Selling Mechanisms and The Australian Housing Market

This thesis examines selling mechanisms relevant mainly to auctions and applicable in the context of the housing market. In the first two chapters a context is described in which the seller of an object has private information about its value that is important to potential buyers. If the seller is unable to reveal this information to the buyers at no cost, the problem of adverse selection arises. Among other examples, auctions of arts, wines, and residential properties are most relevant to the current study. The sellers in these markets observe some private characteristics of their objects that are important to buyers but not revealable to them at no cost. In the first chapter we study some common selling mechanisms in this setting. Specifically, we study an ascending auction with two different reserve price regimes for the seller: first, disclosing the reserve price at the beginning of the auction; and second, keeping the reserve price secret and reserving the right to accept or reject the auction price after the bidding ends. We also study the common posted-price mechanism for the purposes of comparison. Throughout this chapter the assumption is that the seller chooses the mechanism from the ex ante point of view-that is, before observing her signal. Thus, the choice of mechanism itself does not reveal any further information to the buyers. The results in the first chapter suggest that in a one-shot game the seller can realise a higher ex ante expected payoff by choosing the secret reserve price regime than the other two mechanisms. At the end this chapter a dynamic setting is studied to examine the possibility of an extension of these static results to a dynamic case. Most of the results for the one-shot game are extendable to the proposed dynamic game. In the second chapter we study an informed seller's best interest among the two previously mentioned reserve price regimes at the interim stage-that is, after the seller has observed her private information. We study how the seller's expected payoff could change if she observes the signal and then chooses the reserve price mechanism. In this case the choice of mechanism itself could reveal some information to buyers. The results show the conditions under which an informed seller, after observing her signal, chooses to keep the reserve price secret or discloses the reserve price. The last two chapters focus specifically on the housing market. The third chapter adds to the theoretical literature of the housing market by proposing a more realistic selling mechanism applicable to this market: the one in which the seller posts a price to attract potential buyers to make a counteroffer. This game is studied in a dynamic setting with the possibility of more than one potential buyer arriving at each period. In the event that one buyer arrives, the seller engages in negotiation with that buyer; in the event that multiple buyers arrive, the seller runs an auction with a reserve price. This explains why sometimes sale prices are higher than the asking price and at the same time proposes a role for the asking price in this market. Other small variations of this mechanism are also studied for the purposes of comparison. The final chapter is an empirical study of the Sydney housing market. We use comprehensive data on the Sydney housing market composed of 25,489 observations for properties sold in the Sydney region in 2011. We consider the fact that both the seller of the property and the real estate agent have a common goal: to sell the property at the highest possible price in the shortest amount of time. The analysis is divided into two major parts. First, we estimate a two-stage least square model to analyse which parameters affect time on the market for a property. Second, we propose a probit model that estimates the parameters that affect a revision in list prices. The results suggest that overpricing increases time spent on the market, and properties with a revised list price stay on the market for a longer time

The Synthesis and Characterization of Mesoporous Uranium Oxide Catalysts for Oxidative Destructions of Volatile Organic Compounds and Clean Air Protection

