Zillow Project

A look at predicting home values

Project Description

The housing market is hard to predict, but through data analysis and machine learning models we can make predictions on what a house value might be based on different features of the house. Through this project I will explore the Zillow data for 2017 single-family properties, determine what features are effective in predicting house prices, and create a predictive model for these type of properties.

Project Goals

• Identify key features that can be used to create an effective predictive model.
• Use regression models to make house price predictions.
• Use findings to make recommendations and establish a foundation for future work to improve model's performance.

Initial Thoughts & Questions

My initial hypothesis is that square feet and location are strong drivers of home value. 1. Is there a significant relationship between square footage and home value? 2. Is there a significant relationship between the bath-to-bed ratio and home value? 3. Does location have a relationship with home value? 4. Is there a significant relationship between age of the home and home value?

The Plan

Aqcuire the data from Codeup mySQL database

Prepare data

Dropped rows:

• Duplicates
• Rows having 0 bedrooms AND 0 bathrooms
• Rows having more than 10,000 square feet (because these are large and can scew the data)
• Rows containing null values in any column

Created features

• county (names based on the fips code):
  • 6037: LA
  • 6059: Orange
  • 6111: Ventura
• bath_bed_ratio
  • Column displaying bathrooms/bedrooms

Other prep

• Split data into train, validate, and test (65/20/15)

Explore data in search of drivers of churn

• Answer the following initial question
  1. Is there a significant relationship between square footage and home value?
  2. Is there a significant relationship between the bath-to-bed ratio and home value?
  3. Does location have a relationship with home value?
  4. Is there a significant relationship between age of the home and home value?

Develop a model to predict the value of a house

• Use drivers identified through exploration to build different predictive models
• Evaluate models on train and validate data
• Select best model based on highest $r^2 score$
• Evaluate the best model on the test data

Draw conclusions

Data dictionary

Feature	Definition	Type
parcelid	Definition	int
bathroooms	The number of bathrooms in the home.	float
bedrooms	The number of bedrooms in the home.	int
square_feet	Square footage of the house	int
year_built	Year the house was built	int
bath_bed_ratio	The number of bathrooms divided by number of bedrooms	float
county	Name of the county where the house is located	string
2017_age	Age of the house in 2017 (when the data was collected	int
Target variable
home_value	The tax-assessed value of the home.	float

Steps to Reproduce

1. Clone this repo
2. Acquire the data from Codeup mySQL "zillow" database using your personal env.py file where you store your username, password, and host
3. Put the data in the file containing the cloned repo.
4. Run notebook.

Conclusion

Summary

• square_feet seems to be a driver of home value
• bath_bed_ratio seems to be a driver of home value
• county seems to be a driver of home value
• 2017_age seems to be a driver of home value

Recommendations

• We should do more research of the areas where these houses are located to have a better understanding of how location is driving home value.
• Dive into our data and see what information has been corrupted and delete those rows so that we can make sure we are using as accurate data as we can.

Next Steps

• In the next iteration:
  • Look into neighborhoods, and exact location of the houses to see if that will help the model perform better.
  • Look into other features of the house (garage, pool, deck, etc.) to see if they are also drivers of home value.