This paper gives some theoretical instructions of China’s property tax reform which include the reform of land lease system, the design of tax base, tax rates and tax relief, and also provides some interesting property tax proposals for Shanghai and estimates the effects of property tax reform on residence price.This paper selects a case study of Shanghai which can provide useful methods or findings to other cases characterized by similar traits and situations. Through observation and analysis of documentary evidence, the new average residence price and the prices of residences which locate around the inner ring road and the outer ring road are estimated. Moreover, this study uses asset pricing theory, partial equilibrium theory and quantitative simulation analysis to explore the impact of property tax reform on the residence price under the combinations of varied property tax rates, discount rates and tax base. The paper also uses comparative analysis in lots of areas. The data is gathering from National Bureau of Statistics, local bureaus of statistics, World Band, several valuation firms, international and local theses.The author provides 4 proposals of Shanghai. After the simulation analysis, the first proposal is seen as the most mildly proposal with low property rates and small tax base. The total residence value decreases about 4.92 percent of the original value after the property tax reform. The second proposal use graduated property tax rates corresponding to different property value, which may have greater fairness and equality but lower efficiency. The third proposal targets at gaining more tax revenue from villa and luxury apartments and adjusts the poverty gap. The sales price after property tax decreases around 10 percent. The fourth proposal provides the idea that property tax rates can be set according to the location of administrative areas. In the future study, a case study of a certain city combined with precise empirical observations and statistics data is a good direction. Moreover, this study only introduces a simple model and some indicators which affect the house price. However, how to narrow down the indicators and to use an effective model and to use property tax as an effective indicator to affect residence price is the next step.

Investors consider commodity prices to be one of the major criteria critical in the selection of stocks of mining companies. This is done as part of the net present value (NPV) valuation of mining companies. It is believed that one of the three sets of mineral commodities prices, which are: spot price, forward price and long term price, has a greater impact on the share valuation processes used by investors. This research study investigated the extent to which each set of commodity prices influences the valuation process in order to provide investors of mining stocks with a greater understanding of how fluctuations of commodity prices over time affect prices of the mining stocks they hold, intend to sell or buy. A literature review conducted identified different valuation methods employed in valuing share prices. The literature study also revealed price cycles experienced in the commodity sector. Emerging from the literature review was the prominent role of commodity prices in valuing stocks of mining companies. Three mineral commodities namely, gold, silver and copper were used as case studies because stock market data on these commodities is readily available in the public domain. Nine market indices covering all three mineral commodities were selected based on defined criteria that ruled out any ambiguity and tested for correlation with the three sets of mineral commodity prices. An equal number of stocks of individual mining companies, which were not the primary drivers of the indices, were used to validate the results obtained from the indices in order to avoid duplication of the same correlation during cross – checking. Each mineral commodity price was adjusted for operating costs. For market indices, an average operating cost for each index was calculated from the companies in its basket, while for stocks of individual companies, the company’s annual operating costs were used. The data was collected for the period January 2004 to October 2010. This period was further split up into three sub – periods to account for the Global Financial Crisis (GFC) period that started in mid – 2008. This research study concluded that mining stock prices are correlated with mineral commodity prices, but with spot and forward prices exhibiting stronger correlations than long – term price. This finding is useful to mineral stock evaluators and any evaluator of a mineral asset in that where cash flow approach methods are to be used and the commodity price is required to estimate future cash flows, spot price forecasts of that mineral commodity should be used and not the long – term price.