The overall goal of this research was to investigate a new class of mesoporous uranium oxide catalyst used to destroy a range of volatile organic compounds (VOCs), including aromatics and chlorinated organic compounds under conditions which are applicable for U.S. Department of Energy (DOE) facilities and U.S. industries. Uranium oxides are known to have high efficiency and long-term stability when used to destroy volatile organic compounds (VOCs) when compared with some of the commercial catalysts, such as precious metals, TiO2, and Co3O4 catalysts. Two key factors limiting catalytic activities of uranium oxides prepared by conventional methods are small surface area and pore size. To overcome these limitations, mesoporous uranium oxides dispersed on mesoporous oxide hosts were synthesized and tested for VOC destruction efficiency. This research work consisted of synthesizing potential depleted uranium catalysts, as well as testing and structural characterization. A plug flow microreactor was built and used to measure the catalytic performance of toluene, chlorobenzene, and trichloroethylene (TCE) v. catalyst temperature. These data result in so-called ‘light-off’ curves. Destruction efficiency generally increases with temperature. These VOCs were chosen as typical of those found in anticipated air pollution controls applications. The catalysts have been tested for the destruction of VOCs at space velocities of 84,000 mlg-1h-1. A gas chromatograph and a sampling mass spectrometer were used to measure conversion and reaction by-products. The X-ray diffraction (XRD), transmission electron microscope (TEM) and Brunauer-Emmett-Teller (BET) adsorption-desorption isotherm surface area measurements were used to provide information about the compositions present in the catalyst. Catalysts were synthesized using a variety of methods, most notably a method that includes template-moderated co-synthesis of mesoporous oxides. Catalyst activity was improved by supporting the uranium oxide on silica. Dopants (e.g., Cr, Co, K, Br, Fe, Ca, Mg, Cu, Pt, La, Ce, Sr) were added to the urania to improve the catalytic activity. Catalyst performance was compared with that of conventional catalysts. Further improvement was made by supporting uranium on titanium oxide. This co-assembled formulated catalyst was found to be competitive with noble and metal catalysts for catalytic oxidation of VOCs and chlorinated VOCs. The variation in the U:Ti ratio indicated that optimal activity for oxidation of toluene, chlorobenzene, and trichloroethylene was obtained for U:Ti ratio of 1:20 at calcinations temperature of 600˚C. It was stable to deactivation and operates efficiently in the presence of large amounts of water. The gained experiences of this project can be used in the clean up of emissions from soil vapor extraction wells, which are in use to remove VOCs from ground water at DOE’s Hanford and Savannah River sites. This work was conducted under the auspices of the U.S. Department of Energy’s (EM-21) Depleted Uranium Research and Development Program

Permit market auctions with allowance reserves

This article investigates pollution permit auctions that incorporate allowance reserves. In these auctions the sale of a fixed quantity of permits is supplemented by an additional permit reserve. This reserve automatically releases permits if a sufficiently high price is triggered. The main justifications for implementing an allowance reserve are to reduce price volatility as well as assisting in cost containment. We show—paradoxically—that incorporating an allowance reserve into a permit auction can decrease firms’ payoffs, increase the clearing price, and increase compliance costs. This has implications for all major cap-and-trade markets, including the US Regional Greenhouse Gas Initiative

Consignment auctions

This article investigates pollution permit consignment auctions. In this process firms obtain an initial endowment of permits that must be consigned to the auctioneer for sale. In the auction, firms bid for permits, obtain their equilibrium permit allocations, and receive revenue from their consigned permits. It has been proposed that this auction is politically attractive and generates clear price discovery. We provide the first theoretical analysis of this kind of auction. We show, in most cases, the auction does not provide a clear price signal. Our results have policy implications for many permit markets, including the California Cap-and-Trade Program

Towards a blockchain-based trustful mechanism for IoT-enabled data trading systems

Internet of Things (IoT) devices generate and collect massive amounts of IoT data. Monetizing the flood of data generated by the IoT devices has enabled the creation of IoT data trading systems where individuals and businesses may trade data. In the current IoT data trading systems, a third-party broker collects and manages IoT data for buyers who would like to promote their services and make more profit. However, there are three main challenges that may hinder the development of secure IoT data trading systems. First, there is a lack of data transparency and ownership. While the economic value of IoT data is increasing, it is not very well known how this data can be conceptualized, measured, and monetized in a trusted and transparent way. Second, the literature lacks studies about performance models to demonstrate IoT data trading system usability in real-world systems. Third, the reputation of the trading parties is an important attribute that affects their profitability and trading prosperity. However, current reputation systems are prone to malicious manipulation and single point of failure. This thesis identifies and addresses the three above challenges for IoT data trading systems. First, this thesis introduces a trustful IoT data trading system based on the blockchain as a means of providing anonymity, security, transparency, and mutual trust for participants. Using a game-theoretic approach, this study develops a strategic negotiation model that maximizes data buyers’ utility. To ensure that data owners’ IoT data are accessible by trustful buyers, a novel mechanism design is used to impede untruthful buyers from accessing the IoT data. Second, this thesis evaluates the performance of the blockchain-based IoT data trading system using the Hyperledger blockchain. Unlike existing research, this study measures and analyzes transaction throughput, latency, elapsed time, and resource consumption (memory consumption, CPU utilization, and disc read/write operations). Third, this thesis proposes a blockchain-based reputation system capable of avoiding failures by enhancing the Raft consensus mechanism. This thesis also proposes an adaptive learning mechanism that allows the data providers and consumers to enhance their reputation and review credibility scores. Lastly, this thesis carries out extensive theoretical analysis with respect to economic and security properties

