Expert judgement in the Processes of Commercial Property Market Forecasting

In this paper we investigate the role of judgement in the formation of forecasts in commercial real estate markets. Based on interview surveys with the majority of forecast producers, we find that real estate forecasters are using a range of inputs and data sets to form models to predict an array of variables for a range of locations. The findings suggest that forecasts need to be acceptable to their users (and purchasers) and consequently forecasters generally have incentives to avoid presenting contentious or conspicuous forecasts. Where extreme forecasts are generated by a model, forecasters often engage in ‘self-censorship’ or are ‘censored’ following in-house consultation. It is concluded that the forecasting process is more complex than merely carrying out econometric modelling and that the impact of the influences within this process vary considerably across different organizational contexts.

Uncertainty in Integrated Regional Models

This paper examines the nature of uncertainty in integrated econometric+input-output (ECIO) regional models. We focus on three sources of uncertainty: [a] econometric model parameter uncertainty; [b] econometric disturbance term uncertainty; and [c] input-output coefficient uncertainty. Through a series of Monte Carlo simulations we analyze the relative importance of each component as well as the question of how their interaction may propagate through the integrated model to affect the distributions of the endogenous variables. Our results suggest that there is no simple answer to the question of which source of uncertainty is most important in an integrated model. Instead, that answer is conditioned upon the focus of the analysis and whether the industry specific or macro level variables are of central concerns.regional econometric model, input-output, integrated, uncertainty

The Production and Consumption of Commercial Real Estate Market Forecasts

Whilst the vast majority of the research on property market forecasting has concentrated on statistical methods of forecasting future rents, this report investigates the process of property market forecast production with particular reference to the level and effect of judgemental intervention in this process. Expectations of future investment performance at the levels of individual asset, sector, region, country and asset class are crucial to stock selection and tactical and strategic asset allocation decisions.  Given their centrality to investment performance, we focus on the process by which forecasts of rents and yields are generated and expectations formed.  A review of the wider literature on forecasting suggests that there are strong grounds to expect that forecast outcomes are not the result of purely mechanical calculations.Real Estate, Forecast, Real Estate Markets, Commercial Real Estate

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Socio-hydrological modelling: a review asking “why, what and how?”

Interactions between humans and the environment are occurring on a scale that has never previously been seen; the scale of human interaction with the water cycle, along with the coupling present between social and hydrological systems, means that decisions that impact water also impact people. Models are often used to assist in decision-making regarding hydrological systems, and so in order for effective decisions to be made regarding water resource management, these interactions and feedbacks should be accounted for in models used to analyse systems in which water and humans interact. This paper reviews literature surrounding aspects of socio-hydrological modelling. It begins with background information regarding the current state of socio-hydrology as a discipline, before covering reasons for modelling and potential applications. Some important concepts that underlie socio-hydrological modelling efforts are then discussed, including ways of viewing socio-hydrological systems, space and time in modelling, complexity, data and model conceptualisation. Several modelling approaches are described, the stages in their development detailed and their applicability to socio-hydrological cases discussed. Gaps in research are then highlighted to guide directions for future research. The review of literature suggests that the nature of socio-hydrological study, being interdisciplinary, focusing on complex interactions between human and natural systems, and dealing with long horizons, is such that modelling will always present a challenge; it is, however, the task of the modeller to use the wide range of tools afforded to them to overcome these challenges as much as possible. The focus in socio-hydrology is on understanding the human–water system in a holistic sense, which differs from the problem solving focus of other water management fields, and as such models in socio-hydrology should be developed with a view to gaining new insight into these dynamics. There is an essential choice that socio-hydrological modellers face in deciding between representing individual system processes or viewing the system from a more abstracted level and modelling it as such; using these different approaches has implications for model development, applicability and the insight that they are capable of giving, and so the decision regarding how to model the system requires thorough consideration of, among other things, the nature of understanding that is sought

A question of order: The role of collective taste as a strategy to cope with demand uncertainty in the womenswear fashion industry

