Machine Learning and Data Mining Insights into Monthly Housing Price Dynamics in Connecticut, USA

Abstract

In today’s market place housing and housing associated goods and services constitutes important value for consumers. As indicated in the Maslow’s hierarcy of needs figure, sheltering, housing is one of the most basic needs of human beings. Later wants and demands take place, individuals engage in behaviours to afford their housing needs by either renting, buying a house or buying hotel, motel or other housing services. If demand of the consumers meet the available housing options and recently constructed housing projects, rent and selling prices of the housing options may be in a balance meaning a significant increase and decrease in the prices are unlikely to occur. If the demand passes the supply it would be on the spot to wait for increases in the housing market, whereas a lower demand in a normal supply scenario is likely to lower the prices in the marketplace for consumers. In this research average housing prices according to location considering date have been ananlysed using machine learning techniques available in data mining and computer science literature.

Keywords: Data Mining, Machine Learning, Society

DOI: 10.54941/ahfe1007583