An investigation of auctions in the Regional Greenhouse Gas Initiative

The Regional Greenhouse Gas Initiative (RGGI), as the largest cap-and-trade system in the United States, employs quarterly auctions to distribute emissions permits to firms. This study examines firm behavior and auction performance from both theoretical and empirical perspectives. We utilize auction theory to offer theoretical insights regarding the optimal bidding behavior of firms participating in these auctions. Subsequently, we analyze data from the past 58 RGGI auctions to assess the relevant parameters, employing panel random effects and machine learning models. Our findings indicate that most significant policy changes within RGGI, such as the Cost Containment Reserve, positively impacted the auction clearing price. Furthermore, we identify critical parameters, including the number of bidders and the extent of their demand in the auction, demonstrating their influence on the auction clearing price. This paper presents valuable policy insights for all cap-and-trade systems that allocate permits through auctions, as we employ data from an established market to substantiate the efficacy of policies and the importance of specific parameters

An investigation of auctions in the Regional Greenhouse Gas Initiative

The Regional Greenhouse Gas Initiative (RGGI), as the largest cap-and-trade system in the United States, employs quarterly auctions to distribute emissions permits to firms. This study examines firm behavior and auction performance from both theoretical and empirical perspectives. We utilize auction theory to offer theoretical insights regarding the optimal bidding behavior of firms participating in these auctions. Subsequently, we analyze data from the past 58 RGGI auctions to assess the relevant parameters, employing panel random effects and machine learning models. Our findings indicate that most significant policy changes within RGGI, such as the Cost Containment Reserve, positively impacted the auction clearing price. Furthermore, we identify critical parameters, including the number of bidders and the extent of their demand in the auction, demonstrating their influence on the auction clearing price. This paper presents valuable policy insights for all cap-and-trade systems that allocate permits through auctions, as we employ data from an established market to substantiate the efficacy of policies and the importance of specific parameters

A new characterization of equilibrium in multiple-object uniform-price auctions

This paper characterizes equilibrium bidding behavior in a multi-unit uniform-price auction. As posited by Noussair (1995) and Engelbrecht-Wiggans and Kahn (1998), when bidders demand two units and valuations are independent, equilibrium bidding behaviour entails bidding their values for the first unit, and bidding below their valuations for the second unit. We identify some errors in the analysis of Noussair (1995) and Engelbrecht-Wiggans and Kahn (1998), and provide a characterization of equilibrium behavior which involves a threshold value that separates bidders who bid zero for the second unit from those who shade their bids for the second unit

Multi-Unit Auctions: A Survey of Theoretical Literature

This article aims to provide a comprehensive survey of the theoretical research on multi-unit auctions to help identify the gap in this literature. Multi-unit auctions have been extensively used in practise and account for significant amount of transactions in some real-world markets. However, the theoretical research on these auctions has attract less attention compared to single unit auctions. The focus of this article is on those research that study multi-unit auctions for the sale of multiple units of homogeneous objects to potential buyers with more than one unit demand. The articles are categorized based on the assumptions of their models regarding bidders' values and the type of auction. Further the gap in this literature is identified with those areas that require further theoretical research