Genetic Programming Forecasting of Real Estate Prices of Residential Single Family Homes in Southern California

Created by W.Langdon from gp-bibliography.bib Revision:1.7177

@Article{Kaboudan:2008:JREL,

• author = "Mak Kaboudan",
• title = "Genetic Programming Forecasting of Real Estate Prices of Residential Single Family Homes in Southern California",
• journal = "Journal of Real Estate Literature",
• year = "2008",
• volume = "16",
• number = "2",
• pages = "217--240",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "0927-7544",
• publisher = "American Real Estate Society",
• URL = "http://aresjournals.org/doi/abs/10.5555/reli.16.2.a881465823035385",
• broken = "http://ares.metapress.com/content/a881465823035385/",
• abstract = "Use of an artificial intelligence technique, genetic programming (GP), is introduced here to predict real estate residential single family home prices. GP is a computerised random search technique that can deliver regression-like models. Spatiotemporal model specifications of periodic average neighbourhood prices are implemented to predict individual property prices. Average price variations are explained in terms of changes in home attributes, spatial attributes, and temporal economic variables. Quarterly data (2000-2005) from two cities in Southern California are used to obtain GP and standard statistical models (generalised least square - GLS). Results obtained suggest that forecasts from city neighborhood average price GP equations may have advantage over forecasts from GLS equations and over forecasts from models estimated using city aggregated data.",
• notes = "house price prediction in US of america http://business.fullerton.edu/finance/jrel/",

}

Genetic Programming entries for Mahmoud A Kaboudan

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