A genetic programming/neural network multi-agent system to forecast the S\&P/Case-Shiller home price index for Los Angeles

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InCollection{Kaboudan:2011:chen,

• author = "Mak Kaboudan",
• title = "A genetic programming/neural network multi-agent system to forecast the {S\&P/Case-Shiller} home price index for {Los Angeles}",
• publisher = "IGI Global",
• year = "2011",
• booktitle = "Multi-Agent Applications with Evolutionary Computation and Biologically Inspired Technologies: Intelligent Techniques for Ubiquity and Optimization",
• editor = "Shu-Heng Chen and Yasushi Kambayashi and Hiroshi Sato",
• chapter = "1",
• pages = "1--18",
• email = "Mak_kaboudan@Relands.edu",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "1-60566-898-2",
• URL = "http://www.igi-global.com/bookstore/Chapter.aspx?TitleId=46196",
• DOI = "doi:10.4018/978-1-60566-898-7.ch001",
• 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 using academically well-established statistical models. In this chapter, several modelling 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.",

}

Genetic Programming entries for Mahmoud A Kaboudan

Citations