The rise and growth of online social networks have spurred tremendous changes in our understanding of human behavior. Social scientists and companies have devised new tools to analyze the vast amounts of data obtained from these networks. Such advances have had two major consequences. First, it has allowed firms to significantly improve their segmentation and targeting strategies. Second, it also modified how problems are conceptualized. For example, books, academic papers, or webpages are now being studied under methods developed for social network analysis. This dissertation contributes to both applications. Essays 1 and 2 describe efficient targeting strategies in situations where access to information or computing power is costly. Although existing “seeding” methods have been quite successful in social networks, they often do not account for firms' limited computing power or assume that firms are omniscient. Essay 3 focuses on the art industry by conceptualizing paintings as items connected to each other in a network through their visual similarities. Indeed, we still do not perfectly understand what makes art financially valuable and even major auction houses are at awe when paintings are sold at prices multiple times higher than what they expected. In particular, we aim to quantify how an art piece's visual features and historical importance may impact prices and assess how auction houses and their marketing efforts may modify how art is evaluated and valued. This dissertation has three essays. In the first essay, we analyze how the friendship paradox, which states that your friends have more friends than you, may be generalized to situations where relationships are asymmetric. Indeed, the result assumes symmetric relations: if two people are friends, then each is the other's friend. For social networks that satisfy this assumption (e.g., Facebook), the friendship paradox implies that firms can potentially achieve faster and more widespread diffusion of information by seeding it with the friends of a group of people than with people in the group itself. We generalize the result to allow one-sided (leader/follower) relations and examine the implications for seeding in social networks where messages can be sent only by a leader to his/her followers. We obtain necessary and sufficient conditions under which the highest number of followers is obtained by seeding with (1) leaders, (2) followers, and (3) individuals chosen by ignoring the distinction between leaders and followers. We examine the seeding implications of the results for a subset of Twitter users. The second essay furthers our understanding of the friendship paradox and relates it to beta centrality and eigenvector centrality. We generalize the results to asymmetric relations, define two beta centrality measures and relate them to the singular vectors of the associated directed graph. Our first generalization shows that the expected number of k removed friends is no smaller than the expected number of k-1 removed friends when k is an even number. Such a relation does not necessarily exist when k is an odd number. As k increases to infinity, the limiting value of the expected number of k removed friends converges to the largest eigenvalue of the associated undirected graph. We interpret beta centrality to be a weighted sum of an infinite series of the numbers of k removed friends. It approaches eigenvector centrality when the weighting parameter becomes arbitrarily close to the inverse of the limiting value of the expected number of k removed friends. We further generalize these results to asymmetric relations (say, between followers and leaders) that can be represented by directed graphs. We show that the last person in a randomly selected alternating sequence of 2k+1 leaders and followers (followers and leaders) has no fewer followers (leaders) than the last person in a randomly selected alternating sequence of 2k followers and leaders (leaders and followers). As k increases to infinity, the expected number of leaders of the last person in a randomly selected sequence of 2k alternating leaders and followers converges to a value proportional to the largest singular value of the associated directed graph. Similarly, the expected number of followers of the last person in a randomly selected sequence of 2k alternating followers and leaders converges to a (different) value proportional to the largest singular value of the associated directed graph. We show that there is a reciprocal relation between the limiting expected values of leaders and followers. We generalize beta centrality to asymmetric relations and relate the limiting values of beta centrality scores for followers and leaders to the singular vectors of the associated directed graph. The third essay focuses on the art market. Auction houses hold auctions regularly throughout the year. However, once or twice a year, art investors and wealthy consumers attend highly selective marquee events: day and evening sales. Those carefully designed and highly marketed events often generate a lot of excitement for connoisseurs as most paintings get sold for tremendous amounts of money. But what makes those paintings special? We investigate how art is evaluated across those three types of auctions. Specifically, we build a deep learning model to summarize the paintings into a low dimensional representation space where each factor encodes a specific feature of the paintings’ aesthetics and further utilize those components to create “network” variables that will determine how influential and creative a painting is. We use those predictors in hedonic regression models to study how art returns differs across the three types of sales and subsequently analyze whether the paintings are evaluated differently. In particular, we find that paintings sold in evening sales generated an annualized return of 14.33% in the period 1999-2018 - more than three times the returns of paintings sold in regular or day auctions. Finally, we adopt a propensity score matching approach to create a homogeneous population of paintings - based on their likelihood to be auctioned in an evening sale - to assess the causal impact of being featured in an evening sale and find that such highlight increases a painting's price by almost $6 million.

