Data Driven Marketing in Real Estate: Forecasting House Prices and Uncovering Influential Factors
DOI:
10.47353/ecbis.v4i1.248Published:
2025-11-25Downloads
Abstract
In recent years, the housing market has faced significant challenges, including fluctuating prices and declining sales. To solve this issue, there was an increasing need for more sophisticated methods to predict housing prices accurately. This study aimed to provide real estate marketers with a tool to enhance their pricing tactics and mitigate the decline in home sales by predicting house prices using machine learning techniques. Several parameters were considered in this study, such as location, number of bedrooms, number of bathrooms, land area, building area, and number of carports. Linear regression and neural network methods were used to develop predictive models. The findings showed that the neural network method was more accurate than linear regression, which made it a better tool for real estate pricing strategies, with land area and number of carports being the most influential aspects in house price prediction.
Keywords:
House price prediction Machine learning Data-driven marketing linear regression neural netReferences
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