- Predict Zillow Home/Rent values using data models.
- Utilize a variety of publicly available data sources to develop new indices / feature engineer to aid in this prediction.
- What factors most strongly account for the pricing of homes/rent?
- How do past prices impact future prices?
- What constitutes a good metric -- is it 1) useful for prediction, 2) easily interpretable for the sake of real-world applicability?
- How will we measure success from a business/user perspective?
- Are the predictions accurate? (Can our models predict the future sale/ rent price within a very slim margin of error).
- Do our predictors provide new insights / unique value? We want to ensure we don’t “reinvent the wheel” (i.e, fail to provide a model with substantial value)
Population: process and model will be used to predict Zillow home/rent values for multi-family homes in the United States.
Timeframe: Zillow’s latest dataset has 7 years of rental indices for the United States, but it does not include the full history. Therefore, we will use data from rentals spanning between September 2010 and January 2020.
Target variable: Home value index (in US dollars).
Questions to answer with business and risk:
- What’s the default definition of the index?
- Which features from the American Community Survey (ACS) should be considered?
- How do we forecast prices 1,2,5 years from now given this data does not account for the COVID-19 pandemic?
Zillow Historical ZRI Data Multi Family Homes:
Description: historical ZRI data from 1/2014 - 1/2021
Zillow Historical ZORI Data Multi Family Homes:
Description: most recent ZORI data
Source: https://www.zillow.com/research/data/
ACS Data:
Description: Data from ACS surveys
Source: https://console.cloud.google.com/marketplace/product/united-states-census-bureau/acs?project=rf-etl
Casey Hoffman: Academic research
Douglas Pizac: Academic research
Ethan Zien: Advertising
Eugenia Dickson: Building design & BIM