A Multi-Agent System Forecast of the S&P/Case-Shiller LA Home Price Index

A Multi-Agent System Forecast of the S&P/Case-Shiller LA Home Price Index

Mak Kaboudan
ISBN13: 9781605668987|ISBN10: 1605668982|EISBN13: 9781605668994
DOI: 10.4018/978-1-60566-898-7.ch001
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MLA

Kaboudan, Mak. "A Multi-Agent System Forecast of the S&P/Case-Shiller LA Home Price Index." Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization, edited by Shu-Heng Chen, et al., IGI Global, 2011, pp. 1-18. https://doi.org/10.4018/978-1-60566-898-7.ch001

APA

Kaboudan, M. (2011). A Multi-Agent System Forecast of the S&P/Case-Shiller LA Home Price Index. In S. Chen, Y. Kambayashi, & H. Sato (Eds.), Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization (pp. 1-18). IGI Global. https://doi.org/10.4018/978-1-60566-898-7.ch001

Chicago

Kaboudan, Mak. "A Multi-Agent System Forecast of the S&P/Case-Shiller LA Home Price Index." In Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization, edited by Shu-Heng Chen, Yasushi Kambayashi, and Hiroshi Sato, 1-18. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60566-898-7.ch001

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Abstract

Successful decision-making by home-owners, lending institutions, and real estate developers among others is dependent on obtaining reasonable forecasts of residential home prices. For decades, home-price forecasts were produced by agents utilizing academically well-established statistical models. In this chapter, several modeling agents will compete and cooperate to produce a single forecast. A cooperative multi-agent system (MAS) is developed and used to obtain monthly forecasts (April 2008 through March 2010) of the S&P/Case-Shiller home price index for Los Angeles, CA (LXXR). Monthly housing market demand and supply variables including conventional 30-year fixed real mortgage rate, real personal income, cash out loans, homes for sale, change in housing inventory, and construction material price index are used to find different independent models that explain percentage change in LXXR. An agent then combines the forecasts obtained from the different models to obtain a final prediction.

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