USING MACHINE LEARNING ALGORITHMS TO ANALYZE IMPACT OF CRIME ON PROPERTY VALUES

Abstract

Since predictions of property values require an integration of a great deal of data, machine learning can play an important role. There are many factors influencing the sale price of private properties. In this paper, we focus on how different types of crimes affect the residential property sale price in an urban county in the USA - Pierce County, Washington. We are interested in what machine learning algorithms can be more effective in predicting the sales values. We worked on two sets of data. The first data source includes the physical attributes of a property, such as the square footage, quality, the year built and/or remodeled. The second data source contains the crime data for Pierce County from July 2018 to July 2019. We combined these two data sources into one full dataset. Then, we divided the full dataset into three clusters using the EM (Expectation Maximization) algorithm. The full and three clustered datasets were used to build two sets of data mining models - one with crime data, and the other without them. We built our models using three algorithms - decision trees, artificial neural networks, and random forests. In total, we created 32 models and evaluated them by calculating prediction errors of each model. We found that the random forest models produced the lowest values of errors. Using those models, we concluded that crime is a significant factor in predicting the sale price of residential properties in Pierce County, Washington.