This project delves into a meticulous analysis of factors influencing house pricing in a targeted Boston locality for Terro's Real Estate Agency, leveraging a dataset comprising 506 houses. The exploration covers crucial aspects such as crime rates, industrial proportions, and socioeconomic indicators to unravel the intricate relationships impacting property values. Terro's Real Estate, specializing in precise pricing through comprehensive geographic feature analysis, utilizes this study to deliver invaluable insights to clients.
The project begins with a detailed exploration, providing essential summary statistics for each variable. Visualizations like the histogram of average house prices highlight concentration ranges, while covariance and correlation matrices unveil key variable relationships. Initial regression models, incorporating significant variables like LSTAT, establish a baseline understanding. Subsequent models, incorporating multiple factors, refine predictions. Identifying statistically significant variables enhances model explanatory power, exemplified by a regression model achieving an impressive adjusted R-squared value of 0.6887, elucidating 68.87% of the variability in house prices.
These findings deepen the understanding of how variables such as average room numbers and socioeconomic status intricately impact house prices. The derived regression equation equips Terro's Real Estate Agency to make informed decisions, providing clients with reliable pricing insights. This commitment to precision aligns with industry standards, showcasing the agency's excellence in property valuation and its adeptness in navigating the dynamic real estate market. Addressing potential challenges encountered during the analysis would offer a realistic perspective and further enrich the project's narrative.