Time-Series Modeling

Introduction

For this project, I utilized my Time Series Analysis skills to forecast real estate prices of zipcodes using data from Zillow.

Objectives

The CEO of PoManns Capital Real Estate hired my consultancy to answer the following quesitons based on the Zillow Data data:

  • Question: What are the top 5 best zipcodes for PoManns Capital to invest in Houston, Texas?

The Project

Our project team used the OSEMN framework to help PoManns Capital select the top 5 best zipcodes to invest in short to long term periods.

  • Obtaining Data - We used data from Zillow with data from all over the United States
  • Scrubbing Data - We broke down the data by country, then by state, then by city, then by zip code
  • Exploring Data - We did a time-series analysis to see which zip codes had trends, seasonality, and other features that may influence price.
  • Modeling Data - We used a method to select the best features in predicting price
  • We trained the model on the entire dataset
  • We did some forecasts to see which zip codes would yield better return on investments in the short to long term periods
  • Interpreting Data -
  • Short Term - We looked at forecasts for the first year and the average Return on Investment.
  • Mid Term - We looked at forecasts for the couple years and the average Return on Investment.
  • Long Term - We looked at forecasts for three years and the average Return on Investment.

The Results

The entire framework can be found in the repository. The EDA can be found in the Mod-4-EDA.ipynb Notebook; forecasts for each zipcode can be found in the Mod-4-Forecasts.ipynb Notebook, and the project results can be found in the Mod-4-Project.ipynb Notebook. Our results are below:

  • Question: What are the top 5 best zipcodes for PoManns Capital to invest in Houston, Texas?
  • 77028
  • 77033
  • 77029
  • 77051
  • 77078