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    Essays on China's real estate market : a thesis presented in fulfilment of the requirement for the degree of Doctor of Philosophy in Economics at Massey University, Manawatu Campus, New Zealand
    (Massey University, 2024-04-10) Mao, Yiran
    This thesis examines the factors that affect the real estate market in China from the perspectives of sentiment, place-based policies, and tariff shocks. The results are presented in three stand-alone empirical chapters. Chapter 2 probes the influence of sentiment on house prices within China. We construct a novel social media sentiment index, which quantified the tone of Weibo posts relating to "housing market'' from 2010 to 2020 across China's 35 largest cities. This index can predict house price changes up to six quarters ahead, even after factoring in economic fundamentals. These findings, robust to numerous checks, are not driven by announced policy modifications, unobserved fundamentals, or censorship bias and therefore reinforce theories of social learning and, to a minor degree, of animal spirits. Chapter 3 investigates the impact of the merger of suburbs into urban districts on property prices, using Beijing as an example and utilizing a difference-in-differences approach, within an event study framework. The results show that such mergers lead to a substantial surge in house prices in the rezoned areas. In contrast, the non-rezoned border districts experience a decline, with localized impacts in both scenarios. The merger negatively affects the economically disadvantaged, evident by the pronounced decline in house prices for low-priced properties in non-rezoned border districts and a smaller increase in rezoned ones. Further analysis reveals that the merger has a positive spillover effect in surrounding counties, with the effect decreasing as the distance to the rezoned districts increased. Chapter 4 analyzes the impacts of the US-China tariff war on commercial building rents across Chinese cities using Bartik-style tariff exposure proxies. This analysis finds a one percentage point increase in the US tariff exposure resulted in a 1.03 percent decrease in commercial building rent growth after one quarter, ceteris paribus. In contrast, China's retaliatory tariff has no significant impact on the growth of commercial building rents. Additionally, the analysis reveals differences in rent responses, with areas of elevated US dependence showing intensified detrimental effects, while superior financial conditions, societal stability, innovation, and geographical placement showing mitigated effects. Furthermore, the chapter reports that tariff exposures from the US and China exerted their influences through different channels, subtly affecting rent growth. The insights derived from this thesis are pivotal for policy formulation in developing nations. Firstly, sentiment, prominently reflected through social media, exerts a tangible and foreseeable impact on real estate valuations. This indicates that policymakers should monitor public sentiment as a precursor for probable escalations or depreciation in property markets. Secondly, urban planning and rezoning decisions can induce significant impacts on housing values in local and neighboring markets. Thus, it is imperative for policymakers to judiciously evaluate the implications of such initiatives, particularly their repercussions on less affluent demographics. Lastly, external economic disruptions, like the US-China tariff war, can profoundly influence commercial real estate rents, especially those cities intertwined with international trade. The adverse effects are palpable in both China and the US, indicating a need for policymakers to fortify the robustness of property markets against external perturbations by diversifying economic partnerships and instituting provisional strategies.
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    An experimental analysis of information aggregation in decision markets : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Economics at Massey University, Albany, New Zealand
    (Massey University, 2023) ElKashef, Pansye
    Knowledge in a society is often distributed amongst different individuals, each holding different pieces of information. By aggregating these dispersed, different pieces of information, accurate forecasts can be generated, adding potential to improve decision-making processes. Decision markets are economic mechanisms to concurrently predict the future and decide on it. They incentivize “expert” individuals to predict the consequences of each of a set of possible actions and then select an action based on these predictions. Decision markets rely on scoring rules (payment schemes) to guarantee that experts are properly incentivized to truthfully reveal their beliefs whilst using decision rules to translate aggregated forecasts into decisions. In this thesis, we present an experimental study of information aggregation in decision markets. Objective of the study is to provide a proof-of-principle for the functioning of decision markets. Market prices are dependent on the private signals given to participants, signifying that signal constellations are the primary determinant of final market prices. We find that decision markets work in aggregating private information and that the incentive compatibility of the decision rule matters for information aggregation. Upon exploring behavioural attributes that might be linked to individual trading performance, we discover that the decision rules also have an impact on participants’ behaviour in the market but we find no evidence that individual behavioural attributes has any influence on market efficiency. Our findings can inform future experiments about decision making processes and real world decision market applications.