Though strong branding and a distinctive product range are often identified as important factors for companies' economic success (see, e.g., Robinson [1999]) many UK womenswear retailers offer surprisingly similar products. The author argues that product sameness amongst many high street womenswear retailers in the UK is a deliberate strategy employed by industry practitioners to limit demand uncertainty. Based on an empirical study of UK high street womenswear retailers the author argues that the womenswear fashion industry, like other industries operating in markets faced by high levels of demand uncertainty (Crane 1992), has adopted strategies to minimise economic risks. The author explores how fashion information created by companies/groups in the quaternary industry sector is used as inspirational sources for fashion workers at retailing level and contributes to the development of a collective taste amongst them. Collective taste in the context of this research is seen not as a by-product of interaction as theorised by Blumer (1969), but as a strategy to cope with the inherent demand uncertainty experienced by firms operating in the womenswear market and as a means for establishing some orderliness in the fashion system.div_PaSAspers, Patrik. 2006. Contextual Knowledge. Current Sociology 54 (5): 745-763. Accessed July 31, 2014 doi: 10.1177/0011392106066814 Blumer, Herbert. 1969. Fashion: From Class Differentiation to Collective Selection.- The Sociological Quarterly 10: 275-291. Accessed August 15, 2014 doi: 10.1111/j.1533-8525.1969.tb01292.x. Bourdieu, Pierre avec Delsaut, Yvette. 1975. Le Couturier et sa Griffe: Contribution une Thorie de la Magie.- Actes de la Recherche en Sciences Sociales 1 (1): 7-36. Bourdieu, Pierre. 1984. Distinction: A Social Critique of the Judgement of Taste. London: Routledge & Kegan Paul. Braham, Peter. 1997. Fashion: Unpacking a Cultural Production.- In Production of Culture/Cultures of Production, edited by Paul du Gay, 121-165. London: Sage. Caves, Richard. 2000. Creative Industries: Contracts between Art and Commerce. Cambridge, Mass; London: Harvard University Press. Color Marketing Group. 2014. About Color Marketing Group-. Accessed June 13. http://www.colormarketing.org/about-cmg. Costantino, Maria. 1998. Fashion Marketing and PR. London: BT Batsford. Crane, Diane. 1992. The Production of Culture: Media and the Urban Arts. London: Sage. Davis, Fred. 1991. Herbert Blumer and the Study of Fashion - a Reminiscence and a Critique.- Symbolic Interaction 14 (1): 1-21. Davis, Fred. 1992. Fashion, Culture, and Identity. Chicago: The University of Chicago Press. De Vany, Arthur. 2004. Hollywood Economics: How Extreme Uncertainty Shapes the Film Industry. London: Routledge. Dempster, Anna M. 2006. Managing Uncertainty in Creative Industries.- Creativity and Innovation Management 15 (3): 224-233. Dempster, Anna M. 2005. Entrepreneurial Reactions to Uncertainty in the Creative Industries.- Paper presented at the Conference on The Creative Industries and Intellectual Property, London, May 22-23. Entwistle, Joanne. 2009. The Aesthetic Economy of Fashion: Markets and Value in Clothing and Modelling. New York: Berg Ewing, Elizabeth. 1993. History of Twentieth Century Fashion. 3rd ed. London: B. T. Batsford. Fine, Ben and Leopold, Ellen. 1993. The World of Consumption. London: Routledge. Finkelstein, Joanne. 1998. Fashion: An Introduction. New York: New York University Press. Gilbert, David 2006 From Paris to Shanghai: The Changing Geographies of Fashion's World Cities.- In Fashion's World Cities, edited by Christopher Breward and David Gilbert, 3 -32. Oxford and New York: Berg. Godley, Andrew. 1998. Competitiveness in the Clothing Industry: The Economics of Fashion in UK Womenswear, 1880-1950.- Journal of Fashion Marketing and Management 2 (2): 125-36. Goldman, William. 1996. Adventures in the Screen Trade: A Personal View of Hollywood and Screenwriting. New York: Warner Books. Gronow, Jukka. 2001. The Sociology of Taste. Taylor & Francis e-Library Harvey, David. 1989. The Condition of Post-modernity: An Enquiry into the Origin of Cultural Change. Oxford: Blackwell. Hirsch, Paul M. 1972. Processing Fads and Fashions: An Organization-Set Analysis of Cultural Industry Systems.- American Journal of Sociology 77 (4): 639-659. Intercolor. 