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In this chapter, I will review the existing researches about the interaction nexus between real estate market and macroeconomics while analyzing and summarizing the data structure and the methodologies used. Considering China’s specific national conditions and policies, I will shed light on Chinese housing empirical studies, and estimate their research from different economic aspects, expecting to provide a useful perspective for my further research.
Housing price is the price formed by both supply and demand sides in the real estate market. According to the fluctuations in property prices in each country, housing prices generally have three characteristics: periodicity, city differences, and bubble. Periodicity refers to how real estate price fluctuations are cyclically or periodically associated with both microeconomic and macroeconomics fluctuations.
Early in the 1960’s, after Richard Muth (1960) rigorously developed a housing market competitive theory, a lot of economist studied the housing market from the perspective of microeconomics. In 1969, under a lot of assumptions, Olsen (1969) found that if the housing market were perfectly competitive, the poor would not pay more per unit for housing. However, in the survey done by Richard Arnott (1987), which reviewed the microeconomic modeling of the housing sector developed at that time, it was found that even if the competitive theory of housing market is reasonably sophisticated and well developed, it is still hard to ascertain the adequacy of it in explaining the effects of a particular housing policy since there are no well-articulated alternative models.
Then, in later years, scholars focused more on the study of the relationship between the real estate market and macroeconomic fundamentals. According to business cycle theory, there is interaction between real estate prices and macroeconomic fundamental variables. One or more macroeconomic variables will cause fluctuations in real estate prices, but, in the meantime, changes in the real estate industry also will lead to macroeconomic volatility. In the change process, they formed a mutually reinforcing interaction mechanism. On the basis of the existing literature, macroeconomics affect real estate prices primarily through the real estate supply and demand, which can be subdivided into GDP, income, consumption, interest rates, exchange rates, inflation, construction costs, land prices, bank credit, and other basic economic variables. In order to understand the impact of real estate price fluctuations on the macroeconomics, most existing studies analyzed from the perspective that the prices affect total consumption and total investment.
Since there is a close relationship between real estate prices and macroeconomic volatility, the empirical research of their interactive relationship has always been very important in the field of economics. At present, the relevant research literatures can be divided into two categories: (a) The first type mainly analyzes the relationship between real estate prices and the whole macroeconomic fundamentals; (b) The second type analyzes the relationship between real estate prices and one or several specific macro-basic variables (GDP, income, interest rates, investment and so on). We will now detail the two types.
2.2 Housing prices and macroeconomic fundamentals
The real estate industry has become a mature industry in many developed countries. According to existing literature, most of the economists’ empirical research is derived primarily from the perspective of equilibrium theory. Based on the traditional regression analysis model, they used more independent linear systems, numerical economic models and others to analyze the dataGenerally speaking, the macroeconomic fundamentals will affect the investment, credit, and also, the change of interest rate will affect the supply of real estate. On the other hand, economic growth will affect the income and thus affect the demand for real estate. According to equilibrium theory, under the market competition mechanism, the market will eventually be cleared through real estate prices.
However, Case and Shiller(1987, 1989, 1990) found that the housing market does not appear to be very efficient; it is contrary to the efficient market hypothesis. Then, in Clapp and Giaccotto’s study (1994), they not only confirmed Case and Shiller’s (1987, 1989, 1990) result but also found macroeconomic changes have a good predictive ability for real estate prices. Clapp and Giaccotto (1994) used the data of East Hartford, Manchester, and West Hartford over the period from October 1, 1981, to September 30, 1988, with 2 methods: the repeat sales method and the assessed value (AV) method. They found that the local unemployment and expected inflation have considerable forecasting ability for the housing prices; and compare with the first-time house, the repeat housing index is more sensitive in the short run due to the lagged economic factors; It showed the housing market does not meet the efficient market hypothesis (Clapp and Giaccotto, 1994).
With a much longer data set than common literature, Holly and Jones (1997) provided a more comprehensive perspective on the behavior of housing prices in UK. In order to seek the co-integrating relationships between housing prices and long run, they ran a regression with the housing prices and economic factors such as real income, the user cost, and building society lending. The results showed that, with the exception of population, almost all the factors were rejected at the 1% level in the unit root test, and that the most important determinant of real housing prices was real income; the dynamic adjustment of housing prices is asymmetrical; it depends on whether housing prices are below or above the long run equilibrium. When housing prices are above equilibrium, they seem to adjust back more quickly (Holly and Jones, 1997).
