Thinking About the \u27Ming China\u27 Anew: The Ethnocultural Space In A Diverse Empire-With Special Reference to the \u27Miao Territory\u27

By examining the cultural identity of China\u27s Ming dynasty, this essay challenges two prevalent perceptions of the Ming in existing literature: to presume a monolithic socio-ethno-cultural Chinese empire and to equate the Ming Empire with China (Zhongguo, the “middle kingdom”). It shows that the Ming constructed China as an ethnocultural space rather than a political entity. In essence, China was defined as a Han domain that the Han people inhabited and where Han values were produced, practiced, and preserved in contrast to those of non-Han “barbarians,” be they domestic or foreign. The “Great Ming”—the dynastic title—cannot be confused with China, the ethnocultural space. For the Ming ruling elite, the “Miao territory” in western Huguang and eastern Guizhou provinces represented a land “beyond the pale of civilization” (huawai), which was outside and different from China. The Ming construction of the ethnocultural China connects the imperial heritage to China\u27s modern identity

Housing prices and consumption : the case of China

The rapid soaring housing prices in Chinese residential property market have attracted increasing worldwide attention in recent years. Facing the rising concerns about both the stability and sustainability of Chinese housing market prices dynamics, this study aims at investigating the impacts of changes in housing wealth on consumption in China. Previous studies on this subject usually use country level data with relatively shorter sample period, or individual time series for a single or a few cities. Recent development in literatures suggests that panel data have the more heightened capacity for modeling the complexity of human behavior than a single cross-section or time series data can possibly allow. In this study, in order to identify both long-term and short-term elasticity of consumption with respect to housing wealth, panel framework of ECM is constructed, with quarterly data from 23 cities throughout China, covering the period of 2005Q1-2010Q4. The estimation results confirm large and highly significant positive housing wealth effect on consumption in both long-run and short-run for China. Furthermore, due to the potential endogeneity problem driven by the fact that housing prices are highly correlated with income, instrumental variable estimations are also implemented. The resulting empirical findings confirm that changes in housing values can exert large and positive impacts on household consumption, even after this potential endogeneity bias is controlled for

BIG DATA FOR COMPREHENSIVE ANALYSIS OF REAL ESTATE MARKET

This Culminating Experience Project explored the application of big data in the real estate industry in order to address the problem of analyzing the accurate property estimates value. The research questions were: (Q1): What are the benefits and advantages of utilizing big data in the real estate market? (Q2): What are the trends in the application of big data in the real estate market? (Q3): What are the challenges in applying big data in the real estate market? (Q4): What are the methods and processes of applying big data in appraisal of assets in the real estate market? To answer these four questions, this study used qualitative and quantitative methodology, content analysis conducted on data collected through Google Scholar, and One Search for industry reports, conference papers, and select literature about big data adoption trends in the real estate industry. The findings were as follows: (Q1): The benefits of using big data analytics are to help clients to make the right decisions and advice, have higher efficiency for appraisals, better risk evaluation of risk in the real estate industry simplifying applications in valuations and pricing. (Q2): there is anecdotal evidence that real estate has already started adopting big data. Adoption is most likely to be beneficial for first mover industry players at the top of the industry pyramid including investment banks, commercial banks, and mortgage banks that hold the highest interest in the real estate industry. (Q3): complexity of big data solutions and the costs of implementation are a major challenge while smaller players such as real estate agents and brokers do not find utility or justification for the huge investment in big data. (Q4): the development of algorithms remains as the main process of applying big data solutions as there are no off-the-shelf big data solutions for the real estate industry. Adoption of Machine learning (ML) and Artificial Intelligence (AI) in real estate would help buyers and sellers to learn from data and make informed decisions. The conclusions of the culminating experience project are Real Estate Industry has a low adoption of big data solutions because many of the players in the industry have not yet learned how to translate big data to business objectives. Areas of further studies include the development of models and algorithms for use by the real estate industry