TITLE:
A Data Mining Model by Using ANN for Predicting Real Estate Market: Comparative Study
AUTHORS:
Itedal Sabri Hashim Bahia
KEYWORDS:
Cascade Forward Back Propagation (CFBP); Feed Forward Back Propagation (FFBP); Data Mining; House Price
JOURNAL NAME:
International Journal of Intelligence Science,
Vol.3 No.4,
October
8,
2013
ABSTRACT: This paper aims to demonstrate the importance and possible value of housing
predictive power which provides independent real estate market forecasts on home
prices by using data mining tasks. A (FFBP) network model and (CFBP) network
model are one of these tasks used in this research to compare results of them. We estimate the median value of owner occupied homes in Boston suburbs
given 13 neighborhood attributes. An estimator can be found by fitting the inputs and targets.
This data set has 506 samples. “ousing inputs” is a 13 × 506 matrix. The “housing
targets” is a 1 × 506 matrix of median values of owner-occupied homes in
$1000’s. The
result in this paper concludes that which one of the two networks
appears to be a better indicator of the output data to target data network
structure than maximizing predict. The CFBP network which is the best
result from the Output_network for all samples are found from the equation
output = 0.95 * Target + 1.2. The regression value is approximately 1,
(R = 0.964). That means the Output_network is matching to the target data set (Median value
of owner-occupied homes in $1000’s), and the percent
correctly predict in the simulation sample is 96%.