But, Brown, Haiyan, and McGillivray (1997) thought that since the early 1980s, the UK housing market had suffered a number of structural changes; consequently, the parameter was instable, meaning those models that assume the underling data-generating process are not appropriate. Under an assumption that the economic system is unstable, they adopted the Time Varying Coefficient (TVC) methodology, and found TVC specification outperforms the alternative constant parameter specifications of housing prices. Because most of the models have failed to predict the 1992 housing price downturn, part of further research was planned to use the TVC specification to examine the model’s forecasting ability beyond 1992.
Using the data in the past 25 years of 6 European countries (France, Germany, Italy, Spain, Sweden and the UK), Iacoviello (2002) established dynamics of house prices by using a tractable value at risk framework in a straightforward way, which we call SVAR model. He pointed out that house price inflation is highly sensitive to the forces driving economic fluctuations; different housing and credit market institutions play different role in the IS-LM Phillips curve paradigm, but this relationship might change with the changing of institutions; in addition, regulatory legal structure and new monetary policy also will affect that relationship (Iacoviello, 2002). Similarly, using the SVAR model, DeHaant and Sterken (2004) studied 13 developed countries’ real estate markets. Their results showed that, to one country, compared with stock, housing plays a more important role in consumption and output; when housing price raise 1%, consumption will raise 0.75%; when housing price raise 1.5%, GDP will raise 0.4% (DeHaant and Sterken, 2004).
In the Asian market, Quigley (2002) pointed out that, although most of the existed models can generate patterns of housing price changes over time in response to varying conditions in economic fundamentals, there was little research on the effect of changes in property markets upon subsequent economic conditions. With his empirical study, he determined that economic fundamentals do not explain most of the variation in the housing prices in short run, and that there were many bubbles in Asian property market during the late 1990’s (Quigley, 2002). At the same time, Miki Seko (2003) adopted the SVAR model to analyze the Japanese housing prices. In his paper, the results showed there is a strong relationship between Japanese housing market and it’s economic fundamentals; and by analyzing the economic factors, the development of the real estate market can be predicted (Miki Seko, 2003).
It is clear that housing is not just a normal consumption goods, it is a large share of the overall macro-economy. Significant fluctuations in macro-economy would cause significant volatility in housing market. On the other hand, the volatility in housing market also implies the fluctuations in macro-economy. However, the interactive nexus between housing market and the different aspects of macro-economy is different. Thus, besides the studies that analyzed the macro fundamentals-housing market, some economists study from different angles to examine the interactive nexus between housing market and one or several specified macro variables.
2.3 macro-basic variables
2.3.1 Supply and demand
Theoretically, price is determined by supply and demand sides. In the housing market, the relationship between supply and demand is formed by many macroeconomic factors, and with the changes in these factors, supply and demand continues to change. Therefore, some economists thought the greatest impact on housing prices comes from the supply and demand, and have dedicated their research in this area.
Normally, in the real estate industry, the supply side is mainly affected by land price, facilities costs, construction tax, construction exploration and design cost, and so on. And, among them, land price is the most important factor.
Since housing is a product, it is not just a demand price, but also a supply price. In the real estate economic activities, land purchase and development is the beginning and the foundation, and land purchase cost is the most important part of housing costs. From the supply perspective, the land price fluctuations are an important factor in housing price volatility. On the contrary, due to land supply is restricted by the natural; there is a lack of flexibility. Therefore, land price is mainly decided by its demand side, which is mainly composed by the real estate business. The real estate industry has a huge impact on the land market as well.
In order to examine the interactive nexus between housing price and land price, Peng and Wheaton (1994) analyzed the Hong Kong market. Because Hong Kong is a small island with a fixed boundary, it would be clear what the influence of land supply on housing prices. Using a modified stock-flow model, their results showed that the supply restrictions in Hong Kong have caused higher housing prices but not lower housing output (Peng and Wheaton, 1994). Similar outcomes can be found in Alyousha and Tsoukis’ (1999) study. They employed the quarterly data from England and Wales from the period Q1, 1981-Q2, 1994 to explore the implications of intertemporal optimization for house and land prices (Alyousha and Tsoukis, 1999).
