Post-COVID California Property Price Trend Prediction

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Here, based on the transaction records of the real estate transaction market provided by the RMDS laboratory, our group have compiled a pipeline of processes for analyzing the value of real estate and predicting the price of real estate, including a analytical and visualized jupyter notebook, executable python files and a series of output data files/figures. Most of the input data files used in this project come from real life data on Zillow, and some data extracted by web crawlers and python API. In property value analysis, we use a series of regression methods to infer the historical value of the house (catboost) and the future value of the house (other regression methods) by fitting the house transaction information and the characteristics of the house. In real estate price prediction, we used Gaussian Process regression to help us predict property price through a time series of house prices with different time intervals. We believe that our method can promote the stability of the real estate market and provide valuable suggestions to real estate investors.

Language: Python
Are you a contestant for RMDS 2021 Data Science Competition? Yes, I am a contestant from USA
Collaborators: Zhenchen Hong, Jinglin Zhao, Manlin Zhang, Yuncong Ma
Type: Real Estate
Release Date: Jun 13, 2021
Last Updated: Jun 14, 2021

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