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M. Stanley, A. Alastair, M. Dylan and D. Patterson, “Neural Networks: The Prediction of Residential Values,” Journal of Property Valuation & Investment, Vol. 16, No. 1, 1998, pp. 57-70. http://dx.doi.org/10.1108/14635789810205128

has been cited by the following article:

• 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%.