Adopting a simple housing flow supply model, which is based on the Euler equation (Hall, 1978), they found that, under a perfect competition, house prices are co-integrated with land prices and house building costs. But, through the Granger test, Hall (1987) found housing price is not the land price’s cause. Also, after an econometric analysis of American cities, Edward, Joseph and Hilber (2002) determined that land price was positively correlated with regional economic development, the level of human capital, and have no direct relationship with housing price.
As for demand side, existing research usually examined from the aspects which are disposable income, GDP, property taxation, population and so on. There is a large diverse literature related to the housing and taxation because it is clearly that property taxation would directly affect the housing purchasing decisions, and further affect the housing demand. Just like United States, the tax system seems to favor housing ownership in many countries. Thus, Dimasi (1987) employed a computable, spatial general equilibrium model; and found out that differential tax treatment on land and capital can cause a significant social welfare loss. Many other general equilibrium models also found out tax policies that favor the housing sector would lead to a significantly negative impact on both housing sector and aggregate income.
From another special perspective, Mankiw and Weil (1989) examined the relations between demography-induced changes in housing demand and real house prices in the United States. They thought that the “Baby Boom generation” into its house-buying ages was the major cause of the increase in housing prices in the 1970s and the housing demand would grow more slowly in the next decade because of the population structure. Changes in housing demand will further affect the housing price (Mankiw and Weil, 1989)). However, unlike the estimations of Mankiw and Weil (1989), Gary and James (1990) using postwar data from Canada, and found that even if the demographic patterns were similar in Canada and United States, the aggregate time series correlation between shifting demographics and real house prices is distinctly different. From the empirical analysis, they considered there is a statistically insignificant, but in most cases, demographic demand is negative associated with house prices (Gary and James, 1990).
2.3.2 Monetary policy
Generally speaking, as an overall policy, monetary policy is mainly concerned to control the trend and fluctuations of aggregate demand; the impact on the real estate market and the sensitivity of the housing price should be limited. However, as the changing in the structure of global financial markets and developing in real estate industry, the nexus between them has become more and more close, financial sector has become an important reference index in the housing market. It is also proved in Alan, John and Brian’s (2005) study. They found, in eighteen major industrial countries, certain financial conditions (ample liquidity, low interest rates, and financial deregulation) were usually present in past housing price surges, and could conceivably raise the probability of the intensity or the occurrence of the rise.
As for interest rate, considering from the supply side, when it decline, real estate investment and real estate mortgage loans will continuously pour into the real estate industry, and promote housing prices continuing to rise. But, as for the demand side increasing in interest rates will directly affect consumer’s credit repayment costs; so that some consumers would out of the housing market, which affecting the real estate demand, and further led to corresponding changes in real estate prices. By studying the impact of real and nominal interest rates on real estate prices, Harris (1989) thought that changes in real interest rates could explain the market price level; nominal interest rates affect housing price only when the real estate value is expected to rise.
Among the monetary policy, bank credit and investment are the most important determinates. As the real estate industry is capital-intensive industry, and most of the funds come from the bank credit and investment, the change in bank load will significantly affect the supply of real estate industry. Besides, a large part of real estate loans are mortgage loans, the value of real estate products in the market determines the size of the loan amount in this industry. In 2004, Davis and Zhu (2004) discovered, in the long term, bank credit is positively correlated with house prices, and effect of housing price on the bank credit is very significant, but in their paper, the reverse impact was still uncertain.
Matteo (2005) developed and estimated a monetary business cycle model with nominal loans and collateral constraints tied to housing values. Since collateral effects allow the model match the positive response of real spending to a housing prices shock, Matteo (2005) found fall in the housing prices will reinforced the impact negative monetary shock on real rate, consumption and output. Similarly, based on the Hong Kong sample, Gerlach and Peng’s (2005) thought property prices would determine bank lending, but, it was interesting that they found bank lending does not appear to influence property prices in Hong Kong.
Empirical evidence shows that there is a cyclical movements and volatility in the housing market, and obviously, this kind of cyclical movements would relate to the economic cycles. Economics found that it would be useful and interesting to explore these movements in the housing market, thus many studies examined the housing-economy cycle relationship from both qualitative and quantitative aspects. Greenwood and Hercowitz (1991) and Baxter (1996) build up a dynamic general equilibrium models to reproduce the co-movement of business and residential investment that observed in the US. Davis and Heathcote (2001) also considered that, in the US, the residential investment lead the cycle while the non-residential investment lags the cycle, and this co-movement between housing market and macro-economy has been documented for several countries.
