Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Mathematical Sciences

Committee Member

Dr. Thomas Springer, Committee Chair

Committee Member

Dr. Elaine Worzala, Committee Co-Chair

Committee Member

Dr. James Brannan

Committee Member

Dr. James Spencer


Four phenomena can be observed in China's housing in the past 16 years: First, the vacancy rate of new condominium properties has increased significantly. Second, housing prices have been increasing very rapidly. In fact, the prices have rarely decreased even when strict housing policies are mandated. Third, housing transactions are active, as indicated by the new condominiums that are sold very quickly. Four, new construction activities are also very active. Phenomena 2, 3, and 4 are inherently consistent, but the coexistence of phenomena 2, 3, and 4 with phenomenon 1 is very perplexing. Thus, the following research questions can be raised: how can housing prices keep increasing despite high vacancy rates? How can new condominiums be sold quickly when vacancy rates are high? How can construction activities continue when vacancy rates are high? This is a puzzle. This puzzle is connected with another puzzle: the excessive liquidity in China's housing market has been oddly coexisting with the insufficient demand in China's consumer market in past 16 years. The second puzzle can is easily observed even though it has been largely ignored over past 16 years. The following hypotheses are put forward:

Hypothesis 1: At a certain time point, higher-income households will spend a lower proportion of their income on consumption compared with lower-income households. If this hypothesis can be verified, then severe income inequality will lead to an overly-high aggregate savings rate and an extremely low aggregate consumption rate;

Hypothesis 2: Overly-high aggregate savings rates and extremely low aggregate consumption rates caused by severe income inequality will induce high investment demand in the virtual sector rather than the real sector. As a result, the virtual sector will boom while the real sector will decline.

Hypothesis 3: Given a declining real sector, investors prefer houses to other kinds of assets in the virtual sector due to the unique features of houses. This leads to rapidly rising housing prices and overdevelopment. Testing the Hypotheses above is a big challenge because the Gini Coefficient announced by China's government is not trustworthy. The data about GDP and per capita disposable income are not reliable, too. Great effort was exerted in collecting actual data on the variables mentioned above. These efforts include establishing rapport with officials in the National Bureau of Statistics of China, obtaining special access to the database of academic or non-profit research institutes and buying data from private institutes in China. Through these efforts, improved quarterly data of GDP, housing policy, monetary supply for 70 cities from 2000 to 2016 are obtained , although the data on Gini coefficient remain unreliable. The theoretical work in this study includes: First, the economic relationship between income levels and consumption rates, that is, higher-income households will spend a lower proportion of their income on consumption compared with lower-income households, is confirmed by economic data. Moreover, the new economic relationship is explained using Modern Portfolio Theory and information cost. Second, by mathematical proof, it turns out that more severe income inequality will lead to a higher aggregate saving rate as well as a lower aggregate consumption rate under this economic relationship. A theoretical model further shows that the aggregate savings rate caused by income equality will result in investor's preference for virtual assets rather than real assets or consumption goods. Third, a unique feature of houses is found, which can be used to explain why houses are preferred over other virtual assets and why housing bubbles can last longer than speculative bubbles of other virtual assets, such as commercial properties, stocks and mutual funds. The unique feature of housing is that the utility an owner obtains from living in his own house is greater than the utility a tenant gets from living in the same house if it was leased. Therefore, market rents, which are the "price" of the "price" of the utility a tenants get, cannot fully reflect the market fundamentals of a house due to the existence of non-rent utility. As a result, house prices lose the signal for market fundamentals and can continuously increase in the long term. This is the reason why houses are preferred by investors. Fourth, a new measure which is called Ratio of Gross Domestic Income to Gross Domestic Product (RGG) is built in place of the Gini coefficient as a measure for income equality. The official data are not trustworthy because unreported income is not taken into account. If unreported income can be measured and employed in the analysis of income inequality, a better measure for income inequality can be obtained. The following is the problem-solving process. Step 1, it is basic fact that the high income level families possess nearly all of the unreported income in China. Therefore, a high proportion of unreported income in total income implies a severe income inequality. Step 2, Gross Domestic Product should be equal to Gross Domestic Income. Hence, when GDI is less than the GDP, the gap between them should be the unreported income. As a result, a high Ratio of GDI to GDP(RGG) indicates a severe income inequality. In this way, a new index RGG is built to be used to measure inequality. The empirical study is conducted after theoretical work. The results show: First, no matter what models is taken and no matter how 70 cities are grouped, the coefficient of RGG, the measure for income inequality, is consistently negative. Since a high RGG implying a severe income inequality, the negative coefficient shows that the correlation between income inequality and housing prices is positive. Precisely, more severe income inequality leads to housing prices. Second, no matter what models is taken and no matter how 70 cities is grouped, housing prices are always significantly affected by RGG. In other words, the positive correlation between income inequality and housing prices is confirmed statistically, which is exactly what this study intends to show. Third, in order to show how important income inequality is to housing prices, the fitting degree (R2) is checked after the independent variable is removed one by one. In all three models, we find that the fitting degree drops the most when RGG is removed. This shows that income inequality plays the most important role in housing prices. Then the following conclusions are drawn based on the theoretical study and the empirical evidence. First, at a certain point, a high-income family spends a lower proportion of its income on consumption compared with a low-income family. In other words, the higher the level of a family's income in a society at a certain time point, the lower the proportion of its consumption accounted for in its income. As a result, severe income inequality will lead to over-high aggregate savings rate and over-low aggregate consumption rate. Second, over-high aggregate savings rate and over-low aggregate consumption rate caused by severe income inequality will induce high investment demand in the virtual sector and weak demand in the real sector. As a result, the virtual sector will progress, whereas the real sector will decline. Third, in the context of a declining real sector, investors prefer houses to other types of assets in the virtual sector owing to the unique features of houses. Thus, housing prices will increase along with the high vacant rate, the high new construction and the quick sale of houses. Those phenomena are "weird" but can be observed everywhere in current China. The innovations of this study include: illustrating the new relationship between income level and consumption rate which can bri dge income inequality and aggregate savings rate; establishing RGG as the new measure for income inequality which can be a substitute for Gini coefficient; finding the unique feature of house which can explain why investors prefer houses in all virtual assets. Theoretically, the study has the following contributions. (1) providing empirical evidence that rapidly increasing housing prices are positively related to income inequality; (2) adding a new explanation for housing bubbles by revealing the linkage from income inequality to the housing bubble; (3) showing a new channel through which income inequality will damage the sustainability of economic growth, thus enhancing the importance of income inequality issue in economic growth theories. At least two implications which are of great significance to developing countries can be drawn from this study. First, severe income distribution inequality gives rise to the abnormal coexistence of excess liquidity and demand insufficiency, further bringing about the deepening recession of real sector, as well as the increasing prosperity of the virtual sector. Second, an increasingly overheated virtual economy brings huge risk to the whole financial system of the country. A dwindling economy gradually deteriorates employment, further threatens long-term economic growth potential, and weakens competitive advantage in the global economy. Since many countries, including the U.S., have also been experiencing increasing income inequality and real estate bubbles, this research will ultimately remind policy-makers to take income inequality into account when they make policies to deal with housing bubble and overdevelopment, finance market instability and unsustainable economic growth in both developing countries and developed countries.



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