![Sample our Built Environment journals, sign in here to start your access, Latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5926/TOC-Subject-Sample-2021-Built-Environment.jpg"})
Abstract
Use of an artificial intelligence technique, genetic programming (GP), is introduced here to predict real estate residential single-family home prices. GP is a computerized random search technique that can deliver regression-like models. Spatiotemporal model specifications of periodic average neighborhood prices are implemented to predict individual property prices. Average price variations are explained in terms of changes in home attributes, spatial attributes, and temporal economic variables. Quarterly data (2000-2005) from two cities in Southern California are utilized to obtain GP and standard statistical models. The results suggest that forecasts from city neighborhood average price GP equations may have an advantage over forecasts from GLS equations and over forecasts from models estimated using city aggregated data.