Also, economics often analyze real property market tie to “long cycles”. Gottlieb (1976) considered, the amplitudes of housing cycles are larger than typical business cycles, and the periodicity might be significantly longer than those of the business cycle. For instance, Ball (1998) showed, in UK, new commercial property cycles have a 10 years duration while they are independent of the business cycle. Employing the cross-country data and the Kalman Filter technique, Ball (1999) again found significant long cycles of new construction, which with periodicity of 20-30 years in both residential and non-residential real estate markets.
As we can see, the importance and sensitivity of real estate prices attracted a large number of scholars to concerned. Based on the review above, the existing literatures are mainly adopting the cross-section data and time series data, so that the specific econometric methods of housing models are mostly focusing on: traditional ordinary least squares model (OLS), value at risk model (VAR), tractable value at risk framework in a straightforward way (SVAR), co-integration and so on.
2.4 Empirical evidence in the Chinese context
Compare with developed countries, Chinese real estate market started relatively late. But along with China’s rapid economic development, the real estate industry is also showing a good development trend. As real estate investment occupies a very high proportion of total investment in fixed assets, and the volatility in real estate market is closely related to macroeconomic and national policy, the issue of housing prices is not only related to a city’s development, but also related to financial security and the living cost of ordinary people. Thus, Chinese economists have also attached great importance to the development of the real estate market, and conducted extensive research. However, since the late development of China’s statistical system, the database is not perfect, most of the Chinese scholars just analyzed the relationship between housing market and macroeconomic theoretically, empirical studies are relatively small.
First, because of the importance impact of macro fundamentals on real estate prices, using appropriate data and models to estimate the nexus between them has always been the focus of Chinese economists. Adopting the housing index and macro fundamental data (1995-2002) of 14 cities, Shen and Liu (2004) employed a mixed regression, and empirically examined the relationship between housing prices and economic fundamentals. The results showed the impact of macro fundamentals on housing market is quite different in different cities. The explain model was significant affected by the city characteristics (Shen and Liu, 2004).
Song and Wei (2009) using a co-integration and vector error modified model, and considered that, in long run, there is a long-term stability of the dynamic equilibrium between real estate prices and macroeconomic; but when short-term imbalances, it becomes into a negative feedback mechanism. Song and Wei (2009) also found that fluctuation of GDP and inflation is the Granger cause of housing price volatility and the impact of interest rates is not significant. Based on partial least-squares regression (PLS), Wang and Xie (2010) estimate the annual data of China within the period of 1999-2008. They thought land prices, capital size and national wealth are the top three factors that affect China’s price changes at present; although the influence of long/middle-term loan rate is weak, money supply do play a very prominent role in China’s housing prices volatility (Wang and Xie, 2010).
In addition to the analysis of real estate market and macro fundamentals, Chinese economists also studied the housing market from different economic perspective and tie to their own national circumstances and policies.
2.4.2 Land price
As the reforming of Chinese housing system and land system, the housing sales prices were climbing higher and higher until the financial crisis in 2008, but, after a short depression, the price still maintain the rising trend. General view is that, due to the land purchase cost is the main cost which constitute the housing costs, high land prices is the main reason of high housing prices. Especially after the Ministry of Land Resources released two new policy  of land sale, more people think that the skyrocketed of housing prices is because of the high land prices. The policies require that any commercial, tourist, entertainment, commercial housing and other kinds of business land must be transferred by tender, auction or listing mode. After the new land policies, the land transfer cost rose sharply; and almost in the same period, the housing prices have skyrocketed as well.
Thus, from the point of view of China Real Estate Association, Yang (2003), Bao (2004) and Cheng (2004) thought since a large number of land transactions using auctions, land prices increased dramatically. And land purchase costs account for 30% percent of the housing prices, hence construction costs raised, further driving a rapidly rise in housing prices; this “Cost-push theory” was also supported by a large number of real estate developers (Yang, 2003; Bao, 2004; Cheng, 2004). But, Ministry of Land Resources hold the opposite view. Deputy Minister Fu (2006) considered that even if the tender, auction or listing transaction mode will lead an increase in land prices, it might not raise the housing price, the most important factor affecting housing prices is still supply and demand in the housing market. On the contrary, Fu (2006) thought, land is a production factor of real estate industry; the demand for land is generated by the demand for housing, therefore, huge demand in housing market and the rapidly increase in housing prices makes demand for land, and further drive the land prices rise.
