ESTIMATING PROBLEMS WITH NEURAL NETWORKS
 

 

 

 

 

 

 

 

 

 

 

 

 

A neural network takes specific inputs—in this case the information from the housing sheet—and turns them into a specific output, an appraised value for the house. The list of inputs is well defined because of two factors: extensive use of the multiple listing service (MLS) to share information about the housing market among different real estate agents and standardization of housing descrip­ tions for mortgages sold on secondary markets. The desired output is well defined as well—a specific dollar amount. In addition, there is a wealth of experience in the form of previous sales for teaching the network how to value a house. TI P Neural networks are good for prediction and estimation problems. A good problem has the following three characteristics:

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The inputs are well understood. You have a good idea of which features of the data are important, but not necessarily how to combine them. Experience is available. You have plenty of examples where both the inputs and the output are known. These known cases are used to train the network. The first step in setting up a neural network to calculate estimated housing values is determining a set of features that affect the sales price. Some possible common features are In practice, these features work for homes in a single geographical area. To extend the appraisal example to han­ dle homes in many neighborhoods, the input data would include zip code information, neighborhood demographics, and other neighborhood quality- of-life indicators, such as ratings of schools and proximity to transportation. To simplify the example, these additional features are not included here.