A Group Utility Maximizer Mechanism for Land Assembly

A Group Utility Maximizer Mechanism for Land Assembly Buyers requiring large parcels of land for development purposes engage several owners in a bilateral trade seeking monetary payment in exchange for land – a business transaction most often referred to in the literature as the problem of land assembly. To avoid the holdout problem which is a typical consequence of such negotiations necessitates a good pricing strategy that meets the subjective valuation considerations of the owners and protecting property rights to warrant a fair and efficient outcome. Several approaches have been proposed in the literature that includes contributions from game theory in the form of Nash bargaining, Bayesian theory for incomplete information, auction theory and Mechanism Design which have individually enriched this field and proposed credible solutions. In this paper, we consider a setting that has a single buyer and N sellers. We take a Mechanism Design approach to study the assembly problem in a utilitarian framework, where we associate risk-averse utility functions with the sellers. Given a set of reserve prices reported by sellers, and their risk-aversion behaviour, we seek an incentive-compatible mechanism that simultaneously maximizes the sum of individual expected utilities while delivering a Pareto optimal per-seller penalty-reward structure. We show how this mechanism, inspired and adapted from the actuarial Risk Exchange concept in the Insurance industry, can be fruitfully applied to the land assembly problem, yielding an efficient and optimal solution to the holdout problem, while making very minimal demands on knowledge of sellers valuations. The working of the mechanism is illustrated with a simple example. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Real Estate Finance and Economics Springer Journals

A Group Utility Maximizer Mechanism for Land Assembly

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Springer US
Copyright © 2012 by Springer Science+Business Media, LLC
Economics / Management Science; Regional/Spatial Science; Finance/Investment/Banking
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