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:1.3872

@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