AUTOMATING APPRAISALS
 

 

 

 

 

 

 

 

 

 

 

 

Why would we want to automate appraisals? Clearly, automated appraisals could help real estate agents better match prospective buyers to prospective homes, improving the productivity of even inexperienced agents. Another use would be to set up kiosks or Web pages where prospective buyers could describe the homes that they wanted—and get immediate feedback on how much their dream homes cost. Perhaps an unexpected application is in the secondary mortgage market. Good, consistent appraisals are critical to assessing the risk of individual loans and loan portfolios, because one major factor affecting default is the proportion of the value of the property at risk. If the loan value is more than 100 percent of the market value, the risk of default goes up considerably. Once the loan has been made, how can the market value be calculated? For this purpose, Freddie Mac, the Federal Home Loan Mortgage Corporation, developed a product called Loan Prospector that does these appraisals automatically for homes throughout the United States. Loan Prospector was originally based on neural network technology developed by a San Diego company HNC, which has since been merged into Fair Isaac.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Back to the example. This neural network mimics an appraiser who estimates the market value of a house based on features of the property She knows that houses in one part of town are worth more than those in other areas. Additional bedrooms, a larger garage, the style of the house, and the size of the lot are other factors that figure into her mental calculation. She is not applying some set formula, but balancing her experience and knowledge of the sales prices of similar homes. And, her knowledge about housing prices is not static. She is aware of recent sale prices for homes throughout the region and can recognize trends in prices over time—finetuning her calculation to fit the latest data. Artificial Neural Networks 215 The appraiser or real estate agent is a good example of a human expert in a well- defined domain. Houses are described by a fixed set of standard features taken into account by the expert and turned into an appraised value. In 1992, researchers at IBM recognized this as a good problem for neural networks.