2014. History of Intercolor.- Accessed June 13. http://www.intercolor.nu/history.html. International Colour Authority. 2012. The History of ICA.- Accessed June 8. http://www.colourforecasting.com/thehistoryofica. Ryan, John and Peterson, Richard A. 1982. The Product Image: The Fate of Creativity in Country Music Songwriting.- Sage Annual Reviews of Communication Research. 10: 11-32. King, Julie. 2011.-Colour Forecasting: An Investigation into how its Development and Use Impacts on Accuracy.: PhD diss., University of the Arts London. Lash, Scott and Urry, John. 1994. Economies of Signs and Space. London: Sage. Lipovetsky, Gilles. 1994. The Empire of Fashion: Dressing Modern Democracy. Princeton, New Jersey: The Princeton University Press. May, Tim. 2011. Social Research: Issues, Methods and Process. 4th ed. Maidenhead, England: McGraw Hill. McClatchey, Caroline. 2011. Fashion Week: From the Catwalk to the Street.- BBC New Magazine, September 22. http://www.bbc.co.uk/news/magazine-14984468 Meyer, Heinz-Dieter. 2000. Taste Formation in Pluralistic Societies: The Role of Rhetorics and Institutions. International Sociology 15 (1): 33-56. Accessed July, 28 doi: 10.1177/0268580900015001003. Premire Vision. 2009. The Show.- Accessed September 5. http://www.premiervision.fr. Robinson, P. 1999. Marketing Fashion: Strategies and Trends for Fashion Brands. London: Informa Retail & Consumer. Rocamora, Agnes. 2002. Fields of Fashion: Critical Insights into Bourdieu's Sociology of Culture. Journal of Consumer Culture 2 (3): 341-362. Accessed July, 28 doi: 10.1177/146954050200200303. Rousso, Chelsea. 2012. Fashion Forward: A Guide to Fashion Forecasting. Fairchild Publications. Rueling, Charles-Clemens. 2000. Theories of (Management?) Fashion: The contributions of Veblen, Simmel, Blumer, and Bourdieu.- Accessed July, 29 http://archive-ouverte.unige.ch/unige:5868. Sagot-Duvauroux, Dominique. 2011 Art Markets- In A Handbook of Cultural Economics, 2nd ed. Edited by Ruth Towse 33-42. Cheltenham: Edward Elgar Publishing Limited. Schulz, Susanne. 2008. Our Lady Hates Viscose: The Role of the Customer Image in High Street Fashion Production.- Cultural Sociology 2 (3): 385-405. Sharpe, Michael. 2013. Copying is so clich.- The Glasgow Guardian, October 30. http://glasgowguardian.co.uk/2013/12/19/copying-is-so-cliche/. Shields, Rachel. 2007. From the Catwalk to the High Street.- The Independent, October 28. http://www.independent.co.uk/news/uk/home-news/from-the-catwalk-to-the-high-street-398152.html. Simmel, Georg. 1904 Fashion International Quarterly 10: 130-155. Stylesight. 2014. Runway Analysis-. Accessed February 2. http://www.stylesight.com/en/content/runway-analysis. Thompson, John B. 2010. Merchants of Culture. Cambridge: Polity Press. UK Fashion and Textile Association. 2014. About: Who we are-. Accessed June 13. http://www.ukft.org/aboutus.php?page=about&psub=whoweare&cat=1. Veblen, Thorstein. 1994. The Theory of the Leisure Class. Dover Publications, Inc. Wilson, Elizabeth and Taylor, Lou. 1989. Through the Looking Glass: A History of Dress from 1860 to the Present Day. London: BBC Books.8pub3865pub

Encoding Seasonal Climate Predictions for Demand Forecasting with Modular Neural Network

Current time-series forecasting problems use short-term weather attributes as exogenous inputs. However, in specific time-series forecasting solutions (e.g., demand prediction in the supply chain), seasonal climate predictions are crucial to improve its resilience. Representing mid to long-term seasonal climate forecasts is challenging as seasonal climate predictions are uncertain, and encoding spatio-temporal relationship of climate forecasts with demand is complex. We propose a novel modeling framework that efficiently encodes seasonal climate predictions to provide robust and reliable time-series forecasting for supply chain functions. The encoding framework enables effective learning of latent representations -- be it uncertain seasonal climate prediction or other time-series data (e.g., buyer patterns) -- via a modular neural network architecture. Our extensive experiments indicate that learning such representations to model seasonal climate forecast results in an error reduction of approximately 13\% to 17\% across multiple real-world data sets compared to existing demand forecasting methods.Comment: 15 page