Abstract
A major challenge faced by the real estate sector is in obtaining credible information regarding people's preferences. Information available in statistical offices are seldom updated. Recent advancements in data collection technologies have led to numerous changes in the conventional techniques used to assess the real estate sector. Currently, the onset of big data technologies has facilitated the prediction of market trends and consumer behavior. Social networking sites such as Facebook and Twitter utilize enormous amounts of user data to identify patterns and trends to predict market behavior. In this study, we explore the application of big data retrieved online through techniques such as web scraping to evaluate trends in the real estate sector in the Kingdom of Saudi Arabia. Specifically, this study employs a novel and improved methodology, called the median absolute deviation test, which uses mathematical formulae to obtain concrete data that are congruent with the real estate sector. Additionally, it employs a starter relapse to identify exceptions using the Cook's distance strategy, to differentiate irregularities dependent on the mix of perceptions. Several suitable ways are deduced to collect data from web sources, and the results demonstrate the applicability of big data to the real estate sector.