A Project that uses Zillow research data on Quandl, Prophet for time series forecasting, Altair for vega-lite charts and Folium for an creating interactive map.
Review of United States’ housing data since the housing market collapsed in 2008 to identify how the market has or has not rebounded between then and 2019, determine what consumer/market factors affect prices, and show what prices may look like in the future.
Buying a home in NYC, what Neighborhoods are the best value? This project seeks to understand the fundamental factors that explain differences in residential real estate prices across NYC.
This project uses clustering to find drivers of error in Zestimates of single-unit Zillow properties in 2017. I will demonstrate how this data can be used for quality control and preventing errors in the future.
A data analysis of the U.S. housing market using Zillow Research and Consumer Price Index (CPI) to compare housing costs and cost of living across multiple U.S. cities.
Analyzed and visualized the dataset for the Zillow data competition on Kaggle, applied different regression models to predict errors in Zillow home value prediction, and identified the important features that induce prediction errors to improve the original algorithm.
A python script that scrapes all of the images from your saved homes on Zillow. Each house gets its own folder where the image is saved. Add your Zillow sign in info into zillow0.py