However, Wang and Wu (2009) did not agree both of them. Employing the panel data from 28 regions, they found, in China, although land prices promoting housing prices in long-run and housing prices driving an increase in land prices in both long-run and short-run, this mechanism depends on the region. Wand and Wu (2009) thought that the interaction between land prices and housing prices is different in different regions, so the relationship between them should be implement regional studies and cannot be generalized.
2.4.3 Bank credit
After the 1997 Asian financial crisis, in order to stimulate economic growth, China implemented a proactive fiscal and monetary policy: repeatedly issued bonds, reduced interest rates several times, vigorously infrastructure; real estate industry become a national priority support industry and the financial sector continue to increase the real estate credit. But until now, China’s banking system is still not perfect; most of the loans are mortgage loans, therefore, value of real estate products in the market will directly determine the size of credit.
Typically, the credit will play two roles in the housing market. If the real estate prices cyclical rising, since financial institutions anticipate the housing prices can keep rising in the following, banks will relax lending conditions, thus, the increasing housing prices will directly lead to the upswing in real estate bank credit. Because of land and real estate products supply is very inelastic in the short-term, to some extent, the upswing in real estate bank credit will further push up house prices increase. By the same token, the decline in house prices leads to a decline in the quality of bank assets, reduce the size of bank funds, so banks will abate the amount of credit, which will further decrease the housing prices.
Based on the panel data of credit and housing market, Li (2004) considered that among China’s current macro-economic control policy, credit policy play the most significant role in the real estate market. He also believed the flexibility of supply side and demand side is different, so the impact of monetary policy on the supply is greater than that on demand (Li, 2004). Employing the error correction model and VAR model, Zhong and Yan (2009) thought that there existed a stable equilibrium relationship between the volatility of real estate prices and credit in long-run. After the Granger test, Zhong and Yan (2009) found real estate prices and the amount of real estate credit influence each other and they both are the Granger cause for each other. Studying on the East Asian financial crisis, Xiang and Li (2005) also believed bank credit expansion played a very important role in the formation of the real estate bubble in East Asian countries. Thus, in order to ensure the health of China’s real estate development, it should strengthen the financial system construction and regulation (Xiang and Li, 2005).
In addition, through calculating the Lerner index  (Lerner, 1934) of the real estate market in China, Li (2005) considered the level of monopoly in China’s real estate market is very high. Even if as the market economy developing, the competition in the real estate market will gradually get better, but this process will be very slow (Li, 2005). And from another special perspective, Yin (2010) thought the existence of North paradox  behavior (North, 1981) in the local government is an important cause of housing price fluctuations. Local government is lack of intrinsic motivation to stabilize the real estate market; local government’s various “rescue” policies are also mainly based on the purpose of obtain more land transfer fees; thus just depends on local governments’ behavior can not maintain healthy and sustainable development of the real estate market, the central government should implement more effective macroeconomic policies (Yin, 2010).
Comparing with foreign literatures, China’s real estate market research also adopting cross-section data, time series data, especially panel data. Relevant econometric methods are: co-integration approach, Granger test, error correction model (ECM), and panel data model; in the meantime, the analysis about the impact of macroeconomic policy is also Chinese economists’ priority concerns.
However, for the following aspects, China’s research is still inadequate:
The studies on macroeconomic policy are more focused on the theoretical analysis; they are lack of a comprehensive empirical analysis.
Currently, the analysis of macroeconomic fluctuations is mainly under an assumption of closed economy. But, with economic globalization, China’s real estate market will be more affected by international economic development, so the discussion of the relationship between the real estate prices and macro economic fluctuations that under an open economy is more meaningful.
There is no analysis of government expenditure in China’s real estate literatures. However, according to macroeconomic theory, government investment will promote private investment, thereby affecting the real estate investment and price. So, the empirical quantitative estimation about the real estate prices and government spending will contribute to the in-depth analysis of the relationship between the government and the real estate market.
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