Undervalued Housing Price

Introduction / Business Problem

This section provides a description of the problem and a discussion of the background.

The intent of this project is to identify areas with undervalued housing price in the United States. The relevant terms are explained as follows:

The area here is analyzed on ZIP Code level.

According to wikipedia,

a ZIP Code is a postal code used by the United States Postal Service (USPS). As of October 2019, there are 41,702 ZIP Codes in the United States. ZIP Codes are used not only for tracking of mail, but also in gathering geographical statistics in the United States.
This analysis used Zillow Home Value Index (ZHVI) to represent actual housing price of a given month in a ZIP Code area. The detailed definitation of ZHVI can be found in section Data Source 1 below.
This analysis compares the venues (and maybe some other geographical statistics) of a ZIP Code area with the current housing price of that area. A machine learning model would be built given the housing price and the geographical distribution of venues in that area. Then, the fitted model will predict the house price based on the venues. An area would be identified as undervalued if the actual housing price is much lower than the housing price predicted from the venues in this area.

Data

This section provides a description of the data and how it will be used to solve the problem.

Data Source 1: ZHVI dataset

According to Zillow website,

Zillow Home Value Index (ZHVI): A smoothed, seasonally adjusted measure of the typical home value and market changes across a given region and housing type. It reflects the typical value for homes in the 35th to 65th percentile range.

For example, if ZHVI was $400,000 in Seattle one month, that indicates that 50 percent of homes in the area are worth more than $400,000 and 50 percent are worth less (adjusting for seasonal fluctuations– e.g. prices tend to be low in December).

This dataset stores housing price (Zillow Home Value Index) data:

for a period from Jan 1996 to Dec 2020
for 30,330 ZIP Code areas
monthly data are provided

This dataset is used in the following ways:

The analysis will use the most recent monthly housing price (Dec 2020) to represent the current housing price.
The location information including ZIP Code, city name, and state name would be used as location input to Foursquare API to pull venue data.

Data Source 2: Foursquare

Foursquare is an independent location data platform that provides place API.

This data source is used to get venue distribution of a given ZIP Code area.

Others

A link to your Notebook on your Github repository, showing your code. (15 marks)
A full report consisting of all of the following components (15 marks):

Introduction where you discuss the business problem and who would be interested in this project. Data where you describe the data that will be used to solve the problem and the source of the data. Methodology section which represents the main component of the report where you discuss and describe any exploratory data analysis that you did, any inferential statistical testing that you performed, if any, and what machine learnings were used and why. Results section where you discuss the results. Discussion section where you discuss any observations you noted and any recommendations you can make based on the results. Conclusion section where you conclude the report.