San Diego Housing Data Analysis - Part 2. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

Predicting housing prices in San Diego County. The intended public benefit from the project is to provide further insights and guidance to homeowners, investors and government sectors.
The baseline project went a long way in meeting this business challenge. Several important factors that determine the house values, such as regionality (coastal, inland etc.), proximity of schools, seasonality and house size and stability in the San Diego county are built in the baseline model.
We intend to expand this to include:
Macroeconomic factors: mortgage rates
Microeconomic/regional factors, such as employment, migration, Crime
Our second challenge is to improve the housing price forecast models and hence accuracy of price prediction by 10% over baseline.

本数据集旨在预测圣迭戈县（San Diego County）的住宅房价。本项目的预期社会效益在于为住宅业主、投资者以及政府部门提供更具参考价值的分析结论与决策指引。此前的基准项目在应对该业务挑战方面已取得显著进展。基准模型已纳入多项影响圣迭戈县住宅价值的核心因素，包括区域属性（沿海、内陆等）、周边学校的邻近程度、季节波动性、住宅面积以及房产价值稳定性等。本项目拟对该基准模型进行扩展，新增两类影响因子：一是宏观经济因素，如抵押贷款利率；二是微观经济/区域因素，如就业状况、人口迁移、犯罪率等。本项目的第二项挑战在于优化住宅房价预测模型，使预测准确率相较于基准模型提升10%。