Quantitative insights into the Winnipeg rental sector: A data-driven analytical approach using geographic and property metrics

Abstract

In the dynamic rental market of Winnipeg, accurately predicting rental property prices is essential for a wide range of stakeholders, including landlords, tenants, prospective renters, property managers, and urban planners. Traditional rental market assessments often fail to incorporate advanced analytical techniques, leading to less precise price forecasts and hindering strategic decision-making. This paper aims to bridge this gap by developing sophisticated predictive models using a dataset that contains rental property information as well as demographic and socio-economic information in Winnipeg. This paper highlights the importance of integrating advanced computational methods in rental market analysis, which can significantly benefit economic planning and personal investment decisions in urban environments. By utilizing both machine learning and statistical learning methods, this paper seeks to improve the accuracy of rental price estimations across different neighborhoods in Winnipeg.

Keywords

• Machine learning
• Statistical learning