perform well, even for small areas, which suggests that changes in local labor force participation rates can be approximated by national changes. In fact, the mean absolute percent errors are low even when the previously calculated population projections are used. The unemployment projections do not do as well, and using 1980 census labor force data instead of previously calculated labor force projections offers no improvement in the results. A two-step study is needed to determine why the errors are so large. First, the state level changes from 1970 to 1980 should be compared with national changes to determine the difference in unemployment rate changes by race and sex. Second, state level data on the occupational mix should be examined for the relationship between this and the changes in state level unemployment rates by race and sex. It is hypothesized that the first step will show that even state level changes in unemployment rates by race and sex cannot be well approximated by the national changes and the second step will show that some of the variation can be explained by state differences in occupational mix. Further studies should be made to determine how the calculation of national changes affects the results. Changes in labor force participation rates seem to follow trends, and therefore extrapolation may not have much effect on the results. Although using the actual changes in unemployment from 1970 to the latest year available as a proxy for the changes from 1970 to the target year may have a serious detrimental effect on the unemployment projections, if some of the variation in the national changes in race and sex can be explained and some adjustments made, the actual changes may be the best proxy to use.