This paper extends the scope of Earnings per share ("EPS") studies by incorporating Bushman et al. (2004)'s conceptual framework of corporate transparency to illustrate how the disclosure requirement of an accounting rule governing EPS could have far-reaching effects on the information environment in US. Informed participants are having a keener edger over average investors in using EPS as a guide to investment value. EPS signals a summary measure of firm performance to market participants. The market reactions to EPS and change in per share earnings provide a distinct opportunity to gauge the informativeness of earnings. The information role will nevertheless derail whenever there is an equity change. The accounting rule stipulates the use of a theoretical construct, the weighted average number of shares, in the denominator for EPS, which the average investor is unable to interpret as the number of shares at the reporting date is the actual, not average number of shares. Relative to the actual-share EPS, the average-share EPS will either inflate or deflate the per share earnings. The informed investors, who can substitute actual number of shares for the theoretical construct, are hence bestowed by the accounting rule an information advantage over the average investors. Earnings response coefficient is significant with denominator of EPS substituted while the explanatory power of theoretical-denominator EPS abates when it is contemporary with the denominator substituted EPS. Financial analysts' expertise in the provision of idiosyncratic information to the market has been compromised by the average-share EPS, which is reflected heretofore in proforma earnings forecasts errors. Proforma earnings use a numerator different from accounting rules and to further temper the denominator with the actual number of shares will make pro-forma EPS forecast unintelligible to users. The unintended consequence of inflating or deflating the per share earnings misleads average investors in their decision-making process. Analysts should not issue proforma earnings forecast while researchers should abstain from using theoretical-denominator EPS for sample firms with equity change as their policy prescriptions may further aggravate the problem. A simple remedy to change the accounting rule, SFAS No. 128 is eminently anticipated, if not warranted.

In the aftermath of the recent financial crisis, an environment of historically low interest rates and extensive household indebtedness in the OECD countries have triggered a vivid debate on whether central banks should react to house price fluctuations in their pursuit of monetary policy. In Sweden, a period of low policy rates and house price inflation was halted when the central bank increased the interest rates in 2010. This paper studies whether the Riksbank reacted to house prices in 1993-2013. Using Bayesian methods and quarterly data, I estimate a DSGE model with patient and impatient households, where the central bank reacts to house price inflation. The results suggest that the Riksbank did respond to house prices during the sample period. The findings are robust and plausible from an economic point of view.

Challenges associated with ensuring the accuracy and reliability of cost estimation of highway bid items, especially during the conceptual phase of a project, are of significant interest to state highway agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. A systematic literature review was performed to determine research trends, identify, categorize the factors influencing highway unit prices, and assess the performance of conceptual cost prediction models. This research proposes a geographic information system (GIS)?based methodology that leverages vast historical bid data for unit-price estimation and the robust GIS capabilities with consideration of the effects of project-specific location and variations due to cost escalation on different bid items. A comparison of the three spatial interpolation techniques operationalized in this research revealed that disjunctive and empirical Bayesian kriging models led to more accurate cost prediction than ordinary kriging algorithms.

Information availability plays a fundamental role in decision-making for business operations. The present dissertation aims to develop frameworks and algorithms in order to guide a decision-maker in environments with limited information. In particular, in the first part, we study the fundamental problem of designing optimal auctions while relaxing the widely used assumption of common prior. We are able to characterize (near-)optimal mechanisms and associated performance. In the second part of the dissertation, we focus on data-driven pricing in the low sample regime. More precisely, we study the fundamental problem of a seller pricing a product based on historical information consisting of one sample of the willingness-to-pay distribution. By drawing connection with the statistical theory of reliability, we propose a novel approach, using dynamic programming, to characterize near-optimal data-driven pricing algorithms and their performance. In the last part of the dissertation, we delve into the detailed practical operations of the online display advertising marketplace from an information structure perspective. In particular, we analyze the tactical role of intermediaries within this marketplace and their impact on the value chain. In turn, we make the case that under some market conditions, there is a potential for Pareto improvement by adjusting the role of these intermediaries.

