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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 (costal, 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. Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) Catovic, Amer; Ghajar, Hessam Jalali; Begnazarov, Serdar; Khattar, Shammi; Vural, Volkan (2019). San Diego Housing Data Analysis - Part 2. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0891459
Type
dataset
Identifier
ark:/20775/bb4782925w
Language
English
Subject
Regression San Diego (Calif.) Capstone projects Housing Data Science & Engineering Master of Advanced Study (DSE MAS) Task: Regression DSE MAS - 2019 Cohort
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