an empirical Aplication inreal wstate valuation

Topics: Real estate, Data mining, Real estate appraisal Pages: 4 (2505 words) Published: April 17, 2015
From: FLAIRS-02 Proceedings. Copyright © 2002, AAAI ( All rights reserved.

Data Mining: An Empirical Application in Real Estate Valuation Ruben D. Jaen
Florida International University
University Park, PC236
Miami, Fl 33199

This paper presents the insights gained from applying data
mining techniques, in particular neural networks for the
purposes of developing an intelligent model used to predict
real estate property values based on variety of factors. A
dataset of over one thousand transactions in real estate
properties was used. The dataset included 15 variables
obtained from the multiple listing system (MLS) database
and captured information on transactions taking place
during a period of three years. The results from applying
data mining techniques to predict real estate values are
promising. Future plans and recommendations for further
expanding the study are given.

Keywords: Data mining, real estate valuations, home

The factors that determine housing prices are of interest to urban planners, developers, real estate professionals, and
financial executives as well as most American
homeowners. According to a 1998 Federal Reserve survey
(Kennickell, et al., 2000), 66.2 percent of U.S. households
are homeowners and housing investment amounts to 33
percent of household net worth. The number of new
home sales as well as home resales are an important
component of the U.S. economy and data concerning these
transactions is closely tracked for the purpose of gauging
economic activity and formulating appropriate monetary
and fiscal policies. This paper examines the factors that
determine housing prices in a sample of over 1000 home
sales in Miami-Dade County during the period of 19992001.
Sales of homes take place in the marketplace dictated by
the usual rules of supply and demand. Since this is not a
perfect market, there is a great latitude for judgment in...

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