The objective of this paper is to empirically compare the predictive power of the hedonic model with an artificial neural network model on house price prediction. A sample of 200 houses in Christchurch, New Zealand is randomly selected from the Harcourt website. Factors including house size, house age, house type, number of bedrooms, number of bathrooms, number of garages, amenities around the house and geographical location are considered. Empirical results support the potential of artificial neural network on house price prediction, although previous studies have commented on its black box nature and achieved different conclusions.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The views expressed in this paper are those of the authors and do not necessarily reflect the views and policies of the Asian Development Bank (ADB) or its Board of Governors or the governments they represent. Terms of use: Documents inADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use.By making any designation of or reference to a particular territory or geographic area, or by using the term "country" in this document, ADB does not intend to make any judgments as to the legal or other status of any territory or area. Note: In this publication, "$" refers to US dollars.The ADB Economics Working Paper Series is a forum for stimulating discussion and eliciting feedback on ongoing and recently completed research and policy studies undertaken by the Asian Development Bank (ADB) staff, consultants, or resource persons. The series deals with key economic and development problems, particularly those facing the Asia and Pacific region; as well as conceptual, analytical, or methodological issues relating to project/program economic analysis, and statistical data and measurement. The series aims to enhance the knowledge on Asia's development and policy challenges; strengthen analytical rigor and quality of ADB's country partnership strategies, and its subregional and country operations; and improve the quality and availability of statistical data and development indicators for monitoring development effectiveness.

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We use hedonic prices and purchase quantities to consider what can be learned about household willingness to pay for baskets of organic products and how this varies across households. We use rich scanner data on food purchases by a large number of households to compute household speci…c lower and upper bounds on willingness to pay for various baskets of organic products. These bounds provide information about willingness to pay for organic without imposing restrictive assumptions on preferences. We show that the reasons households are willing to pay vary, with quality being the most important, health concerns coming second, and environmental concerns lagging far behind. We also show how these methods can be used for example by stores to provide robust upper bounds on the revenue implication of introducing a new line of organic products. JEL: D12, L11, L81, Q51, C81 Correspondence: rgri¢ th@ifs.org.uk, l.nesheim@ucl.ac.uk Acknowledgement: The authors would like to thank James Banks, Richard Blun-dell, Martin Browning, Ian Crawford, Andrew Leicester, Aviv Nevo, Ariel Pakes and Carol Propper for many helpful comments. Financial support from the ESRC through the ESRC Centre for the Microeconomic Analysis of Public Policy at IFS (CPP) and the ESRC Centre for Microdata Methods and Practice (CeMMAP) is gratefully acknowledged. All errors remain the responsibility of the authors.

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A data warehouse stores information that is collected from multiple, heterogeneous information sources for the purpose of complex querying and analysis. Information in the warehouse is typically stored in the form of materialized views, which represent pre-computed portions of frequently asked queries. One of the most important tasks when designing a warehouse is the selection of materialized views to be maintained in the warehouse. The goal is to select a set of views in such a way as to minimize the total query response time over all queries, given a limited amount of time for maintaining the views (maintenance-cost view selection problem). In this paper, we propose an efficient solution to the maintenance-cost view selection problem using a genetic algorithm for computing a near-optimal set of views. Specifically, we explore the maintenance-cost view selection problem in the context of OR view graphs. We show that our approach represents a dramatic improvement in time complexity over existing search-based approaches using heuristics. Our analysis shows that the algorithm consistently yields a solution that lies within 10% of the optimal query benefit while at the same time exhibiting only a linear increase in execution time. We have implemented a prototype version of our algorithm which is used to simulate the measurements used in the analysis of our approach.

The purpose of this study is to discuss the various issues regarding de‐industrialisation and to systematically analyse the causes of this phenomenon. In addition, the effect of the recent increase in foreign direct investment on de‐industrialisation will be analysed. Unlike extant studies, this study employs a more reliable method of estimation, known as the generalised method of moments system, for establishing the importance of foreign direct investment (FDI) with regard to de‐industrialisation. For a general assessment of the different factors of de‐industrialisation, including FDI, this study classifies the causes of de‐industrialisation in OECD countries into external and internal factors. As a result, the analysis has proved that not only internal and external factors are major factors of de‐industrialisation, FDI inflow and outflow are also major factors.