This study is made up of two parts which explore some of the effects upon individual households making up a sample of a low income neighborhood in Portland. The first section in Chapter II presents a survey of sample households in our identified area, securing personal information given by adult members along with their responses to questions concerning effects on their budgets and the adaptations they have made to reduce the cost of fuel. The second section compares costs of fuel for January, February, and March of 1975 with the same months of 1974 for another sample in the same area. Amount of fuel was tabulated along with costs, and percentage increases or decreases were charted. The fortunate coincidence of the same three-month average temperature in downtown Portland for both years gave an especially good comparison. The monthly use of gas could be more accurately measured than the use of oil. None of our interviewed sample were found to use electricity as a primary source of heat.

The objectives of this dissertation are to explain the production behavior of OPEC's member countries from 1971 to 1987 and to determine whether there was any structural shift in OPEC's production behavior after the organization attempted to assign a quota to each member. This study focused on political and social as well as economic variables, in order to overcome the misspecification of previous models. In order to achieve the above objectives, the study used the following four models, with modifications: the cartel, competitive, target revenue, and property rights models. The double log multiple linear regression technique was used to operationalize the cartel, competitive, and target revenue models; simple linear regression was used to estimate the property rights model. The cartel model was based not only on economic variables but also on social and political variables. The internal political instability of each OPEC country was measured by the number of armed attacks within the country. The structural shift in OPEC's production behavior between the 1971-1982 period and the 1983-1987 period was evaluated using the Chow-test. The Chow-test showed no significant difference between these two periods for OPEC overall or for individual members. Thus, the two periods were combined so that the study was performed for the entire 1971-1987 period. Because this period of analysis was relatively short, alternative models were applied to pool the data and thereby increase the reliability of the model estimates. A cross-sectional correlated and time-wise auto-regressive model (CCTA) was selected to pool the data and to estimate OPEC's production coefficients. Then each individual OPEC member's production model was estimated and compared to the pooled model. The results indicate that OPEC behaved as a cartel, and that a partial market-sharing hypothesis was significant for all 11 OPEC members. These findings indicate that OPEC was a loose cartel, with only partially effective cooperation on production decisions. Political instability was found to be significant (at the 10-percent level) overall, and it negatively affected production. It was also significant at the 5-percent level for the price-pusher group (Iran, Venezuela, and Algeria). This group was also the only one pooled using least squares with dummy variables (LSDV), because of its common slope and different intercepts. Overall results suggest that OPEC members were basing their production decisions on crude oil prices, excess production capacity, and each member's share of total OPEC output.

iv Abstract As real estate is an important part of the wealth composition of households, this the impact of the financial stimulus that was observed throughout 2020 and how it might have affected housing prices in various European countries. For this the thesis runs a Vector Error Correction Model with the following independent variables: population, exchange rate, inflation, short-term interest rate, unemployment rate and the compensation of employees. The time frame for this regression is restricted to 2000Q1 to 2019Q4 in order to exclude the housing price development throughout 2020 that is already affected by the financial stimulus. These regression results are then used in combination with the 2020 actuals of all independent variables to approximate the expected housing price without financial stimulus. This gives an indication of a potential overpricing in the markets and provides an understanding of how financial stimulus might be connected to housing prices. Another analysis in this thesis then provides an understanding of the leader-follower relationship of housing prices and rental prices and provides an analysis on how this might be connected to the level of home ownership in a particular market. JEL Classification F62, J11, R30 Keywords Real Estate, Covid-19, Financial Stimulus Title The effect...